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b96ab4b763
# Docs: improvements in the `retrievers/examples/` notebooks Its primary purpose is to make the Jupyter notebook examples **consistent** and more suitable for first-time viewers. - add links to the integration source (if applicable) with a short description of this source; - removed `_retriever` suffix from the file names (where it existed) for consistency; - removed ` retriever` from the notebook title (where it existed) for consistency; - added code to install necessary Python package(s); - added code to set up the necessary API Key. - very small fixes in notebooks from other folders (for consistency): - docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb - docs/modules/indexes/vectorstores/examples/pinecone.ipynb - docs/modules/models/llms/integrations/cohere.ipynb - fixed misspelling in langchain/retrievers/time_weighted_retriever.py comment (sorry, about this change in a .py file ) ## Who can review @dev2049
169 lines
4.0 KiB
Plaintext
169 lines
4.0 KiB
Plaintext
{
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"cells": [
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{
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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"
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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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"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": 2,
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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": 2,
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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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"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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"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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}
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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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