Aletheia runs keyword and vector retrieval side by side, reranks the merged results, and measures quality with evaluation pipelines and observability tooling. FastAPI services over PostgreSQL, OpenSearch, and Qdrant, with Redis-backed background jobs.
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Category
AI Systems
Type
Platform
Priority
Flagship
Overview
What this project is
Aletheia runs keyword and vector retrieval side by side, reranks the merged results, and measures quality with evaluation pipelines and observability tooling. FastAPI services over PostgreSQL, OpenSearch, and Qdrant, with Redis-backed background jobs.
Problem
Why it matters
Retrieval systems fail quietly. Without evaluation loops and observability there is no way to tell why a query returned weak results or whether a ranking change actually helped.
Solution
Approach
One platform that serves hybrid retrieval and reranking behind an API, scores results with evaluation pipelines, and exposes ranking behavior for inspection.
Architecture
System shape and stack
Architecture
Retrieval quality as an observable system
The project is framed around retrieval, reranking, evaluation, and operational visibility rather than a single chat surface.
System Shape
Backend, index, and evaluation loops
The stack points toward API services, database state, background queues, vector search, keyword search, and evaluation workflows working together.
- Python
- FastAPI
- PostgreSQL
- Redis/RQ
- OpenSearch
- Qdrant
- SQLAlchemy
- Alembic
- Docker
- Next.js
Technical Highlights
Visible technical signal
- Keyword and vector retrieval merged with reranking
- Evaluation pipelines that score retrieval quality
- Observability into how rankings are produced
What It Proves
Builder signal
Search infrastructure engineering: index design, ranking pipelines, evaluation methodology, and the backend services that hold them together.