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Simple vectoreDB\n- [LLamaindex](https://docs.llamaindex.ai/en/stable/) - RAG framework\n- [Milvus](https://milvus.io) - Scalable vectorDB --\u003e\n\n\n## Prerequisites  \n - Tekton \n - OpenShift \n - Nvidia Operator installed \n - Kostomize CLI\n  \n## Usage\n\n- Build the embedding container under `images/rag` using the `build.sh` file.\n- Add secrets resources to the secret folder and to link the secretes to the pipeline service account \n- Copy and edit the pipeline configuration file `pipeline/data-pipeline.yaml` \n- add you file adn resources to th `kustomization.yaml` file \n- Deploy the pipeline pipeline using the following command: \n```\noc apply -k . \n``` \n- Run the pipeline \n- Results:\n    - Container image in the specified location with the embedding json files  \n    - GitHub Release zip file which contains the embedding files and metadata files\n\n\n## Folders \n - images - base images containing the tekton tasks \n\n\n\u003c!-- ## To Do \n- [V] define model in config \n- [V] multiple vector db support: chromadb,llmindex\n- [] milvus vector db support \n- [] change defult mode \n- [] add meta data file  --\u003e\n\n## References  \nhttps://docs.llamaindex.ai/en/stable/getting_started/concepts.html\nhttps://docs.llamaindex.ai/en/stable/understanding/using_llms/using_llms.htm\nhttps://blog.llamaindex.ai/build-and-scale-a-powerful-query-engine-with-llamaindex-and-ray-bfb456404bc4\nhttps://tekton.dev/docs/pipelines/pipelines/ ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frhecosystemappeng%2Flightspeed-rag-pipeline","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frhecosystemappeng%2Flightspeed-rag-pipeline","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frhecosystemappeng%2Flightspeed-rag-pipeline/lists"}