Needle

Indexes documents by converting them into semantic embeddings and storing them in a vector database, enabling similarity-based retrieval without keyword matching. The system processes documents through an embedding pipeline that chunks content, generates vector representations, and persists them in a searchable index optimized for production workloads. This approach enables semantic understanding of document content rather than relying on lexical matching.

Retrieves documents from the indexed collection by computing similarity between a query embedding and stored document embeddings, then ranks results by relevance score. The retrieval system converts incoming queries into the same embedding space as indexed documents, performs vector similarity search (likely using cosine similarity or dot product), and returns ranked results with confidence scores. This enables context-aware document selection for LLM prompts.

Exposes document search and retrieval as an MCP (Model Context Protocol) tool that Claude and other MCP-compatible clients can invoke directly. The implementation registers search functions as MCP resources with defined input schemas and output formats, allowing language models to call document retrieval as part of their reasoning loop without requiring external API calls or custom integration code. This enables seamless integration of RAG into Claude conversations and agentic workflows.

Accepts documents in multiple formats (PDF, TXT, Markdown, code files) and converts them into a unified internal representation for indexing. The ingestion pipeline likely includes format-specific parsers that extract text content, preserve structure metadata, and normalize content before chunking and embedding. This abstraction allows users to index heterogeneous document collections without format-specific preprocessing.

Splits documents into semantically coherent chunks before embedding, using strategies that preserve meaning boundaries (e.g., paragraph-aware or sentence-aware chunking rather than fixed-size windows). The chunking system balances chunk size for embedding quality against retrieval granularity, ensuring that individual chunks contain enough context to be meaningful while remaining small enough for efficient retrieval and LLM context windows. This prevents embedding fragmented content that loses semantic meaning.

Provides a complete, production-ready RAG system with built-in considerations for scalability, reliability, and operational concerns. The system includes indexing, retrieval, MCP integration, and likely includes features like error handling, logging, monitoring hooks, and deployment patterns suitable for production workloads. This eliminates the need to assemble RAG components from multiple libraries and handle production concerns separately.

Abstracts the underlying vector database implementation, allowing Needle to work with different vector storage backends without exposing database-specific details to users. The abstraction layer handles index creation, embedding storage, similarity search, and result retrieval through a unified interface, enabling users to swap vector database implementations (e.g., Pinecone, Weaviate, Milvus) without changing application code. This decouples RAG logic from infrastructure choices.

Selects and ranks retrieved documents based on the LLM's context window constraints, ensuring that the final prompt with documents and query fits within token limits. The system likely tracks token counts for retrieved chunks, prioritizes high-relevance documents, and may truncate or exclude lower-relevance results to fit within context budgets. This prevents context overflow errors and optimizes information density in prompts.