feat: Integrate Milvus, Ollama, PDF ingestion, and Streamlit UI #439
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Key changes include: - Replaced Weaviate with Milvus as the vector database. - Added support for Ollama for both embeddings and language model generation, configurable alongside existing OpenAI integrations. - Implemented ingestion pipeline to process PDF documents from a local `knowledge_docs` folder. Includes text extraction and metadata assignment. A placeholder is included for your logic to extract source URLs from PDF content for citations. - Developed a Streamlit-based frontend for your interaction, allowing model selection and displaying chat history with sources. - Updated the backend server (`backend/server.py`) to correctly serve the LangServe graph using FastAPI. - Provided comprehensive `README.md` documentation covering setup, configuration, data preparation (including the PDF URL extraction task), and instructions for running all components. - Removed Weaviate dependencies and updated project configurations.
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Key changes include:
knowledge_docsfolder. Includes text extraction and metadata assignment. A placeholder is included for your logic to extract source URLs from PDF content for citations.backend/server.py) to correctly serve the LangServe graph using FastAPI.README.mddocumentation covering setup, configuration, data preparation (including the PDF URL extraction task), and instructions for running all components.