mirror of https://github.com/hwchase17/langchain
Add template for Pinecone + Multi-Query (#12353)
Lance Martin
7 months ago
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GitHub
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MIT License
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Copyright (c) 2023 LangChain, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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# RAG Pinecone multi query
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This template performs RAG using Pinecone and OpenAI with the [multi-query retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever).
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This will use an LLM to generate multiple queries from different perspectives for a given user input query.
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For each query, it retrieves a set of relevant documents and takes the unique union across all queries for answer synthesis.
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## Pinecone
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This template uses Pinecone as a vectorstore and requires that `PINECONE_API_KEY`, `PINECONE_ENVIRONMENT`, and `PINECONE_INDEX` are set.
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## LLM
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Be sure that `OPENAI_API_KEY` is set in order to the OpenAI models.
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[tool.poetry]
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name = "rag-pinecone-multi-query"
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version = "0.1.0"
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description = ""
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authors = ["Lance Martin <lance@langchain.dev>"]
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readme = "README.md"
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[tool.poetry.dependencies]
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python = ">=3.8.1,<4.0"
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langchain = ">=0.0.313, <0.1"
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openai = ">=0.28.1"
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tiktoken = ">=0.5.1"
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pinecone-client = ">=2.2.4"
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[tool.langserve]
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export_module = "rag_pinecone_multi_query"
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export_attr = "chain"
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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "681a5d1e",
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"metadata": {},
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"source": [
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"## Connect to template"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d774be2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langserve.client import RemoteRunnable\n",
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"rag_app_pinecone = RemoteRunnable('http://localhost:8000/rag-pinecone-multi-query')\n",
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"rag_app_pinecone.invoke(\"What are the different types of agent memory\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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from rag_pinecone_multi_query.chain import chain
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__all__ = ["chain"]
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import os
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import pinecone
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from operator import itemgetter
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from langchain.vectorstores import Pinecone
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from langchain.prompts import ChatPromptTemplate
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.schema.output_parser import StrOutputParser
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from langchain.retrievers.multi_query import MultiQueryRetriever
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from langchain.schema.runnable import RunnablePassthrough, RunnableParallel
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if os.environ.get("PINECONE_API_KEY", None) is None:
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raise Exception("Missing `PINECONE_API_KEY` environment variable.")
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if os.environ.get("PINECONE_ENVIRONMENT", None) is None:
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raise Exception("Missing `PINECONE_ENVIRONMENT` environment variable.")
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PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test")
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### Ingest code - you may need to run this the first time
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# Load
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# from langchain.document_loaders import WebBaseLoader
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# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
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# data = loader.load()
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# # Split
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# from langchain.text_splitter import RecursiveCharacterTextSplitter
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# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
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# all_splits = text_splitter.split_documents(data)
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# # Add to vectorDB
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# vectorstore = Pinecone.from_documents(
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# documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME
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# )
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# retriever = vectorstore.as_retriever()
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# Set up index with multi query retriever
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vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, OpenAIEmbeddings())
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model = ChatOpenAI(temperature=0)
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retriever = MultiQueryRetriever.from_llm(
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retriever=vectorstore.as_retriever(), llm=model
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)
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# RAG prompt
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template = """Answer the question based only on the following context:
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{context}
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Question: {question}
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"""
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prompt = ChatPromptTemplate.from_template(template)
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# RAG
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model = ChatOpenAI()
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chain = (
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RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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| prompt
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| model
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| StrOutputParser()
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)
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "681a5d1e",
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"metadata": {},
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"source": [
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"## Connect to template\n",
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"\n",
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"`Context`\n",
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" \n",
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"* LangServe apps gives you access to templates.\n",
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"* Templates LLM pipeline (runnables or chains) end-points accessible via FastAPI.\n",
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"* The environment for these templates is managed by Poetry.\n",
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"\n",
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"`Create app`\n",
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"\n",
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"* Install LangServe and create an app.\n",
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"* This will create a new Poetry environment /\n",
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"```\n",
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"pip install < to add > \n",
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"langchain serve new my-app\n",
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"cd my-app\n",
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"```\n",
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"\n",
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"`Add templates`\n",
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"\n",
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"* When we add a template, we update the Poetry config file with the necessary dependencies.\n",
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"* It also automatically installed these template dependencies in your Poetry environment\n",
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"```\n",
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"langchain serve add rag-pinecone\n",
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"```\n",
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"\n",
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"`Start FastAPI server`\n",
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"\n",
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"```\n",
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"langchain start\n",
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"```\n",
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"\n",
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"Note, we can now look at the endpoints:\n",
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"\n",
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"http://127.0.0.1:8000/docs#\n",
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"\n",
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"And look specifically at our loaded template:\n",
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"\n",
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"http://127.0.0.1:8000/docs#/default/invoke_rag_pinecone_invoke_post\n",
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" \n",
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"We can also use remote runnable to call it:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d774be2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langserve.client import RemoteRunnable\n",
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"rag_app_pinecone = RemoteRunnable('http://localhost:8000/rag-pinecone')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,45 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "681a5d1e",
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"metadata": {},
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"source": [
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"## Connect to template"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d774be2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langserve.client import RemoteRunnable\n",
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"rag_app_pinecone = RemoteRunnable('http://localhost:8000/rag-pinecone')\n",
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"rag_app_pinecone.invoke(\"How does agent memory work?\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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