Aquiles-RAG is a high-performance Retrieval-Augmented Generation (RAG) solution built on Redis. It offers a high-level interface through FastAPI REST APIs
Project description
Aquiles-RAG
Description
Aquiles-RAG is a high-performance Retrieval-Augmented Generation (RAG) solution built on Redis. It offers a high-level interface through FastAPI REST APIs to:
- Create RAG indexes in Redis.
- Send raw text alongside its embeddings (the client must chunk the text and compute embeddings before submission).
- Query the index to retrieve the most relevant chunks.
Features
- Optimized Performance: Uses Redis as a vector search engine.
- Simple API: Endpoints for index creation, insertion, and querying.
- Extensible: Basic implementation ready for enhancements and integration into ML pipelines.
High-Level Architecture
Here's a diagram illustrating how Aquiles-RAG connects clients to Redis using an asynchronous FastAPI server:
Usage
Create Index
curl -X POST http://localhost:5500/create/index \
-H 'Content-Type: application/json' \
-d '{"indexname": "my_index"}'
Send RAG
curl -X POST http://localhost:5500/rag/create \
-H 'Content-Type: application/json' \
-d '{
"index": "my_index",
"raw_text": "Full text goes here...",
"embeddings": [0.12, 0.34, ...]
}'
Query RAG
curl -X POST http://localhost:5500/rag/query-rag \
-H 'Content-Type: application/json' \
-d '{
"index": "my_index",
"embeddings": [0.56, 0.78, ...],
"top_k": 5
}'
Command-Line Interface (CLI)
Usage Examples
# Save configuration
aquiles-rag configs --local True --host redis.local --port 6380
# Start server
aquiles-rag serve
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