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https://github.com/kennethreitz/langchain.git
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mv module integrations docs (#8101)
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Bagatur
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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": "683953b3",
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"metadata": {},
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"source": [
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"# Pinecone\n",
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"\n",
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">[Pinecone](https://docs.pinecone.io/docs/overview) is a vector database with broad functionality.\n",
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"\n",
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"This notebook shows how to use functionality related to the `Pinecone` vector database.\n",
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"\n",
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"To use Pinecone, you must have an API key. \n",
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"Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart)."
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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": "b4c41cad-08ef-4f72-a545-2151e4598efe",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install pinecone-client openai tiktoken"
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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": "c1e38361-c1fe-4ac6-86e9-c90ebaf7ae87",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import getpass\n",
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"\n",
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"PINECONE_API_KEY = getpass.getpass(\"Pinecone API Key:\")"
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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": "02a536e0-d603-4d79-b18b-1ed562977b40",
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"metadata": {},
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"outputs": [],
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"source": [
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"PINECONE_ENV = getpass.getpass(\"Pinecone Environment:\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "320af802-9271-46ee-948f-d2453933d44b",
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"metadata": {},
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"source": [
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"We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
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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": "ffea66e4-bc23-46a9-9580-b348dfe7b7a7",
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"metadata": {},
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"outputs": [],
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"source": [
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
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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": "aac9563e",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import Pinecone\n",
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"from langchain.document_loaders import TextLoader"
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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": "a3c3999a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.document_loaders import TextLoader\n",
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"\n",
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"loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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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": "6e104aee",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pinecone\n",
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"\n",
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"# initialize pinecone\n",
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"pinecone.init(\n",
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" api_key=PINECONE_API_KEY, # find at app.pinecone.io\n",
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" environment=PINECONE_ENV, # next to api key in console\n",
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")\n",
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"\n",
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"index_name = \"langchain-demo\"\n",
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"\n",
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"docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)\n",
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"\n",
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"# if you already have an index, you can load it like this\n",
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"# docsearch = Pinecone.from_existing_index(index_name, embeddings)\n",
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"\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = docsearch.similarity_search(query)"
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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": "9c608226",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(docs[0].page_content)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "86a4b96b",
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"metadata": {},
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"source": [
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"### Adding More Text to an Existing Index\n",
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"\n",
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"More text can embedded and upserted to an existing Pinecone index using the `add_texts` function\n"
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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": "38a7a60e",
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"metadata": {},
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"outputs": [],
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"source": [
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"index = pinecone.Index(\"langchain-demo\")\n",
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"vectorstore = Pinecone(index, embeddings.embed_query, \"text\")\n",
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"\n",
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"vectorstore.add_texts(\"More text!\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "d46d1452",
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"metadata": {},
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"source": [
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"### Maximal Marginal Relevance Searches\n",
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"\n",
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"In addition to using similarity search in the retriever object, you can also use `mmr` as retriever.\n"
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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": "a359ed74",
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"metadata": {},
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"outputs": [],
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"source": [
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"retriever = docsearch.as_retriever(search_type=\"mmr\")\n",
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"matched_docs = retriever.get_relevant_documents(query)\n",
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"for i, d in enumerate(matched_docs):\n",
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" print(f\"\\n## Document {i}\\n\")\n",
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" print(d.page_content)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "7c477287",
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"metadata": {},
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"source": [
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"Or use `max_marginal_relevance_search` directly:"
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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": "9ca82740",
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"metadata": {},
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"outputs": [],
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"source": [
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"found_docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10)\n",
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"for i, doc in enumerate(found_docs):\n",
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" print(f\"{i + 1}.\", doc.page_content, \"\\n\")"
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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": "3.10.6"
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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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