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Leonid Ganeline b96ab4b763 docs retriever improvements (#4430 )

# 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

2023-05-17 15:29:22 -07:00

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Pinecone

Pinecone is a vector database with broad functionality.

This notebook shows how to use functionality related to the Pinecone vector database.

To use Pinecone, you must have an API key. Here are the installation instructions.

In [ ]:

!pip install pinecone-client

In [ ]:

import os
import getpass

PINECONE_API_KEY = getpass.getpass('Pinecone API Key:')

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PINECONE_ENV = getpass.getpass('Pinecone Environment:')

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.

In [ ]:

os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')

In [2]:

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Pinecone
from langchain.document_loaders import TextLoader

In [2]:

from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

In [ ]:

import pinecone 

# initialize pinecone
pinecone.init(
    api_key=PINECONE_API_KEY,  # find at app.pinecone.io
    environment=PINECONE_ENV  # next to api key in console
)

index_name = "langchain-demo"

docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)

# if you already have an index, you can load it like this
# docsearch = Pinecone.from_existing_index(index_name, embeddings)

query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)

In [ ]:

print(docs[0].page_content)

In [ ]:

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Pinecone

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