rag_api
RAG API is a FastAPI-based service that integrates Langchain with PostgreSQL/pgvector to enable document indexing and retrieval organized by file ID. It provides asynchronous, scalable endpoints for managing embeddings and vector-based search, with support for multiple embedding providers (OpenAI, Bedrock, Azure, HuggingFace, and others).
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | danny-avila/rag_api |
| Owner | danny-avila |
| Primary language | Python |
| License | MIT — OSI-approved |
| Stars | 860 |
| Forks | 375 |
| Open issues | 44 |
| Latest release | v0.8.0 (2026-04-21) |
| Last updated | 2026-06-18 |
| Source | https://github.com/danny-avila/rag_api |
What rag_api is
Python FastAPI application built on Langchain that uses PostgreSQL with pgvector extension for vector storage and similarity search. Supports configurable embedding providers, async operations, JWT authentication, batch processing, and query optimization via distance thresholding. Deployable via Docker or local Python environment.
Get the rag_api source
Clone the repository and explore it locally.
git clone https://github.com/danny-avila/rag_api.gitcd rag_api# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- PostgreSQL/pgvector setup is mandatory; Docker Compose simplifies local development but production deployments need managed database configuration (schema, connection pooling settings, extension handling).
- Embedding provider credentials (RAG_OPENAI_API_KEY, Bedrock, Azure, etc.) must be securely managed; multiple environment variable options exist to avoid conflicts with parent systems like LibreChat.
- Chunk size, overlap, batch size, and distance threshold tuning directly impact search quality and cost; defaults provided but require inspection and adjustment based on embedding model and use case.
- Async operations and connection pooling require careful configuration for remote PostgreSQL (PG_POOL_PRE_PING, PG_POOL_RECYCLE settings) to handle idle timeouts and network latency.
- JWT authentication is optional but strongly recommended for production; requires upstream token generation and is verification-only (no issuance mechanism included).
When to avoid it — and what to weigh
- Requiring Out-of-Box UI — This is API-only with no built-in frontend. Integration requires separate client application development or embedding into an existing system like LibreChat.
- Simple Single-File Use Cases — The ID-based organizational model adds complexity suited for multi-file, multi-project scenarios. Simpler document search needs may be overengineered by this architecture.
- No PostgreSQL/pgvector Infrastructure — Requires PostgreSQL with pgvector extension pre-configured. While Docker Compose is provided, managed Postgres adoption requires additional configuration (e.g., schema setup, superuser constraints noted in docs).
- Production Security Without External Validation — JWT authentication is basic (verification-only, no token issuance). Requires secure token generation from external system. Not suitable for standalone secure deployments without upstream auth infrastructure.
License & commercial use
MIT License. Permissive, OSI-compliant license allowing commercial use, modification, and distribution with attribution.
MIT license permits commercial use without restriction. However, ensure that any external dependencies (Langchain, FastAPI, embedding provider SDKs) comply with your commercial requirements. No warranty implied; review dependency licenses before production deployment.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
No claims made regarding security posture. JWT authentication is optional and verification-only (requires external token issuance). Credential handling for embedding providers must be managed via environment variables (no secrets management built-in). Database credentials similarly environment-based. No encryption at rest, TLS, or audit logging mentioned. Requires review of secret management strategy for production use.
Alternatives to consider
LangChain Community Vector Store (direct)
Use LangChain's built-in vector store integrations directly in your application if you don't need a separate microservice. Reduces deployment complexity but couples logic to your main application.
Weaviate or Pinecone (managed vector DB)
Fully managed vector databases with hosted infrastructure, automatic scaling, and built-in API. Avoid self-hosted PostgreSQL/pgvector complexity but increases cost and vendor lock-in.
Vespa or Milvus (open-source vector engines)
Alternative open-source vector databases with their own APIs and scaling characteristics. Similar deployment overhead to pgvector but different operational and performance trade-offs.
Build on rag_api with DEV.co software developers
Explore RAG API for scalable document retrieval, or let our team help you evaluate integration complexity and embedding strategy for your specific use case.
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rag_api FAQ
Can I use this without PostgreSQL?
How do I integrate this with an existing LLM system?
What embedding models are recommended?
Is this production-ready?
Software developers & web developers for hire
From first prototype to production, DEV.co delivers software development services around tools like rag_api. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across vector databases and beyond.
Ready to Implement RAG Search in Your Application?
Explore RAG API for scalable document retrieval, or let our team help you evaluate integration complexity and embedding strategy for your specific use case.