diff --git a/Makefile b/Makefile index 420eafc0d..5aa3d01b2 100644 --- a/Makefile +++ b/Makefile @@ -17,7 +17,7 @@ setup: ## Set up the Python development environment $(VENV_BIN)/pip install -r requirements/lint-requirements.txt testenv: setup ## Set up the Python test environment - $(VENV_BIN)/pip install -e ".[simple_framework]" + $(VENV_BIN)/pip install -e ".[default]" .PHONY: fmt fmt: setup ## Format Python code diff --git a/dbgpt/app/component_configs.py b/dbgpt/app/component_configs.py index 5a466f801..77d5634a6 100644 --- a/dbgpt/app/component_configs.py +++ b/dbgpt/app/component_configs.py @@ -30,7 +30,7 @@ def initialize_components( system_app.register_instance(controller) # Register global default RAGGraphFactory - # from dbgpt.rag.graph_engine.graph_factory import DefaultRAGGraphFactory + # from dbgpt.graph.graph_factory import DefaultRAGGraphFactory # system_app.register(DefaultRAGGraphFactory) diff --git a/dbgpt/app/initialization/embedding_component.py b/dbgpt/app/initialization/embedding_component.py index 6d8aa2b6e..a20bd5220 100644 --- a/dbgpt/app/initialization/embedding_component.py +++ b/dbgpt/app/initialization/embedding_component.py @@ -3,7 +3,7 @@ import logging from typing import Any, Type, TYPE_CHECKING from dbgpt.component import ComponentType, SystemApp -from dbgpt.rag.embedding_engine.embedding_factory import EmbeddingFactory +from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory if TYPE_CHECKING: from langchain.embeddings.base import Embeddings diff --git a/dbgpt/app/knowledge/_cli/knowledge_client.py b/dbgpt/app/knowledge/_cli/knowledge_client.py index b5aad084f..5ab24d368 100644 --- a/dbgpt/app/knowledge/_cli/knowledge_client.py +++ b/dbgpt/app/knowledge/_cli/knowledge_client.py @@ -14,10 +14,10 @@ DocumentQueryRequest, ) -from dbgpt.rag.embedding_engine.knowledge_type import KnowledgeType from dbgpt.app.knowledge.request.request import DocumentSyncRequest from dbgpt.app.knowledge.request.request import KnowledgeSpaceRequest +from dbgpt.rag.knowledge.base import KnowledgeType HTTP_HEADERS = {"Content-Type": "application/json"} diff --git a/dbgpt/app/knowledge/api.py b/dbgpt/app/knowledge/api.py index 798de4b2c..753334c43 100644 --- a/dbgpt/app/knowledge/api.py +++ b/dbgpt/app/knowledge/api.py @@ -2,6 +2,7 @@ import shutil import tempfile import logging +from typing import List from fastapi import APIRouter, Depends, File, UploadFile, Form @@ -13,10 +14,10 @@ from dbgpt.app.openapi.api_v1.api_v1 import no_stream_generator, stream_generator from dbgpt.app.openapi.api_view_model import Result -from dbgpt.rag.embedding_engine.embedding_engine import EmbeddingEngine -from dbgpt.rag.embedding_engine.embedding_factory import EmbeddingFactory +from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory from dbgpt.app.knowledge.service import KnowledgeService +from dbgpt.rag.knowledge.factory import KnowledgeFactory from dbgpt.app.knowledge.request.request import ( KnowledgeQueryRequest, KnowledgeQueryResponse, @@ -27,9 +28,14 @@ SpaceArgumentRequest, EntityExtractRequest, DocumentSummaryRequest, + KnowledgeSyncRequest, ) from dbgpt.app.knowledge.request.request import KnowledgeSpaceRequest +from dbgpt.rag.knowledge.base import ChunkStrategy +from dbgpt.rag.retriever.embedding import EmbeddingRetriever +from dbgpt.storage.vector_store.base import VectorStoreConfig +from dbgpt.storage.vector_store.connector import VectorStoreConnector from dbgpt.util.tracer import root_tracer, SpanType from dbgpt.util.api_utils import _check_api_key @@ -104,6 +110,39 @@ def document_add(space_name: str, request: KnowledgeDocumentRequest): return Result.failed(code="E000X", msg=f"document add error {e}") +@router.get("/knowledge/document/chunkstrategies", dependencies=[Depends(_check_api_key)]) +def chunk_strategies(): + """Get chunk strategies""" + print(f"/document/chunkstrategies:") + try: + return Result.succ( + [ + { + "strategy": strategy.name, + "name": strategy.value[2], + "description": strategy.value[3], + "parameters": strategy.value[1], + "suffix": [ + knowledge.document_type().value + for knowledge in KnowledgeFactory.subclasses() + if strategy in knowledge.support_chunk_strategy() + and knowledge.document_type() is not None + ], + "type": set( + [ + knowledge.type().value + for knowledge in KnowledgeFactory.subclasses() + if strategy in knowledge.support_chunk_strategy() + ] + ), + } + for strategy in ChunkStrategy + ] + ) + except Exception as e: + return Result.failed(code="E000X", msg=f"chunk strategies error {e}") + + @router.post("/knowledge/{space_name}/document/list", dependencies=[Depends(_check_api_key)]) def document_list(space_name: str, query_request: DocumentQueryRequest): print(f"/document/list params: {space_name}, {query_request}") @@ -190,6 +229,18 @@ def document_sync(space_name: str, request: DocumentSyncRequest): return Result.failed(code="E000X", msg=f"document sync error {e}") +@router.post("/knowledge/{space_name}/document/sync_batch", dependencies=[Depends(_check_api_key)]) +def batch_document_sync(space_name: str, request: List[KnowledgeSyncRequest]): + logger.info(f"Received params: {space_name}, {request}") + try: + doc_ids = knowledge_space_service.batch_document_sync( + space_name=space_name, sync_requests=request + ) + return Result.succ({"tasks": doc_ids}) + except Exception as e: + return Result.failed(code="E000X", msg=f"document sync error {e}") + + @router.post("/knowledge/{space_name}/chunk/list", dependencies=[Depends(_check_api_key)]) def document_list(space_name: str, query_request: ChunkQueryRequest): print(f"/document/list params: {space_name}, {query_request}") @@ -205,15 +256,23 @@ def similar_query(space_name: str, query_request: KnowledgeQueryRequest): embedding_factory = CFG.SYSTEM_APP.get_component( "embedding_factory", EmbeddingFactory ) - client = EmbeddingEngine( - model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL], - vector_store_config={"vector_store_name": space_name}, - embedding_factory=embedding_factory, + config = VectorStoreConfig( + name=space_name, + embedding_fn=embedding_factory.create( + EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL] + ), + ) + vector_store_connector = VectorStoreConnector( + vector_store_type=CFG.VECTOR_STORE_TYPE, + vector_store_config=config, + ) + retriever = EmbeddingRetriever( + top_k=query_request.top_k, vector_store_connector=vector_store_connector ) - docs = client.similar_search(query_request.query, query_request.top_k) + chunks = retriever.retrieve(query_request.query) res = [ - KnowledgeQueryResponse(text=d.page_content, source=d.metadata["source"]) - for d in docs + KnowledgeQueryResponse(text=d.content, source=d.metadata["source"]) + for d in chunks ] return {"response": res} @@ -255,7 +314,7 @@ async def entity_extract(request: EntityExtractRequest): logger.info(f"Received params: {request}") try: from dbgpt.app.scene import ChatScene - from dbgpt._private.chat_util import llm_chat_response_nostream + from dbgpt.util.chat_util import llm_chat_response_nostream import uuid chat_param = { diff --git a/dbgpt/app/knowledge/document_db.py b/dbgpt/app/knowledge/document_db.py index 983bb001c..d101d8ee3 100644 --- a/dbgpt/app/knowledge/document_db.py +++ b/dbgpt/app/knowledge/document_db.py @@ -1,4 +1,5 @@ from datetime import datetime +from typing import List from sqlalchemy import Column, String, DateTime, Integer, Text, func @@ -51,6 +52,12 @@ def create_knowledge_document(self, document: KnowledgeDocumentEntity): return doc_id def get_knowledge_documents(self, query, page=1, page_size=20): + """Get a list of documents that match the given query. + Args: + query: A KnowledgeDocumentEntity object containing the query parameters. + page: The page number to return. + page_size: The number of documents to return per page. + """ session = self.get_raw_session() print(f"current session:{session}") knowledge_documents = session.query(KnowledgeDocumentEntity) @@ -85,6 +92,23 @@ def get_knowledge_documents(self, query, page=1, page_size=20): session.close() return result + def documents_by_ids(self, ids) -> List[KnowledgeDocumentEntity]: + """Get a list of documents by their IDs. + Args: + ids: A list of document IDs. + Returns: + A list of KnowledgeDocumentEntity objects. + """ + session = self.get_raw_session() + print(f"current session:{session}") + knowledge_documents = session.query(KnowledgeDocumentEntity) + knowledge_documents = knowledge_documents.filter( + KnowledgeDocumentEntity.id.in_(ids) + ) + result = knowledge_documents.all() + session.close() + return result + def get_documents(self, query): session = self.get_raw_session() print(f"current session:{session}") diff --git a/dbgpt/app/knowledge/request/request.py b/dbgpt/app/knowledge/request/request.py index 79b40f306..e87ca14ad 100644 --- a/dbgpt/app/knowledge/request/request.py +++ b/dbgpt/app/knowledge/request/request.py @@ -3,6 +3,8 @@ from dbgpt._private.pydantic import BaseModel from fastapi import UploadFile +from dbgpt.rag.chunk_manager import ChunkParameters + class KnowledgeQueryRequest(BaseModel): """query: knowledge query""" @@ -43,6 +45,8 @@ class DocumentQueryRequest(BaseModel): """doc_name: doc path""" doc_name: str = None + """doc_ids: doc ids""" + doc_ids: Optional[List] = None """doc_type: doc type""" doc_type: str = None """status: status""" @@ -76,6 +80,20 @@ class DocumentSyncRequest(BaseModel): chunk_overlap: Optional[int] = None +class KnowledgeSyncRequest(BaseModel): + """Sync request""" + + """doc_ids: doc ids""" + doc_id: int + + """model_name: model name""" + model_name: Optional[str] = None + + """chunk_parameters: chunk parameters + """ + chunk_parameters: ChunkParameters + + class ChunkQueryRequest(BaseModel): """id: id""" diff --git a/dbgpt/app/knowledge/service.py b/dbgpt/app/knowledge/service.py index 0f45c8d40..e32224180 100644 --- a/dbgpt/app/knowledge/service.py +++ b/dbgpt/app/knowledge/service.py @@ -1,13 +1,26 @@ import json import logging from datetime import datetime - +from typing import List + +from dbgpt.model import DefaultLLMClient +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.chunk_manager import ChunkParameters +from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory +from dbgpt.rag.knowledge.base import KnowledgeType +from dbgpt.rag.knowledge.factory import KnowledgeFactory +from dbgpt.rag.text_splitter.text_splitter import ( + RecursiveCharacterTextSplitter, + SpacyTextSplitter, +) +from dbgpt.serve.rag.assembler.embedding import EmbeddingAssembler +from dbgpt.serve.rag.assembler.summary import SummaryAssembler +from dbgpt.storage.vector_store.base import VectorStoreConfig from dbgpt.storage.vector_store.connector import VectorStoreConnector from dbgpt._private.config import Config from dbgpt.configs.model_config import ( EMBEDDING_MODEL_CONFIG, - KNOWLEDGE_UPLOAD_ROOT_PATH, ) from dbgpt.component import ComponentType from dbgpt.util.executor_utils import ExecutorFactory, blocking_func_to_async @@ -32,6 +45,7 @@ SpaceArgumentRequest, DocumentSyncRequest, DocumentSummaryRequest, + KnowledgeSyncRequest, ) from enum import Enum @@ -106,7 +120,10 @@ def create_knowledge_document(self, space, request: KnowledgeDocumentRequest): content=request.content, result="", ) - return knowledge_document_dao.create_knowledge_document(document) + doc_id = knowledge_document_dao.create_knowledge_document(document) + if doc_id is None: + raise Exception(f"create document failed, {request.doc_name}") + return doc_id def get_knowledge_space(self, request: KnowledgeSpaceRequest): """get knowledge space @@ -171,36 +188,75 @@ def get_knowledge_documents(self, space, request: DocumentQueryRequest): Args: - space: Knowledge Space Name - request: DocumentQueryRequest + Returns: + - res DocumentQueryResponse """ - query = KnowledgeDocumentEntity( - doc_name=request.doc_name, - doc_type=request.doc_type, - space=space, - status=request.status, - ) res = DocumentQueryResponse() - res.data = knowledge_document_dao.get_knowledge_documents( - query, page=request.page, page_size=request.page_size - ) - res.total = knowledge_document_dao.get_knowledge_documents_count(query) - res.page = request.page + if request.doc_ids and len(request.doc_ids) > 0: + res.data = knowledge_document_dao.documents_by_ids(request.doc_ids) + else: + query = KnowledgeDocumentEntity( + doc_name=request.doc_name, + doc_type=request.doc_type, + space=space, + status=request.status, + ) + res.data = knowledge_document_dao.get_knowledge_documents( + query, page=request.page, page_size=request.page_size + ) + res.total = knowledge_document_dao.get_knowledge_documents_count(query) + res.page = request.page return res + def batch_document_sync( + self, space_name, sync_requests: List[KnowledgeSyncRequest] + ) -> List[int]: + """batch sync knowledge document chunk into vector store + Args: + - space: Knowledge Space Name + - sync_requests: List[KnowledgeSyncRequest] + Returns: + - List[int]: document ids + """ + doc_ids = [] + for sync_request in sync_requests: + docs = knowledge_document_dao.documents_by_ids([sync_request.doc_id]) + if len(docs) == 0: + raise Exception( + f"there are document called, doc_id: {sync_request.doc_id}" + ) + doc = docs[0] + if ( + doc.status == SyncStatus.RUNNING.name + or doc.status == SyncStatus.FINISHED.name + ): + raise Exception( + f" doc:{doc.doc_name} status is {doc.status}, can not sync" + ) + chunk_parameters = sync_request.chunk_parameters + if "Automatic" == chunk_parameters.chunk_strategy: + space_context = self.get_space_context(space_name) + chunk_parameters.chunk_size = ( + CFG.KNOWLEDGE_CHUNK_SIZE + if space_context is None + else int(space_context["embedding"]["chunk_size"]) + ) + chunk_parameters.chunk_overlap = ( + CFG.KNOWLEDGE_CHUNK_OVERLAP + if space_context is None + else int(space_context["embedding"]["chunk_overlap"]) + ) + self._sync_knowledge_document(space_name, doc, chunk_parameters) + doc_ids.append(doc.id) + return doc_ids + def sync_knowledge_document(self, space_name, sync_request: DocumentSyncRequest): """sync knowledge document chunk into vector store Args: - space: Knowledge Space Name - sync_request: DocumentSyncRequest """ - from dbgpt.rag.embedding_engine.embedding_engine import EmbeddingEngine - from dbgpt.rag.embedding_engine.embedding_factory import EmbeddingFactory - from dbgpt.rag.embedding_engine.pre_text_splitter import PreTextSplitter - from langchain.text_splitter import ( - RecursiveCharacterTextSplitter, - SpacyTextSplitter, - ) - - # import langchain is very very slow!!! + from dbgpt.rag.text_splitter.pre_text_splitter import PreTextSplitter doc_ids = sync_request.doc_ids self.model_name = sync_request.model_name or CFG.LLM_MODEL @@ -234,6 +290,11 @@ def sync_knowledge_document(self, space_name, sync_request: DocumentSyncRequest) if sync_request.chunk_overlap: chunk_overlap = sync_request.chunk_overlap separators = sync_request.separators or None + from dbgpt.rag.chunk_manager import ChunkParameters + + chunk_parameters = ChunkParameters( + chunk_size=chunk_size, chunk_overlap=chunk_overlap + ) if CFG.LANGUAGE == "en": text_splitter = RecursiveCharacterTextSplitter( separators=separators, @@ -244,7 +305,7 @@ def sync_knowledge_document(self, space_name, sync_request: DocumentSyncRequest) else: if separators and len(separators) > 1: raise ValueError( - "SpacyTextSplitter do not support multiple separators" + "SpacyTextSplitter do not support multipsle separators" ) try: separator = "\n\n" if not separators else separators[0] @@ -266,48 +327,51 @@ def sync_knowledge_document(self, space_name, sync_request: DocumentSyncRequest) pre_separator=sync_request.pre_separator, text_splitter_impl=text_splitter, ) - embedding_factory = CFG.SYSTEM_APP.get_component( - "embedding_factory", EmbeddingFactory - ) - client = EmbeddingEngine( - knowledge_source=doc.content, - knowledge_type=doc.doc_type.upper(), - model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL], - vector_store_config={ - "vector_store_name": space_name, - "vector_store_type": CFG.VECTOR_STORE_TYPE, - }, - text_splitter=text_splitter, - embedding_factory=embedding_factory, - ) - chunk_docs = client.read() - # update document status - doc.status = SyncStatus.RUNNING.name - doc.chunk_size = len(chunk_docs) - doc.gmt_modified = datetime.now() - knowledge_document_dao.update_knowledge_document(doc) - executor = CFG.SYSTEM_APP.get_component( - ComponentType.EXECUTOR_DEFAULT, ExecutorFactory - ).create() - executor.submit(self.async_doc_embedding, client, chunk_docs, doc) - logger.info(f"begin save document chunks, doc:{doc.doc_name}") - # save chunk details - chunk_entities = [ - DocumentChunkEntity( - doc_name=doc.doc_name, - doc_type=doc.doc_type, - document_id=doc.id, - content=chunk_doc.page_content, - meta_info=str(chunk_doc.metadata), - gmt_created=datetime.now(), - gmt_modified=datetime.now(), - ) - for chunk_doc in chunk_docs - ] - document_chunk_dao.create_documents_chunks(chunk_entities) - + chunk_parameters.text_splitter = text_splitter + self._sync_knowledge_document(space_name, doc, chunk_parameters) return doc.id + def _sync_knowledge_document( + self, + space_name, + doc: KnowledgeDocumentEntity, + chunk_parameters: ChunkParameters, + ) -> List[Chunk]: + """sync knowledge document chunk into vector store""" + embedding_factory = CFG.SYSTEM_APP.get_component( + "embedding_factory", EmbeddingFactory + ) + embedding_fn = embedding_factory.create( + model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL] + ) + from dbgpt.storage.vector_store.base import VectorStoreConfig + + config = VectorStoreConfig(name=space_name, embedding_fn=embedding_fn) + vector_store_connector = VectorStoreConnector( + vector_store_type=CFG.VECTOR_STORE_TYPE, + vector_store_config=config, + ) + knowledge = KnowledgeFactory.create( + datasource=doc.content, + knowledge_type=KnowledgeType.get_by_value(doc.doc_type), + ) + assembler = EmbeddingAssembler.load_from_knowledge( + knowledge=knowledge, + chunk_parameters=chunk_parameters, + vector_store_connector=vector_store_connector, + ) + chunk_docs = assembler.get_chunks() + doc.status = SyncStatus.RUNNING.name + doc.chunk_size = len(chunk_docs) + doc.gmt_modified = datetime.now() + knowledge_document_dao.update_knowledge_document(doc) + executor = CFG.SYSTEM_APP.get_component( + ComponentType.EXECUTOR_DEFAULT, ExecutorFactory + ).create() + executor.submit(self.async_doc_embedding, assembler, chunk_docs, doc) + logger.info(f"begin save document chunks, doc:{doc.doc_name}") + return chunk_docs + async def document_summary(self, request: DocumentSummaryRequest): """get document summary Args: @@ -318,20 +382,46 @@ async def document_summary(self, request: DocumentSummaryRequest): if len(documents) != 1: raise Exception(f"can not found document for {request.doc_id}") document = documents[0] - query = DocumentChunkEntity( - document_id=request.doc_id, + from dbgpt.model.cluster import WorkerManagerFactory + + worker_manager = CFG.SYSTEM_APP.get_component( + ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory + ).create() + chunk_parameters = ChunkParameters( + chunk_strategy="CHUNK_BY_SIZE", + chunk_size=CFG.KNOWLEDGE_CHUNK_SIZE, + chunk_overlap=CFG.KNOWLEDGE_CHUNK_OVERLAP, + ) + chunk_entities = document_chunk_dao.get_document_chunks( + DocumentChunkEntity(document_id=document.id) ) - chunks = document_chunk_dao.get_document_chunks(query, page=1, page_size=100) - if len(chunks) == 0: + if ( + document.status not in [SyncStatus.RUNNING.name] + and len(chunk_entities) == 0 + ): + self._sync_knowledge_document( + space_name=document.space, + doc=document, + chunk_parameters=chunk_parameters, + ) + knowledge = KnowledgeFactory.create( + datasource=document.content, + knowledge_type=KnowledgeType.get_by_value(document.doc_type), + ) + assembler = SummaryAssembler( + knowledge=knowledge, + model_name=request.model_name, + llm_client=DefaultLLMClient(worker_manager=worker_manager), + language=CFG.LANGUAGE, + chunk_parameters=chunk_parameters, + ) + summary = await assembler.generate_summary() + + if len(assembler.get_chunks()) == 0: raise Exception(f"can not found chunks for {request.doc_id}") - from langchain.schema import Document - chunk_docs = [Document(page_content=chunk.content) for chunk in chunks] - return await self.async_document_summary( - model_name=request.model_name, - chunk_docs=chunk_docs, - doc=document, - conn_uid=request.conv_uid, + return await self._llm_extract_summary( + summary, request.conv_uid, request.model_name ) def update_knowledge_space( @@ -354,15 +444,13 @@ def delete_space(self, space_name: str): if len(spaces) == 0: raise Exception(f"delete error, no space name:{space_name} in database") space = spaces[0] - vector_config = {} - vector_config["vector_store_name"] = space.name - vector_config["vector_store_type"] = CFG.VECTOR_STORE_TYPE - vector_config["chroma_persist_path"] = KNOWLEDGE_UPLOAD_ROOT_PATH - vector_client = VectorStoreConnector( - vector_store_type=CFG.VECTOR_STORE_TYPE, ctx=vector_config + config = VectorStoreConfig(name=space.name) + vector_store_connector = VectorStoreConnector( + vector_store_type=CFG.VECTOR_STORE_TYPE, + vector_store_config=config, ) # delete vectors - vector_client.delete_vector_name(space.name) + vector_store_connector.delete_vector_name(space.name) document_query = KnowledgeDocumentEntity(space=space.name) # delete chunks documents = knowledge_document_dao.get_documents(document_query) @@ -385,15 +473,13 @@ def delete_document(self, space_name: str, doc_name: str): raise Exception(f"there are no or more than one document called {doc_name}") vector_ids = documents[0].vector_ids if vector_ids is not None: - vector_config = {} - vector_config["vector_store_name"] = space_name - vector_config["vector_store_type"] = CFG.VECTOR_STORE_TYPE - vector_config["chroma_persist_path"] = KNOWLEDGE_UPLOAD_ROOT_PATH - vector_client = VectorStoreConnector( - vector_store_type=CFG.VECTOR_STORE_TYPE, ctx=vector_config + config = VectorStoreConfig(name=space_name) + vector_store_connector = VectorStoreConnector( + vector_store_type=CFG.VECTOR_STORE_TYPE, + vector_store_config=config, ) # delete vector by ids - vector_client.delete_by_ids(vector_ids) + vector_store_connector.delete_by_ids(vector_ids) # delete chunks document_chunk_dao.raw_delete(documents[0].id) # delete document @@ -432,7 +518,7 @@ def async_knowledge_graph(self, chunk_docs, doc): f"async_knowledge_graph, doc:{doc.doc_name}, chunk_size:{len(chunk_docs)}, begin embedding to graph store" ) try: - from dbgpt.rag.graph_engine.graph_factory import RAGGraphFactory + from dbgpt.rag.graph.graph_factory import RAGGraphFactory rag_engine = CFG.SYSTEM_APP.get_component( ComponentType.RAG_GRAPH_DEFAULT.value, RAGGraphFactory @@ -446,54 +532,38 @@ def async_knowledge_graph(self, chunk_docs, doc): logger.error(f"document build graph failed:{doc.doc_name}, {str(e)}") return knowledge_document_dao.update_knowledge_document(doc) - async def async_document_summary(self, model_name, chunk_docs, doc, conn_uid): - """async document extract summary - Args: - - model_name: str - - chunk_docs: List[Document] - - doc: KnowledgeDocumentEntity - """ - texts = [doc.page_content for doc in chunk_docs] - from dbgpt.util.prompt_util import PromptHelper - - prompt_helper = PromptHelper() - from dbgpt.app.scene.chat_knowledge.summary.prompt import prompt - - texts = prompt_helper.repack(prompt_template=prompt.template, text_chunks=texts) - logger.info( - f"async_document_summary, doc:{doc.doc_name}, chunk_size:{len(texts)}, begin generate summary" - ) - space_context = self.get_space_context(doc.space) - if space_context and space_context.get("summary"): - summary = await self._mapreduce_extract_summary( - docs=texts, - model_name=model_name, - max_iteration=int(space_context["summary"]["max_iteration"]), - concurrency_limit=int(space_context["summary"]["concurrency_limit"]), - ) - else: - summary = await self._mapreduce_extract_summary( - docs=texts, model_name=model_name - ) - return await self._llm_extract_summary(summary, conn_uid, model_name) - - def async_doc_embedding(self, client, chunk_docs, doc): + def async_doc_embedding(self, assembler, chunk_docs, doc): """async document embedding into vector db Args: - client: EmbeddingEngine Client - chunk_docs: List[Document] - doc: KnowledgeDocumentEntity """ + logger.info( - f"async doc sync, doc:{doc.doc_name}, chunk_size:{len(chunk_docs)}, begin embedding to vector store-{CFG.VECTOR_STORE_TYPE}" + f"async doc embedding sync, doc:{doc.doc_name}, chunks length is {len(chunk_docs)}, begin embedding to vector store-{CFG.VECTOR_STORE_TYPE}" ) try: - vector_ids = client.knowledge_embedding_batch(chunk_docs) + vector_ids = assembler.persist() doc.status = SyncStatus.FINISHED.name doc.result = "document embedding success" if vector_ids is not None: doc.vector_ids = ",".join(vector_ids) logger.info(f"async document embedding, success:{doc.doc_name}") + # save chunk details + chunk_entities = [ + DocumentChunkEntity( + doc_name=doc.doc_name, + doc_type=doc.doc_type, + document_id=doc.id, + content=chunk_doc.content, + meta_info=str(chunk_doc.metadata), + gmt_created=datetime.now(), + gmt_modified=datetime.now(), + ) + for chunk_doc in chunk_docs + ] + document_chunk_dao.create_documents_chunks(chunk_entities) except Exception as e: doc.status = SyncStatus.FAILED.name doc.result = "document embedding failed" + str(e) @@ -578,65 +648,3 @@ async def _llm_extract_summary( **{"chat_param": chat_param}, ) return chat - - async def _mapreduce_extract_summary( - self, - docs, - model_name: str = None, - max_iteration: int = 5, - concurrency_limit: int = 3, - ): - """Extract summary by mapreduce mode - map -> multi async call llm to generate summary - reduce -> merge the summaries by map process - Args: - docs:List[str] - model_name:model name str - max_iteration:max iteration will call llm to summary - concurrency_limit:the max concurrency threads to call llm - Returns: - Document: refine summary context document. - """ - from dbgpt.app.scene import ChatScene - from dbgpt._private.chat_util import llm_chat_response_nostream - import uuid - - tasks = [] - if len(docs) == 1: - return docs[0] - else: - max_iteration = max_iteration if len(docs) > max_iteration else len(docs) - for doc in docs[0:max_iteration]: - chat_param = { - "chat_session_id": uuid.uuid1(), - "current_user_input": "", - "select_param": doc, - "model_name": model_name, - "model_cache_enable": True, - } - tasks.append( - llm_chat_response_nostream( - ChatScene.ExtractSummary.value(), **{"chat_param": chat_param} - ) - ) - from dbgpt._private.chat_util import run_async_tasks - - summary_iters = await run_async_tasks( - tasks=tasks, concurrency_limit=concurrency_limit - ) - summary_iters = list( - filter( - lambda content: "LLMServer Generate Error" not in content, - summary_iters, - ) - ) - from dbgpt.util.prompt_util import PromptHelper - from dbgpt.app.scene.chat_knowledge.summary.prompt import prompt - - prompt_helper = PromptHelper() - summary_iters = prompt_helper.repack( - prompt_template=prompt.template, text_chunks=summary_iters - ) - return await self._mapreduce_extract_summary( - summary_iters, model_name, max_iteration, concurrency_limit - ) diff --git a/dbgpt/app/scene/base_chat.py b/dbgpt/app/scene/base_chat.py index 1e5cf7070..a5e9fb26a 100644 --- a/dbgpt/app/scene/base_chat.py +++ b/dbgpt/app/scene/base_chat.py @@ -11,6 +11,7 @@ from dbgpt.core.interface.prompt import PromptTemplate from dbgpt.core.interface.message import ModelMessage, ModelMessageRoleType from dbgpt.core.interface.message import OnceConversation +from dbgpt.model.cluster import WorkerManagerFactory from dbgpt.util import get_or_create_event_loop from dbgpt.util.executor_utils import ExecutorFactory, blocking_func_to_async from dbgpt.util.tracer import root_tracer, trace @@ -58,6 +59,9 @@ def __init__(self, chat_param: Dict): chat_param["model_name"] if chat_param["model_name"] else CFG.LLM_MODEL ) self.llm_echo = False + self.worker_manager = CFG.SYSTEM_APP.get_component( + ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory + ).create() self.model_cache_enable = chat_param.get("model_cache_enable", False) ### load prompt template @@ -162,6 +166,10 @@ async def __call_base(self): "BaseChat.__call_base.prompt_template.format", metadata=metadata ): current_prompt = self.prompt_template.format(**input_values) + ### prompt context token adapt according to llm max context length + current_prompt = await self.prompt_context_token_adapt( + prompt=current_prompt + ) self.current_message.add_system_message(current_prompt) llm_messages = self.generate_llm_messages() @@ -169,6 +177,7 @@ async def __call_base(self): # Not new server mode, we convert the message format(List[ModelMessage]) to list of dict # fix the error of "Object of type ModelMessage is not JSON serializable" when passing the payload to request.post llm_messages = list(map(lambda m: m.dict(), llm_messages)) + payload = { "model": self.llm_model, "prompt": self.generate_llm_text(), @@ -431,6 +440,39 @@ def current_ai_response(self) -> str: return message.content return None + async def prompt_context_token_adapt(self, prompt) -> str: + """prompt token adapt according to llm max context length""" + model_metadata = await self.worker_manager.get_model_metadata( + {"model": self.llm_model} + ) + current_token_count = await self.worker_manager.count_token( + {"model": self.llm_model, "prompt": prompt} + ) + if current_token_count == -1: + logger.warning( + "tiktoken not installed, please `pip install tiktoken` first" + ) + template_define_token_count = 0 + if len(self.prompt_template.template_define) > 0: + template_define_token_count = await self.worker_manager.count_token( + { + "model": self.llm_model, + "prompt": self.prompt_template.template_define, + } + ) + current_token_count += template_define_token_count + if ( + current_token_count + self.prompt_template.max_new_tokens + ) > model_metadata.context_length: + prompt = prompt[ + : ( + model_metadata.context_length + - self.prompt_template.max_new_tokens + - template_define_token_count + ) + ] + return prompt + def generate(self, p) -> str: """ generate context for LLM input diff --git a/dbgpt/app/scene/chat_dashboard/chat.py b/dbgpt/app/scene/chat_dashboard/chat.py index 53d1084f5..7fe91b516 100644 --- a/dbgpt/app/scene/chat_dashboard/chat.py +++ b/dbgpt/app/scene/chat_dashboard/chat.py @@ -63,14 +63,11 @@ async def generate_input_values(self) -> Dict: try: table_infos = await blocking_func_to_async( self._executor, - client.get_similar_tables, + client.get_db_summary, self.db_name, self.current_user_input, self.top_k, ) - # table_infos = client.get_similar_tables( - # dbname=self.db_name, query=self.current_user_input, topk=self.top_k - # ) print("dashboard vector find tables:{}", table_infos) except Exception as e: print("db summary find error!" + str(e)) diff --git a/dbgpt/app/scene/chat_factory.py b/dbgpt/app/scene/chat_factory.py index 4afcaf6a9..258020fac 100644 --- a/dbgpt/app/scene/chat_factory.py +++ b/dbgpt/app/scene/chat_factory.py @@ -19,22 +19,14 @@ def get_implementation(chat_mode, **kwargs): from dbgpt.app.scene.chat_dashboard.prompt import prompt from dbgpt.app.scene.chat_knowledge.v1.chat import ChatKnowledge from dbgpt.app.scene.chat_knowledge.v1.prompt import prompt - from dbgpt.app.scene.chat_knowledge.inner_db_summary.chat import ( - InnerChatDBSummary, - ) - from dbgpt.app.scene.chat_knowledge.inner_db_summary.prompt import prompt from dbgpt.app.scene.chat_knowledge.extract_triplet.chat import ExtractTriplet from dbgpt.app.scene.chat_knowledge.extract_triplet.prompt import prompt from dbgpt.app.scene.chat_knowledge.extract_entity.chat import ExtractEntity from dbgpt.app.scene.chat_knowledge.extract_entity.prompt import prompt - from dbgpt.app.scene.chat_knowledge.summary.chat import ExtractSummary - from dbgpt.app.scene.chat_knowledge.summary.prompt import prompt from dbgpt.app.scene.chat_knowledge.refine_summary.chat import ( ExtractRefineSummary, ) from dbgpt.app.scene.chat_knowledge.refine_summary.prompt import prompt - from dbgpt.app.scene.chat_knowledge.rewrite.chat import QueryRewrite - from dbgpt.app.scene.chat_knowledge.rewrite.prompt import prompt from dbgpt.app.scene.chat_data.chat_excel.excel_analyze.chat import ChatExcel from dbgpt.app.scene.chat_data.chat_excel.excel_analyze.prompt import prompt from dbgpt.app.scene.chat_data.chat_excel.excel_learning.prompt import prompt diff --git a/dbgpt/app/scene/chat_knowledge/inner_db_summary/chat.py b/dbgpt/app/scene/chat_knowledge/inner_db_summary/chat.py deleted file mode 100644 index 637296b68..000000000 --- a/dbgpt/app/scene/chat_knowledge/inner_db_summary/chat.py +++ /dev/null @@ -1,40 +0,0 @@ -from typing import Dict -from dbgpt.app.scene import BaseChat, ChatScene - -from dbgpt.util.tracer import trace - - -class InnerChatDBSummary(BaseChat): - chat_scene: str = ChatScene.InnerChatDBSummary.value() - - """Number of results to return from the query""" - - def __init__( - self, - chat_session_id, - user_input, - db_select, - db_summary, - ): - """ """ - super().__init__( - chat_mode=ChatScene.InnerChatDBSummary, - chat_session_id=chat_session_id, - current_user_input=user_input, - select_param=db_select, - ) - - self.db_input = db_select - self.db_summary = db_summary - - @trace() - async def generate_input_values(self) -> Dict: - input_values = { - "db_input": self.db_input, - "db_profile_summary": self.db_summary, - } - return input_values - - @property - def chat_type(self) -> str: - return ChatScene.InnerChatDBSummary.value diff --git a/dbgpt/app/scene/chat_knowledge/inner_db_summary/out_parser.py b/dbgpt/app/scene/chat_knowledge/inner_db_summary/out_parser.py deleted file mode 100644 index 02501d55c..000000000 --- a/dbgpt/app/scene/chat_knowledge/inner_db_summary/out_parser.py +++ /dev/null @@ -1,17 +0,0 @@ -import logging -from dbgpt.core.interface.output_parser import BaseOutputParser - -logger = logging.getLogger(__name__) - - -class NormalChatOutputParser(BaseOutputParser): - def parse_prompt_response(self, model_out_text): - clean_str = super().parse_prompt_response(model_out_text) - print("clean prompt response:", clean_str) - return clean_str - - def parse_view_response(self, ai_text, data) -> str: - return ai_text - - def get_format_instructions(self) -> str: - pass diff --git a/dbgpt/app/scene/chat_knowledge/inner_db_summary/prompt.py b/dbgpt/app/scene/chat_knowledge/inner_db_summary/prompt.py deleted file mode 100644 index 4053956a5..000000000 --- a/dbgpt/app/scene/chat_knowledge/inner_db_summary/prompt.py +++ /dev/null @@ -1,45 +0,0 @@ -import json - -from dbgpt.core.interface.prompt import PromptTemplate -from dbgpt._private.config import Config -from dbgpt.app.scene import ChatScene - -from dbgpt.app.scene.chat_knowledge.inner_db_summary.out_parser import ( - NormalChatOutputParser, -) - - -CFG = Config() - -PROMPT_SCENE_DEFINE = """""" - -_DEFAULT_TEMPLATE = """ -Based on the following known database information?, answer which tables are involved in the user input. -Known database information:{db_profile_summary} -Input:{db_input} -You should only respond in JSON format as described below and ensure the response can be parsed by Python json.loads - - -""" -PROMPT_RESPONSE = """You must respond in JSON format as following format: -{response} -The response format must be JSON, and the key of JSON must be "table". -""" - - -RESPONSE_FORMAT = {"table": ["orders", "products"]} - - -PROMPT_NEED_STREAM_OUT = False - -prompt = PromptTemplate( - template_scene=ChatScene.InnerChatDBSummary.value(), - input_variables=["db_profile_summary", "db_input", "response"], - response_format=json.dumps(RESPONSE_FORMAT, indent=4), - template_define=PROMPT_SCENE_DEFINE, - template=_DEFAULT_TEMPLATE + PROMPT_RESPONSE, - stream_out=PROMPT_NEED_STREAM_OUT, - output_parser=NormalChatOutputParser(is_stream_out=PROMPT_NEED_STREAM_OUT), -) - -CFG.prompt_template_registry.register(prompt, is_default=True) diff --git a/dbgpt/app/scene/chat_knowledge/refine_summary/prompt.py b/dbgpt/app/scene/chat_knowledge/refine_summary/prompt.py index 5877af216..7e94168ce 100644 --- a/dbgpt/app/scene/chat_knowledge/refine_summary/prompt.py +++ b/dbgpt/app/scene/chat_knowledge/refine_summary/prompt.py @@ -19,7 +19,6 @@ _DEFAULT_TEMPLATE_EN = """ We have provided an existing summary up to a certain point: {existing_answer}\nWe have the opportunity to refine the existing summary (only if needed) with some more context below. \nBased on the previous reasoning, please summarize the final conclusion in accordance with points 1.2.and 3. - """ _DEFAULT_TEMPLATE = ( diff --git a/dbgpt/app/scene/chat_knowledge/rewrite/chat.py b/dbgpt/app/scene/chat_knowledge/rewrite/chat.py deleted file mode 100644 index 4514d7016..000000000 --- a/dbgpt/app/scene/chat_knowledge/rewrite/chat.py +++ /dev/null @@ -1,32 +0,0 @@ -from typing import Dict - -from dbgpt.app.scene import BaseChat, ChatScene - -from dbgpt.app.scene.chat_knowledge.rewrite.prompt import prompt - - -class QueryRewrite(BaseChat): - chat_scene: str = ChatScene.QueryRewrite.value() - - """query rewrite by llm""" - - def __init__(self, chat_param: Dict): - """ """ - chat_param["chat_mode"] = ChatScene.QueryRewrite - super().__init__( - chat_param=chat_param, - ) - - self.nums = chat_param["select_param"] - self.current_user_input = chat_param["current_user_input"] - - async def generate_input_values(self): - input_values = { - "nums": self.nums, - "original_query": self.current_user_input, - } - return input_values - - @property - def chat_type(self) -> str: - return ChatScene.QueryRewrite.value diff --git a/dbgpt/app/scene/chat_knowledge/rewrite/out_parser.py b/dbgpt/app/scene/chat_knowledge/rewrite/out_parser.py deleted file mode 100644 index 8e08b7d04..000000000 --- a/dbgpt/app/scene/chat_knowledge/rewrite/out_parser.py +++ /dev/null @@ -1,42 +0,0 @@ -import logging - -from dbgpt.core.interface.output_parser import BaseOutputParser - -logger = logging.getLogger(__name__) - - -class QueryRewriteParser(BaseOutputParser): - def __init__(self, is_stream_out: bool, **kwargs): - super().__init__(is_stream_out=is_stream_out, **kwargs) - - def parse_prompt_response(self, response, max_length: int = 128): - lowercase = True - try: - results = [] - response = response.strip() - - if response.startswith("queries:"): - response = response[len("queries:") :] - - queries = response.split(",") - if len(queries) == 1: - queries = response.split("，") - if len(queries) == 1: - queries = response.split("?") - if len(queries) == 1: - queries = response.split("？") - for k in queries: - rk = k - if lowercase: - rk = rk.lower() - s = rk.strip() - if s == "": - continue - results.append(s) - except Exception as e: - logger.error(f"parse query rewrite prompt_response error: {e}") - return [] - return results - - def parse_view_response(self, speak, data) -> str: - return data diff --git a/dbgpt/app/scene/chat_knowledge/rewrite/prompt.py b/dbgpt/app/scene/chat_knowledge/rewrite/prompt.py deleted file mode 100644 index 3157f6d74..000000000 --- a/dbgpt/app/scene/chat_knowledge/rewrite/prompt.py +++ /dev/null @@ -1,41 +0,0 @@ -from dbgpt.core.interface.prompt import PromptTemplate -from dbgpt._private.config import Config -from dbgpt.app.scene import ChatScene -from .out_parser import QueryRewriteParser - -CFG = Config() - - -PROMPT_SCENE_DEFINE = """You are a helpful assistant that generates multiple search queries based on a single input query.""" - -_DEFAULT_TEMPLATE_ZH = """请根据原问题优化生成{nums}个相关的搜索查询，这些查询应与原始查询相似并且是人们可能会提出的可回答的搜索问题。请勿使用任何示例中提到的内容，确保所有生成的查询均独立于示例，仅基于提供的原始查询。请按照以下逗号分隔的格式提供: 'queries：'： -"original_query：{original_query}\n" -"queries：\n" -""" - -_DEFAULT_TEMPLATE_EN = """ -Generate {nums} search queries related to: {original_query}, Provide following comma-separated format: 'queries: '\n": - "original query:: {original_query}\n" - "queries:\n" -""" - -_DEFAULT_TEMPLATE = ( - _DEFAULT_TEMPLATE_EN if CFG.LANGUAGE == "en" else _DEFAULT_TEMPLATE_ZH -) - -PROMPT_RESPONSE = """""" - - -PROMPT_NEED_NEED_STREAM_OUT = True - -prompt = PromptTemplate( - template_scene=ChatScene.QueryRewrite.value(), - input_variables=["nums", "original_query"], - response_format=None, - template_define=PROMPT_SCENE_DEFINE, - template=_DEFAULT_TEMPLATE + PROMPT_RESPONSE, - stream_out=PROMPT_NEED_NEED_STREAM_OUT, - output_parser=QueryRewriteParser(is_stream_out=PROMPT_NEED_NEED_STREAM_OUT), -) - -CFG.prompt_template_registry.register(prompt, is_default=True) diff --git a/dbgpt/app/scene/chat_knowledge/summary/chat.py b/dbgpt/app/scene/chat_knowledge/summary/chat.py deleted file mode 100644 index 2dea92cf0..000000000 --- a/dbgpt/app/scene/chat_knowledge/summary/chat.py +++ /dev/null @@ -1,28 +0,0 @@ -from typing import Dict - -from dbgpt.app.scene import BaseChat, ChatScene - - -class ExtractSummary(BaseChat): - chat_scene: str = ChatScene.ExtractSummary.value() - - """get summary by llm""" - - def __init__(self, chat_param: Dict): - """ """ - chat_param["chat_mode"] = ChatScene.ExtractSummary - super().__init__( - chat_param=chat_param, - ) - - self.user_input = chat_param["select_param"] - - async def generate_input_values(self): - input_values = { - "context": self.user_input, - } - return input_values - - @property - def chat_type(self) -> str: - return ChatScene.ExtractSummary.value diff --git a/dbgpt/app/scene/chat_knowledge/summary/out_parser.py b/dbgpt/app/scene/chat_knowledge/summary/out_parser.py deleted file mode 100644 index e1f11696b..000000000 --- a/dbgpt/app/scene/chat_knowledge/summary/out_parser.py +++ /dev/null @@ -1,28 +0,0 @@ -import logging -from typing import List, Tuple - -from dbgpt.core.interface.output_parser import BaseOutputParser, ResponseTye - -logger = logging.getLogger(__name__) - - -class ExtractSummaryParser(BaseOutputParser): - def __init__(self, is_stream_out: bool, **kwargs): - super().__init__(is_stream_out=is_stream_out, **kwargs) - - def parse_prompt_response( - self, response, max_length: int = 128 - ) -> List[Tuple[str, str, str]]: - # clean_str = super().parse_prompt_response(response) - print("clean prompt response:", response) - return response - - def parse_view_response(self, speak, data) -> str: - ### tool out data to table view - return data - - def parse_model_nostream_resp(self, response: ResponseTye, sep: str) -> str: - try: - return super().parse_model_nostream_resp(response, sep) - except Exception as e: - return str(e) diff --git a/dbgpt/app/scene/chat_knowledge/summary/prompt.py b/dbgpt/app/scene/chat_knowledge/summary/prompt.py deleted file mode 100644 index 5e63e71ac..000000000 --- a/dbgpt/app/scene/chat_knowledge/summary/prompt.py +++ /dev/null @@ -1,46 +0,0 @@ -from dbgpt.core.interface.prompt import PromptTemplate -from dbgpt._private.config import Config -from dbgpt.app.scene import ChatScene - -from dbgpt.app.scene.chat_knowledge.summary.out_parser import ExtractSummaryParser - -CFG = Config() - -# PROMPT_SCENE_DEFINE = """You are an expert Q&A system that is trusted around the world.\nAlways answer the query using the provided context information, and not prior knowledge.\nSome rules to follow:\n1. Never directly reference the given context in your answer.\n2. Avoid statements like 'Based on the context, ...' or 'The context information ...' or anything along those lines.""" - -PROMPT_SCENE_DEFINE = """A chat between a curious user and an artificial intelligence assistant, who very familiar with database related knowledge. -The assistant gives helpful, detailed, professional and polite answers to the user's questions.""" - -_DEFAULT_TEMPLATE_ZH = """请根据提供的上下文信息的进行精简地总结: -{context} -答案尽量精确和简单,不要过长，长度控制在100字左右 -""" - -_DEFAULT_TEMPLATE_EN = """ -Write a quick summary of the following context: -{context} -the summary should be as concise as possible and not overly lengthy.Please keep the answer within approximately 200 characters. -""" - -_DEFAULT_TEMPLATE = ( - _DEFAULT_TEMPLATE_EN if CFG.LANGUAGE == "en" else _DEFAULT_TEMPLATE_ZH -) - -PROMPT_RESPONSE = """""" - - -RESPONSE_FORMAT = """""" - -PROMPT_NEED_NEED_STREAM_OUT = False - -prompt = PromptTemplate( - template_scene=ChatScene.ExtractSummary.value(), - input_variables=["context"], - response_format=None, - template_define=PROMPT_SCENE_DEFINE, - template=_DEFAULT_TEMPLATE + PROMPT_RESPONSE, - stream_out=PROMPT_NEED_NEED_STREAM_OUT, - output_parser=ExtractSummaryParser(is_stream_out=PROMPT_NEED_NEED_STREAM_OUT), -) - -CFG.prompt_template_registry.register(prompt, is_default=True) diff --git a/dbgpt/app/scene/chat_knowledge/v1/chat.py b/dbgpt/app/scene/chat_knowledge/v1/chat.py index 82953b8d3..496e65af4 100644 --- a/dbgpt/app/scene/chat_knowledge/v1/chat.py +++ b/dbgpt/app/scene/chat_knowledge/v1/chat.py @@ -5,6 +5,7 @@ from dbgpt.app.scene import BaseChat, ChatScene from dbgpt._private.config import Config +from dbgpt.component import ComponentType from dbgpt.configs.model_config import ( EMBEDDING_MODEL_CONFIG, @@ -19,7 +20,9 @@ # NOTE: the import is required for register the chat_knowledge scene # from dbgpt.app.scene.chat_knowledge.v1.prompt import prompt from dbgpt.app.knowledge.service import KnowledgeService -from dbgpt.util.executor_utils import blocking_func_to_async +from dbgpt.model import DefaultLLMClient +from dbgpt.model.cluster import WorkerManagerFactory +from dbgpt.rag.retriever.rewrite import QueryRewrite from dbgpt.util.tracer import trace CFG = Config() @@ -38,8 +41,7 @@ def __init__(self, chat_param: Dict): - model_name:(str) llm model name - select_param:(str) space name """ - from dbgpt.rag.embedding_engine.embedding_engine import EmbeddingEngine - from dbgpt.rag.embedding_engine.embedding_factory import EmbeddingFactory + from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory self.knowledge_space = chat_param["select_param"] chat_param["chat_mode"] = ChatScene.ChatKnowledge @@ -62,17 +64,37 @@ def __init__(self, chat_param: Dict): if self.space_context is None or self.space_context.get("prompt") is None else int(self.space_context["prompt"]["max_token"]) ) - vector_store_config = { - "vector_store_name": self.knowledge_space, - "vector_store_type": CFG.VECTOR_STORE_TYPE, - } embedding_factory = CFG.SYSTEM_APP.get_component( "embedding_factory", EmbeddingFactory ) - self.knowledge_embedding_client = EmbeddingEngine( - model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL], - vector_store_config=vector_store_config, - embedding_factory=embedding_factory, + from dbgpt.rag.retriever.embedding import EmbeddingRetriever + from dbgpt.storage.vector_store.connector import VectorStoreConnector + + embedding_fn = embedding_factory.create( + model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL] + ) + from dbgpt.storage.vector_store.base import VectorStoreConfig + + config = VectorStoreConfig(name=self.knowledge_space, embedding_fn=embedding_fn) + vector_store_connector = VectorStoreConnector( + vector_store_type=CFG.VECTOR_STORE_TYPE, + vector_store_config=config, + ) + query_rewrite = None + self.worker_manager = CFG.SYSTEM_APP.get_component( + ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory + ).create() + self.llm_client = DefaultLLMClient(worker_manager=self.worker_manager) + if CFG.KNOWLEDGE_SEARCH_REWRITE: + query_rewrite = QueryRewrite( + llm_client=self.llm_client, + model_name=self.llm_model, + language=CFG.LANGUAGE, + ) + self.embedding_retriever = EmbeddingRetriever( + top_k=self.top_k, + vector_store_connector=vector_store_connector, + query_rewrite=query_rewrite, ) self.prompt_template.template_is_strict = False self.relations = None @@ -114,51 +136,33 @@ async def generate_input_values(self) -> Dict: if self.space_context and self.space_context.get("prompt"): self.prompt_template.template_define = self.space_context["prompt"]["scene"] self.prompt_template.template = self.space_context["prompt"]["template"] - from dbgpt.rag.retriever.reinforce import QueryReinforce + from dbgpt.util.chat_util import run_async_tasks - # query reinforce, get similar queries - query_reinforce = QueryReinforce( - query=self.current_user_input, model_name=self.llm_model - ) - queries = [] - if CFG.KNOWLEDGE_SEARCH_REWRITE: - queries = await query_reinforce.rewrite() - print("rewrite queries:", queries) - queries.append(self.current_user_input) - from dbgpt._private.chat_util import run_async_tasks - - # similarity search from vector db - tasks = [self.execute_similar_search(query) for query in queries] - docs_with_scores = await run_async_tasks(tasks=tasks, concurrency_limit=1) - candidates_with_scores = reduce(lambda x, y: x + y, docs_with_scores) - # candidates document rerank - from dbgpt.rag.retriever.rerank import DefaultRanker - - ranker = DefaultRanker(self.top_k) - candidates_with_scores = ranker.rank(candidates_with_scores) + tasks = [self.execute_similar_search(self.current_user_input)] + candidates_with_scores = await run_async_tasks(tasks=tasks, concurrency_limit=1) + candidates_with_scores = reduce(lambda x, y: x + y, candidates_with_scores) self.chunks_with_score = [] if not candidates_with_scores or len(candidates_with_scores) == 0: print("no relevant docs to retrieve") context = "no relevant docs to retrieve" else: self.chunks_with_score = [] - for d, score in candidates_with_scores: + for chunk in candidates_with_scores: chucks = self.chunk_dao.get_document_chunks( - query=DocumentChunkEntity(content=d.page_content), + query=DocumentChunkEntity(content=chunk.content), document_ids=self.document_ids, ) if len(chucks) > 0: - self.chunks_with_score.append((chucks[0], score)) + self.chunks_with_score.append((chucks[0], chunk.score)) - context = [doc.page_content for doc, _ in candidates_with_scores] + context = "\n".join([doc.content for doc in candidates_with_scores]) - context = context[: self.max_token] if CFG.KNOWLEDGE_CHAT_SHOW_RELATIONS: self.relations = list( set( [ os.path.basename(str(d.metadata.get("source", ""))) - for d, _ in candidates_with_scores + for d in candidates_with_scores ] ) ) @@ -206,7 +210,8 @@ def parse_source_view(self, chunks_with_score: List): references_list = list(references_dict.values()) references_ele.set("references", json.dumps(references_list)) html = ET.tostring(references_ele, encoding="utf-8") - return html.decode("utf-8") + reference = html.decode("utf-8") + return reference.replace("\\n", "") @property def chat_type(self) -> str: @@ -218,10 +223,6 @@ def get_space_context(self, space_name): async def execute_similar_search(self, query): """execute similarity search""" - return await blocking_func_to_async( - self._executor, - self.knowledge_embedding_client.similar_search_with_scores, - query, - self.top_k, - self.recall_score, + return await self.embedding_retriever.aretrieve_with_scores( + query, self.recall_score ) diff --git a/dbgpt/app/scene/operator/_experimental.py b/dbgpt/app/scene/operator/_experimental.py index 7ebe2c4c7..c422846c0 100644 --- a/dbgpt/app/scene/operator/_experimental.py +++ b/dbgpt/app/scene/operator/_experimental.py @@ -2,16 +2,20 @@ from dataclasses import dataclass import datetime import os + +from dbgpt.configs.model_config import PILOT_PATH from dbgpt.core.awel import MapOperator from dbgpt.core.interface.prompt import PromptTemplate from dbgpt._private.config import Config from dbgpt.app.scene import ChatScene from dbgpt.core.interface.message import OnceConversation from dbgpt.core.interface.message import ModelMessage, ModelMessageRoleType - +from dbgpt.rag.retriever.embedding import EmbeddingRetriever from dbgpt.storage.chat_history.base import BaseChatHistoryMemory from dbgpt.storage.chat_history.chat_hisotry_factory import ChatHistory +from dbgpt.storage.vector_store.base import VectorStoreConfig +from dbgpt.storage.vector_store.connector import VectorStoreConnector # TODO move global config CFG = Config() @@ -184,23 +188,14 @@ def __init__(self, **kwargs): async def map(self, input_value: ChatContext) -> ChatContext: from dbgpt.configs.model_config import EMBEDDING_MODEL_CONFIG - from dbgpt.rag.embedding_engine.embedding_engine import EmbeddingEngine - from dbgpt.rag.embedding_engine.embedding_factory import EmbeddingFactory + from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory # TODO, decompose the current operator into some atomic operators knowledge_space = input_value.select_param - vector_store_config = { - "vector_store_name": knowledge_space, - "vector_store_type": CFG.VECTOR_STORE_TYPE, - } embedding_factory = self.system_app.get_component( "embedding_factory", EmbeddingFactory ) - knowledge_embedding_client = EmbeddingEngine( - model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL], - vector_store_config=vector_store_config, - embedding_factory=embedding_factory, - ) + space_context = await self._get_space_context(knowledge_space) top_k = ( CFG.KNOWLEDGE_SEARCH_TOP_SIZE @@ -219,16 +214,28 @@ async def map(self, input_value: ChatContext) -> ChatContext: ] input_value.prompt_template.template = space_context["prompt"]["template"] + config = VectorStoreConfig( + name=knowledge_space, + embedding_fn=embedding_factory.create( + EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL] + ), + ) + vector_store_connector = VectorStoreConnector( + vector_store_type=CFG.VECTOR_STORE_TYPE, + vector_store_config=config, + ) + embedding_retriever = EmbeddingRetriever( + top_k=top_k, vector_store_connector=vector_store_connector + ) docs = await self.blocking_func_to_async( - knowledge_embedding_client.similar_search, + embedding_retriever.retrieve, input_value.current_user_input, - top_k, ) if not docs or len(docs) == 0: print("no relevant docs to retrieve") context = "no relevant docs to retrieve" else: - context = [d.page_content for d in docs] + context = [d.content for d in docs] context = context[:max_token] relations = list( set([os.path.basename(str(d.metadata.get("source", ""))) for d in docs]) diff --git a/dbgpt/app/static/404.html b/dbgpt/app/static/404.html index 4ef1abcda..53505aaee 100644 --- a/dbgpt/app/static/404.html +++ b/dbgpt/app/static/404.html @@ -1 +1 @@ -404: This page could not be found

404

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\ No newline at end of file diff --git a/dbgpt/app/static/404/index.html b/dbgpt/app/static/404/index.html index 4ef1abcda..53505aaee 100644 --- a/dbgpt/app/static/404/index.html +++ b/dbgpt/app/static/404/index.html @@ -1 +1 @@ -404: This page could not be found

404

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o=RegExp(`(${r.map(e=>"?"===e?"\\?":e).join("|")})`),i=!o.test(e);if(!i){let t=e.indexOf(n);t>0&&!o.test(e.substring(0,t))&&(i=!0)}return i}(e,n,r);if(i&&!a){let t=e.match(this.interpolator.nestingRegexp);if(t&&t.length>0)return{key:e,namespaces:o};let i=e.split(n);(n!==r||n===r&&this.options.ns.indexOf(i[0])>-1)&&(o=i.shift()),e=i.join(r)}return"string"==typeof o&&(o=[o]),{key:e,namespaces:o}}translate(e,t,n){if("object"!=typeof t&&this.options.overloadTranslationOptionHandler&&(t=this.options.overloadTranslationOptionHandler(arguments)),"object"==typeof t&&(t={...t}),t||(t={}),null==e)return"";Array.isArray(e)||(e=[String(e)]);let r=void 0!==t.returnDetails?t.returnDetails:this.options.returnDetails,o=void 0!==t.keySeparator?t.keySeparator:this.options.keySeparator,{key:i,namespaces:a}=this.extractFromKey(e[e.length-1],t),l=a[a.length-1],s=t.lng||this.language,c=t.appendNamespaceToCIMode||this.options.appendNamespaceToCIMode;if(s&&"cimode"===s.toLowerCase()){if(c){let e=t.nsSeparator||this.options.nsSeparator;return r?{res:`${l}${e}${i}`,usedKey:i,exactUsedKey:i,usedLng:s,usedNS:l}:`${l}${e}${i}`}return r?{res:i,usedKey:i,exactUsedKey:i,usedLng:s,usedNS:l}:i}let u=this.resolve(e,t),f=u&&u.res,d=u&&u.usedKey||i,p=u&&u.exactUsedKey||i,h=Object.prototype.toString.apply(f),m=void 0!==t.joinArrays?t.joinArrays:this.options.joinArrays,g=!this.i18nFormat||this.i18nFormat.handleAsObject,v="string"!=typeof f&&"boolean"!=typeof f&&"number"!=typeof f;if(g&&f&&v&&0>["[object Number]","[object Function]","[object RegExp]"].indexOf(h)&&!("string"==typeof m&&"[object Array]"===h)){if(!t.returnObjects&&!this.options.returnObjects){this.options.returnedObjectHandler||this.logger.warn("accessing an object - but returnObjects options is not enabled!");let e=this.options.returnedObjectHandler?this.options.returnedObjectHandler(d,f,{...t,ns:a}):`key '${i} (${this.language})' returned an object instead of string.`;return r?(u.res=e,u):e}if(o){let e="[object Array]"===h,n=e?[]:{},r=e?p:d;for(let e in f)if(Object.prototype.hasOwnProperty.call(f,e)){let i=`${r}${o}${e}`;n[e]=this.translate(i,{...t,joinArrays:!1,ns:a}),n[e]===i&&(n[e]=f[e])}f=n}}else if(g&&"string"==typeof m&&"[object Array]"===h)(f=f.join(m))&&(f=this.extendTranslation(f,e,t,n));else{let r=!1,a=!1,c=void 0!==t.count&&"string"!=typeof t.count,d=w.hasDefaultValue(t),p=c?this.pluralResolver.getSuffix(s,t.count,t):"",h=t.ordinal&&c?this.pluralResolver.getSuffix(s,t.count,{ordinal:!1}):"",m=t[`defaultValue${p}`]||t[`defaultValue${h}`]||t.defaultValue;!this.isValidLookup(f)&&d&&(r=!0,f=m),this.isValidLookup(f)||(a=!0,f=i);let g=t.missingKeyNoValueFallbackToKey||this.options.missingKeyNoValueFallbackToKey,v=g&&a?void 0:f,y=d&&m!==f&&this.options.updateMissing;if(a||r||y){if(this.logger.log(y?"updateKey":"missingKey",s,l,i,y?m:f),o){let e=this.resolve(i,{...t,keySeparator:!1});e&&e.res&&this.logger.warn("Seems the loaded translations were in flat JSON format instead of nested. Either set keySeparator: false on init or make sure your translations are published in nested format.")}let e=[],n=this.languageUtils.getFallbackCodes(this.options.fallbackLng,t.lng||this.language);if("fallback"===this.options.saveMissingTo&&n&&n[0])for(let t=0;t{let o=d&&r!==f?r:v;this.options.missingKeyHandler?this.options.missingKeyHandler(e,l,n,o,y,t):this.backendConnector&&this.backendConnector.saveMissing&&this.backendConnector.saveMissing(e,l,n,o,y,t),this.emit("missingKey",e,l,n,f)};this.options.saveMissing&&(this.options.saveMissingPlurals&&c?e.forEach(e=>{this.pluralResolver.getSuffixes(e,t).forEach(n=>{r([e],i+n,t[`defaultValue${n}`]||m)})}):r(e,i,m))}f=this.extendTranslation(f,e,t,u,n),a&&f===i&&this.options.appendNamespaceToMissingKey&&(f=`${l}:${i}`),(a||r)&&this.options.parseMissingKeyHandler&&(f="v1"!==this.options.compatibilityAPI?this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?`${l}:${i}`:i,r?f:void 0):this.options.parseMissingKeyHandler(f))}return r?(u.res=f,u):f}extendTranslation(e,t,n,r,o){var i=this;if(this.i18nFormat&&this.i18nFormat.parse)e=this.i18nFormat.parse(e,{...this.options.interpolation.defaultVariables,...n},n.lng||this.language||r.usedLng,r.usedNS,r.usedKey,{resolved:r});else if(!n.skipInterpolation){let a;n.interpolation&&this.interpolator.init({...n,interpolation:{...this.options.interpolation,...n.interpolation}});let l="string"==typeof e&&(n&&n.interpolation&&void 0!==n.interpolation.skipOnVariables?n.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables);if(l){let t=e.match(this.interpolator.nestingRegexp);a=t&&t.length}let s=n.replace&&"string"!=typeof n.replace?n.replace:n;if(this.options.interpolation.defaultVariables&&(s={...this.options.interpolation.defaultVariables,...s}),e=this.interpolator.interpolate(e,s,n.lng||this.language,n),l){let t=e.match(this.interpolator.nestingRegexp),r=t&&t.length;a1&&void 0!==arguments[1]?arguments[1]:{};return"string"==typeof e&&(e=[e]),e.forEach(e=>{if(this.isValidLookup(t))return;let l=this.extractFromKey(e,a),s=l.key;n=s;let c=l.namespaces;this.options.fallbackNS&&(c=c.concat(this.options.fallbackNS));let u=void 0!==a.count&&"string"!=typeof a.count,f=u&&!a.ordinal&&0===a.count&&this.pluralResolver.shouldUseIntlApi(),d=void 0!==a.context&&("string"==typeof a.context||"number"==typeof a.context)&&""!==a.context,p=a.lngs?a.lngs:this.languageUtils.toResolveHierarchy(a.lng||this.language,a.fallbackLng);c.forEach(e=>{this.isValidLookup(t)||(i=e,!x[`${p[0]}-${e}`]&&this.utils&&this.utils.hasLoadedNamespace&&!this.utils.hasLoadedNamespace(i)&&(x[`${p[0]}-${e}`]=!0,this.logger.warn(`key "${n}" for languages "${p.join(", ")}" won't get resolved as namespace "${i}" was not yet loaded`,"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),p.forEach(n=>{let i;if(this.isValidLookup(t))return;o=n;let l=[s];if(this.i18nFormat&&this.i18nFormat.addLookupKeys)this.i18nFormat.addLookupKeys(l,s,n,e,a);else{let e;u&&(e=this.pluralResolver.getSuffix(n,a.count,a));let t=`${this.options.pluralSeparator}zero`,r=`${this.options.pluralSeparator}ordinal${this.options.pluralSeparator}`;if(u&&(l.push(s+e),a.ordinal&&0===e.indexOf(r)&&l.push(s+e.replace(r,this.options.pluralSeparator)),f&&l.push(s+t)),d){let n=`${s}${this.options.contextSeparator}${a.context}`;l.push(n),u&&(l.push(n+e),a.ordinal&&0===e.indexOf(r)&&l.push(n+e.replace(r,this.options.pluralSeparator)),f&&l.push(n+t))}}for(;i=l.pop();)this.isValidLookup(t)||(r=i,t=this.getResource(n,e,i,a))}))})}),{res:t,usedKey:n,exactUsedKey:r,usedLng:o,usedNS:i}}isValidLookup(e){return void 0!==e&&!(!this.options.returnNull&&null===e)&&!(!this.options.returnEmptyString&&""===e)}getResource(e,t,n){let r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(e,t,n,r):this.resourceStore.getResource(e,t,n,r)}static hasDefaultValue(e){let t="defaultValue";for(let n in e)if(Object.prototype.hasOwnProperty.call(e,n)&&t===n.substring(0,t.length)&&void 0!==e[n])return!0;return!1}}function C(e){return e.charAt(0).toUpperCase()+e.slice(1)}class S{constructor(e){this.options=e,this.supportedLngs=this.options.supportedLngs||!1,this.logger=i.create("languageUtils")}getScriptPartFromCode(e){if(!(e=v(e))||0>e.indexOf("-"))return null;let t=e.split("-");return 2===t.length?null:(t.pop(),"x"===t[t.length-1].toLowerCase())?null:this.formatLanguageCode(t.join("-"))}getLanguagePartFromCode(e){if(!(e=v(e))||0>e.indexOf("-"))return e;let t=e.split("-");return this.formatLanguageCode(t[0])}formatLanguageCode(e){if("string"==typeof e&&e.indexOf("-")>-1){let t=["hans","hant","latn","cyrl","cans","mong","arab"],n=e.split("-");return this.options.lowerCaseLng?n=n.map(e=>e.toLowerCase()):2===n.length?(n[0]=n[0].toLowerCase(),n[1]=n[1].toUpperCase(),t.indexOf(n[1].toLowerCase())>-1&&(n[1]=C(n[1].toLowerCase()))):3===n.length&&(n[0]=n[0].toLowerCase(),2===n[1].length&&(n[1]=n[1].toUpperCase()),"sgn"!==n[0]&&2===n[2].length&&(n[2]=n[2].toUpperCase()),t.indexOf(n[1].toLowerCase())>-1&&(n[1]=C(n[1].toLowerCase())),t.indexOf(n[2].toLowerCase())>-1&&(n[2]=C(n[2].toLowerCase()))),n.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?e.toLowerCase():e}isSupportedCode(e){return("languageOnly"===this.options.load||this.options.nonExplicitSupportedLngs)&&(e=this.getLanguagePartFromCode(e)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(e)>-1}getBestMatchFromCodes(e){let t;return e?(e.forEach(e=>{if(t)return;let n=this.formatLanguageCode(e);(!this.options.supportedLngs||this.isSupportedCode(n))&&(t=n)}),!t&&this.options.supportedLngs&&e.forEach(e=>{if(t)return;let n=this.getLanguagePartFromCode(e);if(this.isSupportedCode(n))return t=n;t=this.options.supportedLngs.find(e=>{if(e===n||!(0>e.indexOf("-")&&0>n.indexOf("-"))&&0===e.indexOf(n))return e})}),t||(t=this.getFallbackCodes(this.options.fallbackLng)[0]),t):null}getFallbackCodes(e,t){if(!e)return[];if("function"==typeof e&&(e=e(t)),"string"==typeof e&&(e=[e]),"[object Array]"===Object.prototype.toString.apply(e))return e;if(!t)return e.default||[];let n=e[t];return n||(n=e[this.getScriptPartFromCode(t)]),n||(n=e[this.formatLanguageCode(t)]),n||(n=e[this.getLanguagePartFromCode(t)]),n||(n=e.default),n||[]}toResolveHierarchy(e,t){let n=this.getFallbackCodes(t||this.options.fallbackLng||[],e),r=[],o=e=>{e&&(this.isSupportedCode(e)?r.push(e):this.logger.warn(`rejecting language code not found in supportedLngs: ${e}`))};return"string"==typeof e&&(e.indexOf("-")>-1||e.indexOf("_")>-1)?("languageOnly"!==this.options.load&&o(this.formatLanguageCode(e)),"languageOnly"!==this.options.load&&"currentOnly"!==this.options.load&&o(this.getScriptPartFromCode(e)),"currentOnly"!==this.options.load&&o(this.getLanguagePartFromCode(e))):"string"==typeof e&&o(this.formatLanguageCode(e)),n.forEach(e=>{0>r.indexOf(e)&&o(this.formatLanguageCode(e))}),r}}let E=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],$={1:function(e){return Number(e>1)},2:function(e){return Number(1!=e)},3:function(e){return 0},4:function(e){return Number(e%10==1&&e%100!=11?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2)},5:function(e){return Number(0==e?0:1==e?1:2==e?2:e%100>=3&&e%100<=10?3:e%100>=11?4:5)},6:function(e){return Number(1==e?0:e>=2&&e<=4?1:2)},7:function(e){return Number(1==e?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2)},8:function(e){return Number(1==e?0:2==e?1:8!=e&&11!=e?2:3)},9:function(e){return Number(e>=2)},10:function(e){return Number(1==e?0:2==e?1:e<7?2:e<11?3:4)},11:function(e){return Number(1==e||11==e?0:2==e||12==e?1:e>2&&e<20?2:3)},12:function(e){return Number(e%10!=1||e%100==11)},13:function(e){return Number(0!==e)},14:function(e){return Number(1==e?0:2==e?1:3==e?2:3)},15:function(e){return Number(e%10==1&&e%100!=11?0:e%10>=2&&(e%100<10||e%100>=20)?1:2)},16:function(e){return Number(e%10==1&&e%100!=11?0:0!==e?1:2)},17:function(e){return Number(1==e||e%10==1&&e%100!=11?0:1)},18:function(e){return Number(0==e?0:1==e?1:2)},19:function(e){return Number(1==e?0:0==e||e%100>1&&e%100<11?1:e%100>10&&e%100<20?2:3)},20:function(e){return Number(1==e?0:0==e||e%100>0&&e%100<20?1:2)},21:function(e){return Number(e%100==1?1:e%100==2?2:e%100==3||e%100==4?3:0)},22:function(e){return Number(1==e?0:2==e?1:(e<0||e>10)&&e%10==0?2:3)}},O=["v1","v2","v3"],k=["v4"],j={zero:0,one:1,two:2,few:3,many:4,other:5};class P{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};this.languageUtils=e,this.options=t,this.logger=i.create("pluralResolver"),(!this.options.compatibilityJSON||k.includes(this.options.compatibilityJSON))&&("undefined"==typeof Intl||!Intl.PluralRules)&&(this.options.compatibilityJSON="v3",this.logger.error("Your environment seems not to be Intl API compatible, use an Intl.PluralRules polyfill. 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e&&(e.indexOf("-")>-1||e.indexOf("_")>-1)?("languageOnly"!==this.options.load&&o(this.formatLanguageCode(e)),"languageOnly"!==this.options.load&&"currentOnly"!==this.options.load&&o(this.getScriptPartFromCode(e)),"currentOnly"!==this.options.load&&o(this.getLanguagePartFromCode(e))):"string"==typeof e&&o(this.formatLanguageCode(e)),n.forEach(e=>{0>r.indexOf(e)&&o(this.formatLanguageCode(e))}),r}}let 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r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:{},o=t.split(this.formatSeparator),i=o.reduce((e,t)=>{let{formatName:o,formatOptions:i}=function(e){let t=e.toLowerCase().trim(),n={};if(e.indexOf("(")>-1){let r=e.split("(");t=r[0].toLowerCase().trim();let o=r[1].substring(0,r[1].length-1);if("currency"===t&&0>o.indexOf(":"))n.currency||(n.currency=o.trim());else if("relativetime"===t&&0>o.indexOf(":"))n.range||(n.range=o.trim());else{let e=o.split(";");e.forEach(e=>{if(!e)return;let[t,...r]=e.split(":"),o=r.join(":").trim().replace(/^'+|'+$/g,"");n[t.trim()]||(n[t.trim()]=o),"false"===o&&(n[t.trim()]=!1),"true"===o&&(n[t.trim()]=!0),isNaN(o)||(n[t.trim()]=parseInt(o,10))})}}return{formatName:t,formatOptions:n}}(t);if(this.formats[o]){let t=e;try{let a=r&&r.formatParams&&r.formatParams[r.interpolationkey]||{},l=a.locale||a.lng||r.locale||r.lng||n;t=this.formats[o](e,l,{...i,...r,...a})}catch(e){this.logger.warn(e)}return t}return this.logger.warn(`there was no format function 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\ No newline at end of file diff --git a/dbgpt/rag/chunk.py b/dbgpt/rag/chunk.py new file mode 100644 index 000000000..ddfb8cd90 --- /dev/null +++ b/dbgpt/rag/chunk.py @@ -0,0 +1,116 @@ +import json +import uuid +from typing import Any, Dict + +from pydantic import Field, BaseModel + + +class Document(BaseModel): + """Document including document content, document metadata.""" + + content: str = (Field(default="", description="document text content"),) + + metadata: Dict[str, Any] = ( + Field( + default_factory=dict, + description="metadata fields", + ), + ) + + def set_content(self, content: str) -> None: + """Set the content""" + self.content = content + + def get_content(self) -> str: + return self.content + + @classmethod + def langchain2doc(cls, document): + """Transformation from Langchain to Chunk Document format.""" + metadata = document.metadata or {} + return cls(content=document.page_content, metadata=metadata) + + @classmethod + def doc2langchain(cls, chunk): + """Transformation from Chunk to Langchain Document format.""" + from langchain.schema import Document as LCDocument + + return LCDocument(page_content=chunk.content, metadata=chunk.metadata) + + +class Chunk(Document): + """ + Document Chunk including chunk content, chunk metadata, chunk summary, chunk relations. + """ + + chunk_id: str = Field( + default_factory=lambda: str(uuid.uuid4()), description="unique id for the chunk" + ) + content: str = Field(default="", description="chunk text content") + + metadata: Dict[str, Any] = ( + Field( + default_factory=dict, + description="metadata fields", + ), + ) + score: float = Field(default=0.0, description="chunk text similarity score") + summary: str = Field(default="", description="chunk text summary") + separator: str = Field( + default="\n", + description="Separator between metadata fields when converting to string.", + ) + + def to_dict(self, **kwargs: Any) -> Dict[str, Any]: + data = self.dict(**kwargs) + data["class_name"] = self.class_name() + return data + + def to_json(self, **kwargs: Any) -> str: + data = self.to_dict(**kwargs) + return json.dumps(data) + + def __hash__(self): + return hash((self.chunk_id,)) + + def __eq__(self, other): + return self.chunk_id == other.chunk_id + + @classmethod + def from_dict(cls, data: Dict[str, Any], **kwargs: Any): # type: ignore + if isinstance(kwargs, dict): + data.update(kwargs) + + data.pop("class_name", None) + return cls(**data) + + @classmethod + def from_json(cls, data_str: str, **kwargs: Any): # type: ignore + data = json.loads(data_str) + return cls.from_dict(data, **kwargs) + + @classmethod + def langchain2chunk(cls, document): + """Transformation from Langchain to Chunk Document format.""" + metadata = document.metadata or {} + return cls(content=document.page_content, metadata=document.metadata) + + @classmethod + def llamaindex2chunk(cls, node): + """Transformation from LLama-Index to Chunk Document format.""" + metadata = node.metadata or {} + return cls(content=node.content, metadata=metadata) + + @classmethod + def chunk2langchain(cls, chunk): + """Transformation from Chunk to Langchain Document format.""" + from langchain.schema import Document as LCDocument + + return LCDocument(page_content=chunk.content, metadata=chunk.metadata) + + @classmethod + def chunk2llamaindex(cls, chunk): + """Transformation from Chunk to LLama-Index Document format.""" + from llama_index.schema import TextNode + + return TextNode(text=chunk.content, metadata=chunk.metadata) diff --git a/dbgpt/rag/chunk_manager.py b/dbgpt/rag/chunk_manager.py new file mode 100644 index 000000000..f07fe4d3c --- /dev/null +++ b/dbgpt/rag/chunk_manager.py @@ -0,0 +1,137 @@ +from enum import Enum +from typing import Optional, List, Any + +from pydantic import BaseModel, Field + +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.extractor.base import Extractor +from dbgpt.rag.knowledge.base import ChunkStrategy, Knowledge + + +class SplitterType(Enum): + """splitter type""" + + LANGCHAIN = "langchain" + LLAMA_INDEX = "llama-index" + USER_DEFINE = "user_define" + + +class ChunkParameters(BaseModel): + """ChunkParameters""" + + chunk_strategy: str = Field( + default=None, + description="chunk strategy", + ) + text_splitter: Optional[Any] = Field( + default=None, + description="text splitter", + ) + + splitter_type: SplitterType = Field( + default=SplitterType.USER_DEFINE, + description="splitter type", + ) + + chunk_size: int = Field( + default=512, + description="chunk size", + ) + chunk_overlap: int = Field( + default=50, + description="chunk overlap", + ) + separator: str = Field( + default="\n", + description="chunk separator", + ) + + +class ChunkManager: + """ChunkManager""" + + def __init__( + self, + knowledge: Knowledge = None, + chunk_parameter: Optional[ChunkParameters] = None, + extractor: Optional[Extractor] = None, + ): + """ + Args: + knowledge: (Knowledge) Knowledge datasource. + chunk_parameter: (Optional[ChunkParameter]) Chunk parameter. + extractor: (Optional[Extractor]) Extractor to use for summarization. + """ + self._knowledge = knowledge + + self._extractor = extractor + self._chunk_parameters = chunk_parameter or ChunkParameters() + self._chunk_strategy = ( + chunk_parameter.chunk_strategy + or self._knowledge.default_chunk_strategy().name + ) + self._text_splitter = chunk_parameter.text_splitter + self._splitter_type = chunk_parameter.splitter_type + + def split(self, documents) -> List[Chunk]: + """Split a document into chunks.""" + text_splitter = self._select_text_splitter() + if SplitterType.LANGCHAIN == self._splitter_type: + documents = text_splitter.split_documents(documents) + return [Chunk.langchain2chunk(document) for document in documents] + elif SplitterType.LLAMA_INDEX == self._splitter_type: + nodes = text_splitter.split_text(documents) + return [Chunk.llamaindex2chunk(node) for node in nodes] + else: + return text_splitter.split_documents(documents) + + def split_with_summary( + self, document: Any, chunk_strategy: ChunkStrategy + ) -> List[Chunk]: + """Split a document into chunks and summary""" + + raise NotImplementedError + + @property + def chunk_parameters(self) -> ChunkParameters: + return self._chunk_parameters + + def set_text_splitter( + self, + text_splitter, + splitter_type: Optional[SplitterType] = SplitterType.LANGCHAIN, + ) -> None: + """Add text splitter.""" + self._text_splitter = text_splitter + self._splitter_type = splitter_type + + def get_text_splitter( + self, + ) -> Any: + """get text splitter.""" + return self._select_text_splitter() + + def _select_text_splitter( + self, + ): + """Select text splitter by chunk strategy.""" + if self._text_splitter: + return self._text_splitter + if not self._chunk_strategy or "Automatic" == self._chunk_strategy: + self._chunk_strategy = self._knowledge.default_chunk_strategy().name + if self._chunk_strategy not in [ + support_chunk_strategy.name + for support_chunk_strategy in self._knowledge.support_chunk_strategy() + ]: + current_type = self._knowledge.type().value + if self._knowledge.document_type(): + current_type = self._knowledge.document_type().value + raise ValueError( + f"{current_type} knowledge not supported chunk strategy {self._chunk_strategy} " + ) + strategy = ChunkStrategy[self._chunk_strategy] + return strategy.match( + chunk_size=self._chunk_parameters.chunk_size, + chunk_overlap=self._chunk_parameters.chunk_overlap, + separator=self._chunk_parameters.separator, + ) diff --git a/dbgpt/app/scene/chat_knowledge/__init__.py b/dbgpt/rag/embedding/__init__.py similarity index 100% rename from dbgpt/app/scene/chat_knowledge/__init__.py rename to dbgpt/rag/embedding/__init__.py diff --git a/dbgpt/rag/embedding_engine/embedding_factory.py b/dbgpt/rag/embedding/embedding_factory.py similarity index 87% rename from dbgpt/rag/embedding_engine/embedding_factory.py rename to dbgpt/rag/embedding/embedding_factory.py index 589e148d3..d63ad968f 100644 --- a/dbgpt/rag/embedding_engine/embedding_factory.py +++ b/dbgpt/rag/embedding/embedding_factory.py @@ -3,12 +3,15 @@ from typing import Any, Type, TYPE_CHECKING from dbgpt.component import BaseComponent +from dbgpt.rag.embedding.embeddings import HuggingFaceEmbeddings if TYPE_CHECKING: - from langchain.embeddings.base import Embeddings + from dbgpt.rag.embedding.embeddings import Embeddings class EmbeddingFactory(BaseComponent, ABC): + """Abstract base class for EmbeddingFactory.""" + name = "embedding_factory" @abstractmethod @@ -41,6 +44,4 @@ def create( if embedding_cls: return embedding_cls(**new_kwargs) else: - from langchain.embeddings import HuggingFaceEmbeddings - return HuggingFaceEmbeddings(**new_kwargs) diff --git a/dbgpt/rag/embedding/embeddings.py b/dbgpt/rag/embedding/embeddings.py new file mode 100644 index 000000000..cdc338508 --- /dev/null +++ b/dbgpt/rag/embedding/embeddings.py @@ -0,0 +1,363 @@ +import asyncio +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Optional + +import requests +from pydantic import Field, Extra, BaseModel + +DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2" +DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large" +DEFAULT_BGE_MODEL = "BAAI/bge-large-en" +DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: " +DEFAULT_QUERY_INSTRUCTION = ( + "Represent the question for retrieving supporting documents: " +) +DEFAULT_QUERY_BGE_INSTRUCTION_EN = ( + "Represent this question for searching relevant passages: " +) +DEFAULT_QUERY_BGE_INSTRUCTION_ZH = "为这个句子生成表示以用于检索相关文章：" + + +class Embeddings(ABC): + """Interface for embedding models.""" + + """refer to https://github.com/langchain-ai/langchain/tree/master/libs/langchain/langchain/embeddings""" + + @abstractmethod + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Embed search docs.""" + + @abstractmethod + def embed_query(self, text: str) -> List[float]: + """Embed query text.""" + + async def aembed_documents(self, texts: List[str]) -> List[List[float]]: + """Asynchronous Embed search docs.""" + return await asyncio.get_running_loop().run_in_executor( + None, self.embed_documents, texts + ) + + async def aembed_query(self, text: str) -> List[float]: + """Asynchronous Embed query text.""" + return await asyncio.get_running_loop().run_in_executor( + None, self.embed_query, text + ) + + +class HuggingFaceEmbeddings(BaseModel, Embeddings): + """HuggingFace sentence_transformers embedding models. + To use, you should have the ``sentence_transformers`` python package installed. + Refer to https://github.com/langchain-ai/langchain/tree/master/libs/langchain/langchain/embeddings + Example: + .. code-block:: python + + from .embeddings import HuggingFaceEmbeddings + + model_name = "sentence-transformers/all-mpnet-base-v2" + model_kwargs = {'device': 'cpu'} + encode_kwargs = {'normalize_embeddings': False} + hf = HuggingFaceEmbeddings( + model_name=model_name, + model_kwargs=model_kwargs, + encode_kwargs=encode_kwargs + ) + """ + + client: Any #: :meta private: + model_name: str = DEFAULT_MODEL_NAME + """Model name to use.""" + cache_folder: Optional[str] = None + """Path to store models. + Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.""" + model_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Keyword arguments to pass to the model.""" + encode_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Keyword arguments to pass when calling the `encode` method of the model.""" + multi_process: bool = False + """Run encode() on multiple GPUs.""" + + def __init__(self, **kwargs: Any): + """Initialize the sentence_transformer.""" + super().__init__(**kwargs) + try: + import sentence_transformers + + except ImportError as exc: + raise ImportError( + "Could not import sentence_transformers python package. " + "Please install it with `pip install sentence-transformers`." + ) from exc + + self.client = sentence_transformers.SentenceTransformer( + self.model_name, cache_folder=self.cache_folder, **self.model_kwargs + ) + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.forbid + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Compute doc embeddings using a HuggingFace transformer model. + + Args: + texts: The list of texts to embed. + + Returns: + List of embeddings, one for each text. + """ + import sentence_transformers + + texts = list(map(lambda x: x.replace("\n", " "), texts)) + if self.multi_process: + pool = self.client.start_multi_process_pool() + embeddings = self.client.encode_multi_process(texts, pool) + sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool) + else: + embeddings = self.client.encode(texts, **self.encode_kwargs) + + return embeddings.tolist() + + def embed_query(self, text: str) -> List[float]: + """Compute query embeddings using a HuggingFace transformer model. + + Args: + text: The text to embed. + + Returns: + Embeddings for the text. + """ + return self.embed_documents([text])[0] + + +class HuggingFaceInstructEmbeddings(BaseModel, Embeddings): + """Wrapper around sentence_transformers embedding models. + + To use, you should have the ``sentence_transformers`` + and ``InstructorEmbedding`` python packages installed. + + Example: + .. code-block:: python + + from langchain.embeddings import HuggingFaceInstructEmbeddings + + model_name = "hkunlp/instructor-large" + model_kwargs = {'device': 'cpu'} + encode_kwargs = {'normalize_embeddings': True} + hf = HuggingFaceInstructEmbeddings( + model_name=model_name, + model_kwargs=model_kwargs, + encode_kwargs=encode_kwargs + ) + """ + + client: Any #: :meta private: + model_name: str = DEFAULT_INSTRUCT_MODEL + """Model name to use.""" + cache_folder: Optional[str] = None + """Path to store models. + Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.""" + model_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Keyword arguments to pass to the model.""" + encode_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Keyword arguments to pass when calling the `encode` method of the model.""" + embed_instruction: str = DEFAULT_EMBED_INSTRUCTION + """Instruction to use for embedding documents.""" + query_instruction: str = DEFAULT_QUERY_INSTRUCTION + """Instruction to use for embedding query.""" + + def __init__(self, **kwargs: Any): + """Initialize the sentence_transformer.""" + super().__init__(**kwargs) + try: + from InstructorEmbedding import INSTRUCTOR + + self.client = INSTRUCTOR( + self.model_name, cache_folder=self.cache_folder, **self.model_kwargs + ) + except ImportError as e: + raise ImportError("Dependencies for InstructorEmbedding not found.") from e + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.forbid + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Compute doc embeddings using a HuggingFace instruct model. + + Args: + texts: The list of texts to embed. + + Returns: + List of embeddings, one for each text. + """ + instruction_pairs = [[self.embed_instruction, text] for text in texts] + embeddings = self.client.encode(instruction_pairs, **self.encode_kwargs) + return embeddings.tolist() + + def embed_query(self, text: str) -> List[float]: + """Compute query embeddings using a HuggingFace instruct model. + + Args: + text: The text to embed. + + Returns: + Embeddings for the text. + """ + instruction_pair = [self.query_instruction, text] + embedding = self.client.encode([instruction_pair], **self.encode_kwargs)[0] + return embedding.tolist() + + +class HuggingFaceBgeEmbeddings(BaseModel, Embeddings): + """HuggingFace BGE sentence_transformers embedding models. + + To use, you should have the ``sentence_transformers`` python package installed. + refer to https://github.com/langchain-ai/langchain/tree/master/libs/langchain/langchain/embeddings + Example: + .. code-block:: python + + from langchain.embeddings import HuggingFaceBgeEmbeddings + + model_name = "BAAI/bge-large-en" + model_kwargs = {'device': 'cpu'} + encode_kwargs = {'normalize_embeddings': True} + hf = HuggingFaceBgeEmbeddings( + model_name=model_name, + model_kwargs=model_kwargs, + encode_kwargs=encode_kwargs + ) + """ + + client: Any #: :meta private: + model_name: str = DEFAULT_BGE_MODEL + """Model name to use.""" + cache_folder: Optional[str] = None + """Path to store models. + Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.""" + model_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Keyword arguments to pass to the model.""" + encode_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Keyword arguments to pass when calling the `encode` method of the model.""" + query_instruction: str = DEFAULT_QUERY_BGE_INSTRUCTION_EN + """Instruction to use for embedding query.""" + + def __init__(self, **kwargs: Any): + """Initialize the sentence_transformer.""" + super().__init__(**kwargs) + try: + import sentence_transformers + + except ImportError as exc: + raise ImportError( + "Could not import sentence_transformers python package. " + "Please install it with `pip install sentence_transformers`." + ) from exc + + self.client = sentence_transformers.SentenceTransformer( + self.model_name, cache_folder=self.cache_folder, **self.model_kwargs + ) + if "-zh" in self.model_name: + self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.forbid + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Compute doc embeddings using a HuggingFace transformer model. + + Args: + texts: The list of texts to embed. + + Returns: + List of embeddings, one for each text. + """ + texts = [t.replace("\n", " ") for t in texts] + embeddings = self.client.encode(texts, **self.encode_kwargs) + return embeddings.tolist() + + def embed_query(self, text: str) -> List[float]: + """Compute query embeddings using a HuggingFace transformer model. + + Args: + text: The text to embed. + + Returns: + Embeddings for the text. + """ + text = text.replace("\n", " ") + embedding = self.client.encode( + self.query_instruction + text, **self.encode_kwargs + ) + return embedding.tolist() + + +class HuggingFaceInferenceAPIEmbeddings(BaseModel, Embeddings): + """Embed texts using the HuggingFace API. + + Requires a HuggingFace Inference API key and a model name. + """ + + api_key: str + """Your API key for the HuggingFace Inference API.""" + model_name: str = "sentence-transformers/all-MiniLM-L6-v2" + """The name of the model to use for text embeddings.""" + + @property + def _api_url(self) -> str: + return ( + "https://api-inference.huggingface.co" + "/pipeline" + "/feature-extraction" + f"/{self.model_name}" + ) + + @property + def _headers(self) -> dict: + return {"Authorization": f"Bearer {self.api_key}"} + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Get the embeddings for a list of texts. + + Args: + texts (Documents): A list of texts to get embeddings for. + + Returns: + Embedded texts as List[List[float]], where each inner List[float] + corresponds to a single input text. + + Example: + .. code-block:: python + + from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings + + hf_embeddings = HuggingFaceInferenceAPIEmbeddings( + api_key="your_api_key", + model_name="sentence-transformers/all-MiniLM-l6-v2" + ) + texts = ["Hello, world!", "How are you?"] + hf_embeddings.embed_documents(texts) + """ + response = requests.post( + self._api_url, + headers=self._headers, + json={ + "inputs": texts, + "options": {"wait_for_model": True, "use_cache": True}, + }, + ) + return response.json() + + def embed_query(self, text: str) -> List[float]: + """Compute query embeddings using a HuggingFace transformer model. + + Args: + text: The text to embed. + + Returns: + Embeddings for the text. + """ + return self.embed_documents([text])[0] diff --git a/dbgpt/app/scene/chat_knowledge/inner_db_summary/__init__.py b/dbgpt/rag/embedding/tests/__init__.py similarity index 100% rename from dbgpt/app/scene/chat_knowledge/inner_db_summary/__init__.py rename to dbgpt/rag/embedding/tests/__init__.py diff --git a/dbgpt/rag/embedding_engine/__init__.py b/dbgpt/rag/embedding_engine/__init__.py deleted file mode 100644 index afe9b9dc3..000000000 --- a/dbgpt/rag/embedding_engine/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from dbgpt.rag.embedding_engine.source_embedding import SourceEmbedding, register -from dbgpt.rag.embedding_engine.embedding_engine import EmbeddingEngine -from dbgpt.rag.embedding_engine.knowledge_type import KnowledgeType -from dbgpt.rag.embedding_engine.pre_text_splitter import PreTextSplitter - -__all__ = [ - "SourceEmbedding", - "register", - "EmbeddingEngine", - "KnowledgeType", - "PreTextSplitter", -] diff --git a/dbgpt/rag/embedding_engine/csv_embedding.py b/dbgpt/rag/embedding_engine/csv_embedding.py deleted file mode 100644 index 475e97ddd..000000000 --- a/dbgpt/rag/embedding_engine/csv_embedding.py +++ /dev/null @@ -1,64 +0,0 @@ -from typing import List, Optional - -from langchain.schema import Document -from langchain.text_splitter import ( - TextSplitter, - SpacyTextSplitter, - RecursiveCharacterTextSplitter, -) - -from dbgpt.rag.embedding_engine import SourceEmbedding, register -from dbgpt.rag.embedding_engine.loader.csv_loader import NewCSVLoader - - -class CSVEmbedding(SourceEmbedding): - """csv embedding for read csv document.""" - - def __init__( - self, - file_path, - vector_store_config, - source_reader: Optional = None, - text_splitter: Optional[TextSplitter] = None, - ): - """Initialize with csv path. - Args: - - file_path: data source path - - vector_store_config: vector store config params. - - source_reader: Optional[BaseLoader] - - text_splitter: Optional[TextSplitter] - """ - super().__init__( - file_path, vector_store_config, source_reader=None, text_splitter=None - ) - self.file_path = file_path - self.vector_store_config = vector_store_config - self.source_reader = source_reader or None - self.text_splitter = text_splitter or None - - @register - def read(self): - """Load from csv path.""" - if self.source_reader is None: - self.source_reader = NewCSVLoader(self.file_path) - if self.text_splitter is None: - try: - self.text_splitter = SpacyTextSplitter( - pipeline="zh_core_web_sm", - chunk_size=100, - chunk_overlap=100, - ) - except Exception: - self.text_splitter = RecursiveCharacterTextSplitter( - chunk_size=100, chunk_overlap=50 - ) - - return self.source_reader.load_and_split(self.text_splitter) - - @register - def data_process(self, documents: List[Document]): - i = 0 - for d in documents: - documents[i].page_content = d.page_content.replace("\n", "") - i += 1 - return documents diff --git a/dbgpt/rag/embedding_engine/embedding_engine.py b/dbgpt/rag/embedding_engine/embedding_engine.py deleted file mode 100644 index 04ef73561..000000000 --- a/dbgpt/rag/embedding_engine/embedding_engine.py +++ /dev/null @@ -1,145 +0,0 @@ -from typing import Optional - -from langchain.text_splitter import TextSplitter - -from dbgpt.rag.embedding_engine.embedding_factory import ( - EmbeddingFactory, - DefaultEmbeddingFactory, -) -from dbgpt.rag.embedding_engine.knowledge_type import ( - get_knowledge_embedding, - KnowledgeType, -) -from dbgpt.storage.vector_store.connector import VectorStoreConnector - - -class EmbeddingEngine: - """EmbeddingEngine provide a chain process include(read->text_split->data_process->index_store) for knowledge document embedding into vector store. - 1.knowledge_embedding:knowledge document source into vector store.(Chroma, Milvus, Weaviate) - 2.similar_search: similarity search from vector_store - how to use reference:https://db-gpt.readthedocs.io/en/latest/modules/knowledge.html - how to integrate:https://db-gpt.readthedocs.io/en/latest/modules/knowledge/pdf/pdf_embedding.html - Example: - .. code-block:: python - embedding_model = "your_embedding_model" - vector_store_type = "Chroma" - chroma_persist_path = "your_persist_path" - vector_store_config = { - "vector_store_name": "document_test", - "vector_store_type": vector_store_type, - "chroma_persist_path": chroma_persist_path, - } - - # it can be .md,.pdf,.docx, .csv, .html - document_path = "your_path/test.md" - embedding_engine = EmbeddingEngine( - knowledge_source=document_path, - knowledge_type=KnowledgeType.DOCUMENT.value, - model_name=embedding_model, - vector_store_config=vector_store_config, - ) - # embedding document content to vector store - embedding_engine.knowledge_embedding() - """ - - def __init__( - self, - model_name, - vector_store_config, - knowledge_type: Optional[str] = KnowledgeType.DOCUMENT.value, - knowledge_source: Optional[str] = None, - source_reader: Optional = None, - text_splitter: Optional[TextSplitter] = None, - embedding_factory: EmbeddingFactory = None, - ): - """Initialize with knowledge embedding client, model_name, vector_store_config, knowledge_type, knowledge_source - Args: - - model_name: model_name - - vector_store_config: vector store config: Dict - - knowledge_type: Optional[KnowledgeType] - - knowledge_source: Optional[str] - - source_reader: Optional[BaseLoader] - - text_splitter: Optional[TextSplitter] - - embedding_factory: EmbeddingFactory - """ - self.knowledge_source = knowledge_source - self.model_name = model_name - self.vector_store_config = vector_store_config - self.knowledge_type = knowledge_type - if not embedding_factory: - embedding_factory = DefaultEmbeddingFactory() - self.embeddings = embedding_factory.create(model_name=self.model_name) - self.vector_store_config["embeddings"] = self.embeddings - self.source_reader = source_reader - self.text_splitter = text_splitter - - def knowledge_embedding(self): - """source embedding is chain process.read->text_split->data_process->index_store""" - self.knowledge_embedding_client = self.init_knowledge_embedding() - self.knowledge_embedding_client.source_embedding() - - def knowledge_embedding_batch(self, docs): - """Deprecation""" - # docs = self.knowledge_embedding_client.read_batch() - return self.knowledge_embedding_client.index_to_store(docs) - - def read(self): - """Deprecation""" - self.knowledge_embedding_client = self.init_knowledge_embedding() - return self.knowledge_embedding_client.read_batch() - - def init_knowledge_embedding(self): - return get_knowledge_embedding( - self.knowledge_type, - self.knowledge_source, - self.vector_store_config, - self.source_reader, - self.text_splitter, - ) - - def similar_search(self, text, topk): - """vector db similar search in vector database. - Return topk docs. - Args: - - text: query text - - topk: top k - """ - vector_client = VectorStoreConnector( - self.vector_store_config["vector_store_type"], self.vector_store_config - ) - # https://github.com/chroma-core/chroma/issues/657 - ans = vector_client.similar_search(text, topk) - return ans - - def similar_search_with_scores(self, text, topk, score_threshold: float = 0.3): - """ - similar_search_with_score in vector database. - Return docs and relevance scores in the range [0, 1]. - Args: - doc(str): query text - topk(int): return docs nums. Defaults to 4. - score_threshold(float): score_threshold: Optional, a floating point value between 0 to 1 to - filter the resulting set of retrieved docs,0 is dissimilar, 1 is most similar. - """ - vector_client = VectorStoreConnector( - self.vector_store_config["vector_store_type"], self.vector_store_config - ) - ans = vector_client.similar_search_with_scores(text, topk, score_threshold) - return ans - - def vector_exist(self): - """vector db is exist""" - vector_client = VectorStoreConnector( - self.vector_store_config["vector_store_type"], self.vector_store_config - ) - return vector_client.vector_name_exists() - - def delete_by_ids(self, ids): - """delete vector db by ids - Args: - - ids: vector ids - """ - vector_client = VectorStoreConnector( - self.vector_store_config["vector_store_type"], self.vector_store_config - ) - vector_client.delete_by_ids(ids=ids) diff --git a/dbgpt/rag/embedding_engine/encode_text_loader.py b/dbgpt/rag/embedding_engine/encode_text_loader.py deleted file mode 100644 index 2b7344f18..000000000 --- a/dbgpt/rag/embedding_engine/encode_text_loader.py +++ /dev/null @@ -1,26 +0,0 @@ -from typing import List, Optional -import chardet - -from langchain.docstore.document import Document -from langchain.document_loaders.base import BaseLoader - - -class EncodeTextLoader(BaseLoader): - """Load text files.""" - - def __init__(self, file_path: str, encoding: Optional[str] = None): - """Initialize with file path.""" - self.file_path = file_path - self.encoding = encoding - - def load(self) -> List[Document]: - """Load from file path.""" - with open(self.file_path, "rb") as f: - raw_text = f.read() - result = chardet.detect(raw_text) - if result["encoding"] is None: - text = raw_text.decode("utf-8") - else: - text = raw_text.decode(result["encoding"]) - metadata = {"source": self.file_path} - return [Document(page_content=text, metadata=metadata)] diff --git a/dbgpt/rag/embedding_engine/knowledge_type.py b/dbgpt/rag/embedding_engine/knowledge_type.py deleted file mode 100644 index f185f0ac4..000000000 --- a/dbgpt/rag/embedding_engine/knowledge_type.py +++ /dev/null @@ -1,107 +0,0 @@ -from enum import Enum - -from dbgpt.rag.embedding_engine.csv_embedding import CSVEmbedding -from dbgpt.rag.embedding_engine.markdown_embedding import MarkdownEmbedding -from dbgpt.rag.embedding_engine.pdf_embedding import PDFEmbedding -from dbgpt.rag.embedding_engine.ppt_embedding import PPTEmbedding -from dbgpt.rag.embedding_engine.string_embedding import StringEmbedding -from dbgpt.rag.embedding_engine.url_embedding import URLEmbedding -from dbgpt.rag.embedding_engine.word_embedding import WordEmbedding - -DocumentEmbeddingType = { - ".txt": (MarkdownEmbedding, {}), - ".md": (MarkdownEmbedding, {}), - ".html": (MarkdownEmbedding, {}), - ".pdf": (PDFEmbedding, {}), - ".doc": (WordEmbedding, {}), - ".docx": (WordEmbedding, {}), - ".csv": (CSVEmbedding, {}), - ".ppt": (PPTEmbedding, {}), - ".pptx": (PPTEmbedding, {}), -} - - -class KnowledgeType(Enum): - DOCUMENT = "DOCUMENT" - URL = "URL" - TEXT = "TEXT" - OSS = "OSS" - S3 = "S3" - NOTION = "NOTION" - MYSQL = "MYSQL" - TIDB = "TIDB" - CLICKHOUSE = "CLICKHOUSE" - OCEANBASE = "OCEANBASE" - ELASTICSEARCH = "ELASTICSEARCH" - HIVE = "HIVE" - PRESTO = "PRESTO" - KAFKA = "KAFKA" - SPARK = "SPARK" - YOUTUBE = "YOUTUBE" - - -def get_knowledge_embedding( - knowledge_type, - knowledge_source, - vector_store_config=None, - source_reader=None, - text_splitter=None, -): - match knowledge_type: - case KnowledgeType.DOCUMENT.value: - extension = "." + knowledge_source.rsplit(".", 1)[-1] - if extension in DocumentEmbeddingType: - knowledge_class, knowledge_args = DocumentEmbeddingType[extension] - embedding = knowledge_class( - knowledge_source, - vector_store_config=vector_store_config, - source_reader=source_reader, - text_splitter=text_splitter, - **knowledge_args, - ) - return embedding - raise ValueError(f"Unsupported knowledge document type '{extension}'") - case KnowledgeType.URL.value: - embedding = URLEmbedding( - file_path=knowledge_source, - vector_store_config=vector_store_config, - source_reader=source_reader, - text_splitter=text_splitter, - ) - return embedding - case KnowledgeType.TEXT.value: - embedding = StringEmbedding( - file_path=knowledge_source, - vector_store_config=vector_store_config, - source_reader=source_reader, - text_splitter=text_splitter, - ) - return embedding - case KnowledgeType.OSS.value: - raise Exception("OSS have not integrate") - case KnowledgeType.S3.value: - raise Exception("S3 have not integrate") - case KnowledgeType.NOTION.value: - raise Exception("NOTION have not integrate") - case KnowledgeType.MYSQL.value: - raise Exception("MYSQL have not integrate") - case KnowledgeType.TIDB.value: - raise Exception("TIDB have not integrate") - case KnowledgeType.CLICKHOUSE.value: - raise Exception("CLICKHOUSE have not integrate") - case KnowledgeType.OCEANBASE.value: - raise Exception("OCEANBASE have not integrate") - case KnowledgeType.ELASTICSEARCH.value: - raise Exception("ELASTICSEARCH have not integrate") - case KnowledgeType.HIVE.value: - raise Exception("HIVE have not integrate") - case KnowledgeType.PRESTO.value: - raise Exception("PRESTO have not integrate") - case KnowledgeType.KAFKA.value: - raise Exception("KAFKA have not integrate") - case KnowledgeType.SPARK.value: - raise Exception("SPARK have not integrate") - case KnowledgeType.YOUTUBE.value: - raise Exception("YOUTUBE have not integrate") - case _: - raise Exception("unknown knowledge type") diff --git a/dbgpt/rag/embedding_engine/loader/chn_document_splitter.py b/dbgpt/rag/embedding_engine/loader/chn_document_splitter.py deleted file mode 100644 index 5bf06ea8c..000000000 --- a/dbgpt/rag/embedding_engine/loader/chn_document_splitter.py +++ /dev/null @@ -1,55 +0,0 @@ -import re -from typing import List - -from langchain.text_splitter import CharacterTextSplitter - - -class CHNDocumentSplitter(CharacterTextSplitter): - def __init__(self, pdf: bool = False, sentence_size: int = None, **kwargs): - super().__init__(**kwargs) - self.pdf = pdf - self.sentence_size = sentence_size - - def split_text(self, text: str) -> List[str]: - if self.pdf: - text = re.sub(r"\n{3,}", r"\n", text) - text = re.sub("\s", " ", text) - text = re.sub("\n\n", "", text) - - text = re.sub(r"([;；.!?。！？\?])([^”’])", r"\1\n\2", text) - text = re.sub(r'(\.{6})([^"’”」』])', r"\1\n\2", text) - text = re.sub(r'(\…{2})([^"’”」』])', r"\1\n\2", text) - text = re.sub(r'([;；!?。！？\?]["’”」』]{0,2})([^;；!?，。！？\?])', r"\1\n\2", text) - text = text.rstrip() - ls = [i for i in text.split("\n") if i] - for ele in ls: - if len(ele) > self.sentence_size: - ele1 = re.sub(r'([,，.]["’”」』]{0,2})([^,，.])', r"\1\n\2", ele) - ele1_ls = ele1.split("\n") - for ele_ele1 in ele1_ls: - if len(ele_ele1) > self.sentence_size: - ele_ele2 = re.sub( - r'([\n]{1,}| {2,}["’”」』]{0,2})([^\s])', r"\1\n\2", ele_ele1 - ) - ele2_ls = ele_ele2.split("\n") - for ele_ele2 in ele2_ls: - if len(ele_ele2) > self.sentence_size: - ele_ele3 = re.sub( - '( ["’”」』]{0,2})([^ ])', r"\1\n\2", ele_ele2 - ) - ele2_id = ele2_ls.index(ele_ele2) - ele2_ls = ( - ele2_ls[:ele2_id] - + [i for i in ele_ele3.split("\n") if i] - + ele2_ls[ele2_id + 1 :] - ) - ele_id = ele1_ls.index(ele_ele1) - ele1_ls = ( - ele1_ls[:ele_id] - + [i for i in ele2_ls if i] - + ele1_ls[ele_id + 1 :] - ) - - id = ls.index(ele) - ls = ls[:id] + [i for i in ele1_ls if i] + ls[id + 1 :] - return ls diff --git a/dbgpt/rag/embedding_engine/loader/csv_loader.py b/dbgpt/rag/embedding_engine/loader/csv_loader.py deleted file mode 100644 index 7297a1a5b..000000000 --- a/dbgpt/rag/embedding_engine/loader/csv_loader.py +++ /dev/null @@ -1,76 +0,0 @@ -"""Loads a CSV file into a list of documents. - -Each document represents one row of the CSV file. Every row is converted into a -key/value pair and outputted to a new line in the document's page_content. - -The source for each document loaded from csv is set to the value of the -`file_path` argument for all documents by default. -You can override this by setting the `source_column` argument to the -name of a column in the CSV file. -The source of each document will then be set to the value of the column -with the name specified in `source_column`. - -Output Example: - .. code-block:: txt - - column1: value1 - column2: value2 - column3: value3 -""" -from typing import Optional, Dict, List -import csv -from langchain.document_loaders.base import BaseLoader -from langchain.schema import Document - - -class NewCSVLoader(BaseLoader): - def __init__( - self, - file_path: str, - source_column: Optional[str] = None, - csv_args: Optional[Dict] = None, - encoding: Optional[str] = None, - ): - """ - - Args: - file_path: The path to the CSV file. - source_column: The name of the column in the CSV file to use as the source. - Optional. Defaults to None. - csv_args: A dictionary of arguments to pass to the csv.DictReader. - Optional. Defaults to None. - encoding: The encoding of the CSV file. Optional. Defaults to None. - """ - self.file_path = file_path - self.source_column = source_column - self.encoding = encoding - self.csv_args = csv_args or {} - - def load(self) -> List[Document]: - """Load data into document objects.""" - - docs = [] - with open(self.file_path, newline="", encoding=self.encoding) as csvfile: - csv_reader = csv.DictReader(csvfile, **self.csv_args) # type: ignore - for i, row in enumerate(csv_reader): - strs = [] - for k, v in row.items(): - if k is None or v is None: - continue - strs.append(f"{k.strip()}: {v.strip()}") - content = "\n".join(strs) - try: - source = ( - row[self.source_column] - if self.source_column is not None - else self.file_path - ) - except KeyError: - raise ValueError( - f"Source column '{self.source_column}' not found in CSV file." - ) - metadata = {"source": source, "row": i} - doc = Document(page_content=content, metadata=metadata) - docs.append(doc) - - return docs diff --git a/dbgpt/rag/embedding_engine/loader/docx_loader.py b/dbgpt/rag/embedding_engine/loader/docx_loader.py deleted file mode 100644 index 458254b66..000000000 --- a/dbgpt/rag/embedding_engine/loader/docx_loader.py +++ /dev/null @@ -1,28 +0,0 @@ -from typing import List, Optional - -from langchain.docstore.document import Document -from langchain.document_loaders.base import BaseLoader -import docx - - -class DocxLoader(BaseLoader): - """Load docx files.""" - - def __init__(self, file_path: str, encoding: Optional[str] = None): - """Initialize with file path.""" - self.file_path = file_path - self.encoding = encoding - - def load(self) -> List[Document]: - """Load from file path.""" - docs = [] - doc = docx.Document(self.file_path) - content = [] - for i in range(len(doc.paragraphs)): - para = doc.paragraphs[i] - text = para.text - content.append(text) - docs.append( - Document(page_content="".join(content), metadata={"source": self.file_path}) - ) - return docs diff --git a/dbgpt/rag/embedding_engine/loader/pdf_loader.py b/dbgpt/rag/embedding_engine/loader/pdf_loader.py deleted file mode 100644 index bbeead0cd..000000000 --- a/dbgpt/rag/embedding_engine/loader/pdf_loader.py +++ /dev/null @@ -1,55 +0,0 @@ -"""Loader that loads image files.""" -import os -from typing import List - -import fitz -from langchain.document_loaders.unstructured import UnstructuredFileLoader -from paddleocr import PaddleOCR - - -class UnstructuredPaddlePDFLoader(UnstructuredFileLoader): - """Loader that uses unstructured to load image files, such as PNGs and JPGs.""" - - def _get_elements(self) -> List: - def pdf_ocr_txt(filepath, dir_path="tmp_files"): - full_dir_path = os.path.join(os.path.dirname(filepath), dir_path) - if not os.path.exists(full_dir_path): - os.makedirs(full_dir_path) - filename = os.path.split(filepath)[-1] - ocr = PaddleOCR(lang="ch", use_gpu=False, show_log=False) - doc = fitz.open(filepath) - txt_file_path = os.path.join(full_dir_path, "%s.txt" % (filename)) - img_name = os.path.join(full_dir_path, ".tmp.png") - with open(txt_file_path, "w", encoding="utf-8") as fout: - for i in range(doc.page_count): - page = doc[i] - text = page.get_text("") - fout.write(text) - fout.write("\n") - - img_list = page.get_images() - for img in img_list: - pix = fitz.Pixmap(doc, img[0]) - - pix.save(img_name) - - result = ocr.ocr(img_name) - ocr_result = [i[1][0] for line in result for i in line] - fout.write("\n".join(ocr_result)) - os.remove(img_name) - return txt_file_path - - txt_file_path = pdf_ocr_txt(self.file_path) - from unstructured.partition.text import partition_text - - return partition_text(filename=txt_file_path, **self.unstructured_kwargs) - - -if __name__ == "__main__": - filepath = os.path.join( - os.path.dirname(os.path.dirname(__file__)), "content", "samples", "test_py.pdf" - ) - loader = UnstructuredPaddlePDFLoader(filepath, mode="elements") - docs = loader.load() - for doc in docs: - print(doc) diff --git a/dbgpt/rag/embedding_engine/loader/ppt_loader.py b/dbgpt/rag/embedding_engine/loader/ppt_loader.py deleted file mode 100644 index 0bfaf9679..000000000 --- a/dbgpt/rag/embedding_engine/loader/ppt_loader.py +++ /dev/null @@ -1,28 +0,0 @@ -from typing import List, Optional - -from langchain.docstore.document import Document -from langchain.document_loaders.base import BaseLoader -from pptx import Presentation - - -class PPTLoader(BaseLoader): - """Load PPT files.""" - - def __init__(self, file_path: str, encoding: Optional[str] = None): - """Initialize with file path.""" - self.file_path = file_path - self.encoding = encoding - - def load(self) -> List[Document]: - """Load from file path.""" - pr = Presentation(self.file_path) - docs = [] - for slide in pr.slides: - for shape in slide.shapes: - if hasattr(shape, "text") and shape.text: - docs.append( - Document( - page_content=shape.text, metadata={"source": slide.slide_id} - ) - ) - return docs diff --git a/dbgpt/rag/embedding_engine/markdown_embedding.py b/dbgpt/rag/embedding_engine/markdown_embedding.py deleted file mode 100644 index de47cfdab..000000000 --- a/dbgpt/rag/embedding_engine/markdown_embedding.py +++ /dev/null @@ -1,68 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -from typing import List, Optional - -import markdown -from bs4 import BeautifulSoup -from langchain.schema import Document -from langchain.text_splitter import ( - SpacyTextSplitter, - CharacterTextSplitter, - RecursiveCharacterTextSplitter, - TextSplitter, -) - -from dbgpt.rag.embedding_engine import SourceEmbedding, register -from dbgpt.rag.embedding_engine.encode_text_loader import EncodeTextLoader - - -class MarkdownEmbedding(SourceEmbedding): - """markdown embedding for read markdown document.""" - - def __init__( - self, - file_path, - vector_store_config, - source_reader: Optional = None, - text_splitter: Optional[TextSplitter] = None, - ): - """Initialize raw text word path.""" - super().__init__( - file_path, vector_store_config, source_reader=None, text_splitter=None - ) - self.file_path = file_path - self.vector_store_config = vector_store_config - self.source_reader = source_reader or None - self.text_splitter = text_splitter or None - - @register - def read(self): - """Load from markdown path.""" - if self.source_reader is None: - self.source_reader = EncodeTextLoader(self.file_path) - if self.text_splitter is None: - try: - self.text_splitter = SpacyTextSplitter( - pipeline="zh_core_web_sm", - chunk_size=100, - chunk_overlap=100, - ) - except Exception: - self.text_splitter = RecursiveCharacterTextSplitter( - chunk_size=100, chunk_overlap=50 - ) - - return self.source_reader.load_and_split(self.text_splitter) - - @register - def data_process(self, documents: List[Document]): - i = 0 - for d in documents: - content = markdown.markdown(d.page_content) - soup = BeautifulSoup(content, "html.parser") - for tag in soup(["!doctype", "meta", "i.fa"]): - tag.extract() - documents[i].page_content = soup.get_text() - documents[i].page_content = documents[i].page_content.replace("\n", " ") - i += 1 - return documents diff --git a/dbgpt/rag/embedding_engine/pdf_embedding.py b/dbgpt/rag/embedding_engine/pdf_embedding.py deleted file mode 100644 index 4c93df228..000000000 --- a/dbgpt/rag/embedding_engine/pdf_embedding.py +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -from typing import List, Optional - -from langchain.document_loaders import PyPDFLoader -from langchain.schema import Document -from langchain.text_splitter import ( - SpacyTextSplitter, - RecursiveCharacterTextSplitter, - TextSplitter, -) - -from dbgpt.rag.embedding_engine import SourceEmbedding, register - - -class PDFEmbedding(SourceEmbedding): - """pdf embedding for read pdf document.""" - - def __init__( - self, - file_path, - vector_store_config, - source_reader: Optional = None, - text_splitter: Optional[TextSplitter] = None, - ): - """Initialize pdf word path. - Args: - - file_path: data source path - - vector_store_config: vector store config params. - - source_reader: Optional[BaseLoader] - - text_splitter: Optional[TextSplitter] - """ - super().__init__( - file_path, vector_store_config, source_reader=None, text_splitter=None - ) - self.file_path = file_path - self.vector_store_config = vector_store_config - self.source_reader = source_reader or None - self.text_splitter = text_splitter or None - - @register - def read(self): - """Load from pdf path.""" - if self.source_reader is None: - self.source_reader = PyPDFLoader(self.file_path) - if self.text_splitter is None: - try: - self.text_splitter = SpacyTextSplitter( - pipeline="zh_core_web_sm", - chunk_size=100, - chunk_overlap=100, - ) - except Exception: - self.text_splitter = RecursiveCharacterTextSplitter( - chunk_size=100, chunk_overlap=50 - ) - - return self.source_reader.load_and_split(self.text_splitter) - - @register - def data_process(self, documents: List[Document]): - i = 0 - for d in documents: - documents[i].page_content = d.page_content.replace("\n", "") - i += 1 - return documents diff --git a/dbgpt/rag/embedding_engine/ppt_embedding.py b/dbgpt/rag/embedding_engine/ppt_embedding.py deleted file mode 100644 index e52107bd9..000000000 --- a/dbgpt/rag/embedding_engine/ppt_embedding.py +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -from typing import List, Optional - -from langchain.schema import Document -from langchain.text_splitter import ( - SpacyTextSplitter, - RecursiveCharacterTextSplitter, - TextSplitter, -) - -from dbgpt.rag.embedding_engine import SourceEmbedding, register -from dbgpt.rag.embedding_engine.loader.ppt_loader import PPTLoader - - -class PPTEmbedding(SourceEmbedding): - """ppt embedding for read ppt document.""" - - def __init__( - self, - file_path, - vector_store_config, - source_reader: Optional = None, - text_splitter: Optional[TextSplitter] = None, - ): - """Initialize ppt word path. - Args: - - file_path: data source path - - vector_store_config: vector store config params. - - source_reader: Optional[BaseLoader] - - text_splitter: Optional[TextSplitter] - """ - super().__init__( - file_path, vector_store_config, source_reader=None, text_splitter=None - ) - self.file_path = file_path - self.vector_store_config = vector_store_config - self.source_reader = source_reader or None - self.text_splitter = text_splitter or None - - @register - def read(self): - """Load from ppt path.""" - if self.source_reader is None: - self.source_reader = PPTLoader(self.file_path) - if self.text_splitter is None: - try: - self.text_splitter = SpacyTextSplitter( - pipeline="zh_core_web_sm", - chunk_size=100, - chunk_overlap=100, - ) - except Exception: - self.text_splitter = RecursiveCharacterTextSplitter( - chunk_size=100, chunk_overlap=50 - ) - - return self.source_reader.load_and_split(self.text_splitter) - - @register - def data_process(self, documents: List[Document]): - i = 0 - for d in documents: - documents[i].page_content = d.page_content.replace("\n", "") - i += 1 - return documents diff --git a/dbgpt/rag/embedding_engine/search_milvus.py b/dbgpt/rag/embedding_engine/search_milvus.py deleted file mode 100644 index bf8794b34..000000000 --- a/dbgpt/rag/embedding_engine/search_milvus.py +++ /dev/null @@ -1,61 +0,0 @@ -# from langchain.embeddings import HuggingFaceEmbeddings -# from langchain.vectorstores import Milvus -# from pymilvus import Collection,utility -# from pymilvus import datasource, DataType, FieldSchema, CollectionSchema -# -# # milvus = datasource.connect( -# # alias="default", -# # host='localhost', -# # port="19530" -# # ) -# # collection = Collection("book") -# -# -# # Get an existing collection. -# # collection.load() -# # -# # search_params = {"metric_type": "L2", "params": {}, "offset": 5} -# # -# # results = collection.search( -# # data=[[0.1, 0.2]], -# # anns_field="book_intro", -# # param=search_params, -# # limit=10, -# # expr=None, -# # output_fields=['book_id'], -# # consistency_level="Strong" -# # ) -# # -# # # get the IDs of all returned hits -# # results[0].ids -# # -# # # get the distances to the query vector from all returned hits -# # results[0].distances -# # -# # # get the value of an output field specified in the search request. -# # # vector fields are not supported yet. -# # hit = results[0][0] -# # hit.entity.get('title') -# -# # milvus = datasource.connect( -# # alias="default", -# # host='localhost', -# # port="19530" -# # ) -# from dbgpt.vector_store.milvus_store import MilvusStore -# -# data = ["aaa", "bbb"] -# model_name = "xx/all-MiniLM-L6-v2" -# embeddings = HuggingFaceEmbeddings(model_name=model_name) -# -# # text_embeddings = Text2Vectors() -# mivuls = MilvusStore(cfg={"url": "127.0.0.1", "port": "19530", "alias": "default", "table_name": "test_k"}) -# -# mivuls.insert(["textc","tezt2"]) -# print("success") -# ct -# # mivuls.from_texts(texts=data, embedding=embeddings) -# # docs, -# # embedding=embeddings, -# # connection_args={"host": "127.0.0.1", "port": "19530", "alias": "default"} -# # ) diff --git a/dbgpt/rag/embedding_engine/source_embedding.py b/dbgpt/rag/embedding_engine/source_embedding.py deleted file mode 100644 index 2ab279531..000000000 --- a/dbgpt/rag/embedding_engine/source_embedding.py +++ /dev/null @@ -1,126 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -from abc import ABC, abstractmethod -from typing import Dict, List, Optional - -from langchain.text_splitter import TextSplitter - -from dbgpt.storage.vector_store.connector import VectorStoreConnector - -registered_methods = [] - - -def register(method): - registered_methods.append(method.__name__) - return method - - -class SourceEmbedding(ABC): - """base class for read data source embedding pipeline. - include data read, data process, data split, data to vector, data index vector store - Implementations should implement the method - """ - - def __init__( - self, - file_path, - vector_store_config: {}, - source_reader: Optional = None, - text_splitter: Optional[TextSplitter] = None, - embedding_args: Optional[Dict] = None, - ): - """Initialize with Loader url, model_name, vector_store_config - Args: - - file_path: data source path - - vector_store_config: vector store config params. - - source_reader: Optional[BaseLoader] - - text_splitter: Optional[TextSplitter] - - embedding_args: Optional - """ - self.file_path = file_path - self.vector_store_config = vector_store_config or {} - self.source_reader = source_reader or None - self.text_splitter = text_splitter or None - self.embedding_args = embedding_args - self.embeddings = self.vector_store_config.get("embeddings", None) - - @abstractmethod - @register - def read(self) -> List[ABC]: - """read datasource into document objects.""" - - @register - def data_process(self, text): - """pre process data. - Args: - - text: raw text - """ - - @register - def text_splitter(self, text_splitter: TextSplitter): - """add text split chunk - Args: - - text_splitter: TextSplitter - """ - pass - - @register - def text_to_vector(self, docs): - """transform vector - Args: - - docs: List[Document] - """ - pass - - @register - def index_to_store(self, docs): - """index to vector store - Args: - - docs: List[Document] - """ - self.vector_client = VectorStoreConnector( - self.vector_store_config["vector_store_type"], self.vector_store_config - ) - return self.vector_client.load_document(docs) - - @register - def similar_search(self, doc, topk): - """vector store similarity_search - Args: - - query: query - """ - self.vector_client = VectorStoreConnector( - self.vector_store_config["vector_store_type"], self.vector_store_config - ) - # https://github.com/chroma-core/chroma/issues/657 - ans = self.vector_client.similar_search(doc, topk) - # ans = self.vector_client.similar_search(doc, 1) - return ans - - def vector_name_exist(self): - self.vector_client = VectorStoreConnector( - self.vector_store_config["vector_store_type"], self.vector_store_config - ) - return self.vector_client.vector_name_exists() - - def source_embedding(self): - """read()->data_process()->text_split()->index_to_store()""" - if "read" in registered_methods: - text = self.read() - if "data_process" in registered_methods: - text = self.data_process(text) - if "text_split" in registered_methods: - self.text_split(text) - if "text_to_vector" in registered_methods: - self.text_to_vector(text) - if "index_to_store" in registered_methods: - self.index_to_store(text) - - def read_batch(self): - if "read" in registered_methods: - text = self.read() - if "data_process" in registered_methods: - text = self.data_process(text) - if "text_split" in registered_methods: - self.text_split(text) - return text diff --git a/dbgpt/rag/embedding_engine/string_embedding.py b/dbgpt/rag/embedding_engine/string_embedding.py deleted file mode 100644 index 38c301391..000000000 --- a/dbgpt/rag/embedding_engine/string_embedding.py +++ /dev/null @@ -1,66 +0,0 @@ -from typing import List, Optional - -from langchain.schema import Document -from langchain.text_splitter import ( - TextSplitter, - SpacyTextSplitter, - RecursiveCharacterTextSplitter, -) - -from dbgpt.rag.embedding_engine import SourceEmbedding, register - - -class StringEmbedding(SourceEmbedding): - """string embedding for read string document.""" - - def __init__( - self, - file_path, - vector_store_config, - source_reader: Optional = None, - text_splitter: Optional[TextSplitter] = None, - ): - """Initialize raw text word path. - Args: - - file_path: data source path - - vector_store_config: vector store config params. - - source_reader: Optional[BaseLoader] - - text_splitter: Optional[TextSplitter] - """ - super().__init__( - file_path=file_path, - vector_store_config=vector_store_config, - source_reader=None, - text_splitter=None, - ) - self.file_path = file_path - self.vector_store_config = vector_store_config - self.source_reader = source_reader or None - self.text_splitter = text_splitter or None - - @register - def read(self): - """Load from String path.""" - metadata = {"source": "raw text"} - docs = [Document(page_content=self.file_path, metadata=metadata)] - if self.text_splitter is None: - try: - self.text_splitter = SpacyTextSplitter( - pipeline="zh_core_web_sm", - chunk_size=500, - chunk_overlap=100, - ) - except Exception: - self.text_splitter = RecursiveCharacterTextSplitter( - chunk_size=500, chunk_overlap=100 - ) - return self.text_splitter.split_documents(docs) - return docs - - @register - def data_process(self, documents: List[Document]): - i = 0 - for d in documents: - documents[i].page_content = d.page_content.replace("\n", "") - i += 1 - return documents diff --git a/dbgpt/rag/embedding_engine/url_embedding.py b/dbgpt/rag/embedding_engine/url_embedding.py deleted file mode 100644 index 4e49e11b5..000000000 --- a/dbgpt/rag/embedding_engine/url_embedding.py +++ /dev/null @@ -1,69 +0,0 @@ -from typing import List, Optional - -from bs4 import BeautifulSoup -from langchain.document_loaders import WebBaseLoader -from langchain.schema import Document -from langchain.text_splitter import ( - SpacyTextSplitter, - RecursiveCharacterTextSplitter, - TextSplitter, -) - -from dbgpt.rag.embedding_engine import SourceEmbedding, register - - -class URLEmbedding(SourceEmbedding): - """url embedding for read url document.""" - - def __init__( - self, - file_path, - vector_store_config, - source_reader: Optional = None, - text_splitter: Optional[TextSplitter] = None, - ): - """Initialize url word path. - Args: - - file_path: data source path - - vector_store_config: vector store config params. - - source_reader: Optional[BaseLoader] - - text_splitter: Optional[TextSplitter] - """ - super().__init__( - file_path, vector_store_config, source_reader=None, text_splitter=None - ) - self.file_path = file_path - self.vector_store_config = vector_store_config or None - self.source_reader = source_reader or None - self.text_splitter = text_splitter or None - - @register - def read(self): - """Load from url path.""" - if self.source_reader is None: - self.source_reader = WebBaseLoader(web_path=self.file_path) - if self.text_splitter is None: - try: - self.text_splitter = SpacyTextSplitter( - pipeline="zh_core_web_sm", - chunk_size=100, - chunk_overlap=100, - ) - except Exception: - self.text_splitter = RecursiveCharacterTextSplitter( - chunk_size=100, chunk_overlap=50 - ) - - return self.source_reader.load_and_split(self.text_splitter) - - @register - def data_process(self, documents: List[Document]): - i = 0 - for d in documents: - content = d.page_content.replace("\n", "") - soup = BeautifulSoup(content, "html.parser") - for tag in soup(["!doctype", "meta"]): - tag.extract() - documents[i].page_content = soup.get_text() - i += 1 - return documents diff --git a/dbgpt/rag/embedding_engine/word_embedding.py b/dbgpt/rag/embedding_engine/word_embedding.py deleted file mode 100644 index 4207d604d..000000000 --- a/dbgpt/rag/embedding_engine/word_embedding.py +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -from typing import List, Optional - -from langchain.schema import Document -from langchain.text_splitter import ( - SpacyTextSplitter, - RecursiveCharacterTextSplitter, - TextSplitter, -) - -from dbgpt.rag.embedding_engine import SourceEmbedding, register -from dbgpt.rag.embedding_engine.loader.docx_loader import DocxLoader - - -class WordEmbedding(SourceEmbedding): - """word embedding for read word document.""" - - def __init__( - self, - file_path, - vector_store_config, - source_reader: Optional = None, - text_splitter: Optional[TextSplitter] = None, - ): - """Initialize with word path. - Args: - - file_path: data source path - - vector_store_config: vector store config params. - - source_reader: Optional[BaseLoader] - - text_splitter: Optional[TextSplitter] - """ - super().__init__( - file_path, vector_store_config, source_reader=None, text_splitter=None - ) - self.file_path = file_path - self.vector_store_config = vector_store_config - self.source_reader = source_reader or None - self.text_splitter = text_splitter or None - - @register - def read(self): - """Load from word path.""" - if self.source_reader is None: - self.source_reader = DocxLoader(self.file_path) - if self.text_splitter is None: - try: - self.text_splitter = SpacyTextSplitter( - pipeline="zh_core_web_sm", - chunk_size=100, - chunk_overlap=100, - ) - except Exception: - self.text_splitter = RecursiveCharacterTextSplitter( - chunk_size=100, chunk_overlap=50 - ) - - return self.source_reader.load_and_split(self.text_splitter) - - @register - def data_process(self, documents: List[Document]): - i = 0 - for d in documents: - documents[i].page_content = d.page_content.replace("\n", "") - i += 1 - return documents diff --git a/dbgpt/rag/extracter/base.py b/dbgpt/rag/extracter/base.py deleted file mode 100644 index 6ace7872a..000000000 --- a/dbgpt/rag/extracter/base.py +++ /dev/null @@ -1,19 +0,0 @@ -from abc import abstractmethod, ABC -from typing import List, Dict - -from langchain.schema import Document - - -class Extractor(ABC): - """Extractor Base class, it's apply for Summary Extractor, Keyword Extractor, Triplets Extractor, Question Extractor, etc.""" - - def __init__(self): - pass - - @abstractmethod - def extract(self, chunks: List[Document]) -> List[Dict]: - """Extracts chunks. - - Args: - nodes (Sequence[Document]): nodes to extract metadata from - """ diff --git a/dbgpt/rag/extracter/summary.py b/dbgpt/rag/extracter/summary.py deleted file mode 100644 index f7c9bac8f..000000000 --- a/dbgpt/rag/extracter/summary.py +++ /dev/null @@ -1,95 +0,0 @@ -from typing import List - -from langchain.schema import Document - -from dbgpt._private.llm_metadata import LLMMetadata -from dbgpt.rag.extracter.base import Extractor - - -class SummaryExtractor(Extractor): - """Summary Extractor, it can extract document summary.""" - - def __init__(self, model_name: str = None, llm_metadata: LLMMetadata = None): - self.model_name = (model_name,) - self.llm_metadata = (llm_metadata or LLMMetadata,) - - async def extract(self, chunks: List[Document]) -> str: - """async document extract summary - Args: - - model_name: str - - chunk_docs: List[Document] - """ - texts = [doc.page_content for doc in chunks] - from dbgpt.util.prompt_util import PromptHelper - - prompt_helper = PromptHelper() - from dbgpt.app.scene.chat_knowledge.summary.prompt import prompt - - texts = prompt_helper.repack(prompt_template=prompt.template, text_chunks=texts) - return await self._mapreduce_extract_summary( - docs=texts, model_name=self.model_name, llm_metadata=self.llm_metadata - ) - - async def _mapreduce_extract_summary( - self, - docs, - model_name, - llm_metadata: LLMMetadata, - ): - """Extract summary by mapreduce mode - map -> multi async call llm to generate summary - reduce -> merge the summaries by map process - Args: - docs:List[str] - model_name:model name str - llm_metadata:LLMMetadata - Returns: - Document: refine summary context document. - """ - from dbgpt.app.scene import ChatScene - from dbgpt._private.chat_util import llm_chat_response_nostream - import uuid - - tasks = [] - if len(docs) == 1: - return docs[0] - else: - max_iteration = ( - llm_metadata.max_chat_iteration - if len(docs) > llm_metadata.max_chat_iteration - else len(docs) - ) - for doc in docs[0:max_iteration]: - chat_param = { - "chat_session_id": uuid.uuid1(), - "current_user_input": "", - "select_param": doc, - "model_name": model_name, - "model_cache_enable": True, - } - tasks.append( - llm_chat_response_nostream( - ChatScene.ExtractSummary.value(), **{"chat_param": chat_param} - ) - ) - from dbgpt._private.chat_util import run_async_tasks - - summary_iters = await run_async_tasks( - tasks=tasks, concurrency_limit=llm_metadata.concurrency_limit - ) - summary_iters = list( - filter( - lambda content: "LLMServer Generate Error" not in content, - summary_iters, - ) - ) - from dbgpt.util.prompt_util import PromptHelper - from dbgpt.app.scene.chat_knowledge.summary.prompt import prompt - - prompt_helper = PromptHelper() - summary_iters = prompt_helper.repack( - prompt_template=prompt.template, text_chunks=summary_iters - ) - return await self._mapreduce_extract_summary( - summary_iters, model_name, max_iteration, llm_metadata.concurrency_limit - ) diff --git a/dbgpt/app/scene/chat_knowledge/rewrite/__init__.py b/dbgpt/rag/extractor/__init__.py similarity index 100% rename from dbgpt/app/scene/chat_knowledge/rewrite/__init__.py rename to dbgpt/rag/extractor/__init__.py diff --git a/dbgpt/rag/extractor/base.py b/dbgpt/rag/extractor/base.py new file mode 100644 index 000000000..0ea072fe4 --- /dev/null +++ b/dbgpt/rag/extractor/base.py @@ -0,0 +1,45 @@ +from abc import abstractmethod, ABC +from typing import List + +from dbgpt.core import LLMClient +from dbgpt.rag.chunk import Chunk + + +class Extractor(ABC): + """Extractor Base class, it's apply for Summary Extractor, Keyword Extractor, Triplets Extractor, Question Extractor, etc.""" + + def __init__(self, llm_client: LLMClient) -> None: + """Initialize the Extractor.""" + self._llm_client = llm_client + + def extract(self, chunks: List[Chunk]) -> str: + """Extracts chunks. + + Args: + chunks (List[Chunk]): extract metadata from chunks + """ + return self._extract(chunks) + + async def aextract(self, chunks: List[Chunk]) -> str: + """Async Extracts chunks. + + Args: + chunks (List[Chunk]): extract metadata from chunks + """ + return await self._aextract(chunks) + + @abstractmethod + def _extract(self, chunks: List[Chunk]) -> str: + """Extracts chunks. + + Args: + chunks (List[Chunk]): extract metadata from chunks + """ + + @abstractmethod + async def _aextract(self, chunks: List[Chunk]) -> str: + """Async Extracts chunks. + + Args: + chunks (List[Chunk]): extract metadata from chunks + """ diff --git a/dbgpt/rag/extractor/summary.py b/dbgpt/rag/extractor/summary.py new file mode 100644 index 000000000..936240b15 --- /dev/null +++ b/dbgpt/rag/extractor/summary.py @@ -0,0 +1,135 @@ +from typing import List, Optional + +from dbgpt._private.llm_metadata import LLMMetadata +from dbgpt.core import LLMClient, ModelRequest, ModelMessageRoleType +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.extractor.base import Extractor +from dbgpt.util import utils +from dbgpt.util.chat_util import run_async_tasks + +SUMMARY_PROMPT_TEMPLATE_ZH = """请根据提供的上下文信息的进行精简地总结: +{context} +答案尽量精确和简单,不要过长，长度控制在100字左右 +""" + +SUMMARY_PROMPT_TEMPLATE_EN = """ +Write a quick summary of the following context: +{context} +the summary should be as concise as possible and not overly lengthy.Please keep the answer within approximately 200 characters. +""" + + +class SummaryExtractor(Extractor): + """Summary Extractor, it can extract document summary.""" + + def __init__( + self, + llm_client: Optional[LLMClient], + model_name: Optional[str] = None, + llm_metadata: Optional[LLMMetadata] = None, + language: Optional[str] = "en", + max_iteration_with_llm: Optional[int] = 5, + concurrency_limit_with_llm: Optional[int] = 3, + ): + self._llm_client = llm_client + self._model_name = model_name + self.llm_metadata = llm_metadata or LLMMetadata + self._language = language + self._concurrency_limit_with_llm = concurrency_limit_with_llm + self._prompt_template = ( + SUMMARY_PROMPT_TEMPLATE_EN + if language == "en" + else SUMMARY_PROMPT_TEMPLATE_ZH + ) + self._concurrency_limit_with_llm = concurrency_limit_with_llm + self._max_iteration_with_llm = max_iteration_with_llm + self._concurrency_limit_with_llm = concurrency_limit_with_llm + + """Initialize the Extractor. + Args: + llm_client: (Optional[LLMClient]): The LLM client. Defaults to None. + model_name: str + llm_metadata: LLMMetadata + language: (Optional[str]): The language of the prompt. Defaults to "en". + max_iteration_with_llm: (Optional[int]): The max iteration with llm. Defaults to 5. + concurrency_limit_with_llm: (Optional[int]): The concurrency limit with llm. Defaults to 3. + """ + + async def _aextract(self, chunks: List[Chunk]) -> str: + """async document extract summary + Args: + - model_name: str + - chunk_docs: List[Document] + """ + texts = [doc.content for doc in chunks] + from dbgpt.util.prompt_util import PromptHelper + + prompt_helper = PromptHelper() + texts = prompt_helper.repack( + prompt_template=self._prompt_template, text_chunks=texts + ) + if len(texts) == 1: + summary_outs = await self._llm_run_tasks(chunk_texts=texts) + return summary_outs[0] + else: + return await self._mapreduce_extract_summary(docs=texts) + + def _extract(self, chunks: List[Chunk]) -> str: + """document extract summary + Args: + - chunk_docs: List[Document] + """ + loop = utils.get_or_create_event_loop() + return loop.run_until_complete(self._aextract(chunks=chunks)) + + async def _mapreduce_extract_summary( + self, + docs: List[str], + ) -> str: + """Extract summary by mapreduce mode + map -> multi async call llm to generate summary + reduce -> merge the summaries by map process + Args: + docs:List[str] + Returns: + summary: str + """ + if len(docs) == 1: + return docs[0] + else: + summary_outs = await self._llm_run_tasks( + chunk_texts=docs[0 : self._max_iteration_with_llm] + ) + from dbgpt.util.prompt_util import PromptHelper + + prompt_helper = PromptHelper() + summary_outs = prompt_helper.repack( + prompt_template=self._prompt_template, text_chunks=summary_outs + ) + return await self._mapreduce_extract_summary(docs=summary_outs) + + async def _llm_run_tasks(self, chunk_texts: List[str]) -> List[str]: + """llm run tasks + Args: + chunk_texts: List[str] + Returns: + summary_outs: List[str] + """ + tasks = [] + for chunk_text in chunk_texts: + from dbgpt.core import ModelMessage + + prompt = self._prompt_template.format(context=chunk_text) + messages = [ModelMessage(role=ModelMessageRoleType.SYSTEM, content=prompt)] + request = ModelRequest(model=self._model_name, messages=messages) + tasks.append(self._llm_client.generate(request)) + summary_results = await run_async_tasks( + tasks=tasks, concurrency_limit=self._concurrency_limit_with_llm + ) + summary_outs = [model_out.text for model_out in summary_results] + return list( + filter( + lambda model_out: "LLMServer Generate Error" not in model_out, + summary_outs, + ) + ) diff --git a/dbgpt/app/scene/chat_knowledge/summary/__init__.py b/dbgpt/rag/extractor/tests/__init__.py similarity index 100% rename from dbgpt/app/scene/chat_knowledge/summary/__init__.py rename to dbgpt/rag/extractor/tests/__init__.py diff --git a/dbgpt/rag/extractor/tests/test_summary_extractor.py b/dbgpt/rag/extractor/tests/test_summary_extractor.py new file mode 100644 index 000000000..0beba44c1 --- /dev/null +++ b/dbgpt/rag/extractor/tests/test_summary_extractor.py @@ -0,0 +1,40 @@ +import unittest +from unittest.mock import AsyncMock, MagicMock + +from dbgpt._private.llm_metadata import LLMMetadata +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.extractor.summary import SummaryExtractor + + +class MockLLMClient: + async def generate(self, request): + return MagicMock(text=f"Summary for: {request.messages[0].content}") + + +class TestSummaryExtractor(unittest.IsolatedAsyncioTestCase): + async def asyncSetUp(self): + self.llm_client = MockLLMClient() + self.llm_client.generate = AsyncMock(side_effect=self.llm_client.generate) + + self.extractor = SummaryExtractor( + llm_client=self.llm_client, + model_name="test_model_name", + llm_metadata=LLMMetadata(), + language="en", + max_iteration_with_llm=2, + concurrency_limit_with_llm=1, + ) + + async def test_single_chunk_extraction(self): + single_chunk = [Chunk(content="This is a test content.")] + summary = await self.extractor._aextract(chunks=single_chunk) + self.assertEqual("This is a test content" in summary, True) + + async def test_multiple_chunks_extraction(self): + chunks = [Chunk(content=f"Content {i}") for i in range(4)] + summary = await self.extractor._aextract(chunks=chunks) + self.assertTrue(summary.startswith("Summary for:")) + + +if __name__ == "__main__": + unittest.main() diff --git a/dbgpt/rag/embedding_engine/external/__init__.py b/dbgpt/rag/graph/__init__.py similarity index 100% rename from dbgpt/rag/embedding_engine/external/__init__.py rename to dbgpt/rag/graph/__init__.py diff --git a/dbgpt/rag/graph_engine/graph_engine.py b/dbgpt/rag/graph/graph_engine.py similarity index 93% rename from dbgpt/rag/graph_engine/graph_engine.py rename to dbgpt/rag/graph/graph_engine.py index 6cfd5176b..50e66c4c9 100644 --- a/dbgpt/rag/graph_engine/graph_engine.py +++ b/dbgpt/rag/graph/graph_engine.py @@ -4,10 +4,10 @@ from langchain.schema import Document from langchain.text_splitter import RecursiveCharacterTextSplitter -from dbgpt.rag.embedding_engine import KnowledgeType -from dbgpt.rag.embedding_engine.knowledge_type import get_knowledge_embedding -from dbgpt.rag.graph_engine.index_struct import KG -from dbgpt.rag.graph_engine.node import TextNode +from dbgpt.rag.embedding import KnowledgeType +from dbgpt.rag.embedding.knowledge_type import get_knowledge_embedding +from dbgpt.rag.graph.index_struct import KG +from dbgpt.rag.graph.node import TextNode from dbgpt.util import utils logger = logging.getLogger(__name__) @@ -88,7 +88,7 @@ def _extract_triplets(self, text: str) -> List[Tuple[str, str, str]]: def _llm_extract_triplets(self, text: str) -> List[Tuple[str, str, str]]: """Extract triplets from text by llm""" from dbgpt.app.scene import ChatScene - from dbgpt._private.chat_util import llm_chat_response_nostream + from dbgpt.util.chat_util import llm_chat_response_nostream import uuid chat_param = { @@ -123,7 +123,7 @@ def _build_index_from_docs(self, documents: List[Document]) -> KG: return index_struct def search(self, query): - from dbgpt.rag.graph_engine.graph_search import RAGGraphSearch + from dbgpt.rag.graph.graph_search import RAGGraphSearch graph_search = RAGGraphSearch(graph_engine=self) return graph_search.search(query) diff --git a/dbgpt/rag/graph_engine/graph_factory.py b/dbgpt/rag/graph/graph_factory.py similarity index 93% rename from dbgpt/rag/graph_engine/graph_factory.py rename to dbgpt/rag/graph/graph_factory.py index 0d6fffc69..c868190e5 100644 --- a/dbgpt/rag/graph_engine/graph_factory.py +++ b/dbgpt/rag/graph/graph_factory.py @@ -20,7 +20,7 @@ def __init__( super().__init__(system_app=system_app) self._default_model_name = default_model_name self.kwargs = kwargs - from dbgpt.rag.graph_engine.graph_engine import RAGGraphEngine + from dbgpt.rag.graph.graph_engine import RAGGraphEngine self.rag_engine = RAGGraphEngine(model_name="proxyllm") diff --git a/dbgpt/rag/graph_engine/graph_search.py b/dbgpt/rag/graph/graph_search.py similarity index 95% rename from dbgpt/rag/graph_engine/graph_search.py rename to dbgpt/rag/graph/graph_search.py index eb5cfdbcd..055e9aa5a 100644 --- a/dbgpt/rag/graph_engine/graph_search.py +++ b/dbgpt/rag/graph/graph_search.py @@ -6,8 +6,8 @@ from langchain.schema import Document -from dbgpt.rag.graph_engine.node import BaseNode, TextNode, NodeWithScore -from dbgpt.rag.graph_engine.search import BaseSearch, SearchMode +from dbgpt.rag.graph.node import BaseNode, TextNode, NodeWithScore +from dbgpt.rag.graph.search import BaseSearch, SearchMode logger = logging.getLogger(__name__) DEFAULT_NODE_SCORE = 1000.0 @@ -19,7 +19,7 @@ class RAGGraphSearch(BaseSearch): """RAG Graph Search. args: - graph_engine RAGGraphEngine. + graph RAGGraphEngine. model_name (str): model name (see :ref:`Prompt-Templates`). text_qa_template (Optional[BasePromptTemplate]): A Question Answering Prompt @@ -45,7 +45,7 @@ def __init__( **kwargs: Any, ) -> None: """Initialize params.""" - from dbgpt.rag.graph_engine.graph_engine import RAGGraphEngine + from dbgpt.rag.graph.graph_engine import RAGGraphEngine self.graph_engine: RAGGraphEngine = graph_engine self.model_name = model_name or self.graph_engine.model_name @@ -78,7 +78,7 @@ async def _extract_subject_entities(self, query_str: str) -> Set[str]: async def _extract_entities_by_llm(self, text: str) -> Set[str]: """extract subject entities from text by llm""" from dbgpt.app.scene import ChatScene - from dbgpt._private.chat_util import llm_chat_response_nostream + from dbgpt.util.chat_util import llm_chat_response_nostream import uuid chat_param = { diff --git a/dbgpt/rag/graph_engine/index_struct.py b/dbgpt/rag/graph/index_struct.py similarity index 98% rename from dbgpt/rag/graph_engine/index_struct.py rename to dbgpt/rag/graph/index_struct.py index 63f92fa74..e09d68c83 100644 --- a/dbgpt/rag/graph_engine/index_struct.py +++ b/dbgpt/rag/graph/index_struct.py @@ -12,8 +12,8 @@ from dataclasses_json import DataClassJsonMixin -from dbgpt.rag.graph_engine.index_type import IndexStructType -from dbgpt.rag.graph_engine.node import TextNode, BaseNode +from dbgpt.rag.graph.index_type import IndexStructType +from dbgpt.rag.graph.node import TextNode, BaseNode # TODO: legacy backport of old Node class Node = TextNode diff --git a/dbgpt/rag/graph_engine/index_type.py b/dbgpt/rag/graph/index_type.py similarity index 100% rename from dbgpt/rag/graph_engine/index_type.py rename to dbgpt/rag/graph/index_type.py diff --git a/dbgpt/rag/graph_engine/kv_index.py b/dbgpt/rag/graph/kv_index.py similarity index 100% rename from dbgpt/rag/graph_engine/kv_index.py rename to dbgpt/rag/graph/kv_index.py diff --git a/dbgpt/rag/graph_engine/node.py b/dbgpt/rag/graph/node.py similarity index 100% rename from dbgpt/rag/graph_engine/node.py rename to dbgpt/rag/graph/node.py diff --git a/dbgpt/rag/graph_engine/search.py b/dbgpt/rag/graph/search.py similarity index 100% rename from dbgpt/rag/graph_engine/search.py rename to dbgpt/rag/graph/search.py diff --git a/dbgpt/rag/embedding_engine/loader/__init__.py b/dbgpt/rag/knowledge/__init__.py similarity index 100% rename from dbgpt/rag/embedding_engine/loader/__init__.py rename to dbgpt/rag/knowledge/__init__.py diff --git a/dbgpt/rag/knowledge/base.py b/dbgpt/rag/knowledge/base.py new file mode 100644 index 000000000..ff739f990 --- /dev/null +++ b/dbgpt/rag/knowledge/base.py @@ -0,0 +1,141 @@ +from abc import abstractmethod, ABC +from enum import Enum +from typing import Optional, Any, List + +from dbgpt.rag.chunk import Document +from dbgpt.rag.text_splitter.text_splitter import ( + RecursiveCharacterTextSplitter, + MarkdownHeaderTextSplitter, + ParagraphTextSplitter, + CharacterTextSplitter, + PageTextSplitter, + SeparatorTextSplitter, +) + + +class DocumentType(Enum): + PDF = "pdf" + CSV = "csv" + MARKDOWN = "md" + PPTX = "pptx" + DOCX = "docx" + TXT = "txt" + HTML = "html" + + +class KnowledgeType(Enum): + DOCUMENT = "DOCUMENT" + URL = "URL" + TEXT = "TEXT" + + @property + def type(self): + return DocumentType + + @classmethod + def get_by_value(cls, value): + """Get Enum member by value""" + for member in cls: + if member.value == value: + return member + raise ValueError(f"{value} is not a valid value for {cls.__name__}") + + +class ChunkStrategy(Enum): + """chunk strategy""" + + CHUNK_BY_SIZE = ( + RecursiveCharacterTextSplitter, + [ + {"param_name": "chunk_size", "param_type": "int", "default_value": 512}, + {"param_name": "chunk_overlap", "param_type": "int", "default_value": 50}, + ], + "chunk size", + "split document by chunk size", + ) + CHUNK_BY_PAGE = (PageTextSplitter, [], "page", "split document by page") + CHUNK_BY_PARAGRAPH = ( + ParagraphTextSplitter, + [{"param_name": "separator", "param_type": "string", "default_value": "\n"}], + "paragraph", + "split document by paragraph", + ) + CHUNK_BY_SEPARATOR = ( + SeparatorTextSplitter, + [{"param_name": "separator", "param_type": "string", "default_value": "\n"}], + "separator", + "split document by separator", + ) + CHUNK_BY_MARKDOWN_HEADER = ( + MarkdownHeaderTextSplitter, + [], + "markdown header", + "split document by markdown header", + ) + + def __init__(self, splitter_class, parameters, alias, description): + self.splitter_class = splitter_class + self.parameters = parameters + self.alias = alias + self.description = description + + def match(self, *args, **kwargs): + return self.value[0](*args, **kwargs) + + +class Knowledge(ABC): + type: KnowledgeType = None + + def __init__( + self, + path: Optional[str] = None, + knowledge_type: Optional[KnowledgeType] = None, + data_loader: Optional = None, + **kwargs: Any, + ) -> None: + """Initialize with Knowledge arguments.""" + self._path = path + self._type = knowledge_type + self._data_loader = data_loader + + def load(self): + """Load knowledge from data_loader""" + documents = self._load() + return self._postprocess(documents) + + @classmethod + def type(cls) -> KnowledgeType: + """Get knowledge type""" + + @classmethod + def document_type(cls) -> Any: + """Get document type""" + return None + + def _postprocess(self, docs: List[Document]) -> List[Document]: + """Post process knowledge from data_loader""" + return docs + + @abstractmethod + def _load(self): + """Preprocess knowledge from data_loader""" + + @classmethod + def support_chunk_strategy(cls) -> List[ChunkStrategy]: + """support chunk strategy""" + return [ + ChunkStrategy.CHUNK_BY_SIZE, + ChunkStrategy.CHUNK_BY_PAGE, + ChunkStrategy.CHUNK_BY_PARAGRAPH, + ChunkStrategy.CHUNK_BY_MARKDOWN_HEADER, + ChunkStrategy.CHUNK_BY_SEPARATOR, + ] + + def default_chunk_strategy(self) -> ChunkStrategy: + return ChunkStrategy.CHUNK_BY_SIZE + + def support_chunk_strategy(self): + return [ + ChunkStrategy.CHUNK_BY_SIZE, + ChunkStrategy.CHUNK_BY_SEPARATOR, + ] diff --git a/dbgpt/rag/knowledge/csv.py b/dbgpt/rag/knowledge/csv.py new file mode 100644 index 000000000..24ee2ad82 --- /dev/null +++ b/dbgpt/rag/knowledge/csv.py @@ -0,0 +1,87 @@ +from typing import Optional, Any, List +import csv +from dbgpt.rag.chunk import Document +from dbgpt.rag.knowledge.base import ( + KnowledgeType, + Knowledge, + ChunkStrategy, + DocumentType, +) + + +class CSVKnowledge(Knowledge): + """CSV Knowledge""" + + def __init__( + self, + file_path: Optional[str] = None, + knowledge_type: Optional[KnowledgeType] = KnowledgeType.DOCUMENT, + source_column: Optional[str] = None, + encoding: Optional[str] = "utf-8", + loader: Optional[Any] = None, + **kwargs: Any, + ) -> None: + """Initialize csv with Knowledge arguments. + Args: + file_path:(Optional[str]) file path + knowledge_type:(KnowledgeType) knowledge type + source_column:(Optional[str]) source column + encoding:(Optional[str]) csv encoding + loader:(Optional[Any]) loader + """ + self._path = file_path + self._type = knowledge_type + self._loader = loader + self._encoding = encoding + self._source_column = source_column + + def _load(self) -> List[Document]: + """Load csv document from loader""" + if self._loader: + documents = self._loader.load() + else: + docs = [] + with open(self._path, newline="", encoding=self._encoding) as csvfile: + csv_reader = csv.DictReader(csvfile) + for i, row in enumerate(csv_reader): + strs = [] + for k, v in row.items(): + if k is None or v is None: + continue + strs.append(f"{k.strip()}: {v.strip()}") + content = "\n".join(strs) + try: + source = ( + row[self._source_column] + if self._source_column is not None + else self._path + ) + except KeyError: + raise ValueError( + f"Source column '{self._source_column}' not found in CSV file." + ) + metadata = {"source": source, "row": i} + doc = Document(content=content, metadata=metadata) + docs.append(doc) + + return docs + return [Document.langchain2doc(lc_document) for lc_document in documents] + + @classmethod + def support_chunk_strategy(cls) -> List[ChunkStrategy]: + return [ + ChunkStrategy.CHUNK_BY_SIZE, + ChunkStrategy.CHUNK_BY_SEPARATOR, + ] + + @classmethod + def default_chunk_strategy(cls) -> ChunkStrategy: + return ChunkStrategy.CHUNK_BY_SIZE + + @classmethod + def type(cls) -> KnowledgeType: + return KnowledgeType.DOCUMENT + + @classmethod + def document_type(cls) -> DocumentType: + return DocumentType.CSV diff --git a/dbgpt/rag/knowledge/docx.py b/dbgpt/rag/knowledge/docx.py new file mode 100644 index 000000000..df99a988d --- /dev/null +++ b/dbgpt/rag/knowledge/docx.py @@ -0,0 +1,72 @@ +from typing import Optional, Any, List + +from dbgpt.rag.chunk import Document +from dbgpt.rag.knowledge.base import ( + KnowledgeType, + Knowledge, + ChunkStrategy, + DocumentType, +) +import docx + + +class DocxKnowledge(Knowledge): + """Docx Knowledge""" + + def __init__( + self, + file_path: Optional[str] = None, + knowledge_type: Any = KnowledgeType.DOCUMENT, + encoding: Optional[str] = "utf-8", + loader: Optional[Any] = None, + **kwargs: Any, + ) -> None: + """Initialize with Knowledge arguments. + Args: + file_path:(Optional[str]) file path + knowledge_type:(KnowledgeType) knowledge type + encoding:(Optional[str]) csv encoding + loader:(Optional[Any]) loader + """ + self._path = file_path + self._type = knowledge_type + self._loader = loader + self._encoding = encoding + + def _load(self) -> List[Document]: + """Load docx document from loader""" + if self._loader: + documents = self._loader.load() + else: + docs = [] + doc = docx.Document(self._path) + content = [] + for i in range(len(doc.paragraphs)): + para = doc.paragraphs[i] + text = para.text + content.append(text) + docs.append( + Document(content="\n".join(content), metadata={"source": self._path}) + ) + return docs + return [Document.langchain2doc(lc_document) for lc_document in documents] + + @classmethod + def support_chunk_strategy(cls) -> List[ChunkStrategy]: + return [ + ChunkStrategy.CHUNK_BY_SIZE, + ChunkStrategy.CHUNK_BY_PARAGRAPH, + ChunkStrategy.CHUNK_BY_SEPARATOR, + ] + + @classmethod + def default_chunk_strategy(cls) -> ChunkStrategy: + return ChunkStrategy.CHUNK_BY_SIZE + + @classmethod + def type(cls) -> KnowledgeType: + return KnowledgeType.DOCUMENT + + @classmethod + def document_type(cls) -> DocumentType: + return DocumentType.DOCX diff --git a/dbgpt/rag/knowledge/factory.py b/dbgpt/rag/knowledge/factory.py new file mode 100644 index 000000000..845acbbe1 --- /dev/null +++ b/dbgpt/rag/knowledge/factory.py @@ -0,0 +1,143 @@ +from typing import Optional +from typing import List + +from dbgpt.rag.knowledge.base import KnowledgeType, Knowledge +from dbgpt.rag.knowledge.string import StringKnowledge +from dbgpt.rag.knowledge.url import URLKnowledge + + +class KnowledgeFactory: + """Knowledge Factory to create knowledge from file path and url""" + + def __init__( + self, + file_path: Optional[str] = None, + knowledge_type: Optional[KnowledgeType] = KnowledgeType.DOCUMENT, + ): + """Initialize with Knowledge Factory arguments. + Args: + param file_path: path of the file to convert + param knowledge_type: type of knowledge + """ + self._file_path = file_path + self._knowledge_type = knowledge_type + + @classmethod + def create( + cls, + datasource: Optional[str] = None, + knowledge_type: Optional[KnowledgeType] = KnowledgeType.DOCUMENT, + ): + """create knowledge from file path, url or text + Args: + datasource: path of the file to convert + knowledge_type: type of knowledge + Example: + .. code-block:: python + >>> from dbgpt.rag.knowledge.factory import KnowledgeFactory + >>> url_knowlege = KnowledgeFactory.create(datasource="https://www.baidu.com", knowledge_type=KnowledgeType.URL) + >>> doc_knowlege = KnowledgeFactory.create(datasource="path/to/document.pdf", knowledge_type=KnowledgeType.DOCUMENT) + """ + match knowledge_type: + case KnowledgeType.DOCUMENT: + return cls.from_file_path( + file_path=datasource, knowledge_type=knowledge_type + ) + case KnowledgeType.URL: + return cls.from_url(url=datasource, knowledge_type=knowledge_type) + case KnowledgeType.TEXT: + return cls.from_text(text=datasource, knowledge_type=knowledge_type) + case _: + raise Exception(f"Unsupported knowledge type '{knowledge_type}'") + + @classmethod + def from_file_path( + cls, + file_path: Optional[str] = None, + knowledge_type: Optional[KnowledgeType] = KnowledgeType.DOCUMENT, + ) -> Knowledge: + """Create knowledge from path + Args: + param file_path: path of the file to convert + param knowledge_type: type of knowledge + Example: + .. code-block:: python + >>> from dbgpt.rag.knowledge.factory import KnowledgeFactory + >>> doc_knowlege = KnowledgeFactory.create(datasource="path/to/document.pdf", knowledge_type=KnowledgeType.DOCUMENT) + """ + factory = cls(file_path=file_path, knowledge_type=knowledge_type) + return factory._select_document_knowledge( + file_path=file_path, knowledge_type=knowledge_type + ) + + @staticmethod + def from_url( + url: Optional[str] = None, + knowledge_type: Optional[KnowledgeType] = KnowledgeType.URL, + ) -> Knowledge: + """Create knowledge from url + Args: + param url: url of the file to convert + param knowledge_type: type of knowledge + Example: + .. code-block:: python + >>> from dbgpt.rag.knowledge.factory import KnowledgeFactory + >>> url_knowlege = KnowledgeFactory.create(datasource="https://www.baidu.com", knowledge_type=KnowledgeType.URL) + """ + return URLKnowledge( + url=url, + knowledge_type=knowledge_type, + ) + + @staticmethod + def from_text( + text: str = None, + knowledge_type: Optional[KnowledgeType] = KnowledgeType.TEXT, + ) -> Knowledge: + """Create knowledge from text + Args: + param text: text to convert + param knowledge_type: type of knowledge + """ + return StringKnowledge( + text=text, + knowledge_type=knowledge_type, + ) + + def _select_document_knowledge(self, **kwargs): + """Select document knowledge from file path""" + extension = self._file_path.rsplit(".", 1)[-1] + knowledge_classes = self._get_knowledge_subclasses() + implementation = None + for cls in knowledge_classes: + if cls.document_type() and cls.document_type().value == extension: + implementation = cls(**kwargs) + if implementation is None: + raise Exception(f"Unsupported knowledge document type '{extension}'") + return implementation + + @classmethod + def all_types(cls): + """get all knowledge types""" + return [knowledge.type().value for knowledge in cls._get_knowledge_subclasses()] + + @classmethod + def subclasses(cls): + """get all knowledge subclasses""" + return cls._get_knowledge_subclasses() + + @staticmethod + def _get_knowledge_subclasses() -> List[Knowledge]: + """get all knowledge subclasses""" + from dbgpt.rag.knowledge.base import Knowledge + from dbgpt.rag.knowledge.pdf import PDFKnowledge + from dbgpt.rag.knowledge.docx import DocxKnowledge + from dbgpt.rag.knowledge.markdown import MarkdownKnowledge + from dbgpt.rag.knowledge.csv import CSVKnowledge + from dbgpt.rag.knowledge.txt import TXTKnowledge + from dbgpt.rag.knowledge.pptx import PPTXKnowledge + from dbgpt.rag.knowledge.html import HTMLKnowledge + from dbgpt.rag.knowledge.url import URLKnowledge + from dbgpt.rag.knowledge.string import StringKnowledge + + return Knowledge.__subclasses__() diff --git a/dbgpt/rag/knowledge/html.py b/dbgpt/rag/knowledge/html.py new file mode 100644 index 000000000..eb3dce643 --- /dev/null +++ b/dbgpt/rag/knowledge/html.py @@ -0,0 +1,84 @@ +from typing import Optional, Any, List + +import chardet + +from dbgpt.rag.chunk import Document +from dbgpt.rag.knowledge.base import ( + Knowledge, + KnowledgeType, + ChunkStrategy, + DocumentType, +) + + +class HTMLKnowledge(Knowledge): + """HTML Knowledge""" + + def __init__( + self, + file_path: Optional[str] = None, + knowledge_type: KnowledgeType = KnowledgeType.DOCUMENT, + loader: Optional[Any] = None, + **kwargs: Any, + ) -> None: + """Initialize with Knowledge arguments. + Args: + file_path:(Optional[str]) file path + knowledge_type:(KnowledgeType) knowledge type + loader:(Optional[Any]) loader + """ + self._path = file_path + self._type = knowledge_type + self._loader = loader + + def _load(self) -> List[Document]: + """Load html document from loader""" + if self._loader: + documents = self._loader.load() + else: + with open(self._path, "rb") as f: + raw_text = f.read() + result = chardet.detect(raw_text) + if result["encoding"] is None: + text = raw_text.decode("utf-8") + else: + text = raw_text.decode(result["encoding"]) + metadata = {"source": self._path} + return [Document(content=text, metadata=metadata)] + + return [Document.langchain2doc(lc_document) for lc_document in documents] + + def _postprocess(self, documents: List[Document]): + i = 0 + for d in documents: + import markdown + + content = markdown.markdown(d.content) + from bs4 import BeautifulSoup + + soup = BeautifulSoup(content, "html.parser") + for tag in soup(["!doctype", "meta", "i.fa"]): + tag.extract() + documents[i].content = soup.get_text() + documents[i].content = documents[i].content.replace("\n", " ") + i += 1 + return documents + + @classmethod + def support_chunk_strategy(cls): + return [ + ChunkStrategy.CHUNK_BY_SIZE, + ChunkStrategy.CHUNK_BY_SEPARATOR, + ] + + @classmethod + def default_chunk_strategy(cls) -> ChunkStrategy: + return ChunkStrategy.CHUNK_BY_SIZE + + @classmethod + def type(cls) -> KnowledgeType: + return KnowledgeType.DOCUMENT + + @classmethod + def document_type(cls) -> DocumentType: + return DocumentType.HTML diff --git a/dbgpt/rag/extracter/__init__.py b/dbgpt/rag/knowledge/json.py similarity index 100% rename from dbgpt/rag/extracter/__init__.py rename to dbgpt/rag/knowledge/json.py diff --git a/dbgpt/rag/knowledge/markdown.py b/dbgpt/rag/knowledge/markdown.py new file mode 100644 index 000000000..c707c893b --- /dev/null +++ b/dbgpt/rag/knowledge/markdown.py @@ -0,0 +1,65 @@ +from typing import Optional, Any, List + +from dbgpt.rag.chunk import Document +from dbgpt.rag.knowledge.base import ( + KnowledgeType, + Knowledge, + ChunkStrategy, + DocumentType, +) + + +class MarkdownKnowledge(Knowledge): + """Markdown Knowledge""" + + def __init__( + self, + file_path: Optional[str] = None, + knowledge_type: KnowledgeType = KnowledgeType.DOCUMENT, + encoding: Optional[str] = "utf-8", + loader: Optional[Any] = None, + **kwargs: Any, + ) -> None: + """Initialize with Knowledge arguments. + Args: + file_path:(Optional[str]) file path + knowledge_type:(KnowledgeType) knowledge type + encoding:(Optional[str]) encoding + loader:(Optional[Any]) loader + """ + self._path = file_path + self._type = knowledge_type + self._loader = loader + self._encoding = encoding + + def _load(self) -> List[Document]: + """Load markdown document from loader""" + if self._loader: + documents = self._loader.load() + else: + with open(self._path, encoding=self._encoding, errors="ignore") as f: + markdown_text = f.read() + metadata = {"source": self._path} + documents = [Document(content=markdown_text, metadata=metadata)] + return documents + return [Document.langchain2doc(lc_document) for lc_document in documents] + + @classmethod + def support_chunk_strategy(cls) -> List[ChunkStrategy]: + return [ + ChunkStrategy.CHUNK_BY_SIZE, + ChunkStrategy.CHUNK_BY_MARKDOWN_HEADER, + ChunkStrategy.CHUNK_BY_SEPARATOR, + ] + + @classmethod + def default_chunk_strategy(cls) -> ChunkStrategy: + return ChunkStrategy.CHUNK_BY_MARKDOWN_HEADER + + @classmethod + def type(cls) -> KnowledgeType: + return KnowledgeType.DOCUMENT + + @classmethod + def document_type(cls) -> DocumentType: + return DocumentType.MARKDOWN diff --git a/dbgpt/rag/knowledge/pdf.py b/dbgpt/rag/knowledge/pdf.py new file mode 100644 index 000000000..87be838ca --- /dev/null +++ b/dbgpt/rag/knowledge/pdf.py @@ -0,0 +1,88 @@ +from typing import Optional, Any, List + +from dbgpt.rag.chunk import Document +from dbgpt.rag.knowledge.base import ( + Knowledge, + KnowledgeType, + ChunkStrategy, + DocumentType, +) + + +class PDFKnowledge(Knowledge): + """PDF Knowledge""" + + def __init__( + self, + file_path: Optional[str] = None, + knowledge_type: KnowledgeType = KnowledgeType.DOCUMENT, + loader: Optional = None, + language: Optional[str] = "zh", + **kwargs: Any, + ) -> None: + """Initialize with PDF Knowledge arguments. + Args: + file_path:(Optional[str]) file path + knowledge_type:(KnowledgeType) knowledge type + loader:(Optional[Any]) loader + """ + self._path = file_path + self._type = knowledge_type + self._loader = loader + self._language = language + + def _load(self) -> List[Document]: + """Load pdf document from loader""" + if self._loader: + documents = self._loader.load() + else: + import pypdf + + pages = [] + documents = [] + with open(self._path, "rb") as file: + reader = pypdf.PdfReader(file) + for page_num in range(len(reader.pages)): + page = reader.pages[page_num] + pages.append((page.extract_text(), page_num)) + + # cleaned_pages = [] + for page, page_num in pages: + lines = page.splitlines() + + cleaned_lines = [] + for line in lines: + if self._language == "en": + words = list(line) + else: + words = line.split() + digits = [word for word in words if any(i.isdigit() for i in word)] + cleaned_lines.append(line) + page = "\n".join(cleaned_lines) + # cleaned_pages.append(page) + metadata = {"source": self._path, "page": page_num} + # text = "\f".join(cleaned_pages) + document = Document(content=page, metadata=metadata) + documents.append(document) + return documents + return [Document.langchain2doc(lc_document) for lc_document in documents] + + @classmethod + def support_chunk_strategy(cls) -> List[ChunkStrategy]: + return [ + ChunkStrategy.CHUNK_BY_SIZE, + ChunkStrategy.CHUNK_BY_PAGE, + ChunkStrategy.CHUNK_BY_SEPARATOR, + ] + + @classmethod + def default_chunk_strategy(cls) -> ChunkStrategy: + return ChunkStrategy.CHUNK_BY_SIZE + + @classmethod + def type(cls) -> KnowledgeType: + return KnowledgeType.DOCUMENT + + @classmethod + def document_type(cls) -> DocumentType: + return DocumentType.PDF diff --git a/dbgpt/rag/knowledge/pptx.py b/dbgpt/rag/knowledge/pptx.py new file mode 100644 index 000000000..431d51444 --- /dev/null +++ b/dbgpt/rag/knowledge/pptx.py @@ -0,0 +1,72 @@ +from typing import Optional, Any, List + +from dbgpt.rag.chunk import Document +from dbgpt.rag.knowledge.base import ( + Knowledge, + KnowledgeType, + ChunkStrategy, + DocumentType, +) + + +class PPTXKnowledge(Knowledge): + """PPTX Knowledge""" + + def __init__( + self, + file_path: Optional[str] = None, + knowledge_type: KnowledgeType = KnowledgeType.DOCUMENT, + loader: Optional = None, + language: Optional[str] = "zh", + **kwargs: Any, + ) -> None: + """Initialize with PDF Knowledge arguments. + Args: + file_path:(Optional[str]) file path + knowledge_type:(KnowledgeType) knowledge type + loader:(Optional[Any]) loader + """ + self._path = file_path + self._type = knowledge_type + self._loader = loader + self._language = language + + def _load(self) -> List[Document]: + """Load pdf document from loader""" + if self._loader: + documents = self._loader.load() + else: + from pptx import Presentation + + pr = Presentation(self._path) + docs = [] + for slide in pr.slides: + content = "" + for shape in slide.shapes: + if hasattr(shape, "text") and shape.text: + content += shape.text + docs.append( + Document(content=content, metadata={"source": slide.slide_id}) + ) + return docs + return [Document.langchain2doc(lc_document) for lc_document in documents] + + @classmethod + def support_chunk_strategy(cls) -> List[ChunkStrategy]: + return [ + ChunkStrategy.CHUNK_BY_SIZE, + ChunkStrategy.CHUNK_BY_PAGE, + ChunkStrategy.CHUNK_BY_SEPARATOR, + ] + + @classmethod + def default_chunk_strategy(cls) -> ChunkStrategy: + return ChunkStrategy.CHUNK_BY_SIZE + + @classmethod + def type(cls) -> KnowledgeType: + return KnowledgeType.DOCUMENT + + @classmethod + def document_type(cls) -> DocumentType: + return DocumentType.PPTX diff --git a/dbgpt/rag/knowledge/string.py b/dbgpt/rag/knowledge/string.py new file mode 100644 index 000000000..3b8fd7d5a --- /dev/null +++ b/dbgpt/rag/knowledge/string.py @@ -0,0 +1,48 @@ +from typing import Optional, Any, List + +from dbgpt.rag.chunk import Document +from dbgpt.rag.knowledge.base import KnowledgeType, Knowledge, ChunkStrategy + + +class StringKnowledge(Knowledge): + """String Knowledge""" + + def __init__( + self, + text: str = None, + knowledge_type: KnowledgeType = KnowledgeType.TEXT, + encoding: Optional[str] = "utf-8", + loader: Optional[Any] = None, + **kwargs: Any, + ) -> None: + """Initialize with Knowledge arguments. + Args: + text:(str) text + knowledge_type:(KnowledgeType) knowledge type + encoding:(encoding) csv encoding + loader:(loader) loader + """ + self._text = text + self._type = knowledge_type + self._loader = loader + self._encoding = encoding + + def _load(self) -> List[Document]: + """load raw text from loader""" + metadata = {"source": "raw text"} + docs = [Document(content=self._text, metadata=metadata)] + return docs + + @classmethod + def support_chunk_strategy(cls) -> List[ChunkStrategy]: + return [ + ChunkStrategy.CHUNK_BY_SIZE, + ChunkStrategy.CHUNK_BY_SEPARATOR, + ] + + def default_chunk_strategy(cls) -> ChunkStrategy: + return ChunkStrategy.CHUNK_BY_SIZE + + @classmethod + def type(cls): + return KnowledgeType.TEXT diff --git a/dbgpt/rag/graph_engine/__init__.py b/dbgpt/rag/knowledge/tests/__init__.py similarity index 100% rename from dbgpt/rag/graph_engine/__init__.py rename to dbgpt/rag/knowledge/tests/__init__.py diff --git a/dbgpt/rag/knowledge/tests/test_csv.py b/dbgpt/rag/knowledge/tests/test_csv.py new file mode 100644 index 000000000..0f8029354 --- /dev/null +++ b/dbgpt/rag/knowledge/tests/test_csv.py @@ -0,0 +1,31 @@ +import pytest +from unittest.mock import MagicMock, mock_open, patch + +from dbgpt.rag.knowledge.csv import CSVKnowledge + +MOCK_CSV_DATA = "id,name,age\n1,John Doe,30\n2,Jane Smith,25\n3,Bob Johnson,40" + + +@pytest.fixture +def mock_file_open(): + with patch("builtins.open", mock_open(read_data=MOCK_CSV_DATA)) as mock_file: + yield mock_file + + +@pytest.fixture +def mock_csv_dict_reader(): + with patch("csv.DictReader", MagicMock()) as mock_csv: + mock_csv.return_value = iter( + [ + {"id": "1", "name": "John Doe", "age": "30"}, + {"id": "2", "name": "Jane Smith", "age": "25"}, + {"id": "3", "name": "Bob Johnson", "age": "40"}, + ] + ) + yield mock_csv + + +def test_load_from_csv(mock_file_open, mock_csv_dict_reader): + knowledge = CSVKnowledge(file_path="test_data.csv", source_column="name") + documents = knowledge._load() + assert len(documents) == 3 diff --git a/dbgpt/rag/knowledge/tests/test_docx.py b/dbgpt/rag/knowledge/tests/test_docx.py new file mode 100644 index 000000000..e6f1ddc4f --- /dev/null +++ b/dbgpt/rag/knowledge/tests/test_docx.py @@ -0,0 +1,28 @@ +import pytest +from unittest.mock import MagicMock, patch + +from dbgpt.rag.knowledge.docx import DocxKnowledge + + +@pytest.fixture +def mock_docx_document(): + mock_document = MagicMock() + mock_document.paragraphs = [ + MagicMock(text="This is the first paragraph."), + MagicMock(text="This is the second paragraph."), + ] + with patch("docx.Document", return_value=mock_document): + yield mock_document + + +def test_load_from_docx(mock_docx_document): + file_path = "test_document.docx" + knowledge = DocxKnowledge(file_path=file_path) + documents = knowledge._load() + + assert len(documents) == 1 + assert ( + documents[0].content + == "This is the first paragraph.\nThis is the second paragraph." + ) + assert documents[0].metadata["source"] == file_path diff --git a/dbgpt/rag/knowledge/tests/test_html.py b/dbgpt/rag/knowledge/tests/test_html.py new file mode 100644 index 000000000..9cb123c5c --- /dev/null +++ b/dbgpt/rag/knowledge/tests/test_html.py @@ -0,0 +1,45 @@ +import pytest +from unittest.mock import mock_open, patch + +from dbgpt.rag.knowledge.html import HTMLKnowledge + +MOCK_HTML_CONTENT = b""" + + +Test HTML + + +

This is a paragraph.

+ + +""" + +MOCK_CHARDET_RESULT = {"encoding": "utf-8", "confidence": 0.99} + + +@pytest.fixture +def mock_file_open(): + with patch( + "builtins.open", mock_open(read_data=MOCK_HTML_CONTENT), create=True + ) as mock_file: + yield mock_file + + +@pytest.fixture +def mock_chardet_detect(): + with patch("chardet.detect", return_value=MOCK_CHARDET_RESULT) as mock_detect: + yield mock_detect + + +def test_load_from_html(mock_file_open, mock_chardet_detect): + file_path = "test_document.html" + knowledge = HTMLKnowledge(file_path=file_path) + documents = knowledge._load() + + assert len(documents) == 1 + assert "This is a paragraph." in documents[0].content + assert documents[0].metadata["source"] == file_path + + mock_file_open.assert_called_once_with(file_path, "rb") + + mock_chardet_detect.assert_called_once() diff --git a/dbgpt/rag/knowledge/tests/test_markdown.py b/dbgpt/rag/knowledge/tests/test_markdown.py new file mode 100644 index 000000000..619055e10 --- /dev/null +++ b/dbgpt/rag/knowledge/tests/test_markdown.py @@ -0,0 +1,28 @@ +import pytest +from unittest.mock import mock_open, patch + +from dbgpt.rag.knowledge.markdown import MarkdownKnowledge + +MOCK_MARKDOWN_DATA = """# Header 1 +This is some text under header 1. + +## Header 2 +This is some text under header 2. +""" + + +@pytest.fixture +def mock_file_open(): + with patch("builtins.open", mock_open(read_data=MOCK_MARKDOWN_DATA)) as mock_file: + yield mock_file + + +# 定义测试函数 +def test_load_from_markdown(mock_file_open): + file_path = "test_document.md" + knowledge = MarkdownKnowledge(file_path=file_path) + documents = knowledge._load() + + assert len(documents) == 1 + assert documents[0].content == MOCK_MARKDOWN_DATA + assert documents[0].metadata["source"] == file_path diff --git a/dbgpt/rag/knowledge/tests/test_pdf.py b/dbgpt/rag/knowledge/tests/test_pdf.py new file mode 100644 index 000000000..a4b130246 --- /dev/null +++ b/dbgpt/rag/knowledge/tests/test_pdf.py @@ -0,0 +1,36 @@ +import pytest +from unittest.mock import MagicMock, patch, mock_open + +from dbgpt.rag.knowledge.pdf import PDFKnowledge + +MOCK_PDF_PAGES = [ + ("This is the content of the first page.", 0), + ("This is the content of the second page.", 1), +] + + +@pytest.fixture +def mock_pdf_open_and_reader(): + mock_pdf_file = mock_open() + mock_reader = MagicMock() + mock_reader.pages = [ + MagicMock(extract_text=MagicMock(return_value=page[0])) + for page in MOCK_PDF_PAGES + ] + with patch("builtins.open", mock_pdf_file): + with patch("pypdf.PdfReader", return_value=mock_reader) as mock: + yield mock + + +def test_load_from_pdf(mock_pdf_open_and_reader): + file_path = "test_document.pdf" + knowledge = PDFKnowledge(file_path=file_path) + documents = knowledge._load() + + assert len(documents) == len(MOCK_PDF_PAGES) + for i, document in enumerate(documents): + assert MOCK_PDF_PAGES[i][0] in document.content + assert document.metadata["source"] == file_path + assert document.metadata["page"] == MOCK_PDF_PAGES[i][1] + + # diff --git a/dbgpt/rag/knowledge/tests/test_txt.py b/dbgpt/rag/knowledge/tests/test_txt.py new file mode 100644 index 000000000..ecdb241de --- /dev/null +++ b/dbgpt/rag/knowledge/tests/test_txt.py @@ -0,0 +1,37 @@ +import pytest +from unittest.mock import mock_open, patch + +from dbgpt.rag.knowledge.txt import TXTKnowledge + +MOCK_TXT_CONTENT = b"Sample text content for testing.\nAnother line of text." + +MOCK_CHARDET_RESULT = {"encoding": "utf-8", "confidence": 0.99} + + +@pytest.fixture +def mock_file_open(): + with patch( + "builtins.open", mock_open(read_data=MOCK_TXT_CONTENT), create=True + ) as mock_file: + yield mock_file + + +@pytest.fixture +def mock_chardet_detect(): + with patch("chardet.detect", return_value=MOCK_CHARDET_RESULT) as mock_detect: + yield mock_detect + + +# 定义测试函数 +def test_load_from_txt(mock_file_open, mock_chardet_detect): + file_path = "test_document.txt" + knowledge = TXTKnowledge(file_path=file_path) + documents = knowledge._load() + + assert len(documents) == 1 + assert "Sample text content for testing." in documents[0].content + assert documents[0].metadata["source"] == file_path + + mock_file_open.assert_called_once_with(file_path, "rb") + + mock_chardet_detect.assert_called_once() diff --git a/dbgpt/rag/knowledge/txt.py b/dbgpt/rag/knowledge/txt.py new file mode 100644 index 000000000..57b3fcdd7 --- /dev/null +++ b/dbgpt/rag/knowledge/txt.py @@ -0,0 +1,68 @@ +from typing import Optional, Any, List + +import chardet + +from dbgpt.rag.chunk import Document +from dbgpt.rag.knowledge.base import ( + Knowledge, + KnowledgeType, + ChunkStrategy, + DocumentType, +) + + +class TXTKnowledge(Knowledge): + """TXT Knowledge""" + + def __init__( + self, + file_path: Optional[str] = None, + knowledge_type: KnowledgeType = KnowledgeType.DOCUMENT, + loader: Optional[Any] = None, + **kwargs: Any, + ) -> None: + """Initialize with Knowledge arguments. + Args: + file_path:(Optional[str]) file path + knowledge_type:(KnowledgeType) knowledge type + loader:(Optional[Any]) loader + """ + self._path = file_path + self._type = knowledge_type + self._loader = loader + + def _load(self) -> List[Document]: + """Load txt document from loader""" + if self._loader: + documents = self._loader.load() + else: + with open(self._path, "rb") as f: + raw_text = f.read() + result = chardet.detect(raw_text) + if result["encoding"] is None: + text = raw_text.decode("utf-8") + else: + text = raw_text.decode(result["encoding"]) + metadata = {"source": self._path} + return [Document(content=text, metadata=metadata)] + + return [Document.langchain2doc(lc_document) for lc_document in documents] + + @classmethod + def support_chunk_strategy(cls): + return [ + ChunkStrategy.CHUNK_BY_SIZE, + ChunkStrategy.CHUNK_BY_SEPARATOR, + ] + + @classmethod + def default_chunk_strategy(cls) -> ChunkStrategy: + return ChunkStrategy.CHUNK_BY_SIZE + + @classmethod + def type(cls) -> KnowledgeType: + return KnowledgeType.DOCUMENT + + @classmethod + def document_type(cls) -> DocumentType: + return DocumentType.TXT diff --git a/dbgpt/rag/knowledge/url.py b/dbgpt/rag/knowledge/url.py new file mode 100644 index 000000000..01b4e4a8e --- /dev/null +++ b/dbgpt/rag/knowledge/url.py @@ -0,0 +1,55 @@ +from typing import Optional, Any, List + +from dbgpt.rag.chunk import Document +from dbgpt.rag.knowledge.base import KnowledgeType, Knowledge, ChunkStrategy + + +class URLKnowledge(Knowledge): + def __init__( + self, + url: Optional[str] = None, + knowledge_type: KnowledgeType = KnowledgeType.URL, + source_column: Optional[str] = None, + encoding: Optional[str] = "utf-8", + loader: Optional[Any] = None, + **kwargs: Any, + ) -> None: + """Initialize with Knowledge arguments. + Args: + url:(Optional[str]) url + knowledge_type:(KnowledgeType) knowledge type + source_column:(Optional[str]) source column + encoding:(Optional[str]) csv encoding + loader:(Optional[Any]) loader + """ + self._path = url + self._type = knowledge_type + self._loader = loader + self._encoding = encoding + self._source_column = source_column + + def _load(self) -> List[Document]: + """Fetch URL document from loader""" + if self._loader: + documents = self._loader.load() + else: + from langchain.document_loaders import WebBaseLoader + + web_reader = WebBaseLoader(web_path=self._path) + documents = web_reader.load() + return [Document.langchain2doc(lc_document) for lc_document in documents] + + @classmethod + def support_chunk_strategy(cls) -> List[ChunkStrategy]: + return [ + ChunkStrategy.CHUNK_BY_SIZE, + ChunkStrategy.CHUNK_BY_SEPARATOR, + ] + + @classmethod + def default_chunk_strategy(cls) -> ChunkStrategy: + return ChunkStrategy.CHUNK_BY_SIZE + + @classmethod + def type(cls): + return KnowledgeType.URL diff --git a/dbgpt/rag/retriever/base.py b/dbgpt/rag/retriever/base.py new file mode 100644 index 000000000..2af4ab99e --- /dev/null +++ b/dbgpt/rag/retriever/base.py @@ -0,0 +1,99 @@ +from abc import ABC, abstractmethod +from enum import Enum +from typing import List, Tuple +from dbgpt.rag.chunk import Chunk + + +class RetrieverStrategy(str, Enum): + """Retriever strategy. + Args: + - EMBEDDING: embedding retriever + - KEYWORD: keyword retriever + - HYBRID: hybrid retriever + """ + + EMBEDDING = "embedding" + KEYWORD = "keyword" + HYBRID = "hybrid" + + +class BaseRetriever(ABC): + """Base retriever.""" + + def retrieve(self, query: str) -> List[Chunk]: + """ + Args: + query (str): query text + Returns: + List[Chunk]: list of chunks + """ + return self._retrieve(query) + + async def aretrieve(self, query: str) -> List[Chunk]: + """ + Args: + query (str): async query text + Returns: + List[Chunk]: list of chunks + """ + return await self._aretrieve(query) + + def retrieve_with_scores(self, query: str, score_threshold: float) -> List[Chunk]: + """ + Args: + query (str): query text + score_threshold (float): score threshold + """ + return self._retrieve_with_score(query, score_threshold) + + async def aretrieve_with_scores( + self, query: str, score_threshold: float + ) -> List[Chunk]: + """ + Args: + query (str): query text + score_threshold (float): score threshold + Returns: + List[Chunk]: list of chunks + """ + return await self._aretrieve_with_score(query, score_threshold) + + @abstractmethod + def _retrieve(self, query: str) -> List[Chunk]: + """Retrieve knowledge chunks. + Args: + query (str): query text + Returns: + List[Chunk]: list of chunks + """ + + @abstractmethod + async def _aretrieve(self, query: str) -> List[Chunk]: + """Async Retrieve knowledge chunks. + Args: + query (str): query text + Returns: + List[Chunk]: list of chunks + """ + + @abstractmethod + def _retrieve_with_score(self, query: str, score_threshold: float) -> List[Chunk]: + """Retrieve knowledge chunks with score. + Args: + query (str): query text + score_threshold (float): score threshold + Returns: + List[Chunk]: list of chunks + """ + + @abstractmethod + async def _aretrieve_with_score( + self, query: str, score_threshold: float + ) -> List[Chunk]: + """Async Retrieve knowledge chunks with score. + Args: + query (str): query text + score_threshold (float): score threshold + Returns: + List[Chunk]: list of chunks + """ diff --git a/dbgpt/rag/retriever/db_struct.py b/dbgpt/rag/retriever/db_struct.py new file mode 100644 index 000000000..ad1c84e3f --- /dev/null +++ b/dbgpt/rag/retriever/db_struct.py @@ -0,0 +1,152 @@ +from functools import reduce +from typing import List, Optional + +from dbgpt.util.chat_util import run_async_tasks +from dbgpt.datasource.rdbms.base import RDBMSDatabase +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.retriever.base import BaseRetriever +from dbgpt.rag.retriever.rerank import Ranker, DefaultRanker +from dbgpt.storage.vector_store.connector import VectorStoreConnector + + +class DBStructRetriever(BaseRetriever): + """DBStruct retriever.""" + + def __init__( + self, + top_k: int = 4, + connection: Optional[RDBMSDatabase] = None, + is_embeddings: bool = True, + query_rewrite: bool = False, + rerank: Ranker = None, + vector_store_connector: Optional[VectorStoreConnector] = None, + **kwargs + ): + """ + Args: + top_k (int): top k + connection (Optional[RDBMSDatabase]): RDBMSDatabase connection. + is_embeddings (bool): Whether to query by embeddings in the vector store, Defaults to True. + query_rewrite (bool): query rewrite + rerank (Ranker): rerank + vector_store_connector (VectorStoreConnector): vector store connector + code example: + .. code-block:: python + >>> from dbgpt.datasource.rdbms.conn_sqlite import SQLiteTempConnect + >>> from dbgpt.serve.rag.assembler.db_struct import DBStructAssembler + >>> from dbgpt.storage.vector_store.connector import VectorStoreConnector + >>> from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig + >>> from dbgpt.rag.retriever.embedding import EmbeddingRetriever + + def _create_temporary_connection(): + connect = SQLiteTempConnect.create_temporary_db() + connect.create_temp_tables( + { + "user": { + "columns": { + "id": "INTEGER PRIMARY KEY", + "name": "TEXT", + "age": "INTEGER", + }, + "data": [ + (1, "Tom", 10), + (2, "Jerry", 16), + (3, "Jack", 18), + (4, "Alice", 20), + (5, "Bob", 22), + ], + } + } + ) + return connect + connection = _create_temporary_connection() + vector_store_config = ChromaVectorConfig(name="vector_store_name") + embedding_model_path = "{your_embedding_model_path}" + embedding_fn = embedding_factory.create( + model_name=embedding_model_path + ) + vector_connector = VectorStoreConnector.from_default( + "Chroma", + vector_store_config=vector_store_config, + embedding_fn=embedding_fn + ) + # get db struct retriever + retriever = DBStructRetriever(top_k=3, vector_store_connector=vector_connector) + chunks = retriever.retrieve("show columns from table") + print(f"db struct rag example results:{[chunk.content for chunk in chunks]}") + """ + + self._top_k = top_k + self._is_embeddings = is_embeddings + self._connection = connection + self._query_rewrite = query_rewrite + self._vector_store_connector = vector_store_connector + self._rerank = rerank or DefaultRanker(self._top_k) + + def _retrieve(self, query: str) -> List[Chunk]: + """Retrieve knowledge chunks. + Args: + query (str): query text + """ + if self._is_embeddings: + queries = [query] + candidates = [ + self._vector_store_connector.similar_search(query, self._top_k) + for query in queries + ] + candidates = reduce(lambda x, y: x + y, candidates) + return candidates + else: + from dbgpt.rag.summary.rdbms_db_summary import _parse_db_summary + + table_summaries = _parse_db_summary(self._connection) + return [Chunk(content=table_summary) for table_summary in table_summaries] + + def _retrieve_with_score(self, query: str, score_threshold: float) -> List[Chunk]: + """Retrieve knowledge chunks with score. + Args: + query (str): query text + score_threshold (float): score threshold + """ + return self._retrieve(query) + + async def _aretrieve(self, query: str) -> List[Chunk]: + """Retrieve knowledge chunks. + Args: + query (str): query text + """ + if self._is_embeddings: + queries = [query] + candidates = [self._similarity_search(query) for query in queries] + candidates = await run_async_tasks(tasks=candidates, concurrency_limit=1) + return candidates + else: + from dbgpt.rag.summary.rdbms_db_summary import _parse_db_summary + + table_summaries = await run_async_tasks( + tasks=[self._aparse_db_summary()], concurrency_limit=1 + ) + return [Chunk(content=table_summary) for table_summary in table_summaries] + + async def _aretrieve_with_score( + self, query: str, score_threshold: float + ) -> List[Chunk]: + """Retrieve knowledge chunks with score. + Args: + query (str): query text + score_threshold (float): score threshold + """ + return await self._aretrieve(query) + + async def _similarity_search(self, query) -> List[Chunk]: + """Similar search.""" + return self._vector_store_connector.similar_search( + query, + self._top_k, + ) + + async def _aparse_db_summary(self) -> List[Chunk]: + """Similar search.""" + from dbgpt.rag.summary.rdbms_db_summary import _parse_db_summary + + return _parse_db_summary() diff --git a/dbgpt/rag/retriever/embedding.py b/dbgpt/rag/retriever/embedding.py new file mode 100644 index 000000000..48b738d22 --- /dev/null +++ b/dbgpt/rag/retriever/embedding.py @@ -0,0 +1,146 @@ +from functools import reduce +from typing import List, Optional + +from dbgpt.rag.retriever.rewrite import QueryRewrite +from dbgpt.util.chat_util import run_async_tasks +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.retriever.base import BaseRetriever +from dbgpt.rag.retriever.rerank import Ranker, DefaultRanker +from dbgpt.storage.vector_store.connector import VectorStoreConnector + + +class EmbeddingRetriever(BaseRetriever): + """Embedding retriever.""" + + def __init__( + self, + top_k: int = 4, + query_rewrite: Optional[QueryRewrite] = None, + rerank: Ranker = None, + vector_store_connector: VectorStoreConnector = None, + ): + """ + Args: + top_k (int): top k + query_rewrite (Optional[QueryRewrite]): query rewrite + rerank (Ranker): rerank + vector_store_connector (VectorStoreConnector): vector store connector + code example: + .. code-block:: python + >>> from dbgpt.storage.vector_store.connector import VectorStoreConnector + >>> from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig + >>> from dbgpt.rag.retriever.embedding import EmbeddingRetriever + >>> from dbgpt.rag.embedding.embedding_factory import DefaultEmbeddingFactory + + embedding_factory = DefaultEmbeddingFactory() + from dbgpt.rag.retriever.embedding import EmbeddingRetriever + from dbgpt.storage.vector_store.connector import VectorStoreConnector + + embedding_fn = embedding_factory.create( + model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL] + ) + vector_name = "test" + config = ChromaVectorConfig(name=vector_name, embedding_fn=embedding_fn) + vector_store_connector = VectorStoreConnector( + vector_store_type=""Chroma"", + vector_store_config=config, + ) + embedding_retriever = EmbeddingRetriever( + top_k=3, vector_store_connector=vector_store_connector + ) + chunks = embedding_retriever.retrieve("your query text") + print(f"embedding retriever results:{[chunk.content for chunk in chunks]}") + """ + self._top_k = top_k + self._query_rewrite = query_rewrite + self._vector_store_connector = vector_store_connector + self._rerank = rerank or DefaultRanker(self._top_k) + + def _retrieve(self, query: str) -> List[Chunk]: + """Retrieve knowledge chunks. + Args: + query (str): query text + Return: + List[Chunk]: list of chunks + """ + queries = [query] + candidates = [ + self._vector_store_connector.similar_search(query, self._top_k) + for query in queries + ] + candidates = reduce(lambda x, y: x + y, candidates) + return candidates + + def _retrieve_with_score(self, query: str, score_threshold: float) -> List[Chunk]: + """Retrieve knowledge chunks with score. + Args: + query (str): query text + score_threshold (float): score threshold + Return: + List[Chunk]: list of chunks with score + """ + queries = [query] + candidates_with_score = [ + self._vector_store_connector.similar_search_with_scores( + query, self._top_k, score_threshold + ) + for query in queries + ] + candidates_with_score = reduce(lambda x, y: x + y, candidates_with_score) + candidates_with_score = self._rerank.rank(candidates_with_score) + return candidates_with_score + + async def _aretrieve(self, query: str) -> List[Chunk]: + """Retrieve knowledge chunks. + Args: + query (str): query text + Return: + List[Chunk]: list of chunks + """ + queries = [query] + if self._query_rewrite: + new_queries = await self._query_rewrite.rewrite(origin_query=query, nums=1) + queries.extend(new_queries) + candidates = [self._similarity_search(query) for query in queries] + candidates = await run_async_tasks(tasks=candidates, concurrency_limit=1) + return candidates + + async def _aretrieve_with_score( + self, query: str, score_threshold: float + ) -> List[Chunk]: + """Retrieve knowledge chunks with score. + Args: + query (str): query text + score_threshold (float): score threshold + Return: + List[Chunk]: list of chunks with score + """ + queries = [query] + if self._query_rewrite: + new_queries = await self._query_rewrite.rewrite(origin_query=query, nums=1) + queries.extend(new_queries) + candidates_with_score = [ + self._similarity_search_with_score(query, score_threshold) + for query in queries + ] + candidates_with_score = await run_async_tasks( + tasks=candidates_with_score, concurrency_limit=1 + ) + candidates_with_score = reduce(lambda x, y: x + y, candidates_with_score) + candidates_with_score = self._rerank.rank(candidates_with_score) + return candidates_with_score + + async def _similarity_search(self, query) -> List[Chunk]: + """Similar search.""" + return self._vector_store_connector.similar_search( + query, + self._top_k, + ) + + async def _similarity_search_with_score( + self, query, score_threshold + ) -> List[Chunk]: + """Similar search with score.""" + return self._vector_store_connector.similar_search_with_scores( + query, self._top_k, score_threshold + ) diff --git a/dbgpt/rag/retriever/reinforce.py b/dbgpt/rag/retriever/reinforce.py deleted file mode 100644 index 7296b95a9..000000000 --- a/dbgpt/rag/retriever/reinforce.py +++ /dev/null @@ -1,53 +0,0 @@ -from typing import List - -from dbgpt.app.scene import ChatScene -from dbgpt.app.scene import BaseChat - - -class QueryReinforce: - """ - query reinforce, include query rewrite, query correct - """ - - def __init__( - self, query: str = None, model_name: str = None, llm_chat: BaseChat = None - ): - """query reinforce - Args: - - query: str, user query - - model_name: str, llm model name - """ - self.query = query - self.model_name = model_name - self.llm_chat = llm_chat - - async def rewrite(self) -> List[str]: - """query rewrite""" - from dbgpt._private.chat_util import llm_chat_response_nostream - import uuid - - chat_param = { - "chat_session_id": uuid.uuid1(), - "current_user_input": self.query, - "select_param": 2, - "model_name": self.model_name, - "model_cache_enable": False, - } - tasks = [ - llm_chat_response_nostream( - ChatScene.QueryRewrite.value(), **{"chat_param": chat_param} - ) - ] - from dbgpt._private.chat_util import run_async_tasks - - queries = await run_async_tasks(tasks=tasks, concurrency_limit=1) - queries = list( - filter( - lambda content: "LLMServer Generate Error" not in content, - queries, - ) - ) - return queries[0] - - def correct(self) -> List[str]: - pass diff --git a/dbgpt/rag/retriever/rerank.py b/dbgpt/rag/retriever/rerank.py index dd48be82b..a2a97f4dd 100644 --- a/dbgpt/rag/retriever/rerank.py +++ b/dbgpt/rag/retriever/rerank.py @@ -1,11 +1,13 @@ from abc import ABC -from typing import List, Tuple, Optional +from typing import List, Optional + +from dbgpt.rag.chunk import Chunk class Ranker(ABC): """Base Ranker""" - def __init__(self, topk: int, rank_fn: Optional[callable] = None): + def __init__(self, topk: int, rank_fn: Optional[callable] = None) -> None: """ abstract base ranker Args: @@ -15,7 +17,7 @@ def __init__(self, topk: int, rank_fn: Optional[callable] = None): self.topk = topk self.rank_fn = rank_fn - def rank(self, candidates_with_scores: List, topk: int): + def rank(self, candidates_with_scores: List) -> List[Chunk]: """rank algorithm implementation return topk documents by candidates similarity score Args: candidates_with_scores: List[Tuple] @@ -26,17 +28,17 @@ def rank(self, candidates_with_scores: List, topk: int): pass - def _filter(self, candidates_with_scores: List): + def _filter(self, candidates_with_scores: List) -> List[Chunk]: """filter duplicate candidates documents""" candidates_with_scores = sorted( - candidates_with_scores, key=lambda x: x[1], reverse=True + candidates_with_scores, key=lambda x: x.score, reverse=True ) visited_docs = set() new_candidates = [] - for candidate_doc, score in candidates_with_scores: - if candidate_doc.page_content not in visited_docs: - new_candidates.append((candidate_doc, score)) - visited_docs.add(candidate_doc.page_content) + for candidate_chunk in candidates_with_scores: + if candidate_chunk.content not in visited_docs: + new_candidates.append(candidate_chunk) + visited_docs.add(candidate_chunk.content) return new_candidates @@ -46,7 +48,7 @@ class DefaultRanker(Ranker): def __init__(self, topk: int, rank_fn: Optional[callable] = None): super().__init__(topk, rank_fn) - def rank(self, candidates_with_scores: List[Tuple]): + def rank(self, candidates_with_scores: List[Chunk]) -> List[Chunk]: """Default rank algorithm implementation return topk documents by candidates similarity score Args: @@ -59,11 +61,9 @@ def rank(self, candidates_with_scores: List[Tuple]): candidates_with_scores = self.rank_fn(candidates_with_scores) else: candidates_with_scores = sorted( - candidates_with_scores, key=lambda x: x[1], reverse=True + candidates_with_scores, key=lambda x: x.score, reverse=True ) - return [ - (candidate_doc, score) for candidate_doc, score in candidates_with_scores - ][: self.topk] + return candidates_with_scores[: self.topk] class RRFRanker(Ranker): @@ -72,7 +72,7 @@ class RRFRanker(Ranker): def __init__(self, topk: int, rank_fn: Optional[callable] = None): super().__init__(topk, rank_fn) - def rank(self, candidates_with_scores: List): + def rank(self, candidates_with_scores: List[Chunk]) -> List[Chunk]: """RRF rank algorithm implementation This code implements an algorithm called Reciprocal Rank Fusion (RRF), is a method for combining multiple result sets with different relevance indicators into a single result set. RRF requires no tuning, and the different relevance indicators do not have to be related to each other to achieve high-quality results. RRF uses the following formula to determine the score for ranking each document: diff --git a/dbgpt/rag/retriever/rewrite.py b/dbgpt/rag/retriever/rewrite.py new file mode 100644 index 000000000..fdee0046e --- /dev/null +++ b/dbgpt/rag/retriever/rewrite.py @@ -0,0 +1,103 @@ +from typing import List, Optional +from dbgpt.core import LLMClient, ModelMessage, ModelRequest, ModelMessageRoleType + +REWRITE_PROMPT_TEMPLATE_EN = """ +Generate {nums} search queries related to: {original_query}, Provide following comma-separated format: 'queries: '\n": + "original query:: {original_query}\n" + "queries:\n" +""" + +REWRITE_PROMPT_TEMPLATE_ZH = """请根据原问题优化生成{nums}个相关的搜索查询，这些查询应与原始查询相似并且是人们可能会提出的可回答的搜索问题。请勿使用任何示例中提到的内容，确保所有生成的查询均独立于示例，仅基于提供的原始查询。请按照以下逗号分隔的格式提供: 'queries：'： +"original_query：{original_query}\n" +"queries：\n" +""" + + +class QueryRewrite: + """ + query reinforce, include query rewrite, query correct + """ + + def __init__( + self, + model_name: str = None, + llm_client: Optional[LLMClient] = None, + language: Optional[str] = "en", + ) -> None: + """query rewrite + Args: + - query: (str), user query + - model_name: (str), llm model name + - llm_client: (Optional[LLMClient]) + """ + self._model_name = model_name + self._llm_client = llm_client + self._language = language + self._prompt_template = ( + REWRITE_PROMPT_TEMPLATE_EN + if language == "en" + else REWRITE_PROMPT_TEMPLATE_ZH + ) + + async def rewrite(self, origin_query: str, nums: Optional[int] = 1) -> List[str]: + """query rewrite + Args: + origin_query: str original query + nums: Optional[int] rewrite nums + Returns: + queries: List[str] + """ + from dbgpt.util.chat_util import run_async_tasks + + prompt = self._prompt_template.format(original_query=origin_query, nums=nums) + messages = [ModelMessage(role=ModelMessageRoleType.SYSTEM, content=prompt)] + request = ModelRequest(model=self._model_name, messages=messages) + tasks = [self._llm_client.generate(request)] + queries = await run_async_tasks(tasks=tasks, concurrency_limit=1) + queries = [model_out.text for model_out in queries] + queries = list( + filter( + lambda content: "LLMServer Generate Error" not in content, + queries, + ) + ) + print("rewrite queries:", queries) + return self._parse_llm_output(output=queries[0]) + + def correct(self) -> List[str]: + pass + + def _parse_llm_output(self, output: str) -> List[str]: + """parse llm output + Args: + output: str + Returns: + output: List[str] + """ + lowercase = True + try: + results = [] + response = output.strip() + + if response.startswith("queries:"): + response = response[len("queries:") :] + + queries = response.split(",") + if len(queries) == 1: + queries = response.split("，") + if len(queries) == 1: + queries = response.split("?") + if len(queries) == 1: + queries = response.split("？") + for k in queries: + rk = k + if lowercase: + rk = rk.lower() + s = rk.strip() + if s == "": + continue + results.append(s) + except Exception as e: + print(f"parse query rewrite prompt_response error: {e}") + return [] + return results diff --git a/dbgpt/rag/retriever/tests/__init__.py b/dbgpt/rag/retriever/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/dbgpt/rag/retriever/tests/test_db_struct.py b/dbgpt/rag/retriever/tests/test_db_struct.py new file mode 100644 index 000000000..0596c15fe --- /dev/null +++ b/dbgpt/rag/retriever/tests/test_db_struct.py @@ -0,0 +1,49 @@ +import pytest +from unittest.mock import MagicMock, patch, AsyncMock +from typing import List + +import dbgpt +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.retriever.db_struct import DBStructRetriever +from dbgpt.rag.summary.rdbms_db_summary import _parse_db_summary + + +@pytest.fixture +def mock_db_connection(): + return MagicMock() + + +@pytest.fixture +def mock_vector_store_connector(): + mock_connector = MagicMock() + mock_connector.similar_search.return_value = [Chunk(content="Table summary")] * 4 + return mock_connector + + +@pytest.fixture +def dbstruct_retriever(mock_db_connection, mock_vector_store_connector): + return DBStructRetriever( + connection=mock_db_connection, + vector_store_connector=mock_vector_store_connector, + ) + + +def mock_parse_db_summary() -> str: + """Patch _parse_db_summary method.""" + return "Table summary" + + +# Mocking the _parse_db_summary method in your test function +@patch.object( + dbgpt.rag.summary.rdbms_db_summary, "_parse_db_summary", mock_parse_db_summary +) +def test_retrieve_with_mocked_summary(dbstruct_retriever): + query = "Table summary" + chunks: List[Chunk] = dbstruct_retriever._retrieve(query) + assert isinstance(chunks[0], Chunk) + assert chunks[0].content == "Table summary" + + +async def async_mock_parse_db_summary() -> str: + """Asynchronous patch for _parse_db_summary method.""" + return "Table summary" diff --git a/dbgpt/rag/retriever/tests/test_embedding.py b/dbgpt/rag/retriever/tests/test_embedding.py new file mode 100644 index 000000000..9d8bc7be1 --- /dev/null +++ b/dbgpt/rag/retriever/tests/test_embedding.py @@ -0,0 +1,39 @@ +from unittest.mock import MagicMock +import pytest + +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.retriever.embedding import EmbeddingRetriever + + +@pytest.fixture +def top_k(): + return 4 + + +@pytest.fixture +def query(): + return "test query" + + +@pytest.fixture +def mock_vector_store_connector(): + return MagicMock() + + +@pytest.fixture +def embedding_retriever(top_k, mock_vector_store_connector): + return EmbeddingRetriever( + top_k=top_k, + query_rewrite=False, + vector_store_connector=mock_vector_store_connector, + ) + + +def test_retrieve(query, top_k, mock_vector_store_connector, embedding_retriever): + expected_chunks = [Chunk() for _ in range(top_k)] + mock_vector_store_connector.similar_search.return_value = expected_chunks + + retrieved_chunks = embedding_retriever._retrieve(query) + + mock_vector_store_connector.similar_search.assert_called_once_with(query, top_k) + assert len(retrieved_chunks) == top_k diff --git a/dbgpt/rag/summary/db_summary_client.py b/dbgpt/rag/summary/db_summary_client.py index affb08c60..a58beed51 100644 --- a/dbgpt/rag/summary/db_summary_client.py +++ b/dbgpt/rag/summary/db_summary_client.py @@ -23,41 +23,43 @@ class DBSummaryClient: def __init__(self, system_app: SystemApp): self.system_app = system_app + from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory - def db_summary_embedding(self, dbname, db_type): - """put db profile and table profile summary into vector store""" - from dbgpt.rag.embedding_engine.embedding_factory import EmbeddingFactory - - db_summary_client = RdbmsSummary(dbname, db_type) embedding_factory = self.system_app.get_component( "embedding_factory", EmbeddingFactory ) - embeddings = embedding_factory.create( + self.embeddings = embedding_factory.create( model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL] ) - self.init_db_profile(db_summary_client, dbname, embeddings) + + def db_summary_embedding(self, dbname, db_type): + """put db profile and table profile summary into vector store""" + + db_summary_client = RdbmsSummary(dbname, db_type) + + self.init_db_profile(db_summary_client, dbname) logger.info("db summary embedding success") def get_db_summary(self, dbname, query, topk): """get user query related tables info""" - from dbgpt.rag.embedding_engine.embedding_engine import EmbeddingEngine - from dbgpt.rag.embedding_engine.embedding_factory import EmbeddingFactory - - vector_store_config = { - "vector_store_name": dbname + "_profile", - "vector_store_type": CFG.VECTOR_STORE_TYPE, - } - embedding_factory = CFG.SYSTEM_APP.get_component( - "embedding_factory", EmbeddingFactory - ) - knowledge_embedding_client = EmbeddingEngine( - model_name=EMBEDDING_MODEL_CONFIG[CFG.EMBEDDING_MODEL], + + from dbgpt.storage.vector_store.connector import VectorStoreConnector + from dbgpt.storage.vector_store.base import VectorStoreConfig + + vector_store_config = VectorStoreConfig(name=dbname + "_profile") + vector_connector = VectorStoreConnector.from_default( + CFG.VECTOR_STORE_TYPE, + embedding_fn=self.embeddings, vector_store_config=vector_store_config, - embedding_factory=embedding_factory, ) - table_docs = knowledge_embedding_client.similar_search(query, topk) - ans = [d.page_content for d in table_docs] + from dbgpt.rag.retriever.db_struct import DBStructRetriever + + retriever = DBStructRetriever( + top_k=topk, vector_store_connector=vector_connector + ) + table_docs = retriever.retrieve(query) + ans = [d.content for d in table_docs] return ans def init_db_summary(self): @@ -73,41 +75,30 @@ def init_db_summary(self): f'{item["db_name"]}, {item["db_type"]} summary error!{str(e)}, detail: {message}' ) - def init_db_profile(self, db_summary_client, dbname, embeddings): + def init_db_profile(self, db_summary_client, dbname): """db profile initialization Args: db_summary_client(DBSummaryClient): DB Summary Client dbname(str): dbname - embeddings(SourceEmbedding): embedding for read string document """ - from dbgpt.rag.embedding_engine.string_embedding import StringEmbedding - vector_store_name = dbname + "_profile" - profile_store_config = { - "vector_store_name": vector_store_name, - "vector_store_type": CFG.VECTOR_STORE_TYPE, - "embeddings": embeddings, - } - embedding = StringEmbedding( - file_path=None, - vector_store_config=profile_store_config, - ) - if not embedding.vector_name_exist(): - docs = [] - for table_summary in db_summary_client.table_summaries(): - from langchain.text_splitter import RecursiveCharacterTextSplitter + from dbgpt.storage.vector_store.connector import VectorStoreConnector + from dbgpt.storage.vector_store.base import VectorStoreConfig - text_splitter = RecursiveCharacterTextSplitter( - chunk_size=len(table_summary), chunk_overlap=0 - ) - embedding = StringEmbedding( - file_path=table_summary, - vector_store_config=profile_store_config, - text_splitter=text_splitter, - ) - docs.extend(embedding.read_batch()) - if len(docs) > 0: - embedding.index_to_store(docs) + vector_store_config = VectorStoreConfig(name=vector_store_name) + vector_connector = VectorStoreConnector.from_default( + CFG.VECTOR_STORE_TYPE, + self.embeddings, + vector_store_config=vector_store_config, + ) + if not vector_connector.vector_name_exists(): + from dbgpt.serve.rag.assembler.db_struct import DBStructAssembler + + db_assembler = DBStructAssembler.load_from_connection( + connection=db_summary_client.db, vector_store_connector=vector_connector + ) + if len(db_assembler.get_chunks()) > 0: + db_assembler.persist() else: logger.info(f"Vector store name {vector_store_name} exist") logger.info("initialize db summary profile success...") diff --git a/dbgpt/rag/summary/tests/__init__.py b/dbgpt/rag/summary/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/dbgpt/rag/summary/tests/test_rdbms_summary.py b/dbgpt/rag/summary/tests/test_rdbms_summary.py new file mode 100644 index 000000000..1ccc8aac2 --- /dev/null +++ b/dbgpt/rag/summary/tests/test_rdbms_summary.py @@ -0,0 +1,68 @@ +import unittest +from unittest.mock import Mock, patch + +from dbgpt.rag.summary.rdbms_db_summary import RdbmsSummary + + +class MockRDBMSDatabase(object): + def get_users(self): + return "user1, user2" + + def get_grants(self): + return "grant1, grant2" + + def get_charset(self): + return "utf8" + + def get_collation(self): + return "utf8_general_ci" + + def get_table_names(self): + return ["table1", "table2"] + + def get_columns(self, table_name): + if table_name == "table1": + return [{"name": "column1", "comment": "first column"}, {"name": "column2"}] + return [{"name": "column1"}] + + def get_indexes(self, table_name): + return [{"name": "index1", "column_names": ["column1"]}] + + def get_table_comment(self, table_name): + return {"text": f"{table_name} comment"} + + +class TestRdbmsSummary(unittest.TestCase): + def setUp(self): + self.mock_local_db_manage = Mock() + self.mock_local_db_manage.get_connect.return_value = MockRDBMSDatabase() + self.patcher = patch( + "dbgpt.rag.summary.rdbms_db_summary.CFG.LOCAL_DB_MANAGE", + new=self.mock_local_db_manage, + ) + self.patcher.start() + + def test_rdbms_summary_initialization(self): + rdbms_summary = RdbmsSummary(name="test_db", type="test_type") + self.assertEqual(rdbms_summary.name, "test_db") + self.assertEqual(rdbms_summary.type, "test_type") + self.assertTrue("user info :user1, user2" in rdbms_summary.metadata) + self.assertTrue("grant info:grant1, grant2" in rdbms_summary.metadata) + self.assertTrue("charset:utf8" in rdbms_summary.metadata) + self.assertTrue("collation:utf8_general_ci" in rdbms_summary.metadata) + + def test_table_summaries(self): + rdbms_summary = RdbmsSummary(name="test_db", type="test_type") + summaries = rdbms_summary.table_summaries() + self.assertTrue( + "table1(column1 (first column), column2), and index keys: index1(`column1`) , and table comment: table1 comment" + in summaries + ) + self.assertTrue( + "table2(column1), and index keys: index1(`column1`) , and table comment: table2 comment" + in summaries + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/dbgpt/rag/text_splitter/__init__.py b/dbgpt/rag/text_splitter/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/dbgpt/rag/embedding_engine/pre_text_splitter.py b/dbgpt/rag/text_splitter/pre_text_splitter.py similarity index 51% rename from dbgpt/rag/embedding_engine/pre_text_splitter.py rename to dbgpt/rag/text_splitter/pre_text_splitter.py index cf7cdfdf5..65aa54d86 100644 --- a/dbgpt/rag/embedding_engine/pre_text_splitter.py +++ b/dbgpt/rag/text_splitter/pre_text_splitter.py @@ -1,28 +1,39 @@ from typing import Iterable, List -from langchain.schema import Document -from langchain.text_splitter import TextSplitter + +from dbgpt.rag.chunk import Document, Chunk +from dbgpt.rag.text_splitter.text_splitter import TextSplitter def _single_document_split( document: Document, pre_separator: str ) -> Iterable[Document]: - page_content = document.page_content - for i, content in enumerate(page_content.split(pre_separator)): + content = document.content + for i, content in enumerate(content.split(pre_separator)): metadata = document.metadata.copy() if "source" in metadata: metadata["source"] = metadata["source"] + "_pre_split_" + str(i) - yield Document(page_content=content, metadata=metadata) + yield Chunk(content=content, metadata=metadata) class PreTextSplitter(TextSplitter): + """Split text by pre separator""" + def __init__(self, pre_separator: str, text_splitter_impl: TextSplitter): + """Initialize with Knowledge arguments. + Args: + pre_separator: pre separator + text_splitter_impl: text splitter impl + """ self.pre_separator = pre_separator self._impl = text_splitter_impl - def split_text(self, text: str) -> List[str]: + def split_text(self, text: str, **kwargs) -> List[str]: + """Split text by pre separator""" return self._impl.split_text(text) - def split_documents(self, documents: Iterable[Document]) -> List[Document]: + def split_documents(self, documents: Iterable[Document], **kwargs) -> List[Chunk]: + """Split documents by pre separator""" + def generator() -> Iterable[Document]: for doc in documents: yield from _single_document_split(doc, pre_separator=self.pre_separator) diff --git a/dbgpt/rag/text_splitter/tests/__init__.py b/dbgpt/rag/text_splitter/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/dbgpt/rag/text_splitter/tests/test_splitters.py b/dbgpt/rag/text_splitter/tests/test_splitters.py new file mode 100644 index 000000000..3d3ebb764 --- /dev/null +++ b/dbgpt/rag/text_splitter/tests/test_splitters.py @@ -0,0 +1,65 @@ +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.text_splitter.text_splitter import ( + CharacterTextSplitter, + MarkdownHeaderTextSplitter, +) + + +def test_md_header_text_splitter() -> None: + """unit test markdown splitter by header""" + + markdown_document = ( + "# dbgpt\n\n" + " ## description\n\n" + "my name is dbgpt\n\n" + " ## content\n\n" + "my name is aries" + ) + headers_to_split_on = [ + ("#", "Header 1"), + ("##", "Header 2"), + ] + markdown_splitter = MarkdownHeaderTextSplitter( + headers_to_split_on=headers_to_split_on, + ) + output = markdown_splitter.split_text(markdown_document) + expected_output = [ + Chunk( + content="{'Header 1': 'dbgpt', 'Header 2': 'description'}, my name is dbgpt", + metadata={"Header 1": "dbgpt", "Header 2": "description"}, + ), + Chunk( + content="{'Header 1': 'dbgpt', 'Header 2': 'content'}, my name is aries", + metadata={"Header 1": "dbgpt", "Header 2": "content"}, + ), + ] + assert [output.content for output in output] == [ + output.content for output in expected_output + ] + + +def test_merge_splits() -> None: + """Test merging splits with a given separator.""" + splitter = CharacterTextSplitter(separator=" ", chunk_size=9, chunk_overlap=2) + splits = ["foo", "bar", "baz"] + expected_output = ["foo bar", "baz"] + output = splitter._merge_splits(splits, separator=" ") + assert output == expected_output + + +def test_character_text_splitter() -> None: + """Test splitting by character count.""" + text = "foo bar baz 123" + splitter = CharacterTextSplitter(separator=" ", chunk_size=7, chunk_overlap=3) + output = splitter.split_text(text) + expected_output = ["foo bar", "bar baz", "baz 123"] + assert output == expected_output + + +def test_character_text_splitter_empty_doc() -> None: + """Test splitting by character count doesn't create empty documents.""" + text = "db gpt" + splitter = CharacterTextSplitter(separator=" ", chunk_size=2, chunk_overlap=0) + output = splitter.split_text(text) + expected_output = ["db", "gpt"] + assert output == expected_output diff --git a/dbgpt/rag/text_splitter/text_splitter.py b/dbgpt/rag/text_splitter/text_splitter.py new file mode 100644 index 000000000..201fc6e59 --- /dev/null +++ b/dbgpt/rag/text_splitter/text_splitter.py @@ -0,0 +1,730 @@ +import copy +import logging +import re +from abc import abstractmethod, ABC +from typing import ( + Any, + Callable, + Dict, + Iterable, + List, + Optional, + Tuple, + TypedDict, + Union, +) + +from dbgpt.rag.chunk import Document, Chunk + +logger = logging.getLogger(__name__) + + +class TextSplitter(ABC): + """Interface for splitting text into chunks. + Refer to https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/text_splitter.py + """ + + outgoing_edges = 1 + + def __init__( + self, + chunk_size: int = 4000, + chunk_overlap: int = 200, + length_function: Callable[[str], int] = len, + filters: list = [], + separator: str = "", + ): + """Create a new TextSplitter.""" + if chunk_overlap > chunk_size: + raise ValueError( + f"Got a larger chunk overlap ({chunk_overlap}) than chunk size " + f"({chunk_size}), should be smaller." + ) + self._chunk_size = chunk_size + self._chunk_overlap = chunk_overlap + self._length_function = length_function + self._filter = filters + self._separator = separator + + @abstractmethod + def split_text(self, text: str, **kwargs) -> List[str]: + """Split text into multiple components.""" + + def create_documents( + self, + texts: List[str], + metadatas: Optional[List[dict]] = None, + separator: Optional[str] = None, + **kwargs, + ) -> List[Chunk]: + """Create documents from a list of texts.""" + _metadatas = metadatas or [{}] * len(texts) + chunks = [] + for i, text in enumerate(texts): + for chunk in self.split_text(text, separator, **kwargs): + new_doc = Chunk(content=chunk, metadata=copy.deepcopy(_metadatas[i])) + chunks.append(new_doc) + return chunks + + def split_documents(self, documents: List[Document], **kwargs) -> List[Chunk]: + """Split documents.""" + texts = [doc.content for doc in documents] + metadatas = [doc.metadata for doc in documents] + return self.create_documents(texts, metadatas, **kwargs) + + def _join_docs(self, docs: List[str], separator: str, **kwargs) -> Optional[str]: + text = separator.join(docs) + text = text.strip() + if text == "": + return None + else: + return text + + def _merge_splits( + self, + splits: Iterable[str], + separator: str, + chunk_size: Optional[int] = None, + chunk_overlap: Optional[int] = None, + ) -> List[str]: + # We now want to combine these smaller pieces into medium size + # chunks to send to the LLM. + if chunk_size is None: + chunk_size = self._chunk_size + if chunk_overlap is None: + chunk_overlap = self._chunk_overlap + if separator is None: + separator = self._separator + separator_len = self._length_function(separator) + + docs = [] + current_doc: List[str] = [] + total = 0 + for d in splits: + _len = self._length_function(d) + if ( + total + _len + (separator_len if len(current_doc) > 0 else 0) + > chunk_size + ): + if total > chunk_size: + logger.warning( + f"Created a chunk of size {total}, " + f"which is longer than the specified {chunk_size}" + ) + if len(current_doc) > 0: + doc = self._join_docs(current_doc, separator) + if doc is not None: + docs.append(doc) + # Keep on popping if: + # - we have a larger chunk than in the chunk overlap + # - or if we still have any chunks and the length is long + while total > chunk_overlap or ( + total + _len + (separator_len if len(current_doc) > 0 else 0) + > chunk_size + and total > 0 + ): + total -= self._length_function(current_doc[0]) + ( + separator_len if len(current_doc) > 1 else 0 + ) + current_doc = current_doc[1:] + current_doc.append(d) + total += _len + (separator_len if len(current_doc) > 1 else 0) + doc = self._join_docs(current_doc, separator) + if doc is not None: + docs.append(doc) + return docs + + def clean(self, documents: List[dict], filters: List[str]): + for special_character in filters: + for doc in documents: + doc["content"] = doc["content"].replace(special_character, "") + return documents + + def run( # type: ignore + self, + documents: Union[dict, List[dict]], + meta: Optional[Union[Dict[str, str], List[Dict[str, str]]]] = None, # type: ignore + separator: Optional[str] = None, + chunk_size: Optional[int] = None, + chunk_overlap: Optional[int] = None, + filters: Optional[List[str]] = None, + ): + if separator is None: + separator = self._separator + if chunk_size is None: + chunk_size = self._chunk_size + if chunk_overlap is None: + chunk_overlap = self._chunk_overlap + if filters is None: + filters = self._filter + ret = [] + if type(documents) == dict: # single document + text_splits = self.split_text( + documents["content"], + separator=separator, + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + ) + for i, txt in enumerate(text_splits): + doc = copy.deepcopy(documents) + doc["content"] = txt + + if "meta" not in doc.keys() or doc["meta"] is None: + doc["meta"] = {} + + doc["meta"]["_split_id"] = i + ret.append(doc) + + elif type(documents) == list: # list document + for document in documents: + text_splits = self.split_text( + document["content"], + separator=separator, + chunk_size=chunk_size, + chunk_overlap=chunk_overlap, + ) + for i, txt in enumerate(text_splits): + doc = copy.deepcopy(document) + doc["content"] = txt + + if "meta" not in doc.keys() or doc["meta"] is None: + doc["meta"] = {} + + doc["meta"]["_split_id"] = i + ret.append(doc) + if filters is not None and len(filters) > 0: + ret = self.clean(ret, filters) + result = {"documents": ret} + return result, "output_1" + + +class CharacterTextSplitter(TextSplitter): + """Implementation of splitting text that looks at characters. + Refer to https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/text_splitter.py + """ + + def __init__(self, separator: str = "\n\n", filters: list = [], **kwargs: Any): + """Create a new TextSplitter.""" + super().__init__(**kwargs) + self._separator = separator + self._filter = filters + + def split_text( + self, text: str, separator: Optional[str] = None, **kwargs + ) -> List[str]: + """Split incoming text and return chunks.""" + # First we naively split the large input into a bunch of smaller ones. + if separator is None: + separator = self._separator + if separator: + splits = text.split(separator) + else: + splits = list(text) + return self._merge_splits(splits, separator, **kwargs) + + +class RecursiveCharacterTextSplitter(TextSplitter): + """Implementation of splitting text that looks at characters. + Recursively tries to split by different characters to find one + that works. + Refer to https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/text_splitter.py + """ + + def __init__(self, separators: Optional[List[str]] = None, **kwargs: Any): + """Create a new TextSplitter.""" + super().__init__(**kwargs) + self._separators = separators or ["###", "\n", " ", ""] + + def split_text( + self, text: str, separator: Optional[str] = None, **kwargs + ) -> List[str]: + """Split incoming text and return chunks.""" + final_chunks = [] + # Get appropriate separator to use + separator = self._separators[-1] + for _s in self._separators: + if _s == "": + separator = _s + break + if _s in text: + separator = _s + break + # Now that we have the separator, split the text + if separator: + splits = text.split(separator) + else: + splits = list(text) + # Now go merging things, recursively splitting longer texts. + _good_splits = [] + for s in splits: + if self._length_function(s) < self._chunk_size: + _good_splits.append(s) + else: + if _good_splits: + merged_text = self._merge_splits( + _good_splits, + separator, + chunk_size=kwargs.get("chunk_size", None), + chunk_overlap=kwargs.get("chunk_overlap", None), + ) + final_chunks.extend(merged_text) + _good_splits = [] + other_info = self.split_text(s) + final_chunks.extend(other_info) + if _good_splits: + merged_text = self._merge_splits( + _good_splits, + separator, + chunk_size=kwargs.get("chunk_size", None), + chunk_overlap=kwargs.get("chunk_overlap", None), + ) + final_chunks.extend(merged_text) + return final_chunks + + +class SpacyTextSplitter(TextSplitter): + """Implementation of splitting text that looks at sentences using Spacy. + Refer to https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/text_splitter.py + """ + + def __init__(self, pipeline: str = "zh_core_web_sm", **kwargs: Any) -> None: + """Initialize the spacy text splitter.""" + super().__init__(**kwargs) + try: + import spacy + except ImportError: + raise ImportError( + "Spacy is not installed, please install it with `pip install spacy`." + ) + try: + self._tokenizer = spacy.load(pipeline) + except: + spacy.cli.download(pipeline) + self._tokenizer = spacy.load(pipeline) + + def split_text( + self, text: str, separator: Optional[str] = None, **kwargs + ) -> List[str]: + """Split incoming text and return chunks.""" + if len(text) > 1000000: + self._tokenizer.max_length = len(text) + 100 + splits = (str(s) for s in self._tokenizer(text).sents) + return self._merge_splits(splits, separator, **kwargs) + + +class HeaderType(TypedDict): + """Header type as typed dict.""" + + level: int + name: str + data: str + + +class LineType(TypedDict): + """Line type as typed dict.""" + + metadata: Dict[str, str] + content: str + + +class MarkdownHeaderTextSplitter(TextSplitter): + """Implementation of splitting markdown files based on specified headers. + Refer to https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/text_splitter.py + """ + + outgoing_edges = 1 + + def __init__( + self, + headers_to_split_on: List[Tuple[str, str]] = [ + ("#", "Header 1"), + ("##", "Header 2"), + ("###", "Header 3"), + ("####", "Header 4"), + ("#####", "Header 5"), + ("######", "Header 6"), + ], + return_each_line: bool = False, + filters: list = [], + chunk_size: int = 4000, + chunk_overlap: int = 200, + length_function: Callable[[str], int] = len, + separator="\n", + ): + """Create a new MarkdownHeaderTextSplitter. + + Args: + headers_to_split_on: Headers we want to track + return_each_line: Return each line w/ associated headers + """ + # Output line-by-line or aggregated into chunks w/ common headers + self.return_each_line = return_each_line + self._chunk_size = chunk_size + # Given the headers we want to split on, + # (e.g., "#, ##, etc") order by length + self.headers_to_split_on = sorted( + headers_to_split_on, key=lambda split: len(split[0]), reverse=True + ) + self._filter = filters + self._length_function = length_function + self._separator = separator + self._chunk_overlap = chunk_overlap + + def create_documents( + self, + texts: List[str], + metadatas: Optional[List[dict]] = None, + separator: Optional[str] = None, + **kwargs, + ) -> List[Chunk]: + """Create documents from a list of texts.""" + _metadatas = metadatas or [{}] * len(texts) + chunks = [] + for i, text in enumerate(texts): + for chunk in self.split_text(text, separator, **kwargs): + metadata = chunk.metadata or {} + metadata.update(_metadatas[i]) + new_doc = Chunk(content=chunk.content, metadata=metadata) + chunks.append(new_doc) + return chunks + + def aggregate_lines_to_chunks(self, lines: List[LineType]) -> List[Chunk]: + """Combine lines with common metadata into chunks + Args: + lines: Line of text / associated header metadata + """ + aggregated_chunks: List[LineType] = [] + + for line in lines: + if ( + aggregated_chunks + and aggregated_chunks[-1]["metadata"] == line["metadata"] + ): + # If the last line in the aggregated list + # has the same metadata as the current line, + # append the current content to the last lines's content + aggregated_chunks[-1]["content"] += " \n" + line["content"] + else: + # Otherwise, append the current line to the aggregated list + line["content"] = f"{line['metadata']}, " + line["content"] + aggregated_chunks.append(line) + + return [ + Chunk(content=chunk["content"], metadata=chunk["metadata"]) + for chunk in aggregated_chunks + ] + + def split_text( + self, + text: str, + separator: Optional[str] = None, + chunk_size: Optional[int] = None, + chunk_overlap: Optional[int] = None, + ) -> List[Chunk]: + """Split markdown file + Args: + text: Markdown file""" + if separator is None: + separator = self._separator + if chunk_size is None: + chunk_size = self._chunk_size + if chunk_overlap is None: + chunk_overlap = self._chunk_overlap + + # Split the input text by newline character ("\n"). + lines = text.split(separator) + # Final output + lines_with_metadata: List[LineType] = [] + # Content and metadata of the chunk currently being processed + current_content: List[str] = [] + current_metadata: Dict[str, str] = {} + # Keep track of the nested header structure + # header_stack: List[Dict[str, Union[int, str]]] = [] + header_stack: List[HeaderType] = [] + initial_metadata: Dict[str, str] = {} + for line in lines: + stripped_line = line.strip() + # Check each line against each of the header types (e.g., #, ##) + for sep, name in self.headers_to_split_on: + # Check if line starts with a header that we intend to split on + if stripped_line.startswith(sep) and ( + # Header with no text OR header is followed by space + # Both are valid conditions that sep is being used a header + len(stripped_line) == len(sep) + or stripped_line[len(sep)] == " " + ): + # Ensure we are tracking the header as metadata + if name is not None: + # Get the current header level + current_header_level = sep.count("#") + + # Pop out headers of lower or same level from the stack + while ( + header_stack + and header_stack[-1]["level"] >= current_header_level + ): + # We have encountered a new header + # at the same or higher level + popped_header = header_stack.pop() + # Clear the metadata for the + # popped header in initial_metadata + if popped_header["name"] in initial_metadata: + initial_metadata.pop(popped_header["name"]) + + # Push the current header to the stack + header: HeaderType = { + "level": current_header_level, + "name": name, + "data": stripped_line[len(sep) :].strip(), + } + header_stack.append(header) + # Update initial_metadata with the current header + initial_metadata[name] = header["data"] + + # Add the previous line to the lines_with_metadata + # only if current_content is not empty + if current_content: + lines_with_metadata.append( + { + "content": separator.join(current_content), + "metadata": current_metadata.copy(), + } + ) + current_content.clear() + + break + else: + if stripped_line: + current_content.append(stripped_line) + elif current_content: + lines_with_metadata.append( + { + "content": separator.join(current_content), + "metadata": current_metadata.copy(), + } + ) + current_content.clear() + + current_metadata = initial_metadata.copy() + if current_content: + lines_with_metadata.append( + { + "content": separator.join(current_content), + "metadata": current_metadata, + } + ) + # lines_with_metadata has each line with associated header metadata + # aggregate these into chunks based on common metadata + if not self.return_each_line: + return self.aggregate_lines_to_chunks(lines_with_metadata) + else: + return [ + Document(content=chunk["content"], metadata=chunk["metadata"]) + for chunk in lines_with_metadata + ] + + def clean(self, documents: List[dict], filters: Optional[List[str]] = None): + if filters is None: + filters = self._filter + for special_character in filters: + for doc in documents: + doc["content"] = doc["content"].replace(special_character, "") + return documents + + def _join_docs(self, docs: List[str], separator: str) -> Optional[str]: + text = separator.join(docs) + text = text.strip() + if text == "": + return None + else: + return text + + def _merge_splits( + self, + documents: List[dict], + separator: Optional[str] = None, + chunk_size: Optional[int] = None, + chunk_overlap: [int] = None, + ) -> List[str]: + # We now want to combine these smaller pieces into medium size + # chunks to send to the LLM. + if chunk_size is None: + chunk_size = self._chunk_size + if chunk_overlap is None: + chunk_overlap = self._chunk_overlap + if separator is None: + separator = self._separator + separator_len = self._length_function(separator) + + docs = [] + current_doc: List[str] = [] + total = 0 + for doc in documents: + if doc["metadata"] != {}: + head = sorted( + doc["metadata"].items(), key=lambda x: x[0], reverse=True + )[0][1] + d = head + separator + doc["page_content"] + else: + d = doc["page_content"] + _len = self._length_function(d) + if ( + total + _len + (separator_len if len(current_doc) > 0 else 0) + > chunk_size + ): + if total > chunk_size: + logger.warning( + f"Created a chunk of size {total}, " + f"which is longer than the specified {chunk_size}" + ) + if len(current_doc) > 0: + doc = self._join_docs(current_doc, separator) + if doc is not None: + docs.append(doc) + # Keep on popping if: + # - we have a larger chunk than in the chunk overlap + # - or if we still have any chunks and the length is long + while total > chunk_overlap or ( + total + _len + (separator_len if len(current_doc) > 0 else 0) + > chunk_size + and total > 0 + ): + total -= self._length_function(current_doc[0]) + ( + separator_len if len(current_doc) > 1 else 0 + ) + current_doc = current_doc[1:] + current_doc.append(d) + total += _len + (separator_len if len(current_doc) > 1 else 0) + doc = self._join_docs(current_doc, separator) + if doc is not None: + docs.append(doc) + return docs + + def run( + self, + documents: Union[dict, List[dict]], + meta: Optional[Union[Dict[str, str], List[Dict[str, str]]]] = None, + filters: Optional[List[str]] = None, + chunk_size: Optional[int] = None, + chunk_overlap: Optional[int] = None, + separator: Optional[str] = None, + ): + if filters is None: + filters = self._filter + if chunk_size is None: + chunk_size = self._chunk_size + if chunk_overlap is None: + chunk_overlap = self._chunk_overlap + if separator is None: + separator = self._separator + ret = [] + if type(documents) == list: + for document in documents: + text_splits = self.split_text( + document["content"], separator, chunk_size, chunk_overlap + ) + for i, txt in enumerate(text_splits): + doc = {} + doc["content"] = txt + + if "meta" not in doc.keys() or doc["meta"] is None: + doc["meta"] = {} + + doc["meta"]["_split_id"] = i + ret.append(doc) + elif type(documents) == dict: + text_splits = self.split_text( + documents["content"], separator, chunk_size, chunk_overlap + ) + for i, txt in enumerate(text_splits): + doc = {} + doc["content"] = txt + + if "meta" not in doc.keys() or doc["meta"] is None: + doc["meta"] = {} + + doc["meta"]["_split_id"] = i + ret.append(doc) + if filters is None: + filters = self._filter + if filters is not None and len(filters) > 0: + ret = self.clean(ret, filters) + result = {"documents": ret} + return result, "output_1" + + +class ParagraphTextSplitter(CharacterTextSplitter): + """Implementation of splitting text that looks at paragraphs.""" + + def __init__( + self, + separator="\n", + chunk_size: Optional[int] = 0, + chunk_overlap: Optional[int] = 0, + ): + self._separator = separator + if self._separator is None: + self._separator = "\n" + self._chunk_size = chunk_size + self._chunk_overlap = chunk_overlap + self._is_paragraph = chunk_overlap + + def split_text( + self, text: str, separator: Optional[str] = "\n", **kwargs + ) -> List[str]: + paragraphs = text.strip().split(self._separator) + paragraphs = [p.strip() for p in paragraphs if p.strip() != ""] + return paragraphs + + +class SeparatorTextSplitter(CharacterTextSplitter): + """SeparatorTextSplitter""" + + def __init__(self, separator: str = "\n", filters: list = [], **kwargs: Any): + """Create a new TextSplitter.""" + super().__init__(**kwargs) + self._separator = separator + self._filter = filters + + def split_text( + self, text: str, separator: Optional[str] = None, **kwargs + ) -> List[str]: + """Split incoming text and return chunks.""" + if separator is None: + separator = self._separator + if separator: + splits = text.split(separator) + else: + splits = list(text) + return self._merge_splits(splits, separator, chunk_overlap=0, **kwargs) + + +class PageTextSplitter(TextSplitter): + """PageTextSplitter""" + + def __init__(self, separator: str = "\n\n", filters: list = [], **kwargs: Any): + """Create a new TextSplitter.""" + super().__init__(**kwargs) + self._separator = separator + self._filter = filters + + def split_text( + self, text: str, separator: Optional[str] = None, **kwargs + ) -> List[str]: + """Split incoming text and return chunks.""" + return text + + def create_documents( + self, + texts: List[str], + metadatas: Optional[List[dict]] = None, + separator: Optional[str] = None, + **kwargs, + ) -> List[Chunk]: + """Create documents from a list of texts.""" + _metadatas = metadatas or [{}] * len(texts) + chunks = [] + for i, text in enumerate(texts): + new_doc = Chunk(content=text, metadata=copy.deepcopy(_metadatas[i])) + chunks.append(new_doc) + return chunks diff --git a/dbgpt/rag/embedding_engine/loader/token_splitter.py b/dbgpt/rag/text_splitter/token_splitter.py similarity index 98% rename from dbgpt/rag/embedding_engine/loader/token_splitter.py rename to dbgpt/rag/text_splitter/token_splitter.py index 6e09fbee3..8c347e612 100644 --- a/dbgpt/rag/embedding_engine/loader/token_splitter.py +++ b/dbgpt/rag/text_splitter/token_splitter.py @@ -4,7 +4,7 @@ from dbgpt._private.pydantic import Field, PrivateAttr, BaseModel from dbgpt.util.global_helper import globals_helper -from dbgpt.rag.embedding_engine.loader.splitter_utils import split_by_sep, split_by_char +from dbgpt.util.splitter_utils import split_by_sep, split_by_char DEFAULT_METADATA_FORMAT_LEN = 2 DEFAULT_CHUNK_OVERLAP = 20 diff --git a/dbgpt/serve/rag/api/__init__.py b/dbgpt/serve/rag/api/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/dbgpt/serve/rag/assembler/__init__.py b/dbgpt/serve/rag/assembler/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/dbgpt/serve/rag/assembler/base.py b/dbgpt/serve/rag/assembler/base.py new file mode 100644 index 000000000..b5944c781 --- /dev/null +++ b/dbgpt/serve/rag/assembler/base.py @@ -0,0 +1,50 @@ +from abc import ABC, abstractmethod +from typing import Optional, Any, List + +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.chunk_manager import ChunkManager, ChunkParameters +from dbgpt.rag.extractor.base import Extractor +from dbgpt.rag.knowledge.base import Knowledge +from dbgpt.rag.retriever.base import BaseRetriever + + +class BaseAssembler(ABC): + """Base Assembler""" + + def __init__( + self, + knowledge: Optional[Knowledge] = None, + chunk_parameters: Optional[ChunkParameters] = None, + extractor: Optional[Extractor] = None, + **kwargs: Any, + ) -> None: + """Initialize with Assembler arguments. + Args: + knowledge: (Knowledge) Knowledge datasource. + chunk_parameters: (Optional[ChunkParameters]) ChunkManager to use for chunking. + extractor: (Optional[Extractor]) Extractor to use for summarization.""" + self._knowledge = knowledge + self._chunk_parameters = chunk_parameters or ChunkParameters() + self._extractor = extractor + self._chunk_manager = ChunkManager( + knowledge=self._knowledge, chunk_parameter=self._chunk_parameters + ) + self._chunks = None + self.load_knowledge(self._knowledge) + + def load_knowledge(self, knowledge) -> None: + """Load knowledge Pipeline.""" + documents = knowledge.load() + self._chunks = self._chunk_manager.split(documents) + + @abstractmethod + def as_retriever(self, **kwargs: Any) -> BaseRetriever: + """Return a retriever.""" + + @abstractmethod + def persist(self, chunks: List[Chunk]) -> None: + """Persist chunks.""" + + def get_chunks(self) -> List[Chunk]: + """Return chunks.""" + return self._chunks diff --git a/dbgpt/serve/rag/assembler/db_struct.py b/dbgpt/serve/rag/assembler/db_struct.py new file mode 100644 index 000000000..9d85efbf9 --- /dev/null +++ b/dbgpt/serve/rag/assembler/db_struct.py @@ -0,0 +1,151 @@ +import os +from typing import Optional, Any, List + +from dbgpt.datasource.rdbms.base import RDBMSDatabase +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.chunk_manager import ChunkParameters, ChunkManager +from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory +from dbgpt.rag.knowledge.base import Knowledge, ChunkStrategy +from dbgpt.rag.knowledge.factory import KnowledgeFactory +from dbgpt.rag.retriever.db_struct import DBStructRetriever +from dbgpt.rag.summary.rdbms_db_summary import _parse_db_summary +from dbgpt.serve.rag.assembler.base import BaseAssembler +from dbgpt.storage.vector_store.connector import VectorStoreConnector + + +class DBStructAssembler(BaseAssembler): + """DBStructAssembler + Example: + .. code-block:: python + + from dbgpt.datasource.rdbms.conn_sqlite import SQLiteTempConnect + from dbgpt.serve.rag.assembler.db_struct import DBStructAssembler + from dbgpt.storage.vector_store.connector import VectorStoreConnector + from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig + + connection = SQLiteTempConnect.create_temporary_db() + assembler = DBStructAssembler.load_from_connection( + connection=connection, + embedding_model=embedding_model_path, + ) + assembler.persist() + # get db struct retriever + retriever = assembler.as_retriever(top_k=3) + """ + + def __init__( + self, + connection: RDBMSDatabase = None, + chunk_parameters: Optional[ChunkParameters] = None, + embedding_model: Optional[str] = None, + embedding_factory: Optional[EmbeddingFactory] = None, + vector_store_connector: Optional[VectorStoreConnector] = None, + **kwargs: Any, + ) -> None: + """Initialize with Embedding Assembler arguments. + Args: + connection: (RDBMSDatabase) RDBMSDatabase connection. + knowledge: (Knowledge) Knowledge datasource. + chunk_manager: (Optional[ChunkManager]) ChunkManager to use for chunking. + embedding_model: (Optional[str]) Embedding model to use. + embedding_factory: (Optional[EmbeddingFactory]) EmbeddingFactory to use. + vector_store_connector: (Optional[VectorStoreConnector]) VectorStoreConnector to use. + """ + if connection is None: + raise ValueError("datasource connection must be provided.") + from dbgpt.rag.embedding.embedding_factory import DefaultEmbeddingFactory + + embedding_factory = embedding_factory or DefaultEmbeddingFactory( + default_model_name=os.getenv("EMBEDDING_MODEL") + ) + self._connection = connection + if embedding_model: + embedding_fn = embedding_factory.create(model_name=embedding_model) + self._vector_store_connector = ( + vector_store_connector + or VectorStoreConnector.from_default(embedding_fn=embedding_fn) + ) + super().__init__( + chunk_parameters=chunk_parameters, + **kwargs, + ) + + @classmethod + def load_from_connection( + cls, + connection: RDBMSDatabase = None, + knowledge: Optional[Knowledge] = None, + chunk_parameters: Optional[ChunkParameters] = None, + embedding_model: Optional[str] = None, + embedding_factory: Optional[EmbeddingFactory] = None, + vector_store_connector: Optional[VectorStoreConnector] = None, + ) -> "DBStructAssembler": + """Load document embedding into vector store from path. + Args: + connection: (RDBMSDatabase) RDBMSDatabase connection. + knowledge: (Knowledge) Knowledge datasource. + chunk_parameters: (Optional[ChunkParameters]) ChunkManager to use for chunking. + embedding_model: (Optional[str]) Embedding model to use. + embedding_factory: (Optional[EmbeddingFactory]) EmbeddingFactory to use. + vector_store_connector: (Optional[VectorStoreConnector]) VectorStoreConnector to use. + Returns: + DBStructAssembler + """ + from dbgpt.rag.embedding.embedding_factory import DefaultEmbeddingFactory + + embedding_factory = embedding_factory or DefaultEmbeddingFactory( + default_model_name=embedding_model or os.getenv("EMBEDDING_MODEL_PATH") + ) + chunk_parameters = chunk_parameters or ChunkParameters( + chunk_strategy=ChunkStrategy.CHUNK_BY_SIZE.name, chunk_overlap=0 + ) + + return cls( + connection=connection, + knowledge=knowledge, + embedding_model=embedding_model, + chunk_parameters=chunk_parameters, + embedding_factory=embedding_factory, + vector_store_connector=vector_store_connector, + ) + + def load_knowledge(self, knowledge: Optional[Knowledge] = None) -> None: + table_summaries = _parse_db_summary(self._connection) + self._chunks = [] + self._knowledge = knowledge + for table_summary in table_summaries: + from dbgpt.rag.knowledge.base import KnowledgeType + + self._knowledge = KnowledgeFactory.from_text( + text=table_summary, knowledge_type=KnowledgeType.DOCUMENT + ) + self._chunk_parameters.chunk_size = len(table_summary) + self._chunk_manager = ChunkManager( + knowledge=self._knowledge, chunk_parameter=self._chunk_parameters + ) + self._chunks.extend(self._chunk_manager.split(self._knowledge.load())) + + def get_chunks(self) -> List[Chunk]: + """Return chunk ids.""" + return self._chunks + + def persist(self) -> List[str]: + """Persist chunks into vector store.""" + return self._vector_store_connector.load_document(self._chunks) + + def _extract_info(self, chunks) -> List[Chunk]: + """Extract info from chunks.""" + + def as_retriever(self, top_k: Optional[int] = 4) -> DBStructRetriever: + """ + Args: + top_k:(Optional[int]), default 4 + Returns: + DBStructRetriever + """ + return DBStructRetriever( + top_k=top_k, + connection=self._connection, + is_embeddings=True, + vector_store_connector=self._vector_store_connector, + ) diff --git a/dbgpt/serve/rag/assembler/embedding.py b/dbgpt/serve/rag/assembler/embedding.py new file mode 100644 index 000000000..10138c39e --- /dev/null +++ b/dbgpt/serve/rag/assembler/embedding.py @@ -0,0 +1,116 @@ +import os +from typing import Optional, Any, List + +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.chunk_manager import ChunkParameters +from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory +from dbgpt.rag.knowledge.base import Knowledge +from dbgpt.rag.retriever.embedding import EmbeddingRetriever +from dbgpt.serve.rag.assembler.base import BaseAssembler +from dbgpt.storage.vector_store.connector import VectorStoreConnector + + +class EmbeddingAssembler(BaseAssembler): + """Embedding Assembler + + Example: + + .. code-block:: python + + from dbgpt.rag.assembler import EmbeddingAssembler + + pdf_path = "path/to/document.pdf" + knowledge = KnowledgeFactory.from_file_path(pdf_path) + assembler = EmbeddingAssembler.load_from_knowledge( + knowledge=knowledge, + embedding_model="text2vec", + ) + """ + + def __init__( + self, + knowledge: Knowledge = None, + chunk_parameters: Optional[ChunkParameters] = None, + embedding_model: Optional[str] = None, + embedding_factory: Optional[EmbeddingFactory] = None, + vector_store_connector: Optional[VectorStoreConnector] = None, + **kwargs: Any, + ) -> None: + """Initialize with Embedding Assembler arguments. + Args: + knowledge: (Knowledge) Knowledge datasource. + chunk_parameters: (Optional[ChunkParameters]) ChunkManager to use for chunking. + embedding_model: (Optional[str]) Embedding model to use. + embedding_factory: (Optional[EmbeddingFactory]) EmbeddingFactory to use. + vector_store_connector: (Optional[VectorStoreConnector]) VectorStoreConnector to use. + """ + if knowledge is None: + raise ValueError("knowledge datasource must be provided.") + from dbgpt.rag.embedding.embedding_factory import DefaultEmbeddingFactory + + embedding_factory = embedding_factory or DefaultEmbeddingFactory( + default_model_name=os.getenv("EMBEDDING_MODEL") + ) + if embedding_model: + embedding_fn = embedding_factory.create(model_name=embedding_model) + self._vector_store_connector = ( + vector_store_connector + or VectorStoreConnector.from_default(embedding_fn=embedding_fn) + ) + + super().__init__( + knowledge=knowledge, + chunk_parameters=chunk_parameters, + **kwargs, + ) + + @classmethod + def load_from_knowledge( + cls, + knowledge: Knowledge = None, + chunk_parameters: Optional[ChunkParameters] = None, + embedding_model: Optional[str] = None, + embedding_factory: Optional[EmbeddingFactory] = None, + vector_store_connector: Optional[VectorStoreConnector] = None, + ) -> "EmbeddingAssembler": + """Load document embedding into vector store from path. + Args: + knowledge: (Knowledge) Knowledge datasource. + chunk_parameters: (Optional[ChunkParameters]) ChunkManager to use for chunking. + embedding_model: (Optional[str]) Embedding model to use. + embedding_factory: (Optional[EmbeddingFactory]) EmbeddingFactory to use. + vector_store_connector: (Optional[VectorStoreConnector]) VectorStoreConnector to use. + Returns: + EmbeddingAssembler + """ + from dbgpt.rag.embedding.embedding_factory import DefaultEmbeddingFactory + + embedding_factory = embedding_factory or DefaultEmbeddingFactory( + default_model_name=embedding_model or os.getenv("EMBEDDING_MODEL_PATH") + ) + return cls( + knowledge=knowledge, + chunk_parameters=chunk_parameters, + embedding_model=embedding_model, + embedding_factory=embedding_factory, + vector_store_connector=vector_store_connector, + ) + + def persist(self) -> List[str]: + """Persist chunks into vector store.""" + return self._vector_store_connector.load_document(self._chunks) + + def _extract_info(self, chunks) -> List[Chunk]: + """Extract info from chunks.""" + pass + + def as_retriever(self, top_k: Optional[int] = 4) -> EmbeddingRetriever: + """ + Args: + top_k:(Optional[int]), default 4 + Returns: + EmbeddingRetriever + """ + return EmbeddingRetriever( + top_k=top_k, vector_store_connector=self._vector_store_connector + ) diff --git a/dbgpt/serve/rag/assembler/summary.py b/dbgpt/serve/rag/assembler/summary.py new file mode 100644 index 000000000..a9d4af00e --- /dev/null +++ b/dbgpt/serve/rag/assembler/summary.py @@ -0,0 +1,113 @@ +import os +from typing import Optional, Any, List + +from dbgpt.core import LLMClient +from dbgpt.rag.chunk import Chunk +from dbgpt.rag.chunk_manager import ChunkParameters +from dbgpt.rag.extractor.base import Extractor +from dbgpt.rag.knowledge.base import Knowledge +from dbgpt.rag.retriever.base import BaseRetriever +from dbgpt.serve.rag.assembler.base import BaseAssembler + + +class SummaryAssembler(BaseAssembler): + """Summary Assembler + Example: + .. code-block:: python + + pdf_path = "../../../DB-GPT/docs/docs/awel.md" + OPEN_AI_KEY = "{your_api_key}" + OPEN_AI_BASE = "{your_api_base}" + llm_client = OpenAILLMClient(api_key=OPEN_AI_KEY, api_base=OPEN_AI_BASE) + knowledge = KnowledgeFactory.from_file_path(pdf_path) + chunk_parameters = ChunkParameters(chunk_strategy="CHUNK_BY_SIZE") + assembler = SummaryAssembler.load_from_knowledge( + knowledge=knowledge, + chunk_parameters=chunk_parameters, + llm_client=llm_client, + model_name="gpt-3.5-turbo", + ) + summary = await assembler.generate_summary() + """ + + def __init__( + self, + knowledge: Knowledge = None, + chunk_parameters: Optional[ChunkParameters] = None, + model_name: Optional[str] = None, + llm_client: Optional[LLMClient] = None, + extractor: Optional[Extractor] = None, + language: Optional[str] = "en", + **kwargs: Any, + ) -> None: + """Initialize with Embedding Assembler arguments. + Args: + knowledge: (Knowledge) Knowledge datasource. + chunk_manager: (Optional[ChunkManager]) ChunkManager to use for chunking. + model_name: (Optional[str]) llm model to use. + llm_client: (Optional[LLMClient]) LLMClient to use. + extractor: (Optional[Extractor]) Extractor to use for summarization. + language: (Optional[str]) The language of the prompt. Defaults to "en". + """ + if knowledge is None: + raise ValueError("knowledge datasource must be provided.") + + self._model_name = model_name or os.getenv("LLM_MODEL") + self._llm_client = llm_client + from dbgpt.rag.extractor.summary import SummaryExtractor + + self._extractor = extractor or SummaryExtractor( + llm_client=self._llm_client, model_name=self._model_name + ) + self._language = language + super().__init__( + knowledge=knowledge, + chunk_parameters=chunk_parameters, + extractor=self._extractor, + **kwargs, + ) + + @classmethod + def load_from_knowledge( + cls, + knowledge: Knowledge = None, + chunk_parameters: Optional[ChunkParameters] = None, + model_name: Optional[str] = None, + llm_client: Optional[LLMClient] = None, + extractor: Optional[Extractor] = None, + language: Optional[str] = "en", + **kwargs: Any, + ) -> "SummaryAssembler": + """Load document embedding into vector store from path. + Args: + knowledge: (Knowledge) Knowledge datasource. + chunk_parameters: (Optional[ChunkParameters]) ChunkManager to use for chunking. + model_name: (Optional[str]) llm model to use. + llm_client: (Optional[LLMClient]) LLMClient to use. + extractor: (Optional[Extractor]) Extractor to use for summarization. + language: (Optional[str]) The language of the prompt. Defaults to "en". + Returns: + SummaryAssembler + """ + return cls( + knowledge=knowledge, + chunk_parameters=chunk_parameters, + model_name=model_name, + llm_client=llm_client, + extractor=extractor, + language=language, + **kwargs, + ) + + async def generate_summary(self) -> str: + """Generate summary.""" + return await self._extractor.aextract(self._chunks) + + def persist(self) -> List[str]: + """Persist chunks into store.""" + + def _extract_info(self, chunks) -> List[Chunk]: + """Extract info from chunks.""" + + def as_retriever(self, **kwargs: Any) -> BaseRetriever: + """Return a retriever.""" diff --git a/dbgpt/serve/rag/assembler/tests/__init__.py b/dbgpt/serve/rag/assembler/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/dbgpt/serve/rag/assembler/tests/test_db_struct_assembler.py b/dbgpt/serve/rag/assembler/tests/test_db_struct_assembler.py new file mode 100644 index 000000000..34f15b458 --- /dev/null +++ b/dbgpt/serve/rag/assembler/tests/test_db_struct_assembler.py @@ -0,0 +1,76 @@ +from unittest.mock import MagicMock + +import pytest + +from dbgpt.datasource.rdbms.conn_sqlite import SQLiteTempConnect +from dbgpt.rag.chunk_manager import ChunkParameters, SplitterType +from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory +from dbgpt.rag.knowledge.base import Knowledge +from dbgpt.rag.text_splitter.text_splitter import CharacterTextSplitter +from dbgpt.serve.rag.assembler.embedding import EmbeddingAssembler +from dbgpt.storage.vector_store.connector import VectorStoreConnector + + +@pytest.fixture +def mock_db_connection(): + """Create a temporary database connection for testing.""" + connect = SQLiteTempConnect.create_temporary_db() + connect.create_temp_tables( + { + "user": { + "columns": { + "id": "INTEGER PRIMARY KEY", + "name": "TEXT", + "age": "INTEGER", + }, + "data": [ + (1, "Tom", 10), + (2, "Jerry", 16), + (3, "Jack", 18), + (4, "Alice", 20), + (5, "Bob", 22), + ], + } + } + ) + return connect + + +@pytest.fixture +def mock_chunk_parameters(): + return MagicMock(spec=ChunkParameters) + + +@pytest.fixture +def mock_embedding_factory(): + return MagicMock(spec=EmbeddingFactory) + + +@pytest.fixture +def mock_vector_store_connector(): + return MagicMock(spec=VectorStoreConnector) + + +@pytest.fixture +def mock_knowledge(): + return MagicMock(spec=Knowledge) + + +def test_load_knowledge( + mock_db_connection, + mock_knowledge, + mock_chunk_parameters, + mock_embedding_factory, + mock_vector_store_connector, +): + mock_chunk_parameters.chunk_strategy = "CHUNK_BY_SIZE" + mock_chunk_parameters.text_splitter = CharacterTextSplitter() + mock_chunk_parameters.splitter_type = SplitterType.USER_DEFINE + assembler = EmbeddingAssembler( + knowledge=mock_knowledge, + chunk_parameters=mock_chunk_parameters, + embedding_factory=mock_embedding_factory, + vector_store_connector=mock_vector_store_connector, + ) + assembler.load_knowledge(knowledge=mock_knowledge) + assert len(assembler._chunks) == 0 diff --git a/dbgpt/serve/rag/assembler/tests/test_embedding_assembler.py b/dbgpt/serve/rag/assembler/tests/test_embedding_assembler.py new file mode 100644 index 000000000..0a5d7d21f --- /dev/null +++ b/dbgpt/serve/rag/assembler/tests/test_embedding_assembler.py @@ -0,0 +1,76 @@ +from unittest.mock import MagicMock, patch + +import pytest + +from dbgpt.datasource.rdbms.conn_sqlite import SQLiteTempConnect +from dbgpt.rag.chunk_manager import ChunkParameters, SplitterType +from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory +from dbgpt.rag.knowledge.base import Knowledge +from dbgpt.rag.text_splitter.text_splitter import CharacterTextSplitter +from dbgpt.serve.rag.assembler.db_struct import DBStructAssembler +from dbgpt.storage.vector_store.connector import VectorStoreConnector + + +@pytest.fixture +def mock_db_connection(): + """Create a temporary database connection for testing.""" + connect = SQLiteTempConnect.create_temporary_db() + connect.create_temp_tables( + { + "user": { + "columns": { + "id": "INTEGER PRIMARY KEY", + "name": "TEXT", + "age": "INTEGER", + }, + "data": [ + (1, "Tom", 10), + (2, "Jerry", 16), + (3, "Jack", 18), + (4, "Alice", 20), + (5, "Bob", 22), + ], + } + } + ) + return connect + + +@pytest.fixture +def mock_chunk_parameters(): + return MagicMock(spec=ChunkParameters) + + +@pytest.fixture +def mock_embedding_factory(): + return MagicMock(spec=EmbeddingFactory) + + +@pytest.fixture +def mock_vector_store_connector(): + return MagicMock(spec=VectorStoreConnector) + + +@pytest.fixture +def mock_knowledge(): + return MagicMock(spec=Knowledge) + + +def test_load_knowledge( + mock_db_connection, + mock_knowledge, + mock_chunk_parameters, + mock_embedding_factory, + mock_vector_store_connector, +): + mock_chunk_parameters.chunk_strategy = "CHUNK_BY_SIZE" + mock_chunk_parameters.text_splitter = CharacterTextSplitter() + mock_chunk_parameters.splitter_type = SplitterType.USER_DEFINE + assembler = DBStructAssembler( + connection=mock_db_connection, + chunk_parameters=mock_chunk_parameters, + embedding_factory=mock_embedding_factory, + vector_store_connector=mock_vector_store_connector, + ) + assembler.load_knowledge(knowledge=mock_knowledge) + assert len(assembler._chunks) == 1 diff --git a/dbgpt/storage/metadata/_base_dao.py b/dbgpt/storage/metadata/_base_dao.py index 659e06b2e..770d82c93 100644 --- a/dbgpt/storage/metadata/_base_dao.py +++ b/dbgpt/storage/metadata/_base_dao.py @@ -139,8 +139,9 @@ def create(self, request: REQ) -> RES: RES: The response schema object. """ entry = self.from_request(request) - with self.session() as session: + with self.session(commit=False) as session: session.add(entry) + session.commit() return self.get_one(self.to_request(entry)) def update(self, query_request: QUERY_SPEC, update_request: REQ) -> RES: diff --git a/dbgpt/storage/metadata/db_manager.py b/dbgpt/storage/metadata/db_manager.py index eddd8f4e7..f222dd38a 100644 --- a/dbgpt/storage/metadata/db_manager.py +++ b/dbgpt/storage/metadata/db_manager.py @@ -299,7 +299,8 @@ def init_db( session_options.setdefault("class_", Session) session_options.setdefault("query_cls", self.Query) session_factory = sessionmaker(bind=self._engine, **session_options) - self._session = scoped_session(session_factory) + # self._session = scoped_session(session_factory) + self._session = session_factory self._base.metadata.bind = self._engine def init_default_db( diff --git a/dbgpt/storage/vector_store/base.py b/dbgpt/storage/vector_store/base.py index 799296cbd..2f944fc71 100644 --- a/dbgpt/storage/vector_store/base.py +++ b/dbgpt/storage/vector_store/base.py @@ -1,18 +1,69 @@ from abc import ABC, abstractmethod import math +from typing import Optional, Callable, List, Any + +from pydantic import Field, BaseModel + +from dbgpt.rag.chunk import Chunk + + +class VectorStoreConfig(BaseModel): + """Vector store config.""" + + name: str = Field( + default="dbgpt_collection", + description="The name of vector store, if not set, will use the default name.", + ) + user: Optional[str] = Field( + default=None, + description="The user of vector store, if not set, will use the default user.", + ) + password: Optional[str] = Field( + default=None, + description="The password of vector store, if not set, will use the default password.", + ) + embedding_fn: Optional[Any] = Field( + default=None, + description="The embedding function of vector store, if not set, will use the default embedding function.", + ) class VectorStoreBase(ABC): """base class for vector store database""" @abstractmethod - def load_document(self, documents) -> None: - """load document in vector database.""" + def load_document(self, chunks: List[Chunk]) -> List[str]: + """load document in vector database. + Args: + - chunks: document chunks. + Return: + - ids: chunks ids. + """ pass @abstractmethod - def similar_search(self, text, topk) -> None: - """similar search in vector database.""" + def similar_search(self, text, topk) -> List[Chunk]: + """similar search in vector database. + Args: + - text: query text + - topk: topk + Return: + - chunks: chunks. + """ + pass + + @abstractmethod + def similar_search_with_scores( + self, text, topk, score_threshold: float + ) -> List[Chunk]: + """similar search in vector database with scores. + Args: + - text: query text + - topk: topk + - score_threshold: score_threshold: Optional, a floating point value between 0 to 1 + Return: + - chunks: chunks. + """ pass @abstractmethod @@ -22,12 +73,17 @@ def vector_name_exists(self) -> bool: @abstractmethod def delete_by_ids(self, ids): - """delete vector by ids.""" - pass + """delete vector by ids. + Args: + - ids: vector ids + """ @abstractmethod def delete_vector_name(self, vector_name): - """delete vector name.""" + """delete vector name. + Args: + - vector_name: vector store name + """ pass def _normalization_vectors(self, vectors): diff --git a/dbgpt/storage/vector_store/chroma_store.py b/dbgpt/storage/vector_store/chroma_store.py index 5c17f7a52..8587ff732 100644 --- a/dbgpt/storage/vector_store/chroma_store.py +++ b/dbgpt/storage/vector_store/chroma_store.py @@ -1,30 +1,45 @@ import os import logging -from typing import Any +from typing import Any, List from chromadb.config import Settings from chromadb import PersistentClient -from dbgpt.storage.vector_store.base import VectorStoreBase +from pydantic import Field + +from dbgpt.rag.chunk import Chunk +from dbgpt.storage.vector_store.base import VectorStoreBase, VectorStoreConfig from dbgpt.configs.model_config import PILOT_PATH logger = logging.getLogger(__name__) +class ChromaVectorConfig(VectorStoreConfig): + """Chroma vector store config.""" + + persist_path: str = Field( + default=os.getenv("CHROMA_PERSIST_PATH", None), + description="The password of vector store, if not set, will use the default password.", + ) + collection_metadata: dict = Field( + default=None, + description="the index metadata of vector store, if not set, will use the default metadata.", + ) + + class ChromaStore(VectorStoreBase): """chroma database""" - def __init__(self, ctx: {}) -> None: + def __init__(self, vector_store_config: ChromaVectorConfig) -> None: from langchain.vectorstores import Chroma - self.ctx = ctx - chroma_path = ctx.get( - "CHROMA_PERSIST_PATH", - os.path.join(PILOT_PATH, "data"), + chroma_vector_config = vector_store_config.dict() + chroma_path = chroma_vector_config.get( + "persist_path", os.path.join(PILOT_PATH, "data") ) self.persist_dir = os.path.join( - chroma_path, ctx["vector_store_name"] + ".vectordb" + chroma_path, vector_store_config.name + ".vectordb" ) - self.embeddings = ctx.get("embeddings", None) + self.embeddings = vector_store_config.embedding_fn chroma_settings = Settings( # chroma_db_impl="duckdb+parquet", => deprecated configuration of Chroma persist_directory=self.persist_dir, @@ -32,7 +47,9 @@ def __init__(self, ctx: {}) -> None: ) client = PersistentClient(path=self.persist_dir, settings=chroma_settings) - collection_metadata = {"hnsw:space": "cosine"} + collection_metadata = chroma_vector_config.get("collection_metadata") or { + "hnsw:space": "cosine" + } self.vector_store_client = Chroma( persist_directory=self.persist_dir, embedding_function=self.embeddings, @@ -41,11 +58,15 @@ def __init__(self, ctx: {}) -> None: collection_metadata=collection_metadata, ) - def similar_search(self, text, topk, **kwargs: Any) -> None: + def similar_search(self, text, topk, **kwargs: Any) -> List[Chunk]: logger.info("ChromaStore similar search") - return self.vector_store_client.similarity_search(text, topk, **kwargs) + lc_documents = self.vector_store_client.similarity_search(text, topk, **kwargs) + return [ + Chunk(content=doc.page_content, metadata=doc.metadata) + for doc in lc_documents + ] - def similar_search_with_scores(self, text, topk, score_threshold) -> None: + def similar_search_with_scores(self, text, topk, score_threshold) -> List[Chunk]: """ Chroma similar_search_with_score. Return docs and relevance scores in the range [0, 1]. @@ -55,15 +76,19 @@ def similar_search_with_scores(self, text, topk, score_threshold) -> None: score_threshold(float): score_threshold: Optional, a floating point value between 0 to 1 to filter the resulting set of retrieved docs,0 is dissimilar, 1 is most similar. """ - logger.info("ChromaStore similar search") + logger.info("ChromaStore similar search with scores") docs_and_scores = ( self.vector_store_client.similarity_search_with_relevance_scores( query=text, k=topk, score_threshold=score_threshold ) ) - return docs_and_scores + return [ + Chunk(content=doc.page_content, metadata=doc.metadata, score=score) + for doc, score in docs_and_scores + ] def vector_name_exists(self): + """is vector store name exist.""" logger.info(f"Check persist_dir: {self.persist_dir}") if not os.path.exists(self.persist_dir): return False @@ -72,11 +97,12 @@ def vector_name_exists(self): files = list(filter(lambda f: f != "chroma.sqlite3", files)) return len(files) > 0 - def load_document(self, documents): + def load_document(self, chunks: List[Chunk]) -> List[str]: logger.info("ChromaStore load document") - texts = [doc.page_content for doc in documents] - metadatas = [doc.metadata for doc in documents] - ids = self.vector_store_client.add_texts(texts=texts, metadatas=metadatas) + texts = [chunk.content for chunk in chunks] + metadatas = [chunk.metadata for chunk in chunks] + ids = [chunk.chunk_id for chunk in chunks] + self.vector_store_client.add_texts(texts=texts, metadatas=metadatas, ids=ids) return ids def delete_vector_name(self, vector_name): diff --git a/dbgpt/storage/vector_store/connector.py b/dbgpt/storage/vector_store/connector.py index 41a6a2714..a256e6a87 100644 --- a/dbgpt/storage/vector_store/connector.py +++ b/dbgpt/storage/vector_store/connector.py @@ -1,54 +1,94 @@ +import os +from typing import Optional, List, Callable, Any + +from dbgpt.rag.chunk import Chunk from dbgpt.storage import vector_store -from dbgpt.storage.vector_store.base import VectorStoreBase +from dbgpt.storage.vector_store.base import VectorStoreBase, VectorStoreConfig connector = {} class VectorStoreConnector: + """VectorStoreConnector, can connect different vector db provided load document api_v1 and similar search api_v1. 1.load_document:knowledge document source into vector store.(Chroma, Milvus, Weaviate) 2.similar_search: similarity search from vector_store + 3.similar_search_with_scores: similarity search with similarity score from vector_store + code example: + >>> from dbgpt.storage.vector_store.connector import VectorStoreConnector + + >>> vector_store_config = VectorStoreConfig + >>> vector_store_connector = VectorStoreConnector(vector_store_type="Chroma") """ - def __init__(self, vector_store_type, ctx: {}) -> None: + def __init__( + self, vector_store_type: str, vector_store_config: VectorStoreConfig = None + ) -> None: """initialize vector store connector. Args: - vector_store_type: vector store type Milvus, Chroma, Weaviate - ctx: vector store config params. """ - self.ctx = ctx + self._vector_store_config = vector_store_config self._register() if self._match(vector_store_type): self.connector_class = connector.get(vector_store_type) else: - raise Exception(f"Vector Type Not support. {0}", vector_store_type) + raise Exception(f"Vector Store Type Not support. {0}", vector_store_type) print(self.connector_class) - self.client = self.connector_class(ctx) + self.client = self.connector_class(vector_store_config) + + @classmethod + def from_default( + cls, + vector_store_type: str = None, + embedding_fn: Optional[Any] = None, + vector_store_config: Optional[VectorStoreConfig] = None, + ) -> "VectorStoreConnector": + """initialize default vector store connector.""" + vector_store_type = vector_store_type or os.getenv( + "VECTOR_STORE_TYPE", "Chroma" + ) + from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig - def load_document(self, docs): - """load document in vector database.""" - return self.client.load_document(docs) + vector_store_config = vector_store_config or ChromaVectorConfig() + vector_store_config.embedding_fn = embedding_fn + return cls(vector_store_type, vector_store_config) + + def load_document(self, chunks: List[Chunk]) -> List[str]: + """load document in vector database. + Args: + - chunks: document chunks. + Return chunk ids. + """ + return self.client.load_document(chunks) - def similar_search(self, doc: str, topk: int): + def similar_search(self, doc: str, topk: int) -> List[Chunk]: """similar search in vector database. Args: - doc: query text - topk: topk + Return: + - chunks: chunks. """ return self.client.similar_search(doc, topk) - def similar_search_with_scores(self, doc: str, topk: int, score_threshold: float): + def similar_search_with_scores( + self, doc: str, topk: int, score_threshold: float + ) -> List[Chunk]: """ similar_search_with_score in vector database.. Return docs and relevance scores in the range [0, 1]. Args: - doc(str): query text - topk(int): return docs nums. Defaults to 4. - score_threshold(float): score_threshold: Optional, a floating point value between 0 to 1 to + - doc(str): query text + - topk(int): return docs nums. Defaults to 4. + - score_threshold(float): score_threshold: Optional, a floating point value between 0 to 1 to filter the resulting set of retrieved docs,0 is dissimilar, 1 is most similar. + Return: + - chunks: chunks. """ return self.client.similar_search_with_scores(doc, topk, score_threshold) diff --git a/dbgpt/storage/vector_store/milvus_store.py b/dbgpt/storage/vector_store/milvus_store.py index dc034b776..4f97cdd50 100644 --- a/dbgpt/storage/vector_store/milvus_store.py +++ b/dbgpt/storage/vector_store/milvus_store.py @@ -5,49 +5,111 @@ import os from typing import Any, Iterable, List, Optional, Tuple +from pydantic import Field -from dbgpt.storage.vector_store.base import VectorStoreBase +from dbgpt.rag.chunk import Chunk, Document +from dbgpt.storage.vector_store.base import VectorStoreBase, VectorStoreConfig +from dbgpt.util import string_utils logger = logging.getLogger(__name__) -class MilvusStore(VectorStoreBase): - """Milvus database""" +class MilvusVectorConfig(VectorStoreConfig): + """Milvus vector store config.""" + + uri: str = Field( + default="localhost", + description="The uri of milvus store, if not set, will use the default uri.", + ) + port: str = Field( + default="19530", + description="The port of milvus store, if not set, will use the default port.", + ) + + alias: str = Field( + default="default", + description="The alias of milvus store, if not set, will use the default alias.", + ) + user: str = Field( + default=None, + description="The user of milvus store, if not set, will use the default user.", + ) + password: str = Field( + default=None, + description="The password of milvus store, if not set, will use the default password.", + ) + primary_field: str = Field( + default="pk_id", + description="The primary field of milvus store, if not set, will use the default primary field.", + ) + text_field: str = Field( + default="content", + description="The text field of milvus store, if not set, will use the default text field.", + ) + embedding_field: str = Field( + default="vector", + description="The embedding field of milvus store, if not set, will use the default embedding field.", + ) + metadata_field: str = Field( + default="metadata", + description="The metadata field of milvus store, if not set, will use the default metadata field.", + ) + secure: str = Field( + default="", + description="The secure of milvus store, if not set, will use the default secure.", + ) - def __init__(self, ctx: {}) -> None: - """MilvusStore init.""" - from pymilvus import connections - """init a milvus storage connection. +class MilvusStore(VectorStoreBase): + """Milvus database""" + def __init__(self, vector_store_config: MilvusVectorConfig) -> None: + """MilvusStore init. Args: - ctx ({}): MilvusStore global config. + vector_store_config (MilvusVectorConfig): MilvusStore config. + refer to https://milvus.io/docs/v2.0.x/manage_connection.md """ - # self.configure(cfg) + from pymilvus import connections connect_kwargs = {} - self.uri = ctx.get("MILVUS_URL", os.getenv("MILVUS_URL")) - self.port = ctx.get("MILVUS_PORT", os.getenv("MILVUS_PORT")) - self.username = ctx.get("MILVUS_USERNAME", os.getenv("MILVUS_USERNAME")) - self.password = ctx.get("MILVUS_PASSWORD", os.getenv("MILVUS_PASSWORD")) - self.secure = ctx.get("MILVUS_SECURE", os.getenv("MILVUS_SECURE")) - self.collection_name = ctx.get("vector_store_name", None) - self.embedding = ctx.get("embeddings", None) + milvus_vector_config = vector_store_config.dict() + self.uri = milvus_vector_config.get("uri") or os.getenv( + "MILVUS_URL", "localhost" + ) + self.port = milvus_vector_config.get("post") or os.getenv( + "MILVUS_PORT", "19530" + ) + self.username = milvus_vector_config.get("user") or os.getenv("MILVUS_USER") + self.password = milvus_vector_config.get("password") or os.getenv( + "MILVUS_PASSWORD" + ) + self.secure = milvus_vector_config.get("secure") or os.getenv("MILVUS_SECURE") + + self.collection_name = ( + milvus_vector_config.get("name") or vector_store_config.name + ) + if string_utils.is_all_chinese(self.collection_name): + bytes_str = self.collection_name.encode("utf-8") + hex_str = bytes_str.hex() + self.collection_name = hex_str + + self.embedding = vector_store_config.embedding_fn self.fields = [] - self.alias = "default" + self.alias = milvus_vector_config.get("alias") or "default" # use HNSW by default. self.index_params = { - "metric_type": "L2", "index_type": "HNSW", + "metric_type": "COSINE", "params": {"M": 8, "efConstruction": 64}, } + # use HNSW by default. self.index_params_map = { "IVF_FLAT": {"params": {"nprobe": 10}}, "IVF_SQ8": {"params": {"nprobe": 10}}, "IVF_PQ": {"params": {"nprobe": 10}}, - "HNSW": {"params": {"ef": 10}}, + "HNSW": {"params": {"M": 8, "efConstruction": 64}}, "RHNSW_FLAT": {"params": {"ef": 10}}, "RHNSW_SQ": {"params": {"ef": 10}}, "RHNSW_PQ": {"params": {"ef": 10}}, @@ -55,10 +117,10 @@ def __init__(self, ctx: {}) -> None: "ANNOY": {"params": {"search_k": 10}}, } # default collection schema - self.primary_field = "pk_id" - self.vector_field = "vector" - self.text_field = "content" - self.metadata_field = "metadata" + self.primary_field = milvus_vector_config.get("primary_field") or "pk_id" + self.vector_field = milvus_vector_config.get("embedding_field") or "vector" + self.text_field = milvus_vector_config.get("text_field") or "content" + self.metadata_field = milvus_vector_config.get("metadata_field") or "metadata" if (self.username is None) != (self.password is None): raise ValueError( @@ -75,13 +137,13 @@ def __init__(self, ctx: {}) -> None: # secure=self.secure, ) - def init_schema_and_load(self, vector_name, documents): + def init_schema_and_load(self, vector_name, documents) -> List[str]: """Create a Milvus collection, indexes it with HNSW, load document. Args: vector_name (Embeddings): your collection name. documents (List[str]): Text to insert. Returns: - VectorStore: The MilvusStore vector store. + List[str]: document ids. """ try: from pymilvus import ( @@ -105,7 +167,7 @@ def init_schema_and_load(self, vector_name, documents): alias="default" # secure=self.secure, ) - texts = [d.page_content for d in documents] + texts = [d.content for d in documents] metadatas = [d.metadata for d in documents] embeddings = self.embedding.embed_query(texts[0]) @@ -183,7 +245,7 @@ def _add_documents( import numpy as np text_vector = self.embedding.embed_documents(list(texts)) - insert_dict[self.vector_field] = self._normalization_vectors(text_vector) + insert_dict[self.vector_field] = text_vector except NotImplementedError: insert_dict[self.vector_field] = [ self.embedding.embed_query(x) for x in texts @@ -204,12 +266,11 @@ def _add_documents( self.col.flush() return res.primary_keys - def load_document(self, documents) -> None: + def load_document(self, chunks: List[Chunk]) -> List[str]: """load document in vector database.""" - # self.init_schema_and_load(self.collection_name, documents) batch_size = 500 batched_list = [ - documents[i : i + batch_size] for i in range(0, len(documents), batch_size) + chunks[i : i + batch_size] for i in range(0, len(chunks), batch_size) ] doc_ids = [] for doc_batch in batched_list: @@ -217,7 +278,7 @@ def load_document(self, documents) -> None: doc_ids = [str(doc_id) for doc_id in doc_ids] return doc_ids - def similar_search(self, text, topk): + def similar_search(self, text, topk) -> List[Chunk]: from pymilvus import Collection, DataType """similar_search in vector database.""" @@ -232,17 +293,16 @@ def similar_search(self, text, topk): if x.dtype == DataType.FLOAT_VECTOR or x.dtype == DataType.BINARY_VECTOR: self.vector_field = x.name _, docs_and_scores = self._search(text, topk) - from langchain.schema import Document return [ - Document( + Chunk( metadata=json.loads(doc.metadata.get("metadata", "")), - page_content=doc.page_content, + content=doc.content, ) for doc, _, _ in docs_and_scores ] - def similar_search_with_scores(self, text, topk, score_threshold): + def similar_search_with_scores(self, text, topk, score_threshold) -> List[Chunk]: """Perform a search on a query string and return results with score. For more information about the search parameters, take a look at the pymilvus @@ -286,7 +346,12 @@ def similar_search_with_scores(self, text, topk, score_threshold): if score_threshold is not None: docs_and_scores = [ - (doc, score) + Chunk( + metadata=doc.metadata, + content=doc.content, + score=score, + chunk_id=id, + ) for doc, score, id in docs_and_scores if score >= score_threshold ] @@ -308,22 +373,19 @@ def _search( timeout: Optional[int] = None, **kwargs: Any, ): - from langchain.docstore.document import Document - self.col.load() # use default index params. if param is None: index_type = self.col.indexes[0].params["index_type"] - param = self.index_params_map[index_type] + param = self.index_params_map[index_type].get("params") # query text embedding. query_vector = self.embedding.embed_query(query) - data = [self._normalization_vectors(query_vector)] # Determine result metadata fields. output_fields = self.fields[:] output_fields.remove(self.vector_field) # milvus search. res = self.col.search( - data, + [query_vector], self.vector_field, param, k, @@ -339,13 +401,13 @@ def _search( meta = {x: result.entity.get(x) for x in output_fields} ret.append( ( - Document(page_content=meta.pop(self.text_field), metadata=meta), - self._default_relevance_score_fn(result.distance), + Chunk(content=meta.pop(self.text_field), metadata=meta), + result.distance, result.id, ) ) - return data[0], ret + return ret[0], ret def vector_name_exists(self): from pymilvus import utility diff --git a/dbgpt/storage/vector_store/pgvector_store.py b/dbgpt/storage/vector_store/pgvector_store.py index 001f06d58..9917ca54b 100644 --- a/dbgpt/storage/vector_store/pgvector_store.py +++ b/dbgpt/storage/vector_store/pgvector_store.py @@ -1,6 +1,10 @@ -from typing import Any +from typing import Any, List import logging -from dbgpt.storage.vector_store.base import VectorStoreBase + +from pydantic import Field + +from dbgpt.rag.chunk import Chunk +from dbgpt.storage.vector_store.base import VectorStoreBase, VectorStoreConfig from dbgpt._private.config import Config logger = logging.getLogger(__name__) @@ -8,21 +12,29 @@ CFG = Config() +class PGVectorConfig(VectorStoreConfig): + """PG vector store config.""" + + connection_string: str = Field( + default=None, + description="the connection string of vector store, if not set, will use the default connection string.", + ) + + class PGVectorStore(VectorStoreBase): """`Postgres.PGVector` vector store. To use this, you should have the ``pgvector`` python package installed. """ - def __init__(self, ctx: dict) -> None: + def __init__(self, vector_store_config: PGVectorConfig) -> None: """init pgvector storage""" from langchain.vectorstores import PGVector - self.ctx = ctx - self.connection_string = ctx.get("connection_string", None) - self.embeddings = ctx.get("embeddings", None) - self.collection_name = ctx.get("vector_store_name", None) + self.connection_string = vector_store_config.connection_string + self.embeddings = vector_store_config.embedding_fn + self.collection_name = vector_store_config.name self.vector_store_client = PGVector( embedding_function=self.embeddings, @@ -41,8 +53,9 @@ def vector_name_exists(self): logger.error("vector_name_exists error", e.message) return False - def load_document(self, documents) -> None: - return self.vector_store_client.from_documents(documents) + def load_document(self, chunks: List[Chunk]) -> List[str]: + lc_documents = [Chunk.chunk2langchain(chunk) for chunk in chunks] + return self.vector_store_client.from_documents(lc_documents) def delete_vector_name(self, vector_name): return self.vector_store_client.delete_collection() diff --git a/dbgpt/storage/vector_store/weaviate_store.py b/dbgpt/storage/vector_store/weaviate_store.py index 2124a2208..92ded9980 100644 --- a/dbgpt/storage/vector_store/weaviate_store.py +++ b/dbgpt/storage/vector_store/weaviate_store.py @@ -1,19 +1,36 @@ import os import logging +from typing import List + from langchain.schema import Document +from pydantic import Field from dbgpt._private.config import Config from dbgpt.configs.model_config import KNOWLEDGE_UPLOAD_ROOT_PATH -from dbgpt.storage.vector_store.base import VectorStoreBase +from dbgpt.rag.chunk import Chunk +from dbgpt.storage.vector_store.base import VectorStoreBase, VectorStoreConfig logger = logging.getLogger(__name__) CFG = Config() +class WeaviateVectorConfig(VectorStoreConfig): + """Weaviate vector store config.""" + + weaviate_url: str = Field( + default=os.getenv("WEAVIATE_URL", None), + description="weaviate url address, if not set, will use the default url.", + ) + persist_path: str = Field( + default=os.getenv("WEAVIATE_PERSIST_PATH", None), + description="weaviate persist path.", + ) + + class WeaviateStore(VectorStoreBase): """Weaviate database""" - def __init__(self, ctx: dict) -> None: + def __init__(self, vector_store_config: WeaviateVectorConfig) -> None: """Initialize with Weaviate client.""" try: import weaviate @@ -23,12 +40,11 @@ def __init__(self, ctx: dict) -> None: "Please install it with `pip install weaviate-client`." ) - self.ctx = ctx - self.weaviate_url = ctx.get("WEAVIATE_URL", os.getenv("WEAVIATE_URL")) - self.embedding = ctx.get("embeddings", None) - self.vector_name = ctx["vector_store_name"] + self.weaviate_url = vector_store_config.weaviate_url + self.embedding = vector_store_config.embedding_fn + self.vector_name = vector_store_config.name self.persist_dir = os.path.join( - KNOWLEDGE_UPLOAD_ROOT_PATH, self.vector_name + ".vectordb" + vector_store_config.persist_path, vector_store_config.name + ".vectordb" ) self.vector_store_client = weaviate.Client(self.weaviate_url) @@ -120,11 +136,11 @@ def _default_schema(self) -> None: # Create the schema in Weaviate self.vector_store_client.schema.create(schema) - def load_document(self, documents: list) -> None: + def load_document(self, chunks: List[Chunk]) -> List[str]: """Load documents into Weaviate""" logger.info("Weaviate load document") - texts = [doc.page_content for doc in documents] - metadatas = [doc.metadata for doc in documents] + texts = [doc.content for doc in chunks] + metadatas = [doc.metadata for doc in chunks] # Import data with self.vector_store_client.batch as batch: @@ -134,7 +150,7 @@ def load_document(self, documents: list) -> None: for i in range(len(texts)): properties = { "metadata": metadatas[i]["source"], - "page_content": texts[i], + "content": texts[i], } self.vector_store_client.batch.add_data_object( diff --git a/dbgpt/_private/chat_util.py b/dbgpt/util/chat_util.py similarity index 81% rename from dbgpt/_private/chat_util.py rename to dbgpt/util/chat_util.py index 7f15d7657..1c76a43f5 100644 --- a/dbgpt/_private/chat_util.py +++ b/dbgpt/util/chat_util.py @@ -1,7 +1,6 @@ import asyncio from typing import Coroutine, List, Any - from dbgpt.app.scene import BaseChat, ChatFactory chat_factory = ChatFactory() @@ -43,3 +42,16 @@ async def _execute_task(task): # outputs: List[Any] = asyncio.run(_gather()) return await _gather() + + +def run_tasks( + tasks: List[Coroutine], +) -> List[Any]: + """Run a list of async tasks.""" + tasks_to_execute: List[Any] = tasks + + async def _gather() -> List[Any]: + return await asyncio.gather(*tasks_to_execute) + + outputs: List[Any] = asyncio.run(_gather()) + return outputs diff --git a/dbgpt/util/prompt_util.py b/dbgpt/util/prompt_util.py index d61eb4980..17d994d32 100644 --- a/dbgpt/util/prompt_util.py +++ b/dbgpt/util/prompt_util.py @@ -16,7 +16,7 @@ from dbgpt.util.global_helper import globals_helper from dbgpt._private.llm_metadata import LLMMetadata -from dbgpt.rag.embedding_engine.loader.token_splitter import TokenTextSplitter +from dbgpt.rag.text_splitter.token_splitter import TokenTextSplitter DEFAULT_PADDING = 5 DEFAULT_CHUNK_OVERLAP_RATIO = 0.1 @@ -93,6 +93,11 @@ def __init__( separator=separator, ) + def token_count(self, prompt_template: str) -> int: + """Get token count of prompt template.""" + empty_prompt_txt = get_empty_prompt_txt(prompt_template) + return len(self._tokenizer(empty_prompt_txt)) + @classmethod def from_llm_metadata( cls, diff --git a/dbgpt/rag/embedding_engine/loader/splitter_utils.py b/dbgpt/util/splitter_utils.py similarity index 100% rename from dbgpt/rag/embedding_engine/loader/splitter_utils.py rename to dbgpt/util/splitter_utils.py diff --git a/docs/docs/application/started_tutorial/chat_knowledge.md b/docs/docs/application/started_tutorial/chat_knowledge.md index 24ffc37aa..5fd72eda3 100644 --- a/docs/docs/application/started_tutorial/chat_knowledge.md +++ b/docs/docs/application/started_tutorial/chat_knowledge.md @@ -39,13 +39,23 @@ Document addition currently supports multiple types, such as plain text, URL cra

-Select the corresponding document and click `Finish`. +Select one or more corresponding documents and click `next`.

+### Documents Segmentation + +Choose Document Segmentation, you can choose to segment the document by chunk size, separator, paragraph or markdown header. The default is to segment the document by chunk size. + +and click Process, it will take a few minutes to complete the document segmentation. + +

+ +

+ ### Waiting for document vectorization @@ -63,7 +73,7 @@ Click the `Chat`button to start a conversation with the knowledge base.

- +

diff --git a/docs/static/img/chat_knowledge/chat.png b/docs/static/img/chat_knowledge/chat.png new file mode 100644 index 000000000..bcfb30cda Binary files /dev/null and b/docs/static/img/chat_knowledge/chat.png differ diff --git a/docs/static/img/chat_knowledge/create_knowledge_base.png b/docs/static/img/chat_knowledge/create_knowledge_base.png index 80fc5ffa2..3cc72cf6c 100644 Binary files a/docs/static/img/chat_knowledge/create_knowledge_base.png and b/docs/static/img/chat_knowledge/create_knowledge_base.png differ diff --git a/docs/static/img/chat_knowledge/doc_segmentation.png b/docs/static/img/chat_knowledge/doc_segmentation.png new file mode 100644 index 000000000..6d44ddfd0 Binary files /dev/null and b/docs/static/img/chat_knowledge/doc_segmentation.png differ diff --git a/docs/static/img/chat_knowledge/upload_doc.png b/docs/static/img/chat_knowledge/upload_doc.png index 29e79e983..55638953c 100644 Binary files a/docs/static/img/chat_knowledge/upload_doc.png and b/docs/static/img/chat_knowledge/upload_doc.png differ diff --git a/docs/static/img/chat_knowledge/upload_doc_finish.png b/docs/static/img/chat_knowledge/upload_doc_finish.png index 4bca7c55b..e5f17480e 100644 Binary files a/docs/static/img/chat_knowledge/upload_doc_finish.png and b/docs/static/img/chat_knowledge/upload_doc_finish.png differ diff --git a/docs/static/img/chat_knowledge/waiting_doc_vector.png b/docs/static/img/chat_knowledge/waiting_doc_vector.png index a7680b5b8..a8ab0857d 100644 Binary files a/docs/static/img/chat_knowledge/waiting_doc_vector.png and b/docs/static/img/chat_knowledge/waiting_doc_vector.png differ diff --git a/examples/rag/db_struct_rag_example.py b/examples/rag/db_struct_rag_example.py new file mode 100644 index 000000000..01a97341c --- /dev/null +++ b/examples/rag/db_struct_rag_example.py @@ -0,0 +1,68 @@ +from dbgpt.datasource.rdbms.conn_sqlite import SQLiteTempConnect +from dbgpt.rag.embedding.embedding_factory import DefaultEmbeddingFactory +from dbgpt.serve.rag.assembler.db_struct import DBStructAssembler +from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig +from dbgpt.storage.vector_store.connector import VectorStoreConnector + +"""DB struct rag example. + pre-requirements: + set your embedding model path in your example code. + ``` + embedding_model_path = "{your_embedding_model_path}" + ``` + + Examples: + ..code-block:: shell + python examples/rag/db_struct_rag_example.py +""" + + +def _create_temporary_connection(): + """Create a temporary database connection for testing.""" + connect = SQLiteTempConnect.create_temporary_db() + connect.create_temp_tables( + { + "user": { + "columns": { + "id": "INTEGER PRIMARY KEY", + "name": "TEXT", + "age": "INTEGER", + }, + "data": [ + (1, "Tom", 10), + (2, "Jerry", 16), + (3, "Jack", 18), + (4, "Alice", 20), + (5, "Bob", 22), + ], + } + } + ) + return connect + + +if __name__ == "__main__": + connection = _create_temporary_connection() + + embedding_model_path = "{your_embedding_model_path}" + vector_persist_path = "{your_persist_path}" + embedding_fn = DefaultEmbeddingFactory( + default_model_name=embedding_model_path + ).create() + vector_connector = VectorStoreConnector.from_default( + "Chroma", + vector_store_config=ChromaVectorConfig( + name="vector_name", + persist_path=vector_persist_path, + ), + embedding_fn=embedding_fn, + ) + assembler = DBStructAssembler.load_from_connection( + connection=connection, + vector_store_connector=vector_connector, + ) + assembler.persist() + # get db struct retriever + retriever = assembler.as_retriever(top_k=1) + chunks = retriever.retrieve("show columns from user") + print(f"db struct rag example results:{[chunk.content for chunk in chunks]}") diff --git a/examples/rag/embedding_rag_example.py b/examples/rag/embedding_rag_example.py new file mode 100644 index 000000000..b51a60444 --- /dev/null +++ b/examples/rag/embedding_rag_example.py @@ -0,0 +1,53 @@ +import asyncio + +from dbgpt.rag.chunk_manager import ChunkParameters +from dbgpt.rag.embedding.embedding_factory import DefaultEmbeddingFactory +from dbgpt.rag.knowledge.factory import KnowledgeFactory +from dbgpt.serve.rag.assembler.embedding import EmbeddingAssembler +from dbgpt.storage.vector_store.chroma_store import ChromaVectorConfig +from dbgpt.storage.vector_store.connector import VectorStoreConnector + +"""Embedding rag example. + pre-requirements: + set your embedding model path in your example code. + ``` + embedding_model_path = "{your_embedding_model_path}" + ``` + + Examples: + ..code-block:: shell + python examples/rag/embedding_rag_example.py +""" + + +async def main(): + file_path = "./docs/docs/awel.md" + vector_persist_path = "{your_persist_path}" + embedding_model_path = "{your_embedding_model_path}" + knowledge = KnowledgeFactory.from_file_path(file_path) + vector_connector = VectorStoreConnector.from_default( + "Chroma", + vector_store_config=ChromaVectorConfig( + name="vector_name", + persist_path=vector_persist_path, + ), + embedding_fn=DefaultEmbeddingFactory( + default_model_name=embedding_model_path + ).create(), + ) + chunk_parameters = ChunkParameters(chunk_strategy="CHUNK_BY_SIZE") + # get embedding assembler + assembler = EmbeddingAssembler.load_from_knowledge( + knowledge=knowledge, + chunk_parameters=chunk_parameters, + vector_store_connector=vector_connector, + ) + assembler.persist() + # get embeddings retriever + retriever = assembler.as_retriever(3) + chunks = await retriever.aretrieve_with_scores("RAG", 0.3) + print(f"embedding rag example results:{chunks}") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/rag/rewrite_rag_example.py b/examples/rag/rewrite_rag_example.py new file mode 100644 index 000000000..f96e53aed --- /dev/null +++ b/examples/rag/rewrite_rag_example.py @@ -0,0 +1,42 @@ +import asyncio +import os + +from dbgpt.model import OpenAILLMClient +from dbgpt.rag.retriever.rewrite import QueryRewrite + +"""Query rewrite example. + pre-requirements: + 1. install openai python sdk + ``` + pip install openai + ``` + 2. set openai key and base + ``` + export OPENAI_API_KEY={your_openai_key} + export OPENAI_API_BASE={your_openai_base} + ``` + or + ``` + import os + os.environ["OPENAI_API_KEY"] = {your_openai_key} + os.environ["OPENAI_API_BASE"] = {your_openai_base} + ``` + Examples: + ..code-block:: shell + python examples/rag/rewrite_rag_example.py +""" + + +async def main(): + query = "compare steve curry and lebron james" + llm_client = OpenAILLMClient() + reinforce = QueryRewrite( + llm_client=llm_client, + model_name="gpt-3.5-turbo", + ) + return await reinforce.rewrite(origin_query=query, nums=1) + + +if __name__ == "__main__": + output = asyncio.run(main()) + print(f"output: \n\n{output}") diff --git a/examples/rag/summary_extractor_example.py b/examples/rag/summary_extractor_example.py new file mode 100644 index 000000000..825c2ce76 --- /dev/null +++ b/examples/rag/summary_extractor_example.py @@ -0,0 +1,47 @@ +import asyncio + +from dbgpt.model import OpenAILLMClient +from dbgpt.rag.chunk_manager import ChunkParameters +from dbgpt.rag.knowledge.factory import KnowledgeFactory +from dbgpt.serve.rag.assembler.summary import SummaryAssembler + +"""Summary extractor example. + pre-requirements: + 1. install openai python sdk + ``` + pip install openai + ``` + 2. set openai key and base + ``` + export OPENAI_API_KEY={your_openai_key} + export OPENAI_API_BASE={your_openai_base} + ``` + or + ``` + import os + os.environ["OPENAI_API_KEY"] = {your_openai_key} + os.environ["OPENAI_API_BASE"] = {your_openai_base} + ``` + Examples: + ..code-block:: shell + python examples/rag/summary_extractor_example.py +""" + + +async def main(): + file_path = "./docs/docs/awel.md" + llm_client = OpenAILLMClient() + knowledge = KnowledgeFactory.from_file_path(file_path) + chunk_parameters = ChunkParameters(chunk_strategy="CHUNK_BY_SIZE") + assembler = SummaryAssembler.load_from_knowledge( + knowledge=knowledge, + chunk_parameters=chunk_parameters, + llm_client=llm_client, + model_name="gpt-3.5-turbo", + ) + return await assembler.generate_summary() + + +if __name__ == "__main__": + output = asyncio.run(main()) + print(f"output: \n\n{output}") diff --git a/web/app/i18n.ts b/web/app/i18n.ts index 1ca891450..d15310b67 100644 --- a/web/app/i18n.ts +++ b/web/app/i18n.ts @@ -10,12 +10,14 @@ const resources = { Owner: 'Owner', Count: 'Count', File_type_Invalid: 'The file type is invalid', - Knowledge_Space_Config: 'Knowledge Space Config', - Choose_a_Datasource_type: 'Choose a Datasource type', - Setup_the_Datasource: 'Setup the Datasource', + Knowledge_Space_Config: 'Space Config', + Choose_a_Datasource_type: 'Datasource type', + Segmentation: 'Segmentation', + No_parameter: `No segementation parameter required.`, Knowledge_Space_Name: 'Knowledge Space Name', Please_input_the_name: 'Please input the name', Please_input_the_owner: 'Please input the owner', + Please_select_file: 'Please select one file', Description: 'Description', Please_input_the_description: 'Please input the description', Next: 'Next', @@ -29,7 +31,7 @@ const resources = { Name: 'Name', Text_Source: 'Text Source(Optional)', Please_input_the_text_source: 'Please input the text source', - Synch: 'Synch', + Sync: 'Sync', Back: 'Back', Finish: 'Finish', Web_Page_URL: 'Web Page URL', @@ -41,7 +43,7 @@ const resources = { Arguments: 'Arguments', Type: 'Type', Size: 'Size', - Last_Synch: 'Last Synch', + Last_Sync: 'Last Sync', Status: 'Status', Result: 'Result', Details: 'Details', @@ -62,6 +64,9 @@ const resources = { Recall_Type: 'recall type', model: 'model', A_model_used: 'A model used to create vector representations of text or other data', + Automatic: 'Automatic', + Process: 'Process', + Automatic_desc: 'Automatically set segmentation and preprocessing rules.', chunk_size: 'chunk_size', The_size_of_the_data_chunks: 'The size of the data chunks used in processing', chunk_overlap: 'chunk_overlap', @@ -187,11 +192,14 @@ const resources = { Count: '文档数', File_type_Invalid: '文件类型错误', Knowledge_Space_Config: '知识库配置', - Choose_a_Datasource_type: '选择数据源类型', - Setup_the_Datasource: '设置数据源', + Choose_a_Datasource_type: '知识库类型', + Segmentation: '分片', + No_parameter: '不需要配置分片参数', + Setup_the_Datasource: '处理', Knowledge_Space_Name: '知识库名称', Please_input_the_name: '请输入名称', Please_input_the_owner: '请输入创建人', + Please_select_file: '请至少选择一个文件', Description: '描述', Please_input_the_description: '请输入描述', Next: '下一步', @@ -205,7 +213,7 @@ const resources = { Name: '名称', Text_Source: '文本来源（可选）', Please_input_the_text_source: '请输入文本来源', - Synch: '同步', + Sync: '同步', Back: '上一步', Finish: '完成', Web_Page_URL: '网页网址', @@ -217,7 +225,7 @@ const resources = { Arguments: '参数', Type: '类型', Size: '切片', - Last_Synch: '上次同步时间', + Last_Sync: '上次同步时间', Status: '状态', Result: '结果', Details: '明细', @@ -239,6 +247,9 @@ const resources = { Recall_Type: '回忆类型', model: '模型', A_model_used: '用于创建文本或其他数据的矢量表示的模型', + Automatic: '自动切片', + Process: '切片处理', + Automatic_desc: '自动设置分割和预处理规则。', chunk_size: '块大小', The_size_of_the_data_chunks: '处理中使用的数据块的大小', chunk_overlap: '块重叠', diff --git a/web/client/api/request.ts b/web/client/api/request.ts index 19a1d1aad..e867f3485 100644 --- a/web/client/api/request.ts +++ b/web/client/api/request.ts @@ -19,8 +19,11 @@ import { DocumentParams, IArguments, IChunkList, + IChunkStrategyResponse, IDocumentResponse, ISpace, + ISyncBatchParameter, + ISyncBatchResponse, } from '@/types/knowledge'; import { UpdatePromptParams, IPrompt, PromptParams } from '@/types/prompt'; @@ -126,8 +129,8 @@ export const saveArguments = (knowledgeName: string, data: ArgumentsParams) => { export const getSpaceList = () => { return POST>('/knowledge/space/list', {}); }; -export const getDocumentList = (knowLedgeName: string, data: Record) => { - return POST, IDocumentResponse>(`/knowledge/${knowLedgeName}/document/list`, data); +export const getDocumentList = (spaceName: string, data: Record>) => { + return POST>, IDocumentResponse>(`/knowledge/${spaceName}/document/list`, data); }; export const addDocument = (knowledgeName: string, data: DocumentParams) => { @@ -138,10 +141,18 @@ export const addSpace = (data: AddKnowledgeParams) => { return POST>(`/knowledge/space/add`, data); }; +export const getChunkStrategies = () => { + return GET>('/knowledge/document/chunkstrategies'); +}; + export const syncDocument = (spaceName: string, data: Record>) => { return POST>, string | null>(`/knowledge/${spaceName}/document/sync`, data); }; +export const syncBatchDocument = (spaceName: string, data: Array) => { + return POST, ISyncBatchResponse>(`/knowledge/${spaceName}/document/sync_batch`, data); +}; + export const uploadDocument = (knowLedgeName: string, data: FormData) => { return POST(`/knowledge/${knowLedgeName}/document/upload`, data); }; diff --git a/web/components/chat/doc-upload.tsx b/web/components/chat/doc-upload.tsx index 555536851..b89af4cb4 100644 --- a/web/components/chat/doc-upload.tsx +++ b/web/components/chat/doc-upload.tsx @@ -1,5 +1,5 @@ import { ChatContext } from '@/app/chat-context'; -import { apiInterceptors, syncDocument, uploadDocument } from '@/client/api'; +import { apiInterceptors, uploadDocument } from '@/client/api'; import useSummary from '@/hooks/use-summary'; import { PaperClipOutlined } from '@ant-design/icons'; @@ -15,12 +15,8 @@ export default function DocUpload(props: IProps) { const { dbParam, setDocId } = useContext(ChatContext); const { onUploadFinish, handleFinish } = props; const summary = useSummary(); - const [loading, setLoading] = useState(false); - const handleSync = async (knowledgeName: string, id: number) => { - await apiInterceptors(syncDocument(knowledgeName, { doc_ids: [id] })); - }; const handleUpload = async (data: any) => { setLoading(true); const formData = new FormData(); @@ -36,7 +32,6 @@ export default function DocUpload(props: IProps) { } setDocId(res[1]); onUploadFinish(); - await handleSync(dbParam || 'default', res?.[1] as number); setLoading(false); handleFinish?.(true); await summary(res[1]); diff --git a/web/components/common/completion-input.tsx b/web/components/common/completion-input.tsx index ebf9ad306..24d929496 100644 --- a/web/components/common/completion-input.tsx +++ b/web/components/common/completion-input.tsx @@ -41,9 +41,9 @@ function CompletionInput({ children, loading, onSubmit, handleFinish, ...props } setDocuments(data?.data!); } - const onUploadFinish = () => { + const onUploadFinish = async () => { uploadCountRef.current += 1; - fetchDocuments(); + await fetchDocuments(); }; return ( diff --git a/web/components/icons/done-icon.tsx b/web/components/icons/done-icon.tsx new file mode 100644 index 000000000..55bb2a2e8 --- /dev/null +++ b/web/components/icons/done-icon.tsx @@ -0,0 +1,21 @@ +export default function DoneIcon() { + return ( + + + + + + ); +} diff --git a/web/components/icons/index.tsx b/web/components/icons/index.tsx index 2ad9f4b68..a1807b2d7 100644 --- a/web/components/icons/index.tsx +++ b/web/components/icons/index.tsx @@ -12,6 +12,10 @@ import SunnySvg from './sunny-svg'; import Knowledge from './knowledge'; import FileDone from './file-done'; import FileSync from './file-sync'; +import PendingIcon from './pending-icon'; +import SyncIcon from './sync-icon'; +import DoneIcon from './done-icon'; +import FileError from './file-error'; export { ColorfulDashboard, @@ -28,4 +32,8 @@ export { Knowledge, FileDone, FileSync, + PendingIcon, + SyncIcon, + DoneIcon, + FileError, }; diff --git a/web/components/icons/pending-icon.tsx b/web/components/icons/pending-icon.tsx new file mode 100644 index 000000000..b76655e74 --- /dev/null +++ b/web/components/icons/pending-icon.tsx @@ -0,0 +1,13 @@ +export default function PendingIcon() { + return ( + + + + + + ); +} diff --git a/web/components/icons/sync-icon.tsx b/web/components/icons/sync-icon.tsx new file mode 100644 index 000000000..b7dcbcabd --- /dev/null +++ b/web/components/icons/sync-icon.tsx @@ -0,0 +1,11 @@ +export default function SyncIcon() { + return ( + + + + ); +} diff --git a/web/components/knowledge/doc-panel.tsx b/web/components/knowledge/doc-panel.tsx index 950219454..6344611ff 100644 --- a/web/components/knowledge/doc-panel.tsx +++ b/web/components/knowledge/doc-panel.tsx @@ -56,7 +56,7 @@ export default function DocPanel(props: IProps) { }), ); setDocuments(data?.data); - setTotal(data?.total); + setTotal(data?.total || 0); setIsLoading(false); } @@ -175,7 +175,7 @@ export default function DocPanel(props: IProps) { >

{t('Size')}:

{document.chunk_size} chunks

-

{t('Last_Synch')}:

+

{t('Last_Sync')}:

{moment(document.last_sync).format('YYYY-MM-DD HH:MM:SS')}

{renderResultTag(document.status, document.result)}

diff --git a/web/components/knowledge/doc-type-form.tsx b/web/components/knowledge/doc-type-form.tsx index ef9782896..5eff13873 100644 --- a/web/components/knowledge/doc-type-form.tsx +++ b/web/components/knowledge/doc-type-form.tsx @@ -12,19 +12,19 @@ export default function DocTypeForm(props: IProps) { const { handleStepChange } = props; const docTypeList = [ { - type: 'text', + type: 'TEXT', title: t('Text'), subTitle: t('Fill your raw text'), iconType: 'TEXT', }, { - type: 'webPage', + type: 'URL', title: t('URL'), subTitle: t('Fetch_the_content_of_a_URL'), iconType: 'WEBPAGE', }, { - type: 'file', + type: 'DOCUMENT', title: t('Document'), subTitle: t('Upload_a_document'), iconType: 'DOCUMENT', diff --git a/web/components/knowledge/doc-upload-form.tsx b/web/components/knowledge/doc-upload-form.tsx index 5024bb70d..81ec91978 100644 --- a/web/components/knowledge/doc-upload-form.tsx +++ b/web/components/knowledge/doc-upload-form.tsx @@ -1,10 +1,12 @@ -import { Button, Form, Input, Switch, Upload, message, Spin } from 'antd'; +import { Button, Form, Input, Upload, Spin, message } from 'antd'; import React, { useState } from 'react'; import { useTranslation } from 'react-i18next'; import { InboxOutlined } from '@ant-design/icons'; -import { apiInterceptors, addDocument, uploadDocument, syncDocument } from '@/client/api'; +import { apiInterceptors, addDocument, uploadDocument } from '@/client/api'; import { RcFile, UploadChangeParam } from 'antd/es/upload'; -import { StepChangeParams } from '@/types/knowledge'; +import { File, StepChangeParams } from '@/types/knowledge'; +import { UploadRequestOption as RcCustomRequestOptions } from 'rc-upload/lib/interface'; +import classNames from 'classnames'; type FileParams = { file: RcFile; @@ -12,13 +14,13 @@ type FileParams = { }; type IProps = { + className: string; handleStepChange: (params: StepChangeParams) => void; spaceName: string; docType: string; }; type FieldType = { - synchChecked: boolean; docName: string; textSource: string; originFileObj: FileParams; @@ -30,18 +32,19 @@ const { Dragger } = Upload; const { TextArea } = Input; export default function DocUploadForm(props: IProps) { - const { handleStepChange, spaceName, docType } = props; + const { className, handleStepChange, spaceName, docType } = props; const { t } = useTranslation(); const [form] = Form.useForm(); const [spinning, setSpinning] = useState(false); + const [files, setFiles] = useState>([]); - const handleFinish = async (data: FieldType) => { - const { synchChecked, docName, textSource, originFileObj, text, webPageUrl } = data; - let res; + const upload = async (data: FieldType) => { + const { docName, textSource, text, webPageUrl } = data; + let docId; setSpinning(true); switch (docType) { - case 'webPage': - res = await apiInterceptors( + case 'URL': + [, docId] = await apiInterceptors( addDocument(spaceName as string, { doc_name: docName, content: webPageUrl, @@ -49,16 +52,8 @@ export default function DocUploadForm(props: IProps) { }), ); break; - case 'file': - const formData = new FormData(); - formData.append('doc_name', docName || originFileObj.file.name); - formData.append('doc_file', originFileObj.file); - formData.append('doc_type', 'DOCUMENT'); - - res = await apiInterceptors(uploadDocument(spaceName as string, formData)); - break; - default: - res = await apiInterceptors( + case 'TEXT': + [, docId] = await apiInterceptors( addDocument(spaceName as string, { doc_name: docName, source: textSource, @@ -68,38 +63,60 @@ export default function DocUploadForm(props: IProps) { ); break; } - synchChecked && handleSync?.(spaceName as string, res?.[1] as number); setSpinning(false); - handleStepChange({ label: 'finish' }); - }; - - const handleSync = async (knowledgeName: string, id: number) => { - await apiInterceptors(syncDocument(knowledgeName, { doc_ids: [id] })); + if (docType === 'DOCUMENT' && files.length < 1) { + return message.error('Upload failed, please re-upload.'); + } else if (docType !== 'DOCUMENT' && !docId) { + return message.error('Upload failed, please re-upload.'); + } + handleStepChange({ + label: 'forward', + files: + docType === 'DOCUMENT' + ? files + : [ + { + name: docName, + doc_id: docId || -1, + }, + ], + }); }; const handleFileChange = ({ file, fileList }: UploadChangeParam) => { - if (!form.getFieldsValue().docName) { - form.setFieldValue('docName', file.name); - } if (fileList.length === 0) { form.setFieldValue('originFileObj', null); } }; - const beforeUpload = () => { - const curFile = form.getFieldsValue().originFileObj; - if (!curFile) { - return false; + const uploadFile = async (options: RcCustomRequestOptions) => { + const { onSuccess, onError, file } = options; + const formData = new FormData(); + const filename = file?.name; + formData.append('doc_name', filename); + formData.append('doc_file', file); + formData.append('doc_type', 'DOCUMENT'); + const [, docId] = await apiInterceptors(uploadDocument(spaceName, formData)); + if (Number.isInteger(docId)) { + onSuccess && onSuccess(docId || 0); + setFiles((files) => { + files.push({ + name: filename, + doc_id: docId || -1, + }); + return files; + }); + } else { + onError && onError({ name: '', message: '' }); } - message.warning(t('Limit_Upload_File_Count_Tips')); - return Upload.LIST_IGNORE; }; - const renderChooseType = () => {}; - const renderText = () => { return ( <> + label={`${t('Name')}:`} name="docName" rules={[{ required: true, message: t('Please_input_the_name') }]}> + + label={`${t('Text_Source')}:`} name="textSource" @@ -107,7 +124,6 @@ export default function DocUploadForm(props: IProps) { > - label={`${t('Text')}:`} name="text" rules={[{ required: true, message: t('Please_input_the_description') }]}>