-
Install Python dependencies:
pip install -r requirements.txt
-
Ensure PostgreSQL is running with
pgvectorinstalled and configured. -
Configure Environment Variables: Set up the following variables in your
.envfile or system environment: OPENAI_API_KEY=your_openai_api_key OLLAMA_API_URL=http://100.108.91.106:11434 DATABASE_URL=postgresql://user:password@localhost:5432/eidolon ACTIVE_LLM=openai # Options: "openai", "ollama"
uvicorn app:app --reload
Access the API at http://127.0.0.1:8000
Use the /query endpoint to send a user query and receive enriched responses.
curl -X POST "http://127.0.0.1:8000/query" \
-H "Content-Type: application/json" \
-d '{"user_query": "Hello!"}'Use the functions in memory.py to insert and retrieve archival data.
- Local Embeddings: Ensure SentenceTransformers is installed and integrated for efficient local embedding generation.
- Metadata: Timestamps are stored and retrieved to provide historical context for responses.
- Custom Backends: Modify config.py to add new LLM backends or customize existing ones.
Contributions are welcome! Please read the CONTRIBUTING.md file for details on how to contribute.