From f7ebd6501dec9ef7428590b95f3ece819e450ed1 Mon Sep 17 00:00:00 2001 From: Cody Hart Date: Mon, 29 Dec 2025 13:36:46 -0500 Subject: [PATCH 1/3] add agentic feature and example project --- examples/agentic-chat-example/README.md | 151 + examples/agentic-chat-example/app.py | 52 + examples/agentic-chat-example/main.py | 11 + examples/agentic-chat-example/pyproject.toml | 20 + .../agentic-chat-example/routes/__init__.py | 6 + examples/agentic-chat-example/routes/chat.py | 39 + .../routes/conversations.py | 86 + examples/agentic-chat-example/tools.py | 153 + examples/agentic-chat-example/uv.lock | 3791 +++++++++++++++++ packages/llmpane-py/llmpane/__init__.py | 19 + packages/llmpane-py/llmpane/agent/__init__.py | 21 + packages/llmpane-py/llmpane/agent/models.py | 40 + .../llmpane-py/llmpane/agent/pydantic_ai.py | 108 + packages/llmpane-py/llmpane/agent/session.py | 134 + packages/llmpane-py/llmpane/models.py | 1 + packages/llmpane-py/llmpane/store/__init__.py | 62 + packages/llmpane-py/llmpane/store/memory.py | 140 + packages/llmpane-py/llmpane/store/models.py | 89 + packages/llmpane-py/llmpane/store/protocol.py | 62 + packages/llmpane-py/pyproject.toml | 11 +- packages/llmpane-py/tests/test_agent.py | 157 + .../llmpane-py/tests/test_store_memory.py | 229 + packages/llmpane-py/uv.lock | 3314 +++++++++++++- 23 files changed, 8693 insertions(+), 3 deletions(-) create mode 100644 examples/agentic-chat-example/README.md create mode 100644 examples/agentic-chat-example/app.py create mode 100644 examples/agentic-chat-example/main.py create mode 100644 examples/agentic-chat-example/pyproject.toml create mode 100644 examples/agentic-chat-example/routes/__init__.py create mode 100644 examples/agentic-chat-example/routes/chat.py create mode 100644 examples/agentic-chat-example/routes/conversations.py create mode 100644 examples/agentic-chat-example/tools.py create mode 100644 examples/agentic-chat-example/uv.lock create mode 100644 packages/llmpane-py/llmpane/agent/__init__.py create mode 100644 packages/llmpane-py/llmpane/agent/models.py create mode 100644 packages/llmpane-py/llmpane/agent/pydantic_ai.py create mode 100644 packages/llmpane-py/llmpane/agent/session.py create mode 100644 packages/llmpane-py/llmpane/store/__init__.py create mode 100644 packages/llmpane-py/llmpane/store/memory.py create mode 100644 packages/llmpane-py/llmpane/store/models.py create mode 100644 packages/llmpane-py/llmpane/store/protocol.py create mode 100644 packages/llmpane-py/tests/test_agent.py create mode 100644 packages/llmpane-py/tests/test_store_memory.py diff --git a/examples/agentic-chat-example/README.md b/examples/agentic-chat-example/README.md new file mode 100644 index 0000000..12e295e --- /dev/null +++ b/examples/agentic-chat-example/README.md @@ -0,0 +1,151 @@ +# Agentic Chat Example + +This example demonstrates llmpane's full agentic chat capabilities: + +- **Server-side persistence** with `ConversationStore` +- **Pydantic AI integration** via `ChatSession` +- **Tool calling** with automatic execution +- **Conversation history** retrieval for reconnection + +## Features Demonstrated + +| Feature | Description | +|---------|-------------| +| `ChatSession` | Combines Pydantic AI agent with conversation persistence | +| `InMemoryStore` | Default conversation store (auto-created) | +| Tool calling | Agent can call `get_order`, `search_products`, etc. | +| SSE streaming | Real-time response streaming with tool use metadata | +| Conversation API | List, retrieve, and delete conversations | + +## Quick Start + +### 1. Set up environment + +```bash +cd examples/agentic-chat-example + +# Sync dependencies (installs llmpane from workspace) +uv sync +``` + +### 2. Set API key + +```bash +export ANTHROPIC_API_KEY="your-api-key-here" +``` + +### 3. Run the server + +```bash +uv run python main.py +``` + +Server starts at http://localhost:8000 + +## API Endpoints + +### Chat + +```bash +# Send a message (creates new conversation) +curl -X POST http://localhost:8000/api/chat \ + -H "Content-Type: application/json" \ + -d '{"message": "What orders do you have?"}' + +# Continue existing conversation +curl -X POST http://localhost:8000/api/chat \ + -H "Content-Type: application/json" \ + -d '{"message": "Tell me about ORD-001", "conversation_id": "conv_abc123"}' +``` + +### Conversations + +```bash +# List all conversations +curl http://localhost:8000/api/conversations + +# Get specific conversation +curl http://localhost:8000/api/conversations/conv_abc123 + +# Delete conversation +curl -X DELETE http://localhost:8000/api/conversations/conv_abc123 +``` + +## Available Tools + +The agent has access to these tools: + +| Tool | Description | +|------|-------------| +| `get_order(order_id)` | Look up order by ID | +| `search_products(query, category?)` | Search products | +| `get_customer(email)` | Look up customer info | +| `list_recent_orders(limit?)` | List recent orders | + +## Example Conversation + +``` +User: What's the status of order ORD-001? + +Agent: [calls get_order("ORD-001")] + Order ORD-001 for Alice Johnson is currently shipped. + It contains Widget Pro and Gadget Mini, with a total of $149.99. + +User: What other products do you have in the widgets category? + +Agent: [calls search_products("", "widgets")] + We have the Widget Pro available for $99.99. It's currently in stock. +``` + +## Architecture + +``` +┌─────────────────────────────────────────────────────────────┐ +│ FastAPI Application │ +├─────────────────────────────────────────────────────────────┤ +│ POST /api/chat │ +│ ├── ChatSession.run() │ +│ │ ├── Get/create conversation from store │ +│ │ ├── Persist user message │ +│ │ ├── Call Pydantic AI agent.run_stream() │ +│ │ ├── Convert events → StreamChunk │ +│ │ └── Persist assistant message │ +│ └── create_sse_response() → SSE stream │ +├─────────────────────────────────────────────────────────────┤ +│ GET /api/conversations │ +│ └── InMemoryStore.list_conversations() │ +├─────────────────────────────────────────────────────────────┤ +│ GET /api/conversations/{id} │ +│ └── InMemoryStore.get_conversation() │ +└─────────────────────────────────────────────────────────────┘ +``` + +## Customization + +### Using a different store + +```python +from llmpane.store import create_store, set_default_store + +# Use Redis for production +store = create_store("redis", url="redis://localhost:6379") +set_default_store(store) +``` + +### Adding more tools + +```python +@agent.tool_plain +def my_custom_tool(param: str) -> dict: + """Description shown to the LLM.""" + return {"result": do_something(param)} +``` + +### Using a different model + +```python +agent = Agent( + "openai:gpt-4o", # or any Pydantic AI supported model + system_prompt="...", +) +``` diff --git a/examples/agentic-chat-example/app.py b/examples/agentic-chat-example/app.py new file mode 100644 index 0000000..bf49941 --- /dev/null +++ b/examples/agentic-chat-example/app.py @@ -0,0 +1,52 @@ +"""Agentic chat example with persistence and tool calling. + +This example demonstrates: +- Server-side conversation persistence with InMemoryStore +- Pydantic AI integration via ChatSession +- Tool calling with automatic execution +- Conversation history retrieval for reconnection +""" + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware + +from routes import chat_router, conversations_router + + +def create_app() -> FastAPI: + """Create and configure the FastAPI application.""" + app = FastAPI( + title="Agentic Chat Example", + description="Chat with tool calling, persistence, and Pydantic AI integration", + version="0.1.0", + ) + + # CORS for development + app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + # Register routes + app.include_router(chat_router) + app.include_router(conversations_router) + + @app.get("/") + async def root(): + return { + "message": "Agentic Chat API", + "docs": "/docs", + "endpoints": { + "chat": "POST /api/chat - Send a message and get streaming response", + "conversations": "GET /api/conversations - List all conversations", + "conversation": "GET /api/conversations/{id} - Get conversation history", + }, + } + + return app + + +app = create_app() diff --git a/examples/agentic-chat-example/main.py b/examples/agentic-chat-example/main.py new file mode 100644 index 0000000..2ef1f72 --- /dev/null +++ b/examples/agentic-chat-example/main.py @@ -0,0 +1,11 @@ +"""Entry point for the agentic chat example server.""" + +import uvicorn + +if __name__ == "__main__": + uvicorn.run( + "app:app", + host="0.0.0.0", + port=8000, + reload=True, + ) diff --git a/examples/agentic-chat-example/pyproject.toml b/examples/agentic-chat-example/pyproject.toml new file mode 100644 index 0000000..26d910a --- /dev/null +++ b/examples/agentic-chat-example/pyproject.toml @@ -0,0 +1,20 @@ +[project] +name = "agentic-chat-example" +version = "0.1.0" +description = "Example: Agentic chat with persistence and tool calling using llmpane + Pydantic AI" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "fastapi>=0.100.0", + "uvicorn[standard]>=0.23.0", + "sse-starlette>=1.6.0", + "pydantic>=2.0.0", + "pydantic-ai>=0.1.0", + "llmpane[fastapi]", +] + +[project.optional-dependencies] +dev = ["httpx>=0.24.0"] + +[tool.uv.sources] +llmpane = { path = "../../packages/llmpane-py", editable = true } diff --git a/examples/agentic-chat-example/routes/__init__.py b/examples/agentic-chat-example/routes/__init__.py new file mode 100644 index 0000000..cdcf9e6 --- /dev/null +++ b/examples/agentic-chat-example/routes/__init__.py @@ -0,0 +1,6 @@ +"""Route exports for the agentic chat example.""" + +from routes.chat import router as chat_router +from routes.conversations import router as conversations_router + +__all__ = ["chat_router", "conversations_router"] diff --git a/examples/agentic-chat-example/routes/chat.py b/examples/agentic-chat-example/routes/chat.py new file mode 100644 index 0000000..303c2df --- /dev/null +++ b/examples/agentic-chat-example/routes/chat.py @@ -0,0 +1,39 @@ +"""Chat endpoint with Pydantic AI integration and tool calling. + +This demonstrates the full agentic chat flow: +1. User sends a message +2. Agent processes with access to tools +3. Agent can call tools automatically +4. Response streams back with tool use metadata +5. Conversation is persisted for later retrieval +""" + + +from llmpane import ChatRequest +from llmpane.agent import ChatSession +from llmpane.store import get_default_store +from llmpane.streaming import create_sse_response + +from fastapi import APIRouter +from tools import agent + +router = APIRouter() + +# Create a ChatSession that combines the agent with persistence +# The default store is an InMemoryStore (created lazily) +session = ChatSession(agent=agent, store=get_default_store()) + + +@router.post("/api/chat") +async def chat(request: ChatRequest): + """Stream a chat response with automatic tool calling and persistence. + + The ChatSession handles: + - Creating/retrieving conversations by ID + - Persisting user and assistant messages + - Converting between llmpane and Pydantic AI formats + - Streaming tool use events as metadata + """ + return create_sse_response( + session.run(request.conversation_id, request.message) + ) diff --git a/examples/agentic-chat-example/routes/conversations.py b/examples/agentic-chat-example/routes/conversations.py new file mode 100644 index 0000000..12baece --- /dev/null +++ b/examples/agentic-chat-example/routes/conversations.py @@ -0,0 +1,86 @@ +"""Conversation management endpoints. + +These endpoints allow clients to: +- List all conversations (for a conversation picker UI) +- Retrieve a specific conversation's history (for reconnection) +- Delete conversations +""" + +from fastapi import APIRouter, HTTPException + +from llmpane.store import get_default_store + +router = APIRouter() + + +@router.get("/api/conversations") +async def list_conversations(limit: int = 50, offset: int = 0): + """List all conversations, ordered by most recently updated. + + Useful for building a conversation picker or sidebar UI. + """ + store = get_default_store() + conversations = await store.list_conversations(limit=limit, offset=offset) + + return { + "conversations": [ + { + "id": conv.id, + "message_count": len(conv.messages), + "created_at": conv.created_at.isoformat(), + "updated_at": conv.updated_at.isoformat(), + "preview": conv.messages[-1].content[:100] if conv.messages else None, + } + for conv in conversations + ], + "total": len(conversations), + "limit": limit, + "offset": offset, + } + + +@router.get("/api/conversations/{conversation_id}") +async def get_conversation(conversation_id: str): + """Get a conversation's full history. + + Useful for: + - Reconnecting to an existing conversation + - Loading conversation history in UI + - Recovering state after page refresh + """ + store = get_default_store() + conversation = await store.get_conversation(conversation_id) + + if not conversation: + raise HTTPException(status_code=404, detail="Conversation not found") + + return { + "id": conversation.id, + "messages": [ + { + "id": msg.id, + "role": msg.role, + "content": msg.content, + "metadata": msg.metadata.model_dump() if msg.metadata else None, + } + for msg in conversation.messages + ], + "created_at": conversation.created_at.isoformat(), + "updated_at": conversation.updated_at.isoformat(), + "state": conversation.state.model_dump() if conversation.state else None, + } + + +@router.delete("/api/conversations/{conversation_id}") +async def delete_conversation(conversation_id: str): + """Delete a conversation. + + Returns success even if conversation doesn't exist (idempotent). + """ + store = get_default_store() + deleted = await store.delete_conversation(conversation_id) + + return { + "deleted": deleted, + "conversation_id": conversation_id, + } diff --git a/examples/agentic-chat-example/tools.py b/examples/agentic-chat-example/tools.py new file mode 100644 index 0000000..59556d3 --- /dev/null +++ b/examples/agentic-chat-example/tools.py @@ -0,0 +1,153 @@ +"""Example tools for the agentic chat. + +This module defines: +1. A Pydantic AI Agent with tools +2. Mock data for demonstration +3. Tool functions that the agent can call + +In a real application, these tools would connect to your actual +databases, APIs, or business logic. +""" + +import os +from datetime import datetime, timedelta +from pydantic_ai import Agent + + +# --- Mock Data --- + +ORDERS = { + "ORD-001": { + "id": "ORD-001", + "customer": "Alice Johnson", + "items": ["Widget Pro", "Gadget Mini"], + "total": 149.99, + "status": "shipped", + "created_at": (datetime.now() - timedelta(days=3)).isoformat(), + }, + "ORD-002": { + "id": "ORD-002", + "customer": "Bob Smith", + "items": ["Mega Bundle", "Premium Support"], + "total": 499.99, + "status": "processing", + "created_at": (datetime.now() - timedelta(days=1)).isoformat(), + }, + "ORD-003": { + "id": "ORD-003", + "customer": "Carol White", + "items": ["Starter Kit"], + "total": 29.99, + "status": "delivered", + "created_at": (datetime.now() - timedelta(days=7)).isoformat(), + }, +} + +PRODUCTS = [ + {"id": "PROD-001", "name": "Widget Pro", "price": 99.99, "category": "widgets", "in_stock": True}, + {"id": "PROD-002", "name": "Gadget Mini", "price": 49.99, "category": "gadgets", "in_stock": True}, + {"id": "PROD-003", "name": "Mega Bundle", "price": 399.99, "category": "bundles", "in_stock": False}, + {"id": "PROD-004", "name": "Starter Kit", "price": 29.99, "category": "kits", "in_stock": True}, + {"id": "PROD-005", "name": "Premium Support", "price": 99.99, "category": "services", "in_stock": True}, +] + +CUSTOMERS = { + "alice@example.com": {"name": "Alice Johnson", "orders": ["ORD-001"], "tier": "gold"}, + "bob@example.com": {"name": "Bob Smith", "orders": ["ORD-002"], "tier": "silver"}, + "carol@example.com": {"name": "Carol White", "orders": ["ORD-003"], "tier": "bronze"}, +} + + +# --- Pydantic AI Agent with Tools --- + +# Check for API key - required for the agent to work +if not os.environ.get("ANTHROPIC_API_KEY"): + raise RuntimeError( + "ANTHROPIC_API_KEY environment variable is required.\n" + "Set it with: export ANTHROPIC_API_KEY='your-api-key-here'" + ) + +# Create the agent +agent = Agent( + "anthropic:claude-sonnet-4-20250514", + system_prompt="""You are a helpful customer service assistant for an e-commerce store. +You have access to tools to look up orders, search products, and find customer information. +Be concise and helpful. When users ask about orders or products, use your tools to find accurate information. +If you can't find something, let the user know politely.""", +) + + +@agent.tool_plain +def get_order(order_id: str) -> dict | str: + """Look up an order by its ID. + + Args: + order_id: The order ID (e.g., "ORD-001") + + Returns: + Order details including items, total, and status + """ + order = ORDERS.get(order_id.upper()) + if order: + return order + return f"Order {order_id} not found" + + +@agent.tool_plain +def search_products(query: str, category: str | None = None) -> list[dict]: + """Search for products by name or category. + + Args: + query: Search term to match against product names + category: Optional category filter (widgets, gadgets, bundles, kits, services) + + Returns: + List of matching products with id, name, price, and availability + """ + results = [] + query_lower = query.lower() + + for product in PRODUCTS: + # Match by name + if query_lower in product["name"].lower(): + if category is None or product["category"] == category: + results.append(product) + # Match by category + elif category and product["category"] == category: + results.append(product) + + return results if results else [{"message": f"No products found matching '{query}'"}] + + +@agent.tool_plain +def get_customer(email: str) -> dict | str: + """Look up customer information by email. + + Args: + email: Customer's email address + + Returns: + Customer details including name, order history, and membership tier + """ + customer = CUSTOMERS.get(email.lower()) + if customer: + return customer + return f"Customer with email {email} not found" + + +@agent.tool_plain +def list_recent_orders(limit: int = 5) -> list[dict]: + """List the most recent orders. + + Args: + limit: Maximum number of orders to return (default 5) + + Returns: + List of recent orders sorted by creation date + """ + sorted_orders = sorted( + ORDERS.values(), + key=lambda x: x["created_at"], + reverse=True, + ) + return 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__getattr__(name: str) -> Any: + """Lazy load optional modules and streaming utilities.""" + # Streaming utilities (require fastapi extra) if name == "create_sse_response": from llmpane.streaming import create_sse_response + return create_sse_response if name == "stream_chunks": from llmpane.streaming import stream_chunks + return stream_chunks + + # Submodules + if name == "store": + from llmpane import store + + return store + if name == "agent": + from llmpane import agent + + return agent + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/packages/llmpane-py/llmpane/agent/__init__.py b/packages/llmpane-py/llmpane/agent/__init__.py new file mode 100644 index 0000000..ac5c5e8 --- /dev/null +++ b/packages/llmpane-py/llmpane/agent/__init__.py @@ -0,0 +1,21 @@ +"""Agent adapter layer for Pydantic AI integration.""" + +from llmpane.agent.models import ToolResult, ToolUse, ToolUseMetadata +from llmpane.agent.session import ChatSession + +__all__ = [ + "ChatSession", + "ToolResult", + "ToolUse", + "ToolUseMetadata", + "stream_agent", +] + + +def __getattr__(name: str): + """Lazy import stream_agent to avoid requiring pydantic-ai at import time.""" + if name == "stream_agent": + from llmpane.agent.pydantic_ai import stream_agent + + return stream_agent + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/packages/llmpane-py/llmpane/agent/models.py b/packages/llmpane-py/llmpane/agent/models.py new file mode 100644 index 0000000..fc83385 --- /dev/null +++ b/packages/llmpane-py/llmpane/agent/models.py @@ -0,0 +1,40 @@ +"""Models for agent tool use in streaming responses.""" + +from typing import Any, Literal + +from pydantic import BaseModel, Field + + +class ToolUse(BaseModel): + """LLM requests to use a tool.""" + + id: str = Field(description="Unique identifier for this tool call") + name: str = Field(description="Name of the tool to invoke") + arguments: dict[str, Any] = Field( + default_factory=dict, + description="Arguments to pass to the tool", + ) + + +class ToolResult(BaseModel): + """Result of tool execution.""" + + tool_use_id: str = Field(description="ID of the tool call this result is for") + content: str = Field(description="Result content from the tool") + is_error: bool = Field( + default=False, + description="True if the tool execution failed", + ) + + +class ToolUseMetadata(BaseModel): + """Metadata for tool use in streaming responses.""" + + tool_use: ToolUse = Field(description="The tool being used") + status: Literal["pending", "running", "completed", "failed"] = Field( + description="Current status of the tool execution" + ) + result: ToolResult | None = Field( + default=None, + description="Result of the tool execution (when completed)", + ) diff --git a/packages/llmpane-py/llmpane/agent/pydantic_ai.py b/packages/llmpane-py/llmpane/agent/pydantic_ai.py new file mode 100644 index 0000000..c3eb0c1 --- /dev/null +++ b/packages/llmpane-py/llmpane/agent/pydantic_ai.py @@ -0,0 +1,108 @@ +"""Pydantic AI adapter for converting agent events to llmpane StreamChunks.""" + +from __future__ import annotations + +import uuid +from collections.abc import AsyncGenerator +from typing import TYPE_CHECKING, Any + +from llmpane.agent.models import ToolResult, ToolUse, ToolUseMetadata +from llmpane.models import StreamChunk + +if TYPE_CHECKING: + from pydantic_ai import Agent + from pydantic_ai.messages import ModelMessage + + +async def stream_agent( + agent: Agent, + prompt: str, + message_history: list[ModelMessage] | None = None, +) -> AsyncGenerator[StreamChunk[ToolUseMetadata], None]: + """Convert Pydantic AI stream events to llmpane StreamChunks. + + Args: + agent: Pydantic AI Agent instance + prompt: User prompt to send to the agent + message_history: Optional conversation history in Pydantic AI format + + Yields: + StreamChunk objects with text deltas and tool use metadata + """ + try: + from pydantic_ai.messages import ( # noqa: F401 + FunctionToolCallEvent, + FunctionToolResultEvent, + PartDeltaEvent, + TextPartDelta, + ) + except ImportError as e: + raise ImportError( + "Pydantic AI is required for agent support. " + "Install with: pip install llmpane[agent]" + ) from e + + try: + async with agent.run_stream( + prompt, message_history=message_history + ) as result: + async for event in result.stream_events(): + chunk = _convert_event_to_chunk(event) + if chunk is not None: + yield chunk + + # Final chunk with message ID + yield StreamChunk(done=True, message_id=f"msg_{uuid.uuid4().hex[:12]}") + + except Exception as e: + yield StreamChunk(error=str(e), done=True) + + +def _convert_event_to_chunk(event: Any) -> StreamChunk[ToolUseMetadata] | None: + """Convert a Pydantic AI event to a StreamChunk.""" + try: + from pydantic_ai.messages import ( + FunctionToolCallEvent, + FunctionToolResultEvent, + PartDeltaEvent, + TextPartDelta, + ) + except ImportError: + return None + + match event: + # Text delta + case PartDeltaEvent(delta=TextPartDelta(content=content)): + return StreamChunk(delta=content) + + # Tool call started + case FunctionToolCallEvent( + tool_name=name, args=args, tool_call_id=tool_id + ): + return StreamChunk( + metadata=ToolUseMetadata( + tool_use=ToolUse( + id=tool_id, + name=name, + arguments=args if isinstance(args, dict) else {}, + ), + status="running", + ) + ) + + # Tool result received + case FunctionToolResultEvent(tool_call_id=tool_id, result=res): + return StreamChunk( + metadata=ToolUseMetadata( + tool_use=ToolUse(id=tool_id, name="", arguments={}), + status="completed", + result=ToolResult( + tool_use_id=tool_id, + content=str(res) if res is not None else "", + ), + ) + ) + + # Ignore other event types + case _: + return None diff --git a/packages/llmpane-py/llmpane/agent/session.py b/packages/llmpane-py/llmpane/agent/session.py new file mode 100644 index 0000000..a09d87b --- /dev/null +++ b/packages/llmpane-py/llmpane/agent/session.py @@ -0,0 +1,134 @@ +"""ChatSession - high-level wrapper combining persistence and agent execution.""" + +from __future__ import annotations + +from collections.abc import AsyncGenerator +from typing import TYPE_CHECKING + +from llmpane.agent.models import ToolUseMetadata +from llmpane.agent.pydantic_ai import stream_agent +from llmpane.models import ChatMessage, MessageRole, StreamChunk +from llmpane.store import ConversationStore, get_default_store + +if TYPE_CHECKING: + from pydantic_ai import Agent + from pydantic_ai.messages import ModelMessage + + +class ChatSession: + """High-level wrapper combining persistence and agent execution. + + Example: + ```python + from pydantic_ai import Agent + from llmpane.agent import ChatSession + + agent = Agent('anthropic:claude-sonnet-4-20250514') + session = ChatSession(agent=agent) + + @router.post("/api/chat") + async def chat(request: ChatRequest): + return create_sse_response( + session.run(request.conversation_id, request.message) + ) + ``` + """ + + def __init__( + self, + agent: Agent, + store: ConversationStore | None = None, + ): + """Initialize a ChatSession. + + Args: + agent: Pydantic AI Agent instance + store: Conversation store (defaults to global InMemoryStore) + """ + self.agent = agent + self.store = store or get_default_store() + + async def run( + self, + conversation_id: str | None, + message: str, + ) -> AsyncGenerator[StreamChunk[ToolUseMetadata], None]: + """Run a chat turn with automatic persistence. + + Args: + conversation_id: Optional conversation ID (created if None/not found) + message: User message content + + Yields: + StreamChunk objects with text deltas and tool use metadata + """ + # Get or create conversation + conv = None + if conversation_id: + conv = await self.store.get_conversation(conversation_id) + if not conv: + conv = await self.store.create_conversation(conversation_id) + + # Persist user message + user_msg = ChatMessage(role=MessageRole.USER, content=message) + await self.store.add_message(conv.id, user_msg) + + # Convert conversation history to Pydantic AI format + history = self._to_pydantic_history(conv.messages) + + # Stream response and accumulate content + accumulated = "" + async for chunk in stream_agent(self.agent, message, history): + accumulated += chunk.delta + yield chunk + + # Persist assistant message (after streaming completes) + if accumulated: + assistant_msg = ChatMessage( + role=MessageRole.ASSISTANT, + content=accumulated, + ) + await self.store.add_message(conv.id, assistant_msg) + + def _to_pydantic_history( + self, messages: list[ChatMessage] + ) -> list[ModelMessage]: + """Convert llmpane ChatMessages to Pydantic AI ModelMessages. + + This handles the format conversion between llmpane's message format + and Pydantic AI's expected history format. + """ + try: + from pydantic_ai.messages import ( + ModelRequest, + ModelResponse, + SystemPromptPart, + TextPart, + UserPromptPart, + ) + except ImportError: + return [] + + result: list[ModelMessage] = [] + + for msg in messages: + match msg.role: + case MessageRole.USER: + result.append( + ModelRequest(parts=[UserPromptPart(content=msg.content)]) + ) + case MessageRole.ASSISTANT: + result.append( + ModelResponse(parts=[TextPart(content=msg.content)]) + ) + case MessageRole.SYSTEM: + result.append( + ModelRequest(parts=[SystemPromptPart(content=msg.content)]) + ) + case MessageRole.TOOL: + # Tool messages are handled differently in Pydantic AI + # They're typically part of the ModelResponse with tool results + # For now, skip them as they're intermediate state + pass + + return result diff --git a/packages/llmpane-py/llmpane/models.py b/packages/llmpane-py/llmpane/models.py index 2e94f08..e309b93 100644 --- a/packages/llmpane-py/llmpane/models.py +++ b/packages/llmpane-py/llmpane/models.py @@ -12,6 +12,7 @@ class MessageRole(str, Enum): USER = "user" ASSISTANT = "assistant" SYSTEM = "system" + TOOL = "tool" # Generic type for custom metadata diff --git a/packages/llmpane-py/llmpane/store/__init__.py b/packages/llmpane-py/llmpane/store/__init__.py new file mode 100644 index 0000000..562f2de --- /dev/null +++ b/packages/llmpane-py/llmpane/store/__init__.py @@ -0,0 +1,62 @@ +"""Conversation persistence layer for llmpane.""" + +from llmpane.store.memory import InMemoryStore +from llmpane.store.models import ( + ActionConversationState, + Conversation, + ConversationState, + RefinementConversationState, +) +from llmpane.store.protocol import ConversationStore + +__all__ = [ + "ActionConversationState", + "Conversation", + "ConversationState", + "ConversationStore", + "InMemoryStore", + "RefinementConversationState", + "create_store", + "get_default_store", + "set_default_store", +] + +_default_store: ConversationStore | None = None + + +def get_default_store() -> ConversationStore: + """Get or create the default in-memory store.""" + global _default_store + if _default_store is None: + _default_store = InMemoryStore() + return _default_store + + +def set_default_store(store: ConversationStore) -> None: + """Set a custom default store.""" + global _default_store + _default_store = store + + +def create_store( + backend: str = "memory", + **kwargs, +) -> ConversationStore: + """Factory function to create stores by backend name.""" + match backend: + case "memory": + return InMemoryStore(**kwargs) + case "file": + from llmpane.store.file import FileStore + + return FileStore(**kwargs) + case "redis": + from llmpane.store.redis import RedisStore + + return RedisStore(**kwargs) + case "sql": + from llmpane.store.sql import SQLStore + + return SQLStore(**kwargs) + case _: + raise ValueError(f"Unknown backend: {backend}") diff --git a/packages/llmpane-py/llmpane/store/memory.py b/packages/llmpane-py/llmpane/store/memory.py new file mode 100644 index 0000000..5c3fbac --- /dev/null +++ b/packages/llmpane-py/llmpane/store/memory.py @@ -0,0 +1,140 @@ +"""In-memory conversation store with TTL and LRU eviction.""" + +import asyncio +import uuid +from datetime import datetime, timedelta, timezone + +from llmpane.models import ChatMessage +from llmpane.store.models import Conversation + + +class InMemoryStore: + """Thread-safe in-memory conversation store with optional TTL.""" + + def __init__( + self, + default_ttl: timedelta | None = timedelta(hours=24), + max_conversations: int = 10000, + ): + self._conversations: dict[str, Conversation] = {} + self._lock = asyncio.Lock() + self._default_ttl = default_ttl + self._max_conversations = max_conversations + self._access_times: dict[str, datetime] = {} + + async def create_conversation( + self, + conversation_id: str | None = None, + ) -> Conversation: + """Create a new conversation, optionally with a specific ID.""" + async with self._lock: + if conversation_id is None: + conversation_id = f"conv_{uuid.uuid4().hex[:12]}" + + conversation = Conversation( + id=conversation_id, + messages=[], + ) + self._conversations[conversation_id] = conversation + self._access_times[conversation_id] = datetime.now(timezone.utc) + + await self._maybe_evict() + + return conversation + + async def get_conversation( + self, + conversation_id: str, + ) -> Conversation | None: + """Retrieve a conversation by ID. Returns None if not found.""" + async with self._lock: + conv = self._conversations.get(conversation_id) + if conv: + self._access_times[conversation_id] = datetime.now(timezone.utc) + return conv + + async def save_conversation( + self, + conversation: Conversation, + ) -> None: + """Persist a conversation (create or update).""" + async with self._lock: + conversation.updated_at = datetime.now(timezone.utc) + self._conversations[conversation.id] = conversation + self._access_times[conversation.id] = datetime.now(timezone.utc) + + async def delete_conversation( + self, + conversation_id: str, + ) -> bool: + """Delete a conversation. Returns True if deleted, False if not found.""" + async with self._lock: + if conversation_id in self._conversations: + del self._conversations[conversation_id] + del self._access_times[conversation_id] + return True + return False + + async def list_conversations( + self, + limit: int = 100, + offset: int = 0, + ) -> list[Conversation]: + """List conversations with pagination, ordered by updated_at descending.""" + async with self._lock: + sorted_convs = sorted( + self._conversations.values(), + key=lambda c: c.updated_at, + reverse=True, + ) + return sorted_convs[offset : offset + limit] + + async def add_message( + self, + conversation_id: str, + message: ChatMessage, + ) -> ChatMessage: + """Add a message to a conversation. Returns message with server-assigned ID.""" + async with self._lock: + conv = self._conversations.get(conversation_id) + if not conv: + raise ValueError(f"Conversation {conversation_id} not found") + + # Assign ID if not present + if message.id is None: + message = message.model_copy( + update={"id": f"msg_{uuid.uuid4().hex[:12]}"} + ) + + conv.add_message(message) + self._access_times[conversation_id] = datetime.now(timezone.utc) + return message + + async def close(self) -> None: + """Close any connections/resources. No-op for in-memory store.""" + pass + + async def _maybe_evict(self) -> None: + """Evict expired or LRU conversations. Must be called with lock held.""" + now = datetime.now(timezone.utc) + + # Remove expired conversations + if self._default_ttl: + expired = [ + cid + for cid, atime in self._access_times.items() + if now - atime > self._default_ttl + ] + for cid in expired: + del self._conversations[cid] + del self._access_times[cid] + + # LRU eviction if over capacity + while len(self._conversations) > self._max_conversations: + oldest = min(self._access_times, key=lambda k: self._access_times[k]) + del self._conversations[oldest] + del self._access_times[oldest] + + def __len__(self) -> int: + """Return number of conversations in store.""" + return len(self._conversations) diff --git a/packages/llmpane-py/llmpane/store/models.py b/packages/llmpane-py/llmpane/store/models.py new file mode 100644 index 0000000..5da7667 --- /dev/null +++ b/packages/llmpane-py/llmpane/store/models.py @@ -0,0 +1,89 @@ +"""Models for conversation persistence.""" + +from datetime import datetime, timezone +from typing import Any, Generic, TypeVar + +from pydantic import BaseModel, Field + +from llmpane.models import ChatMessage, MetadataT + +StateT = TypeVar("StateT", bound="ConversationState | None") + + +class ConversationState(BaseModel): + """Base class for custom session state. Applications can extend this.""" + + pass + + +class Conversation(BaseModel, Generic[MetadataT, StateT]): + """A conversation with messages and session state.""" + + id: str = Field(description="Unique conversation identifier") + messages: list[ChatMessage[MetadataT]] = Field( + default_factory=list, + description="Ordered list of messages in this conversation", + ) + state: StateT | None = Field( + default=None, + description="Custom session state (iteration counters, action statuses, etc.)", + ) + created_at: datetime = Field( + default_factory=lambda: datetime.now(timezone.utc), + description="When the conversation was created", + ) + updated_at: datetime = Field( + default_factory=lambda: datetime.now(timezone.utc), + description="When the conversation was last updated", + ) + metadata: dict[str, Any] = Field( + default_factory=dict, + description="Arbitrary conversation-level metadata", + ) + + def add_message(self, message: ChatMessage[MetadataT]) -> ChatMessage[MetadataT]: + """Add a message and update timestamps.""" + self.messages.append(message) + self.updated_at = datetime.now(timezone.utc) + return message + + def get_message(self, message_id: str) -> ChatMessage[MetadataT] | None: + """Find a message by ID.""" + for msg in self.messages: + if msg.id == message_id: + return msg + return None + + def to_history(self) -> list[ChatMessage[MetadataT]]: + """Export messages in the format expected by ChatRequest.history.""" + return self.messages.copy() + + +class RefinementConversationState(ConversationState): + """State for conversations using the Refinement pattern.""" + + current_iteration: int = Field( + default=0, + description="Current refinement iteration number", + ) + refinement_history: list[dict[str, Any]] = Field( + default_factory=list, + description="History of refinement outputs", + ) + is_finalized: bool = Field( + default=False, + description="Whether the user has accepted a final version", + ) + + +class ActionConversationState(ConversationState): + """State for conversations using the Action pattern.""" + + pending_actions: dict[str, str] = Field( + default_factory=dict, + description="Map of action_id -> message_id for pending actions", + ) + action_history: list[dict[str, Any]] = Field( + default_factory=list, + description="History of action decisions (accepted/rejected)", + ) diff --git a/packages/llmpane-py/llmpane/store/protocol.py b/packages/llmpane-py/llmpane/store/protocol.py new file mode 100644 index 0000000..ac71ca4 --- /dev/null +++ b/packages/llmpane-py/llmpane/store/protocol.py @@ -0,0 +1,62 @@ +"""Protocol definition for conversation storage backends.""" + +from typing import Protocol, runtime_checkable + +from llmpane.models import ChatMessage + + +@runtime_checkable +class ConversationStore(Protocol): + """Protocol for conversation persistence backends.""" + + async def create_conversation( + self, + conversation_id: str | None = None, + ) -> "Conversation": + """Create a new conversation, optionally with a specific ID.""" + ... + + async def get_conversation( + self, + conversation_id: str, + ) -> "Conversation | None": + """Retrieve a conversation by ID. Returns None if not found.""" + ... + + async def save_conversation( + self, + conversation: "Conversation", + ) -> None: + """Persist a conversation (create or update).""" + ... + + async def delete_conversation( + self, + conversation_id: str, + ) -> bool: + """Delete a conversation. Returns True if deleted, False if not found.""" + ... + + async def list_conversations( + self, + limit: int = 100, + offset: int = 0, + ) -> list["Conversation"]: + """List conversations with pagination.""" + ... + + async def add_message( + self, + conversation_id: str, + message: ChatMessage, + ) -> ChatMessage: + """Add a message to a conversation. Returns message with server-assigned ID.""" + ... + + async def close(self) -> None: + """Close any connections/resources. Called on shutdown.""" + ... + + +# Import here to avoid circular imports +from llmpane.store.models import Conversation # noqa: E402 diff --git a/packages/llmpane-py/pyproject.toml b/packages/llmpane-py/pyproject.toml index 253b394..840aab6 100644 --- a/packages/llmpane-py/pyproject.toml +++ b/packages/llmpane-py/pyproject.toml @@ -27,6 +27,13 @@ dependencies = ["pydantic>=2.0.0"] [project.optional-dependencies] fastapi = ["fastapi>=0.100.0", "sse-starlette>=1.6.0"] +# Agent support (Pydantic AI integration) +agent = ["pydantic-ai>=0.1.0"] +# Store backends +file = ["aiofiles>=23.0.0"] +redis = ["redis>=5.0.0"] +sql = ["sqlalchemy[asyncio]>=2.0.0", "aiosqlite>=0.19.0"] +# Development dev = [ "pytest>=7.0.0", "pytest-asyncio>=0.21.0", @@ -34,7 +41,9 @@ dev = [ "ruff>=0.1.0", "mypy>=1.0.0", ] -all = ["llmpane[fastapi,dev]"] +# Bundles +stores = ["llmpane[file,redis,sql]"] +all = ["llmpane[fastapi,agent,stores,dev]"] [project.urls] Homepage = "https://github.com/hartbrook/llmpane" diff --git a/packages/llmpane-py/tests/test_agent.py b/packages/llmpane-py/tests/test_agent.py new file mode 100644 index 0000000..da123b7 --- /dev/null +++ b/packages/llmpane-py/tests/test_agent.py @@ -0,0 +1,157 @@ +"""Tests for agent adapter layer.""" + +import pytest + +from llmpane.agent.models import ToolResult, ToolUse, ToolUseMetadata + + +class TestToolUseModels: + """Tests for tool use models.""" + + def test_tool_use_creation(self): + """ToolUse can be created with required fields.""" + tool_use = ToolUse( + id="call_123", + name="get_weather", + arguments={"city": "London"}, + ) + assert tool_use.id == "call_123" + assert tool_use.name == "get_weather" + assert tool_use.arguments == {"city": "London"} + + def test_tool_use_default_arguments(self): + """ToolUse defaults to empty arguments dict.""" + tool_use = ToolUse(id="call_123", name="get_weather") + assert tool_use.arguments == {} + + def test_tool_result_creation(self): + """ToolResult can be created with required fields.""" + result = ToolResult( + tool_use_id="call_123", + content="Sunny, 22°C", + ) + assert result.tool_use_id == "call_123" + assert result.content == "Sunny, 22°C" + assert result.is_error is False + + def test_tool_result_with_error(self): + """ToolResult can indicate an error.""" + result = ToolResult( + tool_use_id="call_123", + content="City not found", + is_error=True, + ) + assert result.is_error is True + + def test_tool_use_metadata_running(self): + """ToolUseMetadata can represent running state.""" + metadata = ToolUseMetadata( + tool_use=ToolUse(id="call_123", name="get_weather"), + status="running", + ) + assert metadata.status == "running" + assert metadata.result is None + + def test_tool_use_metadata_completed(self): + """ToolUseMetadata can represent completed state with result.""" + metadata = ToolUseMetadata( + tool_use=ToolUse(id="call_123", name="get_weather"), + status="completed", + result=ToolResult( + tool_use_id="call_123", + content="Sunny, 22°C", + ), + ) + assert metadata.status == "completed" + assert metadata.result is not None + assert metadata.result.content == "Sunny, 22°C" + + def test_tool_use_metadata_serialization(self): + """ToolUseMetadata serializes to JSON correctly.""" + metadata = ToolUseMetadata( + tool_use=ToolUse( + id="call_123", + name="get_weather", + arguments={"city": "London"}, + ), + status="running", + ) + data = metadata.model_dump() + assert data["tool_use"]["id"] == "call_123" + assert data["tool_use"]["name"] == "get_weather" + assert data["status"] == "running" + + +class TestChatSessionWithoutPydanticAI: + """Tests for ChatSession that don't require pydantic-ai.""" + + @pytest.mark.asyncio + async def test_chat_session_import(self): + """ChatSession can be imported without pydantic-ai installed.""" + from llmpane.agent import ChatSession + + # ChatSession should be importable + assert ChatSession is not None + + def test_agent_models_import(self): + """Agent models can be imported without pydantic-ai.""" + from llmpane.agent import ToolResult, ToolUse, ToolUseMetadata + + assert ToolUse is not None + assert ToolResult is not None + assert ToolUseMetadata is not None + + +# Tests that require pydantic-ai are marked to skip if not installed +try: + import pydantic_ai # noqa: F401 + + PYDANTIC_AI_AVAILABLE = True +except ImportError: + PYDANTIC_AI_AVAILABLE = False + + +@pytest.mark.skipif(not PYDANTIC_AI_AVAILABLE, reason="pydantic-ai not installed") +class TestChatSessionWithPydanticAI: + """Tests for ChatSession that require pydantic-ai.""" + + @pytest.mark.asyncio + async def test_chat_session_creates_conversation(self): + """ChatSession creates conversation if none exists.""" + from unittest.mock import MagicMock + + from llmpane.agent import ChatSession + from llmpane.store import InMemoryStore + + # Create mock agent + mock_agent = MagicMock() + + # Mock the run_stream to return an empty async context manager + async def mock_run_stream(*args, **kwargs): + class MockResult: + async def stream_events(self): + return + yield # Make it an async generator + + async def __aenter__(self): + return self + + async def __aexit__(self, *args): + pass + + return MockResult() + + mock_agent.run_stream = mock_run_stream + + store = InMemoryStore() + session = ChatSession(agent=mock_agent, store=store) + + # Consume the generator + chunks = [] + async for chunk in session.run(None, "Hello"): + chunks.append(chunk) + + # Should have created a conversation + convs = await store.list_conversations() + assert len(convs) == 1 + assert len(convs[0].messages) >= 1 # At least user message diff --git a/packages/llmpane-py/tests/test_store_memory.py b/packages/llmpane-py/tests/test_store_memory.py new file mode 100644 index 0000000..01e747d --- /dev/null +++ b/packages/llmpane-py/tests/test_store_memory.py @@ -0,0 +1,229 @@ +"""Tests for InMemoryStore.""" + +import asyncio +from datetime import timedelta + +import pytest + +from llmpane.models import ChatMessage, MessageRole +from llmpane.store.memory import InMemoryStore +from llmpane.store.models import Conversation + + +class TestInMemoryStore: + """Tests for InMemoryStore.""" + + @pytest.fixture + def store(self) -> InMemoryStore: + """Create a fresh store for each test.""" + return InMemoryStore() + + @pytest.mark.asyncio + async def test_create_conversation_generates_id(self, store: InMemoryStore): + """Creating a conversation without ID generates one.""" + conv = await store.create_conversation() + assert conv.id.startswith("conv_") + assert len(conv.id) == 17 # "conv_" + 12 hex chars + assert conv.messages == [] + + @pytest.mark.asyncio + async def test_create_conversation_with_id(self, store: InMemoryStore): + """Creating a conversation with specific ID uses that ID.""" + conv = await store.create_conversation("my-custom-id") + assert conv.id == "my-custom-id" + + @pytest.mark.asyncio + async def test_get_conversation_exists(self, store: InMemoryStore): + """Getting an existing conversation returns it.""" + created = await store.create_conversation("test-conv") + retrieved = await store.get_conversation("test-conv") + assert retrieved is not None + assert retrieved.id == created.id + + @pytest.mark.asyncio + async def test_get_conversation_not_found(self, store: InMemoryStore): + """Getting a non-existent conversation returns None.""" + result = await store.get_conversation("does-not-exist") + assert result is None + + @pytest.mark.asyncio + async def test_add_message_assigns_id(self, store: InMemoryStore): + """Adding a message without ID assigns one.""" + await store.create_conversation("test-conv") + msg = ChatMessage(role=MessageRole.USER, content="Hello") + saved_msg = await store.add_message("test-conv", msg) + + assert saved_msg.id is not None + assert saved_msg.id.startswith("msg_") + assert saved_msg.content == "Hello" + + @pytest.mark.asyncio + async def test_add_message_preserves_id(self, store: InMemoryStore): + """Adding a message with ID preserves it.""" + await store.create_conversation("test-conv") + msg = ChatMessage(id="my-msg-id", role=MessageRole.USER, content="Hello") + saved_msg = await store.add_message("test-conv", msg) + + assert saved_msg.id == "my-msg-id" + + @pytest.mark.asyncio + async def test_add_message_updates_conversation(self, store: InMemoryStore): + """Adding a message updates the conversation's message list.""" + await store.create_conversation("test-conv") + msg = ChatMessage(role=MessageRole.USER, content="Hello") + await store.add_message("test-conv", msg) + + conv = await store.get_conversation("test-conv") + assert conv is not None + assert len(conv.messages) == 1 + assert conv.messages[0].content == "Hello" + + @pytest.mark.asyncio + async def test_add_message_to_nonexistent_conversation(self, store: InMemoryStore): + """Adding a message to non-existent conversation raises error.""" + msg = ChatMessage(role=MessageRole.USER, content="Hello") + with pytest.raises(ValueError, match="not found"): + await store.add_message("does-not-exist", msg) + + @pytest.mark.asyncio + async def test_delete_conversation(self, store: InMemoryStore): + """Deleting a conversation removes it.""" + await store.create_conversation("test-conv") + result = await store.delete_conversation("test-conv") + assert result is True + + retrieved = await store.get_conversation("test-conv") + assert retrieved is None + + @pytest.mark.asyncio + async def test_delete_nonexistent_conversation(self, store: InMemoryStore): + """Deleting a non-existent conversation returns False.""" + result = await store.delete_conversation("does-not-exist") + assert result is False + + @pytest.mark.asyncio + async def test_list_conversations(self, store: InMemoryStore): + """Listing conversations returns them ordered by updated_at.""" + await store.create_conversation("conv-1") + await store.create_conversation("conv-2") + await store.create_conversation("conv-3") + + # Add message to conv-1 to make it most recently updated + msg = ChatMessage(role=MessageRole.USER, content="Hello") + await store.add_message("conv-1", msg) + + convs = await store.list_conversations() + assert len(convs) == 3 + assert convs[0].id == "conv-1" # Most recently updated + + @pytest.mark.asyncio + async def test_list_conversations_pagination(self, store: InMemoryStore): + """Listing conversations supports pagination.""" + for i in range(5): + await store.create_conversation(f"conv-{i}") + + page1 = await store.list_conversations(limit=2, offset=0) + page2 = await store.list_conversations(limit=2, offset=2) + + assert len(page1) == 2 + assert len(page2) == 2 + + @pytest.mark.asyncio + async def test_save_conversation(self, store: InMemoryStore): + """Saving a conversation updates it.""" + conv = await store.create_conversation("test-conv") + conv.metadata["key"] = "value" + await store.save_conversation(conv) + + retrieved = await store.get_conversation("test-conv") + assert retrieved is not None + assert retrieved.metadata["key"] == "value" + + @pytest.mark.asyncio + async def test_ttl_eviction(self): + """Expired conversations are evicted.""" + store = InMemoryStore(default_ttl=timedelta(milliseconds=10)) + await store.create_conversation("will-expire") + + # Wait for TTL to pass + await asyncio.sleep(0.02) + + # Create another conversation to trigger eviction + await store.create_conversation("new-conv") + + # Check internal state directly since get_conversation updates access time + assert "will-expire" not in store._conversations + assert "new-conv" in store._conversations + + @pytest.mark.asyncio + async def test_lru_eviction(self): + """LRU eviction removes oldest conversations when over capacity.""" + store = InMemoryStore(max_conversations=2, default_ttl=None) + await store.create_conversation("conv-1") + await store.create_conversation("conv-2") + + # Access conv-1 to make it recently used + await store.get_conversation("conv-1") + + # Create third conversation, should evict conv-2 + await store.create_conversation("conv-3") + + assert await store.get_conversation("conv-1") is not None + assert await store.get_conversation("conv-2") is None + assert await store.get_conversation("conv-3") is not None + + @pytest.mark.asyncio + async def test_len(self, store: InMemoryStore): + """len() returns number of conversations.""" + assert len(store) == 0 + await store.create_conversation("conv-1") + assert len(store) == 1 + await store.create_conversation("conv-2") + assert len(store) == 2 + + @pytest.mark.asyncio + async def test_close(self, store: InMemoryStore): + """close() is a no-op but doesn't error.""" + await store.create_conversation("test-conv") + await store.close() + # Store should still work after close + assert await store.get_conversation("test-conv") is not None + + +class TestConversationModel: + """Tests for the Conversation model.""" + + def test_add_message(self): + """add_message appends to messages list.""" + conv = Conversation(id="test") + msg = ChatMessage(id="msg-1", role=MessageRole.USER, content="Hello") + conv.add_message(msg) + + assert len(conv.messages) == 1 + assert conv.messages[0].content == "Hello" + + def test_get_message_found(self): + """get_message returns message if found.""" + conv = Conversation(id="test") + msg = ChatMessage(id="msg-1", role=MessageRole.USER, content="Hello") + conv.add_message(msg) + + found = conv.get_message("msg-1") + assert found is not None + assert found.content == "Hello" + + def test_get_message_not_found(self): + """get_message returns None if not found.""" + conv = Conversation(id="test") + found = conv.get_message("does-not-exist") + assert found is None + + def test_to_history(self): + """to_history returns a copy of messages.""" + conv = Conversation(id="test") + msg = ChatMessage(id="msg-1", role=MessageRole.USER, content="Hello") + conv.add_message(msg) + + history = conv.to_history() + assert len(history) == 1 + assert history is not conv.messages # Should be a copy 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.../agentic-chat-example/frontend/index.html | 12 + .../frontend/package-lock.json | 1699 +++++++ .../frontend/package.json | 23 + .../agentic-chat-example/frontend/src/App.tsx | 74 + .../frontend/src/index.css | 45 + .../frontend/src/main.tsx | 10 + .../frontend/tsconfig.json | 26 + .../frontend/tsconfig.node.json | 10 + .../frontend/vite.config.ts | 35 + examples/agentic-chat-example/main.py | 2 +- examples/agentic-chat-example/routes/chat.py | 11 +- .../routes/conversations.py | 1 - .../static/assets/index-B_-_AM1y.css | 1 + .../static/assets/index-ChWc1_DA.js | 42 + .../agentic-chat-example/static/index.html | 13 + examples/agentic-chat-example/tools.py | 58 +- examples/fastapi-example/main.py | 1 - examples/fastapi-example/routes/action.py | 4 +- examples/fastapi-example/routes/chat.py | 4 +- examples/fastapi-example/routes/refinement.py | 4 +- packages/llmpane-py/README.md | 46 + packages/llmpane-py/llmpane/__init__.py | 16 + .../llmpane-py/llmpane/agent/pydantic_ai.py | 130 +- packages/llmpane-py/llmpane/agent/session.py | 139 +- packages/llmpane-py/llmpane/models.py | 140 +- packages/llmpane-py/llmpane/store/memory.py | 8 +- packages/llmpane-py/llmpane/streaming.py | 4 +- packages/llmpane-py/tests/test_agent.py | 20 +- packages/llmpane-py/tests/test_models.py | 395 ++ packages/llmpane-react/README.md | 123 + packages/llmpane-react/package.json | 12 +- packages/llmpane-react/pnpm-lock.yaml | 4204 +++++++++++++++++ .../src/components/ActionMessage.tsx | 3 +- .../src/components/ChatApp.test.tsx | 261 + .../llmpane-react/src/components/ChatApp.tsx | 139 + .../llmpane-react/src/components/ChatPane.tsx | 266 +- .../components/ConversationSidebar.test.tsx | 329 ++ .../src/components/ConversationSidebar.tsx | 222 + .../src/components/ImageAttachment.tsx | 72 + .../llmpane-react/src/components/LinkCard.tsx | 101 + .../src/components/RefinementMessage.tsx | 3 +- .../src/components/ToolIndicator.test.tsx | 150 + .../src/components/ToolIndicator.tsx | 104 + .../llmpane-react/src/components/index.ts | 15 + packages/llmpane-react/src/hooks/index.ts | 13 + .../llmpane-react/src/hooks/useChat.test.ts | 61 +- packages/llmpane-react/src/hooks/useChat.ts | 64 +- .../src/hooks/useConversations.test.ts | 256 + .../src/hooks/useConversations.ts | 155 + .../llmpane-react/src/hooks/useImageInput.ts | 193 + packages/llmpane-react/src/index.ts | 49 +- packages/llmpane-react/src/styles/llmpane.css | 280 ++ packages/llmpane-react/src/types.ts | 146 +- 58 files changed, 10142 insertions(+), 166 deletions(-) create mode 100644 examples/agentic-chat-example/frontend/.gitignore create mode 100644 examples/agentic-chat-example/frontend/index.html create mode 100644 examples/agentic-chat-example/frontend/package-lock.json create mode 100644 examples/agentic-chat-example/frontend/package.json create mode 100644 examples/agentic-chat-example/frontend/src/App.tsx create mode 100644 examples/agentic-chat-example/frontend/src/index.css create mode 100644 examples/agentic-chat-example/frontend/src/main.tsx create mode 100644 examples/agentic-chat-example/frontend/tsconfig.json create mode 100644 examples/agentic-chat-example/frontend/tsconfig.node.json create mode 100644 examples/agentic-chat-example/frontend/vite.config.ts create mode 100644 examples/agentic-chat-example/static/assets/index-B_-_AM1y.css create mode 100644 examples/agentic-chat-example/static/assets/index-ChWc1_DA.js create mode 100644 examples/agentic-chat-example/static/index.html create mode 100644 packages/llmpane-react/pnpm-lock.yaml create mode 100644 packages/llmpane-react/src/components/ChatApp.test.tsx create mode 100644 packages/llmpane-react/src/components/ChatApp.tsx create mode 100644 packages/llmpane-react/src/components/ConversationSidebar.test.tsx create mode 100644 packages/llmpane-react/src/components/ConversationSidebar.tsx create mode 100644 packages/llmpane-react/src/components/ImageAttachment.tsx create mode 100644 packages/llmpane-react/src/components/LinkCard.tsx create mode 100644 packages/llmpane-react/src/components/ToolIndicator.test.tsx create mode 100644 packages/llmpane-react/src/components/ToolIndicator.tsx create mode 100644 packages/llmpane-react/src/hooks/useConversations.test.ts create mode 100644 packages/llmpane-react/src/hooks/useConversations.ts create mode 100644 packages/llmpane-react/src/hooks/useImageInput.ts diff --git a/.gitignore b/.gitignore index 2bcb118..7d9847f 100644 --- a/.gitignore +++ b/.gitignore @@ -182,9 +182,9 @@ cython_debug/ .abstra/ # Visual Studio Code -# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore +# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore # that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore -# and can be added to the global gitignore or merged into this file. However, if you prefer, +# and can be added to the global gitignore or merged into this file. However, if you prefer, # you could uncomment the following to ignore the entire vscode folder # .vscode/ @@ -207,4 +207,4 @@ marimo/_lsp/ __marimo__/ # Claude -.claude \ No newline at end of file +.claude diff --git a/README.md b/README.md index c54e0ea..0f7a10f 100644 --- a/README.md +++ b/README.md @@ -18,6 +18,7 @@ the data—llmpane just handles the conversation UI. - **Type-safe end-to-end** - Pydantic models in Python, TypeScript types in React - **SSE streaming** - Real-time token streaming that works naturally with FastAPI +- **Multimodal support** - Send images via paste or file upload for vision-capable LLMs - **Server-side message IDs** - Simplifies database storage and conversation history - **Built-in patterns** - ActionMessage for confirmations, RefinementMessage for iterative outputs - **Fully customizable** - CSS variables for theming, headless mode for complete control @@ -73,6 +74,54 @@ function App() { ## Patterns +### Image Input (Multimodal) + +Users can send images alongside text for vision-capable LLMs: + +```tsx +// Image upload is enabled by default + +``` + +**Frontend features:** +- Paste images from clipboard (Cmd/Ctrl+V) +- Click the image button to select files +- Preview attached images before sending +- Remove individual images with the X button + +**Backend handling with Pydantic AI:** + +```python +from llmpane.agent import ChatSession +from pydantic_ai import Agent + +# Use a vision-capable model +agent = Agent("google-gla:gemini-2.0-flash") +session = ChatSession(agent=agent) + +@app.post("/chat") +async def chat(request: ChatRequest): + # session.run() automatically converts ImagePart to BinaryContent + return create_sse_response( + session.run(request.conversation_id, request.message) + ) +``` + +**Message content types:** + +```python +from llmpane.models import MessageContent, TextPart, ImagePart + +# String for text-only (backward compatible) +message: MessageContent = "Hello, world!" + +# List of parts for multimodal +message: MessageContent = [ + ImagePart(type="image", data="base64...", media_type="image/png"), + TextPart(type="text", text="What's in this image?"), +] +``` + ### ActionMessage For "LLM proposes, user confirms" flows: diff --git a/examples/agentic-chat-example/README.md b/examples/agentic-chat-example/README.md index 12e295e..ba10b48 100644 --- a/examples/agentic-chat-example/README.md +++ b/examples/agentic-chat-example/README.md @@ -26,6 +26,9 @@ cd examples/agentic-chat-example # Sync dependencies (installs llmpane from workspace) uv sync + +# Install frontend dependencies +cd frontend && npm install && cd .. ``` ### 2. Set API key @@ -40,7 +43,26 @@ export ANTHROPIC_API_KEY="your-api-key-here" uv run python main.py ``` -Server starts at http://localhost:8000 +Server starts at http://localhost:8002 + +### 4. Run the frontend (separate terminal) + +```bash +cd frontend +npm run dev +``` + +Frontend starts at http://localhost:5173 + +## Frontend + +The frontend uses `@llmpane/react` components for a complete chat experience: + +- **ChatApp** - All-in-one component with sidebar + chat panel +- **ToolIndicator** - Shows when the agent calls tools +- **Conversation sidebar** - List, create, switch, delete conversations + +The frontend code is minimal (~50 lines) thanks to llmpane's batteries-included approach. ## API Endpoints @@ -48,12 +70,12 @@ Server starts at http://localhost:8000 ```bash # Send a message (creates new conversation) -curl -X POST http://localhost:8000/api/chat \ +curl -X POST http://localhost:8002/api/chat \ -H "Content-Type: application/json" \ -d '{"message": "What orders do you have?"}' # Continue existing conversation -curl -X POST http://localhost:8000/api/chat \ +curl -X POST http://localhost:8002/api/chat \ -H "Content-Type: application/json" \ -d '{"message": "Tell me about ORD-001", "conversation_id": "conv_abc123"}' ``` @@ -62,13 +84,13 @@ curl -X POST http://localhost:8000/api/chat \ ```bash # List all conversations -curl http://localhost:8000/api/conversations +curl http://localhost:8002/api/conversations # Get specific conversation -curl http://localhost:8000/api/conversations/conv_abc123 +curl http://localhost:8002/api/conversations/conv_abc123 # Delete conversation -curl -X DELETE http://localhost:8000/api/conversations/conv_abc123 +curl -X DELETE http://localhost:8002/api/conversations/conv_abc123 ``` ## Available Tools diff --git a/examples/agentic-chat-example/app.py b/examples/agentic-chat-example/app.py index bf49941..0985875 100644 --- a/examples/agentic-chat-example/app.py +++ b/examples/agentic-chat-example/app.py @@ -9,7 +9,6 @@ from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware - from routes import chat_router, conversations_router diff --git a/examples/agentic-chat-example/frontend/.gitignore b/examples/agentic-chat-example/frontend/.gitignore new file mode 100644 index 0000000..1b9632e --- /dev/null +++ b/examples/agentic-chat-example/frontend/.gitignore @@ -0,0 +1,24 @@ +# Dependencies +node_modules/ + +# Build output +dist/ + +# Logs +*.log +npm-debug.log* + +# Editor directories +.idea/ +.vscode/ +*.swp +*.swo + +# OS files +.DS_Store +Thumbs.db + +# Environment +.env +.env.local +.env.*.local diff --git a/examples/agentic-chat-example/frontend/index.html b/examples/agentic-chat-example/frontend/index.html new file mode 100644 index 0000000..e9fdadf --- /dev/null +++ b/examples/agentic-chat-example/frontend/index.html @@ -0,0 +1,12 @@ + + + + + + Agentic Chat - llmpane Demo + + +
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"@types/react": "^18.2.0", + "@types/react-dom": "^18.2.0", + "@vitejs/plugin-react": "^4.0.0", + "typescript": "^5.0.0", + "vite": "^5.0.0" + } +} diff --git a/examples/agentic-chat-example/frontend/src/App.tsx b/examples/agentic-chat-example/frontend/src/App.tsx new file mode 100644 index 0000000..14e4d1b --- /dev/null +++ b/examples/agentic-chat-example/frontend/src/App.tsx @@ -0,0 +1,74 @@ +import { + ChatApp, + ToolIndicator, + type DisplayMessage, + type ToolUseMetadata, +} from "@llmpane/react"; +import "@llmpane/react/styles/llmpane.css"; + +/** Check if metadata contains tool use information */ +function hasToolUse( + metadata: unknown +): metadata is ToolUseMetadata { + return ( + metadata !== null && + typeof metadata === "object" && + "toolUse" in metadata && + "status" in metadata + ); +} + +/** Custom message renderer that shows tool indicators */ +function renderMessage( + message: DisplayMessage, + index: number +) { + const isPending = "isPending" in message && message.isPending; + const isAssistantPending = isPending && message.role === "assistant"; + const showCursor = isAssistantPending && !message.content; + const key = "tempId" in message ? message.tempId : message.id ?? `msg-${index}`; + + return ( +
+
+ {message.role === "user" ? "You" : "Assistant"} +
+
+ {message.content} + {showCursor && } + + {/* Show tool indicator if this message has tool use metadata */} + {message.metadata && hasToolUse(message.metadata) && ( + + )} +
+
+ ); +} + +export default function App() { + return ( +
+
+

Agentic Chat

+

Customer service assistant with tool capabilities

+
+
+ + endpoint="/api/chat" + placeholder="Ask about orders, products, or customers..." + renderMessage={renderMessage} + newButtonLabel="New Conversation" + sidebarEmptyMessage="Start a new conversation" + /> +
+
+ ); +} diff --git a/examples/agentic-chat-example/frontend/src/index.css b/examples/agentic-chat-example/frontend/src/index.css new file mode 100644 index 0000000..fba982e --- /dev/null +++ b/examples/agentic-chat-example/frontend/src/index.css @@ -0,0 +1,45 @@ +* { + box-sizing: border-box; + margin: 0; + padding: 0; +} + +html, +body, +#root { + height: 100%; +} + +body { + font-family: system-ui, -apple-system, sans-serif; + background: #f3f4f6; +} + +.app { + height: 100%; + display: flex; + flex-direction: column; +} + +.app-header { + padding: 1rem 1.5rem; + background: white; + border-bottom: 1px solid #e5e7eb; +} + +.app-header h1 { + font-size: 1.25rem; + font-weight: 600; + color: #111827; + margin-bottom: 0.25rem; +} + +.app-header p { + font-size: 0.875rem; + color: #6b7280; +} + +.app-main { + flex: 1; + overflow: hidden; +} diff --git a/examples/agentic-chat-example/frontend/src/main.tsx b/examples/agentic-chat-example/frontend/src/main.tsx new file mode 100644 index 0000000..9b67590 --- /dev/null +++ b/examples/agentic-chat-example/frontend/src/main.tsx @@ -0,0 +1,10 @@ +import React from "react"; +import ReactDOM from "react-dom/client"; +import App from "./App"; +import "./index.css"; + +ReactDOM.createRoot(document.getElementById("root")!).render( + + + +); diff --git a/examples/agentic-chat-example/frontend/tsconfig.json b/examples/agentic-chat-example/frontend/tsconfig.json new file mode 100644 index 0000000..2feeba2 --- /dev/null +++ b/examples/agentic-chat-example/frontend/tsconfig.json @@ -0,0 +1,26 @@ +{ + "compilerOptions": { + "target": "ES2020", + "useDefineForClassFields": true, + "lib": ["ES2020", "DOM", "DOM.Iterable"], + "module": "ESNext", + "skipLibCheck": true, + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "resolveJsonModule": true, + "isolatedModules": true, + "noEmit": true, + "jsx": "react-jsx", + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "noFallthroughCasesInSwitch": true, + "baseUrl": ".", + "paths": { + "@llmpane/react": ["../../../packages/llmpane-react/src"], + "@llmpane/react/*": ["../../../packages/llmpane-react/src/*"] + } + }, + "include": ["src"], + "references": [{ "path": "./tsconfig.node.json" }] +} diff --git a/examples/agentic-chat-example/frontend/tsconfig.node.json b/examples/agentic-chat-example/frontend/tsconfig.node.json new file mode 100644 index 0000000..42872c5 --- /dev/null +++ b/examples/agentic-chat-example/frontend/tsconfig.node.json @@ -0,0 +1,10 @@ +{ + "compilerOptions": { + "composite": true, + "skipLibCheck": true, + "module": "ESNext", + "moduleResolution": "bundler", + "allowSyntheticDefaultImports": true + }, + "include": ["vite.config.ts"] +} diff --git a/examples/agentic-chat-example/frontend/vite.config.ts b/examples/agentic-chat-example/frontend/vite.config.ts new file mode 100644 index 0000000..a4c3ea5 --- /dev/null +++ b/examples/agentic-chat-example/frontend/vite.config.ts @@ -0,0 +1,35 @@ +import { defineConfig, loadEnv } from "vite"; +import react from "@vitejs/plugin-react"; +import path from "path"; +import { fileURLToPath } from "url"; + +const __dirname = path.dirname(fileURLToPath(import.meta.url)); + +export default defineConfig(({ mode }) => { + const env = loadEnv(mode, process.cwd(), ""); + const backendPort = env.BACKEND_PORT || "8002"; + + return { + plugins: [react()], + resolve: { + alias: { + "@llmpane/react": path.resolve( + __dirname, + "../../../packages/llmpane-react/src" + ), + }, + }, + server: { + proxy: { + "/api": { + target: `http://localhost:${backendPort}`, + changeOrigin: true, + }, + }, + }, + build: { + outDir: "../static", + emptyOutDir: true, + }, + }; +}); diff --git a/examples/agentic-chat-example/main.py b/examples/agentic-chat-example/main.py index 2ef1f72..f8a5839 100644 --- a/examples/agentic-chat-example/main.py +++ b/examples/agentic-chat-example/main.py @@ -6,6 +6,6 @@ uvicorn.run( "app:app", host="0.0.0.0", - port=8000, + port=8002, reload=True, ) diff --git a/examples/agentic-chat-example/routes/chat.py b/examples/agentic-chat-example/routes/chat.py index 303c2df..4e54e3f 100644 --- a/examples/agentic-chat-example/routes/chat.py +++ b/examples/agentic-chat-example/routes/chat.py @@ -8,15 +8,14 @@ 5. Conversation is persisted for later retrieval """ - -from llmpane import ChatRequest +from fastapi import APIRouter from llmpane.agent import ChatSession from llmpane.store import get_default_store from llmpane.streaming import create_sse_response - -from fastapi import APIRouter from tools import agent +from llmpane import ChatRequest + router = APIRouter() # Create a ChatSession that combines the agent with persistence @@ -34,6 +33,4 @@ async def chat(request: ChatRequest): - Converting between llmpane and Pydantic AI formats - Streaming tool use events as metadata """ - return create_sse_response( - session.run(request.conversation_id, request.message) - ) + return create_sse_response(session.run(request.conversation_id, request.message)) diff --git a/examples/agentic-chat-example/routes/conversations.py b/examples/agentic-chat-example/routes/conversations.py index 12baece..2dfc83c 100644 --- a/examples/agentic-chat-example/routes/conversations.py +++ b/examples/agentic-chat-example/routes/conversations.py @@ -7,7 +7,6 @@ """ from fastapi import APIRouter, HTTPException - from llmpane.store import get_default_store router = APIRouter() diff --git a/examples/agentic-chat-example/static/assets/index-B_-_AM1y.css b/examples/agentic-chat-example/static/assets/index-B_-_AM1y.css new file mode 100644 index 0000000..17ed348 --- /dev/null +++ b/examples/agentic-chat-example/static/assets/index-B_-_AM1y.css @@ -0,0 +1 @@ +:root{--llmpane-bg: #ffffff;--llmpane-bg-secondary: #f9fafb;--llmpane-text: #111827;--llmpane-text-secondary: #6b7280;--llmpane-border: #e5e7eb;--llmpane-user-bg: #3b82f6;--llmpane-user-text: #ffffff;--llmpane-assistant-bg: #f3f4f6;--llmpane-assistant-text: #111827;--llmpane-button-bg: #3b82f6;--llmpane-button-text: #ffffff;--llmpane-button-hover: #2563eb;--llmpane-button-disabled: #9ca3af;--llmpane-success: #22c55e;--llmpane-error: #ef4444;--llmpane-warning: #f59e0b;--llmpane-info: #3b82f6;--llmpane-spacing-xs: 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${JSON.stringify(r)}`).join(", ")}function Yd({toolUse:e,status:t,result:n,defaultCollapsed:r=!0,className:l=""}){const[u,i]=R.useState(r),o={pending:"Pending",running:"Running",completed:"Completed",failed:"Failed"};return N.jsxs("div",{className:`llmpane-tool ${l}`,children:[N.jsxs("div",{className:"llmpane-tool__header",children:[N.jsx("div",{className:"llmpane-tool__icon",children:t==="running"?N.jsx("span",{className:"llmpane-tool__spinner"}):t==="completed"?"✓":t==="failed"?"✗":"○"}),N.jsxs("div",{className:"llmpane-tool__info",children:[N.jsx("span",{className:"llmpane-tool__name",children:e.name}),Object.keys(e.arguments).length>0&&N.jsxs("span",{className:"llmpane-tool__args",children:["(",Kd(e.arguments),")"]})]}),N.jsx("span",{className:`llmpane-tool__status llmpane-tool__status--${t}`,children:o[t]})]}),n&&N.jsxs("div",{className:"llmpane-tool__result",children:[N.jsx("button",{type:"button",className:"llmpane-tool__toggle",onClick:()=>i(!u),children:u?"Show result":"Hide result"}),!u&&N.jsx("pre",{className:`llmpane-tool__output ${n.isError?"llmpane-tool__output--error":""}`,children:n.content})]})]})}function Xd(e){return e!==null&&typeof e=="object"&&"toolUse"in e&&"status"in e}function Gd(e,t){const n="isPending"in e&&e.isPending,l=n&&e.role==="assistant"&&!e.content,u="tempId"in e?e.tempId:e.id??`msg-${t}`;return N.jsxs("div",{className:`llmpane-message llmpane-message--${e.role} ${n?"llmpane-message--pending":""}`,children:[N.jsx("div",{className:"llmpane-message__role",children:e.role==="user"?"You":"Assistant"}),N.jsxs("div",{className:"llmpane-message__content",children:[e.content,l&&N.jsx("span",{className:"llmpane-message__loading"}),e.metadata&&Xd(e.metadata)&&N.jsx(Yd,{toolUse:e.metadata.toolUse,status:e.metadata.status,result:e.metadata.result})]})]},u)}function Zd(){return N.jsxs("div",{className:"app",children:[N.jsxs("header",{className:"app-header",children:[N.jsx("h1",{children:"Agentic Chat"}),N.jsx("p",{children:"Customer service assistant with tool capabilities"})]}),N.jsx("main",{className:"app-main",children:N.jsx(Qd,{endpoint:"/api/chat",placeholder:"Ask about orders, products, or customers...",renderMessage:Gd,newButtonLabel:"New Conversation",sidebarEmptyMessage:"Start a new conversation"})})]})}Xl.createRoot(document.getElementById("root")).render(N.jsx(ls.StrictMode,{children:N.jsx(Zd,{})})); diff --git a/examples/agentic-chat-example/static/index.html b/examples/agentic-chat-example/static/index.html new file mode 100644 index 0000000..ae9ff1b --- /dev/null +++ b/examples/agentic-chat-example/static/index.html @@ -0,0 +1,13 @@ + + + + + + Agentic Chat - llmpane Demo + + + + +
+ + diff --git a/examples/agentic-chat-example/tools.py b/examples/agentic-chat-example/tools.py index 59556d3..57559f0 100644 --- a/examples/agentic-chat-example/tools.py +++ b/examples/agentic-chat-example/tools.py @@ -11,8 +11,8 @@ import os from datetime import datetime, timedelta -from pydantic_ai import Agent +from pydantic_ai import Agent # --- Mock Data --- @@ -44,17 +44,55 @@ } PRODUCTS = [ - {"id": "PROD-001", "name": "Widget Pro", "price": 99.99, "category": "widgets", "in_stock": True}, - {"id": "PROD-002", "name": "Gadget Mini", "price": 49.99, "category": "gadgets", "in_stock": True}, - {"id": "PROD-003", "name": "Mega Bundle", "price": 399.99, "category": "bundles", "in_stock": False}, - {"id": "PROD-004", "name": "Starter Kit", "price": 29.99, "category": "kits", "in_stock": True}, - {"id": "PROD-005", "name": "Premium Support", "price": 99.99, "category": "services", "in_stock": True}, + { + "id": "PROD-001", + "name": "Widget Pro", + "price": 99.99, + "category": "widgets", + "in_stock": True, + }, + { + "id": "PROD-002", + "name": "Gadget Mini", + "price": 49.99, + "category": "gadgets", + "in_stock": True, + }, + { + "id": "PROD-003", + "name": "Mega Bundle", + "price": 399.99, + "category": "bundles", + "in_stock": False, + }, + { + "id": "PROD-004", + "name": "Starter Kit", + "price": 29.99, + "category": "kits", + "in_stock": True, + }, + { + "id": "PROD-005", + "name": "Premium Support", + "price": 99.99, + "category": "services", + "in_stock": True, + }, ] CUSTOMERS = { - "alice@example.com": {"name": "Alice Johnson", "orders": ["ORD-001"], "tier": "gold"}, + "alice@example.com": { + "name": "Alice Johnson", + "orders": ["ORD-001"], + "tier": "gold", + }, "bob@example.com": {"name": "Bob Smith", "orders": ["ORD-002"], "tier": "silver"}, - "carol@example.com": {"name": "Carol White", "orders": ["ORD-003"], "tier": "bronze"}, + "carol@example.com": { + "name": "Carol White", + "orders": ["ORD-003"], + "tier": "bronze", + }, } @@ -116,7 +154,9 @@ def search_products(query: str, category: str | None = None) -> list[dict]: elif category and product["category"] == category: results.append(product) - return results if results else [{"message": f"No products found matching '{query}'"}] + return ( + results if results else [{"message": f"No products found matching '{query}'"}] + ) @agent.tool_plain diff --git a/examples/fastapi-example/main.py b/examples/fastapi-example/main.py index 8454486..5dc74ff 100644 --- a/examples/fastapi-example/main.py +++ b/examples/fastapi-example/main.py @@ -3,7 +3,6 @@ import os import uvicorn - from app import app if __name__ == "__main__": diff --git a/examples/fastapi-example/routes/action.py b/examples/fastapi-example/routes/action.py index b2c4cd7..d8c0767 100644 --- a/examples/fastapi-example/routes/action.py +++ b/examples/fastapi-example/routes/action.py @@ -6,11 +6,11 @@ from collections.abc import AsyncGenerator from fastapi import APIRouter +from llmpane.streaming import create_sse_response +from mock_llm import generate_mock_response from pydantic import BaseModel from llmpane import ChatRequest, StreamChunk -from llmpane.streaming import create_sse_response -from mock_llm import generate_mock_response router = APIRouter() diff --git a/examples/fastapi-example/routes/chat.py b/examples/fastapi-example/routes/chat.py index 8807b22..8cc45cf 100644 --- a/examples/fastapi-example/routes/chat.py +++ b/examples/fastapi-example/routes/chat.py @@ -4,11 +4,11 @@ from collections.abc import AsyncGenerator from fastapi import APIRouter - -from llmpane import ChatRequest, StreamChunk from llmpane.streaming import create_sse_response from mock_llm import generate_mock_response +from llmpane import ChatRequest, StreamChunk + router = APIRouter() diff --git a/examples/fastapi-example/routes/refinement.py b/examples/fastapi-example/routes/refinement.py index c156c94..7d6adcb 100644 --- a/examples/fastapi-example/routes/refinement.py +++ b/examples/fastapi-example/routes/refinement.py @@ -5,11 +5,11 @@ from collections.abc import AsyncGenerator from fastapi import APIRouter +from llmpane.streaming import create_sse_response +from mock_llm import generate_mock_response from pydantic import BaseModel from llmpane import ChatRequest, StreamChunk -from llmpane.streaming import create_sse_response -from mock_llm import generate_mock_response router = APIRouter() diff --git a/packages/llmpane-py/README.md b/packages/llmpane-py/README.md index f52f04e..221528f 100644 --- a/packages/llmpane-py/README.md +++ b/packages/llmpane-py/README.md @@ -85,6 +85,52 @@ chunk = StreamChunk(done=True, message_id="msg_123") chunk = StreamChunk(error="Something went wrong", done=True) ``` +## Multimodal Messages (Images) + +Send images alongside text for vision-capable LLMs: + +```python +from llmpane.models import ChatMessage, MessageContent, TextPart, ImagePart + +# Text-only message (backward compatible) +message = ChatMessage(role="user", content="Hello!") + +# Multimodal message with image +message = ChatMessage( + role="user", + content=[ + ImagePart(type="image", data="base64...", media_type="image/png"), + TextPart(type="text", text="What's in this image?"), + ], +) + +# Helper functions +from llmpane.models import get_text_content, has_images + +text = get_text_content(message.content) # "What's in this image?" +contains_images = has_images(message.content) # True +``` + +### With Pydantic AI + +The `ChatSession` automatically converts images to Pydantic AI's `BinaryContent`: + +```python +from pydantic_ai import Agent +from llmpane.agent import ChatSession + +# Use a vision-capable model +agent = Agent("google-gla:gemini-2.0-flash") +session = ChatSession(agent=agent) + +@app.post("/chat") +async def chat(request: ChatRequest): + # Handles both text and multimodal messages + return create_sse_response( + session.run(request.conversation_id, request.message) + ) +``` + ## Custom Metadata Use generics to add type-safe custom metadata: diff --git a/packages/llmpane-py/llmpane/__init__.py b/packages/llmpane-py/llmpane/__init__.py index 33ff2bd..0bdaa46 100644 --- a/packages/llmpane-py/llmpane/__init__.py +++ b/packages/llmpane-py/llmpane/__init__.py @@ -8,9 +8,17 @@ ChatMessage, ChatRequest, ChatResponse, + ContentPart, + ImagePart, + MessageContent, MessageRole, RefinementMetadata, + SimpleChatMessage, + SimpleChatRequest, StreamChunk, + TextPart, + get_text_content, + has_images, ) __version__ = "0.1.0" @@ -21,12 +29,20 @@ "ChatMessage", "ChatRequest", "ChatResponse", + "ContentPart", + "ImagePart", + "MessageContent", "MessageRole", "RefinementMetadata", + "SimpleChatMessage", + "SimpleChatRequest", "StreamChunk", + "TextPart", "__version__", "agent", "create_sse_response", + "get_text_content", + "has_images", "store", "stream_chunks", ] diff --git a/packages/llmpane-py/llmpane/agent/pydantic_ai.py b/packages/llmpane-py/llmpane/agent/pydantic_ai.py index c3eb0c1..21da1a0 100644 --- a/packages/llmpane-py/llmpane/agent/pydantic_ai.py +++ b/packages/llmpane-py/llmpane/agent/pydantic_ai.py @@ -2,10 +2,13 @@ from __future__ import annotations +import os import uuid from collections.abc import AsyncGenerator from typing import TYPE_CHECKING, Any +from loguru import logger + from llmpane.agent.models import ToolResult, ToolUse, ToolUseMetadata from llmpane.models import StreamChunk @@ -13,18 +16,24 @@ from pydantic_ai import Agent from pydantic_ai.messages import ModelMessage +# Enable verbose logging with LLMPANE_DEBUG=1 +_DEBUG = os.environ.get("LLMPANE_DEBUG", "").lower() in ("1", "true") + async def stream_agent( agent: Agent, - prompt: str, + prompt: str | list, message_history: list[ModelMessage] | None = None, + deps: Any = None, ) -> AsyncGenerator[StreamChunk[ToolUseMetadata], None]: """Convert Pydantic AI stream events to llmpane StreamChunks. Args: agent: Pydantic AI Agent instance - prompt: User prompt to send to the agent + prompt: User prompt - string for text-only, or list for multimodal + (list may contain strings and BinaryContent objects) message_history: Optional conversation history in Pydantic AI format + deps: Optional dependencies to pass to agent tools Yields: StreamChunk objects with text deltas and tool use metadata @@ -38,23 +47,38 @@ async def stream_agent( ) except ImportError as e: raise ImportError( - "Pydantic AI is required for agent support. " - "Install with: pip install llmpane[agent]" + "Pydantic AI is required for agent support. " "Install with: pip install llmpane[agent]" ) from e try: - async with agent.run_stream( - prompt, message_history=message_history - ) as result: - async for event in result.stream_events(): - chunk = _convert_event_to_chunk(event) - if chunk is not None: - yield chunk + # Use run_stream_events() which yields raw events including tool calls + event_count = 0 + text_chunks_received = 0 + + async for event in agent.run_stream_events( + prompt, message_history=message_history, deps=deps + ): + event_count += 1 + if _DEBUG: + logger.debug(f"[llmpane] Event #{event_count}: {type(event).__name__} = {event}") + + chunk = _convert_event_to_chunk(event) + if chunk is not None: + if chunk.delta: + text_chunks_received += 1 + yield chunk + + if _DEBUG: + logger.debug( + f"[llmpane] Stream complete: {event_count} events, {text_chunks_received} text chunks" + ) - # Final chunk with message ID - yield StreamChunk(done=True, message_id=f"msg_{uuid.uuid4().hex[:12]}") + # Final chunk with message ID + yield StreamChunk(done=True, message_id=f"msg_{uuid.uuid4().hex[:12]}") except Exception as e: + if _DEBUG: + logger.exception(f"[llmpane] Exception during streaming: {e}") yield StreamChunk(error=str(e), done=True) @@ -65,44 +89,78 @@ def _convert_event_to_chunk(event: Any) -> StreamChunk[ToolUseMetadata] | None: FunctionToolCallEvent, FunctionToolResultEvent, PartDeltaEvent, + PartStartEvent, + TextPart, TextPartDelta, ) except ImportError: return None - match event: - # Text delta - case PartDeltaEvent(delta=TextPartDelta(content=content)): - return StreamChunk(delta=content) + event_type = type(event).__name__ - # Tool call started - case FunctionToolCallEvent( - tool_name=name, args=args, tool_call_id=tool_id - ): + # Handle text streaming events + if isinstance(event, PartStartEvent): + part = getattr(event, "part", None) + if part is not None and isinstance(part, TextPart): + content = getattr(part, "content", "") + if content: + return StreamChunk(delta=content) + return None + + if isinstance(event, PartDeltaEvent): + delta = getattr(event, "delta", None) + if delta is not None and isinstance(delta, TextPartDelta): + content = getattr(delta, "content_delta", "") + if content: + return StreamChunk(delta=content) + return None + + # Handle tool call events + if isinstance(event, FunctionToolCallEvent): + part = getattr(event, "part", None) + if _DEBUG: + logger.debug(f"[llmpane] FunctionToolCallEvent matched! part={part}") + if part is not None: + tool_id = getattr(part, "tool_call_id", "") or "" + tool_name = getattr(part, "tool_name", "") or "" + args = getattr(part, "args", {}) return StreamChunk( metadata=ToolUseMetadata( tool_use=ToolUse( id=tool_id, - name=name, + name=tool_name, arguments=args if isinstance(args, dict) else {}, ), status="running", ) ) + return None - # Tool result received - case FunctionToolResultEvent(tool_call_id=tool_id, result=res): - return StreamChunk( - metadata=ToolUseMetadata( - tool_use=ToolUse(id=tool_id, name="", arguments={}), - status="completed", - result=ToolResult( - tool_use_id=tool_id, - content=str(res) if res is not None else "", - ), - ) + # Handle tool result events + if isinstance(event, FunctionToolResultEvent): + tool_id = getattr(event, "tool_call_id", "") or "" + res = getattr(event, "result", None) + if _DEBUG: + logger.debug( + f"[llmpane] FunctionToolResultEvent matched! tool_id={tool_id}, result={res}" + ) + return StreamChunk( + metadata=ToolUseMetadata( + tool_use=ToolUse(id=tool_id, name="", arguments={}), + status="completed", + result=ToolResult( + tool_use_id=tool_id, + content=str(res) if res is not None else "", + ), ) + ) - # Ignore other event types - case _: - return None + # Unhandled event type - log all attributes for debugging + if _DEBUG: + attrs = ( + {k: v for k, v in vars(event).items() if not k.startswith("_")} + if hasattr(event, "__dict__") + else str(event) + ) + logger.debug(f"[llmpane] Unhandled event type: {event_type}, attrs={attrs}") + return None diff --git a/packages/llmpane-py/llmpane/agent/session.py b/packages/llmpane-py/llmpane/agent/session.py index a09d87b..d8c1ac9 100644 --- a/packages/llmpane-py/llmpane/agent/session.py +++ b/packages/llmpane-py/llmpane/agent/session.py @@ -2,12 +2,21 @@ from __future__ import annotations +import base64 from collections.abc import AsyncGenerator -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any from llmpane.agent.models import ToolUseMetadata from llmpane.agent.pydantic_ai import stream_agent -from llmpane.models import ChatMessage, MessageRole, StreamChunk +from llmpane.models import ( + ChatMessage, + ImagePart, + MessageContent, + MessageRole, + StreamChunk, + TextPart, + get_text_content, +) from llmpane.store import ConversationStore, get_default_store if TYPE_CHECKING: @@ -32,32 +41,46 @@ async def chat(request: ChatRequest): session.run(request.conversation_id, request.message) ) ``` + + With dependencies: + ```python + @dataclass + class MyDeps: + db: Session + + agent = Agent('anthropic:claude-sonnet-4-20250514', deps_type=MyDeps) + deps = MyDeps(db=get_db()) + session = ChatSession(agent=agent, deps=deps) + ``` """ def __init__( self, agent: Agent, store: ConversationStore | None = None, + deps: Any = None, ): """Initialize a ChatSession. Args: agent: Pydantic AI Agent instance store: Conversation store (defaults to global InMemoryStore) + deps: Optional dependencies to pass to agent tools """ self.agent = agent self.store = store or get_default_store() + self.deps = deps async def run( self, conversation_id: str | None, - message: str, + message: MessageContent, ) -> AsyncGenerator[StreamChunk[ToolUseMetadata], None]: """Run a chat turn with automatic persistence. Args: conversation_id: Optional conversation ID (created if None/not found) - message: User message content + message: User message content (string or list of content parts for multimodal) Yields: StreamChunk objects with text deltas and tool use metadata @@ -69,18 +92,32 @@ async def run( if not conv: conv = await self.store.create_conversation(conversation_id) - # Persist user message + # Convert conversation history to Pydantic AI format BEFORE adding new message + history = self._to_pydantic_history(conv.messages) + + # Persist user message after building history to avoid duplication user_msg = ChatMessage(role=MessageRole.USER, content=message) await self.store.add_message(conv.id, user_msg) - # Convert conversation history to Pydantic AI format - history = self._to_pydantic_history(conv.messages) + # Convert message content to Pydantic AI prompt format + prompt = self._to_pydantic_prompt(message) # Stream response and accumulate content accumulated = "" - async for chunk in stream_agent(self.agent, message, history): + async for chunk in stream_agent(self.agent, prompt, history, deps=self.deps): accumulated += chunk.delta - yield chunk + # Add conversation ID to the final chunk + if chunk.done: + yield StreamChunk( + delta=chunk.delta, + metadata=chunk.metadata, + done=True, + error=chunk.error, + message_id=chunk.message_id, + conversation_id=conv.id, + ) + else: + yield chunk # Persist assistant message (after streaming completes) if accumulated: @@ -90,20 +127,74 @@ async def run( ) await self.store.add_message(conv.id, assistant_msg) - def _to_pydantic_history( - self, messages: list[ChatMessage] - ) -> list[ModelMessage]: + def _to_pydantic_prompt(self, content: MessageContent) -> str | list: + """Convert llmpane MessageContent to Pydantic AI prompt format. + + Args: + content: String for text-only, or list of ContentPart for multimodal + + Returns: + String for text-only, or list with text and BinaryContent for multimodal + """ + if isinstance(content, str): + return content + + try: + from pydantic_ai.messages import BinaryContent + except ImportError: + # Fall back to text-only if BinaryContent not available + return get_text_content(content) + + parts: list = [] + for part in content: + if isinstance(part, TextPart): + parts.append(part.text) + elif isinstance(part, ImagePart): + # Decode base64 to bytes for BinaryContent + image_bytes = base64.b64decode(part.data) + parts.append( + BinaryContent(data=image_bytes, media_type=part.media_type) + ) + + return parts + + def _convert_content_to_pydantic(self, content: MessageContent) -> str | list: + """Convert llmpane MessageContent to Pydantic AI UserPromptPart content format. + + This is used for converting message history content. + """ + if isinstance(content, str): + return content + + try: + from pydantic_ai.messages import BinaryContent + except ImportError: + return get_text_content(content) + + parts: list = [] + for part in content: + if isinstance(part, TextPart): + parts.append(part.text) + elif isinstance(part, ImagePart): + image_bytes = base64.b64decode(part.data) + parts.append( + BinaryContent(data=image_bytes, media_type=part.media_type) + ) + + return parts + + def _to_pydantic_history(self, messages: list[ChatMessage]) -> list[ModelMessage]: """Convert llmpane ChatMessages to Pydantic AI ModelMessages. This handles the format conversion between llmpane's message format - and Pydantic AI's expected history format. + and Pydantic AI's expected history format, including multimodal content. """ try: from pydantic_ai.messages import ( ModelRequest, ModelResponse, SystemPromptPart, - TextPart, + TextPart as PydanticTextPart, UserPromptPart, ) except ImportError: @@ -114,16 +205,28 @@ def _to_pydantic_history( for msg in messages: match msg.role: case MessageRole.USER: + # Convert content (may be multimodal) + pydantic_content = self._convert_content_to_pydantic(msg.content) result.append( - ModelRequest(parts=[UserPromptPart(content=msg.content)]) + ModelRequest(parts=[UserPromptPart(content=pydantic_content)]) ) case MessageRole.ASSISTANT: - result.append( - ModelResponse(parts=[TextPart(content=msg.content)]) + # Assistant messages are always text + text = ( + get_text_content(msg.content) + if not isinstance(msg.content, str) + else msg.content ) + result.append(ModelResponse(parts=[PydanticTextPart(content=text)])) case MessageRole.SYSTEM: + # System messages are always text + text = ( + get_text_content(msg.content) + if not isinstance(msg.content, str) + else msg.content + ) result.append( - ModelRequest(parts=[SystemPromptPart(content=msg.content)]) + ModelRequest(parts=[SystemPromptPart(content=text)]) ) case MessageRole.TOOL: # Tool messages are handled differently in Pydantic AI diff --git a/packages/llmpane-py/llmpane/models.py b/packages/llmpane-py/llmpane/models.py index e309b93..4830f27 100644 --- a/packages/llmpane-py/llmpane/models.py +++ b/packages/llmpane-py/llmpane/models.py @@ -1,7 +1,7 @@ """Pydantic models for llmpane - the source of truth for type definitions.""" from enum import Enum -from typing import Generic, TypeVar +from typing import Annotated, Any, Generic, Literal, TypeVar, Union from pydantic import BaseModel, ConfigDict, Field @@ -16,11 +16,60 @@ class MessageRole(str, Enum): # Generic type for custom metadata -MetadataT = TypeVar("MetadataT", bound=BaseModel | None) +MetadataT = TypeVar("MetadataT") + + +# --- Content Part Types for Multimodal Messages --- + + +class TextPart(BaseModel): + """Text content in a message.""" + + type: Literal["text"] = "text" + text: str = Field(description="The text content.") + + +class ImagePart(BaseModel): + """Base64-encoded image content in a message.""" + + type: Literal["image"] = "image" + data: str = Field(description="Base64-encoded image data (without data URI prefix).") + media_type: str = Field( + description="MIME type of the image (e.g., 'image/png', 'image/jpeg').", + pattern=r"^image/(png|jpeg|gif|webp)$", + ) + + +# Discriminated union for content parts +ContentPart = Annotated[Union[TextPart, ImagePart], Field(discriminator="type")] + +# Message content: string for text-only (backward compatible), or list for multimodal +MessageContent = str | list[ContentPart] + + +def get_text_content(content: MessageContent) -> str: + """Extract text content as a string from MessageContent.""" + if isinstance(content, str): + return content + return " ".join(part.text for part in content if isinstance(part, TextPart)) + + +def has_images(content: MessageContent) -> bool: + """Check if MessageContent contains any images.""" + if isinstance(content, str): + return False + return any(isinstance(part, ImagePart) for part in content) class ChatMessage(BaseModel, Generic[MetadataT]): - """A single message in a conversation.""" + """A single message in a conversation. + + Generic over metadata type. Use `SimpleChatMessage` for FastAPI endpoints + to avoid issues with generic type validation. + + Content can be a string (text-only, backward compatible) or a list of + ContentPart objects for multimodal messages with images. + """ model_config = ConfigDict(use_enum_values=True) @@ -29,7 +78,9 @@ class ChatMessage(BaseModel, Generic[MetadataT]): description="Server-assigned message ID. None for pending messages.", ) role: MessageRole - content: str + content: MessageContent = Field( + description="Message content: string for text-only, or list of content parts for multimodal.", + ) metadata: MetadataT | None = Field( default=None, description="Optional custom metadata attached to this message.", @@ -37,11 +88,20 @@ class ChatMessage(BaseModel, Generic[MetadataT]): class ChatRequest(BaseModel, Generic[MetadataT]): - """Request to send a message and get a streaming response.""" + """Request to send a message and get a streaming response. + + Generic over metadata type. Use `SimpleChatRequest` for FastAPI endpoints + to avoid issues with generic type validation. + + Message can be a string (text-only, backward compatible) or a list of + ContentPart objects for multimodal messages with images. + """ model_config = ConfigDict(use_enum_values=True) - message: str = Field(description="The user's message content.") + message: MessageContent = Field( + description="The user's message: string for text-only, or list of content parts for multimodal.", + ) conversation_id: str | None = Field( default=None, description="Optional conversation ID for multi-turn conversations.", @@ -79,6 +139,10 @@ class StreamChunk(BaseModel, Generic[MetadataT]): default=None, description="Server-assigned message ID. Typically sent in the final chunk.", ) + conversation_id: str | None = Field( + default=None, + description="Conversation ID. Sent in the final chunk for new conversations.", + ) class ChatResponse(BaseModel, Generic[MetadataT]): @@ -88,6 +152,46 @@ class ChatResponse(BaseModel, Generic[MetadataT]): conversation_id: str | None = None +# --- Simple (non-generic) versions for FastAPI endpoints --- +# FastAPI has issues with Generic types as request body parameters. +# Use these concrete types for API endpoints. + + +class SimpleChatMessage(BaseModel): + """A chat message with Any metadata. Use for FastAPI endpoints.""" + + model_config = ConfigDict(use_enum_values=True) + + id: str | None = Field(default=None) + role: MessageRole + content: MessageContent = Field( + description="Message content: string for text-only, or list of content parts for multimodal.", + ) + metadata: Any | None = Field(default=None) + + +class SimpleChatRequest(BaseModel): + """A chat request with Any metadata. Use for FastAPI endpoints.""" + + model_config = ConfigDict(use_enum_values=True) + + message: MessageContent = Field( + description="The user's message: string for text-only, or list of content parts for multimodal.", + ) + conversation_id: str | None = Field( + default=None, + description="Optional conversation ID for multi-turn conversations.", + ) + history: list[SimpleChatMessage] = Field( + default_factory=list, + description="Previous messages in this conversation.", + ) + metadata: Any | None = Field( + default=None, + description="Optional custom metadata for this request.", + ) + + # --- Action Message Pattern --- @@ -132,3 +236,27 @@ class RefinementMetadata(BaseModel, Generic[OutputT]): default=False, description="True if the user has accepted this as final.", ) + + +# --- Link Message Pattern --- + + +class Link(BaseModel): + """A navigable link to an entity or resource.""" + + label: str = Field(description="Display text for the link") + url: str = Field(description="Navigation URL") + entity_type: str | None = Field( + default=None, + description="Type of entity (e.g., 'contact', 'gift')", + ) + entity_id: str | None = Field( + default=None, + description="ID of the entity for programmatic access", + ) + + +class LinkMetadata(BaseModel): + """Metadata for messages with navigation links.""" + + links: list[Link] = Field(default_factory=list) diff --git a/packages/llmpane-py/llmpane/store/memory.py b/packages/llmpane-py/llmpane/store/memory.py index 5c3fbac..1fa23d3 100644 --- a/packages/llmpane-py/llmpane/store/memory.py +++ b/packages/llmpane-py/llmpane/store/memory.py @@ -102,9 +102,7 @@ async def add_message( # Assign ID if not present if message.id is None: - message = message.model_copy( - update={"id": f"msg_{uuid.uuid4().hex[:12]}"} - ) + message = message.model_copy(update={"id": f"msg_{uuid.uuid4().hex[:12]}"}) conv.add_message(message) self._access_times[conversation_id] = datetime.now(timezone.utc) @@ -121,9 +119,7 @@ async def _maybe_evict(self) -> None: # Remove expired conversations if self._default_ttl: expired = [ - cid - for cid, atime in self._access_times.items() - if now - atime > self._default_ttl + cid for cid, atime in self._access_times.items() if now - atime > self._default_ttl ] for cid in expired: del self._conversations[cid] diff --git a/packages/llmpane-py/llmpane/streaming.py b/packages/llmpane-py/llmpane/streaming.py index d5cbdff..a106bbc 100644 --- a/packages/llmpane-py/llmpane/streaming.py +++ b/packages/llmpane-py/llmpane/streaming.py @@ -29,9 +29,7 @@ def _serialize_chunk(chunk: StreamChunk[Any]) -> str: camel_data = {_to_camel(k): v for k, v in data.items()} # Handle nested metadata if "metadata" in camel_data and isinstance(camel_data["metadata"], dict): - camel_data["metadata"] = { - _to_camel(k): v for k, v in camel_data["metadata"].items() - } + camel_data["metadata"] = {_to_camel(k): v for k, v in camel_data["metadata"].items()} import json return json.dumps(camel_data) diff --git a/packages/llmpane-py/tests/test_agent.py b/packages/llmpane-py/tests/test_agent.py index da123b7..3d28dfe 100644 --- a/packages/llmpane-py/tests/test_agent.py +++ b/packages/llmpane-py/tests/test_agent.py @@ -126,22 +126,12 @@ async def test_chat_session_creates_conversation(self): # Create mock agent mock_agent = MagicMock() - # Mock the run_stream to return an empty async context manager - async def mock_run_stream(*args, **kwargs): - class MockResult: - async def stream_events(self): - return - yield # Make it an async generator + # Mock run_stream_events to return an empty async generator + async def mock_run_stream_events(*args, **kwargs): + return + yield # Make it an async generator - async def __aenter__(self): - return self - - async def __aexit__(self, *args): - pass - - return MockResult() - - mock_agent.run_stream = mock_run_stream + mock_agent.run_stream_events = mock_run_stream_events store = InMemoryStore() session = ChatSession(agent=mock_agent, store=store) diff --git a/packages/llmpane-py/tests/test_models.py b/packages/llmpane-py/tests/test_models.py index ebb1bb8..c069c25 100644 --- a/packages/llmpane-py/tests/test_models.py +++ b/packages/llmpane-py/tests/test_models.py @@ -8,9 +8,15 @@ ChatMessage, ChatRequest, ChatResponse, + ImagePart, MessageRole, RefinementMetadata, + SimpleChatMessage, + SimpleChatRequest, StreamChunk, + TextPart, + get_text_content, + has_images, ) @@ -195,3 +201,392 @@ class Config(BaseModel): ) assert meta.iteration == 3 assert meta.is_final is True + + +class TestSimpleChatMessage: + """Tests for SimpleChatMessage - non-generic version for FastAPI endpoints.""" + + def test_basic_message(self): + msg = SimpleChatMessage(role=MessageRole.USER, content="Hello!") + assert msg.role == "user" + assert msg.content == "Hello!" + assert msg.id is None + assert msg.metadata is None + + def test_message_with_id(self): + msg = SimpleChatMessage(id="msg_123", role=MessageRole.ASSISTANT, content="Hi there!") + assert msg.id == "msg_123" + assert msg.role == "assistant" + + def test_message_serialization(self): + msg = SimpleChatMessage(id="msg_123", role=MessageRole.USER, content="Test") + data = msg.model_dump() + assert data["id"] == "msg_123" + assert data["role"] == "user" + assert data["content"] == "Test" + + def test_message_with_any_metadata(self): + """SimpleChatMessage accepts any metadata type without generics.""" + msg = SimpleChatMessage( + role=MessageRole.ASSISTANT, + content="Answer", + metadata={"confidence": 0.95, "source": "database"}, + ) + assert msg.metadata is not None + assert msg.metadata["confidence"] == 0.95 + assert msg.metadata["source"] == "database" + + def test_message_from_dict(self): + data = {"role": "user", "content": "Hello from dict"} + msg = SimpleChatMessage.model_validate(data) + assert msg.role == "user" + assert msg.content == "Hello from dict" + + def test_message_from_json(self): + """Test that SimpleChatMessage can be parsed from JSON-like dict.""" + data = { + "id": "msg-001", + "role": "assistant", + "content": "This is a response", + "metadata": {"tool_used": "search"}, + } + msg = SimpleChatMessage.model_validate(data) + assert msg.id == "msg-001" + assert msg.role == "assistant" + assert msg.content == "This is a response" + assert msg.metadata == {"tool_used": "search"} + + +class TestSimpleChatRequest: + """Tests for SimpleChatRequest - non-generic version for FastAPI endpoints.""" + + def test_minimal_request(self): + req = SimpleChatRequest(message="What is Python?") + assert req.message == "What is Python?" + assert req.conversation_id is None + assert req.history == [] + assert req.metadata is None + + def test_request_with_conversation_id(self): + req = SimpleChatRequest(message="Follow up", conversation_id="conv_456") + assert req.conversation_id == "conv_456" + + def test_request_with_history(self): + history = [ + SimpleChatMessage(role=MessageRole.USER, content="Hi"), + SimpleChatMessage(role=MessageRole.ASSISTANT, content="Hello!"), + ] + req = SimpleChatRequest(message="How are you?", history=history) + assert len(req.history) == 2 + assert req.history[0].content == "Hi" + assert req.history[1].role == "assistant" + + def test_request_with_dict_history(self): + """Test that history can be provided as list of dicts.""" + req = SimpleChatRequest( + message="Continue", + history=[ + {"role": "user", "content": "Start"}, + {"role": "assistant", "content": "Started!"}, + ], + ) + assert len(req.history) == 2 + assert req.history[0].content == "Start" + assert isinstance(req.history[0], SimpleChatMessage) + + def test_request_serialization(self): + req = SimpleChatRequest(message="Test", conversation_id="conv_123") + data = req.model_dump() + assert data["message"] == "Test" + assert data["conversation_id"] == "conv_123" + assert data["history"] == [] + + def test_request_with_any_metadata(self): + """SimpleChatRequest accepts any metadata type without generics.""" + req = SimpleChatRequest( + message="Search for data", + metadata={"user_id": 42, "context": "dashboard"}, + ) + assert req.metadata is not None + assert req.metadata["user_id"] == 42 + assert req.metadata["context"] == "dashboard" + + def test_request_from_json(self): + """Test parsing from JSON-like dict (simulates FastAPI request body).""" + data = { + "message": "Find top donors", + "conversation_id": "conv-abc-123", + "history": [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi! How can I help?"}, + ], + "metadata": {"source": "web"}, + } + req = SimpleChatRequest.model_validate(data) + assert req.message == "Find top donors" + assert req.conversation_id == "conv-abc-123" + assert len(req.history) == 2 + assert req.metadata == {"source": "web"} + + def test_request_compatible_with_fastapi(self): + """Test that SimpleChatRequest works without generic type issues. + + This is the primary use case - FastAPI has issues with Generic types + as request body parameters, so SimpleChatRequest provides a concrete + alternative. + """ + # Simulate what FastAPI does: parse JSON to dict, then validate + raw_json = { + "message": "Get statistics", + "conversation_id": None, + "history": [], + } + req = SimpleChatRequest.model_validate(raw_json) + assert req.message == "Get statistics" + assert req.conversation_id is None + assert req.history == [] + + +# --- Multimodal Content Tests --- + + +class TestTextPart: + def test_basic_text_part(self): + part = TextPart(text="Hello, world!") + assert part.type == "text" + assert part.text == "Hello, world!" + + def test_text_part_serialization(self): + part = TextPart(text="Test content") + data = part.model_dump() + assert data["type"] == "text" + assert data["text"] == "Test content" + + def test_text_part_from_dict(self): + data = {"type": "text", "text": "From dict"} + part = TextPart.model_validate(data) + assert part.text == "From dict" + + +class TestImagePart: + def test_basic_image_part(self): + part = ImagePart(data="iVBORw0KGgoAAAANS", media_type="image/png") + assert part.type == "image" + assert part.data == "iVBORw0KGgoAAAANS" + assert part.media_type == "image/png" + + def test_image_part_jpeg(self): + part = ImagePart(data="base64data", media_type="image/jpeg") + assert part.media_type == "image/jpeg" + + def test_image_part_gif(self): + part = ImagePart(data="base64data", media_type="image/gif") + assert part.media_type == "image/gif" + + def test_image_part_webp(self): + part = ImagePart(data="base64data", media_type="image/webp") + assert part.media_type == "image/webp" + + def test_image_part_serialization(self): + part = ImagePart(data="test_data", media_type="image/png") + data = part.model_dump() + assert data["type"] == "image" + assert data["data"] == "test_data" + assert data["media_type"] == "image/png" + + def test_image_part_from_dict(self): + data = {"type": "image", "data": "abc123", "media_type": "image/jpeg"} + part = ImagePart.model_validate(data) + assert part.data == "abc123" + assert part.media_type == "image/jpeg" + + +class TestMessageContent: + """Tests for MessageContent type and helper functions.""" + + def test_get_text_content_from_string(self): + content = "Hello, world!" + assert get_text_content(content) == "Hello, world!" + + def test_get_text_content_from_parts(self): + content = [ + TextPart(text="Hello,"), + TextPart(text="world!"), + ] + assert get_text_content(content) == "Hello, world!" + + def test_get_text_content_skips_images(self): + content = [ + TextPart(text="Check this"), + ImagePart(data="base64data", media_type="image/png"), + TextPart(text="image"), + ] + assert get_text_content(content) == "Check this image" + + def test_has_images_string_content(self): + assert has_images("Just text") is False + + def test_has_images_text_parts_only(self): + content = [TextPart(text="Hello"), TextPart(text="World")] + assert has_images(content) is False + + def test_has_images_with_image(self): + content = [ + TextPart(text="See image:"), + ImagePart(data="base64data", media_type="image/png"), + ] + assert has_images(content) is True + + def test_has_images_only_image(self): + content = [ImagePart(data="base64data", media_type="image/jpeg")] + assert has_images(content) is True + + +class TestMultimodalChatMessage: + """Tests for ChatMessage with multimodal content.""" + + def test_string_content_backward_compat(self): + """String content should still work (backward compatibility).""" + msg = ChatMessage(role=MessageRole.USER, content="Hello!") + assert msg.content == "Hello!" + assert isinstance(msg.content, str) + + def test_multimodal_content(self): + """Test message with mixed text and image content.""" + content = [ + TextPart(text="What is in this image?"), + ImagePart(data="iVBORw0KGgo=", media_type="image/png"), + ] + msg = ChatMessage(role=MessageRole.USER, content=content) + assert isinstance(msg.content, list) + assert len(msg.content) == 2 + assert has_images(msg.content) + + def test_multimodal_message_serialization(self): + """Test that multimodal messages serialize correctly.""" + content = [ + TextPart(text="Analyze:"), + ImagePart(data="abc123", media_type="image/jpeg"), + ] + msg = ChatMessage(role=MessageRole.USER, content=content) + data = msg.model_dump() + assert isinstance(data["content"], list) + assert data["content"][0]["type"] == "text" + assert data["content"][0]["text"] == "Analyze:" + assert data["content"][1]["type"] == "image" + assert data["content"][1]["data"] == "abc123" + + def test_multimodal_message_from_dict(self): + """Test parsing multimodal message from dict.""" + data = { + "role": "user", + "content": [ + {"type": "text", "text": "Describe this:"}, + {"type": "image", "data": "base64", "media_type": "image/png"}, + ], + } + msg = ChatMessage.model_validate(data) + assert len(msg.content) == 2 + assert msg.content[0].text == "Describe this:" + assert msg.content[1].data == "base64" + + +class TestMultimodalChatRequest: + """Tests for ChatRequest with multimodal content.""" + + def test_string_message_backward_compat(self): + """String message should still work (backward compatibility).""" + req = ChatRequest(message="What is Python?") + assert req.message == "What is Python?" + assert isinstance(req.message, str) + + def test_multimodal_message(self): + """Test request with multimodal message.""" + message = [ + TextPart(text="What's in this screenshot?"), + ImagePart(data="screenshotdata", media_type="image/png"), + ] + req = ChatRequest(message=message) + assert isinstance(req.message, list) + assert len(req.message) == 2 + assert has_images(req.message) + + def test_multimodal_request_serialization(self): + """Test that multimodal requests serialize correctly.""" + message = [ + TextPart(text="Caption:"), + ImagePart(data="imgdata", media_type="image/jpeg"), + ] + req = ChatRequest(message=message, conversation_id="conv_123") + data = req.model_dump() + assert isinstance(data["message"], list) + assert data["message"][0]["type"] == "text" + assert data["message"][1]["type"] == "image" + + def test_multimodal_request_from_json(self): + """Test parsing multimodal request from JSON-like dict.""" + data = { + "message": [ + {"type": "text", "text": "What is this?"}, + {"type": "image", "data": "b64data", "media_type": "image/webp"}, + ], + "conversation_id": "conv-abc", + } + req = ChatRequest.model_validate(data) + assert len(req.message) == 2 + assert req.message[1].media_type == "image/webp" + assert req.conversation_id == "conv-abc" + + def test_multimodal_history(self): + """Test request with multimodal messages in history.""" + history = [ + ChatMessage( + role=MessageRole.USER, + content=[ + TextPart(text="Here's a photo:"), + ImagePart(data="photodata", media_type="image/jpeg"), + ], + ), + ChatMessage( + role=MessageRole.ASSISTANT, + content="I see a landscape with mountains.", + ), + ] + req = ChatRequest(message="What color is the sky?", history=history) + assert len(req.history) == 2 + assert has_images(req.history[0].content) + assert not has_images(req.history[1].content) + + +class TestMultimodalSimpleChatMessage: + """Tests for SimpleChatMessage with multimodal content.""" + + def test_string_content_backward_compat(self): + msg = SimpleChatMessage(role=MessageRole.USER, content="Hello!") + assert msg.content == "Hello!" + + def test_multimodal_content(self): + content = [ + {"type": "text", "text": "Check this:"}, + {"type": "image", "data": "base64", "media_type": "image/png"}, + ] + msg = SimpleChatMessage(role=MessageRole.USER, content=content) + assert isinstance(msg.content, list) + assert len(msg.content) == 2 + + +class TestMultimodalSimpleChatRequest: + """Tests for SimpleChatRequest with multimodal content.""" + + def test_string_message_backward_compat(self): + req = SimpleChatRequest(message="Hello") + assert req.message == "Hello" + + def test_multimodal_message(self): + message = [ + {"type": "text", "text": "Analyze:"}, + {"type": "image", "data": "imgdata", "media_type": "image/jpeg"}, + ] + req = SimpleChatRequest(message=message) + assert isinstance(req.message, list) + assert len(req.message) == 2 diff --git a/packages/llmpane-react/README.md b/packages/llmpane-react/README.md index af5e2c9..ccecbb3 100644 --- a/packages/llmpane-react/README.md +++ b/packages/llmpane-react/README.md @@ -52,6 +52,9 @@ The main chat component with built-in message display and input handling. | `autoScroll` | `boolean` | Auto-scroll to new messages (default: true). | | `multiline` | `boolean` | Allow multiline input with Shift+Enter. | | `disabled` | `boolean` | Disable the input. | +| `allowImageUpload` | `boolean` | Enable image paste/upload (default: true). | +| `maxImageSize` | `number` | Max image size in bytes (default: 5MB). | +| `allowedImageTypes` | `string[]` | Allowed MIME types (default: png, jpeg, gif, webp). | ### ActionMessage @@ -95,6 +98,59 @@ interface SQLQuery { /> ``` +### ToolIndicator + +Display tool execution status in agent-style chats. + +```tsx +import { ToolIndicator } from "@llmpane/react"; + + + +// With completed result + +``` + +### LinkCard + +Display navigation links returned by the LLM (e.g., links to entities). + +```tsx +import { LinkCard } from "@llmpane/react"; + + +``` + +### ConversationSidebar + +Sidebar for listing and managing conversations. + +```tsx +import { ConversationSidebar } from "@llmpane/react"; + + setSelectedConversationId(id)} + onNew={() => setSelectedConversationId(null)} + onDelete={(id) => console.log("Deleted:", id)} +/> +``` + ## Hooks ### useChat @@ -126,6 +182,58 @@ function MyChat() { } ``` +### useImageInput + +Hook for managing image attachments before sending. + +```tsx +import { useImageInput } from "@llmpane/react"; +import type { ContentPart } from "@llmpane/react"; + +function MyImageChat() { + const { + pendingImages, + addImage, + addImageFromClipboard, + removeImage, + clearImages, + hasImages, + } = useImageInput({ maxFileSize: 5 * 1024 * 1024 }); + + const handlePaste = (e: ClipboardEvent) => { + for (const item of e.clipboardData?.items ?? []) { + if (item.type.startsWith("image/")) { + addImageFromClipboard(item); + break; + } + } + }; + + const handleSend = () => { + const parts: ContentPart[] = pendingImages.map(img => ({ + type: "image", + data: img.data, + mediaType: img.mediaType, + })); + parts.push({ type: "text", text: inputValue }); + sendMessage(parts); + clearImages(); + }; + + return ( +
+ {pendingImages.map(img => ( + removeImage(img.id)} + /> + ))} +
+ ); +} +``` + ## Custom Metadata Use TypeScript generics for type-safe custom metadata: @@ -187,12 +295,27 @@ All types mirror the Python Pydantic models: ```tsx import type { + // Core message types ChatMessage, ChatRequest, StreamChunk, + MessageContent, + + // Multimodal content parts + TextPart, + ImagePart, + ContentPart, + + // Pattern metadata ActionMetadata, RefinementMetadata, + + // Image input + PendingImage, } from "@llmpane/react"; + +// Type guards for content parts +import { isTextPart, isImagePart, getTextContent, hasImages } from "@llmpane/react"; ``` ## License diff --git a/packages/llmpane-react/package.json b/packages/llmpane-react/package.json index 8cb2464..c77e224 100644 --- a/packages/llmpane-react/package.json +++ b/packages/llmpane-react/package.json @@ -30,19 +30,19 @@ }, "devDependencies": { "@testing-library/jest-dom": "^6.0.0", - "@testing-library/react": "^14.0.0", + "@testing-library/react": "^16.0.0", "@testing-library/user-event": "^14.0.0", - 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a/packages/llmpane-react/src/components/ActionMessage.tsx b/packages/llmpane-react/src/components/ActionMessage.tsx index 0835eae..3dbdb4e 100644 --- a/packages/llmpane-react/src/components/ActionMessage.tsx +++ b/packages/llmpane-react/src/components/ActionMessage.tsx @@ -1,6 +1,7 @@ import { useCallback, type ReactNode } from "react"; import type { ActionMetadata, ActionStatus, ChatMessage } from "../types"; +import { getTextContent } from "../types"; /** Props for the ActionMessage component */ export interface ActionMessageProps { @@ -125,7 +126,7 @@ export function ActionMessage({ {/* Message content */} {message.content && ( -
{message.content}
+
{getTextContent(message.content)}
)} {/* Action card */} diff --git a/packages/llmpane-react/src/components/ChatApp.test.tsx b/packages/llmpane-react/src/components/ChatApp.test.tsx new file mode 100644 index 0000000..0b97a41 --- /dev/null +++ b/packages/llmpane-react/src/components/ChatApp.test.tsx @@ -0,0 +1,261 @@ +import { describe, it, expect, vi, beforeEach, afterEach } from "vitest"; +import { render, screen, waitFor } from "@testing-library/react"; +import userEvent from "@testing-library/user-event"; +import { ChatApp } from "./ChatApp"; + +describe("ChatApp", () => { + const mockEndpoint = "/api/chat"; + + beforeEach(() => { + vi.clearAllMocks(); + }); + + afterEach(() => { + vi.restoreAllMocks(); + }); + + function createMockSSEResponse(chunks: string[]): Response { + let index = 0; + const encoder = new TextEncoder(); + + const stream = new ReadableStream({ + pull(controller) { + if (index < chunks.length) { + controller.enqueue(encoder.encode(chunks[index])); + index++; + } else { + controller.close(); + } + }, + }); + + return new Response(stream, { + status: 200, + headers: { "Content-Type": "text/event-stream" }, + }); + } + + function createMockJsonResponse(data: unknown, status = 200): Response { + return new Response(JSON.stringify(data), { + status, + headers: { "Content-Type": "application/json" }, + }); + } + + it("renders with sidebar by default", async () => { + const mockFetch = vi.fn().mockResolvedValue( + createMockJsonResponse({ + conversations: [], + total: 0, + limit: 50, + offset: 0, + }) + ); + global.fetch = mockFetch; + + const { container } = render(); + + expect(container.querySelector(".llmpane-sidebar")).toBeInTheDocument(); + expect(screen.getByText("New Chat")).toBeInTheDocument(); + }); + + it("renders without sidebar when showSidebar is false", async () => { + const { container } = render( + + ); + + expect(container.querySelector(".llmpane-sidebar")).not.toBeInTheDocument(); + }); + + it("renders chat input", async () => { + const mockFetch = vi.fn().mockResolvedValue( + createMockJsonResponse({ + conversations: [], + total: 0, + limit: 50, + offset: 0, + }) + ); + global.fetch = mockFetch; + + render(); + + expect(screen.getByRole("textbox")).toBeInTheDocument(); + }); + + it("calls onConversationChange when selecting a conversation", async () => { + const mockConversations = [ + { + id: "conv_1", + preview: "Test conversation", + message_count: 2, + created_at: "2025-01-01T00:00:00Z", + updated_at: "2025-01-01T00:00:00Z", + }, + ]; + + const mockFetch = vi.fn().mockResolvedValue( + createMockJsonResponse({ + conversations: mockConversations, + total: 1, + limit: 50, + offset: 0, + }) + ); + global.fetch = mockFetch; + + const onConversationChange = vi.fn(); + render( + + ); + + await waitFor(() => { + expect(screen.getByText("Test conversation")).toBeInTheDocument(); + }); + + await userEvent.click(screen.getByText("Test conversation")); + expect(onConversationChange).toHaveBeenCalledWith("conv_1"); + }); + + it("calls onConversationChange with null when clicking New Chat", async () => { + const mockFetch = vi.fn().mockResolvedValue( + createMockJsonResponse({ + conversations: [], + total: 0, + limit: 50, + offset: 0, + }) + ); + global.fetch = mockFetch; + + const onConversationChange = vi.fn(); + render( + + ); + + await userEvent.click(screen.getByText("New Chat")); + expect(onConversationChange).toHaveBeenCalledWith(null); + }); + + it("applies custom className", async () => { + const mockFetch = vi.fn().mockResolvedValue( + createMockJsonResponse({ + conversations: [], + total: 0, + limit: 50, + offset: 0, + }) + ); + global.fetch = mockFetch; + + const { container } = render( + + ); + + expect(container.querySelector(".my-custom-app")).toBeInTheDocument(); + }); + + it("uses custom new button label", async () => { + const mockFetch = vi.fn().mockResolvedValue( + createMockJsonResponse({ + conversations: [], + total: 0, + limit: 50, + offset: 0, + }) + ); + global.fetch = mockFetch; + + render( + + ); + + expect(screen.getByText("Start Fresh")).toBeInTheDocument(); + }); + + it("passes placeholder to ChatPane", async () => { + const mockFetch = vi.fn().mockResolvedValue( + createMockJsonResponse({ + conversations: [], + total: 0, + limit: 50, + offset: 0, + }) + ); + global.fetch = mockFetch; + + render( + + ); + + expect(screen.getByPlaceholderText("Ask me anything...")).toBeInTheDocument(); + }); + + it("clears conversation when deleted conversation was active", async () => { + const mockConversations = [ + { + id: "conv_1", + preview: "Active conversation", + message_count: 2, + created_at: "2025-01-01T00:00:00Z", + updated_at: "2025-01-01T00:00:00Z", + }, + ]; + + const mockFetch = vi + .fn() + .mockResolvedValueOnce( + createMockJsonResponse({ + conversations: mockConversations, + total: 1, + limit: 50, + offset: 0, + }) + ) + .mockResolvedValueOnce( + createMockJsonResponse({ deleted: true, conversation_id: "conv_1" }) + ) + .mockResolvedValue( + createMockJsonResponse({ + conversations: [], + total: 0, + limit: 50, + offset: 0, + }) + ); + global.fetch = mockFetch; + + const onConversationChange = vi.fn(); + render( + + ); + + await waitFor(() => { + expect(screen.getByText("Active conversation")).toBeInTheDocument(); + }); + + // Select the conversation first + await userEvent.click(screen.getByText("Active conversation")); + expect(onConversationChange).toHaveBeenCalledWith("conv_1"); + + // Delete the conversation + const deleteButton = screen.getByRole("button", { + name: /delete conversation/i, + }); + await userEvent.click(deleteButton); + + // Should be called with null when active conversation is deleted + await waitFor(() => { + expect(onConversationChange).toHaveBeenCalledWith(null); + }); + }); +}); diff --git a/packages/llmpane-react/src/components/ChatApp.tsx b/packages/llmpane-react/src/components/ChatApp.tsx new file mode 100644 index 0000000..89c1184 --- /dev/null +++ b/packages/llmpane-react/src/components/ChatApp.tsx @@ -0,0 +1,139 @@ +import React, { useCallback, useRef, useState } from "react"; + +import type { ChatMessage, ConversationSummary } from "../types"; +import { ChatPane, type ChatPaneProps } from "./ChatPane"; +import type { ConversationSidebarRef } from "./ConversationSidebar"; +import { ConversationSidebar } from "./ConversationSidebar"; + +/** Props for the ChatApp component */ +export interface ChatAppProps + extends Omit, "conversationId"> { + /** Whether to show the conversation sidebar */ + showSidebar?: boolean; + /** Position of the sidebar */ + sidebarPosition?: "left" | "right"; + /** Label for the new chat button */ + newButtonLabel?: string; + /** Message shown when sidebar is empty */ + sidebarEmptyMessage?: string; + /** Called when the active conversation changes */ + onConversationChange?: (id: string | null) => void; + /** Custom class name for the container */ + className?: string; + /** Custom render function for sidebar items */ + renderSidebarItem?: ( + conversation: ConversationSummary, + isActive: boolean + ) => React.ReactNode; +} + +/** + * Complete chat application with sidebar and chat panel. + * + * @example Basic usage + * ```tsx + * + * ``` + * + * @example With custom message rendering + * ```tsx + * } + * onConversationChange={(id) => console.log("Switched to:", id)} + * /> + * ``` + */ +export function ChatApp({ + endpoint, + showSidebar = true, + sidebarPosition = "left", + newButtonLabel = "New Chat", + sidebarEmptyMessage = "No conversations yet", + onConversationChange, + className = "", + renderSidebarItem, + initialMessages, + ...chatPaneProps +}: ChatAppProps) { + const [activeConversationId, setActiveConversationId] = useState< + string | null + >(null); + + // Ref to sidebar for refreshing conversation list + const sidebarRef = useRef(null); + + // Extract base endpoint for conversations API + // If endpoint is "/api/chat", base is "/api" + const baseEndpoint = endpoint.replace(/\/chat$/, ""); + + const handleConversationSelect = useCallback( + (id: string) => { + setActiveConversationId(id); + onConversationChange?.(id); + }, + [onConversationChange] + ); + + const handleNewChat = useCallback(() => { + setActiveConversationId(null); + onConversationChange?.(null); + }, [onConversationChange]); + + const handleConversationDelete = useCallback( + (id: string) => { + // If the deleted conversation was active, clear it + if (id === activeConversationId) { + setActiveConversationId(null); + onConversationChange?.(null); + } + }, + [activeConversationId, onConversationChange] + ); + + // Refresh conversation list when a message completes + const handleMessageComplete = useCallback( + (message: ChatMessage) => { + // Refresh the sidebar to show updated conversation + sidebarRef.current?.refresh(); + // Call the original onComplete if provided + chatPaneProps.onComplete?.(message); + }, + [chatPaneProps] + ); + + const sidebarComponent = showSidebar && ( + + ); + + return ( +
+ {sidebarPosition === "left" && sidebarComponent} + +
+ + key={activeConversationId ?? "new"} + endpoint={endpoint} + conversationId={activeConversationId ?? undefined} + initialMessages={activeConversationId ? undefined : initialMessages} + {...chatPaneProps} + onComplete={handleMessageComplete} + /> +
+ + {sidebarPosition === "right" && sidebarComponent} +
+ ); +} diff --git a/packages/llmpane-react/src/components/ChatPane.tsx b/packages/llmpane-react/src/components/ChatPane.tsx index 19f2cbd..a742412 100644 --- a/packages/llmpane-react/src/components/ChatPane.tsx +++ b/packages/llmpane-react/src/components/ChatPane.tsx @@ -1,16 +1,20 @@ import React, { useCallback, useEffect, + useLayoutEffect, useRef, useState, + type ClipboardEvent, type FormEvent, type KeyboardEvent, type ReactNode, } from "react"; import { useChat, type UseChatOptions } from "../hooks/useChat"; -import type { DisplayMessage } from "../types"; -import { isPendingMessage } from "../types"; +import { useImageInput } from "../hooks/useImageInput"; +import type { ContentPart, DisplayMessage, MessageContent } from "../types"; +import { isImagePart, isPendingMessage, isTextPart } from "../types"; +import { ImageAttachment } from "./ImageAttachment"; /** Props for the ChatPane component */ export interface ChatPaneProps @@ -46,6 +50,42 @@ export interface ChatPaneProps maxInputHeight?: number; /** Whether to disable the input */ disabled?: boolean; + /** Whether to allow image uploads (paste or file select) */ + allowImageUpload?: boolean; + /** Maximum image file size in bytes (default: 5MB) */ + maxImageSize?: number; + /** Allowed image MIME types */ + allowedImageTypes?: string[]; +} + +/** Render message content (handles both string and multimodal content) */ +function renderMessageContent(content: MessageContent): ReactNode { + // String content (text-only) + if (typeof content === "string") { + return {content}; + } + + // Multimodal content (array of parts) + return ( + <> + {content.map((part, index) => { + if (isTextPart(part)) { + return {part.text}; + } + if (isImagePart(part)) { + return ( + Attached image + ); + } + return null; + })} + + ); } /** Default message renderer */ @@ -56,7 +96,12 @@ function DefaultMessage({ }) { const isPending = isPendingMessage(message); const isAssistantPending = isPending && message.role === "assistant"; - const showCursor = isAssistantPending && !message.content; + // Check if content is empty (for showing loading cursor) + const hasContent = + typeof message.content === "string" + ? message.content.length > 0 + : message.content.length > 0; + const showCursor = isAssistantPending && !hasContent; return (
({ {message.role === "user" ? "You" : "Assistant"}
- {message.content} + {renderMessageContent(message.content)} {showCursor && }
@@ -108,6 +153,9 @@ export function ChatPane({ multiline = false, maxInputHeight = 150, disabled = false, + allowImageUpload = true, + maxImageSize, + allowedImageTypes, ...chatOptions }: ChatPaneProps) { const { @@ -119,16 +167,69 @@ export function ChatPane({ clearError, } = useChat(chatOptions); + const { + pendingImages, + addImage, + addImageFromClipboard, + removeImage, + clearImages, + hasImages, + } = useImageInput({ + maxFileSize: maxImageSize, + allowedTypes: allowedImageTypes, + }); + const [inputValue, setInputValue] = useState(""); const messagesEndRef = useRef(null); + const messagesContainerRef = useRef(null); const inputRef = useRef(null); + const fileInputRef = useRef(null); - // Auto-scroll to bottom when new messages arrive - useEffect(() => { - if (autoScroll && messagesEndRef.current) { - messagesEndRef.current.scrollIntoView({ behavior: "smooth" }); + // Track whether user is at the bottom (for "stick to bottom" behavior) + const [isAtBottom, setIsAtBottom] = useState(true); + + // Track last message content for scroll during streaming + const lastMessageContent = messages[messages.length - 1]?.content ?? ""; + + // Check if scrolled to bottom (with small threshold for floating point) + const checkIfAtBottom = useCallback(() => { + const container = messagesContainerRef.current; + if (!container) return true; + const threshold = 50; // pixels from bottom to consider "at bottom" + return ( + container.scrollHeight - container.scrollTop - container.clientHeight < + threshold + ); + }, []); + + // Update isAtBottom when user scrolls + const handleScroll = useCallback(() => { + setIsAtBottom(checkIfAtBottom()); + }, [checkIfAtBottom]); + + // Auto-scroll to bottom when new messages arrive or content streams in + // Only scroll if user is already at the bottom (stick to bottom behavior) + useLayoutEffect(() => { + if (autoScroll && isAtBottom && messagesEndRef.current) { + messagesEndRef.current.scrollIntoView({ + // Use instant scroll during streaming to keep up with rapid updates + behavior: isLoading ? "instant" : "smooth", + block: "end", + }); } - }, [messages, autoScroll]); + }, [messages.length, lastMessageContent, autoScroll, isLoading, isAtBottom]); + + // When user sends a message, always scroll to bottom + const scrollToBottomOnSend = useCallback(() => { + setIsAtBottom(true); + // Small delay to ensure message is rendered + requestAnimationFrame(() => { + messagesEndRef.current?.scrollIntoView({ + behavior: "smooth", + block: "end", + }); + }); + }, []); // Auto-resize textarea for multiline mode useEffect(() => { @@ -139,16 +240,83 @@ export function ChatPane({ } }, [inputValue, multiline, maxInputHeight]); + // Restore focus to input when loading completes + useEffect(() => { + if (!isLoading && inputRef.current) { + inputRef.current.focus(); + } + }, [isLoading]); + + // Handle paste events for images + const handlePaste = useCallback( + (e: ClipboardEvent) => { + if (!allowImageUpload) return; + + const items = e.clipboardData?.items; + if (!items) return; + + for (const item of items) { + if (item.type.startsWith("image/")) { + e.preventDefault(); + addImageFromClipboard(item); + break; + } + } + }, + [allowImageUpload, addImageFromClipboard] + ); + + // Handle file input change + const handleFileSelect = useCallback( + (e: React.ChangeEvent) => { + const file = e.target.files?.[0]; + if (file) { + addImage(file); + } + // Reset input to allow re-selecting the same file + e.target.value = ""; + }, + [addImage] + ); + const handleSubmit = useCallback( async (e?: FormEvent) => { e?.preventDefault(); const trimmed = inputValue.trim(); - if (!trimmed || isLoading || disabled) return; + + // Need either text or images to submit + if ((!trimmed && !hasImages) || isLoading || disabled) return; + + // Build content parts + let content: MessageContent; + if (hasImages) { + const parts: ContentPart[] = []; + + // Add text if present + if (trimmed) { + parts.push({ type: "text", text: trimmed }); + } + + // Add images + for (const img of pendingImages) { + parts.push({ + type: "image", + data: img.data, + mediaType: img.mediaType, + }); + } + + content = parts; + } else { + content = trimmed; + } setInputValue(""); - await sendMessage(trimmed); + clearImages(); + scrollToBottomOnSend(); + await sendMessage(content); }, - [inputValue, isLoading, disabled, sendMessage] + [inputValue, isLoading, disabled, hasImages, pendingImages, sendMessage, clearImages, scrollToBottomOnSend] ); const handleKeyDown = useCallback( @@ -174,10 +342,25 @@ export function ChatPane({ setInputValue(value); }, []); + const hasMessages = messages.length > 0; + + // Build container class - compact when no messages for auto-height + const containerClass = [ + "llmpane-container", + !hasMessages && "llmpane-container--compact", + className, + ] + .filter(Boolean) + .join(" "); + return ( -
+
{/* Messages area */} -
+
{messages.length === 0 ? (

No messages yet. Start a conversation!

@@ -215,6 +398,20 @@ export function ChatPane({
)} + {/* Image preview area */} + {hasImages && ( +
+ {pendingImages.map((img) => ( + removeImage(img.id)} + /> + ))} +
+ )} + {/* Input area */}
{renderInput ? ( @@ -227,12 +424,51 @@ export function ChatPane({ }) ) : ( <> + {/* Hidden file input for image upload */} + {allowImageUpload && ( + + )} + {/* Image upload button */} + {allowImageUpload && ( + + )}