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langchain-vector-stores

Integrate Chroma, Pinecone, FAISS, and in-memory vector stores in LangChain.js.

Updated Feb 13, 2026
Search Tags:#semantic search#embeddings#langchain#RAG#database integration#vector stores
One-click install
npx skills add https://github.com/evanfang0054/x-codegen-agent --skill langchain-vector-stores-evanfang0054
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-vector-stores
Source: https://github.com/evanfang0054/x-codegen-agent/tree/main/.claude/skills/langchain-integrations-vector-stores
Command: npx skills add https://github.com/evanfang0054/x-codegen-agent --skill langchain-vector-stores-evanfang0054

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires @langchain/community, @langchain/pinecone, @pinecone-database/pinecone, @langchain/weaviate, weaviate-ts-client, @langchain/qdrant, @qdrant/js-client-rest, and includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive guide to integrating various vector stores with LangChain, enabling efficient semantic search and RAG capabilities.

Core Features & Use Cases

  • Vector Store Selection: Decision table and guidance on choosing the right vector store (FAISS, Chroma, Pinecone, etc.).
  • Code Examples: Practical TypeScript examples for initializing, adding documents, querying, and using vector stores as retrievers.
  • Use Case: Integrate Pinecone into your RAG application to enable fast and scalable semantic search over your document corpus.

Quick Start

Use the langchain-vector-stores skill to learn how to initialize and query a MemoryVectorStore with example documents.

Frequently Asked Questions about langchain-vector-stores

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I integrate a vector store like Pinecone or Chroma with LangChain for RAG?▼

To integrate a vector store with LangChain for RAG, you initialize the client, add documents using a consistent embedding model, and query the store as a retriever. This Skill provides TypeScript examples for setup and querying across providers like Pinecone and Chroma.

What is the best way to choose a vector database for semantic search in LangChain.js?▼

Choosing a vector database for semantic search depends on your scale and infrastructure needs. This Skill provides a decision table comparing FAISS, Chroma, Pinecone, and in-memory options to help you select the right provider for your specific use case.

Does LangChain vector store integration require a separate database server?▼

LangChain vector store integration sometimes requires a separate database server. While in-memory options need no server, providers like Chroma, Pinecone, and Weaviate require specific server setups or cloud instances to function properly.

How do I use metadata filtering when querying a vector store in LangChain?▼

Metadata filtering in a LangChain vector store narrows your semantic search results based on document attributes. This Skill addresses critical considerations and provides examples for applying metadata filters across different vector database providers.

Why do my vector store query results return irrelevant documents in LangChain?▼

Vector store query results may return irrelevant documents if the embedding model used for querying differs from the one used for ingestion. This Skill emphasizes maintaining embedding consistency to ensure accurate semantic search performance.

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