diff --git a/Dockerfile b/Dockerfile index 2b89af7..5359007 100644 --- a/Dockerfile +++ b/Dockerfile @@ -6,7 +6,7 @@ ENV PYTHONDONTWRITEBYTECODE=1 \ SUPERNOTE_STORAGE_DIR=/data \ SUPERNOTE_CONFIG_DIR=/data/config \ SUPERNOTE_HOST=0.0.0.0 \ - SUPERNOTE_PORT=8080 + SUPERNOTE_PORT=8000 # Create a non-root user RUN groupadd -g 1000 -r supernote && useradd -u 1000 -r -g supernote supernote @@ -32,7 +32,7 @@ RUN mkdir -p /data /data/config && \ # Switch to non-root user USER supernote -EXPOSE 8080 +EXPOSE 8000 VOLUME ["/data"] diff --git a/README.md b/README.md index ede1b3b..8879396 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ **The AI-powered intelligence layer for your Ratta Supernote.** -This toolkit is a self-hosted implementation of the **Supernote Private Cloud** protocol. While Ratta's official private cloud provides a solid and reliable sync foundation, this project extends that experience with an **AI-driven synthesis engine**—transforming your handwritten notes into structured, searchable knowledge using Google Gemini. +This toolkit is a self-hosted implementation of the **Supernote Private Cloud** protocol. While Ratta's official private cloud provides a solid and reliable sync foundation, this project extends that experience with an **AI-driven synthesis engine**—transforming your handwritten notes into structured, searchable knowledge using Google Gemini or Mistral AI.

Supernote Overview @@ -26,7 +26,7 @@ This project is designed to be **fully compatible** with the official Supernote Beyond simple storage, Supernote provides an active processing pipeline to increase the utility of your notes: 1. **Sync**: Your device uploads `.note` files using the official Private Cloud protocol. -2. **Transcribe**: The server extract pages and use Gemini Vision to OCR your handwriting. +2. **Transcribe**: The server extracts pages and uses an AI provider (Gemini or Mistral) to OCR your handwriting. 3. **Synthesize**: AI Analyzers review your journals to find tasks, themes, and summaries. 4. **Index**: Every word is vectorized, enabling semantic search across your entire library. @@ -43,10 +43,15 @@ The integrated frontend allows you to review your notes and AI insights side-by- ### 1. Launch the Cloud -The easiest way to start is with the `all` bundle and a Gemini API key: +The easiest way to start is with the `all` bundle and an AI API key. Choose either Google Gemini or Mistral AI: ```bash -export SUPERNOTE_GEMINI_API_KEY="your-api-key" +# Option A: Google Gemini (default) +export SUPERNOTE_GEMINI_API_KEY="your-gemini-api-key" + +# Option B: Mistral AI +export SUPERNOTE_MISTRAL_API_KEY="your-mistral-api-key" + pip install "supernote[all]" supernote serve ``` @@ -55,16 +60,16 @@ supernote serve ```bash # Create the initial admin account -supernote admin user add you@example.com --url http://localhost:8080 +supernote admin user add you@example.com --url http://localhost:8000 # Authenticate your CLI -supernote cloud login you@example.com --url http://localhost:8080 +supernote cloud login you@example.com --url http://localhost:8000 ``` ### 3. Connect Your Device 1. On your Supernote, go to **Settings > Sync > Private Cloud**. -2. Enter your server URL (e.g., `http://192.168.1.5:8080`). +2. Enter your server URL (e.g., `http://192.168.1.5:8000`). 3. Log in with the email and password you created in Step 2. 4. Tap **Sync** to begin processing your notes. @@ -130,13 +135,26 @@ The notebook parser is a fork and slightly lighter dependency version of [supern ### Run with Docker +The pre-built image is published to the GitHub Container Registry: + +```bash +# Pull and run the latest image +docker run -d \ + -p 8000:8000 \ + -p 8001:8001 \ + -v supernote-data:/data \ + -e SUPERNOTE_GEMINI_API_KEY="your-api-key" \ + ghcr.io/allenporter/supernote:latest +``` + +Or build from source: + ```bash -# Build & Run server docker build -t supernote . -docker run -d -p 8080:8080 -v $(pwd)/storage:/storage supernote serve +docker run -d -p 8000:8000 -v supernote-data:/data supernote ``` -See [Server Documentation](https://github.com/allenporter/supernote/blob/main/supernote/server/README.md) for details. +For a full setup with Docker Compose, see [docker-compose.yml](docker-compose.yml). ### Developer API diff --git a/config-example.yaml b/config-example.yaml index e5230e4..6f77074 100644 --- a/config-example.yaml +++ b/config-example.yaml @@ -5,7 +5,7 @@ --- # Host and port to bind the server to. host: 0.0.0.0 -port: 8080 +port: 8000 # Directory where uploaded files and databases will be stored. storage_dir: storage diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..77bbbf5 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,33 @@ +--- +services: + supernote: + image: ghcr.io/allenporter/supernote:latest + # Alternatively, build from source: + # build: . + restart: unless-stopped + ports: + - "8000:8000" # Main server + - "8001:8001" # MCP server + volumes: + - supernote-data:/data + environment: + # AI Provider — set one of the following: + SUPERNOTE_GEMINI_API_KEY: "" # Google Gemini API key + # SUPERNOTE_MISTRAL_API_KEY: "" # Mistral AI API key (alternative) + + # Storage & server + SUPERNOTE_STORAGE_DIR: /data + SUPERNOTE_CONFIG_DIR: /data/config + SUPERNOTE_HOST: 0.0.0.0 + SUPERNOTE_PORT: "8000" + SUPERNOTE_MCP_PORT: "8001" + + # Optional: set the public-facing base URL (e.g. behind a reverse proxy) + # SUPERNOTE_BASE_URL: "https://supernote.example.com" + # SUPERNOTE_MCP_BASE_URL: "https://mcp.example.com" + + # Optional: enable user self-registration + # SUPERNOTE_ENABLE_REGISTRATION: "true" + +volumes: + supernote-data: diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md index b7ed97b..9d7bf39 100644 --- a/docs/CONTRIBUTING.md +++ b/docs/CONTRIBUTING.md @@ -61,8 +61,9 @@ This script will initialize a virtual environment using `uv`, install dependenci For rapid iteration, run an ephemeral server. It starts with a clean state and a pre-configured debug user. ```bash -# Enable AI features for development -export SUPERNOTE_GEMINI_API_KEY="your_api_key" +# Enable AI features for development (choose one) +export SUPERNOTE_GEMINI_API_KEY="your-gemini-api-key" # Google Gemini (default) +# export SUPERNOTE_MISTRAL_API_KEY="your-mistral-api-key" # Mistral AI (alternative) # Start the ephemeral server supernote serve --ephemeral diff --git a/docs/note_processing_design.md b/docs/note_processing_design.md index 56c27eb..bc8a37f 100644 --- a/docs/note_processing_design.md +++ b/docs/note_processing_design.md @@ -41,11 +41,11 @@ If we don't want to parse the large "Transcript Summary" every time we need a si 1. **Diff Phase**: Parser extracts page streams. Each stream is hashed and compared to the database. 2. **Visual Phase**: Generate PNGs for new/changed pages. Assemble full PDF using cached PNGs for unchanged pages. 3. **Intelligence Phase**: - - Send PNG to Gemini (with retry/backoff) for OCR. + - Send PNG to the configured AI provider (with retry/backoff) for OCR. - **Chunk Embeddings (Page-indexed)**: Generated per-page from raw OCR text. Ideal for "finding the needle in the haystack." 4. **Document Phase**: - **Transcript Generation**: Aggregate all page text into a single "OCR Transcript" `SummaryDO`. - - **Insight Generation**: Prompt Gemini with the transcript to create an "AI Insights" `SummaryDO`. + - **Insight Generation**: Prompt the AI provider with the transcript to create an "AI Insights" `SummaryDO`. - **Vector Indexing**: - **Chunks**: Generate vectors for each page window. Store in-memory index `(file_id, page_index)`. - **Document**: Generate vector for the Insight Summary. Store in-memory index `(file_id)`. @@ -102,7 +102,7 @@ To maintain a resilient pipeline, modules must follow specific error handling pa ### 1. Expectations for `process()` - **No Internal Try/Except (Mostly)**: Modules should let exceptions bubble up. The base class's `run()` method is the centralized error handler. -- **Descriptive Exceptions**: Raise specific exceptions (e.g., `FileNotFoundError`, `GeminiAPIError`) so the automated logs are useful. +- **Descriptive Exceptions**: Raise specific exceptions (e.g., `FileNotFoundError`, `ValueError`) so the automated logs are useful. - **Idempotency is Mandatory**: If `process()` fails halfway (e.g., after writing a file but before updating a DB record), the next attempt must be able to resume or overwrite without creating duplicates or corruption. ### 2. Orchestrator Reaction @@ -118,5 +118,5 @@ To maintain a resilient pipeline, modules must follow specific error handling pa ### Failure Modes & Corner Cases 1. **Dependency Staleness**: If `PageHashingModule` detects a change, it deletes the `SystemTaskDO` entries for `OCR` and `Embedding`. This causes their `run_if_needed` to return `True` on the next run, forcing a re-poll. -2. **Concurrency Limits**: `ProcessorService` limits the number of files processed in parallel. Modules should use internal semaphores (like `GeminiService`) if they have external API rate limits. +2. **Concurrency Limits**: `ProcessorService` limits the number of files processed in parallel. AI service implementations (like `GeminiService` and `MistralService`) use internal semaphores to respect external API rate limits. 3. **Idempotency Requirement**: If a task fails *after* writing data but *before* updating its status to `COMPLETED`, it will be re-run. `process()` must be safe to call again (e.g., using `UPSERT` or overwriting files). diff --git a/pyproject.toml b/pyproject.toml index 03b34cd..ad363c0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,6 +43,7 @@ server = [ "aiofiles>=25.1.0", "aiohttp-remotes>=1.3.0", "google-genai>=1.57.0", + "mistralai>=2.0.0", "mcp>=1.25.0", "aiohttp-asgi>=0.6.1", ] diff --git a/supernote/cli/client.py b/supernote/cli/client.py index 231aa16..1088074 100644 --- a/supernote/cli/client.py +++ b/supernote/cli/client.py @@ -66,7 +66,7 @@ async def async_cloud_login( Args: email: User email/account password: User password - url: Server URL (e.g. http://localhost:8080) + url: Server URL (e.g. http://localhost:8000) verbose: Enable verbose HTTP logging """ setup_logging(verbose) @@ -504,7 +504,7 @@ def add_parser(subparsers): "--password", type=str, help="user password (prompt if omitted)" ) parser_login.add_argument( - "--url", type=str, required=True, help="Server URL (e.g. http://localhost:8080)" + "--url", type=str, required=True, help="Server URL (e.g. http://localhost:8000)" ) parser_login.add_argument( "-v", "--verbose", action="store_true", help="Enable verbose logging" diff --git a/supernote/cli/server.py b/supernote/cli/server.py index b44f163..3bd0929 100644 --- a/supernote/cli/server.py +++ b/supernote/cli/server.py @@ -28,7 +28,7 @@ def serve_run(args: argparse.Namespace) -> None: # Set environment variables for the server process os.environ["SUPERNOTE_EPHEMERAL"] = "true" if not os.getenv("SUPERNOTE_PORT"): - os.environ["SUPERNOTE_PORT"] = "8080" + os.environ["SUPERNOTE_PORT"] = "8000" if not os.getenv("SUPERNOTE_HOST"): os.environ["SUPERNOTE_HOST"] = "127.0.0.1" os.environ["SUPERNOTE_STORAGE_DIR"] = str(tmp_path) diff --git a/supernote/client/__init__.py b/supernote/client/__init__.py index e09c036..899df4d 100644 --- a/supernote/client/__init__.py +++ b/supernote/client/__init__.py @@ -1,7 +1,7 @@ """Client library for accessing Supernote Cloud services. Example: - async with await Supernote.login("email@example.com", "password", host="http://localhost:8080") as sn: + async with await Supernote.login("email@example.com", "password", host="http://localhost:8000") as sn: # Access Web and Device APIs directly through the session object # Example: List root folder using path-based Device API result = await sn.device.list_folder("/") @@ -10,7 +10,7 @@ print(sn.token) # Use an existing token obtained with `LoginClient` - sn = Supernote.from_token("your-token", host="http://localhost:8080") + sn = Supernote.from_token("your-token", host="http://localhost:8000") """ from . import ( diff --git a/supernote/client/api.py b/supernote/client/api.py index 1a92d0a..8af4f73 100644 --- a/supernote/client/api.py +++ b/supernote/client/api.py @@ -15,7 +15,7 @@ class Supernote: """A session-managed entry point for Supernote clients. Example: - async with await Supernote.login("email@example.com", "password", host="http://localhost:8080") as sn: + async with await Supernote.login("email@example.com", "password", host="http://localhost:8000") as sn: # Access Web and Device APIs directly through the session object # Example: List root folder using path-based Device API result = await sn.device.list_folder("/") @@ -24,7 +24,7 @@ class Supernote: print(sn.token) Example using an existing token: - sn = Supernote.from_token("your-token", host="http://localhost:8080") + sn = Supernote.from_token("your-token", host="http://localhost:8000") # Note: When created this way, you are responsible for closing the session # or passing an existing one. """ diff --git a/supernote/server/PLAN.md b/supernote/server/PLAN.md index e156cfc..8f996f5 100644 --- a/supernote/server/PLAN.md +++ b/supernote/server/PLAN.md @@ -45,10 +45,12 @@ - [x] Add test isolation for storage. ## Phase 5: AI & Intelligence (Completed) -- [x] Implement `GeminiService` for LLM interaction. +- [x] Implement `AIService` abstract base class for provider-agnostic AI integration. +- [x] Implement `GeminiService` for Google Gemini LLM interaction. +- [x] Implement `MistralService` for Mistral AI (OCR API, embeddings, chat). - [x] Implement `ProcessorService` background worker. -- [x] Implement `GeminiOcrModule` for handwriting transcription. -- [x] Implement `GeminiEmbeddingModule` for semantic indexing. +- [x] Implement `OcrModule` for handwriting transcription (provider-agnostic). +- [x] Implement `EmbeddingModule` for semantic indexing (provider-agnostic). - [x] Implement `SummaryModule` for AI-generated highlights. - [x] Implement `SearchService` for semantic search. diff --git a/supernote/server/README.md b/supernote/server/README.md index 4ea622a..9dd0e48 100644 --- a/supernote/server/README.md +++ b/supernote/server/README.md @@ -5,7 +5,7 @@ This package provides a self-hosted implementation of the Supernote Cloud server ## Core Features - **Seamless Sync**: Implements the native Supernote sync protocol. -- **AI Synthesis**: Automatically transcribes handwriting and identifies key insights using Google Gemini. +- **AI Synthesis**: Automatically transcribes handwriting and identifies key insights using Google Gemini or Mistral AI. - **Knowledge Exploration**: Cross-notebook semantic search and web-based file browsing. - **Private & Local**: Store your notes and metadata on your own infrastructure. @@ -17,7 +17,7 @@ See the main [README.md](../../README.md) for a quick start guide. - A Supernote device (Nomad, A5 X, A6 X, etc.) - Python 3.13+ or Docker. -- (Recommended) **Gemini API Key** for OCR and Summarization. +- (Recommended) A **Gemini** or **Mistral AI** API key for OCR and Summarization. ### Configuration @@ -26,24 +26,50 @@ The server is configured via `config/config.yaml` or environment variables. For a comprehensive reference, see the [ServerConfig documentation](https://allenporter.github.io/supernote/supernote/server.html#ServerConfig). #### AI Configuration -To enable AI features, set the Gemini API key: + +AI features require an API key from either Google Gemini (default) or Mistral AI. Set one of the following: + ```bash -export SUPERNOTE_GEMINI_API_KEY="your-api-key" +# Option A: Google Gemini (default) +export SUPERNOTE_GEMINI_API_KEY="your-gemini-api-key" + +# Option B: Mistral AI (takes priority when set) +export SUPERNOTE_MISTRAL_API_KEY="your-mistral-api-key" ``` +> **Note on provider switching**: Gemini embeddings are 3072-dimensional while Mistral embeddings are 1024-dimensional. Switching providers after notes have been indexed requires re-processing all files to regenerate embeddings. + +Additional Gemini model settings: + +| Env var | Default | Description | +|---|---|---| +| `SUPERNOTE_GEMINI_OCR_MODEL` | `gemini-3-flash-preview` | Vision model for OCR | +| `SUPERNOTE_GEMINI_EMBEDDING_MODEL` | `gemini-embedding-001` | Embedding model | +| `SUPERNOTE_GEMINI_CHAT_MODEL` | `gemini-2.0-flash` | Chat model for summaries | +| `SUPERNOTE_GEMINI_MAX_CONCURRENCY` | `5` | Max concurrent API calls (minimum 1) | + +Additional Mistral model settings: + +| Env var | Default | Description | +|---|---|---| +| `SUPERNOTE_MISTRAL_OCR_MODEL` | `mistral-ocr-latest` | Dedicated OCR model | +| `SUPERNOTE_MISTRAL_EMBEDDING_MODEL` | `mistral-embed` | Embedding model | +| `SUPERNOTE_MISTRAL_CHAT_MODEL` | `mistral-large-latest` | Chat model for summaries | +| `SUPERNOTE_MISTRAL_MAX_CONCURRENCY` | `5` | Max concurrent API calls (minimum 1) | + ### Running the Server Start the server using the unified `supernote` CLI: ```bash -# Start the server on port 8080 +# Start the server on port 8000 supernote serve ``` To override settings via environment: ```bash -export SUPERNOTE_PORT=8080 +export SUPERNOTE_PORT=8000 export SUPERNOTE_HOST=0.0.0.0 supernote serve ``` @@ -56,7 +82,7 @@ docker build -t supernote . # Run the container docker run -d \ - -p 8080:8080 \ + -p 8000:8000 \ -v $(pwd)/storage:/storage \ -e SUPERNOTE_GEMINI_API_KEY="your-key" \ --name supernote-server \ @@ -68,7 +94,7 @@ docker run -d \ 1. Review the [official Private Cloud setup guide](https://support.supernote.com/Whats-New/setting-up-your-own-supernote-private-cloud-beta). 2. Ensure your Supernote device and server are on the same Wi-Fi network. 3. On your Supernote device, go to **Settings** > **Sync** > **Supernote Cloud**. -4. Select **Private Cloud** and enter your server's IP and port (e.g., `192.168.1.100:8080`). +4. Select **Private Cloud** and enter your server's IP and port (e.g., `192.168.1.100:8000`). 5. Attempt to login using the credentials created via `supernote admin user add`. 6. Configure folders to sync (e.g., `Note`, `Document`, `EXPORT`) in **Settings** > **Drive** > **Private Cloud**. diff --git a/supernote/server/app.py b/supernote/server/app.py index 55d1e8c..cd2d065 100644 --- a/supernote/server/app.py +++ b/supernote/server/app.py @@ -35,13 +35,15 @@ system, ) from .routes.decorators import public_route +from .services.ai_service import AIService from .services.blob import LocalBlobStorage from .services.coordination import SqliteCoordinationService from .services.file import FileService from .services.gemini import GeminiService +from .services.mistral import MistralService from .services.processor import ProcessorService -from .services.processor_modules.gemini_embedding import GeminiEmbeddingModule -from .services.processor_modules.gemini_ocr import GeminiOcrModule +from .services.processor_modules.embedding import EmbeddingModule +from .services.processor_modules.ocr import OcrModule from .services.processor_modules.page_hashing import PageHashingModule from .services.processor_modules.png_conversion import PngConversionModule from .services.processor_modules.summary import SummaryModule @@ -290,15 +292,31 @@ def create_app(config: ServerConfig) -> web.Application: app["file_service"] = file_service app["url_signer"] = UrlSigner(config.auth.secret_key, coordination_service) app["schedule_service"] = ScheduleService(session_manager) - gemini_service = GeminiService( - config.gemini_api_key, max_concurrency=config.gemini_max_concurrency - ) - app["gemini_service"] = gemini_service + ai_service: AIService + if config.mistral_api_key: + logger.info("Using Mistral as AI backend") + ai_service = MistralService( + api_key=config.mistral_api_key, + ocr_model=config.mistral_ocr_model, + embedding_model=config.mistral_embedding_model, + chat_model=config.mistral_chat_model, + max_concurrency=config.mistral_max_concurrency, + ) + else: + logger.info("Using Gemini as AI backend") + ai_service = GeminiService( + api_key=config.gemini_api_key, + ocr_model=config.gemini_ocr_model, + embedding_model=config.gemini_embedding_model, + chat_model=config.gemini_chat_model, + max_concurrency=config.gemini_max_concurrency, + ) + app["ai_service"] = ai_service summary_service = SummaryService(user_service, session_manager) app["summary_service"] = summary_service - search_service = SearchService(session_manager, gemini_service, config) + search_service = SearchService(session_manager, ai_service) app["search_service"] = search_service app["sync_locks"] = {} # user -> (equipment_no, expiry_time) @@ -313,16 +331,17 @@ def create_app(config: ServerConfig) -> web.Application: processor_service.register_modules( hashing=PageHashingModule(file_service=file_service), png=PngConversionModule(file_service=file_service), - ocr=GeminiOcrModule( - file_service=file_service, config=config, gemini_service=gemini_service + ocr=OcrModule( + file_service=file_service, + ai_service=ai_service, ), - embedding=GeminiEmbeddingModule( - file_service=file_service, config=config, gemini_service=gemini_service + embedding=EmbeddingModule( + file_service=file_service, + ai_service=ai_service, ), summary=SummaryModule( file_service=file_service, - config=config, - gemini_service=gemini_service, + ai_service=ai_service, summary_service=summary_service, ), ) diff --git a/supernote/server/config.py b/supernote/server/config.py index 495238c..a2e8958 100644 --- a/supernote/server/config.py +++ b/supernote/server/config.py @@ -60,25 +60,25 @@ class ServerConfig(DataClassYAMLMixin): Env Var: `SUPERNOTE_HOST` """ - port: int = 8080 + port: int = 8000 """Port to bind the server to. Env Var: `SUPERNOTE_PORT` """ - mcp_port: int = 8081 + mcp_port: int = 8001 """Port to bind the MCP server to. Env Var: `SUPERNOTE_MCP_PORT` """ _base_url: str | None = field(default=None, metadata={"name": "base_url"}) - """Base URL for the main server (port 8080). + """Base URL for the main server (port 8000). Used for generating links and for the MCP Authorization Server issuer. """ _mcp_base_url: str | None = field(default=None, metadata={"name": "mcp_base_url"}) - """Base URL for the MCP server (port 8081). + """Base URL for the MCP server (port 8001). Used for RFC 9728 discovery if the server is behind a proxy. """ @@ -127,16 +127,54 @@ class ServerConfig(DataClassYAMLMixin): gemini_embedding_model: str = "gemini-embedding-001" """Gemini model to use for Embeddings. -BaseConfig + Env Var: `SUPERNOTE_GEMINI_EMBEDDING_MODEL` """ + gemini_chat_model: str = "gemini-2.0-flash" + """Gemini model to use for text generation (summaries). + + Env Var: `SUPERNOTE_GEMINI_CHAT_MODEL` + """ + gemini_max_concurrency: int = 5 """Maximum number of concurrent Gemini API calls. Env Var: `SUPERNOTE_GEMINI_MAX_CONCURRENCY` """ + mistral_api_key: str | None = None + """Mistral AI API Key for OCR, embeddings, and summaries. + + When set, Mistral will be used as the AI backend instead of Gemini. + + Env Var: `SUPERNOTE_MISTRAL_API_KEY` + """ + + mistral_ocr_model: str = "mistral-ocr-latest" + """Mistral model to use for OCR. + + Env Var: `SUPERNOTE_MISTRAL_OCR_MODEL` + """ + + mistral_embedding_model: str = "mistral-embed" + """Mistral model to use for Embeddings. + + Env Var: `SUPERNOTE_MISTRAL_EMBEDDING_MODEL` + """ + + mistral_chat_model: str = "mistral-large-latest" + """Mistral model to use for text generation (summaries). + + Env Var: `SUPERNOTE_MISTRAL_CHAT_MODEL` + """ + + mistral_max_concurrency: int = 5 + """Maximum number of concurrent Mistral API calls. + + Env Var: `SUPERNOTE_MISTRAL_MAX_CONCURRENCY` + """ + @property def base_url(self) -> str: """Get the base URL for the main server. @@ -274,9 +312,7 @@ def load( if gemini_api_key := os.getenv("SUPERNOTE_GEMINI_API_KEY"): config.gemini_api_key = gemini_api_key - logger.info( - f"Using SUPERNOTE_GEMINI_API_KEY: xxx...{config.gemini_api_key[-3:]}" - ) + logger.info("Using SUPERNOTE_GEMINI_API_KEY") if gemini_ocr_model := os.getenv("SUPERNOTE_GEMINI_OCR_MODEL"): config.gemini_ocr_model = gemini_ocr_model @@ -288,14 +324,67 @@ def load( f"Using SUPERNOTE_GEMINI_EMBEDDING_MODEL: {config.gemini_embedding_model}" ) + if gemini_chat_model := os.getenv("SUPERNOTE_GEMINI_CHAT_MODEL"): + config.gemini_chat_model = gemini_chat_model + logger.info( + f"Using SUPERNOTE_GEMINI_CHAT_MODEL: {config.gemini_chat_model}" + ) + if gemini_max_concurrency := os.getenv("SUPERNOTE_GEMINI_MAX_CONCURRENCY"): try: - config.gemini_max_concurrency = int(gemini_max_concurrency) + value = int(gemini_max_concurrency) + if value < 1: + logger.warning( + f"SUPERNOTE_GEMINI_MAX_CONCURRENCY={value} is invalid; clamping to 1" + ) + value = 1 + config.gemini_max_concurrency = value logger.info( f"Using SUPERNOTE_GEMINI_MAX_CONCURRENCY: {config.gemini_max_concurrency}" ) except ValueError: - pass + logger.warning( + f"Ignoring invalid SUPERNOTE_GEMINI_MAX_CONCURRENCY={gemini_max_concurrency!r}; must be an integer" + ) + + if mistral_api_key := os.getenv("SUPERNOTE_MISTRAL_API_KEY"): + config.mistral_api_key = mistral_api_key + logger.info("Using SUPERNOTE_MISTRAL_API_KEY") + + if mistral_ocr_model := os.getenv("SUPERNOTE_MISTRAL_OCR_MODEL"): + config.mistral_ocr_model = mistral_ocr_model + logger.info( + f"Using SUPERNOTE_MISTRAL_OCR_MODEL: {config.mistral_ocr_model}" + ) + + if mistral_embedding_model := os.getenv("SUPERNOTE_MISTRAL_EMBEDDING_MODEL"): + config.mistral_embedding_model = mistral_embedding_model + logger.info( + f"Using SUPERNOTE_MISTRAL_EMBEDDING_MODEL: {config.mistral_embedding_model}" + ) + + if mistral_chat_model := os.getenv("SUPERNOTE_MISTRAL_CHAT_MODEL"): + config.mistral_chat_model = mistral_chat_model + logger.info( + f"Using SUPERNOTE_MISTRAL_CHAT_MODEL: {config.mistral_chat_model}" + ) + + if mistral_max_concurrency := os.getenv("SUPERNOTE_MISTRAL_MAX_CONCURRENCY"): + try: + value = int(mistral_max_concurrency) + if value < 1: + logger.warning( + f"SUPERNOTE_MISTRAL_MAX_CONCURRENCY={value} is invalid; clamping to 1" + ) + value = 1 + config.mistral_max_concurrency = value + logger.info( + f"Using SUPERNOTE_MISTRAL_MAX_CONCURRENCY: {config.mistral_max_concurrency}" + ) + except ValueError: + logger.warning( + f"Ignoring invalid SUPERNOTE_MISTRAL_MAX_CONCURRENCY={mistral_max_concurrency!r}; must be an integer" + ) if config.trace_log_file is None: config.trace_log_file = str( diff --git a/supernote/server/services/ai_service.py b/supernote/server/services/ai_service.py new file mode 100644 index 0000000..9f8abcd --- /dev/null +++ b/supernote/server/services/ai_service.py @@ -0,0 +1,39 @@ +import abc +from typing import Any + + +class AIService(abc.ABC): + """Abstract base class for AI service backends (OCR, embeddings, generation).""" + + @property + @abc.abstractmethod + def is_configured(self) -> bool: + """Return True if the service is properly configured with an API key.""" + ... + + @property + @abc.abstractmethod + def provider_name(self) -> str: + """Return the name of the AI provider (e.g. 'GEMINI', 'MISTRAL').""" + ... + + @abc.abstractmethod + async def ocr_image(self, png_data: bytes, prompt: str) -> str: + """Perform OCR on a PNG image using the given prompt. + + Returns the extracted text content. + """ + ... + + @abc.abstractmethod + async def embed_text(self, text: str) -> list[float]: + """Generate an embedding vector for the given text.""" + ... + + @abc.abstractmethod + async def generate_json(self, prompt: str, schema: dict[str, Any]) -> str: + """Generate JSON content following the given schema. + + Returns a JSON string. + """ + ... diff --git a/supernote/server/services/gemini.py b/supernote/server/services/gemini.py index 15bda57..72b23ee 100644 --- a/supernote/server/services/gemini.py +++ b/supernote/server/services/gemini.py @@ -1,19 +1,28 @@ import asyncio -import logging from typing import Any from google import genai from google.genai import types -logger = logging.getLogger(__name__) +from supernote.server.services.ai_service import AIService -class GeminiService: - """Shared service for interacting with Google Gemini API.""" +class GeminiService(AIService): + """AI service implementation using Google Gemini.""" - def __init__(self, api_key: str | None, max_concurrency: int = 5) -> None: + def __init__( + self, + api_key: str | None, + ocr_model: str, + embedding_model: str, + chat_model: str, + max_concurrency: int = 5, + ) -> None: self.api_key = api_key - self.max_concurrency = max_concurrency + self._ocr_model = ocr_model + self._embedding_model = embedding_model + self._chat_model = chat_model + self.max_concurrency = max(1, max_concurrency) self._client: genai.Client | None = None self._semaphore: asyncio.Semaphore | None = None if self.api_key: @@ -25,6 +34,10 @@ def __init__(self, api_key: str | None, max_concurrency: int = 5) -> None: def is_configured(self) -> bool: return self._client is not None + @property + def provider_name(self) -> str: + return "GEMINI" + def _get_semaphore(self) -> asyncio.Semaphore: """Lazy initialization of semaphore to ensure it's in the correct event loop.""" if self._semaphore is None: @@ -64,3 +77,41 @@ async def embed_content( contents=contents, config=config, ) + + async def ocr_image(self, png_data: bytes, prompt: str) -> str: + response = await self.generate_content( + model=self._ocr_model, + contents=[ + types.Content( + parts=[ + types.Part.from_text(text=prompt), + types.Part.from_bytes(data=png_data, mime_type="image/png"), + ] + ) + ], + config={"media_resolution": "media_resolution_high"}, # type: ignore[arg-type] + ) + return response.text or "" + + async def embed_text(self, text: str) -> list[float]: + response = await self.embed_content( + model=self._embedding_model, + contents=text, + ) + if not response.embeddings: + raise ValueError("No embeddings returned from Gemini API") + values = response.embeddings[0].values + if not values: + raise ValueError("Empty embedding values returned from Gemini API") + return list(values) + + async def generate_json(self, prompt: str, schema: dict[str, Any]) -> str: + response = await self.generate_content( + model=self._chat_model, + contents=prompt, + config={ + "response_mime_type": "application/json", + "response_json_schema": schema, + }, + ) + return response.text or "" diff --git a/supernote/server/services/mistral.py b/supernote/server/services/mistral.py new file mode 100644 index 0000000..64c14cd --- /dev/null +++ b/supernote/server/services/mistral.py @@ -0,0 +1,113 @@ +import asyncio +import base64 +import json +from typing import Any, cast + +from mistralai.client import Mistral +from mistralai.client.models import ( + AssistantMessage, + SystemMessage, + ToolMessage, + UserMessage, +) + +from supernote.server.services.ai_service import AIService + + +class MistralService(AIService): + """AI service implementation using Mistral AI.""" + + def __init__( + self, + api_key: str | None, + ocr_model: str, + embedding_model: str, + chat_model: str, + max_concurrency: int = 5, + ) -> None: + self._ocr_model = ocr_model + self._embedding_model = embedding_model + self._chat_model = chat_model + self.max_concurrency = max(1, max_concurrency) + self._client: Mistral | None = None + self._semaphore: asyncio.Semaphore | None = None + if api_key: + self._client = Mistral(api_key=api_key) + + @property + def is_configured(self) -> bool: + return self._client is not None + + @property + def provider_name(self) -> str: + return "MISTRAL" + + def _get_semaphore(self) -> asyncio.Semaphore: + """Lazy initialization of semaphore to ensure it's in the correct event loop.""" + if self._semaphore is None: + self._semaphore = asyncio.Semaphore(self.max_concurrency) + return self._semaphore + + async def ocr_image(self, png_data: bytes, prompt: str) -> str: + # The dedicated OCR API does not accept a prompt — it extracts text + # directly from the document. The `prompt` parameter is part of the + # AIService interface for providers that support vision+instruction + # (e.g. Gemini), and is intentionally unused here. + if self._client is None: + raise ValueError("Mistral API key not configured") + + b64_image = base64.b64encode(png_data).decode() + async with self._get_semaphore(): + response = await self._client.ocr.process_async( + model=self._ocr_model, + document={ + "type": "image_url", + "image_url": f"data:image/png;base64,{b64_image}", + }, + ) + pages = getattr(response, "pages", None) + if not pages: + return "" + return "\n\n".join( + page.markdown for page in pages if getattr(page, "markdown", None) + ) + + async def embed_text(self, text: str) -> list[float]: + if self._client is None: + raise ValueError("Mistral API key not configured") + + async with self._get_semaphore(): + response = await self._client.embeddings.create_async( + model=self._embedding_model, + inputs=[text], + ) + if not response.data: + raise ValueError("No embeddings returned from Mistral API") + embedding = response.data[0].embedding + if embedding is None: + raise ValueError("No embedding values returned from Mistral API") + return list(embedding) + + async def generate_json(self, prompt: str, schema: dict[str, Any]) -> str: + if self._client is None: + raise ValueError("Mistral API key not configured") + + schema_str = json.dumps(schema, separators=(",", ":")) + full_prompt = ( + f"{prompt}\n\nRespond with valid JSON matching this schema:\n{schema_str}" + ) + async with self._get_semaphore(): + response = await self._client.chat.complete_async( + model=self._chat_model, + messages=cast( + list[AssistantMessage | SystemMessage | ToolMessage | UserMessage], + [UserMessage(content=full_prompt)], + ), + response_format={"type": "json_object"}, + ) + content = response.choices[0].message.content if response.choices else "" + return ( + content + if isinstance(content, str) + else json.dumps(content, separators=(",", ":")) + ) diff --git a/supernote/server/services/processor_modules/gemini_embedding.py b/supernote/server/services/processor_modules/embedding.py similarity index 68% rename from supernote/server/services/processor_modules/gemini_embedding.py rename to supernote/server/services/processor_modules/embedding.py index 6af5f2d..5e489b4 100644 --- a/supernote/server/services/processor_modules/gemini_embedding.py +++ b/supernote/server/services/processor_modules/embedding.py @@ -2,32 +2,29 @@ import logging from typing import Optional -from supernote.server.config import ServerConfig from supernote.server.db.session import DatabaseSessionManager +from supernote.server.services.ai_service import AIService from supernote.server.services.file import FileService -from supernote.server.services.gemini import GeminiService from supernote.server.services.processor_modules import ProcessorModule from supernote.server.utils.note_content import get_page_content_by_id logger = logging.getLogger(__name__) -class GeminiEmbeddingModule(ProcessorModule): - """Module responsible for generating embeddings for note pages using Gemini.""" +class EmbeddingModule(ProcessorModule): + """Module responsible for generating embeddings for note pages using AI.""" def __init__( self, file_service: FileService, - config: ServerConfig, - gemini_service: GeminiService, + ai_service: AIService, ) -> None: self.file_service = file_service - self.config = config - self.gemini_service = gemini_service + self.ai_service = ai_service @property def name(self) -> str: - return "GeminiEmbeddingModule" + return "EmbeddingModule" @property def task_type(self) -> str: @@ -43,7 +40,7 @@ async def run_if_needed( if not page_id: return False - if not self.gemini_service.is_configured: + if not self.ai_service.is_configured: return False if not await super().run_if_needed( @@ -52,11 +49,8 @@ async def run_if_needed( return False async with session_manager.session() as session: - # Check Prerequisites (Text Content must exist) content = await get_page_content_by_id(session, file_id, page_id) - if not content or not content.text_content: - # Dependency not met yet (OCR not done or empty page) return False return True @@ -72,11 +66,9 @@ async def process( if not page_id: return - # Get Text Content text_content = "" async with session_manager.session() as session: content = await get_page_content_by_id(session, file_id, page_id) - if not content or not content.text_content: logger.warning( f"No text content found for embedding: file {file_id} page {page_id} (idx {page_index})" @@ -84,27 +76,18 @@ async def process( return text_content = content.text_content - # Call Gemini API - if not self.gemini_service.is_configured: - raise ValueError("Gemini API key not configured") - - model_id = self.config.gemini_embedding_model - response = await self.gemini_service.embed_content( - model=model_id, - contents=text_content, - ) + if not self.ai_service.is_configured: + raise ValueError("AI service not configured") - if not response.embeddings: - raise ValueError("No embeddings returned from Gemini API") - - # Assuming single embedding for the whole text block for now - embedding_values = response.embeddings[0].values + embedding_values = await self.ai_service.embed_text(text_content) + if not embedding_values: + raise ValueError( + f"AI service returned empty embedding for file {file_id} page {page_id}" + ) embedding_json = json.dumps(embedding_values) - # Save Result async with session_manager.session() as session: content = await get_page_content_by_id(session, file_id, page_id) - if content: content.embedding = embedding_json await session.commit() diff --git a/supernote/server/services/processor_modules/gemini_ocr.py b/supernote/server/services/processor_modules/ocr.py similarity index 67% rename from supernote/server/services/processor_modules/gemini_ocr.py rename to supernote/server/services/processor_modules/ocr.py index fee8e98..0b82095 100644 --- a/supernote/server/services/processor_modules/gemini_ocr.py +++ b/supernote/server/services/processor_modules/ocr.py @@ -1,40 +1,66 @@ import logging +from dataclasses import dataclass +from pathlib import Path from typing import Optional -from google.genai import types - -from supernote.server.config import ServerConfig from supernote.server.constants import CACHE_BUCKET from supernote.server.db.models.file import UserFileDO from supernote.server.db.session import DatabaseSessionManager +from supernote.server.services.ai_service import AIService from supernote.server.services.file import FileService -from supernote.server.services.gemini import GeminiService from supernote.server.services.processor_modules import ProcessorModule -from supernote.server.utils.gemini_content import PageMetadata, create_gemini_content from supernote.server.utils.note_content import ( + format_page_metadata, get_page_content_by_id, ) from supernote.server.utils.paths import get_page_png_path +from supernote.server.utils.prompt_loader import PROMPT_LOADER, PromptId logger = logging.getLogger(__name__) -class GeminiOcrModule(ProcessorModule): - """Module responsible for extracting text from note pages using Gemini OCR.""" +@dataclass +class PageMetadata: + file_name: str | None + page_index: int + page_id: str + notebook_create_time: int | None + + @property + def file_name_basis(self) -> str | None: + if self.file_name: + return Path(self.file_name).stem.lower() + return None + + +def _build_ocr_prompt(page_metadata: PageMetadata) -> str: + prompt = PROMPT_LOADER.get_prompt( + PromptId.OCR_TRANSCRIPTION, custom_type=page_metadata.file_name_basis + ) + metadata_block = format_page_metadata( + page_index=page_metadata.page_index, + page_id=page_metadata.page_id, + file_name=page_metadata.file_name, + notebook_create_time=page_metadata.notebook_create_time, + include_section_divider=True, + ) + return f"{metadata_block}\n\n{prompt}" + + +class OcrModule(ProcessorModule): + """Module responsible for extracting text from note pages using AI OCR.""" def __init__( self, file_service: FileService, - config: ServerConfig, - gemini_service: GeminiService, + ai_service: AIService, ) -> None: self.file_service = file_service - self.config = config - self.gemini_service = gemini_service + self.ai_service = ai_service @property def name(self) -> str: - return "GeminiOcrModule" + return "OcrModule" @property def task_type(self) -> str: @@ -50,7 +76,7 @@ async def run_if_needed( if page_index is None: return False - if not self.gemini_service.is_configured: + if not self.ai_service.is_configured: return False if not await super().run_if_needed( @@ -61,7 +87,6 @@ async def run_if_needed( if not page_id: return False - # Check if PNG exists (Prerequisite) png_path = get_page_png_path(file_id, page_id) if not await self.file_service.blob_storage.exists(CACHE_BUCKET, png_path): logger.warning( @@ -83,17 +108,15 @@ async def process( logger.error(f"Page ID required for OCR processing of file {file_id}") return - # Get PNG Content png_path = get_page_png_path(file_id, page_id) - png_data = b"" + chunks = [] async for chunk in self.file_service.blob_storage.get(CACHE_BUCKET, png_path): - png_data += chunk + chunks.append(chunk) + png_data = b"".join(chunks) - # Call Gemini API - if not self.gemini_service.is_configured: - raise ValueError("Gemini API key not configured") + if not self.ai_service.is_configured: + raise ValueError("AI service not configured") - # Get File Info for custom prompt and metadata file_name: Optional[str] = None notebook_create_time: Optional[int] = None async with session_manager.session() as session: @@ -108,21 +131,9 @@ async def process( page_id=page_id, notebook_create_time=notebook_create_time, ) - model_id = self.config.gemini_ocr_model - parts = create_gemini_content(page_metadata, png_data) - response = await self.gemini_service.generate_content( - model=model_id, - contents=[ - types.Content( - parts=parts, - ) - ], - config={"media_resolution": "media_resolution_high"}, # type: ignore[arg-type] - ) - - text_content = response.text if response.text else "" + prompt = _build_ocr_prompt(page_metadata) + text_content = await self.ai_service.ocr_image(png_data, prompt) - # Save Result async with session_manager.session() as session: content = await get_page_content_by_id(session, file_id, page_id) if content: diff --git a/supernote/server/services/processor_modules/summary.py b/supernote/server/services/processor_modules/summary.py index 99b6381..ada824c 100644 --- a/supernote/server/services/processor_modules/summary.py +++ b/supernote/server/services/processor_modules/summary.py @@ -13,13 +13,12 @@ AddSummaryDTO, UpdateSummaryDTO, ) -from supernote.server.config import ServerConfig from supernote.server.db.models.file import UserFileDO from supernote.server.db.models.note_processing import NotePageContentDO from supernote.server.db.models.user import UserDO from supernote.server.db.session import DatabaseSessionManager +from supernote.server.services.ai_service import AIService from supernote.server.services.file import FileService -from supernote.server.services.gemini import GeminiService from supernote.server.services.processor_modules import ProcessorModule from supernote.server.services.summary import SummaryService from supernote.server.utils.note_content import format_page_metadata @@ -69,13 +68,11 @@ class SummaryModule(ProcessorModule): def __init__( self, file_service: FileService, - config: ServerConfig, - gemini_service: GeminiService, + ai_service: AIService, summary_service: SummaryService, ) -> None: self.file_service = file_service - self.config = config - self.gemini_service = gemini_service + self.ai_service = ai_service self.summary_service = summary_service @property @@ -98,7 +95,7 @@ async def run_if_needed( if page_index is not None: return False - if not self.gemini_service.is_configured: + if not self.ai_service.is_configured: return False if not await super().run_if_needed(file_id, session_manager, page_index): @@ -118,7 +115,7 @@ async def process( Generates an AI summary for the given file. 1. Aggregates all OCR text for the file. - 2. Sends to Gemini for summarization and date extraction. + 2. Sends to the AI service for summarization and date extraction. 3. Stores the result as a new Summary. """ logger.info(f"Starting summary generation for file_id={file_id}") @@ -184,7 +181,7 @@ async def process( ), ) - # 5. Generate AI Summary using Gemini + # 5. Generate AI Summary # Determine prompt based on filename/type custom_type = Path(file_do.file_name).stem.lower() @@ -195,27 +192,21 @@ async def process( prompt = f"{prompt_template}\n\nTRANSCRIPT:\n{full_text}" try: - response = await self.gemini_service.generate_content( - model=self.config.gemini_ocr_model, - contents=prompt, - config={ - "response_mime_type": "application/json", - "response_json_schema": build_json_schema( - SummaryResponse - ).to_dict(), - }, + response_text = await self.ai_service.generate_json( + prompt=prompt, + schema=build_json_schema(SummaryResponse).to_dict(), ) except Exception as e: logger.error(f"Failed to generate AI summary for file {file_id}: {e}") - return + raise # Parse JSON response ai_summary = "No summary generated." metadata_str = None - if response.text: + if response_text: try: - data = json.loads(response.text) + data = json.loads(response_text) segments_data = data.get("segments", []) # Format segments into Markdown and collect metadata @@ -264,7 +255,7 @@ async def process( except json.JSONDecodeError: logger.error(f"Failed to parse JSON response for file {file_id}") - ai_summary = response.text + ai_summary = response_text await self._upsert_summary( user_email, @@ -272,7 +263,7 @@ async def process( file_id=file_id, unique_identifier=summary_uuid, content=ai_summary, - data_source="GEMINI", + data_source=self.ai_service.provider_name, source_path=file_do.file_name, metadata=metadata_str, ), diff --git a/supernote/server/services/search.py b/supernote/server/services/search.py index b74a2a1..2011f33 100644 --- a/supernote/server/services/search.py +++ b/supernote/server/services/search.py @@ -7,11 +7,10 @@ import numpy as np from sqlalchemy import select -from supernote.server.config import ServerConfig from supernote.server.db.models.file import UserFileDO from supernote.server.db.models.note_processing import NotePageContentDO from supernote.server.db.session import DatabaseSessionManager -from supernote.server.services.gemini import GeminiService +from supernote.server.services.ai_service import AIService from supernote.server.utils.note_content import format_page_metadata, infer_page_date logger = logging.getLogger(__name__) @@ -34,12 +33,10 @@ class SearchService: def __init__( self, session_manager: DatabaseSessionManager, - gemini_service: GeminiService, - config: ServerConfig, + ai_service: AIService, ) -> None: self.session_manager = session_manager - self.gemini_service = gemini_service - self.config = config + self.ai_service = ai_service async def search_chunks( self, @@ -61,8 +58,8 @@ async def search_chunks( date_after: Optional ISO date string (YYYY-MM-DD). Filter results created after this date. date_before: Optional ISO date string (YYYY-MM-DD). Filter results created before this date. """ - if not self.gemini_service.is_configured: - logger.warning("Search requested but Gemini is not configured") + if not self.ai_service.is_configured: + logger.warning("Search requested but AI service is not configured") return [] # Parse date filters @@ -78,23 +75,17 @@ async def search_chunks( return [] # 1. Embed Query - model_id = self.config.gemini_embedding_model try: - response = await self.gemini_service.embed_content( - model=model_id, - contents=query, - ) - if not response.embeddings: - logger.error("No embeddings returned for query") - return [] - - # Process the embedding values - query_embedding = np.array(response.embeddings[0].values) + embedding_values = await self.ai_service.embed_text(query) + query_embedding = np.array(embedding_values) except (ValueError, RuntimeError, TypeError) as e: logger.error(f"Failed to fetch or process query embedding: {e}") return [] query_norm = np.linalg.norm(query_embedding) + if query_norm == 0: + logger.error("Query embedding has zero norm, cannot compute similarity") + return [] # 2. Fetch Candidates async with self.session_manager.session() as session: @@ -133,10 +124,13 @@ async def search_chunks( try: candidate_embedding = np.array(embedding_list) + candidate_norm = np.linalg.norm(candidate_embedding) + if candidate_norm == 0: + continue # Cosine Similarity score = np.dot(query_embedding, candidate_embedding) / ( - query_norm * np.linalg.norm(candidate_embedding) + query_norm * candidate_norm ) # Date Inference (Phase 2) diff --git a/supernote/server/utils/gemini_content.py b/supernote/server/utils/gemini_content.py deleted file mode 100644 index 57df38c..0000000 --- a/supernote/server/utils/gemini_content.py +++ /dev/null @@ -1,46 +0,0 @@ -"""Library for preparing Gemini Content requests.""" - -from dataclasses import dataclass -from pathlib import Path - -from google.genai import types - -from .note_content import format_page_metadata -from .prompt_loader import PROMPT_LOADER, PromptId - - -@dataclass -class PageMetadata: - file_name: str | None - page_index: int - page_id: str - notebook_create_time: int | None - - @property - def file_name_basis(self) -> str | None: - if self.file_name: - return Path(self.file_name).stem.lower() - return None - - -def create_gemini_content( - page_metadata: PageMetadata, - png_data: bytes, -) -> list[types.Part]: - prompt = PROMPT_LOADER.get_prompt( - PromptId.OCR_TRANSCRIPTION, custom_type=page_metadata.file_name_basis - ) - - metadata_block = format_page_metadata( - page_index=page_metadata.page_index or 0, - page_id=page_metadata.page_id, - file_name=page_metadata.file_name, - notebook_create_time=page_metadata.notebook_create_time, - include_section_divider=True, - ) - prompt = f"{metadata_block}\n\n{prompt}" - - return [ - types.Part.from_text(text=prompt), - types.Part.from_bytes(data=png_data, mime_type="image/png"), - ] diff --git a/tests/server/mcp/test_client.py b/tests/server/mcp/test_client.py index 54cee28..0eccaf1 100644 --- a/tests/server/mcp/test_client.py +++ b/tests/server/mcp/test_client.py @@ -5,7 +5,7 @@ from collections.abc import AsyncGenerator, Generator from contextlib import asynccontextmanager from typing import Any -from unittest.mock import AsyncMock, patch +from unittest.mock import AsyncMock, PropertyMock, patch import httpx import pytest @@ -77,17 +77,15 @@ async def notebook_file_id( def mock_gemini_service() -> Generator[None, None, None]: """Fixture to mock Gemini service.""" # 1. Mock Gemini Service to avoid network calls - mock_embedding_response = AsyncMock() - mock_embedding_response.embeddings = [AsyncMock(values=[0.1] * 768)] - with ( patch( "supernote.server.services.gemini.GeminiService.is_configured", + new_callable=PropertyMock, return_value=True, ), patch( - "supernote.server.services.gemini.GeminiService.embed_content", - return_value=mock_embedding_response, + "supernote.server.services.gemini.GeminiService.embed_text", + AsyncMock(return_value=[0.1] * 768), ), ): yield diff --git a/tests/server/routes/test_extended.py b/tests/server/routes/test_extended.py index b6fa6d1..d8ed3a6 100644 --- a/tests/server/routes/test_extended.py +++ b/tests/server/routes/test_extended.py @@ -1,6 +1,6 @@ from collections.abc import Generator from typing import Any -from unittest.mock import AsyncMock, patch +from unittest.mock import AsyncMock, PropertyMock, patch import pytest @@ -21,17 +21,15 @@ def extended_client(authenticated_client: Client) -> ExtendedClient: def mock_gemini_service() -> Generator[None, None, None]: """Fixture to mock Gemini service.""" # Mock Gemini Service to avoid network calls - mock_embedding_response = AsyncMock() - mock_embedding_response.embeddings = [AsyncMock(values=[1.0, 0.0, 0.0])] - with ( patch( "supernote.server.services.gemini.GeminiService.is_configured", + new_callable=PropertyMock, return_value=True, ), patch( - "supernote.server.services.gemini.GeminiService.embed_content", - return_value=mock_embedding_response, + "supernote.server.services.gemini.GeminiService.embed_text", + AsyncMock(return_value=[1.0, 0.0, 0.0]), ), ): yield diff --git a/tests/server/services/processor_modules/conftest.py b/tests/server/services/processor_modules/conftest.py index c04fe39..dce08de 100644 --- a/tests/server/services/processor_modules/conftest.py +++ b/tests/server/services/processor_modules/conftest.py @@ -1,13 +1,13 @@ from pathlib import Path -from unittest.mock import MagicMock +from unittest.mock import AsyncMock, MagicMock import pytest from supernote.server.config import AuthConfig, ServerConfig from supernote.server.db.session import DatabaseSessionManager +from supernote.server.services.ai_service import AIService from supernote.server.services.blob import BlobStorage from supernote.server.services.file import FileService -from supernote.server.services.gemini import GeminiService from supernote.server.services.user import UserService @@ -32,7 +32,6 @@ def server_config_gemini(tmp_path: Path) -> ServerConfig: conf = ServerConfig( auth=AuthConfig(secret_key="secret"), storage_dir=str(tmp_path), - # db_url is a property, not an init arg. We mock session_manager anyway. ) conf.gemini_api_key = "fake-key" conf.gemini_ocr_model = "gemini-2.0-flash-exp" @@ -41,12 +40,17 @@ def server_config_gemini(tmp_path: Path) -> ServerConfig: @pytest.fixture -def gemini_service(server_config_gemini: ServerConfig) -> GeminiService: - return GeminiService(api_key=server_config_gemini.gemini_api_key) +def mock_ai_service() -> MagicMock: + service = MagicMock(spec=AIService) + service.is_configured = True + service.provider_name = "GEMINI" + service.ocr_image = AsyncMock(return_value="") + service.embed_text = AsyncMock(return_value=[]) + service.generate_json = AsyncMock(return_value="{}") + return service +# Backwards-compatible alias used by older tests @pytest.fixture -def mock_gemini_service() -> MagicMock: - service = MagicMock(spec=GeminiService) - service.is_configured = True - return service +def mock_gemini_service(mock_ai_service: MagicMock) -> MagicMock: + return mock_ai_service diff --git a/tests/server/services/processor_modules/test_gemini_embedding.py b/tests/server/services/processor_modules/test_embedding.py similarity index 51% rename from tests/server/services/processor_modules/test_gemini_embedding.py rename to tests/server/services/processor_modules/test_embedding.py index ee13c83..2b7be23 100644 --- a/tests/server/services/processor_modules/test_gemini_embedding.py +++ b/tests/server/services/processor_modules/test_embedding.py @@ -4,42 +4,35 @@ import pytest from sqlalchemy import select -from supernote.server.config import ServerConfig from supernote.server.db.models.file import UserFileDO from supernote.server.db.models.note_processing import NotePageContentDO, SystemTaskDO from supernote.server.db.session import DatabaseSessionManager from supernote.server.services.file import FileService -from supernote.server.services.processor_modules.gemini_embedding import ( - GeminiEmbeddingModule, -) +from supernote.server.services.processor_modules.embedding import EmbeddingModule @pytest.fixture -def gemini_embedding_module( +def embedding_module( file_service: FileService, - server_config_gemini: ServerConfig, - mock_gemini_service: MagicMock, -) -> GeminiEmbeddingModule: - return GeminiEmbeddingModule( + mock_ai_service: MagicMock, +) -> EmbeddingModule: + return EmbeddingModule( file_service=file_service, - config=server_config_gemini, - gemini_service=mock_gemini_service, + ai_service=mock_ai_service, ) async def test_process_embedding_success( - gemini_embedding_module: GeminiEmbeddingModule, + embedding_module: EmbeddingModule, session_manager: DatabaseSessionManager, - mock_gemini_service: MagicMock, + mock_ai_service: MagicMock, ) -> None: - # Setup Data user_id = 100 file_id = 999 page_index = 0 storage_key = "test_note_storage_key" async with session_manager.session() as session: - # UserFile user_file = UserFileDO( id=file_id, user_id=user_id, @@ -49,7 +42,6 @@ async def test_process_embedding_success( ) session.add(user_file) - # NotePageContent (Pre-existing from OCR) content = NotePageContentDO( file_id=file_id, page_index=page_index, @@ -60,29 +52,17 @@ async def test_process_embedding_success( session.add(content) await session.commit() - # Mock Gemini API Response - mock_response = MagicMock() - mock_embedding = MagicMock() - mock_embedding.values = [0.1, 0.2, 0.3] - mock_response.embeddings = [mock_embedding] - mock_gemini_service.embed_content.return_value = mock_response + mock_ai_service.embed_text.return_value = [0.1, 0.2, 0.3] - # Run full module lifecycle - await gemini_embedding_module.run( + await embedding_module.run( file_id, session_manager, page_index=page_index, page_id="p0" ) - # Verifications - # Verify API Call - call_args = mock_gemini_service.embed_content.call_args - assert call_args is not None - _, kwargs = call_args - assert kwargs["model"] == "text-embedding-004" - assert kwargs["contents"] == "This is the text to embed." + # Verify embed_text was called with the correct text + mock_ai_service.embed_text.assert_called_once_with("This is the text to embed.") # Verify DB Updates async with session_manager.session() as session: - # Check Content Update updated_content = ( ( await session.execute( @@ -100,7 +80,6 @@ async def test_process_embedding_success( embedding_list = json.loads(updated_content.embedding) assert embedding_list == [0.1, 0.2, 0.3] - # Check Task Status task = ( ( await session.execute( @@ -118,26 +97,76 @@ async def test_process_embedding_success( assert task.status == "COMPLETED" +async def test_embedding_fails_on_empty_vector( + embedding_module: EmbeddingModule, + session_manager: DatabaseSessionManager, + mock_ai_service: MagicMock, +) -> None: + """EmbeddingModule marks the task FAILED when the AI service returns an empty vector.""" + user_id = 101 + file_id = 888 + + async with session_manager.session() as session: + from supernote.server.db.models.user import UserDO + + session.add(UserDO(id=user_id, email="e@example.com", password_md5="hash")) + session.add( + UserFileDO( + id=file_id, + user_id=user_id, + storage_key="sk", + file_name="f.note", + directory_id=0, + ) + ) + session.add( + NotePageContentDO( + file_id=file_id, + page_index=0, + page_id="p0", + content_hash="h", + text_content="Some text", + ) + ) + await session.commit() + + mock_ai_service.embed_text.return_value = [] + + # run() catches the ValueError and marks the task FAILED rather than re-raising + await embedding_module.run(file_id, session_manager, page_index=0, page_id="p0") + + async with session_manager.session() as session: + task = ( + ( + await session.execute( + select(SystemTaskDO) + .where(SystemTaskDO.file_id == file_id) + .where(SystemTaskDO.task_type == "EMBEDDING_GENERATION") + .where(SystemTaskDO.key == "page_p0") + ) + ) + .scalars() + .first() + ) + assert task is not None + assert task.status == "FAILED" + + async def test_embedding_run_if_needed_disabled( - gemini_embedding_module: GeminiEmbeddingModule, + embedding_module: EmbeddingModule, session_manager: DatabaseSessionManager, - mock_gemini_service: MagicMock, + mock_ai_service: MagicMock, ) -> None: - # Disable Gemini - mock_gemini_service.is_configured = False + mock_ai_service.is_configured = False - # Should return False assert ( - await gemini_embedding_module.run_if_needed( + await embedding_module.run_if_needed( 1, session_manager, page_index=0, page_id="p0" ) is False ) - # run() should still return True (skipped success) assert ( - await gemini_embedding_module.run( - 1, session_manager, page_index=0, page_id="p0" - ) + await embedding_module.run(1, session_manager, page_index=0, page_id="p0") is True ) diff --git a/tests/server/services/processor_modules/test_gemini_ocr.py b/tests/server/services/processor_modules/test_ocr.py similarity index 54% rename from tests/server/services/processor_modules/test_gemini_ocr.py rename to tests/server/services/processor_modules/test_ocr.py index 856bd38..5869a16 100644 --- a/tests/server/services/processor_modules/test_gemini_ocr.py +++ b/tests/server/services/processor_modules/test_ocr.py @@ -3,52 +3,44 @@ import pytest from sqlalchemy import select -from supernote.server.config import ServerConfig from supernote.server.constants import CACHE_BUCKET from supernote.server.db.models.file import UserFileDO from supernote.server.db.models.note_processing import NotePageContentDO, SystemTaskDO from supernote.server.db.session import DatabaseSessionManager from supernote.server.services.blob import BlobStorage from supernote.server.services.file import FileService -from supernote.server.services.processor_modules.gemini_ocr import GeminiOcrModule +from supernote.server.services.processor_modules.ocr import OcrModule from supernote.server.utils.paths import get_page_png_path from supernote.server.utils.prompt_loader import PromptId @pytest.fixture -def gemini_ocr_module( +def ocr_module( file_service: FileService, - server_config_gemini: ServerConfig, - mock_gemini_service: MagicMock, -) -> GeminiOcrModule: - return GeminiOcrModule( + mock_ai_service: MagicMock, +) -> OcrModule: + return OcrModule( file_service=file_service, - config=server_config_gemini, - gemini_service=mock_gemini_service, + ai_service=mock_ai_service, ) async def test_process_ocr_success( - gemini_ocr_module: GeminiOcrModule, + ocr_module: OcrModule, session_manager: DatabaseSessionManager, blob_storage: BlobStorage, - mock_gemini_service: MagicMock, + mock_ai_service: MagicMock, ) -> None: - # Setup Data user_id = 100 file_id = 999 page_index = 0 storage_key = "test_note_storage_key" - # Create dummy PNG - png_content = b"fake-png-data" - # Create dummy PNG png_content = b"fake-png-data" png_path = get_page_png_path(file_id, "p0") await blob_storage.put(CACHE_BUCKET, png_path, png_content) async with session_manager.session() as session: - # UserFile user_file = UserFileDO( id=file_id, user_id=user_id, @@ -58,7 +50,6 @@ async def test_process_ocr_success( ) session.add(user_file) - # NotePageContent (Pre-existing from hashing) content = NotePageContentDO( file_id=file_id, page_index=page_index, @@ -68,44 +59,29 @@ async def test_process_ocr_success( session.add(content) await session.commit() - # Mock Gemini API Response - mock_response = MagicMock() - mock_response.text = "Handwritten text content" - mock_gemini_service.generate_content.return_value = mock_response + mock_ai_service.ocr_image.return_value = "Handwritten text content" - # Mock PromptLoader - with patch("supernote.server.utils.gemini_content.PROMPT_LOADER") as mock_loader: + with patch( + "supernote.server.services.processor_modules.ocr.PROMPT_LOADER" + ) as mock_loader: mock_loader.get_prompt.return_value = "Transcribe this page." - - # Run full module lifecycle - await gemini_ocr_module.run( + await ocr_module.run( file_id, session_manager, page_index=page_index, page_id="p0" ) - - # Verifications - # Verify PromptLoader called with correct filename mock_loader.get_prompt.assert_called_with( PromptId.OCR_TRANSCRIPTION, custom_type="real" ) - # Verify API Call - call_args = mock_gemini_service.generate_content.call_args + # Verify ocr_image called with correct png_data and prompt containing metadata + call_args = mock_ai_service.ocr_image.call_args assert call_args is not None - _, kwargs = call_args - assert kwargs["model"] == "gemini-2.0-flash-exp" - - content_obj = kwargs["contents"][0] - parts = content_obj.parts - assert len(parts) == 2 - assert "Transcribe this page." in parts[0].text - assert "Notebook Filename: real.note" in parts[0].text - assert parts[1].inline_data.data == png_content - # Verify config passed - assert kwargs["config"] == {"media_resolution": "media_resolution_high"} + called_png, called_prompt = call_args.args + assert called_png == png_content + assert "Transcribe this page." in called_prompt + assert "Notebook Filename: real.note" in called_prompt # Verify DB Updates async with session_manager.session() as session: - # Check Content Update updated_content = ( ( await session.execute( @@ -117,11 +93,9 @@ async def test_process_ocr_success( .scalars() .first() ) - assert updated_content is not None assert updated_content.text_content == "Handwritten text content" - # Check Task Status task = ( ( await session.execute( @@ -134,45 +108,33 @@ async def test_process_ocr_success( .scalars() .first() ) - assert task is not None assert task.status == "COMPLETED" async def test_ocr_run_if_needed_disabled( - gemini_ocr_module: GeminiOcrModule, + ocr_module: OcrModule, session_manager: DatabaseSessionManager, - mock_gemini_service: MagicMock, + mock_ai_service: MagicMock, ) -> None: - # Disable Gemini - mock_gemini_service.is_configured = False + mock_ai_service.is_configured = False - # Should return False assert ( - await gemini_ocr_module.run_if_needed( - 1, session_manager, page_index=0, page_id="p0" - ) + await ocr_module.run_if_needed(1, session_manager, page_index=0, page_id="p0") is False ) - - # run() should still return True (skipped success) - assert ( - await gemini_ocr_module.run(1, session_manager, page_index=0, page_id="p0") - is True - ) + assert await ocr_module.run(1, session_manager, page_index=0, page_id="p0") is True async def test_ocr_with_inferred_date( - gemini_ocr_module: GeminiOcrModule, + ocr_module: OcrModule, session_manager: DatabaseSessionManager, blob_storage: BlobStorage, - mock_gemini_service: MagicMock, + mock_ai_service: MagicMock, ) -> None: - # Setup Data file_id = 123 page_id = "P20231027123456" - # Create dummy PNG png_path = get_page_png_path(file_id, page_id) await blob_storage.put(CACHE_BUCKET, png_path, b"data") @@ -183,23 +145,16 @@ async def test_ocr_with_inferred_date( session.add(NotePageContentDO(file_id=file_id, page_index=0, page_id=page_id)) await session.commit() - # Mock Gemini - mock_response = MagicMock() - mock_response.text = "OCR text" - mock_gemini_service.generate_content.return_value = mock_response + mock_ai_service.ocr_image.return_value = "OCR text" - # Mock PromptLoader - with patch("supernote.server.utils.gemini_content.PROMPT_LOADER") as mock_loader: + with patch( + "supernote.server.services.processor_modules.ocr.PROMPT_LOADER" + ) as mock_loader: mock_loader.get_prompt.return_value = "Prompt" - await gemini_ocr_module.run( - file_id, session_manager, page_index=0, page_id=page_id - ) + await ocr_module.run(file_id, session_manager, page_index=0, page_id=page_id) - # Verify Prompt - call_args = mock_gemini_service.generate_content.call_args - _, kwargs = call_args - prompt_text = kwargs["contents"][0].parts[0].text - assert "--- Page 1 ---" in prompt_text - assert "Notebook Filename: test.note" in prompt_text - assert "Page ID: P20231027123456" in prompt_text - assert "Page Date (Inferred): 2023-10-27" in prompt_text + _, called_prompt = mock_ai_service.ocr_image.call_args.args + assert "--- Page 1 ---" in called_prompt + assert "Notebook Filename: test.note" in called_prompt + assert "Page ID: P20231027123456" in called_prompt + assert "Page Date (Inferred): 2023-10-27" in called_prompt diff --git a/tests/server/services/processor_modules/test_processor_integration.py b/tests/server/services/processor_modules/test_processor_integration.py index c3d3d51..8e22da8 100644 --- a/tests/server/services/processor_modules/test_processor_integration.py +++ b/tests/server/services/processor_modules/test_processor_integration.py @@ -1,3 +1,4 @@ +import json from pathlib import Path from unittest.mock import MagicMock @@ -13,10 +14,8 @@ from supernote.server.services.blob import BlobStorage from supernote.server.services.file import FileService from supernote.server.services.processor import ProcessorService -from supernote.server.services.processor_modules.gemini_embedding import ( - GeminiEmbeddingModule, -) -from supernote.server.services.processor_modules.gemini_ocr import GeminiOcrModule +from supernote.server.services.processor_modules.embedding import EmbeddingModule +from supernote.server.services.processor_modules.ocr import OcrModule from supernote.server.services.processor_modules.page_hashing import PageHashingModule from supernote.server.services.processor_modules.png_conversion import ( PngConversionModule, @@ -54,7 +53,6 @@ async def test_full_processing_pipeline_with_real_file( session_manager: DatabaseSessionManager, blob_storage: BlobStorage, test_note_path: Path, - server_config_gemini: MagicMock, mock_gemini_service: MagicMock, authenticated_client: Client, user_service: UserService, @@ -87,17 +85,18 @@ async def test_full_processing_pipeline_with_real_file( # Register real modules (mostly) hashing = PageHashingModule(processor_service.file_service) png = PngConversionModule(processor_service.file_service) - # Mock Gemini modules because they need API keys - ocr = GeminiOcrModule( - processor_service.file_service, server_config_gemini, mock_gemini_service + # Mock AI modules because they need API keys + ocr = OcrModule( + file_service=processor_service.file_service, + ai_service=mock_gemini_service, ) - embedding = GeminiEmbeddingModule( - processor_service.file_service, server_config_gemini, mock_gemini_service + embedding = EmbeddingModule( + file_service=processor_service.file_service, + ai_service=mock_gemini_service, ) summary = SummaryModule( file_service=processor_service.file_service, - config=server_config_gemini, - gemini_service=mock_gemini_service, + ai_service=mock_gemini_service, summary_service=processor_service.summary_service, ) @@ -109,14 +108,21 @@ async def test_full_processing_pipeline_with_real_file( summary=summary, ) - # Mock Gemini responses - mock_response = MagicMock() - mock_response.text = "Handwritten text content" - mock_gemini_service.generate_content.return_value = mock_response - - mock_embed = MagicMock() - mock_embed.values = [0.1, 0.2, 0.3] - mock_gemini_service.embed_content.return_value = MagicMock(embeddings=[mock_embed]) + # Mock AI service responses + mock_gemini_service.ocr_image.return_value = "Handwritten text content" + mock_gemini_service.embed_text.return_value = [0.1, 0.2, 0.3] + mock_gemini_service.generate_json.return_value = json.dumps( + { + "segments": [ + { + "date_range": "2024-01-01", + "summary": "Mock summary", + "extracted_dates": [], + "page_refs": [], + } + ] + } + ) # Execute Pipeline await processor_service.process_file(file_id) @@ -202,7 +208,8 @@ async def test_full_processing_pipeline_with_real_file( assert transcript.data_source == "OCR" # Check AI summary content - assert ai_summary.content == "Handwritten text content" # Mocked response - assert ai_summary.data_source == "GEMINI" + assert ai_summary.content is not None + assert "Mock summary" in ai_summary.content + assert ai_summary.data_source == mock_gemini_service.provider_name print(f"Integration test passed with {total_pages} pages processed.") diff --git a/tests/server/services/processor_modules/test_summary_module.py b/tests/server/services/processor_modules/test_summary_module.py index 83e0f59..f0b8e1c 100644 --- a/tests/server/services/processor_modules/test_summary_module.py +++ b/tests/server/services/processor_modules/test_summary_module.py @@ -9,7 +9,6 @@ AddSummaryDTO, SummaryItem, ) -from supernote.server.config import ServerConfig from supernote.server.db.models.file import UserFileDO from supernote.server.db.models.note_processing import NotePageContentDO, SystemTaskDO from supernote.server.db.models.user import UserDO @@ -33,14 +32,12 @@ def mock_summary_service() -> MagicMock: @pytest.fixture def summary_module( file_service: FileService, - server_config_gemini: ServerConfig, mock_gemini_service: MagicMock, mock_summary_service: MagicMock, ) -> SummaryModule: return SummaryModule( file_service=file_service, - config=server_config_gemini, - gemini_service=mock_gemini_service, + ai_service=mock_gemini_service, summary_service=mock_summary_service, ) @@ -91,10 +88,8 @@ async def test_summary_success( session.add(p2) await session.commit() - # Mock Gemini AI Response - mock_response = MagicMock() - # Return valid JSON matching the new segmented schema - mock_response.text = json.dumps( + # Mock AI JSON response + mock_gemini_service.generate_json.return_value = json.dumps( { "segments": [ { @@ -106,7 +101,6 @@ async def test_summary_success( ] } ) - mock_gemini_service.generate_content.return_value = mock_response # Mock PromptLoader with patch( @@ -124,13 +118,13 @@ async def test_summary_success( PromptId.SUMMARY_GENERATION, custom_type="real" ) - # Verify Gemini called with populated prompt - call_args = mock_gemini_service.generate_content.call_args + # Verify AI called with populated prompt + call_args = mock_gemini_service.generate_json.call_args assert call_args is not None - _, kwargs = call_args - assert "Page 1 text" in kwargs["contents"] - assert "Page 2 text" in kwargs["contents"] - assert "Generate" in kwargs["contents"] + called_prompt = call_args.kwargs["prompt"] + assert "Page 1 text" in called_prompt + assert "Page 2 text" in called_prompt + assert "Generate" in called_prompt # 1. Transcript Upsert transcript_call = mock_summary_service.add_summary.call_args_list[0] @@ -150,7 +144,7 @@ async def test_summary_success( assert dto_ai.unique_identifier == get_summary_id(storage_key) assert "## 2023-10-27" in dto_ai.content assert "AI Summary Output" in dto_ai.content - assert dto_ai.data_source == "GEMINI" + assert dto_ai.data_source == mock_gemini_service.provider_name # Check Metadata assert dto_ai.metadata is not None @@ -209,9 +203,7 @@ async def test_summary_idempotency_update( ) await session.commit() - # Mock Gemini - mock_response = MagicMock() - mock_response.text = json.dumps( + mock_gemini_service.generate_json.return_value = json.dumps( { "segments": [ { @@ -223,7 +215,6 @@ async def test_summary_idempotency_update( ] } ) - mock_gemini_service.generate_content.return_value = mock_response # Mock Existing Summary existing_summary = SummaryItem(id=11, unique_identifier=get_summary_id(storage_key)) @@ -292,10 +283,7 @@ async def test_summary_transcript_with_dates( ) await session.commit() - # Mock Gemini (minimal) - mock_response = MagicMock() - mock_response.text = json.dumps({"segments": []}) - mock_gemini_service.generate_content.return_value = mock_response + mock_gemini_service.generate_json.return_value = json.dumps({"segments": []}) # Run full module lifecycle await summary_module.run(file_id, session_manager) diff --git a/tests/server/services/test_gemini.py b/tests/server/services/test_gemini.py new file mode 100644 index 0000000..2093db8 --- /dev/null +++ b/tests/server/services/test_gemini.py @@ -0,0 +1,195 @@ +import asyncio +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from supernote.server.services.gemini import GeminiService + + +@pytest.fixture +def service() -> GeminiService: + with patch("supernote.server.services.gemini.genai") as mock_genai: + mock_genai.Client.return_value = MagicMock() + svc = GeminiService( + api_key="fake-key", + ocr_model="gemini-ocr-model", + embedding_model="gemini-embedding-001", + chat_model="gemini-2.0-flash", + max_concurrency=2, + ) + return svc + + +def test_is_configured_with_key() -> None: + with patch("supernote.server.services.gemini.genai"): + svc = GeminiService( + api_key="key", ocr_model="m", embedding_model="m", chat_model="m" + ) + assert svc.is_configured + + +def test_is_configured_without_key() -> None: + svc = GeminiService( + api_key=None, ocr_model="m", embedding_model="m", chat_model="m" + ) + assert not svc.is_configured + + +def test_provider_name(service: GeminiService) -> None: + assert service.provider_name == "GEMINI" + + +def test_max_concurrency_clamped_to_one() -> None: + with patch("supernote.server.services.gemini.genai"): + svc = GeminiService( + api_key="key", + ocr_model="m", + embedding_model="m", + chat_model="m", + max_concurrency=0, + ) + assert svc.max_concurrency == 1 + + +async def test_ocr_image(service: GeminiService) -> None: + mock_response = MagicMock() + mock_response.text = "OCR text output" + service._client.aio.models.generate_content = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.ocr_image(b"png-bytes", prompt="Extract text") + + assert result == "OCR text output" + call_kwargs = service._client.aio.models.generate_content.call_args.kwargs # type: ignore[union-attr] + assert call_kwargs["model"] == "gemini-ocr-model" + + +async def test_ocr_image_empty_text(service: GeminiService) -> None: + mock_response = MagicMock() + mock_response.text = None + service._client.aio.models.generate_content = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.ocr_image(b"png-bytes", prompt="Extract text") + + assert result == "" + + +async def test_ocr_image_not_configured() -> None: + svc = GeminiService( + api_key=None, ocr_model="m", embedding_model="m", chat_model="m" + ) + with pytest.raises(ValueError, match="not configured"): + await svc.ocr_image(b"data", prompt="") + + +async def test_embed_text(service: GeminiService) -> None: + mock_embedding = MagicMock() + mock_embedding.values = [0.1, 0.2, 0.3] + mock_response = MagicMock() + mock_response.embeddings = [mock_embedding] + service._client.aio.models.embed_content = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.embed_text("hello") + + assert result == [0.1, 0.2, 0.3] + service._client.aio.models.embed_content.assert_called_once_with( # type: ignore[union-attr] + model="gemini-embedding-001", + contents="hello", + config=None, + ) + + +async def test_embed_text_no_embeddings(service: GeminiService) -> None: + mock_response = MagicMock() + mock_response.embeddings = [] + service._client.aio.models.embed_content = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + with pytest.raises(ValueError, match="No embeddings"): + await service.embed_text("hello") + + +async def test_embed_text_empty_values(service: GeminiService) -> None: + mock_embedding = MagicMock() + mock_embedding.values = [] + mock_response = MagicMock() + mock_response.embeddings = [mock_embedding] + service._client.aio.models.embed_content = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + with pytest.raises(ValueError, match="Empty embedding values"): + await service.embed_text("hello") + + +async def test_embed_text_not_configured() -> None: + svc = GeminiService( + api_key=None, ocr_model="m", embedding_model="m", chat_model="m" + ) + with pytest.raises(ValueError, match="not configured"): + await svc.embed_text("hello") + + +async def test_generate_json(service: GeminiService) -> None: + mock_response = MagicMock() + mock_response.text = '{"result": "ok"}' + service._client.aio.models.generate_content = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.generate_json("Summarise this", schema={"type": "object"}) + + assert result == '{"result": "ok"}' + call_kwargs = service._client.aio.models.generate_content.call_args.kwargs # type: ignore[union-attr] + assert call_kwargs["model"] == "gemini-2.0-flash" + + +async def test_generate_json_empty_response(service: GeminiService) -> None: + mock_response = MagicMock() + mock_response.text = None + service._client.aio.models.generate_content = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.generate_json("prompt", schema={}) + + assert result == "" + + +async def test_generate_json_not_configured() -> None: + svc = GeminiService( + api_key=None, ocr_model="m", embedding_model="m", chat_model="m" + ) + with pytest.raises(ValueError, match="not configured"): + await svc.generate_json("prompt", schema={}) + + +async def test_concurrency_limit() -> None: + max_concurrency = 2 + + with patch("supernote.server.services.gemini.genai") as mock_genai: + mock_genai.Client.return_value = MagicMock() + service = GeminiService( + api_key="fake-key", + ocr_model="m", + embedding_model="gemini-embedding-001", + chat_model="m", + max_concurrency=max_concurrency, + ) + + active_calls = 0 + max_active_seen = 0 + + async def slow_embed(*args: object, **kwargs: object) -> MagicMock: + nonlocal active_calls, max_active_seen + active_calls += 1 + max_active_seen = max(max_active_seen, active_calls) + try: + await asyncio.sleep(0.05) + mock_embedding = MagicMock() + mock_embedding.values = [0.1] + mock_response = MagicMock() + mock_response.embeddings = [mock_embedding] + return mock_response + finally: + active_calls -= 1 + + service._client.aio.models.embed_content = AsyncMock(side_effect=slow_embed) # type: ignore[union-attr, method-assign] + + tasks = [service.embed_text("text") for _ in range(5)] + await asyncio.gather(*tasks) + + assert max_active_seen == max_concurrency + assert active_calls == 0 diff --git a/tests/server/services/test_mistral.py b/tests/server/services/test_mistral.py new file mode 100644 index 0000000..89fac8b --- /dev/null +++ b/tests/server/services/test_mistral.py @@ -0,0 +1,238 @@ +import asyncio +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from supernote.server.services.mistral import MistralService + + +@pytest.fixture +def service() -> MistralService: + with patch("supernote.server.services.mistral.Mistral"): + return MistralService( + api_key="fake-key", + ocr_model="mistral-ocr-latest", + embedding_model="mistral-embed", + chat_model="mistral-large-latest", + max_concurrency=2, + ) + + +def test_is_configured_with_key() -> None: + with patch("supernote.server.services.mistral.Mistral"): + svc = MistralService( + api_key="key", + ocr_model="m", + embedding_model="m", + chat_model="m", + ) + assert svc.is_configured + + +def test_is_configured_without_key() -> None: + svc = MistralService( + api_key=None, + ocr_model="m", + embedding_model="m", + chat_model="m", + ) + assert not svc.is_configured + + +def test_provider_name(service: MistralService) -> None: + assert service.provider_name == "MISTRAL" + + +async def test_ocr_image(service: MistralService) -> None: + mock_page = MagicMock() + mock_page.markdown = "Extracted text" + mock_response = MagicMock() + mock_response.pages = [mock_page] + service._client.ocr.process_async = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.ocr_image(b"png-bytes", prompt="ignored") + + assert result == "Extracted text" + call_kwargs = service._client.ocr.process_async.call_args.kwargs # type: ignore[union-attr] + assert call_kwargs["model"] == "mistral-ocr-latest" + assert call_kwargs["document"]["type"] == "image_url" + assert call_kwargs["document"]["image_url"].startswith("data:image/png;base64,") + + +async def test_ocr_image_multiple_pages(service: MistralService) -> None: + pages = [MagicMock(markdown=f"Page {i}") for i in range(3)] + mock_response = MagicMock() + mock_response.pages = pages + service._client.ocr.process_async = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.ocr_image(b"png-bytes", prompt="") + + assert result == "Page 0\n\nPage 1\n\nPage 2" + + +async def test_ocr_image_not_configured() -> None: + svc = MistralService( + api_key=None, ocr_model="m", embedding_model="m", chat_model="m" + ) + with pytest.raises(ValueError, match="not configured"): + await svc.ocr_image(b"data", prompt="") + + +async def test_embed_text(service: MistralService) -> None: + mock_embedding = MagicMock() + mock_embedding.embedding = [0.1, 0.2, 0.3] + mock_response = MagicMock() + mock_response.data = [mock_embedding] + service._client.embeddings.create_async = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.embed_text("hello") + + assert result == [0.1, 0.2, 0.3] + service._client.embeddings.create_async.assert_called_once_with( # type: ignore[union-attr] + model="mistral-embed", + inputs=["hello"], + ) + + +async def test_embed_text_empty_response(service: MistralService) -> None: + mock_response = MagicMock() + mock_response.data = [] + service._client.embeddings.create_async = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + with pytest.raises(ValueError, match="No embeddings"): + await service.embed_text("hello") + + +async def test_embed_text_not_configured() -> None: + svc = MistralService( + api_key=None, ocr_model="m", embedding_model="m", chat_model="m" + ) + with pytest.raises(ValueError, match="not configured"): + await svc.embed_text("hello") + + +async def test_generate_json(service: MistralService) -> None: + mock_choice = MagicMock() + mock_choice.message.content = '{"result": "ok"}' + mock_response = MagicMock() + mock_response.choices = [mock_choice] + service._client.chat.complete_async = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.generate_json("Summarise this", schema={"type": "object"}) + + assert result == '{"result": "ok"}' + + +async def test_generate_json_not_configured() -> None: + svc = MistralService( + api_key=None, ocr_model="m", embedding_model="m", chat_model="m" + ) + with pytest.raises(ValueError, match="not configured"): + await svc.generate_json("prompt", schema={}) + + +async def test_ocr_image_empty_pages(service: MistralService) -> None: + mock_response = MagicMock() + mock_response.pages = [] + service._client.ocr.process_async = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.ocr_image(b"png-bytes", prompt="") + + assert result == "" + + +async def test_ocr_image_missing_pages(service: MistralService) -> None: + mock_response = MagicMock(spec=[]) # no 'pages' attribute + service._client.ocr.process_async = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.ocr_image(b"png-bytes", prompt="") + + assert result == "" + + +async def test_ocr_image_skips_pages_without_markdown(service: MistralService) -> None: + page_with_text = MagicMock() + page_with_text.markdown = "Real text" + page_no_markdown = MagicMock(spec=[]) # no 'markdown' attribute + mock_response = MagicMock() + mock_response.pages = [page_with_text, page_no_markdown] + service._client.ocr.process_async = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.ocr_image(b"png-bytes", prompt="") + + assert result == "Real text" + + +async def test_generate_json_non_string_content(service: MistralService) -> None: + import json + + mock_choice = MagicMock() + mock_choice.message.content = {"result": "ok"} # dict, not str + mock_response = MagicMock() + mock_response.choices = [mock_choice] + service._client.chat.complete_async = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.generate_json("prompt", schema={}) + + parsed = json.loads(result) + assert parsed == {"result": "ok"} + + +async def test_generate_json_empty_choices(service: MistralService) -> None: + mock_response = MagicMock() + mock_response.choices = [] + service._client.chat.complete_async = AsyncMock(return_value=mock_response) # type: ignore[union-attr, method-assign] + + result = await service.generate_json("prompt", schema={}) + + assert result == "" + + +def test_max_concurrency_clamped_to_one() -> None: + with patch("supernote.server.services.mistral.Mistral"): + svc = MistralService( + api_key="key", + ocr_model="m", + embedding_model="m", + chat_model="m", + max_concurrency=0, + ) + assert svc.max_concurrency == 1 + + +async def test_concurrency_limit() -> None: + max_concurrency = 2 + + with patch("supernote.server.services.mistral.Mistral"): + service = MistralService( + api_key="fake-key", + ocr_model="mistral-ocr-latest", + embedding_model="mistral-embed", + chat_model="mistral-large-latest", + max_concurrency=max_concurrency, + ) + + active_calls = 0 + max_active_seen = 0 + + async def slow_embed(*args: object, **kwargs: object) -> MagicMock: + nonlocal active_calls, max_active_seen + active_calls += 1 + max_active_seen = max(max_active_seen, active_calls) + try: + await asyncio.sleep(0.05) + mock_data = MagicMock() + mock_data.embedding = [0.1] + mock_response = MagicMock() + mock_response.data = [mock_data] + return mock_response + finally: + active_calls -= 1 + + service._client.embeddings.create_async = AsyncMock(side_effect=slow_embed) # type: ignore[union-attr, method-assign] + + tasks = [service.embed_text("text") for _ in range(5)] + await asyncio.gather(*tasks) + + assert max_active_seen == max_concurrency + assert active_calls == 0 diff --git a/tests/server/services/test_processor.py b/tests/server/services/test_processor.py index aafd9fc..c49405f 100644 --- a/tests/server/services/test_processor.py +++ b/tests/server/services/test_processor.py @@ -428,7 +428,13 @@ async def test_gemini_concurrency_limit() -> None: # Use patch to avoid actually calling the API with patch("google.genai.Client") as mock_client_cls: - service = GeminiService(api_key="fake-key", max_concurrency=max_concurrency) + service = GeminiService( + api_key="fake-key", + ocr_model="test-ocr", + embedding_model="test-embed", + chat_model="test-chat", + max_concurrency=max_concurrency, + ) mock_client = mock_client_cls.return_value service._client = mock_client diff --git a/tests/server/services/test_processor_modules.py b/tests/server/services/test_processor_modules.py index 0a29428..d2e9f98 100644 --- a/tests/server/services/test_processor_modules.py +++ b/tests/server/services/test_processor_modules.py @@ -4,10 +4,8 @@ import pytest from supernote.server.services.processor import ProcessorService -from supernote.server.services.processor_modules.gemini_embedding import ( - GeminiEmbeddingModule, -) -from supernote.server.services.processor_modules.gemini_ocr import GeminiOcrModule +from supernote.server.services.processor_modules.embedding import EmbeddingModule +from supernote.server.services.processor_modules.ocr import OcrModule from supernote.server.services.processor_modules.page_hashing import PageHashingModule from supernote.server.services.processor_modules.png_conversion import ( PngConversionModule, @@ -36,10 +34,10 @@ async def test_explicit_orchestration_flow( png = MagicMock(spec=PngConversionModule) png.run = AsyncMock(return_value=True) - ocr = MagicMock(spec=GeminiOcrModule) + ocr = MagicMock(spec=OcrModule) ocr.run = AsyncMock(return_value=True) - embedding = MagicMock(spec=GeminiEmbeddingModule) + embedding = MagicMock(spec=EmbeddingModule) embedding.run = AsyncMock(return_value=True) summary = MagicMock(spec=SummaryModule) @@ -92,10 +90,10 @@ async def test_dependant_skipping( png = MagicMock(spec=PngConversionModule) png.run = AsyncMock(return_value=False) - ocr = MagicMock(spec=GeminiOcrModule) + ocr = MagicMock(spec=OcrModule) ocr.run = AsyncMock(return_value=True) - embedding = MagicMock(spec=GeminiEmbeddingModule) + embedding = MagicMock(spec=EmbeddingModule) embedding.run = AsyncMock(return_value=True) summary = MagicMock(spec=SummaryModule) diff --git a/tests/server/services/test_search.py b/tests/server/services/test_search.py index 146505a..78bb6e7 100644 --- a/tests/server/services/test_search.py +++ b/tests/server/services/test_search.py @@ -3,7 +3,6 @@ import pytest -from supernote.server.config import ServerConfig from supernote.server.db.models.file import UserFileDO from supernote.server.db.models.note_processing import NotePageContentDO from supernote.server.db.session import DatabaseSessionManager @@ -11,31 +10,28 @@ @pytest.fixture -def mock_gemini_service() -> MagicMock: +def mock_ai_service() -> MagicMock: service = MagicMock() service.is_configured = True - service.embed_content = AsyncMock() + service.embed_text = AsyncMock() return service @pytest.fixture def search_service( session_manager: DatabaseSessionManager, - mock_gemini_service: MagicMock, - server_config: ServerConfig, + mock_ai_service: MagicMock, ) -> SearchService: - server_config.gemini_embedding_model = "text-embedding-004" return SearchService( session_manager=session_manager, - gemini_service=mock_gemini_service, - config=server_config, + ai_service=mock_ai_service, ) async def test_search_chunks_success( search_service: SearchService, session_manager: DatabaseSessionManager, - mock_gemini_service: MagicMock, + mock_ai_service: MagicMock, ) -> None: # Setup Data user_id = 1 @@ -79,12 +75,8 @@ async def test_search_chunks_success( ) await session.commit() - # Mock Gemini Embedding for query "cats" - mock_response = MagicMock() - mock_embedding = MagicMock() - mock_embedding.values = [1.0, 0.0, 0.0] - mock_response.embeddings = [mock_embedding] - mock_gemini_service.embed_content.return_value = mock_response + # Mock embedding for query "cats" + mock_ai_service.embed_text.return_value = [1.0, 0.0, 0.0] # Run Search results = await search_service.search_chunks(user_id=user_id, query="cats", top_n=5) @@ -101,7 +93,7 @@ async def test_search_chunks_success( async def test_search_chunks_with_name_filter( search_service: SearchService, session_manager: DatabaseSessionManager, - mock_gemini_service: MagicMock, + mock_ai_service: MagicMock, ) -> None: # Setup Data user_id = 1 @@ -143,12 +135,7 @@ async def test_search_chunks_with_name_filter( ) await session.commit() - # Mock Gemini Embedding - mock_response = MagicMock() - mock_embedding = MagicMock() - mock_embedding.values = [1.0, 0.0] - mock_response.embeddings = [mock_embedding] - mock_gemini_service.embed_content.return_value = mock_response + mock_ai_service.embed_text.return_value = [1.0, 0.0] # Run Search with name filter "Monthly" results = await search_service.search_chunks( @@ -225,10 +212,58 @@ async def test_get_transcript( assert "Page ID: P20231028120000def" in range_trans +async def test_search_skips_zero_norm_candidate( + search_service: SearchService, + session_manager: DatabaseSessionManager, + mock_ai_service: MagicMock, +) -> None: + """Candidates with all-zero embeddings should be skipped (not cause NaN scores).""" + user_id = 1 + file_id = 101 + + async with session_manager.session() as session: + session.add( + UserFileDO( + id=file_id, user_id=user_id, file_name="Notes.note", directory_id=0 + ) + ) + # Good page + session.add( + NotePageContentDO( + file_id=file_id, + page_index=0, + page_id="p0", + text_content="Relevant content.", + embedding=json.dumps([1.0, 0.0]), + ) + ) + # Zero-norm page — should be skipped entirely + session.add( + NotePageContentDO( + file_id=file_id, + page_index=1, + page_id="p1", + text_content="Zero norm page.", + embedding=json.dumps([0.0, 0.0]), + ) + ) + await session.commit() + + mock_ai_service.embed_text.return_value = [1.0, 0.0] + + results = await search_service.search_chunks(user_id=user_id, query="anything") + + # Only the good page should appear; zero-norm page is silently skipped + assert len(results) == 1 + assert results[0].page_index == 0 + # Score should be finite and valid + assert 0.0 <= results[0].score <= 1.0 + + async def test_search_chunks_with_date_filter_inferred( search_service: SearchService, session_manager: DatabaseSessionManager, - mock_gemini_service: MagicMock, + mock_ai_service: MagicMock, ) -> None: # Setup Data user_id = 1 @@ -263,12 +298,7 @@ async def test_search_chunks_with_date_filter_inferred( ) await session.commit() - # Mock Gemini - mock_response = MagicMock() - mock_embedding = MagicMock() - mock_embedding.values = [1.0, 0.0] - mock_response.embeddings = [mock_embedding] - mock_gemini_service.embed_content.return_value = mock_response + mock_ai_service.embed_text.return_value = [1.0, 0.0] # Filter for Oct 27 results = await search_service.search_chunks( diff --git a/tests/server/test_config.py b/tests/server/test_config.py index c013c08..378d984 100644 --- a/tests/server/test_config.py +++ b/tests/server/test_config.py @@ -24,7 +24,7 @@ def test_server_config_defaults(tmp_path: Path) -> None: config = ServerConfig.load(config_dir) assert config.host == "0.0.0.0" - assert config.port == 8080 + assert config.port == 8000 assert config.storage_dir == "storage" assert config.auth.secret_key != "" # Should be generated in-memory @@ -79,7 +79,7 @@ def test_example_config_is_valid() -> None: config = ServerConfig.load(config_file=config_path) assert config.host == "0.0.0.0" - assert config.port == 8080 + assert config.port == 8000 assert config.storage_dir == "storage" assert config.auth.secret_key == "CHANGE_ME_TO_A_SECURE_RANDOM_STRING" assert 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