diff --git a/tutorials/mistral/evaluate_rag--mistral.ipynb b/tutorials/mistral/evaluate_rag--mistral.ipynb index 6eb33ec0716..0a5a34a7d9b 100644 --- a/tutorials/mistral/evaluate_rag--mistral.ipynb +++ b/tutorials/mistral/evaluate_rag--mistral.ipynb @@ -77,7 +77,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -qq \"arize-phoenix\" \"arize-phoenix-evals>=0.5.0\" \"llama-index==0.10.19\" \"llama-index-llms-mistralai\" \"llama-index-embeddings-mistralai\" \"openinference-instrumentation-mistralai\" \"openinference-instrumentation-llama-index>=1.0.0\" \"llama-index-callbacks-arize-phoenix>=0.1.2\" gcsfs nest_asyncio" + "!pip install -qq \"arize-phoenix\" \"arize-phoenix-evals>=0.5.0\" \"llama-index==0.10.19\" \"llama-index-llms-mistralai\" \"llama-index-embeddings-mistralai\" \"openinference-instrumentation-mistralai>=0.0.2\" \"openinference-instrumentation-llama-index>=1.0.0\" \"llama-index-callbacks-arize-phoenix>=0.1.2\" gcsfs nest_asyncio" ] }, { @@ -130,6 +130,48 @@ "nest_asyncio.apply()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import phoenix as px\n", + "from llama_index.core import set_global_handler\n", + "\n", + "session = px.launch_app()\n", + "set_global_handler(\"arize_phoenix\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's use the OpenInference Mistral Instrumentation as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from openinference.instrumentation.mistralai import MistralAIInstrumentor\n", + "from opentelemetry import trace as trace_api\n", + "from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\n", + "from opentelemetry.sdk import trace as trace_sdk\n", + "from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor\n", + "\n", + "tracer_provider = trace_sdk.TracerProvider()\n", + "tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(\"http://127.0.0.1:6006/v1/traces\")))\n", + "# Optionally, you can also print the spans to the console.\n", + "tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))\n", + "trace_api.set_tracer_provider(tracer_provider)\n", + "\n", + "mistral_instrumentor = MistralAIInstrumentor()\n", + "mistral_instrumentor.instrument()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -138,7 +180,7 @@ "source": [ "import pandas as pd\n", "import phoenix as px\n", - "from llama_index.core import Settings, SimpleDirectoryReader, VectorStoreIndex, set_global_handler\n", + "from llama_index.core import Settings, SimpleDirectoryReader, VectorStoreIndex\n", "from llama_index.core.node_parser import SimpleNodeParser\n", "from llama_index.embeddings.mistralai import MistralAIEmbedding\n", "from llama_index.llms.mistralai import MistralAI\n", @@ -440,7 +482,6 @@ "source": [ "import json\n", "\n", - "from openinference.instrumentation.mistralai import MistralAIInstrumentor\n", "from phoenix.evals import MistralAIModel, llm_generate\n", "\n", "\n", @@ -452,16 +493,15 @@ "\n", "\n", "with using_project(TESTSET_PROJECT): # Collect traces under the project \"testset\"\n", - " mistral_instrumentor = MistralAIInstrumentor()\n", - " mistral_instrumentor.instrument() # Instrument MistralAI to see the synthetic testset generation\n", " questions_df = llm_generate(\n", " dataframe=document_chunks_df,\n", " template=generate_questions_template,\n", - " model=MistralAIModel(model=\"mistral-large-latest\", response_format={\"type\": \"json_object\"}),\n", + " model=MistralAIModel(model=\"mistral-large-latest\"),\n", " output_parser=output_parser,\n", " concurrency=20,\n", " )\n", - " mistral_instrumentor.uninstrument() # No longer needed" + "\n", + "mistral_instrumentor.uninstrument() # No longer needed" ] }, { @@ -1057,11 +1097,11 @@ " )\n", ")\n", "# relaunch phoenix with a primary and corpus dataset to view embeddings\n", - "px.close_app()\n", - "session = px.launch_app(\n", - " primary=px.Dataset(query_df, query_schema, \"query\"),\n", - " corpus=px.Dataset(corpus_df.reset_index(drop=True), corpus_schema, \"corpus\"),\n", - ")" + "# px.close_app()\n", + "# session = px.launch_app(\n", + "# primary=px.Dataset(query_df, query_schema, \"query\"),\n", + "# corpus=px.Dataset(corpus_df.reset_index(drop=True), corpus_schema, \"corpus\"),\n", + "# )" ] }, {