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Decision brief
Key Insights for Using rag_api as an ID-based RAG FastAPI Tool with Langchain and PostgreSQL/pgvector Integration
Good fit when
- When you need rapid integration of REST API services for Retrieval-Augmented Generation (RAG) with robust vector storage.
- If your use case benefits from the efficiency of PostgreSQL/pgvector combination, where scalable vector search operations are required.
Avoid when
- Avoid using if your project cannot leverage PostgreSQL/pgvector due to license or compatibility constraints.
- Not recommended for scenarios where high-level orchestration of multiple APIs and services is necessary without a direct need for FastAPI's simplicity.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (6d since push)
- As of 4d
- Provenance
- Not a fork · Personal account
- As of 4d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install rag_apiSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A FastAPI-based API for ID-based retrieval-augmented generation (RAG) systems, integrated with Langchain and using PostgreSQL/pgvector as the vector database.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 21, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 21, 2026
- Languages
- python
Source: github.language · Aug 21, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 21, 2026)
ollowing commands (preferably in a [virtual environment](https://realpython.com/python-virtual-environments-a-primer/))Source link
Tags
README
Getting Started
- Configure
.envfile based on section below - Setup pgvector database:
- Run an existing PSQL/PGVector setup, or,
- Docker:
docker compose up(also starts RAG API)- or, use docker just for DB:
docker compose -f ./db-compose.yaml up
- or, use docker just for DB:
- Run API:
- Docker:
docker compose up(also starts PSQL/pgvector)- or, use docker just for RAG API:
docker compose -f ./api-compose.yaml up
- or, use docker just for RAG API:
- Local:
- Make sure to setup
DB_HOSTto the correct database hostname - Run the following commands (preferably in a virtual environment)
- Make sure to setup
- Docker:
pip install -r requirements.txt
uvicorn main:app
Clean Install (Local Development)
To do a clean reinstall of all dependencies (e.g., after updating requirements.txt):
---
### Cloud Installation Settings:
#### AWS:
Make sure your RDS Postgres instance adheres to this requirement:
`The pgvector extension version 0.5.0 is available on database instances in Amazon RDS running PostgreSQL 15.4-R2 and higher, 14.9-R2 and higher, 13.12-R2 and higher, and 12.16-R2 and higher in all applicable AWS Regions, including the AWS GovCloud (US) Regions.`
In order to setup RDS Postgres with RAG API, you can follow these steps:
* Create a RDS Instance/Cluster using the provided [AWS Documentation](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_CreateDBInstance.html).
* Login to the RDS Cluster using the Endpoint connection string from the RDS Console or from your IaC Solution output.
* The login is via the *Master User*.
* Create a dedicated database for rag_api:
``` create database rag_api;```.
* Create a dedicated user\role for that database:
``` create role rag;```
* Switch to the database you just created: ```\c rag_api```
* Enable the Vector extension: ```create extension vector;```
* Use the documentation provided above to set up the connection string to the RDS Postgres Instance\Cluster.
Notes:
* Even though you're logging with a Master user, it doesn't have all the super user privileges, that's why we cannot use the command: ```create role x with superuser;```
* If you do not enable the extension, rag_api service will throw an error that it cannot create the extension due to the note above.
For agents
This page has a .md twin and JSON over the API.