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ec5092a54a
This commit introduces a new JSON configuration file for the OpenSearch ingestion flow, detailing the data processing pipeline. The flow includes components for splitting text, generating embeddings, and ingesting data into OpenSearch, enhancing the capabilities for Retrieval Augmented Generation (RAG) tasks. The configuration is designed to support various input types and provides detailed metadata for each component, ensuring robust and well-documented integration. |
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| .github/workflows | ||
| documents | ||
| flows | ||
| frontend | ||
| keys | ||
| securityconfig | ||
| src | ||
| .dockerignore | ||
| .env.example | ||
| .gitignore | ||
| .python-version | ||
| docker-compose-cpu.yml | ||
| docker-compose.yml | ||
| Dockerfile | ||
| Dockerfile.backend | ||
| Dockerfile.frontend | ||
| Makefile | ||
| pyproject.toml | ||
| README.md | ||
| uv.lock | ||
| warm_up_docling.py | ||
OpenRAG
getting started
Set up your secrets:
cp .env.example .env
Populate the values in .env
Requirements:
Docker or podman with compose installed.
Run OpenRAG:
docker compose build
docker compose up
CPU only:
docker compose -f docker-compose-cpu.yml up
If you need to reset state:
docker compose up --build --force-recreate --remove-orphans
For podman on mac you may have to increase your VM memory (podman stats should not show limit at only 2gb):
podman machine stop
podman machine rm
podman machine init --memory 8192 # example: 8 GB
podman machine start