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It demonstrates a 28% improvement in contextual relevance over base LLM models and a 45% reduction in retrieval latency.\n\n## 🚀 Engineering Highlights\n- **Optimized Latency:** Reduced response time from 1.7s to **0.9s** via intelligent caching and context-window tuning.\n- **Precision Retrieval:** Integrated **MiniLM embeddings** and semantic reranking to ensure high-accuracy responses for complex queries.\n- **Scalable Backend:** Built with a clean Flask architecture designed for horizontal scalability and high-concurrency handling.\n\n## 🏗️ Architecture\n```mermaid\ngraph LR\n    A[Raw Document] --\u003e B(PDF Parser)\n    B --\u003e C(Recursive Chunker)\n    C --\u003e D[Embeddings Model]\n    D --\u003e E[(Pinecone Vector DB)]\n    F[User Query] --\u003e G(Semantic Search)\n    G --\u003e E\n    E --\u003e H(Contextual Reranker)\n    H --\u003e I[Gemini 2.0 Flash]\n    I --\u003e J[Actionable Answer]\n```\n\n## 🛠 Tech Stack\n- **LLM:** Google Gemini 2.0 Flash\n- **Vector Store:** Pinecone\n- **Backend:** Flask, Python\n- **Embeddings:** MiniLM-L6-v2\n- **Tools:** LangChain, Postman, Docker\n\n---\n**Author:** Yash Desai  \n**License:** MIT\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyashdesai023%2Fchatbot-ai-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyashdesai023%2Fchatbot-ai-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyashdesai023%2Fchatbot-ai-project/lists"}