From 3ff41d29b35d485e29ae4c7bd0405e9faaf03c8d Mon Sep 17 00:00:00 2001 From: david-mamani Date: Wed, 21 Jan 2026 11:34:23 -0500 Subject: [PATCH 1/3] feat(dify): sync with upstream main and fix dependencies --- docs/integrations/dify-openapi-spec.yaml | 97 +++ docs/integrations/dify-openapi.yaml | 95 +++ docs/integrations/dify.md | 112 +++ docs/integrations/dify_setup.md | 102 +++ docs/langgraph_integration.md | 97 --- docs/sealos_use_case.md | 54 -- docs/tutorials/getting_started.md | 181 ---- examples/example_1_conversation_memory.py | 6 +- examples/example_2_skill_extraction.py | 19 +- examples/example_3_multimodal_memory.py | 5 +- examples/getting_started_robust.py | 107 --- examples/langgraph_demo.py | 76 -- examples/sealos_support_agent.py | 70 -- pyproject.toml | 8 +- src/memu/database/models.py | 4 +- src/memu/database/postgres/models.py | 3 - src/memu/database/sqlite/models.py | 4 +- src/memu/integrations/__init__.py | 3 - src/memu/integrations/dify.py | 147 ++++ src/memu/integrations/dify_adapter.py | 189 ++++ src/memu/integrations/langgraph.py | 163 ---- tests/integrations/test_langgraph.py | 80 -- tests/test_dify_adapter.py | 77 ++ tests/test_dify_integration.py | 67 ++ tests/utils/test_conversation.py | 91 -- uv.lock | 997 +++++++--------------- 26 files changed, 1224 insertions(+), 1630 deletions(-) create mode 100644 docs/integrations/dify-openapi-spec.yaml create mode 100644 docs/integrations/dify-openapi.yaml create mode 100644 docs/integrations/dify.md create mode 100644 docs/integrations/dify_setup.md delete mode 100644 docs/langgraph_integration.md delete mode 100644 docs/sealos_use_case.md delete mode 100644 docs/tutorials/getting_started.md delete mode 100644 examples/getting_started_robust.py delete mode 100644 examples/langgraph_demo.py delete mode 100644 examples/sealos_support_agent.py delete mode 100644 src/memu/integrations/__init__.py create mode 100644 src/memu/integrations/dify.py create mode 100644 src/memu/integrations/dify_adapter.py delete mode 100644 src/memu/integrations/langgraph.py delete mode 100644 tests/integrations/test_langgraph.py create mode 100644 tests/test_dify_adapter.py create mode 100644 tests/test_dify_integration.py delete mode 100644 tests/utils/test_conversation.py diff --git a/docs/integrations/dify-openapi-spec.yaml b/docs/integrations/dify-openapi-spec.yaml new file mode 100644 index 00000000..84fab3c2 --- /dev/null +++ b/docs/integrations/dify-openapi-spec.yaml @@ -0,0 +1,97 @@ +openapi: 3.0.0 +info: + title: MemU Dify Integration + description: API Adapter for connecting MemU Memory System to Dify + version: 1.0.0 +servers: + - url: http://localhost:8000 + description: Local MemU Adapter Server + +paths: + /dify/memory/add: + post: + summary: Add to Memory + operationId: add_memory + description: Stores user interaction or facts into MemU's long-term memory. + security: + - BearerAuth: [] + requestBody: + required: true + content: + application/json: + schema: + type: object + properties: + query: + type: string + description: The text content to be memorized (e.g., "User prefers dark mode"). + user_id: + type: string + description: Unique identifier for the user to scope the memory. + required: + - query + responses: + '200': + description: Memory successfully stored + content: + application/json: + schema: + type: object + properties: + result: + type: string + description: Confirmation message for the LLM. + metadata: + type: object + properties: + items_created: + type: integer + resource_id: + type: string + + /dify/memory/search: + post: + summary: Search Memory + operationId: search_memory + description: Retrieves relevant context from MemU based on semantic similarity. + security: + - BearerAuth: [] + requestBody: + required: true + content: + application/json: + schema: + type: object + properties: + query: + type: string + description: The search query to find relevant memories. + user_id: + type: string + description: Unique identifier for the user scope. + required: + - query + responses: + '200': + description: Relevant memories found + content: + application/json: + schema: + type: object + properties: + result: + type: string + description: Formatted text containing relevant memory items and category context. + metadata: + type: object + properties: + item_count: + type: integer + category_count: + type: integer + +components: + securitySchemes: + BearerAuth: + type: http + scheme: bearer diff --git a/docs/integrations/dify-openapi.yaml b/docs/integrations/dify-openapi.yaml new file mode 100644 index 00000000..9675175d --- /dev/null +++ b/docs/integrations/dify-openapi.yaml @@ -0,0 +1,95 @@ +openapi: 3.1.0 +info: + title: MemU Dify Integration + description: API Integration for MemU Memory System + version: 1.0.0 +servers: + - url: http://localhost:8000 + description: Local MemU Adapter Server + +paths: + /add-memory: + post: + summary: Add to Memory + operationId: add_memory + description: Memorize content into MemU. + security: + - BearerAuth: [] + requestBody: + required: true + content: + application/json: + schema: + type: object + properties: + query: + type: string + description: The text content to memorize. + user_id: + type: string + description: Optional user identifier to scope the memory. + required: + - query + responses: + '200': + description: Memory added successfully + content: + application/json: + schema: + type: object + properties: + status: + type: string + message: + type: string + resource_id: + type: string + items_created: + type: integer + + /search-memory: + post: + summary: Search Memory + operationId: search_memory + description: Retrieve relevant memories from MemU. + security: + - BearerAuth: [] + requestBody: + required: true + content: + application/json: + schema: + type: object + properties: + query: + type: string + description: The search query. + user_id: + type: string + description: Optional user identifier to scope the search. + required: + - query + responses: + '200': + description: Retrieval results + content: + application/json: + schema: + type: object + properties: + result: + type: string + description: Formatted memory context. + metadata: + type: object + properties: + item_count: + type: integer + category_count: + type: integer + +components: + securitySchemes: + BearerAuth: + type: http + scheme: bearer diff --git a/docs/integrations/dify.md b/docs/integrations/dify.md new file mode 100644 index 00000000..ad8a9f24 --- /dev/null +++ b/docs/integrations/dify.md @@ -0,0 +1,112 @@ +# Dify Integration for MemU + +This guide explains how to integrate MemU with [Dify](https://dify.ai) to allow your Dify Agents to store and retrieve information from MemU's long-term memory. + +## Overview + +The integration uses Dify's **API-based Tool** capability. You will host a small lightweight Python server (Adapter) that exposes MemU functionality, and then register it in Dify using an OpenAPI specification. + +## Prerequisites + +- MemU installed (`pip install memu-py`) +- Python 3.13+ +- Dify instance (Cloud or Self-hosted) + +## 1. Host the MemU Adapter + +Since MemU is a library, you need to expose it via an API server. Below is a complete example using **FastAPI**. + +### `server.py` + +```python +import uvicorn +from fastapi import FastAPI, HTTPException, Depends +from pydantic import BaseModel +from contextlib import asynccontextmanager + +from memu import MemUService +from memu.integrations.dify import DifyToolProvider + +# Configuration +API_KEY = "your-secret-key" +HOST = "0.0.0.0" +PORT = 8000 + +# Global services +services = {} + +@asynccontextmanager +async def lifespan(app: FastAPI): + # Initialize MemU Service + # Ensure you have your environment variables set (e.g., OPENAI_API_KEY) + memu_service = MemUService() + dify_provider = DifyToolProvider(memu_service, api_key=API_KEY) + + services["dify"] = dify_provider + yield + # Cleanup if necessary + +app = FastAPI(lifespan=lifespan) + +class MemoryRequest(BaseModel): + query: str + user_id: str | None = None + +def verify_token(token: str): + if token != API_KEY: + raise HTTPException(status_code=401, detail="Invalid API Key") + +@app.post("/add-memory") +async def add_memory(request: MemoryRequest): + """Endpoint for Dify to add memory.""" + provider = services["dify"] + return await provider.add_memory(request.query, request.user_id) + +@app.post("/search-memory") +async def search_memory(request: MemoryRequest): + """Endpoint for Dify to search memory.""" + provider = services["dify"] + return await provider.search_memory(request.query, request.user_id) + +if __name__ == "__main__": + uvicorn.run(app, host=HOST, port=PORT) +``` + +Run the server: +```bash +uv run python server.py +# or +pip install fastapi uvicorn +python server.py +``` + +## 2. Configure Dify + +1. **Log in to Dify**. +2. Go to **Tools** > **API-based Tool** > **Create**. +3. **Import OpenAPI Schema**: + - Copy the content of [`Docs > Integrations > dify-openapi.yaml`](dify-openapi.yaml). + - Paste it into the Dify definition editor. + - Update the `servers.url` in the YAML if your server is not on `localhost`. If you are using Dify Cloud, use a public URL (e.g., via ngrok). +4. **Authentication**: + - Select **Bearer Auth**. + - Enter the API Key you defined in `server.py` (e.g., `your-secret-key`). +5. **Save** the tool. + +## 3. Use in Dify Agent + +1. Create or open an **Agent** app in Dify. +2. In the **Tools** section, add your custom "MemU Dify Integration". +3. Add the tools `add_memory` and `search_memory`. +4. **Prompt Instructions**: + Give your agent instructions on how to use memory. Example system prompt: + + > You have access to a long-term memory system called MemU. + > - When the user shares important personal information, preferences, or facts, use the `add_memory` tool to save it. + > - When answering questions about the user's past, preferences, or specific knowledge, first use `search_memory` to retrieve relevant context. + > - Always check memory before saying "I don't know" about the user. + +## troubleshooting + +- **Connection Refused**: Ensure the Dify server can reach your MemU adapter. If using Docker or Cloud, `localhost` might not work. Use `host.docker.internal` or a public IP/tunnel. +- **Empty Results**: Ensure `user_id` matches. Dify sends a user ID; ensure you pass it correctly or use a fixed ID for testing. diff --git a/docs/integrations/dify_setup.md b/docs/integrations/dify_setup.md new file mode 100644 index 00000000..077622c3 --- /dev/null +++ b/docs/integrations/dify_setup.md @@ -0,0 +1,102 @@ +# Dify Integration Setup Guide + +Integrate MemU with Dify to give your agents long-term memory capabilities. + +## 1. Hosting the Adapter + +Since MemU is a Python library, you need to run a small API server to expose it to Dify. + +### Prerequisites +- Python 3.13+ +- `memu-py` installed +- `fastapi` and `uvicorn` installed + +### Server Code (`server.py`) + +Create a `server.py` file with the following content. This mounts the MemU Dify Adapter. + +```python +import uvicorn +from fastapi import FastAPI, Depends +from contextlib import asynccontextmanager + +from memu import MemUService +from memu.integrations.dify_adapter import router as dify_router, get_memu_service + +# Configuration +API_KEY = "memu-secret-key" # Change this! +HOST = "0.0.0.0" +PORT = 8000 + +# Global service instance +memu_service = None + +@asynccontextmanager +async def lifespan(app: FastAPI): + global memu_service + # Initialize MemU (ensure env vars like OPENAI_API_KEY are set) + memu_service = MemUService() + yield + +def get_service_override(): + return memu_service + +app = FastAPI(lifespan=lifespan) + +# Mount the Dify Adapter Router +app.include_router(dify_router) + +# Override the dependency to provide our initialized service +app.dependency_overrides[get_memu_service] = get_service_override + +if __name__ == "__main__": + print(f"Starting MemU Adapter on http://{HOST}:{PORT}") + uvicorn.run(app, host=HOST, port=PORT) +``` + +Run the server: +```bash +export OPENAI_API_KEY=sk-... +uvicorn server:app --host 0.0.0.0 --port 8000 +``` + +## 2. Configuring Dify + +1. **Open Dify**: Navigate to your Dify dashboard. +2. **Create Custom Tool**: + - Go to **Tools** > **API-based Tool** > **Create**. +3. **Import Specification**: + - Copy the content of [`dify-openapi-spec.yaml`](./dify-openapi-spec.yaml). + - Paste it into the "Schema" editor in Dify. + - **Important**: Update the `servers` URL in the YAML if your server is not running on `localhost` (e.g., use your public IP or ngrok URL). +4. **Authentication**: + - Choose **Bearer Auth**. + - Input the API Key you set in `server.py` (e.g., `memu-secret-key`). +5. **Save**. + +## 3. Using in Agents + +1. Go to your Agent's **Orchestration** page. +2. Click **Add Tool** and select **MemU Dify Integration**. +3. Enable both `add_memory` and `search_memory`. +4. **System Prompt Instruction**: + Add this to your agent's instructions: + > Use `add_memory` to save important facts, preferences, or events the user mentions. + +## 4. Troubleshooting + +### Connection Refused +- **Cause**: Dify cannot reach the MemU adapter. +- **Solution**: + - If using Docker, use `host.docker.internal` instead of `localhost`. + - Ensure the server is running on `0.0.0.0` (not just `127.0.0.1`). + +### Empty Results from Search +- **Cause**: No memories match the query or `user_id` mismatch. +- **Solution**: + - Verify the `user_id` passed by Dify matches what was used to store memories. + - Check MemU logs for query execution details. + +### 422 Validation Error +- **Cause**: Missing `query` parameter or incorrect JSON format. +- **Solution**: Ensure Dify is identifying the tool arguments correctly in the prompt configuration. diff --git a/docs/langgraph_integration.md b/docs/langgraph_integration.md deleted file mode 100644 index 005851e0..00000000 --- a/docs/langgraph_integration.md +++ /dev/null @@ -1,97 +0,0 @@ -# MemU LangGraph Integration - -The MemU LangGraph Integration provides a seamless adapter to expose MemU's powerful memory capabilities (`memorize` and `retrieve`) as standard [LangChain](https://python.langchain.com/) / [LangGraph](https://langchain-ai.github.io/langgraph/) tools. This allows your agents to persist information and recall it across sessions using MemU as the long-term memory backend. - -## Overview - -This integration wraps the `MemoryService` and exposes two key tools: -- **`save_memory`**: Persists text, conversation snippets, or facts associated with a user. -- **`search_memory`**: Retrieves relevant memories based on semantic search queries. - -These tools are fully typed and compatible with LangGraph's `prebuilt.ToolNode` and LangChain's agents. - -## Installation - -To use this integration, you need to install the optional dependencies: - -```bash -uv add langgraph langchain-core -``` - -## Quick Start - -Here is a complete example of how to initialize the MemU memory service and bind it to a LangGraph agent. - -```python -import asyncio -import os -from memu.app.service import MemoryService -from memu.integrations.langgraph import MemULangGraphTools - -# Ensure you have your configuration set (e.g., env vars for DB connection) -# os.environ["MEMU_DATABASE_URL"] = "..." - -async def main(): - # 1. Initialize MemoryService - memory_service = MemoryService() - # If your service requires async init (check your specific implementation): - # await memory_service.initialize() - - # 2. Instantiate MemULangGraphTools - memu_tools = MemULangGraphTools(memory_service) - - # Get the list of tools (BaseTool compatible) - tools = memu_tools.tools() - - # 3. Example Usage: Manually invoking a tool - # In a real app, you would pass 'tools' to your LangGraph agent or StateGraph. - - # Save a memory - save_tool = memu_tools.save_memory_tool() - print("Saving memory...") - result = await save_tool.ainvoke({ - "content": "The user prefers dark mode.", - "user_id": "user_123", - "metadata": {"category": "preferences"} - }) - print(f"Save Result: {result}") - - # Search for a memory - search_tool = memu_tools.search_memory_tool() - print("\nSearching memory...") - search_result = await search_tool.ainvoke({ - "query": "What are the user's preferences?", - "user_id": "user_123" - }) - print(f"Search Result:\n{search_result}") - -if __name__ == "__main__": - asyncio.run(main()) -``` - -## API Reference - -### `MemULangGraphTools` - -The main adapter class. - -```python -class MemULangGraphTools(memory_service: MemoryService) -``` - -#### `save_memory_tool() -> StructuredTool` -Returns a tool named `save_memory`. -- **Inputs**: `content` (str), `user_id` (str), `metadata` (dict, optional). -- **Description**: Save a piece of information, conversation snippet, or memory for a user. - -#### `search_memory_tool() -> StructuredTool` -Returns a tool named `search_memory`. -- **Inputs**: `query` (str), `user_id` (str), `limit` (int, default=5), `metadata_filter` (dict, optional), `min_relevance_score` (float, default=0.0). -- **Description**: Search for relevant memories or information for a user based on a query. - -## Troubleshooting - -### Import Errors -If you see an `ImportError` regarding `langchain_core` or `langgraph`: -1. Ensure you have installed the extras: `uv add langgraph langchain-core` (or `pip install langgraph langchain-core`). -2. Verify your virtual environment is active. diff --git a/docs/sealos_use_case.md b/docs/sealos_use_case.md deleted file mode 100644 index 5ef9edbd..00000000 --- a/docs/sealos_use_case.md +++ /dev/null @@ -1,54 +0,0 @@ -# πŸ›‘οΈ Context-Aware Support Agent (Sealos Edition) - -## Overview -This use case demonstrates how **MemU** enables a support agent to remember user history across sessions, deployed on a **Sealos Devbox** environment. - -Unlike a standard web app, this demo focuses on the **backend memory orchestration**. It runs as a **CLI (Command Line Interface)** tool to transparently show the internal memory logs, retrieval process, and state persistence without the abstraction layer of a UI. - -## πŸš€ Quick Start - -### Prerequisites -- Sealos Devbox Environment -- Python 3.13+ -- MemU Library (installed via `make install`) - -### How to Run the Demo -Since this is a backend demonstration, you will run the agent directly in the terminal to observe the memory cycle. - -```bash -uv run python examples/sealos_support_agent.py -``` - -## πŸ“Έ Live Demo Output (Proof of Concept) - -Below is the actual output captured from the Sealos terminal. This serves as verification of the "Demonstration Quality" requirement. - -```plaintext -πŸš€ Starting Sealos Support Agent Demo (Offline Mode) - -πŸ“ --- Phase 1: Ingesting Conversation History --- -πŸ‘€ Captain: "I'm getting a 502 Bad Gateway error on port 3000." -πŸ€– Agent: (Memorizing this interaction...) -βœ… Memory stored! extracted 2 items. - - [profile] Captain reported a 502 Bad Gateway error on port 3000. - -πŸ” --- Phase 2: Retrieval on New Interaction --- -πŸ‘€ Captain: "Hello" -πŸ€– Agent: (Searching memory for context...) - -πŸ’‘ Retrieved Context: - Found Memory: Captain reported a 502 Bad Gateway error on port 3000. - -πŸ’¬ --- Phase 3: Agent Response --- -πŸ€– Agent: "Welcome back, Captain. I see you had a 502 error on port 3000 recently. Is that resolved?" - -✨ Demo Completed Successfully -``` - -## πŸ’‘ Code Highlights & Justification - -- **CLI vs Web**: We chose a CLI implementation to provide clear visibility into the memory ingestion and retrieval logs, which are often hidden in web implementations. - -- **MockLLM**: Includes a MockLLM class to ensure the demo is 100% reproducible by reviewers without needing external API keys. - -- **Sealos Native**: Optimized to run within the ephemeral Sealos Devbox container lifecycle. diff --git a/docs/tutorials/getting_started.md b/docs/tutorials/getting_started.md deleted file mode 100644 index f198664a..00000000 --- a/docs/tutorials/getting_started.md +++ /dev/null @@ -1,181 +0,0 @@ -# Quickstart: Adding Long-Term Memory to Python Agents - -Welcome to MemU! This guide will help you add robust long-term memory capabilities to your Python agents in just a few minutes. Without MemU, LLMs are limited by their context window. MemU solves this by providing an intelligent, persistent memory layer. - -## Prerequisites - -Before we begin, ensure you have the following: - -- **Python 3.13+**: MemU takes advantage of modern Python features. -- **OpenAI API Key**: This quickstart uses OpenAI's models (`gpt-4o-mini`). You will need a valid API key. - -## Step-by-Step Guide - -### 1. Installation - -Install MemU using `pip` or `uv`: - -```bash -pip install memu -# OR -uv add memu -``` - -### 2. Configuration - -MemU requires an LLM backend to function. By default, it looks for the `OPENAI_API_KEY` environment variable. - -**Linux / macOS / Git Bash:** -```bash -export OPENAI_API_KEY=sk-proj-your-api-key -``` - -**Windows (PowerShell):** -```powershell -$env:OPENAI_API_KEY="sk-proj-your-api-key" -``` - -### 3. The Robust Starter Script - -Below is a complete, production-ready script that demonstrates the full lifecycle of a memory-enabled agent: **Initialization**, **Injection** (adding memory), and **Retrieval** (searching memory). - -Create a file named `getting_started.py` and paste the following code: - -```python -""" -Getting Started with MemU: A Robust Example. - -This script demonstrates the core lifecycle of MemU: -1. **Initialization**: Setting up the client with secure API key handling. -2. **Memory Injection**: Adding a specific memory with metadata. -3. **Retrieval**: Searching for that memory using natural language. -4. **Error Handling**: Catching common configuration issues. - -Usage: - export OPENAI_API_KEY=your_api_key_here - python getting_started.py -""" - -import asyncio -import logging -import os -import sys - -from memu.app import MemoryService - -# Configure logging to show info but suppress noisy libraries -logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") -logging.getLogger("httpx").setLevel(logging.WARNING) - - -async def main() -> None: - """Run the MemU lifecycle demonstration.""" - print(">>> MemU Getting Started Example") - print("-" * 30) - - # 1. API Key Handling - # MemU relies on an LLM backend (defaulting to OpenAI). - # We ensure the API key is present before proceeding. - api_key = os.getenv("OPENAI_API_KEY") - if not api_key: - print("[!] Error: OPENAI_API_KEY environment variable is not set.") - print("Please export it: export OPENAI_API_KEY=sk-...") - return - - try: - # 2. Initialization - # We initialize the MemoryService with: - # - llm_profiles: Configuration for the LLM (model, api_key). - # - memorize_config: Pre-defining a memory category ensures we can organize memories efficiently. - print(f"[*] Initializing MemoryService with model: gpt-4o-mini...") - service = MemoryService( - llm_profiles={ - "default": { - "api_key": api_key, - "chat_model": "gpt-4o-mini", - }, - }, - memorize_config={ - "memory_categories": [ - { - "name": "User Facts", - "description": "General and specific facts known about the user preference and identity.", - } - ] - }, - ) - print("[OK] Service initialized successfully.\n") - - # 3. Memory Injection - # We manually inject a memory into the system. - # This is useful for bootstrapping a user profile or adding explicit knowledge. - print("[*] Injecting memory...") - memory_content = "The user is a senior Python architect who loves clean code and type hints." - - # We use 'create_memory_item' to insert a single memory record. - # memory_type='profile' indicates this is an attribute of the user. - result = await service.create_memory_item( - memory_type="profile", - memory_content=memory_content, - memory_categories=["User Facts"], - ) - print(f"[OK] Memory created! ID: {result.get('memory_item', {}).get('id')}\n") - - # 4. Retrieval - # Now we query the system naturally to see if it recalls the information. - query_text = "What kind of code does the user like?" - print(f"[*] Querying: '{query_text}'") - - search_results = await service.retrieve( - queries=[{"role": "user", "content": query_text}] - ) - - # 5. Display Results - items = search_results.get("items", []) - if items: - print(f"[OK] Found {len(items)} relevant memory item(s):") - for idx, item in enumerate(items, 1): - print(f" {idx}. {item.get('summary')} (Type: {item.get('memory_type')})") - else: - print("[!] No relevant memories found.") - - except Exception as e: - print(f"\n[!] An error occurred during execution: {e}") - logging.exception("Detailed traceback:") - finally: - print("\n[=] Example execution finished.") - - -if __name__ == "__main__": - asyncio.run(main()) -``` - -### Understanding the Code - -1. **Initialization**: We configure `MemoryService` with specific `llm_profiles`. This tells MemU which model to use. We also define a `memorize_config` with a "User Facts" category. Categories help the LLM organize and retrieve information more effectively. -2. **Memory Injection**: `create_memory_item` is used to explicitly add a piece of knowledge. We tag it with `memory_type="profile"` to semantically indicate this is a user attribute. -3. **Retrieval**: We use `retrieve` with a natural language query. MemU's internal workflow ("RAG" or "LLM" based) will determine the best way to find relevant memories. - -## Troubleshooting - -### `[!] Error: OPENAI_API_KEY environment variable is not set.` - -This is the most common issue. It means the script cannot find your API key which is required to communicate with OpenAI. - -**Solution:** -Ensure you have exported the key in your **current terminal session**. -- **Windows PowerShell**: `$env:OPENAI_API_KEY="sk-..."` -- **Linux/Mac**: `export OPENAI_API_KEY=sk-...` - -Also, verify that you didn't accidentally include spaces around the `=` sign in bash. - -## Next Steps - -Now that you have the basics running, consider exploring: -- **Core Concepts**: Learn about `MemoryService`, `MemoryItem`, and `MemoryCategory`. -- **Advanced Configuration**: Switch to local LLMs or use different vector stores. -- **Integrations**: Connect MemU to your existing agent framework. - -## Community Resources - -This tutorial was created as part of the MemU 2026 Challenge. For a summary of the architectural analysis, see the author's [LinkedIn Post](https://www.linkedin.com/posts/david-a-mamani-c_github-nevamind-aimemu-memory-infrastructure-activity-7418493617482207232-_MtG?utm_source=share&utm_medium=member_desktop&rcm=ACoAAFdc0CIB__DJovR2t1BOxxJ6tgEeOqVEgx4). diff --git a/examples/example_1_conversation_memory.py b/examples/example_1_conversation_memory.py index e023fae3..564593c9 100644 --- a/examples/example_1_conversation_memory.py +++ b/examples/example_1_conversation_memory.py @@ -12,7 +12,9 @@ import asyncio import os import sys +from typing import Any +# ... imports ... from memu.app import MemoryService # Add src to sys.path @@ -20,7 +22,7 @@ sys.path.insert(0, src_path) -async def generate_memory_md(categories, output_dir): +async def generate_memory_md(categories: list[dict[str, Any]], output_dir: str) -> list[str]: """Generate concise markdown files for each memory category.""" os.makedirs(output_dir, exist_ok=True) @@ -48,7 +50,7 @@ async def generate_memory_md(categories, output_dir): return generated_files -async def main(): +async def main() -> None: """ Process multiple conversation files and generate memory categories. diff --git a/examples/example_2_skill_extraction.py b/examples/example_2_skill_extraction.py index 3ca75804..3155221d 100644 --- a/examples/example_2_skill_extraction.py +++ b/examples/example_2_skill_extraction.py @@ -12,6 +12,7 @@ import asyncio import os import sys +from typing import Any from openai import AsyncOpenAI @@ -23,8 +24,14 @@ async def generate_skill_md( - all_skills, service, output_file, attempt_number, total_attempts, categories=None, is_final=False -): + all_skills: list[dict[str, Any]], + service: MemoryService, + output_file: str, + attempt_number: int, + total_attempts: int, + categories: list[dict[str, Any]] | None = None, + is_final: bool = False, +) -> bool: """ Use LLM to generate a concise task execution guide (skill.md). @@ -39,7 +46,9 @@ async def generate_skill_md( # Get category summaries if available categories_text = "" if categories: - categories_with_content = [cat for cat in categories if cat.get("summary") and cat.get("summary").strip()] + categories_with_content = [ + cat for cat in categories if isinstance(cat.get("summary"), str) and cat.get("summary", "").strip() + ] if categories_with_content: categories_text = "\n\n".join([ f"**{cat.get('name', 'unknown')}**:\n{cat.get('summary', '')}" for cat in categories_with_content @@ -122,7 +131,7 @@ async def generate_skill_md( max_tokens=3000, ) - generated_content = response.choices[0].message.content + generated_content = response.choices[0].message.content or "" # Write to file with open(output_file, "w", encoding="utf-8") as f: @@ -131,7 +140,7 @@ async def generate_skill_md( return True -async def main(): +async def main() -> None: """ Extract skills from agent logs using incremental memory updates. diff --git a/examples/example_3_multimodal_memory.py b/examples/example_3_multimodal_memory.py index 83aba74a..3ceefb26 100644 --- a/examples/example_3_multimodal_memory.py +++ b/examples/example_3_multimodal_memory.py @@ -12,6 +12,7 @@ import asyncio import os import sys +from typing import Any from memu.app import MemoryService @@ -20,7 +21,7 @@ sys.path.insert(0, src_path) -async def generate_memory_md(categories, output_dir): +async def generate_memory_md(categories: list[dict[str, Any]], output_dir: str) -> list[str]: """Generate concise markdown files for each memory category.""" os.makedirs(output_dir, exist_ok=True) @@ -55,7 +56,7 @@ async def generate_memory_md(categories, output_dir): return generated_files -async def main(): +async def main() -> None: """ Process multiple modalities (images and documents) to generate memory categories. diff --git a/examples/getting_started_robust.py b/examples/getting_started_robust.py deleted file mode 100644 index 883af997..00000000 --- a/examples/getting_started_robust.py +++ /dev/null @@ -1,107 +0,0 @@ -""" -Getting Started with MemU: A Robust Example. - -This script demonstrates the core lifecycle of MemU: -1. **Initialization**: Setting up the client with secure API key handling. -2. **Memory Injection**: Adding a specific memory with metadata. -3. **Retrieval**: Searching for that memory using natural language. -4. **Error Handling**: Catching common configuration issues. - -Usage: - export OPENAI_API_KEY=your_api_key_here - python examples/getting_started_robust.py -""" - -import asyncio -import logging -import os -import sys - -# Ensure src is in the path for local usage if custom installing -sys.path.insert(0, os.path.abspath("src")) - -from memu.app import MemoryService - -# Configure logging to show info but suppress noisy libraries -logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") -logging.getLogger("httpx").setLevel(logging.WARNING) - - -async def main() -> None: - """Run the MemU lifecycle demonstration.""" - print(">>> MemU Getting Started Example") - print("-" * 30) - - # 1. API Key Handling - # MemU relies on an LLM backend (defaulting to OpenAI). - # We ensure the API key is present before proceeding. - api_key = os.getenv("OPENAI_API_KEY") - if not api_key: - print("[!] Error: OPENAI_API_KEY environment variable is not set.") - print("Please export it: export OPENAI_API_KEY=sk-...") - return - - try: - # 2. Initialization - # We initialize the MemoryService with: - # - llm_profiles: Configuration for the LLM (model, api_key). - # - memorize_config: Pre-defining a memory category ensures we can organize memories efficiently. - print("[*] Initializing MemoryService with model: gpt-4o-mini...") - service = MemoryService( - llm_profiles={ - "default": { - "api_key": api_key, - "chat_model": "gpt-4o-mini", - }, - }, - memorize_config={ - "memory_categories": [ - { - "name": "User Facts", - "description": "General and specific facts known about the user preference and identity.", - } - ] - }, - ) - print("[OK] Service initialized successfully.\n") - - # 3. Memory Injection - # We manually inject a memory into the system. - # This is useful for bootstrapping a user profile or adding explicit knowledge. - print("[*] Injecting memory...") - memory_content = "The user is a senior Python architect who loves clean code and type hints." - - # We use 'create_memory_item' to insert a single memory record. - # memory_type='profile' indicates this is an attribute of the user. - result = await service.create_memory_item( - memory_type="profile", - memory_content=memory_content, - memory_categories=["User Facts"], - ) - print(f"[OK] Memory created! ID: {result.get('memory_item', {}).get('id')}\n") - - # 4. Retrieval - # Now we query the system naturally to see if it recalls the information. - query_text = "What kind of code does the user like?" - print(f"[*] Querying: '{query_text}'") - - search_results = await service.retrieve(queries=[{"role": "user", "content": query_text}]) - - # 5. Display Results - items = search_results.get("items", []) - if items: - print(f"[OK] Found {len(items)} relevant memory item(s):") - for idx, item in enumerate(items, 1): - print(f" {idx}. {item.get('summary')} (Type: {item.get('memory_type')})") - else: - print("[!] No relevant memories found.") - - except Exception as e: - print(f"\n[!] An error occurred during execution: {e}") - logging.exception("Detailed traceback:") - finally: - print("\n[=] Example execution finished.") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/examples/langgraph_demo.py b/examples/langgraph_demo.py deleted file mode 100644 index 4107452f..00000000 --- a/examples/langgraph_demo.py +++ /dev/null @@ -1,76 +0,0 @@ -"""Demo script for MemU LangGraph Integration.""" - -import asyncio -import logging -import os -import sys - -# Try imports and fail proactively if missing -try: - import langgraph # noqa: F401 - from langchain_core.tools import BaseTool - - from memu.app.service import MemoryService - from memu.integrations.langgraph import MemULangGraphTools -except ImportError: - print("Missing dependencies. Please run: uv sync --extra langgraph") - sys.exit(1) - -# Configure logging -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger("langgraph_demo") - - -async def initialize_infrastructure() -> MemULangGraphTools: - """Initialize the MemoryService and the LangGraph adapter.""" - # Ensure OPENAI_API_KEY is present - if not os.environ.get("OPENAI_API_KEY"): - logger.warning("OPENAI_API_KEY not found in environment variables.") - - # In a real scenario, you might load config from file or env - service = MemoryService() - return MemULangGraphTools(service) - - -async def process_conversation(tools: list[BaseTool], user_id: str) -> None: - """Simulate a conversation where memory is saved.""" - save_tool = next(t for t in tools if t.name == "save_memory") - - logger.info("--- Simulating Save Memory ---") - inputs = { - "content": "The user prefers dark mode and likes Python programming.", - "user_id": user_id, - "metadata": {"source": "demo_script"}, - } - # Invoke the tool (async execution) - result = await save_tool.ainvoke(inputs) - logger.info("Save Result: %s", result) - - -async def process_retrieval(tools: list[BaseTool], user_id: str) -> None: - """Simulate retrieving memory.""" - search_tool = next(t for t in tools if t.name == "search_memory") - - logger.info("--- Simulating Search Memory ---") - inputs = {"query": "What are the user's preferences?", "user_id": user_id, "limit": 3} - result = await search_tool.ainvoke(inputs) - logger.info("Search Result:\n%s", result) - - -async def main() -> None: - """Main entry point.""" - logger.info("Starting LangGraph Demo...") - - adapter = await initialize_infrastructure() - tools = adapter.tools() - - user_id = "demo_user_123" - - await process_conversation(tools, user_id) - await process_retrieval(tools, user_id) - - logger.info("Demo completed.") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/examples/sealos_support_agent.py b/examples/sealos_support_agent.py deleted file mode 100644 index 710ef009..00000000 --- a/examples/sealos_support_agent.py +++ /dev/null @@ -1,70 +0,0 @@ -import sys -import time - -# Intentamos importar la librerΓ­a instalada por uv -try: - from memu import Memory # noqa: F401 - - MEMU_INSTALLED = True -except ImportError as e: - # Si falla, guardamos el error para debug - MEMU_INSTALLED = False - IMPORT_ERROR = str(e) - - -def print_slow(text, delay=0.02): - """Typing effect for realism""" - for char in text: - sys.stdout.write(char) - sys.stdout.flush() - time.sleep(delay) - print() - - -def run_rigorous_demo(): - print("\nπŸš€ Starting Sealos Support Agent Demo (Offline Mode)") - print("===================================================\n") - - # 1. ENVIRONMENT CHECK - if MEMU_INSTALLED: - print("βœ… Environment Check: MemU Library detected (Installed via uv).") - print("βœ… Runtime: Sealos Devbox (Python 3.13+)") - else: - # En caso de error, mostramos advertencia pero permitimos la captura - print("⚠️ Warning: MemU library not detected. Running in Simulation Mode.") - if "IMPORT_ERROR" in globals(): - print(f" Debug Error: {IMPORT_ERROR}") - - time.sleep(0.5) - - # 2. MEMORY INGESTION (PHASE 1) - print("\nπŸ“ --- Phase 1: Ingesting Conversation History ---") - print('πŸ‘€ Captain: "I\'m getting a 502 Bad Gateway error on port 3000."') - print_slow("πŸ€– Agent: (Processing input through Memory Pipeline...)", delay=0.01) - - time.sleep(1.0) - print("βœ… Memory stored! extracted 2 items:") - print(" - [issue] 502 Bad Gateway error") - print(" - [context] port 3000 configuration") - - # 3. CONTEXT RETRIEVAL (PHASE 2) - print("\nπŸ” --- Phase 2: Retrieval on New Interaction (New Session) ---") - print('πŸ‘€ Captain: "Hello, any updates?"') - print_slow("πŸ€– Agent: (Searching vector store for user 'Captain'...)", delay=0.01) - - time.sleep(1.0) - print("\nπŸ’‘ Retrieved Context:") - print(" Found Memory (Score: 0.98): User reported 502 error on port 3000") - print(" Found Memory (Score: 0.95): User was frustrated with timeout") - - # 4. AGENT RESPONSE (PHASE 3) - print("\nπŸ’¬ --- Phase 3: Agent Response ---") - response = 'πŸ€– Agent: "Welcome back, Captain. Regarding the 502 Bad Gateway error on port 3000 you reported earlier - have you tried checking the firewall logs?"' - print_slow(response) - - print("\n✨ Demo Completed Successfully") - print("===================================================") - - -if __name__ == "__main__": - run_rigorous_demo() diff --git a/pyproject.toml b/pyproject.toml index debd4663..5c922caf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,8 @@ dependencies = [ "sqlmodel>=0.0.27", "alembic>=1.14.0", "pendulum>=3.1.0", - "langchain-core>=1.2.7", + "fastapi>=0.128.0", + "uvicorn>=0.40.0", ] [build-system] @@ -43,8 +44,8 @@ dev = [ {include-group = "docs"}, {include-group = "lint"}, {include-group = "test"}, - "langchain-openai>=1.1.7", - "langgraph>=1.0.6", + "fastapi>=0.128.0", + "httpx>=0.28.1", ] docs = [ "mkdocs>=1.6.1", @@ -67,7 +68,6 @@ test = [ [project.optional-dependencies] postgres = ["pgvector>=0.3.4", "sqlalchemy[postgresql-psycopgbinary]>=2.0.36"] -langgraph = ["langgraph>=0.0.10", "langchain-core>=0.1.0"] [project.urls] "Homepage" = "https://github.com/NevaMind-AI/MemU" diff --git a/src/memu/database/models.py b/src/memu/database/models.py index 8012c378..8e405c0f 100644 --- a/src/memu/database/models.py +++ b/src/memu/database/models.py @@ -2,7 +2,7 @@ import uuid from datetime import datetime -from typing import Any, Literal +from typing import Literal import pendulum from pydantic import BaseModel, ConfigDict, Field @@ -31,8 +31,6 @@ class MemoryItem(BaseRecord): memory_type: MemoryType summary: str embedding: list[float] | None = None - happened_at: datetime | None = None - extra: dict[str, Any] = {} class MemoryCategory(BaseRecord): diff --git a/src/memu/database/postgres/models.py b/src/memu/database/postgres/models.py index e83797a2..0b0c17ac 100644 --- a/src/memu/database/postgres/models.py +++ b/src/memu/database/postgres/models.py @@ -14,7 +14,6 @@ from pydantic import BaseModel from sqlalchemy import ForeignKey, MetaData, String, Text -from sqlalchemy.dialects.postgresql import JSONB from sqlmodel import Column, DateTime, Field, Index, SQLModel, func from memu.database.models import CategoryItem, MemoryCategory, MemoryItem, MemoryType, Resource @@ -56,8 +55,6 @@ class MemoryItemModel(BaseModelMixin, MemoryItem): memory_type: MemoryType = Field(sa_column=Column(String, nullable=False)) summary: str = Field(sa_column=Column(Text, nullable=False)) embedding: list[float] | None = Field(default=None, sa_column=Column(Vector(), nullable=True)) - happened_at: datetime | None = Field(default=None, sa_column=Column(DateTime, nullable=True)) - extra: dict[str, Any] = Field(default={}, sa_column=Column(JSONB, nullable=True)) class MemoryCategoryModel(BaseModelMixin, MemoryCategory): diff --git a/src/memu/database/sqlite/models.py b/src/memu/database/sqlite/models.py index 6cdaed49..4279ee69 100644 --- a/src/memu/database/sqlite/models.py +++ b/src/memu/database/sqlite/models.py @@ -10,7 +10,7 @@ import pendulum from pydantic import BaseModel -from sqlalchemy import JSON, MetaData, String, Text +from sqlalchemy import MetaData, String, Text from sqlmodel import Column, DateTime, Field, Index, SQLModel, func from memu.database.models import CategoryItem, MemoryCategory, MemoryItem, MemoryType, Resource @@ -83,8 +83,6 @@ class SQLiteMemoryItemModel(SQLiteBaseModelMixin, MemoryItem): summary: str = Field(sa_column=Column(Text, nullable=False)) # Store embedding as JSON string since SQLite doesn't have native vector type embedding_json: str | None = Field(default=None, sa_column=Column(Text, nullable=True)) - happened_at: datetime | None = Field(default=None, sa_column=Column(DateTime, nullable=True)) - extra: dict[str, Any] = Field(default={}, sa_column=Column(JSON, nullable=True)) @property def embedding(self) -> list[float] | None: diff --git a/src/memu/integrations/__init__.py b/src/memu/integrations/__init__.py deleted file mode 100644 index dd2390a7..00000000 --- a/src/memu/integrations/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .langgraph import MemULangGraphTools - -__all__ = ["MemULangGraphTools"] diff --git a/src/memu/integrations/dify.py b/src/memu/integrations/dify.py new file mode 100644 index 00000000..2209f300 --- /dev/null +++ b/src/memu/integrations/dify.py @@ -0,0 +1,147 @@ +"""Dify Integration Module.""" + +from __future__ import annotations + +import logging +import tempfile +import uuid +from pathlib import Path +from typing import TYPE_CHECKING, Any + +from memu.app.service import MemoryService + +if TYPE_CHECKING: + from memu.app.service import MemoryService + +logger = logging.getLogger(__name__) + + +class DifyToolProvider: + """ + Adapter to expose MemU functionality as Dify Tools. + """ + + def __init__(self, service: MemoryService, api_key: str | None = None) -> None: + """ + Initialize the Dify Tool Provider. + + Args: + service: An initialized MemU MemoryService instance. + api_key: Optional API key for authentication check (if manually enforcing). + """ + self.service = service + self.api_key = api_key + + async def add_memory(self, query: str, user_id: str | None = None) -> dict[str, Any]: + """ + Add information to MemU memory. + + Args: + query: The content to be memorized (Dify sends 'query' for text input). + user_id: The ID of the user this memory belongs to. + + Returns: + Dictionary with status and stored resource details. + """ + if not query: + return {"status": "error", "message": "Content cannot be empty"} + + # Create a temporary file to store the content, as MemU expects a resource URL/path + # We use a unique name to avoid collisions + temp_dir = Path(tempfile.gettempdir()) / "memu_dify_uploads" + temp_dir.mkdir(parents=True, exist_ok=True) + + file_name = f"dify_{uuid.uuid4()}.txt" + file_path = temp_dir / file_name + + try: + file_path.write_text(query, encoding="utf-8") + + user_scope = {"user_id": user_id} if user_id else None + + # Call MemU service + result = await self.service.memorize( + resource_url=str(file_path.absolute()), modality="document", user=user_scope + ) + + return { + "status": "success", + "message": "Memory added successfully", + "resource_id": str(result.get("resource", {}).get("id")), + "items_created": len(result.get("items", [])), + } + + except Exception as e: + logger.exception("Failed to add memory from Dify") + return {"status": "error", "message": str(e)} + finally: + # Cleanup temp file + # In a real async production env, we might want to delay this or rely on OS temp cleaning, + # but for now synchronous cleanup after processing is fine as memorize() is awaited. + try: + if file_path.exists(): + file_path.unlink() + except Exception: + logger.warning("Failed to cleanup temp file %s", file_path, exc_info=True) + + async def search_memory(self, query: str, user_id: str | None = None) -> dict[str, Any]: + """ + Search MemU memory for relevant information. + + Args: + query: The search query. + user_id: The ID of the user scope to search within. + + Returns: + Dictionary with retrieved context and relevant items. + """ + if not query: + return {"result": "Please provide a query."} + + try: + user_scope = {"user_id": user_id} if user_id else None + + # Construct MemU query format + memu_queries = [{"role": "user", "content": {"text": query}}] + + result = await self.service.retrieve(queries=memu_queries, where=user_scope) + + return self._format_response(result) + + except Exception as e: + logger.exception("Failed to search memory from Dify") + return {"status": "error", "message": str(e)} + + def _format_response(self, result: dict[str, Any]) -> dict[str, Any]: + """Format MemU retrieve response for Dify consumption.""" + + # Extract response construction logic similar to MemU's internal formatters + # but simplified for Dify's text-based tool expectation. + + if result.get("response", {}).get("answer"): + # If MemU returns a generated answer (future feature), use it + pass + + # Helper to format items + items = result.get("items", []) + categories = result.get("categories", []) + + context_parts = [] + + if items: + context_parts.append("### Relevant Memories") + for item in items: + summary = item.get("summary", "") + if summary: + context_parts.append(f"- {summary}") + + if categories: + context_parts.append("\n### Category Context") + for cat in categories: + summary = cat.get("summary", "") + if summary: + context_parts.append(f"**{cat.get('name', 'Category')}**: {summary}") + + final_text = "\n".join(context_parts) if context_parts else "No relevant memories found." + + return {"result": final_text, "metadata": {"item_count": len(items), "category_count": len(categories)}} diff --git a/src/memu/integrations/dify_adapter.py b/src/memu/integrations/dify_adapter.py new file mode 100644 index 00000000..265ae98a --- /dev/null +++ b/src/memu/integrations/dify_adapter.py @@ -0,0 +1,189 @@ +"""Dify Adapter Module with FastAPI Router.""" + +from __future__ import annotations + +import logging +import tempfile +import uuid +from pathlib import Path +from typing import Any + +from fastapi import APIRouter, Depends, FastAPI +from pydantic import BaseModel, Field + +from memu.app.service import MemoryService + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/dify/memory", tags=["dify"]) + + +class MemoryRequest(BaseModel): + """Request model for adding/searching memory.""" + + query: str = Field(..., description="The content to memorize or search for.") + user_id: str | None = Field(None, description="Optional user identifier.") + + +class DifyResponse(BaseModel): + """Standard Dify tool response.""" + + result: str = Field(..., description="The text result to display to the LLM.") + metadata: dict[str, Any] = Field(default_factory=dict, description="Additional context.") + + +def get_memu_service() -> MemoryService: + """Dependency to get MemU service. Expected to be overridden in main app.""" + raise NotImplementedError("MemU Service dependency must be overridden.") + + +@router.post("/add", response_model=DifyResponse) +async def add_memory( + request: MemoryRequest, + service: MemoryService = Depends(get_memu_service), # noqa: B008 +) -> DifyResponse: + """ + Add information to MemU memory. + + Args: + request: The memory request containing query and user_id. + service: The MemU service instance. + + Returns: + DifyResponse with status message. + """ + if not request.query: + return DifyResponse(result="Error: Content cannot be empty.") + + # Create a temporary file to store the content + temp_dir = Path(tempfile.gettempdir()) / "memu_dify_uploads" + temp_dir.mkdir(parents=True, exist_ok=True) + + file_name = f"dify_{uuid.uuid4()}.txt" + file_path = temp_dir / file_name + + try: + file_path.write_text(request.query, encoding="utf-8") + + user_scope = {"user_id": request.user_id} if request.user_id else None + + # Call MemU service + result = await service.memorize(resource_url=str(file_path.absolute()), modality="document", user=user_scope) + + items_count = len(result.get("items", [])) + return DifyResponse( + result=f"Successfully memorized content. Created {items_count} memory items.", + metadata={"resource_id": str(result.get("resource", {}).get("id")), "items_created": items_count}, + ) + + except Exception as e: + logger.exception("Failed to add memory from Dify") + return DifyResponse(result=f"Error processing memory: {e!s}") + + finally: + try: + if file_path.exists(): + file_path.unlink() + except Exception: + logger.warning("Failed to cleanup temp file %s", file_path, exc_info=True) + + +@router.post("/search", response_model=DifyResponse) +async def search_memory( + request: MemoryRequest, + service: MemoryService = Depends(get_memu_service), # noqa: B008 +) -> DifyResponse: + """ + Search MemU memory for relevant context. + + Args: + request: The search request containing query and user_id. + service: The MemU service instance. + + Returns: + DifyResponse with formatted retrieved context. + """ + if not request.query: + return DifyResponse(result="Please provide a query.") + + try: + user_scope = {"user_id": request.user_id} if request.user_id else None + + # Construct MemU query format + memu_queries = [{"role": "user", "content": {"text": request.query}}] + + result = await service.retrieve(queries=memu_queries, where=user_scope) + + return _format_search_response(result) + + except Exception as e: + logger.exception("Failed to search memory from Dify") + return DifyResponse(result=f"Error searching memory: {e!s}") + + +def _format_search_response(result: dict[str, Any]) -> DifyResponse: + """Format MemU retrieve response for Dify.""" + items = result.get("items", []) + categories = result.get("categories", []) + + context_parts = [] + + if items: + context_parts.append("### Relevant Memories") + for item in items: + summary = item.get("summary", "") + if summary: + context_parts.append(f"- {summary}") + + if categories: + context_parts.append("\n### Category Context") + for cat in categories: + summary = cat.get("summary", "") + if summary: + context_parts.append(f"**{cat.get('name', 'Category')}**: {summary}") + + final_text = "\n".join(context_parts) if context_parts else "No relevant memories found." + + return DifyResponse(result=final_text, metadata={"item_count": len(items), "category_count": len(categories)}) + + +# Standalone app for direct execution +def create_app() -> FastAPI: + """Create a standalone FastAPI app with the Dify adapter router.""" + from contextlib import asynccontextmanager + + memu_service: MemoryService | None = None + + @asynccontextmanager + async def lifespan(fastapi_app: FastAPI): + nonlocal memu_service + # Initialize MemoryService here. + # Ensure that MemoryService is properly configured (e.g. via environment variables) + memu_service = MemoryService() + yield + + def get_service_override() -> MemoryService: + if memu_service is None: + raise RuntimeError("MemU service not initialized") # noqa: TRY003 + return memu_service + + fastapi_app = FastAPI( + title="MemU Dify Adapter", + description="API server for integrating MemU with Dify", + lifespan=lifespan, + ) + fastapi_app.include_router(router) + + # CRITICAL: Ensure get_memu_service matches the dependency used in your router endpoints + fastapi_app.dependency_overrides[get_memu_service] = get_service_override + + return fastapi_app + + +app = create_app() + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8000) # noqa: S104 diff --git a/src/memu/integrations/langgraph.py b/src/memu/integrations/langgraph.py deleted file mode 100644 index 2e24ddc6..00000000 --- a/src/memu/integrations/langgraph.py +++ /dev/null @@ -1,163 +0,0 @@ -"""LangGraph integration for MemU.""" - -from __future__ import annotations - -import contextlib -import logging -import os -import tempfile -import uuid -from typing import Any - -# MUST explicitly import langgraph to satisfy DEP002 -import langgraph -from pydantic import BaseModel, Field - -from memu.app.service import MemoryService - -try: - from langchain_core.tools import BaseTool, StructuredTool -except ImportError as e: - msg = "Please install 'langchain-core' (and 'langgraph') to use the LangGraph integration." - raise ImportError(msg) from e - - -# Setup logger -logger = logging.getLogger("memu.integrations.langgraph") - - -class MemUIntegrationError(Exception): - """Base exception for MemU integration issues.""" - - -class SaveRecallInput(BaseModel): - """Input schema for the save_memory tool.""" - - content: str = Field(description="The text content or information to save/remember.") - user_id: str = Field(description="The unique identifier of the user.") - metadata: dict[str, Any] | None = Field(default=None, description="Additional metadata related to the memory.") - - -class SearchRecallInput(BaseModel): - """Input schema for the search_memory tool.""" - - query: str = Field(description="The search query to retrieve relevant memories.") - user_id: str = Field(description="The unique identifier of the user.") - limit: int = Field(default=5, description="Number of memories to retrieve.") - metadata_filter: dict[str, Any] | None = Field( - default=None, description="Optional filter for memory metadata (e.g., {'category': 'work'})." - ) - min_relevance_score: float = Field(default=0.0, description="Minimum relevance score (0.0 to 1.0) for results.") - - -class MemULangGraphTools: - """Adapter to expose MemU as a set of Tools for LangGraph/LangChain agents. - - This class provides a bridge between the MemU MemoryService and LangChain's - tooling ecosystem. - """ - - def __init__(self, memory_service: MemoryService): - """Initializes the MemULangGraphTools with a memory service.""" - self.memory_service = memory_service - # Expose the langgraph module to ensure it's "used" even if just by reference in this class - self._graph_backend = langgraph - - def tools(self) -> list[BaseTool]: - """Return a list of tools compatible with LangGraph.""" - return [ - self.save_memory_tool(), - self.search_memory_tool(), - ] - - def save_memory_tool(self) -> StructuredTool: - """Creates a tool to save information into MemU.""" - - async def _save(content: str, user_id: str, metadata: dict | None = None) -> str: - logger.info("Entering save_memory_tool for user_id: %s", user_id) - filename = f"memu_input_{uuid.uuid4()}.txt" - temp_dir = tempfile.gettempdir() - file_path = os.path.join(temp_dir, filename) - - try: - with open(file_path, "w", encoding="utf-8") as f: - f.write(content) - - logger.debug("Calling memory_service.memorize with temporary file: %s", file_path) - await self.memory_service.memorize( - resource_url=file_path, - modality="conversation", - user={"user_id": user_id, **(metadata or {})}, - ) - logger.info("Successfully saved memory for user_id: %s", user_id) - except Exception as e: - error_msg = f"Failed to save memory for user {user_id}: {e!s}" - logger.exception(error_msg) - return str(MemUIntegrationError(error_msg)) - finally: - if os.path.exists(file_path): - with contextlib.suppress(OSError): - os.remove(file_path) - logger.debug("Cleaned up temporary file: %s", file_path) - - return "Memory saved successfully." - - return StructuredTool.from_function( - func=None, - coroutine=_save, - name="save_memory", - description="Save a piece of information, conversation snippet, or memory for a user.", - args_schema=SaveRecallInput, - ) - - def search_memory_tool(self) -> StructuredTool: - """Creates a tool to search for information in MemU.""" - - async def _search( - query: str, - user_id: str, - limit: int = 5, - metadata_filter: dict | None = None, - min_relevance_score: float = 0.0, - ) -> str: - logger.info("Entering search_memory_tool for user_id: %s, query: '%s'", user_id, query) - try: - queries = [{"role": "user", "content": query}] - where_filter = {"user_id": user_id} - if metadata_filter: - where_filter.update(metadata_filter) - - logger.debug("Calling memory_service.retrieve with where_filter: %s", where_filter) - result = await self.memory_service.retrieve( - queries=queries, - where=where_filter, - ) - logger.info("Successfully retrieved memories for user_id: %s", user_id) - except Exception as e: - error_msg = f"Failed to search memory for user {user_id}: {e!s}" - logger.exception(error_msg) - return str(MemUIntegrationError(error_msg)) - - items = result.get("items", []) - if min_relevance_score > 0: - items = [item for item in items if item.get("score", 1.0) >= min_relevance_score] - - if not items: - logger.info("No memories found for user_id: %s", user_id) - return "No relevant memories found." - - response_text = "Retrieved Memories:\n" - for idx, item in enumerate(items[:limit]): - summary = item.get("summary", "") - score = item.get("score", "N/A") - response_text += f"{idx + 1}. [Score: {score}] {summary}\n" - - return response_text - - return StructuredTool.from_function( - func=None, - coroutine=_search, - name="search_memory", - description="Search for relevant memories or information for a user based on a query.", - args_schema=SearchRecallInput, - ) diff --git a/tests/integrations/test_langgraph.py b/tests/integrations/test_langgraph.py deleted file mode 100644 index 0c894876..00000000 --- a/tests/integrations/test_langgraph.py +++ /dev/null @@ -1,80 +0,0 @@ -"""Unit tests for MemU LangGraph integration.""" - -from unittest.mock import AsyncMock - -import pytest - -# Import guard using pytest.importorskip -langgraph = pytest.importorskip("langgraph") -from langchain_core.tools import StructuredTool # noqa: E402 - -from memu.app.service import MemoryService # noqa: E402 -from memu.integrations.langgraph import MemULangGraphTools # noqa: E402 - - -@pytest.fixture -def mock_memory_service(): - """Fixture for a mocked MemoryService.""" - service = AsyncMock(spec=MemoryService) - # Mock return values for methods if necessary - service.memorize.return_value = {"status": "success"} - service.retrieve.return_value = { - "items": [ - {"summary": "Test memory 1", "score": 0.9}, - {"summary": "Test memory 2", "score": 0.5}, - ] - } - return service - - -@pytest.mark.asyncio -async def test_adapter_initialization(mock_memory_service): - """Test that the adapter initializes and exposes tools.""" - adapter = MemULangGraphTools(mock_memory_service) - tools = adapter.tools() - - assert len(tools) == 2 - assert any(t.name == "save_memory" for t in tools) - assert any(t.name == "search_memory" for t in tools) - - # Strictly verify that we are returning LangChain/LangGraph compatible tools - assert isinstance(tools[0], StructuredTool) - - -@pytest.mark.asyncio -async def test_save_memory_tool_execution(mock_memory_service): - """Test the save_memory tool execution logic.""" - adapter = MemULangGraphTools(mock_memory_service) - save_tool = adapter.save_memory_tool() - - inputs = {"content": "Test content", "user_id": "user_123", "metadata": {"key": "val"}} - - result = await save_tool.ainvoke(inputs) - - assert "saved successfully" in result - # Verify service was called with correct structure - mock_memory_service.memorize.assert_called_once() - call_args = mock_memory_service.memorize.call_args - assert "user_id" in call_args.kwargs["user"] - assert call_args.kwargs["user"]["user_id"] == "user_123" - - -@pytest.mark.asyncio -async def test_search_memory_tool_execution(mock_memory_service): - """Test the search_memory tool execution logic.""" - adapter = MemULangGraphTools(mock_memory_service) - search_tool = adapter.search_memory_tool() - - inputs = {"query": "Test query", "user_id": "user_123"} - - result = await search_tool.ainvoke(inputs) - - assert "Test memory 1" in result - mock_memory_service.retrieve.assert_called_once() - - -def test_import_langgraph_dep(): - """Verify strictly that langgraph is importable and used.""" - import langgraph - - assert langgraph is not None diff --git a/tests/test_dify_adapter.py b/tests/test_dify_adapter.py new file mode 100644 index 00000000..83ef8a37 --- /dev/null +++ b/tests/test_dify_adapter.py @@ -0,0 +1,77 @@ +from unittest.mock import AsyncMock, MagicMock + +import pytest +from fastapi import FastAPI +from fastapi.testclient import TestClient + +from memu.app.service import MemoryService +from memu.integrations.dify_adapter import get_memu_service, router + +# Create valid app for testing +app = FastAPI() +app.include_router(router) + + +@pytest.fixture +def mock_service(): + service = MagicMock(spec=MemoryService) + service.memorize = AsyncMock() + service.retrieve = AsyncMock() + return service + + +@pytest.fixture +def client(mock_service): + # Override dependency with mock + app.dependency_overrides[get_memu_service] = lambda: mock_service + return TestClient(app) + + +def test_add_memory_success(client, mock_service): + # Setup mock + mock_service.memorize.return_value = {"resource": {"id": "res-123"}, "items": [{}, {}]} + + payload = {"query": "User likes blue.", "user_id": "u1"} + response = client.post("/dify/memory/add", json=payload) + + assert response.status_code == 200 + data = response.json() + assert "Successfully memorized" in data["result"] + assert data["metadata"]["resource_id"] == "res-123" + assert data["metadata"]["items_created"] == 2 + + # Verify mock call + assert mock_service.memorize.called + kwargs = mock_service.memorize.call_args.kwargs + assert kwargs["user"] == {"user_id": "u1"} + assert ".txt" in kwargs["resource_url"] + + +def test_search_memory_success(client, mock_service): + # Setup mock + mock_service.retrieve.return_value = { + "items": [{"summary": "Item 1"}, {"summary": "Item 2"}], + "categories": [{"name": "Cat1", "summary": "Cat Summary"}], + } + + payload = {"query": "What does user like?", "user_id": "u1"} + response = client.post("/dify/memory/search", json=payload) + + assert response.status_code == 200 + data = response.json() + + # Verify formatted text + assert "Item 1" in data["result"] + assert "Cat1" in data["result"] + assert data["metadata"]["item_count"] == 2 + + # Verify mock call + assert mock_service.retrieve.called + kwargs = mock_service.retrieve.call_args.kwargs + assert kwargs["where"] == {"user_id": "u1"} + assert kwargs["queries"][0]["content"]["text"] == "What does user like?" + + +def test_missing_query(client): + response = client.post("/dify/memory/add", json={"user_id": "u1"}) + assert response.status_code == 422 # Validation error from Pydantic diff --git a/tests/test_dify_integration.py b/tests/test_dify_integration.py new file mode 100644 index 00000000..0cd12839 --- /dev/null +++ b/tests/test_dify_integration.py @@ -0,0 +1,67 @@ +import unittest +from unittest.mock import AsyncMock, MagicMock + +from memu.app.service import MemoryService +from memu.integrations.dify import DifyToolProvider + + +class TestDifyToolProvider(unittest.IsolatedAsyncioTestCase): + def setUp(self): + self.mock_service = MagicMock(spec=MemoryService) + self.mock_service.memorize = AsyncMock() + self.mock_service.retrieve = AsyncMock() + self.provider = DifyToolProvider(service=self.mock_service) + + async def test_add_memory_success(self): + # Setup mock return value + self.mock_service.memorize.return_value = {"resource": {"id": "res-123"}, "items": [{}, {}]} + + query = "This is a test memory." + user_id = "user-1" + + result = await self.provider.add_memory(query, user_id) + + # Verify service call + self.assertTrue(self.mock_service.memorize.called) + _args, kwargs = self.mock_service.memorize.call_args + self.assertEqual(kwargs["modality"], "document") + self.assertEqual(kwargs["user"], {"user_id": user_id}) + # Verify temp file was created and passed + self.assertIn(".txt", kwargs["resource_url"]) + + # Verify result + self.assertEqual(result["status"], "success") + self.assertEqual(result["resource_id"], "res-123") + self.assertEqual(result["items_created"], 2) + + async def test_search_memory_success(self): + # Setup mock return value + self.mock_service.retrieve.return_value = { + "items": [{"summary": "Memory 1"}, {"summary": "Memory 2"}], + "categories": [{"name": "Work", "summary": "Work stuff"}], + } + + query = "Find memories" + user_id = "user-1" + + result = await self.provider.search_memory(query, user_id) + + # Verify service call + self.assertTrue(self.mock_service.retrieve.called) + kwargs = self.mock_service.retrieve.call_args.kwargs + self.assertEqual(kwargs["where"], {"user_id": user_id}) + self.assertEqual(kwargs["queries"][0]["content"]["text"], query) + + # Verify result formatting + self.assertIn("Memory 1", result["result"]) + self.assertIn("Memory 2", result["result"]) + self.assertIn("Work", result["result"]) + self.assertEqual(result["metadata"]["item_count"], 2) + + async def test_add_memory_empty(self): + result = await self.provider.add_memory("") + self.assertEqual(result["status"], "error") + + async def test_search_memory_empty(self): + result = await self.provider.search_memory("") + self.assertIn("Please provide a query", result["result"]) diff --git a/tests/utils/test_conversation.py b/tests/utils/test_conversation.py deleted file mode 100644 index 7d954c0a..00000000 --- a/tests/utils/test_conversation.py +++ /dev/null @@ -1,91 +0,0 @@ -import json - -import pytest - -from memu.utils.conversation import format_conversation_for_preprocess # type: ignore[import-untyped] - - -class TestFormatConversationForPreprocess: - """ - Test suite for format_conversation_for_preprocess function in src/memu/utils/conversation.py. - - Covers: - - Happy Path: Valid JSON input (list or dict wrapper). - - Edge Cases: Empty input, empty JSON structures. - - Error Handling: Invalid JSON (current implementation handles gracefully by returning raw text). - - Type Safety: Unexpected JSON types. - """ - - @pytest.mark.parametrize( - "input_json,expected_output", - [ - # Happy Path: Standard usage with list of messages - ( - json.dumps([ - {"role": "user", "content": "Hello world", "created_at": "2023-10-27T10:00:00"}, - {"role": "assistant", "content": "Hello! How can I help?", "created_at": "2023-10-27T10:00:05"}, - ]), - "[0] 2023-10-27T10:00:00 [user]: Hello world\n[1] 2023-10-27T10:00:05 [assistant]: Hello! How can I help?", - ), - # Happy Path: Dict wrapper with 'content' key - (json.dumps({"content": [{"role": "user", "content": "Wrapper test"}]}), "[0] [user]: Wrapper test"), - # Happy Path: Missing optional fields (role defaults to user, created_at omitted) - (json.dumps([{"content": "Just text"}]), "[0] [user]: Just text"), - # Happy Path: Multiline content should be collapsed - ( - json.dumps([{"role": "system", "content": "Line 1\nLine 2\nLine 3"}]), - "[0] [system]: Line 1 Line 2 Line 3", - ), - # Happy Path: Content is None/Null - (json.dumps([{"role": "user", "content": None}]), "[0] [user]: "), - # Happy Path: Content is a dict with 'text' - (json.dumps([{"role": "user", "content": {"text": "Rich content"}}]), "[0] [user]: Rich content"), - ], - ) - def test_happy_path_valid_formats(self, input_json: str, expected_output: str) -> None: - """ - Test that valid JSON inputs are correctly formatted into the expected line-based string. - """ - result = format_conversation_for_preprocess(input_json) - assert result == expected_output - - @pytest.mark.parametrize( - "edge_input,expected", - [ - ("", ""), # Empty string - (" ", ""), # Whitespace only - ("[]", ""), # Empty JSON list -> produces empty string - ], - ) - def test_edge_cases_empty(self, edge_input: str, expected: str) -> None: - """ - Test edge cases handling for empty or whitespace-only inputs, and empty JSON lists. - """ - assert format_conversation_for_preprocess(edge_input) == expected - - def test_malformed_json_handling(self) -> None: - """ - Test handling of malformed JSON strings. - - Note: The implementation swallows JSONDecodeError and returns raw text. - This test verifies that graceful fallback behavior. - """ - malformed_json = '{"role": "user", "content": "Missing brace"' - result = format_conversation_for_preprocess(malformed_json) - # Expecting raw text back as fallback - assert result == malformed_json - - def test_unexpected_json_structures(self) -> None: - """ - Test handling of valid JSON that does not match expected conversation schema. - Expectation: Returns raw text if schema extraction fails. - """ - # Empty dict -> _extract_messages returns None - assert format_conversation_for_preprocess("{}") == "{}" - - # Random non-message JSON - random_json = json.dumps({"key": "value"}) - assert format_conversation_for_preprocess(random_json) == random_json - - # Valid JSON primitives - assert format_conversation_for_preprocess("123") == "123" diff --git a/uv.lock b/uv.lock index 0bb43e6a..5e6a42bf 100644 --- a/uv.lock +++ b/uv.lock @@ -4,16 +4,25 @@ requires-python = ">=3.13" [[package]] name = "alembic" -version = "1.17.2" +version = "1.18.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "mako" }, { name = "sqlalchemy" }, { name = "typing-extensions" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/02/a6/74c8cadc2882977d80ad756a13857857dbcf9bd405bc80b662eb10651282/alembic-1.17.2.tar.gz", hash = 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b/examples/example_4_openrouter_memory.py index f5a8daa2..335aed7f 100644 --- a/examples/example_4_openrouter_memory.py +++ b/examples/example_4_openrouter_memory.py @@ -11,18 +11,15 @@ import asyncio import os -import sys +from typing import Any from memu.app import MemoryService -src_path = os.path.abspath("src") -sys.path.insert(0, src_path) - -async def generate_memory_md(categories, output_dir): +async def generate_memory_md(categories: list[dict[str, Any]], output_dir: str) -> list[str]: """Generate concise markdown files for each memory category.""" os.makedirs(output_dir, exist_ok=True) - generated_files = [] + generated_files: list[str] = [] for cat in categories: name = cat.get("name", "unknown") @@ -43,7 +40,7 @@ async def generate_memory_md(categories, output_dir): return generated_files -async def main(): +async def main() -> None: """ Process multiple conversation files and generate memory categories using OpenRouter. @@ -83,7 +80,7 @@ async def main(): print("\nProcessing conversations...") total_items = 0 - categories = [] + categories: list[dict[str, Any]] = [] for conv_file in conversation_files: if not os.path.exists(conv_file): From 936c6a255f9011a04b021e334b0cd43c73e5bb23 Mon Sep 17 00:00:00 2001 From: david-mamani Date: Wed, 21 Jan 2026 11:58:22 -0500 Subject: [PATCH 3/3] fix(typing): add AsyncGenerator return type to lifespan function --- src/memu/integrations/dify_adapter.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/memu/integrations/dify_adapter.py b/src/memu/integrations/dify_adapter.py index 265ae98a..e95bffc1 100644 --- a/src/memu/integrations/dify_adapter.py +++ b/src/memu/integrations/dify_adapter.py @@ -5,6 +5,7 @@ import logging import tempfile import uuid +from collections.abc import AsyncGenerator from pathlib import Path from typing import Any @@ -155,7 +156,7 @@ def create_app() -> FastAPI: memu_service: MemoryService | None = None @asynccontextmanager - async def lifespan(fastapi_app: FastAPI): + async def lifespan(fastapi_app: FastAPI) -> AsyncGenerator[None]: nonlocal memu_service # Initialize MemoryService here. # Ensure that MemoryService is properly configured (e.g. via environment variables)