Fetching metadata from the HF Docker repository...
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feat: add conversation history support to chat service and update Gradio UI for interactive sessions Running
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feat: implement modular RAG architecture, query routing, metadata-filtered retrieval, and a comprehensive offline testing suite.
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refactor: modularize chat service by extracting core logic into dedicated workflow functions
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refactor: move title from launch configuration to Blocks constructor in Gradio interface
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refactor: migrate chat and ingestion logic into dedicated services and consolidate database initialization
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feat: implement AI query router and metadata-based filtering for RAG retrieval with updated UI controls
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feat: introduce VECTOR_SIZE configuration for dynamic Qdrant collection creation
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feat: implement document chunking with configurable size and overlap during ingestion
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refactor: modularize data loading and ingestion logic for improved maintainability
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feat: clear existing collection in Qdrant before running ingestion process
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feat: implement RAG pipeline with Qdrant vector store, Groq LLM integration, and structured prompt management
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feat: migrate LLM from local Flan-T5 to Groq API and add environment configuration files
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Update README.md
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feat: refine system prompt, set max sequence length, and adjust generation parameters for improved response quality
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refactor: replace transformers pipeline with manual model loading and add Gradio UI interface
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refactor: restrict retrieval to the single most relevant document and remove Gradio UI implementation
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refactor: remove code comments, optimize retrieval logic, and refine LLM prompting and output formatting
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refactor: reduce retrieval counts, improve document filtering logic, and enforce stricter prompt constraints for the LLM assistant
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fix: update pipeline task type from text2text-generation to text-generation
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refactor: optimize RAG pipeline with Flant-T5, centralized retrieval, and UI refinements
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feat: update Qdrant indexing, add payload filtering, and switch to Mistral-7B model for RAG pipeline
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refactor: remove unused imports, standardize naming, and update Qdrant filters to use FieldCondition objects
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feat: integrate local LLM pipeline and structured RAG retrieval for policies and products
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refactor: remove redundant section headers, inline helper functions, and simplify retrieval and chat logic
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refactor: optimize data ingestion with type metadata, improve retrieval filtering, and clean up codebase structure
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refactor: implement UUID generation for ingestion and replace local transformer pipeline with placeholder LLM function
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refactor: swap Flan-T5 for GPT-2 causal language model and clean up ingestion and UI code
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refactor: clean up imports, update LLM to flan-t5-small, and refine Gradio UI labels
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refactor: modernize data ingestion, update LLM pipeline implementation, and improve Qdrant configuration handling
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refactor: migrate to local LLM inference using flan-t5 and remove HuggingFace API dependency
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refactor: improve Qdrant initialization, enhance data ingestion, and add robust error handling to RAG pipeline
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feat: implement automated Qdrant collection recreation and improve LLM response handling and UI presentation
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refactor: migrate from langchain-community to langchain-qdrant for vector store integration
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refactor: rename policy and product data files by removing _rag suffix
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refactor: simplify code structure, refine system prompt, and migrate Gradio interface to Blocks API
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feat: implement ingest_to_qdrant function to upload policy and product data to vector store
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feat: add load_files utility to parse policy and product JSON data
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add rag data
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Create app.py
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Create requirements.txt
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initial commit
