Compage gpt

deploy/build-docker-images.sh

@@ -5,8 +5,11 @@ TAG_NAME="v2"
 CORE_IMAGE="ghcr.io/intelops/compage/core:$TAG_NAME"
 APP_IMAGE="ghcr.io/intelops/compage/app:$TAG_NAME"
 UI_IMAGE="ghcr.io/intelops/compage/ui:$TAG_NAME"
+LLM_BACKEND_IMAGE="ghcr.io/intelops/compage/llm_backend:$TAG_NAME"
 
 # create docker images for core, app and ui
 docker build -t $CORE_IMAGE  --network host ../core/
 docker build -t $APP_IMAGE  --network host ../app/
 docker build -t $UI_IMAGE  --network host ../ui/
+ocker build -t $LLM_BACKEND_IMAGE  --network host ../llm_backend/
+

deploy/push-docker-images-to-github.sh

@@ -5,4 +5,5 @@ source build-docker-images.sh
 # push docker images for core, app and ui
 docker push $CORE_IMAGE
 docker push $APP_IMAGE
-docker push $UI_IMAGE
+docker push $UI_IMAGE
+docker push $LLM_BACKEND_IMAGE

llm_backend/Dockerfile

@@ -0,0 +1,11 @@
+FROM python:3.11
+
+RUN mkdir -p /app
+
+WORKDIR /app
+COPY . .
+
+RUN pip install -r requirements.txt
+EXPOSE 8000
+# ENTRYPOINT [ "python", "generate_code.py" ]
+CMD ["uvicorn", "generate_code:app", "--host", "0.0.0.0", "--port", "8000"]

llm_backend/README.md

@@ -0,0 +1,40 @@
+## Requirements
+
+To successfully run the program, make sure you have all the necessary dependencies installed. These dependencies are listed in the `requirements.txt` file. Before executing the program, follow these steps:
+
+## Setting Up the Environment
+
+1. Create a Python virtual environment using either pip or conda. You should use Python version 3.11.4.
+
+2. Activate the newly created virtual environment. This step ensures that the required packages are isolated from your system-wide Python installation.
+
+## Installing Dependencies
+
+3. Install the required dependencies by running the following command in your terminal:
+
+<pre>
+<code >pip install -r requirements.txt</code>
+</pre>
+
+This command will read the `requirements.txt` file and install all the necessary packages into your virtual environment.
+
+## Running the Code
+
+4. Once the dependencies are installed, you can run the program using the following command:
+
+<pre>
+<code>uvicorn generate_code:app --reload</code>
+</pre>
+
+This command starts the Uvicorn server and launches the application. The `--reload` flag enables auto-reloading, which is useful during development.
+
+
+
+
+
+
+
+
+
+
+

llm_backend/generate_code.py

@@ -0,0 +1,142 @@
+# Import libraries
+import os 
+import sys
+import openai
+import json
+import langchain.agents as lc_agents
+import uvicorn
+import pydantic
+
+
+# Import custom modules
+from datetime import datetime
+from dotenv import load_dotenv
+from langchain.llms import OpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import SequentialChain, SimpleSequentialChain, LLMChain
+from langchain.memory import ConversationBufferMemory
+from langchain.llms import OpenAI as lang_open_ai
+from pydantic import BaseModel
+from fastapi import FastAPI, Header
+from fastapi.middleware.cors import CORSMiddleware
+
+
+
+
+
+
+class Item(pydantic.BaseModel):
+    language: str
+    topic: str
+
+
+app = FastAPI()
+origins = ["*"]
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+
+
+@app.get("/ping")
+def ping():
+    return {"message": "Hello World"}
+
+@app.post("/llm_generate_code/")
+async def generate_code(item:Item,apikey: str = Header(None) ):
+    global api_key
+
+    api_key = apikey
+    # Check if the API key has been saved in the memory
+    if not api_key:
+        api_key = api_key
+
+    else:
+        # The API key has already been saved, so don't re-assign it
+        pass
+
+
+    os.environ["OPENAI_API_KEY"] =  api_key  #item.apikey
+
+    code_language = item.language
+    code_topic = item.topic 
+
+    # prompt template for the code generation
+    code_template = PromptTemplate(
+    input_variables=['lang', 'top'],
+    template='Write the code in ' +
+    ' {lang} language' + ' for {top}'\
+    + ' with proper inline comments and maintaining \
+        markdown format of {lang}'
+    )
+
+    code_explain_template = PromptTemplate(
+    input_variables=['top'],
+    template='Explain in detail the working of the generated code and algorithm ' +
+    ' for {top}' + ' in proper markdown format'
+    )
+    code_flow_template = PromptTemplate(
+    input_variables=['top'],
+    template='Generate the diagram flow  ' +
+    ' for {top} in proper markdown format'
+    )
+
+    code_testcase_template = PromptTemplate(
+    input_variables= ['lang', 'top'],
+    template='Generate the unit test cases and codes ' +
+    'and integration test cases with codes  ' +
+    'in  {lang}' + ' for {top} in proper  markdown formats'
+    )
+
+    # use memory for the conversation
+    code_memory = ConversationBufferMemory(
+        input_key='top', memory_key='chat_history')
+    explain_memory = ConversationBufferMemory(
+        input_key='top', memory_key='chat_history')
+    flow_memory = ConversationBufferMemory(
+        input_key='top', memory_key='chat_history')
+    testcase_memory = ConversationBufferMemory(
+        input_key='top', memory_key='chat_history')
+
+    # create the  OpenAI LLM model
+    open_ai_llm = OpenAI( temperature=0.7, max_tokens=1000)
+
+    # create a chain to generate the code
+    code_chain = LLMChain(llm=open_ai_llm, prompt=code_template,
+                      output_key='code', memory=code_memory, verbose=True)
+    #  create another chain to explain the code 
+    code_explain_chain = LLMChain(llm=open_ai_llm, prompt=code_explain_template,
+                          output_key='code_explain', memory=explain_memory, verbose=True)
+
+
+
+    #  create another chain to generate the code flow if needed
+    code_flow_chain = LLMChain(llm=open_ai_llm, prompt=code_flow_template,
+                          output_key='code_flow', memory=flow_memory, verbose=True)
+
+    #  create another chain to generate the code flow if needed
+    code_testcase_chain = LLMChain(llm=open_ai_llm, prompt=code_testcase_template,
+                          output_key='code_unittest', memory=testcase_memory, verbose=True)
+
+    # create a sequential chain to combine both chains
+    sequential_chain = SequentialChain(chains=[code_chain, code_explain_chain, code_flow_chain,\
+                                           code_testcase_chain], input_variables=
+                                   ['lang', 'top'], output_variables=['code', 'code_explain','code_flow', 'code_unittest'])
+
+
+    response = sequential_chain({'lang': code_language, 'top': code_topic})
+
+
+    return  {'code': response['code'], 'code_explain': response['code_explain'],\
+             'code_flow': response['code_flow'], 'code_unittest': response['code_unittest']}
+
+
+
+
+
+

llm_backend/requirements.txt

@@ -0,0 +1,125 @@
+aiohttp==3.8.4
+aiosignal==1.3.1
+altair==4.2.2
+anyio==3.7.1
+async-timeout==4.0.2
+attrs==23.1.0
+backoff==2.2.1
+beautifulsoup4==4.12.2
+blinker==1.6.2
+cachetools==5.3.1
+certifi==2023.5.7
+charset-normalizer==3.2.0
+chromadb==0.3.23
+click==8.1.5
+clickhouse-connect==0.6.6
+cmake==3.26.4
+dataclasses-json==0.5.9
+decorator==5.1.1
+duckdb==0.8.1
+entrypoints==0.4
+fastapi==0.100.0
+filelock==3.12.2
+frozenlist==1.4.0
+fsspec==2023.6.0
+gitdb==4.0.10
+GitPython==3.1.32
+greenlet==2.0.2
+h11==0.14.0
+hnswlib==0.7.0
+httptools==0.6.0
+huggingface-hub==0.16.4
+idna==3.4
+importlib-metadata==6.8.0
+Jinja2==3.1.2
+joblib==1.3.1
+jsonschema==4.18.3
+jsonschema-specifications==2023.6.1
+langchain==0.0.174
+lit==16.0.6
+lz4==4.3.2
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+marshmallow==3.19.0
+marshmallow-enum==1.5.1
+mdurl==0.1.2
+monotonic==1.6
+mpmath==1.3.0
+multidict==6.0.4
+mypy-extensions==1.0.0
+networkx==3.1
+nltk==3.8.1
+numexpr==2.8.4
+numpy==1.25.1
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-cupti-cu11==11.7.101
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.2.10.91
+nvidia-cusolver-cu11==11.4.0.1
+nvidia-cusparse-cu11==11.7.4.91
+nvidia-nccl-cu11==2.14.3
+nvidia-nvtx-cu11==11.7.91
+openai==0.27.2
+openapi-schema-pydantic==1.2.4
+packaging==23.1
+pandas==2.0.3
+Pillow==10.0.0
+posthog==3.0.1
+protobuf==3.20.3
+pyarrow==12.0.1
+pydantic==1.10.11
+pydeck==0.8.1b0
+Pygments==2.15.1
+Pympler==1.0.1
+python-dateutil==2.8.2
+python-dotenv==1.0.0
+pytz==2023.3
+PyYAML==6.0
+referencing==0.29.1
+regex==2023.6.3
+requests==2.31.0
+rich==13.4.2
+rpds-py==0.8.10
+safetensors==0.3.1
+scikit-learn==1.3.0
+scipy==1.11.1
+sentence-transformers==2.2.2
+sentencepiece==0.1.99
+six==1.16.0
+smmap==5.0.0
+sniffio==1.3.0
+soupsieve==2.4.1
+SQLAlchemy==2.0.18
+starlette==0.27.0
+streamlit==1.22.0
+sympy==1.12
+tenacity==8.2.2
+threadpoolctl==3.2.0
+tiktoken==0.3.3
+tokenizers==0.13.3
+toml==0.10.2
+toolz==0.12.0
+torch==2.0.1
+torchvision==0.15.2
+tornado==6.3.2
+tqdm==4.65.0
+transformers==4.30.2
+triton==2.0.0
+typing-inspect==0.9.0
+typing_extensions==4.7.1
+tzdata==2023.3
+tzlocal==5.0.1
+urllib3==2.0.3
+uvicorn==0.22.0
+uvloop==0.17.0
+validators==0.20.0
+watchdog==3.0.0
+watchfiles==0.19.0
+websockets==11.0.3
+wikipedia==1.4.0
+yarl==1.9.2
+zipp==3.16.1
+zstandard==0.21.0