LlamaParse

Open-source library for preprocessing unstructured documents (PDFs, Word, HTML, images) for RAG and LLM applications. Handles extraction, chunking, and cleaning of diverse document types.

LlamaIndex is a Python data framework library for vector search and embedding retrieval, integrating various ANN indexes like HNSW and FAISS without full database dependencies. Supports quantization, multi-modal embeddings, and advanced query engines in Python/Rust backends. Great for prototyping LLM apps and embedded RAG; more developer-friendly and lighter than Milvus, composable vs hnswlib.

AnythingLLM is an open-source, self-hosted AI application with integrated vector storage and retrieval for embeddings, enabling RAG and LLM workflows. Key features include built-in RAG, AI agent support, Docker deployment, and free MIT license. Ideal for RAG prototypes and local deployments, providing cost savings and full control compared to managed services like Pinecone.

RAG-powered text-to-SQL framework that enables natural language querying of SQL databases using vector search for retrieval of relevant schema, documentation, and example queries.

Techniques for managing limited LLM context windows in RAG systems, including chunk selection, summarization, and iterative retrieval. As context windows fill with retrieved documents, strategies ensure the most relevant information reaches the model while respecting token limits.

Using language models to automatically evaluate RAG system outputs, retrieval quality, and answer correctness. LLM-as-judge provides scalable, consistent evaluation of aspects like faithfulness, relevance, and coherence that are difficult to measure with traditional metrics, enabling rapid iteration on RAG systems.