Using LangChain Pinecone Wrapper for Index Creation, Upserts, and Similarity Search

LangChain provides a convenient wrapper for interacting with Pinecone, a popular vector database. This wrapper simplifies the process of creating indexes, upserting vectors, and performing similarity searches. In this comprehensive guide, we will explore how to use the LangChain Pinecone wrapper for these tasks.

Prerequisites

• Pinecone: Ensure you have a Pinecone account and API key.
• LangChain: Install LangChain using pip: pip install langchain
• Pinecone Python SDK: Install the Pinecone Python SDK: pip install pinecone-client

Creating a Pinecone Index

Import Necessary Libraries:

Python

from langchain.vectorstores import Pinecone
import pinecone

Initialize Pinecone:

Python

pinecone.init(
api_key=”YOUR_API_KEY”,
environment=”us-west1-gcp” # Replace with your desired environment
)

Create a Pinecone Index:

Python

index_name = “my_index”
embedding_dimension = 512 # Adjust based on your embeddings
metric = “cosine” # Choose a suitable metric

collection = Pinecone(
index_name=index_name,
embedding_dimension=embedding_dimension,
metric=metric
)

Upserting Vectors

Prepare Vectors: Create a list of vectors to upsert.

Upsert Vectors:

Python

vectors = [
{“id”: “doc1”, “embeddings”: [0.1, 0.2, …]},
{“id”: “doc2”, “embeddings”: [0.3, 0.4, …]}
]

collection.add_embeddings(
embeddings=vectors
)

Performing Similarity Search

Create a Query Embedding:

Python

query_text = “What is the capital of France?”
query_embedding = embedding_model.encode([query_text])

Search for Similar Items:

Python

results = collection.similarity_search(
query=query_embedding,
k=5
)

Accessing Results

The results will be a list of documents with their corresponding similarity scores.

Python

for result in results:
    print(result.metadata["id"])
    print(result.embedding)
    print(result.score)

LangChain’s Pinecone wrapper simplifies the process of creating, updating, and querying vector indexes in Pinecone. By using this wrapper, you can efficiently manage your vector data and perform similarity search operations.