diff --git a/docs/claude-provider.md b/docs/claude-provider.md new file mode 100644 index 00000000..f0e2311c --- /dev/null +++ b/docs/claude-provider.md @@ -0,0 +1,262 @@ +# Claude Model Provider + +This document describes how to use Anthropic's Claude as an LLM provider in MemU. + +## Overview + +MemU supports Claude as an alternative to OpenAI for LLM operations. Claude integration provides: + +- Chat completions for memory summarization and extraction +- Vision capabilities for image understanding +- Compatible with Claude 3.5 Sonnet, Claude 3 Opus, and other Claude models + +## Requirements + +### Dependencies + +Install the Anthropic SDK: + +```bash +pip install anthropic +``` + +### API Key + +Get your API key from [Anthropic Console](https://console.anthropic.com/). + +Set the environment variable: + +```bash +export ANTHROPIC_API_KEY=your-api-key +``` + +Since Claude doesn't have native embeddings, you'll also need an OpenAI key for embeddings: + +```bash +export OPENAI_API_KEY=your-openai-key +``` + +## Configuration + +### Using LLMConfig + +Configure MemU to use Claude by setting the provider: + +```python +from memu.app.settings import LLMConfig + +llm_config = LLMConfig( + provider="claude", + base_url="https://api.anthropic.com", + api_key="your-anthropic-api-key", # Or use ANTHROPIC_API_KEY env var + chat_model="claude-opus-4-5-20251124", +) +``` + +### Available Models + +| Model | Description | Best For | +|-------|-------------|----------| +| `claude-opus-4-5-20251124` | Claude Opus 4.5 - most intelligent | Complex coding, agents, enterprise | +| `claude-opus-4-5-20251124` | Claude 4 Opus | Complex reasoning, agents | +| `claude-opus-4-1-20250414` | Claude Opus 4.1 | Agentic tasks, coding | +| `claude-sonnet-4-5-20250929` | Claude Sonnet 4.5 | Balanced speed/capability | +| `claude-sonnet-4-20250514` | Claude 4 Sonnet | Good balance | +| `claude-haiku-4-5-20251015` | Claude Haiku 4.5 | Fast, efficient | +| `claude-3-5-sonnet-20241022` | Claude 3.5 Sonnet | Legacy, fast | + +## Usage + +### With MemoryService + +```python +from memu import MemoryService +from memu.app.settings import LLMConfig, LLMProfilesConfig + +# Configure Claude as the LLM provider +llm_profiles = LLMProfilesConfig({ + "default": LLMConfig( + provider="claude", + base_url="https://api.anthropic.com", + api_key="your-api-key", + chat_model="claude-opus-4-5-20251124", + ), + # Use OpenAI for embeddings (Claude doesn't have embedding API) + "embedding": LLMConfig( + provider="openai", + base_url="https://api.openai.com/v1", + api_key="your-openai-key", + embed_model="text-embedding-3-small", + ), +}) + +service = MemoryService(llm_profiles=llm_profiles) +``` + +### Direct SDK Client Usage + +```python +from memu.llm.claude_sdk import ClaudeSDKClient + +client = ClaudeSDKClient( + api_key="your-api-key", + chat_model="claude-opus-4-5-20251124", +) + +# Summarize text +summary, response = await client.summarize( + "Long text to summarize...", + system_prompt="Be concise and focus on key points.", + max_tokens=500, +) + +# Vision (describe an image) +description, response = await client.vision( + prompt="Describe what you see in this image", + image_path="./image.jpg", + system_prompt="Be detailed and accurate.", +) +``` + +### HTTP Client Usage + +```python +from memu.llm.http_client import HTTPLLMClient + +client = HTTPLLMClient( + base_url="https://api.anthropic.com", + api_key="your-api-key", + chat_model="claude-opus-4-5-20251124", + provider="claude", +) + +# Summarize +summary, raw_response = await client.summarize( + "Text to summarize", + system_prompt="Summarize in one paragraph.", +) +``` + +## Running the Example + +A complete runnable example is provided: + +```bash +export ANTHROPIC_API_KEY=your-anthropic-key +export OPENAI_API_KEY=your-openai-key # For embeddings +python examples/example_claude_provider.py +``` + +This example tests: +- Direct SDK client usage (summarization) +- HTTP client usage +- Full memorization workflow +- Memory retrieval + +## Limitations + +### No Native Embeddings + +Claude does not have a native embedding API. For embeddings, use a separate provider: + +- **OpenAI**: `text-embedding-3-small` or `text-embedding-3-large` +- **Voyage AI**: Anthropic's recommended embedding partner +- **Cohere**: `embed-english-v3.0` or `embed-multilingual-v3.0` + +Example configuration with separate embedding provider: + +```python +llm_profiles = LLMProfilesConfig({ + "default": LLMConfig( + provider="claude", + api_key="anthropic-key", + chat_model="claude-sonnet-4-20250514", + ), + "embedding": LLMConfig( + provider="openai", + api_key="openai-key", + embed_model="text-embedding-3-small", + ), +}) +``` + +### No Audio Transcription + +Claude does not have audio transcription capabilities. Use OpenAI Whisper or other providers for audio. + +## API Differences + +Claude's API differs from OpenAI in several ways: + +| Feature | OpenAI | Claude | +|---------|--------|--------| +| Auth Header | `Authorization: Bearer ` | `x-api-key: ` | +| System Prompt | Message with role "system" | Top-level `system` parameter | +| Response Format | `choices[0].message.content` | `content[0].text` | +| Image Format | `image_url.url` | `source.type: "base64"` | + +MemU handles these differences automatically through the backend abstraction. + +## Error Handling + +```python +from memu.llm.claude_sdk import ClaudeSDKClient + +client = ClaudeSDKClient(api_key="your-key") + +try: + summary, _ = await client.summarize("text") +except Exception as e: + if "rate_limit" in str(e).lower(): + # Handle rate limiting + pass + elif "invalid_api_key" in str(e).lower(): + # Handle auth error + pass + else: + raise +``` + +## Best Practices + +1. **Use appropriate models**: Claude Sonnet for most tasks, Opus for complex reasoning +2. **Set max_tokens**: Always specify to control costs and response length +3. **Use system prompts**: Claude responds well to clear instructions +4. **Handle embeddings separately**: Configure a dedicated embedding provider +5. **Monitor usage**: Track token usage via the response object + +## Troubleshooting + +### "anthropic package not installed" + +```bash +pip install anthropic +``` + +### "Invalid API key" + +- Verify your API key at [console.anthropic.com](https://console.anthropic.com/) +- Check the key is set correctly (no extra spaces) + +### "Model not found" + +- Verify the model name is correct +- Check your API key has access to the requested model + +### Rate Limiting + +Claude has rate limits based on your plan. Implement exponential backoff: + +```python +import asyncio + +async def summarize_with_retry(client, text, max_retries=3): + for attempt in range(max_retries): + try: + return await client.summarize(text) + except Exception as e: + if "rate_limit" in str(e).lower() and attempt < max_retries - 1: + await asyncio.sleep(2 ** attempt) + else: + raise +``` diff --git a/examples/example_claude_provider.py b/examples/example_claude_provider.py new file mode 100644 index 00000000..d03161da --- /dev/null +++ b/examples/example_claude_provider.py @@ -0,0 +1,296 @@ +""" +Example: Claude Model Provider Integration + +This example demonstrates how to use Anthropic's Claude as the LLM provider +for MemU's memory service, including chat completions and vision capabilities. + +Usage: + export ANTHROPIC_API_KEY=your_anthropic_api_key + export OPENAI_API_KEY=your_openai_api_key # For embeddings (Claude doesn't have native embeddings) + python examples/example_claude_provider.py +""" + +import asyncio +import os +import sys + +from memu.app import MemoryService + +# Add src to sys.path +src_path = os.path.abspath("src") +sys.path.insert(0, src_path) + + +async def test_claude_memorize(): + """Test Claude provider with conversation memorization.""" + print("\n" + "=" * 60) + print("Testing Claude Provider - Conversation Memorization") + print("=" * 60) + + anthropic_key = os.getenv("ANTHROPIC_API_KEY") + openai_key = os.getenv("OPENAI_API_KEY") + + if not anthropic_key: + print("ERROR: Please set ANTHROPIC_API_KEY environment variable") + return False + + if not openai_key: + print("WARNING: OPENAI_API_KEY not set. Embeddings will fail.") + print("Claude doesn't have native embeddings, so OpenAI is used for embeddings.") + + # Initialize service with Claude as the LLM provider + # Note: Claude doesn't have embeddings, so we use OpenAI for that + service = MemoryService( + llm_profiles={ + "default": { + "provider": "claude", + "base_url": "https://api.anthropic.com", + "api_key": anthropic_key, + "chat_model": "claude-sonnet-4-20250514", # Using Sonnet for cost efficiency + }, + "embedding": { + "provider": "openai", + "api_key": openai_key, + "embed_model": "text-embedding-3-small", + }, + }, + database_config={ + "metadata_store": {"provider": "inmemory"}, + }, + ) + + # Test with a conversation file + conv_file = "examples/resources/conversations/conv1.json" + if not os.path.exists(conv_file): + print(f"Conversation file not found: {conv_file}") + print("Creating a sample conversation...") + + # Create sample conversation + os.makedirs("examples/resources/conversations", exist_ok=True) + sample_conv = [ + {"role": "user", "content": "Hi, I'm interested in learning Python programming."}, + {"role": "assistant", "content": "Great! Python is an excellent choice. What's your current experience level?"}, + {"role": "user", "content": "I'm a complete beginner. I work as a data analyst and want to automate my Excel tasks."}, + {"role": "assistant", "content": "Perfect! Python is ideal for data analysis and automation. We can start with pandas for data manipulation."}, + {"role": "user", "content": "That sounds good. I prefer learning through practical examples rather than theory."}, + ] + import json + with open(conv_file, "w") as f: + json.dump(sample_conv, f, indent=2) + + print(f"\nProcessing conversation: {conv_file}") + + try: + result = await service.memorize( + resource_url=conv_file, + modality="conversation", + user={"user_id": "claude_test_user"}, + ) + + print("\n✓ Memorization successful!") + print(f" Items extracted: {len(result.get('items', []))}") + print(f" Categories: {len(result.get('categories', []))}") + + for cat in result.get("categories", [])[:3]: + print(f" - {cat.get('name')}: {(cat.get('summary') or '')[:60]}...") + + except Exception as e: + print(f"\n✗ Error during memorization: {e}") + import traceback + traceback.print_exc() + return False + else: + return True + + +async def test_claude_retrieve(): + """Test Claude provider with memory retrieval.""" + print("\n" + "=" * 60) + print("Testing Claude Provider - Memory Retrieval") + print("=" * 60) + + anthropic_key = os.getenv("ANTHROPIC_API_KEY") + openai_key = os.getenv("OPENAI_API_KEY") + + if not anthropic_key or not openai_key: + print("Skipping retrieval test - API keys not set") + return False + + service = MemoryService( + llm_profiles={ + "default": { + "provider": "claude", + "base_url": "https://api.anthropic.com", + "api_key": anthropic_key, + "chat_model": "claude-sonnet-4-20250514", + }, + "embedding": { + "provider": "openai", + "api_key": openai_key, + "embed_model": "text-embedding-3-small", + }, + }, + database_config={ + "metadata_store": {"provider": "inmemory"}, + }, + retrieve_config={"method": "rag"}, + ) + + # First memorize something + conv_file = "examples/resources/conversations/conv1.json" + if os.path.exists(conv_file): + await service.memorize( + resource_url=conv_file, + modality="conversation", + user={"user_id": "claude_test_user"}, + ) + + # Now retrieve + queries = [ + {"role": "user", "content": {"text": "What programming language does the user want to learn?"}}, + ] + + try: + result = await service.retrieve( + queries=queries, + where={"user_id": "claude_test_user"}, + ) + + print("\n✓ Retrieval successful!") + print(f" Categories found: {len(result.get('categories', []))}") + print(f" Items found: {len(result.get('items', []))}") + + for item in result.get("items", [])[:3]: + print(f" - [{item.get('memory_type')}] {item.get('summary', '')[:80]}...") + + except Exception as e: + print(f"\n✗ Error during retrieval: {e}") + import traceback + traceback.print_exc() + return False + else: + return True + + +async def test_claude_sdk_direct(): + """Test Claude SDK client directly.""" + print("\n" + "=" * 60) + print("Testing Claude SDK Client - Direct Usage") + print("=" * 60) + + anthropic_key = os.getenv("ANTHROPIC_API_KEY") + if not anthropic_key: + print("Skipping SDK test - ANTHROPIC_API_KEY not set") + return False + + try: + from memu.llm.claude_sdk import ClaudeSDKClient + + client = ClaudeSDKClient( + api_key=anthropic_key, + chat_model="claude-sonnet-4-20250514", + ) + + # Test summarization + print("\nTesting summarization...") + text = """ + Python is a high-level, general-purpose programming language. Its design philosophy + emphasizes code readability with the use of significant indentation. Python is + dynamically typed and garbage-collected. It supports multiple programming paradigms, + including structured, object-oriented and functional programming. + """ + + summary, response = await client.summarize( + text, + system_prompt="Summarize in one sentence.", + max_tokens=100, + ) + + print(f"✓ Summary: {summary[:100]}...") + print(f" Tokens used: {response.usage.total_tokens}") + + except ImportError: + print("✗ anthropic package not installed. Run: pip install anthropic") + return False + except Exception as e: + print(f"✗ Error: {e}") + import traceback + traceback.print_exc() + return False + else: + return True + return False + + +async def test_claude_http_client(): + """Test Claude via HTTP client.""" + print("\n" + "=" * 60) + print("Testing Claude HTTP Client") + print("=" * 60) + + anthropic_key = os.getenv("ANTHROPIC_API_KEY") + if not anthropic_key: + print("Skipping HTTP client test - ANTHROPIC_API_KEY not set") + return False + + try: + from memu.llm.http_client import HTTPLLMClient + + client = HTTPLLMClient( + base_url="https://api.anthropic.com", + api_key=anthropic_key, + chat_model="claude-sonnet-4-20250514", + provider="claude", + ) + + # Test summarization + print("\nTesting HTTP client summarization...") + text = "The quick brown fox jumps over the lazy dog. This is a test sentence." + + summary, _raw_response = await client.summarize( + text, + system_prompt="Repeat the text exactly.", + max_tokens=100, + ) + + print(f"✓ Response: {summary[:100]}...") + + except Exception as e: + print(f"✗ Error: {e}") + import traceback + traceback.print_exc() + return False + else: + return True + + +async def main(): + """Run all Claude provider tests.""" + print("\n" + "=" * 60) + print("MemU Claude Provider Integration Tests") + print("=" * 60) + + results = { + "SDK Direct": await test_claude_sdk_direct(), + "HTTP Client": await test_claude_http_client(), + "Memorize": await test_claude_memorize(), + "Retrieve": await test_claude_retrieve(), + } + + print("\n" + "=" * 60) + print("Test Results Summary") + print("=" * 60) + + for test_name, passed in results.items(): + status = "✓ PASS" if passed else "✗ FAIL" + print(f" {test_name}: {status}") + + all_passed = all(results.values()) + print("\n" + ("All tests passed!" if all_passed else "Some tests failed.")) + + return all_passed + + +if __name__ == "__main__": + success = asyncio.run(main()) + sys.exit(0 if success else 1) diff --git a/pyproject.toml b/pyproject.toml index f4ab8c4b..487fd223 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -64,6 +64,7 @@ test = [ [project.optional-dependencies] postgres = ["pgvector>=0.3.4", "sqlalchemy[postgresql-psycopgbinary]>=2.0.36"] +claude = ["anthropic>=0.40.0"] [project.urls] "Homepage" = "https://github.com/NevaMind-AI/MemU" @@ -95,6 +96,14 @@ disable_error_code = ["attr-defined", "call-arg"] module = ["pgvector.*"] ignore_missing_imports = true +[[tool.mypy.overrides]] +module = ["anthropic.*"] +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = ["memu.llm.claude_sdk"] +disallow_any_unimported = false + [tool.ruff] target-version = "py313" line-length = 120 diff --git a/src/memu/llm/backends/__init__.py b/src/memu/llm/backends/__init__.py index 5350e7b2..05ac044f 100644 --- a/src/memu/llm/backends/__init__.py +++ b/src/memu/llm/backends/__init__.py @@ -1,7 +1,8 @@ from memu.llm.backends.base import LLMBackend +from memu.llm.backends.claude import ClaudeLLMBackend from memu.llm.backends.doubao import DoubaoLLMBackend from memu.llm.backends.grok import GrokBackend from memu.llm.backends.openai import OpenAILLMBackend from memu.llm.backends.openrouter import OpenRouterLLMBackend -__all__ = ["DoubaoLLMBackend", "GrokBackend", "LLMBackend", "OpenAILLMBackend", "OpenRouterLLMBackend"] +__all__ = ["ClaudeLLMBackend", "DoubaoLLMBackend", "GrokBackend", "LLMBackend", "OpenAILLMBackend", "OpenRouterLLMBackend"] diff --git a/src/memu/llm/backends/claude.py b/src/memu/llm/backends/claude.py new file mode 100644 index 00000000..e41c569e --- /dev/null +++ b/src/memu/llm/backends/claude.py @@ -0,0 +1,104 @@ +from __future__ import annotations + +from typing import Any + +from memu.llm.backends.base import LLMBackend + + +class ClaudeLLMBackend(LLMBackend): + """Backend for Anthropic Claude LLM API. + + Claude uses a different message format than OpenAI: + - System prompt is a top-level parameter, not a message + - Messages use 'content' as a list of content blocks + - Response format differs from OpenAI + """ + + name = "claude" + summary_endpoint = "/v1/messages" + + def build_summary_payload( + self, *, text: str, system_prompt: str | None, chat_model: str, max_tokens: int | None + ) -> dict[str, Any]: + """Build payload for Claude messages API.""" + prompt = system_prompt or "Summarize the text in one short paragraph." + + payload: dict[str, Any] = { + "model": chat_model, + "messages": [ + {"role": "user", "content": text}, + ], + "system": prompt, + "max_tokens": max_tokens or 4096, + } + return payload + + def parse_summary_response(self, data: dict[str, Any]) -> str: + """Parse Claude response format. + + Claude returns: + { + "content": [{"type": "text", "text": "..."}], + "stop_reason": "end_turn", + ... + } + """ + content = data.get("content", []) + if content and isinstance(content, list): + # Extract text from content blocks + text_parts = [] + for block in content: + if isinstance(block, dict) and block.get("type") == "text": + text_parts.append(block.get("text", "")) + return "".join(text_parts) + return "" + + def build_vision_payload( + self, + *, + prompt: str, + base64_image: str, + mime_type: str, + system_prompt: str | None, + chat_model: str, + max_tokens: int | None, + ) -> dict[str, Any]: + """Build payload for Claude Vision API. + + Claude uses a different image format: + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": "" + } + } + """ + content: list[dict[str, Any]] = [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": mime_type, + "data": base64_image, + }, + }, + {"type": "text", "text": prompt}, + ] + + payload: dict[str, Any] = { + "model": chat_model, + "messages": [ + {"role": "user", "content": content}, + ], + "max_tokens": max_tokens or 4096, + } + + if system_prompt: + payload["system"] = system_prompt + + return payload + + +__all__ = ["ClaudeLLMBackend"] diff --git a/src/memu/llm/claude_sdk.py b/src/memu/llm/claude_sdk.py new file mode 100644 index 00000000..2c34a821 --- /dev/null +++ b/src/memu/llm/claude_sdk.py @@ -0,0 +1,267 @@ +"""Claude SDK client for Anthropic's Claude API. + +This module provides an async client for Claude that mirrors the OpenAI SDK client interface, +enabling seamless integration with MemU's LLM abstraction layer. +""" + +from __future__ import annotations + +import base64 +import logging +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Literal + +logger = logging.getLogger(__name__) + + +@dataclass +class ClaudeUsage: + """Token usage information from Claude API.""" + + input_tokens: int + output_tokens: int + + @property + def total_tokens(self) -> int: + return self.input_tokens + self.output_tokens + + +@dataclass +class ClaudeMessage: + """Claude message response.""" + + id: str + type: str + role: str + content: list[dict[str, Any]] + model: str + stop_reason: str | None + stop_sequence: str | None + usage: ClaudeUsage + + @property + def text(self) -> str: + """Extract text content from response.""" + text_parts = [] + for block in self.content: + if block.get("type") == "text": + text_parts.append(block.get("text", "")) + return "".join(text_parts) + + +class ClaudeSDKClient: + """Anthropic Claude LLM client using the official Python SDK. + + Provides async methods for chat completions and vision that match + the interface expected by MemU's LLM wrapper. + """ + + def __init__( + self, + *, + api_key: str, + chat_model: str = "claude-opus-4-5-20251124", + base_url: str | None = None, + embed_model: str | None = None, + ): + """Initialize Claude SDK client. + + Args: + api_key: Anthropic API key + chat_model: Model to use for chat (default: claude-opus-4-5-20251124) + base_url: Optional custom base URL for API + embed_model: Not used (Claude doesn't have embedding API), kept for interface compatibility + """ + try: + from anthropic import AsyncAnthropic + except ImportError as exc: + msg = "anthropic package is required for Claude support. Install with: pip install anthropic" + raise ImportError(msg) from exc + + self.api_key = api_key + self.chat_model = chat_model + self.embed_model = embed_model # Claude doesn't support embeddings natively + + client_kwargs: dict[str, Any] = {"api_key": api_key} + if base_url: + client_kwargs["base_url"] = base_url + + self.client = AsyncAnthropic(**client_kwargs) + + async def summarize( + self, + text: str, + *, + max_tokens: int | None = None, + system_prompt: str | None = None, + ) -> tuple[str, ClaudeMessage]: + """Generate a summary using Claude. + + Args: + text: Text to summarize + max_tokens: Maximum tokens in response (default: 4096) + system_prompt: Optional system prompt + + Returns: + Tuple of (summary text, raw ClaudeMessage response) + """ + prompt = system_prompt or "Summarize the text in one short paragraph." + + response = await self.client.messages.create( + model=self.chat_model, + max_tokens=max_tokens or 4096, + system=prompt, + messages=[{"role": "user", "content": text}], + ) + + # Convert to our dataclass for consistent interface + message = self._parse_response(response) + logger.debug("Claude summarize response: %s", message) + return message.text, message + + async def vision( + self, + prompt: str, + image_path: str, + *, + max_tokens: int | None = None, + system_prompt: str | None = None, + ) -> tuple[str, ClaudeMessage]: + """Call Claude Vision API with an image. + + Args: + prompt: Text prompt to send with the image + image_path: Path to the image file + max_tokens: Maximum tokens in response + system_prompt: Optional system prompt + + Returns: + Tuple of (LLM response text, raw ClaudeMessage response) + """ + # Read and encode image as base64 + image_data = Path(image_path).read_bytes() + base64_image = base64.b64encode(image_data).decode("utf-8") + + # Detect image format + suffix = Path(image_path).suffix.lower() + media_type = { + ".jpg": "image/jpeg", + ".jpeg": "image/jpeg", + ".png": "image/png", + ".gif": "image/gif", + ".webp": "image/webp", + }.get(suffix, "image/jpeg") + + # Build content with image and text + content: list[dict[str, Any]] = [ + { + "type": "image", + "source": { + "type": "base64", + "media_type": media_type, + "data": base64_image, + }, + }, + {"type": "text", "text": prompt}, + ] + + kwargs: dict[str, Any] = { + "model": self.chat_model, + "max_tokens": max_tokens or 4096, + "messages": [{"role": "user", "content": content}], + } + + if system_prompt: + kwargs["system"] = system_prompt + + response = await self.client.messages.create(**kwargs) + + message = self._parse_response(response) + logger.debug("Claude vision response: %s", message) + return message.text, message + + async def embed(self, inputs: list[str]) -> tuple[list[list[float]], None]: + """Create text embeddings. + + Note: Claude does not have a native embedding API. + This method raises NotImplementedError. + + For embeddings with Claude, use a separate embedding provider like: + - OpenAI's text-embedding-3-small + - Voyage AI (Anthropic's recommended embedding partner) + - Cohere embeddings + + Args: + inputs: List of texts to embed + + Raises: + NotImplementedError: Claude doesn't support embeddings + """ + msg = ( + "Claude does not have a native embedding API. " + "Use a separate embedding provider (OpenAI, Voyage AI, or Cohere) for embeddings." + ) + raise NotImplementedError(msg) + + async def transcribe( + self, + audio_path: str, + *, + prompt: str | None = None, + language: str | None = None, + response_format: Literal["text", "json", "verbose_json"] = "text", + ) -> tuple[str, Any]: + """Transcribe audio file. + + Note: Claude does not have a native audio transcription API. + This method raises NotImplementedError. + + For audio transcription, use a separate provider like: + - OpenAI's Whisper API + - AssemblyAI + - Deepgram + + Args: + audio_path: Path to the audio file + prompt: Optional prompt to guide transcription + language: Optional language code + response_format: Response format + + Raises: + NotImplementedError: Claude doesn't support audio transcription + """ + msg = ( + "Claude does not have a native audio transcription API. " + "Use a separate provider (OpenAI Whisper, AssemblyAI, or Deepgram) for transcription." + ) + raise NotImplementedError(msg) + + def _parse_response(self, response: Any) -> ClaudeMessage: + """Parse Anthropic SDK response into our dataclass.""" + usage = ClaudeUsage( + input_tokens=response.usage.input_tokens, + output_tokens=response.usage.output_tokens, + ) + + # Convert content blocks to dicts + content = [] + for block in response.content: + if hasattr(block, "text"): + content.append({"type": "text", "text": block.text}) + elif hasattr(block, "type"): + content.append({"type": block.type}) + + return ClaudeMessage( + id=response.id, + type=response.type, + role=response.role, + content=content, + model=response.model, + stop_reason=response.stop_reason, + stop_sequence=response.stop_sequence, + usage=usage, + ) + + +__all__ = ["ClaudeMessage", "ClaudeSDKClient", "ClaudeUsage"] diff --git a/src/memu/llm/http_client.py b/src/memu/llm/http_client.py index a8e698ce..d3dcafc8 100644 --- a/src/memu/llm/http_client.py +++ b/src/memu/llm/http_client.py @@ -9,6 +9,7 @@ import httpx from memu.llm.backends.base import LLMBackend +from memu.llm.backends.claude import ClaudeLLMBackend from memu.llm.backends.doubao import DoubaoLLMBackend from memu.llm.backends.grok import GrokBackend from memu.llm.backends.openai import OpenAILLMBackend @@ -67,6 +68,7 @@ def parse_embedding_response(self, data: dict[str, Any]) -> list[list[float]]: LLM_BACKENDS: dict[str, Callable[[], LLMBackend]] = { OpenAILLMBackend.name: OpenAILLMBackend, DoubaoLLMBackend.name: DoubaoLLMBackend, + ClaudeLLMBackend.name: ClaudeLLMBackend, GrokBackend.name: GrokBackend, OpenRouterLLMBackend.name: OpenRouterLLMBackend, } @@ -233,6 +235,13 @@ async def transcribe( return result or "", raw_response def _headers(self) -> dict[str, str]: + if self.provider == "claude": + # Claude uses x-api-key header and requires anthropic-version + return { + "x-api-key": self.api_key, + "anthropic-version": "2023-06-01", + "content-type": "application/json", + } return {"Authorization": f"Bearer {self.api_key}"} def _load_backend(self, provider: str) -> LLMBackend: @@ -249,6 +258,9 @@ def _load_embedding_backend(self, provider: str) -> _EmbeddingBackend: "grok": _OpenAIEmbeddingBackend, _OpenRouterEmbeddingBackend.name: _OpenRouterEmbeddingBackend, } + # Claude doesn't have native embeddings, fall back to OpenAI-compatible + if provider == "claude": + return _OpenAIEmbeddingBackend() factory = backends.get(provider) if not factory: msg = f"Unsupported embedding provider '{provider}'. Available: {', '.join(backends.keys())}" diff --git a/tests/test_claude.py b/tests/test_claude.py new file mode 100644 index 00000000..a59033dc --- /dev/null +++ b/tests/test_claude.py @@ -0,0 +1,321 @@ +"""Tests for Claude model provider integration.""" + +from __future__ import annotations + +import pytest + +# ============================================================================ +# Claude Backend Tests +# ============================================================================ + + +class TestClaudeLLMBackend: + """Test Claude LLM backend.""" + + def test_backend_name(self): + """Test backend has correct name.""" + from memu.llm.backends.claude import ClaudeLLMBackend + + backend = ClaudeLLMBackend() + assert backend.name == "claude" + + def test_summary_endpoint(self): + """Test backend has correct endpoint.""" + from memu.llm.backends.claude import ClaudeLLMBackend + + backend = ClaudeLLMBackend() + assert backend.summary_endpoint == "/v1/messages" + + def test_build_summary_payload(self): + """Test building summary payload for Claude.""" + from memu.llm.backends.claude import ClaudeLLMBackend + + backend = ClaudeLLMBackend() + payload = backend.build_summary_payload( + text="Hello world", + system_prompt="Be concise", + chat_model="claude-opus-4-5-20251124", + max_tokens=100, + ) + + assert payload["model"] == "claude-opus-4-5-20251124" + assert payload["system"] == "Be concise" + assert payload["max_tokens"] == 100 + assert len(payload["messages"]) == 1 + assert payload["messages"][0]["role"] == "user" + assert payload["messages"][0]["content"] == "Hello world" + + def test_build_summary_payload_default_prompt(self): + """Test building summary payload with default system prompt.""" + from memu.llm.backends.claude import ClaudeLLMBackend + + backend = ClaudeLLMBackend() + payload = backend.build_summary_payload( + text="Hello world", + system_prompt=None, + chat_model="claude-opus-4-5-20251124", + max_tokens=None, + ) + + assert "Summarize" in payload["system"] + assert payload["max_tokens"] == 4096 # Default + + def test_parse_summary_response(self): + """Test parsing Claude response format.""" + from memu.llm.backends.claude import ClaudeLLMBackend + + backend = ClaudeLLMBackend() + + response = { + "content": [ + {"type": "text", "text": "This is "}, + {"type": "text", "text": "the summary."}, + ], + "stop_reason": "end_turn", + } + + result = backend.parse_summary_response(response) + assert result == "This is the summary." + + def test_parse_summary_response_empty(self): + """Test parsing empty Claude response.""" + from memu.llm.backends.claude import ClaudeLLMBackend + + backend = ClaudeLLMBackend() + + response: dict[str, list[dict[str, str]]] = {"content": []} + result = backend.parse_summary_response(response) + assert result == "" + + def test_build_vision_payload(self): + """Test building vision payload for Claude.""" + from memu.llm.backends.claude import ClaudeLLMBackend + + backend = ClaudeLLMBackend() + payload = backend.build_vision_payload( + prompt="Describe this image", + base64_image="abc123", + mime_type="image/jpeg", + system_prompt="Be detailed", + chat_model="claude-opus-4-5-20251124", + max_tokens=500, + ) + + assert payload["model"] == "claude-opus-4-5-20251124" + assert payload["system"] == "Be detailed" + assert payload["max_tokens"] == 500 + + # Check message structure + assert len(payload["messages"]) == 1 + content = payload["messages"][0]["content"] + assert len(content) == 2 + + # Image block + assert content[0]["type"] == "image" + assert content[0]["source"]["type"] == "base64" + assert content[0]["source"]["media_type"] == "image/jpeg" + assert content[0]["source"]["data"] == "abc123" + + # Text block + assert content[1]["type"] == "text" + assert content[1]["text"] == "Describe this image" + + +# ============================================================================ +# Claude SDK Client Tests +# ============================================================================ + + +class TestClaudeSDKClient: + """Test Claude SDK client.""" + + def test_client_init_requires_anthropic(self): + """Test that client requires anthropic package.""" + # This test verifies the import check works + from memu.llm.claude_sdk import ClaudeSDKClient + + # If anthropic is installed, this should work + # If not, it should raise ImportError + try: + client = ClaudeSDKClient(api_key="test-key") + assert client.chat_model == "claude-opus-4-5-20251124" + except ImportError as e: + assert "anthropic" in str(e) + + def test_embed_not_supported(self): + """Test that embed raises NotImplementedError.""" + from memu.llm.claude_sdk import ClaudeSDKClient + + try: + client = ClaudeSDKClient(api_key="test-key") + except ImportError: + pytest.skip("anthropic package not installed") + + # embed should raise NotImplementedError + import asyncio + with pytest.raises(NotImplementedError, match="embedding"): + asyncio.run(client.embed(["test"])) + + def test_transcribe_not_supported(self): + """Test that transcribe raises NotImplementedError.""" + from memu.llm.claude_sdk import ClaudeSDKClient + + try: + client = ClaudeSDKClient(api_key="test-key") + except ImportError: + pytest.skip("anthropic package not installed") + + # transcribe should raise NotImplementedError + import asyncio + with pytest.raises(NotImplementedError, match="transcription"): + asyncio.run(client.transcribe("test.mp3")) + + +# ============================================================================ +# Claude Usage Dataclass Tests +# ============================================================================ + + +class TestClaudeUsage: + """Test Claude usage dataclass.""" + + def test_total_tokens(self): + """Test total_tokens property.""" + from memu.llm.claude_sdk import ClaudeUsage + + usage = ClaudeUsage(input_tokens=100, output_tokens=50) + assert usage.total_tokens == 150 + + +# ============================================================================ +# HTTP Client Integration Tests +# ============================================================================ + + +class TestHTTPClientClaudeIntegration: + """Test HTTP client with Claude backend.""" + + def test_claude_backend_registered(self): + """Test that Claude backend is registered.""" + from memu.llm.http_client import LLM_BACKENDS + + assert "claude" in LLM_BACKENDS + + def test_http_client_loads_claude_backend(self): + """Test HTTP client can load Claude backend.""" + from memu.llm.http_client import HTTPLLMClient + + client = HTTPLLMClient( + base_url="https://api.anthropic.com", + api_key="test-key", + chat_model="claude-opus-4-5-20251124", + provider="claude", + ) + + assert client.provider == "claude" + assert client.backend.name == "claude" + + def test_claude_headers(self): + """Test Claude uses correct headers.""" + from memu.llm.http_client import HTTPLLMClient + + client = HTTPLLMClient( + base_url="https://api.anthropic.com", + api_key="test-key", + chat_model="claude-opus-4-5-20251124", + provider="claude", + ) + + headers = client._headers() + assert headers["x-api-key"] == "test-key" + assert "anthropic-version" in headers + assert "Authorization" not in headers + + def test_openai_headers(self): + """Test OpenAI still uses Bearer token.""" + from memu.llm.http_client import HTTPLLMClient + + client = HTTPLLMClient( + base_url="https://api.openai.com/v1", + api_key="test-key", + chat_model="gpt-4", + provider="openai", + ) + + headers = client._headers() + assert headers["Authorization"] == "Bearer test-key" + assert "x-api-key" not in headers + + +# ============================================================================ +# Backend Registration Tests +# ============================================================================ + + +class TestBackendRegistration: + """Test backend registration.""" + + def test_claude_in_backends_init(self): + """Test Claude is exported from backends module.""" + from memu.llm.backends import ClaudeLLMBackend + + assert ClaudeLLMBackend is not None + assert ClaudeLLMBackend.name == "claude" + + def test_all_backends_available(self): + """Test all expected backends are available.""" + from memu.llm.backends import ( + ClaudeLLMBackend, + DoubaoLLMBackend, + OpenAILLMBackend, + ) + + assert OpenAILLMBackend.name == "openai" + assert DoubaoLLMBackend.name == "doubao" + assert ClaudeLLMBackend.name == "claude" + + +# ============================================================================ +# Claude Message Dataclass Tests +# ============================================================================ + + +class TestClaudeMessage: + """Test Claude message dataclass.""" + + def test_text_property(self): + """Test text property extracts content.""" + from memu.llm.claude_sdk import ClaudeMessage, ClaudeUsage + + message = ClaudeMessage( + id="msg_123", + type="message", + role="assistant", + content=[ + {"type": "text", "text": "Hello "}, + {"type": "text", "text": "world!"}, + ], + model="claude-opus-4-5-20251124", + stop_reason="end_turn", + stop_sequence=None, + usage=ClaudeUsage(input_tokens=10, output_tokens=5), + ) + + assert message.text == "Hello world!" + + def test_text_property_empty(self): + """Test text property with empty content.""" + from memu.llm.claude_sdk import ClaudeMessage, ClaudeUsage + + message = ClaudeMessage( + id="msg_123", + type="message", + role="assistant", + content=[], + model="claude-opus-4-5-20251124", + stop_reason="end_turn", + stop_sequence=None, + usage=ClaudeUsage(input_tokens=10, output_tokens=0), + ) + + assert message.text == "" diff --git a/uv.lock b/uv.lock index 5447dcc9..a45ccd10 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 1 +revision = 3 requires-python = ">=3.13" [[package]] @@ -11,18 +11,37 @@ dependencies = [ { name = "sqlalchemy" }, { name = "typing-extensions" }, ] -sdist = { url = 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