PineconeSparseVectorStore

Setup:

# Install required packages
pip install langchain-pinecone pinecone-client

Key init args - indexing params:

text_key (str): The metadata key where the document text will be stored. namespace (str): Pinecone namespace to use. distance_strategy (DistanceStrategy): Strategy for computing distances.

Key init args - client params:

index (pinecone.Index): A Pinecone sparse index. embedding (PineconeSparseEmbeddings): A sparse embeddings model. pinecone_api_key (str): The Pinecone API key. index_name (str): The name of the Pinecone index.

See full list of supported init args and their descriptions in the params section.

Instantiate:

from pinecone import Pinecone
from langchain_pinecone import PineconeSparseVectorStore
from langchain_pinecone.embeddings import PineconeSparseEmbeddings

# Initialize Pinecone client
pc = Pinecone(api_key="your-api-key")

# Get your sparse index
index = pc.Index("your-sparse-index-name")

# Initialize embedding function
embeddings = PineconeSparseEmbeddings()

# Create vector store
vectorstore = PineconeSparseVectorStore(
    index=index,
    embedding=embeddings,
    text_key="content",
    namespace="my-namespace"
)

Add Documents:

from langchain_core.documents import Document

docs = [
    Document(page_content="This is a sparse vector example"),
    Document(page_content="Another document for testing")
]

# Option 1: Add from Document objects
vectorstore.add_documents(docs)

# Option 2: Add from texts
texts = ["Text 1", "Text 2"]
metadatas = [{"source": "source1"}, {"source": "source2"}]
vectorstore.add_texts(texts, metadatas=metadatas)

Update Documents:

Update documents by re-adding them with the same IDs.

ids = ["id1", "id2"]
texts = ["Updated text 1", "Updated text 2"]
metadatas = [{"source": "updated_source1"}, {"source": "updated_source2"}]

vectorstore.add_texts(texts, metadatas=metadatas, ids=ids)

Delete Documents:

# Delete by IDs
vectorstore.delete(ids=["id1", "id2"])

# Delete by filter
vectorstore.delete(filter={"source": "source1"})

# Delete all documents in a namespace
vectorstore.delete(delete_all=True, namespace="my-namespace")

Search:

# Search for similar documents
docs = vectorstore.similarity_search("query text", k=5)

# Search with filters
docs = vectorstore.similarity_search(
    "query text",
    k=5,
    filter={"source": "source1"}
)

# Maximal marginal relevance search for diversity
docs = vectorstore.max_marginal_relevance_search(
    "query text",
    k=5,
    fetch_k=20,
    lambda_mult=0.5
)

Search with score:

# Search with relevance scores
docs_and_scores = vectorstore.similarity_search_with_score(
    "query text",
    k=5
)

for doc, score in docs_and_scores:
    print(f"Score: {score}, Document: {doc.page_content}")

Use as Retriever:

# Create a retriever
retriever = vectorstore.as_retriever()

# Customize retriever
retriever = vectorstore.as_retriever(
    search_type="mmr",
    search_kwargs={"k": 5, "fetch_k": 20, "lambda_mult": 0.5},
    filter={"source": "source1"}
)

# Use the retriever
docs = retriever.get_relevant_documents("query text")