langchain-pinecone

This package contains the LangChain integration with Pinecone.

Installation

pip install -qU langchain langchain-pinecone langchain-openai

And you should configure credentials by setting the following environment variables:

• PINECONE_API_KEY
• OPENAI_API_KEY (optional, for embeddings to use)

Development

Running Tests

The test suite includes both unit tests and integration tests. To run the tests:

# Run unit tests only
make test

# Run integration tests (requires environment variables)
make integration_test

Required Environment Variables for Tests

Integration tests require the following environment variables:

• PINECONE_API_KEY: Required for all integration tests
• OPENAI_API_KEY: Optional, required only for OpenAI embedding tests

You can set these environment variables before running the tests:

export PINECONE_API_KEY="your-api-key"
export OPENAI_API_KEY="your-openai-key"  # Optional

If these environment variables are not set, the integration tests that require them will be skipped.

Usage

Initialization

Before initializing our vector store, let's connect to a Pinecone index. If one named index_name doesn't exist, it will be created.

from pinecone import ServerlessSpec

index_name = "langchain-test-index"  # change if desired

if not pc.has_index(index_name):
    pc.create_index(
        name=index_name,
        dimension=1536,
        metric="cosine",
        spec=ServerlessSpec(
            cloud='aws',
            region='us-east-1'
        )
    )

index = pc.Index(index_name)

Initialize embedding model:

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")

The PineconeVectorStore class exposes the connection to the Pinecone vector store.

from langchain_pinecone import PineconeVectorStore

vector_store = PineconeVectorStore(index=index, embedding=embeddings)

Manage vector store

Once you have created your vector store, we can interact with it by adding and deleting different items.

Add items to vector store

We can add items to our vector store by using the add_documents function.

from uuid import uuid4

from langchain_core.documents import Document

document_1 = Document(
    page_content="I had chocalate chip pancakes and scrambled eggs for breakfast this morning.",
    metadata={"source": "tweet"},
)

document_2 = Document(
    page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
    metadata={"source": "news"},
)

document_3 = Document(
    page_content="Building an exciting new project with LangChain - come check it out!",
    metadata={"source": "tweet"},
)

document_4 = Document(
    page_content="Robbers broke into the city bank and stole $1 million in cash.",
    metadata={"source": "news"},
)

document_5 = Document(
    page_content="Wow! That was an amazing movie. I can't wait to see it again.",
    metadata={"source": "tweet"},
)

document_6 = Document(
    page_content="Is the new iPhone worth the price? Read this review to find out.",
    metadata={"source": "website"},
)

document_7 = Document(
    page_content="The top 10 soccer players in the world right now.",
    metadata={"source": "website"},
)

document_8 = Document(
    page_content="LangGraph is the best framework for building stateful, agentic applications!",
    metadata={"source": "tweet"},
)

document_9 = Document(
    page_content="The stock market is down 500 points today due to fears of a recession.",
    metadata={"source": "news"},
)

document_10 = Document(
    page_content="I have a bad feeling I am going to get deleted :(",
    metadata={"source": "tweet"},
)

documents = [
    document_1,
    document_2,
    document_3,
    document_4,
    document_5,
    document_6,
    document_7,
    document_8,
    document_9,
    document_10,
]
uuids = [str(uuid4()) for _ in range(len(documents))]
vector_store.add_documents(documents=documents, ids=uuids)

Delete items from vector store

vector_store.delete(ids=[uuids[-1]])

Query vector store

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

Query directly

Performing a simple similarity search can be done as follows:

results = vector_store.similarity_search(
    "LangChain provides abstractions to make working with LLMs easy",
    k=2,
    filter={"source": "tweet"},
)
for res in results:
    print(f"* {res.page_content} [{res.metadata}]")

Similarity search with score

You can also search with score:

results = vector_store.similarity_search_with_score(
    "Will it be hot tomorrow?", k=1, filter={"source": "news"}
)
for res, score in results:
    print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")

Query by turning into retriever

You can also transform the vector store into a retriever for easier usage in your chains.

retriever = vector_store.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={"k": 1, "score_threshold": 0.4},
)
retriever.invoke("Stealing from the bank is a crime", filter={"source": "news"})