LlamaIndex

LlamaIndex stores and retrieves document embeddings from Qdrant via its QdrantVectorStore adapter inside a VectorStoreIndex.

→ Production-grade semantic retrieval with Qdrant's filtered search and payload metadata inside LlamaIndex pipelines.

LlamaIndex uses Chroma as a local vector store via its ChromaVectorStore adapter — zero infrastructure required.

→ Fast local RAG development: LlamaIndex handles chunking and indexing, Chroma stores embeddings without any external service.

LlamaIndex stores embeddings in Postgres via its PGVectorStore adapter, colocating vector and relational queries.

→ RAG on top of an existing Postgres database — no separate vector DB, structured and vector queries in the same store.

LlamaIndex connects to Weaviate as a vector store, using its GraphQL API for multimodal and multi-tenant retrieval.

→ Multimodal RAG within LlamaIndex — retrieve across text, images, and structured data from a single Weaviate index.

Langfuse provides a LlamaIndex callback handler that traces every query, retrieval call, and LLM generation within the pipeline.

→ Retrieval-level observability: see which chunks were fetched, at what similarity score, and what the LLM did with them.

LlamaIndex accepts LiteLLM's OpenAI-compatible proxy as its LLM backend, routing all generation through any provider.

→ Model-agnostic LlamaIndex pipelines — swap the generation model without touching retrieval or indexing code.

LlamaIndex connects to Ollama's local API for both completions and embeddings — the same pipeline works fully offline.

→ Fully local RAG: documents indexed and retrieved locally, generation running on local models via Ollama with no API costs.

Ragas evaluates LlamaIndex pipeline outputs using retrieval and generation quality metrics against the source documents.

→ Automated quality scoring for LlamaIndex RAG — faithfulness, context relevance, and answer correctness measurable in CI.

LlamaIndex uses OpenAI's API for both embedding generation and completions via its native adapters.

→ Best-in-class embeddings and generation in LlamaIndex pipelines — ada-002 or text-embedding-3 for retrieval, GPT-4o for generation.

LlamaIndex uses Anthropic's API for generation via its Anthropic LLM class alongside a separate embedding model.

→ Claude-powered RAG answers with strong long-context document understanding and minimal hallucination.

LlamaIndex stores and queries embeddings in Pinecone via its PineconeVectorStore adapter.

→ Managed, auto-scaling vector retrieval for LlamaIndex at production scale with no infrastructure to operate.

LlamaIndex connects to a vLLM-hosted endpoint via its OpenAI-compatible API, treating self-hosted vLLM as a generation provider.

→ LlamaIndex RAG pipelines backed by self-hosted GPU inference — enterprise-grade retrieval and generation with full data residency.