Workflow et Readme

Merge d'agent_V3

GEMINI.md

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+# GEMINI.md - Project Context for Gemini CLI
+
+This document provides a comprehensive overview of the "Rapport-automatique" project for the Gemini CLI, enabling it to understand the project's purpose, architecture, and key components for effective collaboration.
+
+## Project Overview
+
+The goal of this project is to create an AI agent that can automatically write an internship report. The agent uses a Retrieval-Augmented Generation (RAG) system to source information from a collection of notes (weekly internship reports) provided in the `documents_projet/` directory.
+
+The project is built in Python and leverages the LangChain and LangGraph frameworks to create a sophisticated agent.
+
+**Key Technologies:**
+
+*   **Orchestration:** LangChain & LangGraph
+*   **LLM:** `mistral-large-latest` via `ChatMistralAI`
+*   **Vector Database (RAG):** ChromaDB (persisted in `chroma_db/`)
+*   **Embeddings:** `jinaai/jina-embeddings-v3` from HuggingFace
+*   **Document Loading:** `Unstructured` (for `.txt` files)
+*   **Web Search:** Tavily
+*   **Experiment Tracking:** MLflow
+
+## Architecture
+
+The system is designed as a LangGraph agent with a clear, cyclical flow:
+
+1.  **Start (LLM Call):** The agent starts by calling the Mistral LLM (`reponse_question` node) with the current conversation history.
+2.  **Tool Decision:** The LLM decides whether to generate a direct response or use one of its available tools.
+3.  **Conditional Routing:** The `should_continue` function checks the LLM's output. If tool calls are present, the graph transitions to the `tool_node`. Otherwise, the session ends.
+4.  **Tool Execution:** The `tool_node` executes the requested tools (e.g., `search_in_files` for RAG, `internet_search`, file I/O).
+5.  **Loop:** The output of the tools is passed back to the LLM (`reponse_question` node) for it to process the results and decide the next action, continuing the cycle.
+
+The agent's state (`CustomState`) is explicitly managed and includes conversation history, a `todo` list for task management, and the query and results from the RAG system.
+
+## Building and Running
+
+### 1. Setup
+
+**a. Install Dependencies:**
+First, set up and activate a Python virtual environment. Then, install the required packages.
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+**b. Configure Environment Variables:**
+Copy the template `.env.template` file and fill in your API keys (e.g., for Mistral, Tavily).
+
+```bash
+cp AgentReact/.env.template AgentReact/.env
+# Edit AgentReact/.env with your credentials
+```
+
+### 2. Data Ingestion (RAG Setup)
+
+Place your source documents (as `.txt` files) into the `documents_projet/` directory at the project root. Then, run the initialization script to populate the Chroma vector database.
+
+```bash
+python RAG/init.py
+```
+
+### 3. Running the Agent
+
+The main entry point for the agent is `AgentReact/start.py`.
+
+```bash
+python AgentReact/start.py
+```
+
+This script will invoke the agent graph with a hardcoded sample question and print the resulting messages.
+
+## Development Conventions
+
+*   **Modularity:** The code is well-structured into directories for the agent (`AgentReact`), RAG components (`RAG`), and data (`documents_projet`). The agent's logic is further divided into `agent.py` (graph), `nodes.py`, `state.py`, and `tools.py`.
+*   **Singleton Pattern:** The `VectorDatabase` is implemented as a Singleton to ensure a single, shared instance throughout the application.
+*   **State Management:** The agent's state is explicitly defined in `AgentReact/utils/state.py`, making it clear what information is tracked across turns.
+*   **Human-in-the-Loop:** The `ask_human` tool provides a mechanism for the agent to request user input, although the full "supervised tools" workflow from the diagram is not yet implemented.
+*   **Roadmap:** The `roadmap.md` file tracks the project's progress and outlines future development goals, such as moving from ChromaDB to PG Vector and adding PDF generation.