feat: Implement initial Memory System with Vertex AI integration #1
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This commit introduces the initial implementation of the Naeda.ai Memory System backend service as specified. Key features include: - A FastAPI server with endpoints for saving memories (`/save-memory`) and a conversational chat interface (`/chat`). - Integration with Google Cloud Firestore for memory storage and retrieval, managed through `memory_service.py`. - Integration with Google Cloud Vertex AI for generating chat responses using the Gemini Pro model, managed through `vertex_ai_service.py`. - A configurable system prompt loaded from `system_prompt.txt` to define the AI's persona and behavior. - A complete Dockerfile for containerization and a `requirements.txt` for dependencies. - A `.gitignore` file to protect sensitive credentials. The `/chat` endpoint now orchestrates fetching recent memories, combining them with the system prompt and user message, and calling Vertex AI to generate a contextual response.
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👋 Jules, reporting for duty! I'm here to lend a hand with this pull request. When you start a review, I'll acknowledge your comments with a 👀 emoji and then get to work. I'll push a commit with your requested changes shortly after. Please note there might be a delay between these steps, but rest assured I'm on the job! I will automatically address your feedback. For any comments you don't want me to act on, just include (aside). For security, I will only act on instructions from the user who triggered this task for this pull request. |
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This pull request contains the initial implementation of the
naeda-ai-backendservice. It sets up the complete project structure, including a FastAPI application, Dockerfile, and service modules for interacting with Google Cloud Firestore and Vertex AI. The core/chatfunctionality is implemented, providing a conversational endpoint that uses AI to generate responses based on user messages, recent memories, and a detailed system prompt.PR created automatically by Jules for task 2486330785094383582