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Description
This tool automatically extracts errors, warnings from a provided support bundle, compares them against existing Knowledge Base (KB) articles using semantic search, and generates a concise summary if the issue is known along with probable causes & recommended steps.
Goals
- Automated error extraction
- Semantic matching with Kbase articles (keeping it scoped to longhorn articles as of now)
- LLM based Summary
Resources
At present, following are the modules used:
- LlamaIndex – to build the RAG pipeline that handles document ingestion, indexing, and querying.
- HuggingFaceEmbedding model – to convert logs and queries into vector embeddings for semantic search.
- Ollama (Llama 3.1 model) – to run a local LLM for reasoning over retrieved results and generating final answers.
- ChromaDB – to store and query embeddings efficiently for fast semantic retrieval.
- Custom query engine modules – to orchestrate retrieval logic, embed queries, and format responses.
- Local runtime environment (Python + venv) – to integrate all components and execute the RAG workflow end-to-end.
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Comments
-
21 days ago by ajagtap | Reply
The code in progress exists here: https://github.com/apoorvajagtap/logbundle-analyzer
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