diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index ff094d9..e5bfdf1 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -12,7 +12,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ["3.11", "3.12"] + python-version: ["3.11", "3.14"] steps: - uses: actions/checkout@v4 @@ -80,8 +80,8 @@ jobs: - name: Install dependencies run: uv sync --all-extras - - name: Run mypy - run: uv run mypy src + - name: Run ty + run: uv run ty check docs: name: Build Documentation diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index af20849..b4fa2a4 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -12,7 +12,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ['3.11', '3.12'] + python-version: ['3.11', '3.14'] steps: - uses: actions/checkout@v4 @@ -61,5 +61,5 @@ jobs: - name: Run ruff run: uv run ruff check src tests - - name: Run mypy - run: uv run mypy src + - name: Run ty + run: uv run ty check diff --git a/.gitignore b/.gitignore index 9faaa89..fa18afb 100644 --- a/.gitignore +++ b/.gitignore @@ -32,11 +32,6 @@ ENV/ htmlcov/ .tox/ -# Type checking -.mypy_cache/ -.dmypy.json -dmypy.json - # Ruff .ruff_cache/ diff --git a/CHANGELOG.md b/CHANGELOG.md index 8c78d42..e789af6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,34 +7,119 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## [Unreleased] +## [0.1.8] - 2025-10-31 + +### Changed +- Lazy import of `openai` to reduce memory footprint +- Simplified callback inputs and added telemetry support + +## [0.1.7] - 2025-10-31 + ### Added -- History class for conversation management -- Optional tool execution with `auto_execute_tools` parameter -- Comprehensive documentation and examples +- ReAct now passes kwargs through to underlying Predict modules + +### Changed +- Wrapped OpenAI API calls in `_acomplete` for tracing support + +## [0.1.6] - 2025-10-31 + +### Added +- Comprehensive provider configuration guide for LM +- Improved environment variable precedence for multi-provider support ### Changed -- Renamed example files to remove redundant "_example" suffix +- Simplified LM factory implementation +- Updated env var name to `UDSPY_LM_OPENAI_COMPATIBLE_BASE_URL` +- Removed unused history parameter from ReactContext ### Fixed -- Type checking error in settings.py for default_model property +- API key precedence documentation to match implementation -## [0.1.0] - Initial Release +## [0.1.5] - 2025-10-31 + +### Added +- Auto-initialize LM from `UDSPY_LM_MODEL` environment variable +- History tracking integration in ReAct module +- Comprehensive version validation for releases + +## [0.1.4] - 2025-10-31 + +### Changed +- Use `importlib.metadata` for version instead of hardcoded string + +### Fixed +- Type annotations to resolve mypy errors +- Adapter `remaining_content` initialization before conditional assignment + +## [0.1.3] - 2025-10-31 + +### Added +- LM abstraction layer with registry-based factory and multi-provider support (OpenAI, Groq, Bedrock, Ollama) +- LM callable interface: `lm("prompt")` returns text directly, `lm(messages)` returns full response +- DSPy-compatible callback system for telemetry and monitoring +- Confirmation system (renamed from interrupt system) with `ConfirmationRequired`, `ConfirmationRejected`, `ResumeState` +- `set_system_message()` for guaranteed system prompt positioning in History +- `module` and `is_final` attributes on Prediction for nested module support +- String signature support: `Predict("question -> answer")` +- jiter-based JSON streaming parser for adapter +- `parse_and_validate_args` for Pydantic model arguments in tools +- `run_async_with_context` for context preservation in async operations +- Dynamic tool management documentation and examples + +### Changed +- **Breaking**: Settings API simplified to only accept LM instances (`configure(lm=...)` instead of `configure(api_key=..., model=...)`) +- **Breaking**: Restructured ReAct trajectory as `list[Episode]` with typed fields (`thought`, `tool_name`, `tool_args`, `observation`) +- Renamed `_aexecute` to `aexecute` for public API +- Split `tool.py` into `tool/` package +- Split `predict.py` into focused submodules +- Split `utils` into package with `async_support.py`, `formatting.py`, `schema.py` +- Removed `GroqLM` class in favor of OpenAI-compatible API +- Made `emit_event` synchronous with `put_nowait` + +### Fixed +- Streaming queue consumption prioritization +- Tool callback timing moved to actual execution point +- React tool execution `UnboundLocalError` +- Handle models without provider prefix in `_clean_model_name` + +## [0.1.2] - 2025-10-24 + +### Added +- Codecov integration with OIDC tokenless upload +- Pre-release check command + +### Fixed +- Documentation badge URLs and absolute paths + +## [0.1.1] - 2025-10-24 + +### Added +- ReAct module with native tool calling and human-in-the-loop flow +- ChainOfThought module for step-by-step reasoning +- History class for conversation management +- Context manager for thread-safe settings overrides +- Automatic tool calling with streaming support +- Optional tool execution with `auto_execute_tools` parameter +- Architectural Decision Records (ADRs) +- GitHub Actions CI/CD workflows + +### Changed +- Converted `module.py` into `module/` package +- Standardized module execution with `_aexecute` pattern +- Improved field parsing with regex-based extraction + +## [0.1.0] - 2025-10-24 ### Added - Core Predict module with streaming support - Signature system for defining inputs/outputs - ChatAdapter for OpenAI integration -- ChainOfThought module for step-by-step reasoning - Tool calling with `@tool` decorator -- Automatic multi-turn conversation handling -- Context-aware settings management -- Comprehensive test suite (90%+ coverage) -- Documentation with MkDocs - -### Features - Async-first architecture with sync wrappers - Streaming with field-level chunking - Event system for custom streaming events - Native OpenAI tool calling integration - Pydantic-based schemas - Type-safe API with full type hints +- Comprehensive test suite +- Documentation with MkDocs diff --git a/CLAUDE.md b/CLAUDE.md index 82b8b68..dfda704 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -21,7 +21,7 @@ Created a minimal DSPy-inspired library focused on: - This reduces complexity and leverages OpenAI's optimized tool calling 2. **Minimal Dependencies** - - Only `openai` and `pydantic` in core dependencies + - Core dependencies: `openai`, `pydantic`, `tenacity`, `jiter`, `regex` - Keeps the library lightweight and maintainable - Reduces potential dependency conflicts @@ -48,8 +48,30 @@ udspy/ ├── src/udspy/ # Core library code │ ├── signature.py # Signature, InputField, OutputField │ ├── adapter.py # ChatAdapter for formatting -│ ├── module.py # Module, Predict abstractions -│ └── streaming.py # Streaming support +│ ├── module/ # Module abstractions (package) +│ │ ├── base.py # Module base class +│ │ ├── predict.py # Predict module +│ │ ├── chain_of_thought.py # ChainOfThought module +│ │ ├── react.py # ReAct agent module +│ │ └── callbacks.py # Module callback system +│ ├── lm/ # Language model abstraction (package) +│ │ ├── base.py # LM abstract base class +│ │ ├── openai.py # OpenAI implementation +│ │ └── factory.py # LM factory with provider registry +│ ├── tool/ # Tool system (package) +│ │ ├── tool.py # Tool class and ToolCall +│ │ ├── decorator.py # @tool decorator +│ │ └── types.py # Tool-related types +│ ├── utils/ # Utilities (package) +│ │ ├── async_support.py # Async helpers +│ │ ├── formatting.py # Error formatting +│ │ └── schema.py # Schema utilities +│ ├── streaming.py # Streaming support +│ ├── settings.py # Global settings with context manager +│ ├── history.py # Conversation history +│ ├── callback.py # Callback/telemetry system +│ ├── confirmation.py # Human-in-the-loop confirmation +│ └── exceptions.py # Custom exceptions ├── tests/ # Pytest tests ├── docs/ # MkDocs documentation ├── examples/ # Usage examples @@ -134,7 +156,7 @@ result = predictor(question="...") # Uses global-key and gpt-4o-mini async def handle_user(user): user_lm = LM(model="gpt-4o-mini", api_key=user.api_key) with udspy.settings.context(lm=user_lm): - async for chunk in streaming_predictor.stream(...): + async for chunk in streaming_predictor.astream(...): yield chunk ``` @@ -275,7 +297,7 @@ Feature is additive - no migration needed. For complete details on recent architectural decisions, see the full ADRs in [docs/architecture/decisions.md](docs/architecture/decisions.md): - **ADR-008: Module Callbacks and Dynamic Tool Management** - `@module_callback` decorator for runtime tool loading -- **ADR-009: History Management with System Prompts** - Dedicated `system_prompt` property ensures proper positioning +- **ADR-009: History Management with System Prompts** - `set_system_message()` method ensures system prompt is always at position 0 - **ADR-010: LM Callable Interface with String Prompts** - Simple `lm("prompt")` returns text directly --- diff --git a/README.md b/README.md index a1913c5..60143e2 100644 --- a/README.md +++ b/README.md @@ -45,7 +45,7 @@ If resource constraints aren't a concern for your use case, consider [DSPy](http - **Optional Tool Execution**: Control whether tools execute automatically or return for manual handling - **Module Abstraction**: Compose LLM calls with reusable modules (`Predict`, `ChainOfThought`, `ReAct`) - **Streaming Support**: Stream reasoning and output fields incrementally with async generators -- **Minimal Dependencies**: Only requires `openai` and `pydantic` (~10MB total footprint) +- **Minimal Dependencies**: Only requires `openai`, `pydantic`, `tenacity`, `jiter`, and `regex` (~10MB total footprint) ## Installation @@ -137,7 +137,7 @@ print(result.answer) # Tools are automatically executed ### With Human-in-the-Loop ```python -from udspy import ReAct, HumanInTheLoopRequired, tool +from udspy import ReAct, ConfirmationRequired, tool from pydantic import Field import os @@ -159,7 +159,7 @@ agent = ReAct(FileTask, tools=[delete_file]) try: result = agent(request="Delete /tmp/old_data.txt") -except HumanInTheLoopRequired as e: +except ConfirmationRequired as e: print(f"Agent asks: {e.question}") # User confirms: "yes", "no", or provides feedback result = agent.resume("yes", e) diff --git a/docs/architecture/callbacks.md b/docs/architecture/callbacks.md index f2412d9..ca58d17 100644 --- a/docs/architecture/callbacks.md +++ b/docs/architecture/callbacks.md @@ -91,30 +91,16 @@ Configure callbacks globally via `settings.configure()`: ```python import udspy +from udspy import LM +lm = LM(model="gpt-4o-mini", api_key="sk-...") callback = LoggingCallback() udspy.settings.configure( - api_key="sk-...", - model="gpt-4o-mini", + lm=lm, callbacks=[callback] # Applied to all modules and tools ) ``` -### Per-Module Callbacks - -Configure callbacks for specific module instances: - -```python -from udspy import Predict, Signature, InputField, OutputField - -class QA(Signature): - question: str = InputField() - answer: str = OutputField() - -# This callback only applies to this predictor instance -predictor = Predict(QA, callbacks=[LoggingCallback()]) -``` - ### Context-Specific Callbacks Use temporary callbacks within a context: @@ -127,15 +113,20 @@ with udspy.settings.context(callbacks=[DebugCallback()]): ### Combining Callbacks -Callbacks are combined from multiple sources: +Use global callbacks for persistent monitoring and context callbacks for temporary overrides: ```python -# Global callbacks + instance callbacks are all executed -udspy.settings.configure(callbacks=[GlobalCallback()]) -predictor = Predict(QA, callbacks=[InstanceCallback()]) +from udspy import LM + +lm = LM(model="gpt-4o-mini", api_key="sk-...") +udspy.settings.configure(lm=lm, callbacks=[GlobalCallback()]) -# Both GlobalCallback and InstanceCallback will be invoked +# GlobalCallback is invoked result = predictor(question="...") + +# Context temporarily overrides with different callbacks +with udspy.settings.context(callbacks=[DebugCallback()]): + result = predictor(question="...") # Only DebugCallback is invoked ``` ## Callback Execution Flow @@ -219,9 +210,11 @@ Tools like Opik and MLflow that provide DSPy callbacks will work with udspy: ```python # Example with Opik (hypothetical - check Opik docs for actual API) from opik import OpikCallback +from udspy import LM +lm = LM(model="gpt-4o-mini", api_key="sk-...") udspy.settings.configure( - api_key="sk-...", + lm=lm, callbacks=[OpikCallback(project="my-project")] ) @@ -248,11 +241,12 @@ udspy.settings.configure(callbacks=[ ]) ``` -### 2. Use Instance Callbacks for Specific Monitoring +### 2. Use Context Callbacks for Specific Monitoring ```python -# Monitor only critical paths -critical_predictor = Predict(ImportantTask, callbacks=[AlertCallback()]) +# Monitor only critical paths with context-scoped callbacks +with udspy.settings.context(callbacks=[AlertCallback()]): + result = critical_predictor(question="...") ``` ### 3. Use Context Callbacks for Debugging @@ -342,9 +336,11 @@ If you're using DSPy callbacks, migration is straightforward: import dspy dspy.settings.configure(callbacks=[MyCallback()]) -# udspy code - exactly the same! +# udspy code - same callback interface! import udspy -udspy.settings.configure(callbacks=[MyCallback()]) +from udspy import LM +lm = LM(model="gpt-4o-mini", api_key="sk-...") +udspy.settings.configure(lm=lm, callbacks=[MyCallback()]) ``` The callback interface is identical, so existing DSPy callbacks should work without modification. diff --git a/docs/architecture/decisions.md b/docs/architecture/decisions.md index b973302..d3ef0b4 100644 --- a/docs/architecture/decisions.md +++ b/docs/architecture/decisions.md @@ -57,7 +57,7 @@ Use OpenAI's native function calling API directly as the primary approach. #### 2. Minimal Dependencies -Only `openai` and `pydantic` in core dependencies. +Core dependencies: `openai`, `pydantic`, `tenacity` (retry logic), `jiter` (fast JSON), `regex`. **Rationale**: - Keeps the library lightweight (~10MB) @@ -476,12 +476,11 @@ try: except ConfirmationRequired as e: # ReAct enriches context with trajectory state e.context = { - "trajectory": trajectory.copy(), - "iteration": idx, - "input_args": input_args.copy(), + "trajectory": self._context.trajectory.copy(), + "input_args": self._context.input_args.copy(), + "plan": [item.copy() for item in self._context.plan], + "stream": self._context.stream, } - if e.tool_call and tool_call_id: - e.tool_call.call_id = tool_call_id raise # Re-raise for caller ``` @@ -607,11 +606,12 @@ result = agent(question="What is the population of Tokyo?") ### Implementation Approach -1. **Iterative Loop**: Continues until final answer or max iterations -2. **Dynamic Signature**: Extends signature with reasoning_N, tool_name_N, tool_args_N fields -3. **Tool Execution**: Automatically executes tools and adds results to context -4. **Error Handling**: Retries with error feedback if tool execution fails -5. **Human Confirmations**: Integrates with `@confirm_first` for user input +1. **Iterative Loop**: Continues until `finish` tool called or max iterations +2. **Plan System**: Tracks todo/done items via `PlanItem` dicts with `plan_updates` +3. **Dynamic Signature**: Extends signature with `next_thought`, `plan_updates`, `next_tool_name`, `next_tool_args` fields +4. **Tool Execution**: Automatically executes tools and adds results to trajectory +5. **Error Handling**: Retries with error feedback if tool execution fails, warns after consecutive failures +6. **Human Confirmations**: Integrates with `require_confirmation=True` and `enable_ask_to_user` ### Key Features @@ -684,28 +684,37 @@ Introduce a single `aexecute()` method that handles both streaming and non-strea ```python class Module: - async def aexecute(self, *, stream: bool = False, **inputs): - """Core execution logic - handles both streaming and non-streaming.""" + async def aexecute(self, *, stream: bool = False, **inputs) -> Prediction: + """Core execution logic - handles both streaming and non-streaming. + Always returns a Prediction. Emits events via emit_event() to the + active stream queue (if one exists).""" # Implementation here async def astream(self, **inputs): - """Public streaming API.""" - async for event in self.aexecute(stream=True, **inputs): - yield event - - async def aforward(self, **inputs): + """Public streaming API. Sets up a queue, spawns aexecute as a task, + and yields events from the queue.""" + queue = asyncio.Queue() + token = _stream_queue.set(queue) + try: + task = asyncio.create_task(self.aexecute(stream=True, **inputs)) + # yield events from queue until task completes + ... + finally: + _stream_queue.reset(token) + + async def aforward(self, **inputs) -> Prediction: """Public non-streaming API.""" - async for event in self.aexecute(stream=False, **inputs): - if isinstance(event, Prediction): - return event + return await self.aexecute(stream=False, **inputs) ``` ### Implementation Details 1. **Single Source of Truth**: All execution logic in `aexecute()` -2. **Stream Parameter**: Boolean flag controls behavior -3. **Generator Pattern**: Always yields events, even in non-streaming mode -4. **Clean Separation**: Public methods are thin wrappers +2. **Stream Parameter**: Boolean flag controls whether LLM is called in streaming mode +3. **Returns Prediction**: `aexecute()` always returns a `Prediction` (not a generator) +4. **Event Emission**: Events are emitted via `emit_event()` to a ContextVar-based queue +5. **Queue Pattern**: `astream()` sets up the queue, runs `aexecute` as a task, and yields events +6. **Clean Separation**: Public methods are thin wrappers ### Key Benefits @@ -726,7 +735,7 @@ class Module: **Trade-offs**: - Slightly more complex to implement initially - Need to handle both streaming and non-streaming cases in same method -- Generator pattern requires understanding of async generators +- Queue-based event pattern requires understanding of ContextVar and asyncio.Queue ### Before and After @@ -743,18 +752,21 @@ async def aforward(self, **inputs): **After:** ```python -async def aexecute(self, *, stream: bool, **inputs): +async def aexecute(self, *, stream: bool, **inputs) -> Prediction: # 100 lines of logic (used by both) + # Emits events via emit_event() when queue is active ... + return Prediction(...) async def astream(self, **inputs): - async for event in self.aexecute(stream=True, **inputs): - yield event + queue = asyncio.Queue() + token = _stream_queue.set(queue) + task = asyncio.create_task(self.aexecute(stream=True, **inputs)) + # yield events from queue until task completes + ... -async def aforward(self, **inputs): - async for event in self.aexecute(stream=False, **inputs): - if isinstance(event, Prediction): - return event +async def aforward(self, **inputs) -> Prediction: + return await self.aexecute(stream=False, **inputs) ``` ### Naming Rationale @@ -775,20 +787,20 @@ We chose `aexecute()` (without underscore prefix) because: ## Additional Design Decisions -### Field Markers for Parsing +### Output Parsing Strategy -**Decision**: Use `[[ ## field_name ## ]]` markers to delineate fields in completions. +**Decision**: Use JSON format for LLM output parsing, with `[[ ## field_name ## ]]` markers for input formatting. **Rationale**: -- Simple, regex-parseable format -- Clear visual separation -- Consistent with DSPy's approach (proven) -- Fallback when native tools aren't available +- JSON is natively understood by LLMs and reliably generated +- `[[ ## field_name ## ]]` markers used in input formatting (user messages) for clear field delineation +- Output parsing via `json.loads()` is simple and robust +- Fallback parsing for edge cases when JSON is malformed **Trade-offs**: -- Requires careful prompt engineering -- LLM might not always respect markers -- Uses extra tokens +- LLM might occasionally generate malformed JSON (handled by retry logic) +- Input markers use extra tokens +- Dual format (markers for input, JSON for output) requires clear documentation --- @@ -978,7 +990,7 @@ def load_calculator() -> callable: # Get current tools (excluding built-ins) current_tools = [ t for t in context.module.tools.values() - if t.name not in ("finish", "user_clarification") + if t.name not in ("finish", "ask_to_user") ] # Add calculator to available tools @@ -997,10 +1009,10 @@ result = agent(question="What is 157 * 834?") ### Implementation Details -1. **@module_callback Decorator**: Simple marker decorator that adds `__udspy_module_callback__` attribute -2. **Return Value Detection**: After tool execution, check `is_module_callback(result)` +1. **@module_callback Decorator**: Wraps the function in a `ModuleCallback` class instance +2. **Return Value Detection**: After tool execution, check `is_module_callback(result)` which uses `isinstance(obj, ModuleCallback)` 3. **Context Objects**: Pass execution context to callbacks: - - `ReactContext`: Includes trajectory history + - `ReactContext`: Includes trajectory, input_args, plan, and stream flag - `PredictContext`: Includes conversation history - `ModuleContext`: Base with module reference 4. **init_module() Pattern**: Unified method to reinitialize tools and regenerate signatures @@ -1114,7 +1126,7 @@ Chat histories need special handling for system prompts to ensure they're always ### Decision -Implement `History` class with dedicated `system_prompt` property that ensures system messages always appear first: +Implement `History` class with a `set_system_message()` method that ensures system messages always appear first: ```python from udspy import History @@ -1126,9 +1138,9 @@ history.add_message(role="user", content="Hello") history.add_message(role="assistant", content="Hi there!") # System prompt always goes first, even if set later -history.system_prompt = "You are a helpful assistant" +history.set_system_message("You are a helpful assistant") -messages = history.messages +print(history.messages) # [{"role": "system", "content": "You are a helpful assistant"}, # {"role": "user", "content": "Hello"}, # {"role": "assistant", "content": "Hi there!"}] @@ -1138,99 +1150,94 @@ messages = history.messages ```python class History: - def __init__(self, system_prompt: str | None = None): - self._messages: list[dict[str, Any]] = [] - self._system_prompt: str | None = system_prompt - - @property - def system_prompt(self) -> str | None: - return self._system_prompt - - @system_prompt.setter - def system_prompt(self, value: str | None) -> None: - self._system_prompt = value + def __init__(self, messages: list[dict[str, Any]] | None = None): + self.messages: list[dict[str, Any]] = messages or [] + + def set_system_message(self, content: str) -> None: + """Set or replace the system message at position 0.""" + message = {"role": "system", "content": content} + if not self.messages: + self.messages.append(message) + elif self.messages[0]["role"] == "system": + self.messages[0] = message + else: + self.messages.insert(0, message) - @property - def messages(self) -> list[dict[str, Any]]: - """Get all messages with system prompt first (if set).""" - if self._system_prompt: - return [ - {"role": "system", "content": self._system_prompt}, - *self._messages - ] - return self._messages.copy() + def add_user_message(self, content: str) -> None: ... + def add_assistant_message(self, content: str, tool_calls=None) -> None: ... + def add_tool_result(self, tool_call_id: str, content: str) -> None: ... ``` **Key aspects**: -- System prompt stored separately from regular messages -- `messages` property dynamically constructs full list -- No risk of system prompt appearing mid-conversation -- Simple to update system prompt without rebuilding list -- Clear ownership (History manages system message) +- `messages` is a plain public list attribute (simple and direct) +- `set_system_message()` inserts/replaces at position 0 +- No risk of system prompt appearing mid-conversation when using `set_system_message()` +- Predict module automatically calls `set_system_message()` with formatted instructions +- Simple implementation -- no properties or dynamic list construction needed ### Key Features -1. **Dedicated system_prompt property**: Special handling for system messages -2. **Automatic positioning**: System prompt always first in messages list -3. **Mutable**: Can update system prompt at any time, position maintained -4. **Copy support**: `history.copy()` includes system prompt -5. **Clear separation**: Regular messages in `_messages`, system prompt separate +1. **Dedicated set_system_message() method**: Ensures system message at position 0 +2. **Automatic management**: Predict calls `set_system_message()` on each invocation +3. **Mutable**: Can update system message at any time, position maintained +4. **Copy support**: `history.copy()` deep-copies all messages +5. **Simple design**: Plain list attribute -- no hidden state or dynamic construction ### Use Cases -1. **Module initialization**: Set system prompt per module type +1. **Module initialization**: Predict auto-manages system prompt ```python - history = History(system_prompt="You are a ReAct agent. Use tools to solve tasks.") + history = History() + result = predictor(question="What is Python?", history=history) + # history.messages[0] is now the system prompt from the signature ``` -2. **Dynamic prompts**: Update based on context or user +2. **Manual system prompt**: Set before passing to Predict ```python - history.system_prompt = f"You are assisting {user.name}. Use their preferences: {prefs}" + history = History() + history.set_system_message("You are a math tutor") ``` -3. **Tool manipulation**: Tools can safely update system prompt +3. **Dynamic prompts**: Update based on context or user ```python - @tool(name="change_persona") - def change_persona(persona: str) -> str: - # Tool can access and modify history.system_prompt - return f"Changed to {persona} persona" + history.set_system_message(f"You are assisting {user.name}. Use their preferences: {prefs}") ``` -4. **History replay**: Maintain system prompt across sessions +4. **Multi-turn conversations**: System prompt persists correctly ```python - saved_history = history.to_dict() # Save including system prompt - loaded_history = History.from_dict(saved_history) # Restore + history = History() + # System prompt set once by Predict, remains first through all turns + result1 = predictor(question="What is Python?", history=history) + result2 = predictor(question="What are its features?", history=history) ``` -5. **Multi-turn conversations**: System prompt persists correctly +5. **Pre-populated history**: Add prior context, system prompt auto-managed ```python - # System prompt set once, remains first through all turns - for user_msg in conversation: - history.add_message(role="user", content=user_msg) - # System prompt still first + history = History() + history.add_user_message("What is Python?") + history.add_assistant_message("Python is a programming language") + # System prompt will be prepended automatically by Predict + result = predictor(question="What are its features?", history=history) ``` ### Consequences **Benefits**: - System prompt guaranteed to be first (LLM APIs require this) -- Can update system prompt at any time safely -- Clean property-based API +- Can update system prompt at any time safely via `set_system_message()` +- Simple API -- no hidden state or dynamic construction - Prevents common mistakes (system prompt mid-conversation) -- Supports all history manipulation patterns -- No manual list management required +- Predict auto-manages system prompt from signature **Trade-offs**: -- Small overhead constructing messages list on each access (negligible) -- System message can't be treated like regular message (by design) -- Slight complexity in History implementation vs. simple list -- Property access pattern may surprise developers expecting plain list +- Must use `set_system_message()` instead of `add_message("system", ...)` to ensure position 0 +- System message is stored in the messages list (not separately), so direct list manipulation could misplace it +- Simple design trades some safety for transparency ### Alternatives Considered -- **Insert at index 0**: Rejected as error-prone with mutations, easy to forget -- **Validation on add**: Rejected as too restrictive, doesn't prevent mid-conversation insertion -- **Separate system field in messages**: Rejected as doesn't integrate with standard message format +- **Property-based with private `_messages`**: Considered but rejected for simplicity -- adds complexity without sufficient benefit +- **Validation on add**: Rejected as too restrictive - **Manual management**: Status quo before this ADR, too error-prone ### Migration Guide @@ -1239,26 +1246,27 @@ Existing code using `History.add_message()` continues to work unchanged. To use system prompts: -**Create with system prompt**: +**Set system message**: ```python -history = History(system_prompt="You are a helpful assistant") +history = History() +history.set_system_message("You are a helpful assistant") ``` -**Set later**: +**Let Predict manage it** (recommended): ```python history = History() -# ... add messages ... -history.system_prompt = "You are a math tutor" +result = predictor(question="...", history=history) +# System prompt is automatically set from signature ``` **Update dynamically**: ```python -history.system_prompt = f"You are assisting {user.name}" +history.set_system_message(f"You are assisting {user.name}") ``` **Always correctly positioned**: ```python -messages = history.messages # System prompt is always first +print(history.messages[0]) # System prompt is always first ``` ### See Also diff --git a/docs/architecture/lm.md b/docs/architecture/lm.md index fc4bbd8..26374f0 100644 --- a/docs/architecture/lm.md +++ b/docs/architecture/lm.md @@ -303,6 +303,11 @@ from abc import ABC, abstractmethod from typing import Any class BaseLM(ABC): + @property + def model(self) -> str | None: + """Get the default model for this LM instance.""" + return None + @abstractmethod async def acomplete( self, @@ -324,6 +329,25 @@ class BaseLM(ABC): - `stream`: If True, return streaming response - `**kwargs`: Provider-specific parameters (temperature, etc.) +### Callable Interface + +The base `LM` class also provides `acall()` and `__call__()` methods for convenient usage with two modes: + +1. **String prompt**: Returns just the text content (str) +2. **Messages list**: Returns the complete response object + +```python +# Simple string prompt - returns text directly +answer = lm("What is Python?") +answer = await lm.acall("What is Python?") + +# Full messages list - returns response object +response = lm([{"role": "user", "content": "Hello"}], model="gpt-4o") +response = await lm.acall([{"role": "user", "content": "Hello"}], tools=[...]) +``` + +Both `acall()` and `__call__()` are decorated with `@with_callbacks` for automatic telemetry. + ## OpenAILM Implementation `OpenAILM` provides the native OpenAI implementation: @@ -567,7 +591,7 @@ The LM abstraction uses **OpenAI's message format** as the standard: 4. **Store API keys in environment variables** - never hardcode 5. **Use context managers** for multi-tenant scenarios 6. **Always specify a model** to avoid runtime errors -7. **Prefer `settings.lm.client`** over deprecated `settings.aclient` +7. **Access the client via `settings.lm.client`** when needed for direct API calls ### For Provider Implementers diff --git a/docs/architecture/modules.md b/docs/architecture/modules.md index 53ca5e4..d878046 100644 --- a/docs/architecture/modules.md +++ b/docs/architecture/modules.md @@ -28,30 +28,25 @@ class Pipeline(Module): self.summarize = ChainOfThought("text, analysis -> summary") async def aexecute(self, *, stream: bool = False, **inputs): - # First module: get analysis (stream=False since we need full result) - analysis = None - async for event in self.analyze.aexecute(stream=False, text=inputs["text"]): - if isinstance(event, Prediction): - analysis = event - - if not analysis: - raise ValueError("First module did not produce a result") + # First module: get analysis + analysis = await self.analyze.aexecute(stream=False, text=inputs["text"]) # Second module: pass down stream parameter - async for event in self.summarize.aexecute( + # Events emitted by summarize are automatically sent to the active queue + result = await self.summarize.aexecute( stream=stream, text=inputs["text"], analysis=analysis.analysis - ): - yield event + ) + return result ``` ### Streaming Support -All modules support streaming for real-time output: +All modules support streaming for real-time output via `astream()`: ```python -async for event in module.aexecute(stream=True, **inputs): +async for event in module.astream(**inputs): if isinstance(event, OutputStreamChunk): print(event.delta, end="", flush=True) elif isinstance(event, Prediction): @@ -153,7 +148,7 @@ print(result.answer) # "36738" Agent that reasons and acts with tools. Features: - Iterative reasoning and tool usage - Human-in-the-loop support -- Built-in user_clarification and finish tools +- Built-in `ask_to_user` and `finish` tools - Full trajectory tracking **When to use**: For tasks requiring multiple steps, tool usage, or agent-like behavior @@ -187,11 +182,11 @@ To create a custom module: 1. Subclass `Module` 2. Implement `aexecute()` method -3. Yield `StreamEvent` objects during execution -4. Yield final `Prediction` at the end +3. Emit `StreamEvent` objects via `emit_event()` during execution (optional) +4. Return final `Prediction` ```python -from udspy import Module, Prediction, OutputStreamChunk +from udspy import Module, Predict, Prediction class CustomModule(Module): def __init__(self, signature): @@ -201,16 +196,13 @@ class CustomModule(Module): # Custom logic before prediction processed_inputs = preprocess(inputs) - # Call nested module's aexecute, passing down stream parameter - async for event in self.predictor.aexecute(stream=stream, **processed_inputs): - if isinstance(event, Prediction): - # Custom logic after prediction - final_result = postprocess(event) - # Yield final prediction - yield Prediction(**final_result) - else: - # Pass through other events (OutputStreamChunks, etc.) - yield event + # Call nested module -- events are automatically emitted to the + # active stream queue (if one exists from astream()) + result = await self.predictor.aexecute(stream=stream, **processed_inputs) + + # Custom logic after prediction + final_result = postprocess(result) + return Prediction(**final_result) ``` See [Base Module](modules/base.md) for detailed guidance. diff --git a/docs/architecture/modules/base.md b/docs/architecture/modules/base.md index cdfe4e9..7d93c6f 100644 --- a/docs/architecture/modules/base.md +++ b/docs/architecture/modules/base.md @@ -46,7 +46,7 @@ def add_tools(context): # Get current tools (excluding built-ins) current = [ t for t in context.module.tools.values() - if t.name not in ("finish", "user_clarification") + if t.name not in ("finish", "ask_to_user") ] # Add new tools @@ -60,20 +60,21 @@ def add_tools(context): **See also**: [Dynamic Tool Management](../../examples/dynamic_tools.md) for detailed examples. -### `aexecute(*, stream: bool = False, **inputs)` +### `aexecute(*, stream: bool = False, **inputs) -> Prediction` The core execution method that all modules must implement. This is the public API for module execution. - **stream**: If `True`, enables streaming mode for real-time output - **inputs**: Keyword arguments matching the module's signature input fields -- **Returns**: `AsyncGenerator[StreamEvent, None]` that yields events and ends with a `Prediction` +- **Returns**: `Prediction` object with output fields +- **Side effects**: Emits `StreamEvent` objects via `emit_event()` to the active queue (if streaming) ```python class CustomModule(Module): async def aexecute(self, *, stream: bool = False, **inputs): # Implementation here ... - yield Prediction(result=final_result) + return Prediction(result=final_result) ``` ### `aforward(**inputs)` @@ -96,20 +97,23 @@ print(result.answer) ## Streaming Architecture -Modules support streaming through an async generator pattern: +Modules support streaming through a queue-based pattern. Use `astream()` to get events: ```python -async for event in module.aexecute(stream=True, question="Explain AI"): +async for event in module.astream(question="Explain AI"): if isinstance(event, OutputStreamChunk): print(event.delta, end="", flush=True) elif isinstance(event, Prediction): print(f"\nFinal: {event.answer}") ``` -The streaming system yields: -- `StreamChunk` events during generation (with `field` and `delta`) +The streaming system emits: +- `OutputStreamChunk` events during generation (with `field_name` and `delta`) +- `ThoughtStreamChunk` events for reasoning output - A final `Prediction` object with complete results +Internally, `astream()` creates an `asyncio.Queue`, runs `aexecute(stream=True)` as a task, and yields events from the queue. Events are emitted via `emit_event()` using a `ContextVar`-based queue. + ## Module Composition Modules can contain other modules, creating powerful compositions: @@ -123,23 +127,17 @@ class Pipeline(Module): self.summarizer = ChainOfThought("text, analysis -> summary") async def aexecute(self, *, stream: bool = False, **inputs): - # First module: get analysis (stream=False since we need full result) - analysis = None - async for event in self.analyzer.aexecute(stream=False, text=inputs["text"]): - if isinstance(event, Prediction): - analysis = event - - if not analysis: - raise ValueError("First module did not produce a result") + # First module: get analysis + analysis = await self.analyzer.aexecute(stream=False, text=inputs["text"]) # Second module: pass down stream parameter - async for event in self.summarizer.aexecute( + # Events are automatically emitted to the active queue + result = await self.summarizer.aexecute( stream=stream, text=inputs["text"], analysis=analysis.analysis - ): - # Yield all events from second module - yield event + ) + return result ``` ## Design Rationale @@ -179,11 +177,12 @@ To create a custom module: 1. Subclass `Module` 2. Implement `aexecute()` method -3. Yield `StreamEvent` objects during execution -4. Yield final `Prediction` at the end +3. Optionally emit `StreamEvent` objects via `emit_event()` during execution +4. Return final `Prediction` ```python from udspy import Module, Prediction +from udspy.streaming import emit_event, OutputStreamChunk class CustomModule(Module): async def aexecute(self, *, stream: bool = False, **inputs): @@ -191,7 +190,7 @@ class CustomModule(Module): result = process_inputs(inputs) # Return final prediction - yield Prediction(output=result) + return Prediction(output=result) ``` ## See Also diff --git a/docs/architecture/modules/predict.md b/docs/architecture/modules/predict.md index 7ba307f..bcaef28 100644 --- a/docs/architecture/modules/predict.md +++ b/docs/architecture/modules/predict.md @@ -95,32 +95,30 @@ for call in result.tool_calls: ### Auto-execution vs Manual -By default, tools are NOT auto-executed. You control execution: +By default, tools ARE auto-executed (`auto_execute_tools=True`). You can disable this: ```python -# Auto-execute tools -result = await predictor.aexecute( +# Auto-execute tools (default) +result = await predictor.aforward( question="Search for Python", - auto_execute_tools=True ) -# Manual execution -result = await predictor.aexecute( +# Manual execution - tools returned but not executed +result = await predictor.aforward( question="Search for Python", auto_execute_tools=False ) -# Tools are returned in result.tool_calls but not executed +# Tools are available in result.native_tool_calls ``` See [Tool Calling](../../api/tool.md) for more details. ## Streaming -Stream outputs in real-time: +Stream outputs in real-time using `astream()`: ```python -async for event in predictor.aexecute( - stream=True, +async for event in predictor.astream( question="Explain quantum computing" ): if isinstance(event, OutputStreamChunk): @@ -131,17 +129,18 @@ async for event in predictor.aexecute( ### Stream Events -The streaming API yields: -- `StreamChunk(field: str, delta: str)` - Incremental text for a field +The streaming API emits: +- `OutputStreamChunk(field_name: str, delta: str, content: str, is_complete: bool)` - Incremental text for a field +- `ThoughtStreamChunk(...)` - Reasoning/thought output - `Prediction(**outputs)` - Final result with all fields ```python from udspy import OutputStreamChunk, Prediction -async for event in predictor.aexecute(stream=True, **inputs): +async for event in predictor.astream(**inputs): match event: - case OutputStreamChunk(field=field, delta=delta): - print(f"[{field}] {delta}", end="") + case OutputStreamChunk(field_name=field_name, delta=delta): + print(f"[{field_name}] {delta}", end="") case Prediction() as pred: print(f"\n\nComplete: {pred}") ``` @@ -152,7 +151,7 @@ async for event in predictor.aexecute(stream=True, **inputs): ```python # With streaming -async for event in predictor.aexecute(stream=True, question="..."): +async for event in predictor.astream(question="..."): ... # Without streaming @@ -170,25 +169,30 @@ Internally, the synchronous call runs async code using `asyncio.run()` or the cu ## History Management -Predict automatically manages conversation history: +Predict manages conversation history when you pass a `History` object: ```python +from udspy import History + predictor = Predict(QA) +history = History() -# First call -result1 = predictor(question="What is Python?") +# First call - system prompt auto-set, user/assistant messages added +result1 = predictor(question="What is Python?", history=history) -# Second call - includes history -result2 = predictor(question="What are its key features?") +# Second call - uses context from previous turn +result2 = predictor(question="What are its key features?", history=history) # Access history -for msg in predictor.history: - print(f"{msg.role}: {msg.content}") +for msg in history.messages: + print(f"{msg['role']}: {msg.get('content', '')}") # Clear history -predictor.history.clear() +history.clear() ``` +Note: Each call without a `history` parameter creates a fresh `History` internally. To maintain conversation context across calls, pass a shared `History` object. + See [History API](../../api/history.md) for more details. ## Under the Hood diff --git a/docs/architecture/modules/react.md b/docs/architecture/modules/react.md index 55ef3d8..8bf3978 100644 --- a/docs/architecture/modules/react.md +++ b/docs/architecture/modules/react.md @@ -57,32 +57,41 @@ result = agent(question="What is Python?") ReAct automatically provides: - **finish**: Call when task is complete -- **user_clarification**: Ask user for clarification (if enabled) +- **ask_to_user**: Ask user for clarification (if enabled via `enable_ask_to_user=True`) ```python # The agent automatically has these tools available: -# - finish(answer: str) - Complete the task -# - user_clarification(question: str) - Ask user for help +# - finish() - Complete the task +# - ask_to_user(question: str) - Ask user for help (opt-in) ``` ### Trajectory -The trajectory records every step: +The trajectory records every step as a `list[Episode]`: ```python result = agent(question="Calculate 15 * 23") # Access trajectory print(result.trajectory) -# { -# "reasoning_0": "I need to calculate 15 * 23", -# "tool_name_0": "calculator", -# "tool_args_0": {"expression": "15 * 23"}, -# "observation_0": "345", -# "reasoning_1": "I have the answer", -# "tool_name_1": "finish", -# ... -# } +# [ +# { +# "thought": "I need to calculate 15 * 23", +# "tool_name": "calculator", +# "tool_args": {"expression": "15 * 23"}, +# "observation": "345" +# }, +# { +# "thought": "I have the answer", +# "tool_name": "finish", +# "tool_args": {}, +# "observation": "Task completed" +# } +# ] + +# Access plan +print(result.plan) +# [{"task": "Calculate 15 * 23", "status": "done", "done_at_step": 1}] ``` ## Configuration @@ -94,18 +103,24 @@ agent = ReAct(QA, tools=[search], max_iters=10) result = agent(question="...", max_iters=5) # Override per call ``` -### Disable Ask-to-User +### Enable Ask-to-User + +The `ask_to_user` built-in tool is disabled by default. Enable it explicitly: ```python +# Disabled by default agent = ReAct(QA, tools=[search]) + +# Enable ask_to_user tool +agent = ReAct(QA, tools=[search], enable_ask_to_user=True) ``` ## Human-in-the-Loop -ReAct supports tools with require_confirmation that require human confirmation: +ReAct supports tools with `require_confirmation` that require human confirmation: ```python -from udspy import ConfirmationRequired, tool +from udspy import ConfirmationRequired, ConfirmationRejected, tool @tool(name="delete_file", require_confirmation=True) def delete_file(path: str = Field(...)) -> str: @@ -113,10 +128,6 @@ def delete_file(path: str = Field(...)) -> str: agent = ReAct(QA, tools=[delete_file]) -# Note: aresume() is not yet implemented in ReAct -# Use respond_to_confirmation() instead -from udspy import respond_to_confirmation - try: result = await agent.aforward(question="Delete /tmp/test.txt") except ConfirmationRequired as e: @@ -124,12 +135,11 @@ except ConfirmationRequired as e: print(f"Tool: {e.tool_call.name}") print(f"Args: {e.tool_call.args}") - # User approves - respond_to_confirmation(e.confirmation_id, approved=True) - result = await agent.aforward(question="Delete /tmp/test.txt") + # Resume with user approval + result = await agent.aresume("yes", e) - # Or user rejects - respond_to_confirmation(e.confirmation_id, approved=False, status="rejected") + # Or reject (raises ConfirmationRejected) + # result = await agent.aresume("no", e) ``` ### Resumption Flow @@ -137,23 +147,29 @@ except ConfirmationRequired as e: When a confirmation is requested: 1. Agent pauses and raises `ConfirmationRequired` -2. Exception contains saved state and pending tool call +2. Exception contains saved context (trajectory, plan, input_args, pending episode) 3. User reviews and responds -4. Call `aresume(response, saved_state)` to continue +4. Call `aresume(user_response, exception)` to continue the ReAct loop +5. The pending episode is completed with the user's response, then iteration resumes + +The `aresume()` method handles: +- **ask_to_user**: User's response becomes the observation directly +- **Tool confirmations**: "yes"/"y" approves, "no"/"n" raises `ConfirmationRejected`, JSON string edits tool args See [Confirmation API](../../api/confirmation.md) for details. ## Streaming -Stream the agent's reasoning in real-time: +Stream the agent's reasoning in real-time using `astream()`: ```python -async for event in agent.aexecute( - stream=True, +from udspy import OutputStreamChunk, Prediction + +async for event in agent.astream( question="What is quantum computing?" ): if isinstance(event, OutputStreamChunk): - if event.field == "reasoning": + if event.field_name == "next_thought": print(f"Thinking: {event.delta}", end="", flush=True) elif isinstance(event, Prediction): print(f"\n\nAnswer: {event.answer}") @@ -168,12 +184,12 @@ See `examples/react_streaming.py` for a complete example. ReAct uses two internal signatures: 1. **react_signature**: For reasoning and tool selection - - Inputs: Original inputs + trajectory - - Outputs: reasoning - - Tools: All provided tools + finish + - Inputs: Original inputs + `trajectory` + `plan` + - Outputs: `next_thought`, `plan_updates`, `next_tool_name`, `next_tool_args` + - No native tool calling - tool selection is done via JSON in message content 2. **extract_signature**: For extracting final answer - - Inputs: Original inputs + trajectory + - Inputs: Original inputs + `trajectory` - Outputs: Original outputs - Uses ChainOfThought for extraction @@ -181,29 +197,40 @@ ReAct uses two internal signatures: ReAct composes two modules: -- `react_module`: Predict with tools for reasoning/acting +- `react_module`: Predict for reasoning/acting (no native tool calling; tools are described in the prompt and selected via structured output) - `extract_module`: ChainOfThought for final answer extraction +### Plan System + +ReAct uses a plan to track progress: + +- Each iteration, the agent outputs `plan_updates` to add/complete plan items +- Plan items are `PlanItem(task: str, status: "todo"|"done", done_at_step: int|None)` +- When all plan items are done, the loop force-stops +- The final `Prediction` includes the plan via `result.plan` + ### Example Flow ``` User: "What is the capital of France?" -Iteration 0: - Reasoning: "I need to search for France's capital" - Tool: search - Args: {"query": "capital of France"} +Iteration 1: + next_thought: "I need to search for France's capital" + plan_updates: [{"add": "Search for the capital of France"}, {"add": "Return the answer"}] + next_tool_name: "search" + next_tool_args: {"query": "capital of France"} Observation: "Paris is the capital of France" -Iteration 1: - Reasoning: "I have the answer, I can finish" - Tool: finish - Args: {} +Iteration 2: + next_thought: "I found the answer, I can finish" + plan_updates: [{"done": 0}, {"done": 1}] + next_tool_name: "finish" + next_tool_args: {} Observation: "Task completed" -Extract: - Reasoning: "Based on the search, Paris is the capital" - Answer: "Paris" +Extract (ChainOfThought): + reasoning: "Based on the search, Paris is the capital" + answer: "Paris" ``` ## Advanced Usage @@ -277,14 +304,14 @@ This separation ensures: - Final outputs are well-formatted - Trajectory doesn't pollute final answer -### Why user_clarification Tool? +### Why ask_to_user Tool? -The built-in user clarification tool allows agents to: +The built-in `ask_to_user` tool (opt-in via `enable_ask_to_user=True`) allows agents to: - Request clarification when ambiguous - Ask for additional information - Interact naturally with users -It's implemented as a tool with require_confirmation, so users can provide responses that the agent incorporates into its reasoning. +It raises `ConfirmationRequired`, and the user's response is used as the observation via `aresume()`. This enables natural human-in-the-loop interaction. ### Why finish Tool? diff --git a/docs/architecture/overview.md b/docs/architecture/overview.md index 708ad2e..d1f3b68 100644 --- a/docs/architecture/overview.md +++ b/docs/architecture/overview.md @@ -128,11 +128,7 @@ udspy is organized into clear layers with well-defined responsibilities: #### 2. LM Layer (Language Model Abstraction) **What it does**: Provides abstract interface to LLM providers -**Current State**: -- Direct usage of `settings.aclient` (AsyncOpenAI) -- No abstraction yet - coupled to OpenAI - -**Future Design**: +**Current Implementation** (`src/udspy/lm/`): ```python class LM(ABC): """Abstract language model interface.""" @@ -141,39 +137,41 @@ class LM(ABC): async def acomplete( self, messages: list[dict], + *, + model: str | None = None, tools: list[dict] | None = None, stream: bool = False, **kwargs - ) -> AsyncGenerator[ChatCompletion, None] | ChatCompletion: + ) -> Any: """Complete a prompt with optional tools.""" pass + # Also supports: __call__(prompt_or_messages), acall(), complete() + class OpenAILM(LM): - """OpenAI implementation.""" - def __init__(self, client: AsyncOpenAI, model: str): - self.client = client - self.model = model - - async def acomplete(self, messages, tools=None, stream=False, **kwargs): - return await self.client.chat.completions.create( - model=self.model, - messages=messages, - tools=tools, - stream=stream, - **kwargs - ) + """OpenAI implementation with retry logic.""" + def __init__(self, api_key="", base_url=None, default_model=None, client=None, provider="openai"): + ... +``` + +**Factory function** for easy creation: +```python +lm = LM(model="gpt-4o", api_key="sk-...") # OpenAI +lm = LM(model="ollama/llama2") # Ollama +lm = LM(model="groq/llama-3.1-70b", api_key=...) # Groq ``` **Responsibilities**: - Normalize provider-specific APIs -- Handle retries and rate limiting +- Handle retries with tenacity (exponential backoff) - Abstract away provider differences - Provide unified interface for modules +- Support callable interface: `lm("prompt")` returns text directly -**Key Files** (when implemented): -- `lm.py` - Base LM class and interface -- `lm/openai.py` - OpenAI implementation -- `lm/anthropic.py` - Anthropic implementation (future) +**Key Files**: +- `lm/base.py` - Abstract LM class with `acomplete()`, `acall()`, `__call__()` +- `lm/openai.py` - OpenAI implementation with AsyncOpenAI +- `lm/factory.py` - `LM()` factory function with provider auto-detection **DO**: Provider API calls, retry logic, rate limiting **DON'T**: Message formatting (use Adapter), business logic (use Module) @@ -192,9 +190,11 @@ class OpenAILM(LM): **Key Methods**: - `format_instructions(signature)` - Signature → system message -- `format_inputs(signature, inputs)` - Inputs → user message -- `parse_outputs(signature, completion)` - Completion → structured dict -- `tools_to_openai_format(tools)` - Tools → OpenAI schemas +- `format_user_request(signature, inputs)` - Inputs + output instructions → user message +- `parse_outputs(signature, completion)` - JSON completion → structured dict +- `format_tool_schema(tool)` - Tool → OpenAI function schema +- `process_chunk(chunk, module, signature)` - Streaming chunk processing +- `split_reasoning_and_content_delta(response)` - Separate reasoning from content **DO**: Format translation, schema conversion, parsing **DON'T**: LLM API calls (use LM), orchestration (use Module) @@ -208,8 +208,10 @@ class OpenAILM(LM): - Simple list wrapper with convenience methods - No LLM coupling - pure data structure -**Tools** (`src/udspy/tool.py`) -- Wraps functions as tools +**Tools** (`src/udspy/tool/`) +- Wraps functions as tools (`tool.py`) +- `@tool` decorator (`decorator.py`) +- Type definitions: `Tools`, `ToolCall`, `ToolCalls` (`types.py`) - Extracts schemas from type hints - Handles async/sync execution - Integrates with confirmation system @@ -544,7 +546,7 @@ udspy.settings.configure(lm=lm) lm = udspy.settings.lm # Returns OpenAILM instance ``` -**Backward Compatibility**: `settings.aclient` still works but is deprecated. Use `settings.lm` for new code. +**Note**: Use `settings.lm` to access the configured LM instance. There is no `settings.aclient`. ### Context Manager Support @@ -567,10 +569,9 @@ result = predictor(question="...") # Uses global LM ``` **Priority**: -1. Explicit `lm` parameter (highest) -2. `aclient` parameter (creates OpenAILM wrapper) -3. `api_key` parameter (creates new client + LM) -4. Global settings (fallback) +1. Context-specific LM (from `settings.context(lm=...)`) - highest +2. Global LM (from `settings.configure(lm=...)`) +3. Environment variable `UDSPY_LM_MODEL` (auto-creates LM) ### Usage in Predict Module @@ -777,7 +778,7 @@ QA = make_signature( ↓ 2. Schema Extraction (Tool.__init__) ↓ -3. Schema Conversion (Adapter.tools_to_openai_format) +3. Schema Conversion (Adapter.format_tool_schema) ↓ 4. LLM Call (Module → LM) ↓ @@ -1045,11 +1046,10 @@ Events from nested modules bubble up automatically: ```python class Pipeline(Module): async def aexecute(self, *, stream: bool = False, **inputs): - # Nested module emits to same queue - async for event in self.predictor.aexecute(stream=stream, **inputs): - # Events automatically go to active queue - if isinstance(event, Prediction): - return event + # Nested module emits to same queue via emit_event() + result = await self.predictor.aexecute(stream=stream, **inputs) + # Events were already emitted to the active queue during execution + return result ``` --- @@ -1315,31 +1315,23 @@ class MyModule(Module): ### Adding a New LM Provider -1. **Create src/udspy/lm/ package** -2. **Define LM base class** -3. **Implement provider-specific class** -4. **Update settings to use LM** +1. **Subclass the LM base class** from `src/udspy/lm/base.py` +2. **Implement `acomplete()`** method +3. **Register in factory** (optional) or use directly ```python -# src/udspy/lm/base.py -from abc import ABC, abstractmethod - -class LM(ABC): - @abstractmethod - async def acomplete(self, messages, *, tools=None, stream=False, **kwargs): - pass +# my_providers/anthropic.py +from udspy.lm.base import LM as BaseLM -# src/udspy/lm/anthropic.py -class AnthropicLM(LM): - def __init__(self, client, model): - self.client = client - self.model = model +class AnthropicLM(BaseLM): + def __init__(self, api_key: str, default_model: str | None = None): + from anthropic import AsyncAnthropic + self.client = AsyncAnthropic(api_key=api_key) + self._default_model = default_model - async def acomplete(self, messages, *, tools=None, stream=False, **kwargs): - # Convert formats - # Call Anthropic API - # Convert response - pass + async def acomplete(self, messages, *, model=None, tools=None, stream=False, **kwargs): + # Convert OpenAI format to Anthropic format and call API + ... ``` ### Adding Custom Stream Events @@ -1479,7 +1471,7 @@ except ConfirmationRequired as e: ``` Is it about LLM provider API calls? -├─ YES → LM Layer (future: src/udspy/lm/) +├─ YES → LM Layer (src/udspy/lm/) └─ NO ├─ Is it about message formatting or parsing? │ └─ YES → Adapter Layer (src/udspy/adapter.py) @@ -1490,7 +1482,7 @@ Is it about LLM provider API calls? ├─ Is it about conversation storage? │ └─ YES → History (src/udspy/history.py) ├─ Is it about tool definition or execution? - │ └─ YES → Tool (src/udspy/tool.py) + │ └─ YES → Tool (src/udspy/tool/) ├─ Is it about streaming events? │ └─ YES → Streaming (src/udspy/streaming.py) ├─ Is it about human-in-the-loop? @@ -1498,7 +1490,7 @@ Is it about LLM provider API calls? ├─ Is it about telemetry? │ └─ YES → Callbacks (src/udspy/callback.py) └─ Is it a utility used everywhere? - └─ YES → Utils (src/udspy/utils.py) + └─ YES → Utils (src/udspy/utils/) ``` ### Should this be a new module? @@ -1536,10 +1528,10 @@ Does it talk to an LLM provider API? | Layer | Responsibilities | Key Files | |-------|-----------------|-----------| | **Module** | Business logic, orchestration, composition | `module/predict.py`, `module/react.py` | -| **LM** | Provider API calls, retries, format conversion | *(Future)* `lm/openai.py`, `lm/anthropic.py` | +| **LM** | Provider API calls, retries, format conversion | `lm/base.py`, `lm/openai.py`, `lm/factory.py` | | **Adapter** | Message formatting, output parsing, schema conversion | `adapter.py` | | **Signature** | I/O contracts, validation | `signature.py` | -| **Tool** | Function wrapping, schema extraction, execution | `tool.py` | +| **Tool** | Function wrapping, schema extraction, execution | `tool/tool.py`, `tool/decorator.py`, `tool/types.py` | | **History** | Conversation storage | `history.py` | | **Streaming** | Event queue, chunks, emission | `streaming.py` | | **Confirmation** | Suspend/resume, human-in-the-loop | `confirmation.py` | diff --git a/docs/architecture/streaming.md b/docs/architecture/streaming.md index c58d36d..e66cbf2 100644 --- a/docs/architecture/streaming.md +++ b/docs/architecture/streaming.md @@ -20,11 +20,11 @@ async for event in predictor.astream(question="Explain AI"): ## Stream Events -The streaming API yields two types of events: +The streaming API yields three types of events: ### OutputStreamChunk -Incremental text updates for a specific field: +Incremental text updates for a specific output field: ```python class OutputStreamChunk(StreamEvent): @@ -44,6 +44,21 @@ async for event in predictor.astream(question="..."): print(f"\n--- {event.field_name} complete ---") ``` +### ThoughtStreamChunk + +Incremental text updates for reasoning/thought fields (used by ChainOfThought): + +```python +class ThoughtStreamChunk(StreamEvent): + module: Module # Module that generated this chunk + field_name: str # Which thought field (e.g., "reasoning") + delta: str # New text since last chunk + content: str # Full accumulated text so far + is_complete: bool # Whether field is done streaming +``` + +`ThoughtStreamChunk` has the same attributes as `OutputStreamChunk` but is a separate class, allowing consumers to distinguish reasoning output from final output fields. + ### Prediction Final result with all output fields: @@ -221,12 +236,13 @@ This allows tools and nested modules to emit events without explicit queue passi ### Event Flow 1. Module's `astream(**inputs)` is called -2. Queue is created and set in context -3. Internal `aexecute(stream=True)` is called -4. Module yields `StreamChunk` events as text arrives -5. Tools can call `emit_event()` to inject custom events -6. Module yields final `Prediction` when complete -7. Queue is cleaned up +2. An `asyncio.Queue` is created and set in `_stream_queue` ContextVar +3. `aexecute(stream=True)` runs as a background task +4. Module emits `OutputStreamChunk`/`ThoughtStreamChunk` events via `emit_event()` as text arrives +5. Tools can also call `emit_event()` to inject custom events into the queue +6. Module emits final `Prediction` when complete +7. `astream()` yields events from the queue until the sentinel `None` is received +8. Queue is cleaned up ### Non-Streaming Mode diff --git a/docs/examples/advanced.md b/docs/examples/advanced.md index 17134c8..339b522 100644 --- a/docs/examples/advanced.md +++ b/docs/examples/advanced.md @@ -8,6 +8,7 @@ Build complex modules from simpler ones: ```python from udspy import Module, Predict, Prediction +from udspy.signature import make_signature class ChainOfThought(Module): """Answer questions with explicit reasoning.""" @@ -128,12 +129,12 @@ def mock_openai_response(content: str) -> ChatCompletion: ) def test_with_mock(): - mock_client = MagicMock() - mock_client.chat.completions.create.return_value = mock_openai_response( + mock_lm = MagicMock() + mock_lm.acomplete.return_value = mock_openai_response( "[[ ## answer ## ]]\nTest answer" ) - udspy.settings.configure(client=mock_client) + udspy.settings.configure(lm=mock_lm) predictor = Predict(QA) result = predictor(question="Test?") diff --git a/docs/examples/basic_usage.md b/docs/examples/basic_usage.md index cf1702f..56574e2 100644 --- a/docs/examples/basic_usage.md +++ b/docs/examples/basic_usage.md @@ -107,11 +107,10 @@ predictor = Predict( ## Global Defaults ```python -udspy.settings.configure( - api_key="sk-...", - model="gpt-4o-mini", - temperature=0.7, -) +from udspy import LM + +lm = LM(model="gpt-4o-mini", api_key="sk-...") +udspy.settings.configure(lm=lm, temperature=0.7) # All predictors use these defaults unless overridden predictor = Predict(QA) @@ -157,11 +156,11 @@ def test_qa(): ], ) - # Configure mock client - mock_client = MagicMock() - mock_client.chat.completions.create.return_value = mock_response + # Configure mock LM + mock_lm = MagicMock() + mock_lm.acomplete.return_value = mock_response - udspy.settings.configure(client=mock_client) + udspy.settings.configure(lm=mock_lm) # Test predictor = Predict(QA) diff --git a/docs/examples/confirmation.md b/docs/examples/confirmation.md index 693df84..b58bb15 100644 --- a/docs/examples/confirmation.md +++ b/docs/examples/confirmation.md @@ -160,7 +160,7 @@ def run_with_editing(agent, question): else: user_response = response - resume_state = ResumeState(e, response) + resume_state = ResumeState(e, user_response) # Usage agent = ReAct( @@ -216,7 +216,7 @@ def run_with_confirmations( except ConfirmationRequired as e: if on_confirmation: # Use custom handler - user_response = on_confirmation(e) + response = on_confirmation(e) else: # Default CLI handler print(f"\n⚠️ {e.question}") @@ -320,10 +320,10 @@ async def resume_agent(request: ResumeRequest): session = sessions[request.session_id] try: + resume = ResumeState(session["state"], request.user_response) result = await agent.aforward( question=session["question"], - resume_state=session["state"], - user_response=request.user_response + resume_state=resume ) # Clean up session on success @@ -388,7 +388,7 @@ async def agent_worker( # Wait for user response user_response = await req.get_response() - resume_state = ResumeState(e, response) + resume_state = ResumeState(e, user_response) async def confirmation_handler(confirmation_queue: Queue[ConfirmationRequest]): """Handler that processes confirmation requests from queue.""" @@ -588,7 +588,7 @@ async def run_with_timeout(agent, question): timeout_seconds=30, default_response="no" ) - resume_state = ResumeState(e, response) + resume_state = ResumeState(e, user_response) ``` ## Testing Confirmations @@ -610,14 +610,13 @@ async def test_agent_with_confirmation(): assert "delete" in e.question.lower() assert e.tool_call.name == "delete_file" - # Programmatically approve - respond_to_confirmation(e.confirmation_id, approved=True) + # Resume with approval + resume = ResumeState(e, "yes") # Resume - should complete result = await agent.aforward( question="Delete /tmp/test.txt", - resume_state=e, - user_response="yes" + resume_state=resume ) assert "deleted" in result.answer.lower() diff --git a/docs/examples/context_settings.md b/docs/examples/context_settings.md index 84625f1..ddf3267 100644 --- a/docs/examples/context_settings.md +++ b/docs/examples/context_settings.md @@ -112,13 +112,14 @@ Context managers work seamlessly with async code: ```python import asyncio -from udspy import LM +import udspy +from udspy import LM, Predict async def generate_response(question: str, user_api_key: str): user_lm = LM(model="gpt-4o-mini", api_key=user_api_key) with udspy.settings.context(lm=user_lm): - predictor = StreamingPredict(QA) - async for chunk in predictor.stream(question=question): + predictor = Predict(QA) + async for chunk in predictor.astream(question=question): yield chunk # Handle multiple users concurrently diff --git a/docs/examples/dynamic_tools.md b/docs/examples/dynamic_tools.md index afccbbd..a9f03c4 100644 --- a/docs/examples/dynamic_tools.md +++ b/docs/examples/dynamic_tools.md @@ -42,7 +42,7 @@ def load_calculator() -> callable: # Get current tools (excluding built-ins) current_tools = [ t for t in context.module.tools.values() - if t.name not in ("finish", "user_clarification") + if t.name not in ("finish", "ask_to_user") ] # Add calculator to available tools @@ -98,7 +98,7 @@ def my_callback(context: ReactContext): # Access trajectory history trajectory = context.trajectory - thoughts = [v for k, v in trajectory.items() if k.startswith("thought_")] + thoughts = [episode["thought"] for episode in trajectory] # Modify tools context.module.init_module(tools=current_tools + [new_tool]) @@ -142,7 +142,7 @@ def load_tools(category: str = Field(...)) -> callable: def add_tools(context): current = [ t for t in context.module.tools.values() - if t.name not in ("finish", "user_clarification", "load_tools") + if t.name not in ("finish", "ask_to_user", "load_tools") ] new_tools = [] @@ -249,7 +249,7 @@ def callback(context): ### Built-in Tools -ReAct's built-in tools (`finish`, user clarification) are automatically preserved when you call `init_module()`. You don't need to include them manually. +ReAct's built-in tools (`finish`, `ask_to_user`) are automatically preserved when you call `init_module()`. You don't need to include them manually. ### Error Handling diff --git a/docs/examples/react.md b/docs/examples/react.md index 8e8d382..236bd16 100644 --- a/docs/examples/react.md +++ b/docs/examples/react.md @@ -75,7 +75,7 @@ result = agent(task="Find information about React") print(result.result) ``` -## User Clarification with user clarification +## User Clarification with ask_to_user When the user's request is ambiguous, the agent can ask for clarification: @@ -85,7 +85,7 @@ from udspy import ConfirmationRequired, ResumeState agent = ReAct( ResearchTask, tools=[search], - enable_user_clarification=True # Enable clarification requests + enable_ask_to_user=True # Enable clarification requests ) try: @@ -99,29 +99,29 @@ except ConfirmationRequired as e: response = "The Python programming language" # Resume execution - result = agent.resume(user_response, e) + result = agent.resume(response, e) print(result.answer) ``` -### Configuring user clarification +### Configuring ask_to_user -The user clarification tool is enabled by default and can be called by the agent whenever clarification is needed: +The ask_to_user tool is disabled by default but can be enabled so the agent can ask clarifying questions: ```python agent = ReAct( ResearchTask, tools=[search], - enable_user_clarification=True, # Default: enabled + enable_ask_to_user=True, # Enable clarification requests (default: False) ) ``` -To disable user clarification entirely: +To disable ask_to_user entirely: ```python agent = ReAct( ResearchTask, tools=[search], - enable_user_clarification=False # No clarification requests + enable_ask_to_user=False # No clarification requests (default) ) ``` @@ -162,34 +162,30 @@ except ConfirmationRequired as e: ## Accessing the Trajectory -The trajectory contains all reasoning steps and tool calls: +The trajectory is a `list[Episode]` where each episode contains `thought`, `tool_name`, `tool_args`, and `observation`: ```python result = agent(question="What is 2 + 2?") -# Access trajectory -for i in range(10): # Max iterations - observation_key = f"observation_{i}" - if observation_key not in result.trajectory: - break - +# Access trajectory (list of Episode dicts) +for i, episode in enumerate(result.trajectory): print(f"Step {i + 1}:") - print(f" Reasoning: {result.trajectory.get(f'reasoning_{i}', '')}") - print(f" Tool: {result.trajectory[f'tool_name_{i}']}") - print(f" Args: {result.trajectory[f'tool_args_{i}']}") - print(f" Observation: {result.trajectory[observation_key]}") + print(f" Thought: {episode['thought']}") + print(f" Tool: {episode['tool_name']}") + print(f" Args: {episode['tool_args']}") + print(f" Observation: {episode['observation']}") ``` Example trajectory: ``` Step 1: - Reasoning: I need to calculate 2 + 2 + Thought: I need to calculate 2 + 2 Tool: calculator Args: {'expression': '2 + 2'} Observation: 4 Step 2: - Reasoning: I have the answer + Thought: I have the answer Tool: finish Args: {} Observation: Task completed @@ -202,7 +198,7 @@ agent = ReAct( signature=ResearchTask, # Task signature tools=[search, calculator], # Available tools max_iters=10, # Maximum reasoning steps (default: 10) - enable_user_clarification=True # Enable user clarification (default: True) + enable_ask_to_user=True # Enable ask_to_user tool (default: False) ) ``` @@ -211,7 +207,7 @@ agent = ReAct( - **`signature`**: Signature class or string format (`"input -> output"`) - **`tools`**: List of tool functions (decorated with `@tool`) or `Tool` objects - **`max_iters`**: Maximum number of reasoning iterations before stopping -- **`enable_user_clarification`**: Whether to enable the user clarification tool +- **`enable_ask_to_user`**: Whether to enable the ask_to_user tool (default: False) ## Async Support @@ -246,13 +242,13 @@ Signals that the agent has collected enough information to answer: This is automatically selected by the LLM when it has sufficient information. -### user clarification (if enabled) +### ask_to_user (if enabled) Requests clarification from the user: ```python # Agent internally calls: -# Tool: user_clarification +# Tool: ask_to_user # Args: {"question": "What topic would you like to know about?"} ``` @@ -302,7 +298,7 @@ def api_call(endpoint: str = Field(...)) -> str: # Agent will see error in observation and can: # 1. Try a different tool # 2. Retry with different args -# 3. Ask user for help (using user_clarification tool) +# 3. Ask user for help (using ask_to_user tool) ``` ### State Management @@ -323,7 +319,7 @@ except ConfirmationRequired as e: # Later, restore and continue response = input(f"{saved_question} ") - result = agent.resume(user_response, saved_state) + result = agent.resume(response, saved_state) ``` ## DSPy Compatibility @@ -423,23 +419,23 @@ Reduce `max_iters` or improve tool descriptions: ### Agent asks for clarification too often -Disable or restrict user clarification: +Disable or restrict ask_to_user: ```python agent = ReAct( signature, tools=tools, - enable_user_clarification=False # Disable entirely + enable_ask_to_user=False # Disable entirely ) ``` -Or disable it completely if the agent should never ask for clarification: +Or disable it completely if the agent should never ask for clarification (this is the default): ```python agent = ReAct( signature, tools=tools, - enable_user_clarification=False # Disable user clarification + enable_ask_to_user=False # Disable ask_to_user (default) ) ``` diff --git a/docs/examples/streaming.md b/docs/examples/streaming.md index 18bc66d..2a83599 100644 --- a/docs/examples/streaming.md +++ b/docs/examples/streaming.md @@ -6,7 +6,8 @@ Learn how to use streaming for better user experience. ```python import asyncio -from udspy import StreamingPredict, Signature, InputField, OutputField +from udspy import Predict, Signature, InputField, OutputField +from udspy.streaming import OutputStreamChunk class QA(Signature): """Answer questions.""" @@ -14,9 +15,9 @@ class QA(Signature): answer: str = OutputField() async def main(): - predictor = StreamingPredict(QA) + predictor = Predict(QA) - async for chunk in predictor.stream(question="What is AI?"): + async for chunk in predictor.astream(question="What is AI?"): if isinstance(chunk, OutputStreamChunk): print(chunk.delta, end="", flush=True) @@ -35,11 +36,11 @@ class ReasonedQA(Signature): answer: str = OutputField() async def main(): - predictor = StreamingPredict(ReasonedQA) + predictor = Predict(ReasonedQA) print("Question: What is the sum of first 10 primes?\n") - async for item in predictor.stream( + async for item in predictor.astream( question="What is the sum of first 10 primes?" ): if isinstance(item, OutputStreamChunk): @@ -70,8 +71,8 @@ app = FastAPI() @app.get("/ask") async def ask_question(question: str): async def generate(): - predictor = StreamingPredict(QA) - async for chunk in predictor.stream(question=question): + predictor = Predict(QA) + async for chunk in predictor.astream(question=question): if isinstance(chunk, OutputStreamChunk) and not chunk.is_complete: # chunk.delta contains the new incremental text # chunk.content contains the full accumulated text so far diff --git a/examples/basic_usage.py b/examples/basic_usage.py index 28673d9..a598d6f 100644 --- a/examples/basic_usage.py +++ b/examples/basic_usage.py @@ -34,7 +34,7 @@ # APPROACH 2: Structured signatures (for complex outputs) print("=== Structured Signatures ===\n") - udspy.settings.configure() + udspy.settings.configure(lm=lm) class QA(Signature): """Answer questions concisely and accurately.""" diff --git a/justfile b/justfile index cccb564..ee99497 100644 --- a/justfile +++ b/justfile @@ -19,7 +19,7 @@ test-cov: lint: uv run ruff check src tests examples uv run ruff format --check src tests examples - uv run mypy src + uv run ty check # Format code and fix linting issues fmt: @@ -28,10 +28,10 @@ fmt: # Run type checker only typecheck: - uv run mypy src + uv run ty check -# Run all checks (lint, test) -check: lint test +# Run all checks (lint, typecheck, test) +check: lint typecheck test # Pre-release checks - run everything that CI runs release-check: @@ -65,15 +65,26 @@ docs-deploy: # Clean build artifacts clean: - rm -rf dist build *.egg-info htmlcov .coverage .pytest_cache .mypy_cache .ruff_cache + rm -rf dist build *.egg-info htmlcov .coverage .pytest_cache .ruff_cache # Build package build: uv build -# Run example -example name: - uv run python examples/{{name}}.py +# Run example (e.g., just example basic_usage, or just example to run all) +example name='*': + #!/usr/bin/env bash + uv run python -c "import udspy; udspy.settings.configure(); udspy.settings.lm" 2>&1 || { + echo "" + echo "Set UDSPY_LM_MODEL before running examples, e.g.:" + echo " export UDSPY_LM_MODEL=gpt-4o-mini" + exit 1 + } + trap 'exit 130' INT + for f in examples/{{name}}.py; do + echo "▶ Running $f..." + uv run python "$f" + done # Bump version and create release branch (e.g., just bump-release 0.1.4) bump-release version: diff --git a/pyproject.toml b/pyproject.toml index d5f449f..892bfd5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ readme = "README.md" requires-python = ">=3.11" license = { text = "MIT" } authors = [ - { name = "Your Name", email = "your.email@example.com" } + { name = "Davide Silvestri", email = "davide@baserow.io" } ] classifiers = [ "Development Status :: 3 - Alpha", @@ -16,6 +16,7 @@ classifiers = [ "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Scientific/Engineering :: Artificial Intelligence", ] @@ -33,8 +34,7 @@ dev = [ "pytest-asyncio>=0.23.0", "pytest-cov>=4.1.0", "ruff>=0.3.0", - "mypy>=1.8.0", - "types-regex>=2024.0.0", + "ty", ] docs = [ "mkdocs>=1.5.0", @@ -50,6 +50,10 @@ build-backend = "hatchling.build" line-length = 100 target-version = "py311" +[tool.ruff.lint.isort] +known-first-party = ["udspy"] +section-order = ["future", "standard-library", "third-party", "first-party", "local-folder"] + [tool.ruff.lint] select = [ "E", # pycodestyle errors @@ -69,17 +73,8 @@ ignore = [ quote-style = "double" indent-style = "space" -[tool.mypy] -python_version = "3.11" -strict = false -check_untyped_defs = true -disallow_untyped_defs = true -disallow_any_unimported = false -no_implicit_optional = true -warn_return_any = false -warn_unused_ignores = true -warn_redundant_casts = true -warn_unused_configs = true +[tool.ty.src] +include = ["src"] [tool.pytest.ini_options] testpaths = ["tests"] diff --git a/src/udspy/callback.py b/src/udspy/callback.py index f010599..b98bc26 100644 --- a/src/udspy/callback.py +++ b/src/udspy/callback.py @@ -5,6 +5,7 @@ observability tools that support DSPy callbacks. """ +import asyncio import functools import inspect import logging @@ -214,7 +215,7 @@ async def async_wrapper(instance: Any, *args: Any, **kwargs: Any) -> Any: try: results = await fn(instance, *args, **kwargs) return results - except Exception as e: + except (Exception, asyncio.CancelledError) as e: exception = e raise finally: diff --git a/src/udspy/confirmation.py b/src/udspy/confirmation.py index e597027..2177eaf 100644 --- a/src/udspy/confirmation.py +++ b/src/udspy/confirmation.py @@ -268,7 +268,7 @@ def _generate_confirmation_id( """ import json - func_name = func.__name__ + func_name = getattr(func, "__name__", repr(func)) # Convert positional args to kwargs for consistent handling sig = inspect.signature(func) diff --git a/src/udspy/exceptions.py b/src/udspy/exceptions.py index 5c226e9..d06cd7c 100644 --- a/src/udspy/exceptions.py +++ b/src/udspy/exceptions.py @@ -9,13 +9,13 @@ class AdapterParseError(Exception): def __init__( self, adapter_name: str, - signature: Signature | None, + signature: type[Signature] | None, lm_response: str, message: str | None = None, parsed_result: dict[str, Any] | None = None, ): self.adapter_name = adapter_name - self.signature = signature + self.signature: type[Signature] | None = signature self.lm_response = lm_response self.parsed_result = parsed_result diff --git a/src/udspy/lm/base.py b/src/udspy/lm/base.py index 22f9865..8b6957e 100644 --- a/src/udspy/lm/base.py +++ b/src/udspy/lm/base.py @@ -172,8 +172,9 @@ async def acall( # Ignore tools parameter for string prompts (already enforced by overload) messages = [{"role": "user", "content": prompt_or_messages}] response = await self.acomplete(messages, model=model, stream=stream, **kwargs) - if hasattr(response, "choices") and len(response.choices) > 0: - message = response.choices[0].message + choices = getattr(response, "choices", []) + if choices and len(choices) > 0: + message = choices[0].message if hasattr(message, "content") and message.content: return message.content return str(response) @@ -256,8 +257,9 @@ def __call__( # Ignore tools parameter for string prompts (already enforced by overload) messages = [{"role": "user", "content": prompt_or_messages}] response = self.complete(messages, model=model, stream=stream, **kwargs) - if hasattr(response, "choices") and len(response.choices) > 0: - message = response.choices[0].message + choices = getattr(response, "choices", []) + if choices and len(choices) > 0: + message = choices[0].message if hasattr(message, "content") and message.content: return message.content return str(response) diff --git a/src/udspy/module/base.py b/src/udspy/module/base.py index e313874..0bd4c04 100644 --- a/src/udspy/module/base.py +++ b/src/udspy/module/base.py @@ -167,6 +167,12 @@ async def astream( break finally: + if not task.done(): + task.cancel() + try: + await task + except asyncio.CancelledError: + pass try: _stream_queue.reset(token) except (ValueError, LookupError): diff --git a/src/udspy/module/callbacks.py b/src/udspy/module/callbacks.py index 5c2dc58..35f2e7b 100644 --- a/src/udspy/module/callbacks.py +++ b/src/udspy/module/callbacks.py @@ -8,7 +8,7 @@ from udspy import History from udspy.module.base import Module from udspy.module.predict import Predict - from udspy.module.react import Episode, ReAct + from udspy.module.react import Episode, PlanItem, ReAct class ModuleCallback: @@ -126,6 +126,7 @@ class ReactContext(ModuleContext): module: The ReAct module instance trajectory: List of completed episodes input_args: Original input arguments to the module + plan: System-maintained plan (list of PlanItem dicts) stream: Whether streaming is enabled """ @@ -134,6 +135,7 @@ def __init__( module: "ReAct", trajectory: list["Episode"], input_args: dict[str, Any], + plan: list["PlanItem"] | None = None, stream: bool = False, ): """Initialize ReAct context. @@ -142,11 +144,13 @@ def __init__( module: The ReAct module instance trajectory: Current trajectory (list of episodes) input_args: Original input arguments + plan: System-maintained plan stream: Whether streaming is enabled """ super().__init__(module) self.trajectory = trajectory self.input_args = input_args + self.plan: list[PlanItem] = plan if plan is not None else [] self.stream = stream diff --git a/src/udspy/module/react.py b/src/udspy/module/react.py index 8b107e5..047029c 100644 --- a/src/udspy/module/react.py +++ b/src/udspy/module/react.py @@ -8,7 +8,8 @@ from typing import Any, Literal, TypedDict from udspy.callback import with_callbacks -from udspy.confirmation import ConfirmationRequired +from udspy.confirmation import ConfirmationRejected, ConfirmationRequired +from udspy.decorators import suspendable from udspy.history import History from udspy.module.base import Module from udspy.module.callbacks import ReactContext, is_module_callback @@ -16,9 +17,9 @@ from udspy.module.predict import Predict from udspy.signature import Signature, make_signature from udspy.streaming import Prediction, emit_event -from udspy.tool import Tool, Tools +from udspy.tool import Tool, ToolCall, Tools from udspy.utils.async_support import execute_function_async -from udspy.utils.formatting import format_tool_exception +from udspy.utils.formatting import format_tool_exception, format_validation_error # Rebuild Tools model to resolve forward references Tools.model_rebuild() @@ -26,6 +27,14 @@ logger = logging.getLogger(__name__) +class PlanItem(TypedDict): + """A single item in the agent's plan.""" + + task: str + status: Literal["todo", "done"] + done_at_step: int | None + + class Episode(TypedDict): """Typed dict for a single ReAct episode (thought -> tool calls -> observation).""" @@ -102,6 +111,7 @@ def __init__( tools: list[Callable | Tool], *, max_iters: int = 10, + enable_ask_to_user: bool = False, **kwargs: Any, ): """Initialize ReAct module. @@ -110,6 +120,8 @@ def __init__( signature: Signature defining inputs and outputs, or signature string tools: List of tool functions (decorated with @tool) or Tool objects max_iters: Maximum number of reasoning iterations (default: 10) + enable_ask_to_user: Whether to add an ask_to_user built-in tool + that lets the agent ask the user clarifying questions """ if isinstance(signature, str): signature = Signature.from_string(signature) @@ -117,6 +129,7 @@ def __init__( self.signature = signature self.user_signature = signature self.max_iters = max_iters + self._enable_ask_to_user = enable_ask_to_user self._kwargs = kwargs self._context: ReactContext | None = None # Current execution context @@ -129,7 +142,7 @@ def _init_tools(self) -> None: self._add_builtin_tools() def _add_builtin_tools(self) -> None: - """Add built-in finish tool.""" + """Add built-in tools (finish, and optionally ask_to_user).""" outputs = ", ".join([f"`{k}`" for k in self.signature.get_output_fields().keys()]) def finish_tool() -> str: # pyright: ignore[reportUnusedParameter] @@ -142,6 +155,21 @@ def finish_tool() -> str: # pyright: ignore[reportUnusedParameter] description=f"Call this when you have all information needed to produce {outputs}", ) + if self._enable_ask_to_user: + + def ask_to_user_func(question: str) -> str: + """Ask the user a clarifying question.""" + raise ConfirmationRequired( + question=question, + tool_call=ToolCall(name="ask_to_user", args={"question": question}), + ) + + self.tools["ask_to_user"] = Tool( + func=ask_to_user_func, + name="ask_to_user", + description="Ask the user a clarifying question when the request is ambiguous", + ) + def _rebuild_signatures(self) -> None: """Rebuild react and extract signatures with current tools. @@ -164,12 +192,20 @@ def _build_react_signature(self) -> type[Signature]: instr.extend( [ - f"You are an Agent. In each episode, you will be given the fields {inputs} as input. And you can see your past trajectory so far.", - f"Your goal is to use one or more of the supplied tools to collect any necessary information for producing {outputs}.\n", - "To do this, you will interleave next_thought, next_tool_name, and next_tool_args in each turn, and also when finishing the task.", - "After each step, you receive a resulting observation, which gets appended to your trajectory.\n", - "When writing next_thought, you may reason about the current situation and plan for future steps.", - "When selecting the next_tool_name and its next_tool_args, the tool must be one of:\n", + f"You are an Agent. Given {inputs} and your trajectory, use tools to produce {outputs}.", + "You respond with next_thought, plan_updates, next_tool_name, and next_tool_args each turn.\n", + "next_thought: reason about the user request vs what the trajectory and plan show is already done. " + "If a tool succeeded, mark that plan item done and move on. " + "If a tool failed, retry ONCE only if the fix is obvious from the error. " + "After two similar failures, call `finish` and report the issue. " + "NEVER repeat a tool call with the same or similar arguments that already succeeded.\n", + "plan_updates: a list of updates to the plan. " + 'Use {"add": "task description"} to add a new todo item. ' + 'Use {"done": } to mark the item at that index as done. ' + "On your first turn, add all the steps needed. On subsequent turns, mark completed items done and add new ones if needed. " + "When all items are done, call `finish`.\n", + "The current plan is provided as input. Items marked [done at step N] are completed — do not redo them.\n", + "When selecting next_tool_name and next_tool_args, the tool must be one of:\n", ] ) @@ -177,19 +213,21 @@ def _build_react_signature(self) -> type[Signature]: instr.extend( [ "IMPORTANT: You must respond with a JSON object in your message content containing the fields: " - '{"next_thought": "...", "next_tool_name": "...", "next_tool_args": {...}}.', + '{"next_thought": "...", "plan_updates": [{"add": "..."}, {"done": 0}], "next_tool_name": "...", "next_tool_args": {...}}.', "NEVER use function calling or tool calling syntax - return the JSON as plain text in your response.", ] ) react_input_fields: dict[str, type] = { "trajectory": str, + "plan": str, } for name, field_info in self.user_signature.get_input_fields().items(): react_input_fields[name] = field_info.annotation or str react_output_fields: dict[str, type] = { "next_thought": str, + "plan_updates": list[dict[str, Any]], "next_tool_name": Literal[*self.tools.keys()], # type: ignore[dict-item] "next_tool_args": dict[str, Any], } @@ -274,6 +312,34 @@ def _format_trajectory(self, trajectory: list[Episode]) -> str: return "\n".join(lines) + @staticmethod + def _format_plan(plan: list[PlanItem]) -> str: + """Format the plan as a string for the LLM input.""" + if not plan: + return "No plan yet. Use plan_updates to create one." + + lines = [] + for i, item in enumerate(plan): + if item["status"] == "done": + lines.append(f"[{i}] [done at step {item['done_at_step']}] {item['task']}") + else: + lines.append(f"[{i}] [todo] {item['task']}") + return "\n".join(lines) + + @staticmethod + def _apply_plan_updates( + plan: list[PlanItem], updates: list[dict[str, Any]], step_number: int + ) -> None: + """Apply LLM-proposed plan updates to the canonical plan in place.""" + for update in updates: + if "add" in update and isinstance(update["add"], str): + plan.append({"task": update["add"], "status": "todo", "done_at_step": None}) + elif "done" in update: + idx = update["done"] + if isinstance(idx, int) and 0 <= idx < len(plan) and plan[idx]["status"] == "todo": + plan[idx]["status"] = "done" + plan[idx]["done_at_step"] = step_number + async def _execute_tool_call(self, tool_name: str, tool_args: dict[str, Any]) -> str: """Execute a single tool call and return observation. @@ -310,17 +376,40 @@ async def _execute_tool_call(self, tool_name: str, tool_args: dict[str, Any]) -> e.context = { "trajectory": self._context.trajectory.copy(), "input_args": self._context.input_args.copy(), + "plan": [item.copy() for item in self._context.plan], "stream": self._context.stream, } raise except Exception as e: - parts = [ - f"Traceback '{tool_name}': {format_tool_exception(e)}.", - ] - if tool is not None: - parts.append(f"Expected tool args schema: {tool.parameters}.") + from pydantic import ValidationError + logger.warning(f"Tool execution failed: {e}") - return " ".join(parts) + + if isinstance(e, (ValidationError, TypeError)): + observation = format_validation_error(tool_name, e, tool) + else: + observation = f"Runtime error in '{tool_name}': {format_tool_exception(e)}" + + # Count consecutive failures for this tool to help the LLM + # decide when to stop retrying. + if self._context is not None: + consecutive = 0 + for ep in reversed(self._context.trajectory): + if ep["tool_name"] == tool_name and ep["observation"].startswith( + ("Runtime error", "Validation error") + ): + consecutive += 1 + else: + break + if consecutive >= 1: + observation += ( + f"\n\nWARNING: '{tool_name}' has now failed " + f"{consecutive + 1} times in a row. " + "Consider a different approach or call `finish` " + "to report the issue." + ) + + return observation async def _execute_iteration( self, @@ -348,14 +437,18 @@ async def _execute_iteration( input_args = self._context.input_args # Normal flow: get next thought and tool calls from LLM + plan = self._context.plan formatted_trajectory = self._format_trajectory(trajectory) + formatted_plan = self._format_plan(plan) pred = await self.react_module.aexecute( stream=stream, **input_args, trajectory=formatted_trajectory, + plan=formatted_plan, ) thought = pred.get("next_thought", "").strip() + plan_updates = pred.get("plan_updates", []) tool_name = pred.get("next_tool_name", None) if tool_name not in self.tools: raise ValueError( @@ -365,7 +458,17 @@ async def _execute_iteration( ) tool_args = pred.get("next_tool_args", None) - observation = await self._execute_tool_call(tool_name, tool_args) + try: + observation = await self._execute_tool_call(tool_name, tool_args) + except ConfirmationRequired as e: + # Save the current iteration state so aresume() can complete it + e.context["pending_thought"] = thought + e.context["pending_plan_updates"] = plan_updates + raise + + # Apply plan updates after tool execution (step number = next trajectory index) + step_number = len(trajectory) + 1 + self._apply_plan_updates(plan, plan_updates, step_number) episode: Episode = { "thought": thought, @@ -376,14 +479,22 @@ async def _execute_iteration( trajectory.append(episode) should_stop = tool_name == "finish" + + # System guard: force stop if all plan items are done + if not should_stop and plan and all(item["status"] == "done" for item in plan): + logger.info("All plan items done — forcing stop") + should_stop = True + return should_stop + @suspendable @with_callbacks async def aexecute( self, *, stream: bool = False, _trajectory: list[Episode] | None = None, + _plan: list[PlanItem] | None = None, history: History | None = None, **input_args: Any, ) -> Prediction: @@ -392,6 +503,7 @@ async def aexecute( Args: stream: Passed to sub-modules _trajectory: Internal - restored trajectory for resumption (list of completed episodes) + _plan: Internal - restored plan for resumption history: History object for streaming (not used currently) **input_args: Input values matching signature's input fields @@ -408,7 +520,11 @@ async def aexecute( # Set up React context for this execution self._context = ReactContext( - module=self, trajectory=trajectory, input_args=input_args, stream=stream + module=self, + trajectory=trajectory, + input_args=input_args, + plan=_plan, + stream=stream, ) try: @@ -447,6 +563,7 @@ async def aexecute( **result_dict, reasoning=extract["reasoning"], trajectory=trajectory, + plan=self._context.plan, module=self, ) emit_event(prediction) @@ -454,3 +571,100 @@ async def aexecute( finally: # Clean up context self._context = None + + async def aresume(self, user_response: str, saved_state: Any) -> Prediction: + """Resume ReAct execution after a ConfirmationRequired exception. + + Completes the interrupted iteration using the saved state and user's + response, then continues the ReAct loop. + + Args: + user_response: The user's response (e.g. "yes", "no", or JSON edits) + saved_state: The ConfirmationRequired exception with saved context + + Returns: + Final Prediction after completing the ReAct loop + + Raises: + ConfirmationRejected: If user rejected the operation + ConfirmationRequired: If another confirmation is needed during execution + """ + from udspy.confirmation import respond_to_confirmation + + exc = saved_state + ctx = exc.context + + trajectory = ctx.get("trajectory", []) + input_args = ctx.get("input_args", {}) + plan = ctx.get("plan", []) + stream = ctx.get("stream", False) + + tool_name = exc.tool_call.name if exc.tool_call else None + tool_args = exc.tool_call.args if exc.tool_call else {} + + # Handle ask_to_user: user's response becomes the observation directly + if tool_name == "ask_to_user": + observation = f"User response: {user_response}" + elif tool_name is not None: + # Handle tool confirmation + response_lower = user_response.lower().strip() + + if response_lower in ("no", "n"): + raise ConfirmationRejected( + message=f"User rejected execution of {tool_name}", + confirmation_id=exc.confirmation_id, + tool_call=exc.tool_call, + ) + + # Set approval in confirmation context before re-executing the tool + if response_lower.startswith("{"): + try: + edited_args = json.loads(user_response) + respond_to_confirmation( + exc.confirmation_id, + approved=True, + data=edited_args, + status="edited", + ) + except json.JSONDecodeError: + respond_to_confirmation(exc.confirmation_id, approved=True) + else: + respond_to_confirmation(exc.confirmation_id, approved=True) + + # Re-execute the tool (now with approval set in confirmation context) + self._context = ReactContext( + module=self, + trajectory=trajectory, + input_args=input_args, + plan=plan, + stream=stream, + ) + try: + observation = await self._execute_tool_call(tool_name, tool_args) + finally: + self._context = None + else: + observation = f"Unknown tool confirmation for: {exc.question}" + + # Complete the pending episode + pending_thought = ctx.get("pending_thought", "") + pending_plan_updates = ctx.get("pending_plan_updates", []) + + step_number = len(trajectory) + 1 + self._apply_plan_updates(plan, pending_plan_updates, step_number) + + episode: Episode = { + "thought": pending_thought, + "tool_name": tool_name, + "tool_args": tool_args, + "observation": observation, + } + trajectory.append(episode) + + # Continue the ReAct loop + return await self.aexecute( + stream=stream, + _trajectory=trajectory, + _plan=plan, + **input_args, + ) diff --git a/src/udspy/settings.py b/src/udspy/settings.py index 05996f7..a407d17 100644 --- a/src/udspy/settings.py +++ b/src/udspy/settings.py @@ -69,6 +69,9 @@ def on_lm_start(self, call_id, instance, inputs): """ if lm: self._lm = lm + elif self._lm is None: + if model := os.getenv("UDSPY_LM_MODEL"): + self._lm = LM(model) if callbacks is not None: self._callbacks = callbacks @@ -91,7 +94,7 @@ def lm(self) -> BaseLM: if context_lm is not None: return context_lm - if model := os.getenv("USDPY_LM_MODEL"): + if model := os.getenv("UDSPY_LM_MODEL"): self._lm = LM(model) if self._lm is None: diff --git a/src/udspy/signature.py b/src/udspy/signature.py index acd953e..450f85d 100644 --- a/src/udspy/signature.py +++ b/src/udspy/signature.py @@ -60,7 +60,7 @@ def OutputField( ) -class SignatureMeta(type(BaseModel)): # type: ignore[misc] +class SignatureMeta(type(BaseModel)): """Metaclass for Signature that validates field types.""" def __new__( @@ -76,12 +76,12 @@ def __new__( if name == "Signature": return cls - for field_name, field_info in cls.model_fields.items(): + for field_name, field_info in cls.model_fields.items(): # ty: ignore[unresolved-attribute] if not isinstance(field_info, FieldInfo): continue json_schema_extra = field_info.json_schema_extra or {} - field_type = json_schema_extra.get("__udspy_field_type") # type: ignore[union-attr] + field_type = json_schema_extra.get("__udspy_field_type") if field_type not in ("input", "output"): raise TypeError( @@ -113,7 +113,7 @@ def get_input_fields(cls) -> dict[str, FieldInfo]: return { name: field_info for name, field_info in cls.model_fields.items() - if (field_info.json_schema_extra or {}).get("__udspy_field_type") == "input" # type: ignore[union-attr] + if (field_info.json_schema_extra or {}).get("__udspy_field_type") == "input" } @classmethod @@ -122,7 +122,7 @@ def get_output_fields(cls) -> dict[str, FieldInfo]: return { name: field_info for name, field_info in cls.model_fields.items() - if (field_info.json_schema_extra or {}).get("__udspy_field_type") == "output" # type: ignore[union-attr] + if (field_info.json_schema_extra or {}).get("__udspy_field_type") == "output" } @classmethod @@ -236,7 +236,7 @@ def make_signature( sig = create_model( "DynamicSignature", __base__=Signature, - **fields, # type: ignore + **fields, ) if instructions: diff --git a/src/udspy/tool/tool.py b/src/udspy/tool/tool.py index 55d3755..a646fa7 100644 --- a/src/udspy/tool/tool.py +++ b/src/udspy/tool/tool.py @@ -68,7 +68,7 @@ def __init__( """ super().__init__( func=func, - name=name or func.__name__, + name=name or getattr(func, "__name__", ""), description=description or inspect.getdoc(func) or "", require_confirmation=require_confirmation, ) @@ -280,7 +280,7 @@ def get_output_type_or_schema(self, resolve_defs: bool = True) -> str | JsonSche return resolve_json_schema_reference(schema)["properties"] else: return schema - elif (valid_type := type_map.get(return_type)) is not None: # type: ignore[arg-type] + elif (valid_type := type_map.get(return_type)) is not None: return valid_type else: raise ValueError( diff --git a/src/udspy/utils/__init__.py b/src/udspy/utils/__init__.py index c7af122..def77d0 100644 --- a/src/udspy/utils/__init__.py +++ b/src/udspy/utils/__init__.py @@ -7,7 +7,7 @@ """ from udspy.utils.async_support import ensure_sync_context, execute_function_async -from udspy.utils.formatting import format_tool_exception +from udspy.utils.formatting import format_tool_exception, format_validation_error from udspy.utils.schema import minimize_schema, resolve_json_schema_reference __all__ = [ @@ -16,4 +16,5 @@ "resolve_json_schema_reference", "minimize_schema", "format_tool_exception", + "format_validation_error", ] diff --git a/src/udspy/utils/async_support.py b/src/udspy/utils/async_support.py index c9fc742..cc5d852 100644 --- a/src/udspy/utils/async_support.py +++ b/src/udspy/utils/async_support.py @@ -76,6 +76,16 @@ async def execute_function_async( return await loop.run_in_executor(None, partial(ctx.run, lambda: func(**kwargs))) +def _cancel_all_tasks(loop: asyncio.AbstractEventLoop) -> None: + """Cancel all pending tasks on the loop (mirrors asyncio.run internals).""" + to_cancel = asyncio.all_tasks(loop) + if not to_cancel: + return + for task in to_cancel: + task.cancel() + loop.run_until_complete(asyncio.gather(*to_cancel, return_exceptions=True)) + + def run_async_with_context(coro: Any) -> Any: """Run an async coroutine with the current context preserved. @@ -109,7 +119,25 @@ def run_async_with_context(coro: Any) -> Any: Both use the same pattern: copy_context() + ctx.run() """ ctx = contextvars.copy_context() - return ctx.run(asyncio.run, coro) + + # Suppress "Event loop is closed" errors from httpx/AsyncOpenAI client cleanup. + # These are harmless GC-triggered warnings when the async client outlives the loop. + def _ignore_loop_closed(loop: asyncio.AbstractEventLoop, context: dict[str, Any]) -> None: + if "Event loop is closed" in str(context.get("exception", "")): + return + loop.default_exception_handler(context) + + loop = asyncio.new_event_loop() + loop.set_exception_handler(_ignore_loop_closed) + try: + return ctx.run(loop.run_until_complete, coro) + finally: + try: + _cancel_all_tasks(loop) + loop.run_until_complete(loop.shutdown_asyncgens()) + loop.run_until_complete(loop.shutdown_default_executor()) + finally: + loop.close() __all__ = [ diff --git a/src/udspy/utils/formatting.py b/src/udspy/utils/formatting.py index 0b67e5d..70d005f 100644 --- a/src/udspy/utils/formatting.py +++ b/src/udspy/utils/formatting.py @@ -1,22 +1,45 @@ """Formatting utility functions.""" -def format_tool_exception(err: BaseException, *, limit: int = 5) -> str: +def format_tool_exception(err: BaseException, *, limit: int = 3, max_length: int = 1000) -> str: """ - Return a one-string traceback summary. + Return a concise error summary with the error message first (most useful + for the LLM), followed by abbreviated stack frames if space allows. + * `limit` - how many stack frames to keep (from the innermost outwards). + * `max_length` - maximum length of the returned string. """ import traceback - return ( - "\n" - + "".join( - traceback.format_exception(type(err), err, err.__traceback__, limit=limit) - ).strip() - ) + # Error message first — this is what the LLM needs most + error_line = f"{type(err).__name__}: {err}" + + # Then abbreviated stack frames for context + tb_lines = traceback.format_tb(err.__traceback__, limit=limit) + frames = "".join(tb_lines).strip() + + formatted = f"{error_line}\n{frames}" if frames else error_line + + if len(formatted) > max_length: + formatted = formatted[:max_length] + "..." + + return formatted + + +def format_validation_error(tool_name: str, err: BaseException, tool=None) -> str: + """ + Format a validation/type error with the expected schema appended, + so the LLM knows how to fix its arguments. + """ + + parts = [f"Validation error for '{tool_name}': {err}."] + if tool is not None: + parts.append(f"Expected tool args schema: {tool.parameters}.") + return " ".join(parts) __all__ = [ "format_tool_exception", + "format_validation_error", ] diff --git a/tests/test_cancellation.py b/tests/test_cancellation.py new file mode 100644 index 0000000..000339b --- /dev/null +++ b/tests/test_cancellation.py @@ -0,0 +1,182 @@ +"""Tests for asyncio cancellation support.""" + +import asyncio + +import pytest + +import udspy +from udspy import BaseCallback, InputField, OutputField, Predict, Prediction, Signature, settings +from udspy.streaming import OutputStreamChunk + + +class QA(Signature): + """Answer questions.""" + + question: str = InputField() + answer: str = OutputField() + + +class SlowQA(Signature): + """Answer questions slowly for cancellation testing.""" + + question: str = InputField() + answer: str = OutputField() + + +def _make_slow_streaming_mock(delay: float = 0.05): + """Create a slow streaming mock that yields chunks with delays.""" + from openai.types.chat import ChatCompletionChunk + from openai.types.chat.chat_completion_chunk import Choice, ChoiceDelta + + async def mock_create(**kwargs): + async def mock_stream(): + parts = ['{"answer": "word1', " word2", " word3", " word4", ' word5"}'] + for i, part in enumerate(parts): + await asyncio.sleep(delay) + yield ChatCompletionChunk( + id="test", + model="gpt-4o-mini", + object="chat.completion.chunk", + created=1234567890, + choices=[ + Choice( + index=0, + delta=ChoiceDelta( + content=part, + role="assistant" if i == 0 else None, + ), + finish_reason="stop" if i == len(parts) - 1 else None, + ) + ], + ) + + return mock_stream() + + return mock_create + + +@pytest.mark.asyncio +async def test_astream_break_cancels_task(): + """Breaking out of astream() should cancel the internal LLM task.""" + settings.lm.client.chat.completions.create = _make_slow_streaming_mock(delay=0.05) + + predictor = Predict(SlowQA) + events = [] + + async for event in predictor.astream(question="Tell me something"): + events.append(event) + if isinstance(event, OutputStreamChunk) and len(events) >= 2: + break # Break early — should cancel the internal task + + # We got some events but not the full prediction + assert len(events) >= 1 + assert not any(isinstance(e, Prediction) for e in events) + + # Give the event loop a tick to process the cancellation + await asyncio.sleep(0.1) + + # Verify no lingering tasks (aside from the current test task) + remaining = [t for t in asyncio.all_tasks() if not t.done() and t is not asyncio.current_task()] + for t in remaining: + # None of the remaining tasks should be our LLM streaming task + assert "aexecute" not in str(t.get_coro()), ( + "LLM task was not cancelled after breaking out of astream()" + ) + + +@pytest.mark.asyncio +async def test_aforward_cancel_propagates(): + """Cancelling an aforward() task should propagate CancelledError.""" + + # Use a slow mock so we can cancel mid-flight + async def slow_create(**kwargs): + await asyncio.sleep(10) # Long enough that we always cancel first + + settings.lm.client.chat.completions.create = slow_create + + predictor = Predict(QA) + + task = asyncio.create_task(predictor.aforward(question="Slow question")) + + # Let it start + await asyncio.sleep(0.05) + + task.cancel() + + with pytest.raises(asyncio.CancelledError): + await task + + +@pytest.mark.asyncio +async def test_callback_receives_cancelled_error(): + """Callbacks should receive CancelledError as the exception on cancellation.""" + + class CancellationRecorder(BaseCallback): + def __init__(self): + self.end_exception = None + self.end_called = False + + def on_module_end(self, call_id, outputs, exception): + self.end_called = True + self.end_exception = exception + + callback = CancellationRecorder() + + async def slow_create(**kwargs): + await asyncio.sleep(10) + + settings.lm.client.chat.completions.create = slow_create + + predictor = Predict(QA) + + with udspy.settings.context(callbacks=[callback]): + task = asyncio.create_task(predictor.aforward(question="Will be cancelled")) + await asyncio.sleep(0.05) + task.cancel() + + with pytest.raises(asyncio.CancelledError): + await task + + assert callback.end_called, "on_module_end should have been called" + assert isinstance(callback.end_exception, asyncio.CancelledError), ( + f"Expected CancelledError, got {type(callback.end_exception)}" + ) + + +@pytest.mark.asyncio +async def test_astream_full_consumption_no_cancel(): + """Fully consuming astream() should not trigger cancellation — regression check.""" + + async def mock_create(**kwargs): + async def mock_stream(): + from openai.types.chat import ChatCompletionChunk + from openai.types.chat.chat_completion_chunk import Choice, ChoiceDelta + + yield ChatCompletionChunk( + id="test", + model="gpt-4o-mini", + object="chat.completion.chunk", + created=1234567890, + choices=[ + Choice( + index=0, + delta=ChoiceDelta(content='{"answer": "Paris"}', role="assistant"), + finish_reason="stop", + ) + ], + ) + + return mock_stream() + + settings.lm.client.chat.completions.create = mock_create + + predictor = Predict(QA) + events = [] + + async for event in predictor.astream(question="Capital of France?"): + events.append(event) + + # Should have completed normally with a Prediction + predictions = [e for e in events if isinstance(e, Prediction)] + assert len(predictions) == 1 + assert predictions[0].answer == "Paris" diff --git a/tests/test_edge_cases.py b/tests/test_edge_cases.py index ad90227..47bdf0a 100644 --- a/tests/test_edge_cases.py +++ b/tests/test_edge_cases.py @@ -380,16 +380,16 @@ class SimpleTask(Signature): # Mock response with malformed JSON in tool arguments - note the invalid JSON in args response = make_mock_response( - '{"next_thought": "I\'ll use the test tool", ' + '{"next_thought": "I\'ll use the test tool", "plan_updates": [], ' '"next_tool_name": "TestTool", "next_tool_args": {"x": invalid_json}}' # Malformed JSON in content ) response2 = make_mock_response( - '{"next_thought": "Let me try again", "next_tool_name": "finish", "next_tool_args": {}}' + '{"next_thought": "Let me try again", "plan_updates": [], "next_tool_name": "finish", "next_tool_args": {}}' ) response3 = make_mock_response( - '{"next_thought": "Finished", "next_tool_name": "finish", "next_tool_args": {}}' + '{"next_thought": "Finished", "plan_updates": [], "next_tool_name": "finish", "next_tool_args": {}}' ) response4 = make_mock_response('{"reasoning": "Completed", "result": "Completed"}') diff --git a/tests/test_module_callbacks.py b/tests/test_module_callbacks.py index 468e171..3a39c9e 100644 --- a/tests/test_module_callbacks.py +++ b/tests/test_module_callbacks.py @@ -305,11 +305,11 @@ def callback(context): # Mock LLM responses responses = [ # Load tools - '{"next_thought": "I need to load weather tools", "next_tool_name": "load_tools", "next_tool_args": {"category": "weather"}}', + '{"next_thought": "I need to load weather tools", "plan_updates": [], "next_tool_name": "load_tools", "next_tool_args": {"category": "weather"}}', # Use weather tool - '{"next_thought": "Now check the weather", "next_tool_name": "weather_tool", "next_tool_args": {"location": "Tokyo"}}', + '{"next_thought": "Now check the weather", "plan_updates": [], "next_tool_name": "weather_tool", "next_tool_args": {"location": "Tokyo"}}', # Finish - '{"next_thought": "I have the answer", "next_tool_name": "finish", "next_tool_args": {}}', + '{"next_thought": "I have the answer", "plan_updates": [], "next_tool_name": "finish", "next_tool_args": {}}', # Extract answer (ChainOfThought reasoning) '{"reasoning": "Extracting final answer from trajectory", "answer": "The weather in Tokyo is Sunny"}', ] @@ -588,9 +588,9 @@ def callback(context): return callback responses = [ - '{"next_thought": "I need to load the calculator", "next_tool_name": "load_calculator", "next_tool_args": {}}', - '{"next_thought": "Now I can calculate", "next_tool_name": "calculator", "next_tool_args": {"expression": "25 * 4"}}', - '{"next_thought": "I have the answer", "next_tool_name": "finish", "next_tool_args": {}}', + '{"next_thought": "I need to load the calculator", "plan_updates": [], "next_tool_name": "load_calculator", "next_tool_args": {}}', + '{"next_thought": "Now I can calculate", "plan_updates": [], "next_tool_name": "calculator", "next_tool_args": {"expression": "25 * 4"}}', + '{"next_thought": "I have the answer", "plan_updates": [], "next_tool_name": "finish", "next_tool_args": {}}', '{"reasoning": "Extracting answer", "answer": "100"}', ] @@ -650,11 +650,11 @@ def callback(context): return callback responses = [ - '{"next_thought": "Load calculator", "next_tool_name": "load_calculator", "next_tool_args": {}}', - '{"next_thought": "Load formatter", "next_tool_name": "load_formatter", "next_tool_args": {}}', - '{"next_thought": "Use tools", "next_tool_name": "calculator", "next_tool_args": {"expression": "10 + 5"}}', - '{"next_thought": "Format result", "next_tool_name": "formatter", "next_tool_args": {"number": "15"}}', - '{"next_thought": "Done", "next_tool_name": "finish", "next_tool_args": {}}', + '{"next_thought": "Load calculator", "plan_updates": [], "next_tool_name": "load_calculator", "next_tool_args": {}}', + '{"next_thought": "Load formatter", "plan_updates": [], "next_tool_name": "load_formatter", "next_tool_args": {}}', + '{"next_thought": "Use tools", "plan_updates": [], "next_tool_name": "calculator", "next_tool_args": {"expression": "10 + 5"}}', + '{"next_thought": "Format result", "plan_updates": [], "next_tool_name": "formatter", "next_tool_args": {"number": "15"}}', + '{"next_thought": "Done", "plan_updates": [], "next_tool_name": "finish", "next_tool_args": {}}', '{"reasoning": "Completed", "answer": "Formatted: 15"}', ] @@ -688,8 +688,8 @@ def valid_tool() -> str: return "Valid tool executed" responses = [ - '{"next_thought": "Try invalid tool", "next_tool_name": "nonexistent_tool", "next_tool_args": {}}', - '{"next_thought": "Handle error", "next_tool_name": "finish", "next_tool_args": {}}', + '{"next_thought": "Try invalid tool", "plan_updates": [], "next_tool_name": "nonexistent_tool", "next_tool_args": {}}', + '{"next_thought": "Handle error", "plan_updates": [], "next_tool_name": "finish", "next_tool_args": {}}', '{"reasoning": "Error handled", "answer": "Tool not found"}', ] diff --git a/tests/test_react.py b/tests/test_react.py index bb8167e..b49734e 100644 --- a/tests/test_react.py +++ b/tests/test_react.py @@ -41,12 +41,12 @@ async def test_react_basic_execution() -> None: # Mock LLM responses for ReAct loop (using next_thought/next_tool_name/next_tool_args format) # First call: agent decides to call search tool react_response = make_mock_response( - '{"next_thought": "I should search for information about Python", "next_tool_name": "search", "next_tool_args": {"query": "Python programming language"}}' + '{"next_thought": "I should search for information about Python", "plan_updates": [{"add": "search for Python info"}, {"add": "compile answer"}], "next_tool_name": "search", "next_tool_args": {"query": "Python programming language"}}' ) # Second call: agent decides to finish react_finish_response = make_mock_response( - '{"next_thought": "I have the information I need", "next_tool_name": "finish", "next_tool_args": {}}' + '{"next_thought": "I have the information I need", "plan_updates": [{"done": 0}, {"done": 1}], "next_tool_name": "finish", "next_tool_args": {}}' ) # Extract call: final answer extraction @@ -86,7 +86,7 @@ async def mock_create(**kwargs): # type: ignore[no-untyped-def] async def test_react_string_signature() -> None: """Test ReAct with string signature format.""" react_response = make_mock_response( - '{"next_thought": "Finish", "next_tool_name": "finish", "next_tool_args": {}}' + '{"next_thought": "Finish", "plan_updates": [], "next_tool_name": "finish", "next_tool_args": {}}' ) extract_response = make_mock_response('{"reasoning": "Completed", "result": "Done"}') @@ -116,7 +116,7 @@ async def mock_create(**kwargs): # type: ignore[no-untyped-def] async def test_react_tool_confirmation() -> None: """Test ReAct with tool requiring confirmation.""" react_response = make_mock_response( - '{"next_thought": "Delete the file", "next_tool_name": "delete_file", "next_tool_args": {"path": "/tmp/test.txt"}}' + '{"next_thought": "Delete the file", "plan_updates": [{"add": "delete the file"}], "next_tool_name": "delete_file", "next_tool_args": {"path": "/tmp/test.txt"}}' ) async def mock_create(**kwargs): # type: ignore[no-untyped-def] @@ -144,7 +144,7 @@ async def mock_create(**kwargs): # type: ignore[no-untyped-def] def test_react_forward_sync() -> None: """Test sync forward() method.""" react_response = make_mock_response( - '{"next_thought": "Finish", "next_tool_name": "finish", "next_tool_args": {}}' + '{"next_thought": "Finish", "plan_updates": [], "next_tool_name": "finish", "next_tool_args": {}}' ) extract_response = make_mock_response('{"reasoning": "Completed", "answer": "Test answer"}') @@ -189,7 +189,7 @@ async def test_react_with_string_signature() -> None: """Test ReAct with string signature format.""" finish_response = make_mock_response( - '{"next_thought": "Reasoning", "next_tool_name": "finish", "next_tool_args": {}}' + '{"next_thought": "Reasoning", "plan_updates": [], "next_tool_name": "finish", "next_tool_args": {}}' ) extract_response = make_mock_response('{"reasoning": "Completed", "result": "Task completed"}') @@ -213,3 +213,43 @@ async def mock_create(**_kwargs): # type: ignore[no-untyped-def] # Trajectory is now a list of episodes assert isinstance(result.trajectory, list) assert "result" in result + + +@pytest.mark.asyncio +async def test_react_plan_all_done_forces_stop() -> None: + """Test that system forces stop when all plan items are marked done.""" + # First call: agent adds a plan item and marks it done, but calls search instead of finish + react_response = make_mock_response( + '{"next_thought": "Searching", "plan_updates": [{"add": "search"}, {"done": 0}], "next_tool_name": "search", "next_tool_args": {"query": "test"}}' + ) + + # This should NOT be reached — system should force stop after first call + react_second_response = make_mock_response( + '{"next_thought": "Again", "plan_updates": [{"add": "another search"}], "next_tool_name": "search", "next_tool_args": {"query": "test2"}}' + ) + + extract_response = make_mock_response('{"reasoning": "Done", "answer": "Result"}') + + call_count = 0 + + async def mock_create(**kwargs): # type: ignore[no-untyped-def] + nonlocal call_count + call_count += 1 + + if call_count == 1: + return react_response + elif call_count == 2: + return extract_response + else: + return react_second_response + + mock_aclient = settings.lm.client + mock_aclient.chat.completions.create = mock_create + + react = ReAct(QA, tools=[search_tool]) + result = await react.aforward(question="Test question") + + # Should have stopped after 1 react call + 1 extract call = 2 total + assert call_count == 2 + assert len(result.trajectory) == 1 + 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