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déploiement en cloud

  • Financial Agentic RAG

    A Python-based AI Agent that uses retrieval-augmented generation to analyze financial documents and answer domain-specific queries.
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    What is Financial Agentic RAG?
    Financial Agentic RAG combines document ingestion, embedding-based retrieval, and GPT-powered generation to deliver an interactive financial analysis assistant. The agent pipelines balance search and generative AI: PDFs, spreadsheets, and reports are vectorized, enabling contextual retrieval of relevant content. When a user submits a question, the system fetches top-matching segments and conditions the language model to produce concise, accurate financial insights. Deployable locally or in the cloud, it supports custom data connectors, prompt templating, and vector stores like Pinecone or FAISS.
    Financial Agentic RAG Core Features
    • Retrieval-Augmented Generation pipeline
    • Multi-format financial document ingestion
    • Embeddings-based semantic search
    • GPT-driven answer synthesis
    • Custom prompt templating
    • Configurable vector store support

  • Query-Bot

    An AI-powered chat app that uses GPT-3.5 Turbo to ingest documents and answer user queries in real-time.
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    What is Query-Bot?
    Query-Bot integrates document ingestion, text chunking, and vector embeddings to build a searchable index from PDFs, text files, and Word documents. Using LangChain and OpenAI GPT-3.5 Turbo, it processes user queries by retrieving relevant document passages and generating concise answers. The Streamlit-based UI allows users to upload files, track conversation history, and adjust settings. It can be deployed locally or on cloud environments, offering an extensible framework for custom agents and knowledge bases.
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