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| # SwiftQuery AI Documentation | |
| ## Overview | |
| The AI module in SwiftQuery powers intelligent features such as Retrieval-Augmented Generation (RAG) for patient data, similar case search, and clinical note enhancement. It leverages **Google Gemini** for generation, **CyborgDB** for vector storage, and **SentenceTransformers** for embeddings. | |
| ## Key Components | |
| ### 1. Patient RAG Pipeline (`ai_agents/patient_rag.py`) | |
| **Class**: `SimilarPatientRAGPipeline` | |
| This pipeline manages the context for AI interactions. | |
| - **Session Management**: | |
| - `start_patient_session(patient_id)`: Initializes an *ephemeral, in-memory* vector store for a specific patient. It extracts data (History, Consultations, Tests, Chats) from MongoDB, chunks it, and ingests it into CyborgDB. | |
| - `end_patient_session(patient_id)`: Cleans up the in-memory store to free resources. | |
| - **RAG Query**: | |
| - `rag_query(query, patient_id)`: Retrieves relevant chunks from the patient's active session store and uses Google Gemini to generate a context-aware answer. | |
| - **Similar Patient Search**: | |
| - `similar_patient_search(query)`: Builds/uses a *global anonymized* vector store containing clinical data from all patients. It finds cases with similar symptoms or conditions to aid diagnosis. | |
| ### 2. Clinical Note Enhancer (`ai_agents/clinical_summarizer.py`) | |
| **Class**: `ClinicalNoteEnhancer` | |
| This tool improves the quality of raw clinical text entered by assistants. | |
| - **Function**: `enhance_text(text)` | |
| - Uses Gemini to rephrase medical text for clarity, grammar, and professionalism without altering facts. | |
| - **Batch Processing**: `enhance_patient_records(patient_id)` | |
| - Iterates through a patient's entire record (History, Consultations, Reports) and enhances all text fields. Supports a `dry_run` mode to preview changes. | |
| ### 3. Chat Interface (`utils/blueprints/chat.py`) | |
| - **Route**: `/api/chat` | |
| - **Logic**: | |
| - Receives user message. | |
| - If the message starts with `/similar`, it triggers `rag_pipeline.similar_patient_search`. | |
| - Otherwise, it triggers `rag_pipeline.rag_query`. | |
| - Logs the interaction to `chatsession` and `chatmessage` collections. | |
| ## Dependency Configuration | |
| - **Embeddings**: `all-MiniLM-L6-v2` (via SentenceTransformers). | |
| - **LLM**: `gemini-flash-latest` (via Google Generative AI). | |
| - **Vector DB**: `CyborgDB` (in-memory execution). | |
| ## Error Handling | |
| - All AI failures (e.g., API limits, network issues) are caught and logged. | |
| - The UI receives graceful error messages instead of server crashes. | |
| - Sessions attempt auto-recovery if a context is not found in memory (e.g., due to worker restarts). | |