Enterprise Knowledge Retrieval
EDDI provides a complete Retrieval-Augmented Generation pipeline with native support for multiple embedding providers, vector stores, and a zero-infrastructure RAG option via HTTP calls.
RAG Capabilities
- 7 Embedding Providers: OpenAI, Ollama, Azure OpenAI, Mistral, Amazon Bedrock, Cohere, Google Vertex AI
- 6 Vector Stores: pgvector, Chroma, In-Memory, MongoDB Atlas, Elasticsearch, Qdrant
- httpCall RAG: Zero-infrastructure RAG via any search API (BM25, Elasticsearch, custom endpoints)
- REST Ingestion API: Async document ingestion with status tracking and batch processing
- Hybrid Search: Combine dense vector retrieval with sparse keyword matching for optimal recall
Flexible Deployment
RAG is fully configuration-driven. Choose your embedding provider and vector store via JSON configuration, no code changes needed. The httpCall RAG option lets you use any existing search infrastructure (Elasticsearch, Solr, custom APIs) without deploying a separate vector database.