diff --git a/.env.example b/.env.example index 5ed5a47..44bf234 100644 --- a/.env.example +++ b/.env.example @@ -63,6 +63,25 @@ LLM_MAX_TOKENS= # Disable caching (true/false) - when true, skips reading cache but still writes LLM_NOCACHE=false +# ============================================================================== +# PYDANTIC AI STANDARD API KEYS +# ============================================================================== +# These are the standard env vars that PydanticAI expects for each provider +# Set these to use the providers with default configuration + +# OpenAI API key (for openai provider) +OPENAI_API_KEY=your-openai-api-key + +# Anthropic API key (for anthropic provider) +ANTHROPIC_API_KEY=your-anthropic-api-key + +# Mistral API key (for mistral provider) +MISTRAL_API_KEY=your-mistral-api-key + +# Azure OpenAI (for azure provider) +AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com +AZURE_OPENAI_API_KEY=your-azure-api-key + # Rate limiting settings # 0 = use provider-specific default (azure/openai/anthropic: 60 RPM, ollama/lmstudio: unlimited) LLM_RATE_LIMIT_RPM=0 @@ -104,7 +123,7 @@ LLM_LMSTUDIO_MODEL=local-model # Naming: LLM_{NAME}_* -> CLI name is lowercase with hyphens (AZURE_GPT4MINI -> azure-gpt4mini) # # Required fields per model: -# LLM_{NAME}_PROVIDER - Provider type: azure, openai, anthropic, ollama, lmstudio, mistral, claudecode +# LLM_{NAME}_PROVIDER - Provider type: azure, openai, anthropic, ollama, lmstudio, mistral # LLM_{NAME}_MODEL - Model name/identifier # LLM_{NAME}_URL - API endpoint URL # LLM_{NAME}_KEY - API key (optional for local providers) @@ -164,33 +183,26 @@ LLM_LMSTUDIO_LOCAL_PROVIDER=lmstudio LLM_LMSTUDIO_LOCAL_MODEL=local-model LLM_LMSTUDIO_LOCAL_URL=http://localhost:1234/v1/chat/completions -# --- CLAUDE CODE (via Agent SDK CLI) --- +# --- ANTHROPIC (Direct API) --- -# Uses your authenticated Claude Code session - no API key needed! -# Requires: Claude Code extension installed and authenticated -# +# Anthropic Claude models via direct API # Model Pricing (per Million Tokens): # haiku: $1/$5 (fastest, cheapest) # sonnet: $3/$15 (balanced) -# opus: $5/$25 (most capable) - -# Claude Haiku (via Claude Code) -> use as: claudecode-haiku -LLM_CLAUDECODE_HAIKU_PROVIDER=claudecode -LLM_CLAUDECODE_HAIKU_MODEL=haiku -LLM_CLAUDECODE_HAIKU_URL=cli -LLM_CLAUDECODE_HAIKU_KEY= - -# Claude Sonnet (via Claude Code) -> use as: claudecode-sonnet -LLM_CLAUDECODE_SONNET_PROVIDER=claudecode -LLM_CLAUDECODE_SONNET_MODEL=sonnet -LLM_CLAUDECODE_SONNET_URL=cli -LLM_CLAUDECODE_SONNET_KEY= - -# Claude Opus (via Claude Code) -> use as: claudecode-opus -LLM_CLAUDECODE_OPUS_PROVIDER=claudecode -LLM_CLAUDECODE_OPUS_MODEL=opus -LLM_CLAUDECODE_OPUS_URL=cli -LLM_CLAUDECODE_OPUS_KEY= + +# Anthropic Claude Haiku -> use as: anthropic-haiku +LLM_ANTHROPIC_HAIKU_PROVIDER=anthropic +LLM_ANTHROPIC_HAIKU_MODEL=claude-haiku-4-5-20251001 +LLM_ANTHROPIC_HAIKU_URL=https://api.anthropic.com/v1/messages +LLM_ANTHROPIC_HAIKU_KEY=your-anthropic-api-key +LLM_ANTHROPIC_HAIKU_STRUCTURED_OUTPUT=true + +# Anthropic Claude Sonnet -> use as: anthropic-sonnet +LLM_ANTHROPIC_SONNET_PROVIDER=anthropic +LLM_ANTHROPIC_SONNET_MODEL=claude-sonnet-4-5-20251101 +LLM_ANTHROPIC_SONNET_URL=https://api.anthropic.com/v1/messages +LLM_ANTHROPIC_SONNET_KEY=your-anthropic-api-key +LLM_ANTHROPIC_SONNET_STRUCTURED_OUTPUT=true # ============================================================================== # TOKEN LIMITS FOR CHUNKING diff --git a/CHANGELOG.md b/CHANGELOG.md index 5a71356..afb32b5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,7 +6,21 @@ Deriving ArchiMate models from code using knowledge graphs, heuristics and LLM's # v0.6.x - Deriva (December 2025 - January 2026) -## v0.6.7 - (January 15 2026) +## v0.6.8 - PydanticAI Migration (Unreleased) + +### LLM Adapter Rewrite +- **PydanticAI Integration**: Replaced custom REST provider implementations with PydanticAI library +- **Code Reduction**: Same (or better) llm adapter with way less code, deleted entire `providers.py` +- **Native Structured Output**: PydanticAI handles validation and retry automatically +- **Removed ClaudeCode Provider**: Use `anthropic` provider directly instead (CLI subprocess no longer supported) + +### Configuration +- **Standard API Keys**: Added PydanticAI standard env vars (`OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `MISTRAL_API_KEY`, `AZURE_OPENAI_*`) +- **Updated .env.example**: Removed `claudecode` provider, added Anthropic direct API configs + +--- + +## v0.6.7 - Gotta save some Tokens (January 15 2026) ### Caching & Performance - **Graph Cache**: New `cache.py` in graph adapter with hash-based cache for expensive graph queries diff --git a/deriva/adapters/llm/__init__.py b/deriva/adapters/llm/__init__.py index ae2c9b7..e56cfc9 100644 --- a/deriva/adapters/llm/__init__.py +++ b/deriva/adapters/llm/__init__.py @@ -1,11 +1,15 @@ """ LLM Manager - A multi-provider LLM abstraction with caching and structured output. +Uses PydanticAI for model-agnostic LLM interactions. + Supports: - Azure OpenAI - OpenAI - Anthropic - Ollama +- Mistral +- LM Studio Example: from deriva.adapters.llm import LLMManager @@ -27,9 +31,12 @@ class Concept(BaseModel): from __future__ import annotations from .cache import CacheManager, cached_llm_call +from .manager import LLMManager +from .model_registry import VALID_PROVIDERS, get_pydantic_ai_model from .models import ( APIError, BaseResponse, + BenchmarkModelConfig, CachedResponse, CacheError, ConfigurationError, @@ -38,24 +45,9 @@ class Concept(BaseModel): LLMError, LLMResponse, ResponseType, - StructuredOutputMixin, ValidationError, ) -from .providers import ( - AnthropicProvider, - AzureOpenAIProvider, - BaseProvider, - CompletionResult, - LLMProvider, - LMStudioProvider, - OllamaProvider, - OpenAIProvider, - ProviderConfig, - ProviderError, - create_provider, -) from .rate_limiter import RateLimitConfig, RateLimiter, get_default_rate_limit -from .manager import LLMManager __all__ = [ # Main service @@ -67,20 +59,10 @@ class Concept(BaseModel): "CachedResponse", "FailedResponse", "LLMResponse", - # Structured output - "StructuredOutputMixin", - # Providers - "LLMProvider", - "BaseProvider", - "AzureOpenAIProvider", - "OpenAIProvider", - "AnthropicProvider", - "OllamaProvider", - "LMStudioProvider", - "ProviderConfig", - "CompletionResult", - "ProviderError", - "create_provider", + # Configuration + "BenchmarkModelConfig", + "VALID_PROVIDERS", + "get_pydantic_ai_model", # Cache "CacheManager", "cached_llm_call", @@ -96,4 +78,4 @@ class Concept(BaseModel): "ValidationError", ] -__version__ = "1.0.0" +__version__ = "2.0.0" diff --git a/deriva/adapters/llm/manager.py b/deriva/adapters/llm/manager.py index 91e0662..fa0e801 100644 --- a/deriva/adapters/llm/manager.py +++ b/deriva/adapters/llm/manager.py @@ -1,14 +1,15 @@ """ LLM Manager - Unified interface for LLM API calls with caching and structured output. -This module provides the LLMManager class which handles all LLM operations -with multi-provider support, intelligent caching, and type-safe structured output. +Uses PydanticAI for model-agnostic LLM interactions. Supported Providers: - Azure OpenAI - OpenAI - Anthropic - - Ollama (local) + - Ollama + - Mistral + - LM Studio Usage: from deriva.adapters.llm import LLMManager @@ -27,25 +28,21 @@ class Concept(BaseModel): result = llm.query("Extract concept...", response_model=Concept) print(result.name) # Type-safe access - - # Check response type - if response.response_type == "cached": - print("Response was cached") """ from __future__ import annotations -import json import logging import os -from datetime import UTC, datetime from pathlib import Path from typing import Any, TypeVar, overload from dotenv import load_dotenv -from pydantic import BaseModel, ValidationError as PydanticValidationError +from pydantic import BaseModel +from pydantic_ai import Agent from .cache import CacheManager +from .model_registry import VALID_PROVIDERS, get_pydantic_ai_model from .models import ( APIError, BenchmarkModelConfig, @@ -56,32 +53,12 @@ class Concept(BaseModel): LLMResponse, ValidationError, ) -from .providers import ProviderConfig, ProviderError, create_provider +from .rate_limiter import RateLimitConfig, RateLimiter, get_default_rate_limit logger = logging.getLogger(__name__) - -def _strip_markdown_json(content: str) -> str: - """Strip markdown code blocks from JSON content. - - Some providers (e.g., Anthropic) return JSON wrapped in markdown: - ```json - {"key": "value"} - ``` - - This function extracts the JSON content. - """ - import re - - content = content.strip() - - # Pattern to match ```json ... ``` or ``` ... ``` - pattern = r"^```(?:json|JSON)?\s*\n?(.*?)\n?```$" - match = re.match(pattern, content, re.DOTALL) - if match: - return match.group(1).strip() - - return content +# Type variable for structured output +T = TypeVar("T", bound=BaseModel) def load_benchmark_models() -> dict[str, BenchmarkModelConfig]: @@ -94,14 +71,6 @@ def load_benchmark_models() -> dict[str, BenchmarkModelConfig]: LLM_{NAME}_URL (optional) LLM_{NAME}_KEY (optional, direct key) LLM_{NAME}_KEY_ENV (optional, env var name for key) - LLM_{NAME}_STRUCTURED_OUTPUT (optional, "true" to enable) - - Example .env: - LLM_AZURE_GPT4_PROVIDER=azure - LLM_AZURE_GPT4_MODEL=gpt-4 - LLM_AZURE_GPT4_URL=https://... - LLM_AZURE_GPT4_KEY=sk-... - LLM_AZURE_GPT4_STRUCTURED_OUTPUT=true Returns: Dict mapping config name to BenchmarkModelConfig @@ -110,13 +79,11 @@ def load_benchmark_models() -> dict[str, BenchmarkModelConfig]: configs: dict[str, BenchmarkModelConfig] = {} - # Find all LLM_*_PROVIDER entries (but not legacy LLM_PROVIDER) prefix = "LLM_" suffix = "_PROVIDER" for key, value in os.environ.items(): if key.startswith(prefix) and key.endswith(suffix) and key != "LLM_PROVIDER": - # Extract the name part (e.g., "AZURE_GPT4" from "LLM_AZURE_GPT4_PROVIDER") name = key[len(prefix) : -len(suffix)] provider = value @@ -124,13 +91,10 @@ def load_benchmark_models() -> dict[str, BenchmarkModelConfig]: api_url = os.getenv(f"{prefix}{name}_URL") api_key = os.getenv(f"{prefix}{name}_KEY") api_key_env = os.getenv(f"{prefix}{name}_KEY_ENV") - structured_output_str = os.getenv(f"{prefix}{name}_STRUCTURED_OUTPUT", "") - structured_output = structured_output_str.lower() in ("true", "1", "yes") if not model: - continue # Skip incomplete configs + continue - # Convert name to lowercase with hyphens (AZURE_GPT4 -> azure-gpt4) friendly_name = name.lower().replace("_", "-") try: @@ -141,28 +105,24 @@ def load_benchmark_models() -> dict[str, BenchmarkModelConfig]: api_url=api_url, api_key=api_key, api_key_env=api_key_env, - structured_output=structured_output, ) except ValueError: - # Invalid provider, skip continue return configs -# Type variable for structured output -T = TypeVar("T", bound=BaseModel) - - class LLMManager: """ Manages LLM API calls with intelligent caching and structured output support. + Uses PydanticAI for model interactions with caching layer on top. + Features: - - Multi-provider support (Azure OpenAI, OpenAI, Anthropic, Ollama) + - Multi-provider support (Azure OpenAI, OpenAI, Anthropic, Ollama, Mistral, LM Studio) - Automatic caching of responses - Structured output with Pydantic models - - JSON schema validation + - Rate limiting - Response type indicators (live/cached/failed) Example: @@ -199,17 +159,19 @@ def __init__(self): cache_path = project_root / cache_path self.cache = CacheManager(str(cache_path)) - # Create provider with rate limiting - provider_config = ProviderConfig( - api_url=self.config["api_url"], - api_key=self.config["api_key"], - model=self.config["model"], - timeout=self.config.get("timeout", 60), - requests_per_minute=self.config.get("requests_per_minute", 0), - min_request_delay=self.config.get("min_request_delay", 0.0), - rate_limit_retries=self.config.get("rate_limit_retries", 3), + # Initialize rate limiter + rpm = self.config.get("requests_per_minute", 0) + if rpm <= 0: + rpm = get_default_rate_limit(self.config["provider"]) + self._rate_limiter = RateLimiter( + config=RateLimitConfig( + requests_per_minute=rpm, + min_request_delay=self.config.get("min_request_delay", 0.0), + ) ) - self.provider = create_provider(self.config["provider"], provider_config) + + # Get PydanticAI model + self._pydantic_model = get_pydantic_ai_model(self.config) # Store config values for easy access self.model = self.config["model"] @@ -217,8 +179,7 @@ def __init__(self): self.cache_ttl = self.config.get("cache_ttl", 0) self.nocache = self.config.get("nocache", False) self.temperature = self.config.get("temperature", 0.7) - self.max_tokens = self.config.get("max_tokens") # None = provider default - self.structured_output = self.config.get("structured_output", False) + self.max_tokens = self.config.get("max_tokens") @classmethod def from_config( @@ -228,15 +189,11 @@ def from_config( max_retries: int = 3, timeout: int = 60, temperature: float | None = None, - nocache: bool = True, # Default to no cache for benchmarking + nocache: bool = True, ) -> "LLMManager": """ Create an LLMManager from explicit configuration. - This factory method allows creating LLMManager instances without - relying on environment variables, useful for benchmarking multiple - models in the same process. - Args: config: BenchmarkModelConfig with provider/model settings cache_dir: Directory for response caching @@ -247,23 +204,15 @@ def from_config( Returns: Configured LLMManager instance - - Raises: - ConfigurationError: If configuration is invalid """ load_dotenv(override=True) - # Get temperature from env if not provided effective_temperature = ( - temperature - if temperature is not None - else float(os.getenv("LLM_TEMPERATURE", "0.7")) + temperature if temperature is not None else float(os.getenv("LLM_TEMPERATURE", "0.7")) ) - # Create instance without calling __init__ instance = object.__new__(cls) - # Build config dict from BenchmarkModelConfig instance.config = { "provider": config.provider, "api_url": config.get_api_url(), @@ -274,39 +223,34 @@ def from_config( "timeout": timeout, "temperature": effective_temperature, "nocache": nocache, - "structured_output": config.structured_output, } - # Validate instance._validate_config() - # Initialize cache manager cache_path = Path(cache_dir) if not cache_path.is_absolute(): project_root = Path(__file__).parent.parent.parent.parent cache_path = project_root / cache_path instance.cache = CacheManager(str(cache_path)) - # Create provider with rate limiting - provider_config = ProviderConfig( - api_url=instance.config["api_url"], - api_key=instance.config["api_key"], - model=instance.config["model"], - timeout=timeout, - requests_per_minute=int(os.getenv("LLM_RATE_LIMIT_RPM", "0")), - min_request_delay=float(os.getenv("LLM_RATE_LIMIT_DELAY", "0.0")), - rate_limit_retries=int(os.getenv("LLM_RATE_LIMIT_RETRIES", "3")), + rpm = int(os.getenv("LLM_RATE_LIMIT_RPM", "0")) + if rpm <= 0: + rpm = get_default_rate_limit(config.provider) + instance._rate_limiter = RateLimiter( + config=RateLimitConfig( + requests_per_minute=rpm, + min_request_delay=float(os.getenv("LLM_RATE_LIMIT_DELAY", "0.0")), + ) ) - instance.provider = create_provider(config.provider, provider_config) - # Store config values + instance._pydantic_model = get_pydantic_ai_model(instance.config) + instance.model = config.model instance.max_retries = max_retries instance.cache_ttl = 0 instance.nocache = nocache instance.temperature = effective_temperature - instance.max_tokens = None # None = use provider default - instance.structured_output = config.structured_output + instance.max_tokens = None return instance @@ -316,41 +260,21 @@ def provider_name(self) -> str: return self.config.get("provider", "unknown") def _load_config_from_env(self) -> dict[str, Any]: - """ - Load configuration from environment variables (.env file). - - Supports two modes: - 1. LLM_DEFAULT_MODEL: References a benchmark model config (e.g., "azure-gpt4mini") - 2. Legacy mode: Uses LLM_PROVIDER with provider-specific settings - - Returns: - Configuration dictionary - - Raises: - ConfigurationError: If required env vars are missing - """ - # Check if using a benchmark model as default + """Load configuration from environment variables.""" default_model = os.getenv("LLM_DEFAULT_MODEL") if default_model: - # Load benchmark model configs and use the specified one benchmark_models = load_benchmark_models() if default_model not in benchmark_models: - available = ( - ", ".join(benchmark_models.keys()) if benchmark_models else "none" - ) - raise ConfigurationError( - f"LLM_DEFAULT_MODEL '{default_model}' not found. Available: {available}" - ) + available = ", ".join(benchmark_models.keys()) if benchmark_models else "none" + raise ConfigurationError(f"LLM_DEFAULT_MODEL '{default_model}' not found. Available: {available}") config = benchmark_models[default_model] provider = config.provider api_url = config.get_api_url() api_key = config.get_api_key() model = config.model else: - # Legacy mode: use LLM_PROVIDER with provider-specific settings provider = os.getenv("LLM_PROVIDER", "azure") - # Get provider-specific settings if provider == "azure": api_url = os.getenv("LLM_AZURE_API_URL") api_key = os.getenv("LLM_AZURE_API_KEY") @@ -362,23 +286,22 @@ def _load_config_from_env(self) -> dict[str, Any]: elif provider == "anthropic": api_url = "https://api.anthropic.com/v1/messages" api_key = os.getenv("LLM_ANTHROPIC_API_KEY") - model = os.getenv("LLM_ANTHROPIC_MODEL", "claude-3-5-sonnet-20241022") + model = os.getenv("LLM_ANTHROPIC_MODEL", "claude-sonnet-4-20250514") elif provider == "ollama": - api_url = os.getenv( - "LLM_OLLAMA_API_URL", "http://localhost:11434/api/chat" - ) - api_key = None # Ollama doesn't require an API key + api_url = os.getenv("LLM_OLLAMA_API_URL", "http://localhost:11434/api/chat") + api_key = None model = os.getenv("LLM_OLLAMA_MODEL", "llama3.2") + elif provider == "mistral": + api_url = "https://api.mistral.ai/v1/chat/completions" + api_key = os.getenv("LLM_MISTRAL_API_KEY") + model = os.getenv("LLM_MISTRAL_MODEL", "mistral-large-latest") elif provider == "lmstudio": - api_url = os.getenv( - "LLM_LMSTUDIO_API_URL", "http://localhost:1234/v1/chat/completions" - ) - api_key = None # LM Studio doesn't require an API key (local) + api_url = os.getenv("LLM_LMSTUDIO_API_URL", "http://localhost:1234/v1/chat/completions") + api_key = None model = os.getenv("LLM_LMSTUDIO_MODEL", "local-model") else: raise ConfigurationError(f"Unknown LLM provider: {provider}") - # Parse max_tokens - None if not set or empty max_tokens_str = os.getenv("LLM_MAX_TOKENS", "") max_tokens = int(max_tokens_str) if max_tokens_str else None @@ -392,103 +315,27 @@ def _load_config_from_env(self) -> dict[str, Any]: "max_retries": int(os.getenv("LLM_MAX_RETRIES", "3")), "timeout": int(os.getenv("LLM_TIMEOUT", "60")), "temperature": float(os.getenv("LLM_TEMPERATURE", "0.7")), - "max_tokens": max_tokens, # None = use provider default + "max_tokens": max_tokens, "nocache": os.getenv("LLM_NOCACHE", "false").strip("'\"").lower() == "true", - # Rate limiting configuration "requests_per_minute": int(os.getenv("LLM_RATE_LIMIT_RPM", "0")), "min_request_delay": float(os.getenv("LLM_RATE_LIMIT_DELAY", "0.0")), - "rate_limit_retries": int(os.getenv("LLM_RATE_LIMIT_RETRIES", "3")), } def _validate_config(self) -> None: - """ - Validate configuration has required fields. - - Raises: - ConfigurationError: If required fields are missing - """ - # Ollama, LM Studio, and ClaudeCode don't require api_key - if self.config["provider"] in ("ollama", "lmstudio", "claudecode"): - required_fields = ["provider", "api_url", "model"] + """Validate configuration has required fields.""" + provider = self.config.get("provider", "") + if provider not in VALID_PROVIDERS: + raise ConfigurationError(f"Invalid provider: {provider}. Must be one of {VALID_PROVIDERS}") + + # Ollama and LM Studio don't require api_key + if provider in ("ollama", "lmstudio"): + required_fields = ["provider", "model"] else: - required_fields = ["provider", "api_url", "api_key", "model"] + required_fields = ["provider", "api_key", "model"] missing = [f for f in required_fields if not self.config.get(f)] - if missing: - raise ConfigurationError( - f"Missing required config fields: {', '.join(missing)}" - ) - - def _validate_prompt(self, prompt: str) -> None: - """ - Validate prompt input. - - Args: - prompt: The prompt to validate - - Raises: - ValidationError: If prompt is invalid - """ - if not prompt or not isinstance(prompt, str): - raise ValidationError("Prompt must be a non-empty string") - - if len(prompt.strip()) == 0: - raise ValidationError("Prompt cannot be empty or whitespace only") - - def _build_messages( - self, - prompt: str, - system_prompt: str | None = None, - ) -> list[dict[str, str]]: - """ - Build message list for the provider. - - Args: - prompt: The user prompt - system_prompt: Optional system prompt - - Returns: - List of message dictionaries - """ - messages = [] - if system_prompt: - messages.append({"role": "system", "content": system_prompt}) - messages.append({"role": "user", "content": prompt}) - return messages - - def _augment_prompt_for_schema( - self, - prompt: str, - schema: dict[str, Any] | None = None, - response_model: type[BaseModel] | None = None, - ) -> str: - """ - Augment prompt with schema instructions. - - Args: - prompt: Original prompt - schema: Raw JSON schema dict - response_model: Pydantic model for structured output - - Returns: - Augmented prompt with schema instructions - """ - if response_model: - schema = response_model.model_json_schema() - - if not schema: - return prompt - - schema_str = json.dumps(schema, indent=2) - return f"""{prompt} - -Respond with a valid JSON object matching this schema: -```json -{schema_str} -``` - -Return only the JSON object, no additional text.""" + raise ConfigurationError(f"Missing required config fields: {', '.join(missing)}") @overload def query( @@ -541,252 +388,126 @@ def query( max_tokens: Maximum tokens in response use_cache: Whether to use caching (default: True) system_prompt: Optional system prompt - bench_hash: Optional benchmark hash (e.g., "repo:model:run") for per-run - cache isolation. Off by default. When set, allows resuming - failed benchmark runs with cache enabled. + bench_hash: Optional benchmark hash for per-run cache isolation Returns: If response_model is provided: Validated Pydantic model instance or FailedResponse Otherwise: LiveResponse, CachedResponse, or FailedResponse """ - # Use configured values if not explicitly provided - effective_temperature = ( - temperature if temperature is not None else self.temperature - ) + effective_temperature = temperature if temperature is not None else self.temperature effective_max_tokens = max_tokens if max_tokens is not None else self.max_tokens - # Determine if we need JSON mode - json_mode = schema is not None or response_model is not None - - # Augment prompt with schema if needed - effective_prompt = self._augment_prompt_for_schema( - prompt, schema, response_model - ) - # Generate cache key cache_key = CacheManager.generate_cache_key( - effective_prompt, + prompt, self.model, response_model.model_json_schema() if response_model else schema, bench_hash=bench_hash, ) - # Determine cache behavior: - # - read_cache: Whether to read from cache (disabled by nocache) - # - write_cache: Whether to write to cache (always enabled unless use_cache=False) read_cache = use_cache and not self.nocache - write_cache = use_cache # Always write to cache if use_cache is True + write_cache = use_cache try: - self._validate_prompt(prompt) + # Validate prompt + if not prompt or not isinstance(prompt, str) or len(prompt.strip()) == 0: + raise ValidationError("Prompt must be a non-empty string") - # Check cache (only if reading is enabled) - # NOTE: We skip cached errors to allow retries on transient failures + # Check cache if read_cache: cached = self.cache.get(cache_key) if cached and not cached.get("is_error"): content = cached["content"] - - # Skip cached responses with empty content (invalid) - if not content or not content.strip(): - # Invalid cached response, fall through to make fresh API call - pass - # If response_model, parse and return the model - elif response_model: - try: - return response_model.model_validate_json(content) - except PydanticValidationError: - # Cached content doesn't match model, invalidate and retry - pass - else: - return CachedResponse( - prompt=cached["prompt"], - model=cached["model"], - content=content, - cache_key=cached["cache_key"], - cached_at=cached["cached_at"], - ) - - # Build initial messages (keep original for retry efficiency) - messages = self._build_messages(effective_prompt, system_prompt) - original_messages = messages.copy() - - # Attempt API call with retries - last_error = None - - for attempt in range(self.max_retries): - try: - # Only pass json_schema if structured_output is enabled - effective_schema = schema if self.structured_output else None - result = self.provider.complete( - messages=messages, - temperature=effective_temperature, - max_tokens=effective_max_tokens, - json_mode=json_mode, - json_schema=effective_schema, - ) - - content = result.content - - # Validate JSON if schema provided - if json_mode: - # Strip markdown code blocks (some providers wrap JSON in ```json...```) - # Skip if provider already stripped it (e.g., ClaudeCode) - if not result.markdown_stripped: - content = _strip_markdown_json(content) - - # Validate content is non-empty before parsing - if not content or not content.strip(): - if attempt < self.max_retries - 1: - last_error = "LLM returned empty content" - # Append retry hint as conversation turn (saves tokens vs rebuilding prompt) - messages = original_messages + [ - {"role": "assistant", "content": content or ""}, - { - "role": "user", - "content": f"Error: {last_error}. Please provide valid JSON.", - }, - ] - continue - raise ValidationError( - "LLM returned empty content after all retries" + if content and content.strip(): + if response_model: + try: + return response_model.model_validate_json(content) + except Exception: + pass # Cache miss, continue to API call + else: + return CachedResponse( + prompt=cached["prompt"], + model=cached["model"], + content=content, + cache_key=cached["cache_key"], + cached_at=cached["cached_at"], ) - try: - parsed = json.loads(content) - - # If response_model, validate and return - if response_model: - try: - validated = response_model.model_validate(parsed) - - # Cache the raw content - if write_cache: - self.cache.set_response( - cache_key, - content, - prompt, - self.model, - result.usage, - ) - - return validated - - except PydanticValidationError as e: - if attempt < self.max_retries - 1: - last_error = f"Validation error: {e}" - # Append retry hint as conversation turn (saves tokens) - messages = original_messages + [ - {"role": "assistant", "content": content}, - { - "role": "user", - "content": f"Error: {last_error}. Fix the response.", - }, - ] - continue - raise ValidationError( - f"Failed to validate response after {self.max_retries} attempts: {e}" - ) - - except json.JSONDecodeError as e: - if attempt < self.max_retries - 1: - last_error = f"Invalid JSON: {e}" - # Append retry hint as conversation turn (saves tokens) - messages = original_messages + [ - {"role": "assistant", "content": content}, - { - "role": "user", - "content": f"Error: {last_error}. Return valid JSON.", - }, - ] - continue - raise ValidationError( - f"Failed to generate valid JSON after {self.max_retries} attempts: {e}" - ) + # Rate limit + self._rate_limiter.wait_if_needed() - # Success! Cache the response - if write_cache: - self.cache.set_response( - cache_key, content, prompt, self.model, result.usage - ) - - return LiveResponse( - prompt=prompt, - model=self.model, - content=content, - usage=result.usage, - finish_reason=result.finish_reason, - ) - - except ProviderError as e: - last_error = str(e) - if attempt < self.max_retries - 1: - continue - raise APIError(str(e)) from e - - raise APIError( - f"Failed after {self.max_retries} attempts. Last error: {last_error}" + # Create PydanticAI agent + output_type = response_model if response_model else str + agent: Agent[None, Any] = Agent( + model=self._pydantic_model, + output_type=output_type, + system_prompt=system_prompt or "", + retries=self.max_retries, ) - except (ValidationError, APIError) as e: - # NOTE: Errors are NOT cached to allow retries on transient failures - logger.warning(f"LLM query failed with {type(e).__name__}: {e}") + # Run query + result = agent.run_sync( + prompt, + model_settings={ + "temperature": effective_temperature, + "max_tokens": effective_max_tokens, + }, + ) + + self._rate_limiter.record_success() + + # Extract usage + usage = None + if hasattr(result, "usage") and result.usage: + usage = { + "prompt_tokens": getattr(result.usage, "request_tokens", 0) or 0, + "completion_tokens": getattr(result.usage, "response_tokens", 0) or 0, + "total_tokens": getattr(result.usage, "total_tokens", 0) or 0, + } + + # Handle response + if response_model: + # Cache the serialized model + if write_cache: + content = result.output.model_dump_json() if hasattr(result.output, "model_dump_json") else str(result.output) + self.cache.set_response(cache_key, content, prompt, self.model, usage) + return result.output + else: + content = str(result.output) if result.output else "" + if write_cache: + self.cache.set_response(cache_key, content, prompt, self.model, usage) + return LiveResponse( + prompt=prompt, + model=self.model, + content=content, + usage=usage, + finish_reason="stop", + ) + + except ValidationError as e: + logger.warning(f"LLM query failed with ValidationError: {e}") return FailedResponse( prompt=prompt, model=self.model, error=str(e), - error_type=type(e).__name__, + error_type="ValidationError", ) except Exception as e: - # NOTE: Errors are NOT cached to allow retries on transient failures + logger.warning(f"LLM query failed with {type(e).__name__}: {e}") return FailedResponse( prompt=prompt, model=self.model, - error=f"Unexpected error: {e}", - error_type="UnexpectedError", + error=str(e), + error_type=type(e).__name__, ) - def _cache_error( - self, cache_key: str, prompt: str, error: str, error_type: str - ) -> None: - """ - Cache an error response to prevent retrying failed prompts. - - Args: - cache_key: The cache key - prompt: The original prompt - error: Error message - error_type: Type of error - """ - error_data = { - "prompt": prompt, - "model": self.model, - "error": error, - "error_type": error_type, - "is_error": True, - "cached_at": datetime.now(UTC).isoformat().replace("+00:00", "Z"), - "cache_key": cache_key, - } - - cache_file = self.cache.cache_dir / f"{cache_key}.json" - try: - with open(cache_file, "w", encoding="utf-8") as f: - json.dump(error_data, f, indent=2) - except Exception as e: - logger.warning("Failed to cache error response: %s", e) - def clear_cache(self) -> None: """Clear all cached responses.""" self.cache.clear_all() def get_cache_stats(self) -> dict[str, Any]: - """ - Get cache statistics. - - Returns: - Dictionary with cache statistics - """ + """Get cache statistics.""" return self.cache.get_cache_stats() def get_token_usage_stats(self) -> dict[str, Any]: @@ -794,19 +515,14 @@ def get_token_usage_stats(self) -> dict[str, Any]: Aggregate token usage statistics from all cached entries. Returns: - Dictionary with: - - total_prompt_tokens: Total input tokens across all cached calls - - total_completion_tokens: Total output tokens across all cached calls - - total_tokens: Sum of prompt + completion tokens - - total_calls: Number of cached LLM calls - - avg_prompt_tokens: Average input tokens per call - - avg_completion_tokens: Average output tokens per call + Dictionary with token usage statistics """ + import json + total_prompt = 0 total_completion = 0 total_calls = 0 - # Scan cached files for usage data for cache_file in self.cache.cache_dir.glob("*.json"): try: with open(cache_file, encoding="utf-8") as f: @@ -825,11 +541,9 @@ def get_token_usage_stats(self) -> dict[str, Any]: "total_tokens": total_prompt + total_completion, "total_calls": total_calls, "avg_prompt_tokens": total_prompt / total_calls if total_calls else 0, - "avg_completion_tokens": total_completion / total_calls - if total_calls - else 0, + "avg_completion_tokens": total_completion / total_calls if total_calls else 0, } def __repr__(self) -> str: """String representation.""" - return f"LLMManager(provider={self.provider.name}, model={self.model})" + return f"LLMManager(provider={self.provider_name}, model={self.model})" diff --git a/deriva/adapters/llm/model_registry.py b/deriva/adapters/llm/model_registry.py new file mode 100644 index 0000000..84c11b2 --- /dev/null +++ b/deriva/adapters/llm/model_registry.py @@ -0,0 +1,98 @@ +""" +Model registry for PydanticAI provider configuration. + +Maps Deriva environment config to PydanticAI model identifiers. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from pydantic_ai.models import Model + +# Valid provider names +VALID_PROVIDERS = frozenset({"azure", "openai", "anthropic", "ollama", "mistral", "lmstudio"}) + + +def get_pydantic_ai_model(config: dict[str, Any]) -> "Model | str": + """ + Convert Deriva config to PydanticAI model. + + Args: + config: Dict with provider, model, api_url, api_key + + Returns: + PydanticAI model string or Model instance + + Raises: + ValueError: If provider is unknown + """ + provider = config.get("provider", "").lower() + model = config.get("model", "") + api_url = config.get("api_url") + api_key = config.get("api_key") + + if provider == "openai": + return f"openai:{model}" + + elif provider == "anthropic": + return f"anthropic:{model}" + + elif provider == "mistral": + return f"mistral:{model}" + + elif provider == "ollama": + return f"ollama:{model}" + + elif provider == "azure": + # Azure uses OpenAI-compatible API with custom endpoint via AzureProvider + from pydantic_ai.models.openai import OpenAIChatModel + from pydantic_ai.providers.azure import AzureProvider + + azure_endpoint = _normalize_azure_url(api_url) if api_url else None + azure_provider = AzureProvider( + azure_endpoint=azure_endpoint, + api_key=api_key, + api_version="2024-06-01", + ) + return OpenAIChatModel(model, provider=azure_provider) + + elif provider == "lmstudio": + # LM Studio uses OpenAI-compatible API via OpenAIProvider with custom base_url + from pydantic_ai.models.openai import OpenAIChatModel + from pydantic_ai.providers.openai import OpenAIProvider + + base_url = _normalize_openai_url(api_url) if api_url else "http://localhost:1234/v1" + openai_provider = OpenAIProvider(base_url=base_url) + return OpenAIChatModel(model, provider=openai_provider) + + else: + raise ValueError(f"Unknown provider: {provider}. Valid providers: {VALID_PROVIDERS}") + + +def _normalize_azure_url(url: str) -> str: + """Normalize Azure OpenAI URL to endpoint format for PydanticAI.""" + # Azure URLs should be the endpoint without /chat/completions + url = url.rstrip("/") + if url.endswith("/chat/completions"): + url = url[: -len("/chat/completions")] + # Also remove /openai/deployments/... if present + if "/openai/deployments/" in url: + idx = url.index("/openai/deployments/") + url = url[:idx] + return url + + +def _normalize_openai_url(url: str) -> str: + """Normalize OpenAI-compatible URL to base URL format.""" + url = url.rstrip("/") + if url.endswith("/chat/completions"): + url = url[: -len("/chat/completions")] + # Ensure /v1 suffix for OpenAI-compatible APIs + if not url.endswith("/v1"): + if not url.endswith("/"): + url = url + "/v1" + else: + url = url + "v1" + return url diff --git a/deriva/adapters/llm/models.py b/deriva/adapters/llm/models.py index 8037f02..ad96d96 100644 --- a/deriva/adapters/llm/models.py +++ b/deriva/adapters/llm/models.py @@ -9,15 +9,16 @@ from enum import Enum from typing import Any, Literal +from pydantic import BaseModel + # Re-export exceptions for backwards compatibility from deriva.common.exceptions import APIError as APIError from deriva.common.exceptions import CacheError as CacheError from deriva.common.exceptions import ConfigurationError as ConfigurationError from deriva.common.exceptions import LLMError as LLMError from deriva.common.exceptions import ValidationError as ValidationError -from pydantic import BaseModel -from deriva.adapters.llm.providers import VALID_PROVIDERS +from .model_registry import VALID_PROVIDERS __all__ = [ # Exceptions (re-exported) @@ -34,7 +35,6 @@ "CachedResponse", "FailedResponse", "LLMResponse", - "StructuredOutputMixin", ] @@ -48,17 +48,13 @@ class BenchmarkModelConfig: """ Configuration for a specific LLM model used in benchmarking. - This allows explicit configuration of models rather than relying on - environment variables, enabling multi-model comparison in benchmarks. - Attributes: name: Friendly name for the model config (e.g., "azure-gpt4") - provider: Provider type: azure, openai, anthropic, ollama - model: Model identifier (e.g., "gpt-4", "claude-3-5-sonnet-20241022") + provider: Provider type: azure, openai, anthropic, ollama, mistral, lmstudio + model: Model identifier (e.g., "gpt-4", "claude-sonnet-4-20250514") api_url: API endpoint URL (optional, uses provider default if not set) api_key: API key (optional, reads from api_key_env if not set) api_key_env: Environment variable name for API key - structured_output: Enable JSON schema enforcement at API level (default: False) """ name: str @@ -67,14 +63,11 @@ class BenchmarkModelConfig: api_url: str | None = None api_key: str | None = None api_key_env: str | None = None - structured_output: bool = False def __post_init__(self): """Validate provider.""" if self.provider not in VALID_PROVIDERS: - raise ValueError( - f"Invalid provider: {self.provider}. Must be one of {VALID_PROVIDERS}" - ) + raise ValueError(f"Invalid provider: {self.provider}. Must be one of {VALID_PROVIDERS}") def get_api_key(self) -> str | None: """Get API key from direct value or environment variable.""" @@ -89,12 +82,12 @@ def get_api_url(self) -> str: if self.api_url: return self.api_url - # Provider defaults defaults = { "openai": "https://api.openai.com/v1/chat/completions", "anthropic": "https://api.anthropic.com/v1/messages", "ollama": "http://localhost:11434/api/chat", "lmstudio": "http://localhost:1234/v1/chat/completions", + "mistral": "https://api.mistral.ai/v1/chat/completions", } return defaults.get(self.provider, "") @@ -122,9 +115,7 @@ class LiveResponse(BaseResponse): response_type: Literal[ResponseType.LIVE] = ResponseType.LIVE content: str - usage: dict[str, Any] | None = ( - None # Can contain nested dicts (e.g., completion_tokens_details) - ) + usage: dict[str, Any] | None = None finish_reason: str | None = None @@ -147,91 +138,3 @@ class FailedResponse(BaseResponse): # Type alias for any response type LLMResponse = LiveResponse | CachedResponse | FailedResponse - - -# ============================================================================= -# Structured Output Support -# ============================================================================= - - -class StructuredOutputMixin(BaseModel): - """ - Mixin for models used as structured output schemas. - - Inherit from this to use your model with llm_manager.query(response_model=YourModel). - - Example: - class BusinessConcept(StructuredOutputMixin): - name: str = Field(description="The concept name") - type: str = Field(description="The concept type") - description: str = Field(description="Brief description") - - result = llm_manager.query( - prompt="Extract business concepts from...", - response_model=BusinessConcept - ) - """ - - model_config = {"extra": "forbid"} - - @classmethod - def to_prompt_schema(cls) -> str: - """ - Generate a prompt-friendly schema description. - - Returns: - String description of the expected JSON structure - """ - schema = cls.model_json_schema() - return _format_schema_for_prompt(schema) - - -def _format_schema_for_prompt(schema: dict[str, Any], indent: int = 0) -> str: - """ - Format a JSON schema into a readable prompt description. - - Args: - schema: JSON schema dict - indent: Current indentation level - - Returns: - Formatted string description - """ - lines = [] - prefix = " " * indent - - if "properties" in schema: - lines.append(f"{prefix}{{") - props = schema["properties"] - required = set(schema.get("required", [])) - - for name, prop in props.items(): - prop_type = prop.get("type", "any") - description = prop.get("description", "") - req_marker = " (required)" if name in required else " (optional)" - - # Handle nested objects - if prop_type == "object" and "properties" in prop: - lines.append(f'{prefix} "{name}": {req_marker}') - lines.append(_format_schema_for_prompt(prop, indent + 2)) - # Handle arrays - elif prop_type == "array": - items = prop.get("items", {}) - items_type = items.get("type", "any") - lines.append( - f'{prefix} "{name}": [{items_type}...]{req_marker} - {description}' - ) - # Handle enums - elif "enum" in prop: - enum_vals = ", ".join(f'"{v}"' for v in prop["enum"]) - lines.append( - f'{prefix} "{name}": one of [{enum_vals}]{req_marker} - {description}' - ) - else: - lines.append( - f'{prefix} "{name}": {prop_type}{req_marker} - {description}' - ) - - lines.append(f"{prefix}}}") - - return "\n".join(lines) diff --git a/deriva/adapters/llm/providers.py b/deriva/adapters/llm/providers.py deleted file mode 100644 index addf3cd..0000000 --- a/deriva/adapters/llm/providers.py +++ /dev/null @@ -1,934 +0,0 @@ -""" -LLM Provider abstraction layer. - -Defines a Protocol for LLM providers and implementations for: -- Azure OpenAI -- OpenAI -- Anthropic -- Ollama -- Claude Code (via Agent SDK CLI) -""" - -from __future__ import annotations - -import json -import subprocess -import sys -from abc import ABC, abstractmethod -from dataclasses import dataclass -from typing import Any, Protocol, runtime_checkable - -import requests - -from deriva.common.exceptions import ProviderError as ProviderError -from .rate_limiter import ( - RateLimitConfig, - RateLimiter, - get_default_rate_limit, - parse_retry_after, -) - -# Valid provider names - shared between providers and benchmark models -VALID_PROVIDERS = frozenset( - {"azure", "openai", "anthropic", "ollama", "claudecode", "mistral", "lmstudio"} -) - - -def _add_additional_properties_false(schema: dict[str, Any]) -> dict[str, Any]: - """ - Recursively add additionalProperties: false to all object types in a JSON schema. - - Anthropic's structured output API requires this for all object types. - - Args: - schema: JSON schema dict - - Returns: - Modified schema with additionalProperties: false on all objects - """ - if not isinstance(schema, dict): - return schema - - result = schema.copy() - - # If this is an object type, add additionalProperties: false - if result.get("type") == "object": - result["additionalProperties"] = False - - # Recursively process properties - if "properties" in result: - result["properties"] = { - k: _add_additional_properties_false(v) - for k, v in result["properties"].items() - } - - # Recursively process items (for arrays) - if "items" in result: - result["items"] = _add_additional_properties_false(result["items"]) - - # Recursively process $defs (Pydantic puts nested models here) - if "$defs" in result: - result["$defs"] = { - k: _add_additional_properties_false(v) for k, v in result["$defs"].items() - } - - # Recursively process anyOf, oneOf, allOf - for key in ("anyOf", "oneOf", "allOf"): - if key in result: - result[key] = [ - _add_additional_properties_false(item) for item in result[key] - ] - - return result - - -__all__ = [ - "VALID_PROVIDERS", - "ProviderConfig", - "CompletionResult", - "ProviderError", - "LLMProvider", - "BaseProvider", - "AzureOpenAIProvider", - "OpenAIProvider", - "AnthropicProvider", - "OllamaProvider", - "ClaudeCodeProvider", - "MistralProvider", - "LMStudioProvider", - "create_provider", -] - - -@dataclass -class ProviderConfig: - """Configuration for an LLM provider.""" - - api_url: str - api_key: str | None - model: str - timeout: int = 60 - # Rate limiting configuration - requests_per_minute: int = 0 # 0 = use provider default - min_request_delay: float = 0.0 # Minimum seconds between requests - rate_limit_retries: int = 3 # Max retries on rate limit (429) errors - - -@dataclass -class CompletionResult: - """Raw result from a provider completion call.""" - - content: str - usage: dict[str, int] | None = None - finish_reason: str | None = None - raw_response: dict[str, Any] | None = None - markdown_stripped: bool = False # True if provider already stripped markdown - - -@runtime_checkable -class LLMProvider(Protocol): - """Protocol defining the interface for LLM providers.""" - - @property - def name(self) -> str: - """Provider name identifier.""" - ... - - def complete( - self, - messages: list[dict[str, str]], - temperature: float = 0.7, - max_tokens: int | None = None, - json_mode: bool = False, - json_schema: dict[str, Any] | None = None, - ) -> CompletionResult: - """ - Send a completion request to the provider. - - Args: - messages: List of message dicts with 'role' and 'content' - temperature: Sampling temperature (0-2) - max_tokens: Maximum tokens in response - json_mode: Whether to request JSON output - json_schema: Optional JSON schema for structured output (OpenAI only) - - Returns: - CompletionResult with content and metadata - - Raises: - ProviderError: If the API call fails - """ - ... - - -class BaseProvider(ABC): - """Base class for LLM providers with common functionality.""" - - def __init__(self, config: ProviderConfig): - self.config = config - self._rate_limiter = self._create_rate_limiter() - - def _create_rate_limiter(self) -> RateLimiter: - """Create rate limiter with provider-specific defaults.""" - # Use configured RPM or fall back to provider default - rpm = self.config.requests_per_minute - if rpm <= 0: - rpm = get_default_rate_limit(self.name) - - rate_config = RateLimitConfig( - requests_per_minute=rpm, - min_request_delay=self.config.min_request_delay, - ) - return RateLimiter(config=rate_config) - - @property - @abstractmethod - def name(self) -> str: - """Provider name identifier.""" - ... - - @abstractmethod - def complete( - self, - messages: list[dict[str, str]], - temperature: float = 0.7, - max_tokens: int | None = None, - json_mode: bool = False, - json_schema: dict[str, Any] | None = None, - ) -> CompletionResult: - """Send a completion request.""" - ... - - def _make_request( - self, headers: dict[str, str], body: dict[str, Any] - ) -> dict[str, Any]: - """Make HTTP request to provider API with rate limiting.""" - import time - - last_error = None - max_retries = self.config.rate_limit_retries - - for attempt in range(max_retries + 1): - # Wait for rate limit if needed - self._rate_limiter.wait_if_needed() - - try: - response = requests.post( - self.config.api_url, - headers=headers, - json=body, - timeout=self.config.timeout, - ) - - # Handle rate limit response (429) - if response.status_code == 429: - # Extract retry-after from headers if available - retry_after = parse_retry_after(dict(response.headers)) - backoff = self._rate_limiter.record_rate_limit(retry_after) - if attempt < max_retries: - time.sleep(backoff) - continue - raise ProviderError( - f"{self.name} rate limited after {max_retries + 1} attempts" - ) - - response.raise_for_status() - self._rate_limiter.record_success() - return response.json() - - except requests.exceptions.Timeout as e: - last_error = e - # Timeout might be due to overload - apply backoff - if attempt < max_retries: - backoff = self._rate_limiter.record_rate_limit() - time.sleep(backoff) - continue - raise ProviderError(f"{self.name} API request timed out") from e - - except requests.exceptions.RequestException as e: - # Check if this is a rate limit error from the response - if hasattr(e, "response") and e.response is not None: - if e.response.status_code == 429: - # Extract retry-after from headers if available - retry_after = parse_retry_after(dict(e.response.headers)) - backoff = self._rate_limiter.record_rate_limit(retry_after) - if attempt < max_retries: - time.sleep(backoff) - continue - # Log full error response for debugging - try: - import logging - - error_body = e.response.text[:500] - logging.getLogger(__name__).error( - f"{self.name} API error response: {error_body}" - ) - except Exception: - pass - raise ProviderError(f"{self.name} API request failed: {e}") from e - - except json.JSONDecodeError as e: - raise ProviderError(f"{self.name} returned invalid JSON") from e - - raise ProviderError( - f"{self.name} failed after {max_retries + 1} attempts: {last_error}" - ) - - -class AzureOpenAIProvider(BaseProvider): - """Azure OpenAI provider implementation.""" - - @property - def name(self) -> str: - return "azure" - - def complete( - self, - messages: list[dict[str, str]], - temperature: float = 0.7, - max_tokens: int | None = None, - json_mode: bool = False, - json_schema: dict[str, Any] | None = None, - ) -> CompletionResult: - headers = { - "Content-Type": "application/json", - "api-key": self.config.api_key or "", - } - - body: dict[str, Any] = { - "messages": messages, - "temperature": temperature, - } - - if max_tokens: - body["max_tokens"] = max_tokens - - if json_schema: - # Use structured output with strict schema enforcement - body["response_format"] = { - "type": "json_schema", - "json_schema": json_schema, - } - elif json_mode: - body["response_format"] = {"type": "json_object"} - - response = self._make_request(headers, body) - - try: - content = response["choices"][0]["message"]["content"] - usage = response.get("usage") - finish_reason = response["choices"][0].get("finish_reason") - return CompletionResult( - content=content, - usage=usage, - finish_reason=finish_reason, - raw_response=response, - ) - except (KeyError, IndexError) as e: - raise ProviderError(f"Unexpected Azure response format: {e}") from e - - -class OpenAIProvider(BaseProvider): - """OpenAI provider implementation.""" - - @property - def name(self) -> str: - return "openai" - - def complete( - self, - messages: list[dict[str, str]], - temperature: float = 0.7, - max_tokens: int | None = None, - json_mode: bool = False, - json_schema: dict[str, Any] | None = None, - ) -> CompletionResult: - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {self.config.api_key}", - } - - body: dict[str, Any] = { - "model": self.config.model, - "messages": messages, - "temperature": temperature, - } - - if max_tokens: - # GPT-5+ models require max_completion_tokens instead of max_tokens - if self.config.model.startswith("gpt-5"): - body["max_completion_tokens"] = max_tokens - else: - body["max_tokens"] = max_tokens - - if json_schema: - # Use structured output with strict schema enforcement - # Handle both wrapped format (with name/schema) and raw JSON schema - raw_schema = json_schema.get("schema", json_schema) - # OpenAI requires additionalProperties: false on all object types - fixed_schema = _add_additional_properties_false(raw_schema) - body["response_format"] = { - "type": "json_schema", - "json_schema": { - "name": json_schema.get("name", "response"), - "strict": json_schema.get("strict", True), - "schema": fixed_schema, - }, - } - elif json_mode: - body["response_format"] = {"type": "json_object"} - - response = self._make_request(headers, body) - - try: - content = response["choices"][0]["message"].get("content", "") - if not content or not content.strip(): - raise ProviderError("OpenAI returned empty content") - - usage = response.get("usage") - finish_reason = response["choices"][0].get("finish_reason") - return CompletionResult( - content=content, - usage=usage, - finish_reason=finish_reason, - raw_response=response, - ) - except (KeyError, IndexError) as e: - raise ProviderError(f"Unexpected OpenAI response format: {e}") from e - - -class AnthropicProvider(BaseProvider): - """Anthropic Claude provider implementation.""" - - @property - def name(self) -> str: - return "anthropic" - - def complete( - self, - messages: list[dict[str, str]], - temperature: float = 0.7, - max_tokens: int | None = None, - json_mode: bool = False, - json_schema: dict[str, Any] | None = None, - ) -> CompletionResult: - headers = { - "Content-Type": "application/json", - "x-api-key": self.config.api_key or "", - "anthropic-version": "2023-06-01", - } - - # Add beta header for structured outputs if schema provided - if json_schema: - headers["anthropic-beta"] = "structured-outputs-2025-11-13" - - # Anthropic uses a different message format - extract system message - system_message = None - user_messages = [] - for msg in messages: - if msg["role"] == "system": - system_message = msg["content"] - else: - user_messages.append(msg) - - body: dict[str, Any] = { - "model": self.config.model, - "messages": user_messages, - "temperature": temperature, - "max_tokens": max_tokens or 4096, # Anthropic requires max_tokens - } - - if system_message: - body["system"] = system_message - - # Anthropic uses output_format (not response_format) - if json_schema: - # Extract raw schema (handle both wrapped and raw formats) - raw_schema = json_schema.get("schema", json_schema) - # Anthropic requires additionalProperties: false on all object types - fixed_schema = _add_additional_properties_false(raw_schema) - body["output_format"] = { - "type": "json_schema", - "schema": fixed_schema, - } - - response = self._make_request(headers, body) - - try: - # Anthropic returns content as a list of content blocks - content_blocks = response.get("content", []) - if not content_blocks: - raise ProviderError("Anthropic response has no content blocks") - - content = "" - for block in content_blocks: - block_type = block.get("type") - if block_type == "text": - content += block.get("text", "") - elif block_type == "json": - # When using output_format with json_schema, content may be in json block - import json - - json_content = block.get("json") - if json_content is not None: - content += ( - json.dumps(json_content) - if isinstance(json_content, dict) - else str(json_content) - ) - - if not content.strip(): - raise ProviderError("Anthropic response has no text content") - - usage = response.get("usage") - # Map Anthropic usage format to standard format - if usage: - usage = { - "prompt_tokens": usage.get("input_tokens", 0), - "completion_tokens": usage.get("output_tokens", 0), - "total_tokens": usage.get("input_tokens", 0) - + usage.get("output_tokens", 0), - } - - finish_reason = response.get("stop_reason") - return CompletionResult( - content=content, - usage=usage, - finish_reason=finish_reason, - raw_response=response, - ) - except (KeyError, IndexError) as e: - raise ProviderError(f"Unexpected Anthropic response format: {e}") from e - - -class OllamaProvider(BaseProvider): - """Ollama local LLM provider implementation.""" - - @property - def name(self) -> str: - return "ollama" - - def complete( - self, - messages: list[dict[str, str]], - temperature: float = 0.7, - max_tokens: int | None = None, - json_mode: bool = False, - json_schema: dict[str, Any] | None = None, - ) -> CompletionResult: - headers = { - "Content-Type": "application/json", - } - - body: dict[str, Any] = { - "model": self.config.model, - "messages": messages, - "stream": False, - "options": { - "temperature": temperature, - }, - } - - if max_tokens: - body["options"]["num_predict"] = max_tokens - - if json_schema: - # Ollama supports structured output via format parameter - # Pass the schema directly (or extract from nested structure) - schema = json_schema.get("schema", json_schema) - body["format"] = schema - elif json_mode: - body["format"] = "json" - - response = self._make_request(headers, body) - - try: - content = response["message"]["content"] - # Ollama provides different usage metrics - usage = None - if "eval_count" in response or "prompt_eval_count" in response: - usage = { - "prompt_tokens": response.get("prompt_eval_count", 0), - "completion_tokens": response.get("eval_count", 0), - "total_tokens": response.get("prompt_eval_count", 0) - + response.get("eval_count", 0), - } - - finish_reason = "stop" if response.get("done") else None - return CompletionResult( - content=content, - usage=usage, - finish_reason=finish_reason, - raw_response=response, - ) - except (KeyError, IndexError) as e: - raise ProviderError(f"Unexpected Ollama response format: {e}") from e - - -class LMStudioProvider(BaseProvider): - """LM Studio local LLM provider implementation (OpenAI-compatible API). - - LM Studio provides an OpenAI-compatible REST API at localhost:1234. - No API key required - runs locally. - """ - - @property - def name(self) -> str: - return "lmstudio" - - def complete( - self, - messages: list[dict[str, str]], - temperature: float = 0.7, - max_tokens: int | None = None, - json_mode: bool = False, - json_schema: dict[str, Any] | None = None, - ) -> CompletionResult: - headers = { - "Content-Type": "application/json", - } - # LMStudio uses json_schema format - - body: dict[str, Any] = { - "model": self.config.model, - "messages": messages, - "temperature": temperature, - } - - if max_tokens: - body["max_tokens"] = max_tokens - - if json_schema: - # Use structured output with strict schema enforcement - body["response_format"] = { - "type": "json_schema", - "json_schema": { - "name": json_schema.get("name", "response"), - "strict": json_schema.get("strict", True), - "schema": json_schema.get("schema", json_schema), - }, - } - elif json_mode: - # LM Studio uses json_schema format (not json_object like OpenAI) - body["response_format"] = { - "type": "json_schema", - "json_schema": { - "name": "response", - "strict": False, - "schema": {"type": "object"}, - }, - } - - response = self._make_request(headers, body) - - try: - content = response["choices"][0]["message"]["content"] - usage = response.get("usage") - finish_reason = response["choices"][0].get("finish_reason") - return CompletionResult( - content=content, - usage=usage, - finish_reason=finish_reason, - raw_response=response, - ) - except (KeyError, IndexError) as e: - raise ProviderError(f"Unexpected LM Studio response format: {e}") from e - - -class MistralProvider(BaseProvider): - """Mistral AI provider implementation (OpenAI-compatible API).""" - - @property - def name(self) -> str: - return "mistral" - - def complete( - self, - messages: list[dict[str, str]], - temperature: float = 0.7, - max_tokens: int | None = None, - json_mode: bool = False, - json_schema: dict[str, Any] | None = None, - ) -> CompletionResult: - headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {self.config.api_key}", - } - - body: dict[str, Any] = { - "model": self.config.model, - "messages": messages, - "temperature": temperature, - } - - if max_tokens: - body["max_tokens"] = max_tokens - - if json_schema: - # Use structured output with strict schema enforcement - body["response_format"] = { - "type": "json_schema", - "json_schema": { - "name": json_schema.get("name", "response"), - "strict": json_schema.get("strict", True), - "schema": json_schema.get("schema", json_schema), - }, - } - elif json_mode: - body["response_format"] = {"type": "json_object"} - - response = self._make_request(headers, body) - - try: - content = response["choices"][0]["message"]["content"] - usage = response.get("usage") - finish_reason = response["choices"][0].get("finish_reason") - return CompletionResult( - content=content, - usage=usage, - finish_reason=finish_reason, - raw_response=response, - ) - except (KeyError, IndexError) as e: - raise ProviderError(f"Unexpected Mistral response format: {e}") from e - - -class ClaudeCodeProvider(BaseProvider): - """ - Claude Code provider using the Agent SDK CLI. - - This provider uses the `claude` CLI command to send requests, - leveraging the user's authenticated Claude Code session. - No API key required - uses OAuth authentication from Claude Code. - """ - - # Model aliases: CLI accepts short names (opus, sonnet, haiku) - MODEL_ALIASES = { - "opus": "opus", - "sonnet": "sonnet", - "haiku": "haiku", - "claude-opus-4-5-20251101": "opus", - "claude-sonnet-4-5-20250929": "sonnet", - "claude-haiku-4-5-20251001": "haiku", - "claude-sonnet-4-20250514": "sonnet", - "claude-3-5-sonnet-20241022": "sonnet", - "claude-3-5-haiku-20241022": "haiku", - } - - def __init__(self, config: ProviderConfig): - super().__init__(config) - self._cli_verified = False - - def _verify_cli(self) -> None: - """Verify that the claude CLI is available (once per instance).""" - if self._cli_verified: - return - try: - # shell=True needed on Windows where claude is a .cmd script - # encoding='utf-8' for consistent output handling - result = subprocess.run( - ["claude", "--version"], - capture_output=True, - text=True, - encoding="utf-8", - timeout=10, - check=False, - shell=(sys.platform == "win32"), - ) - if result.returncode != 0: - raise ProviderError( - "Claude CLI error. Is Claude Code installed and authenticated?" - ) - self._cli_verified = True - except FileNotFoundError as exc: - raise ProviderError( - "Claude CLI not found. Install Claude Code: https://claude.ai/claude-code" - ) from exc - except subprocess.TimeoutExpired as exc: - raise ProviderError("Claude CLI version check timed out") from exc - - def _resolve_model(self, model: str) -> str: - """Resolve model name to Claude Code CLI model flag.""" - return self.MODEL_ALIASES.get(model.lower(), model) - - def _format_prompt(self, messages: list[dict[str, str]]) -> str: - """Convert chat messages to a single prompt string for the CLI.""" - parts = [] - for msg in messages: - role = msg.get("role", "user") - content = msg.get("content", "") - if role == "system": - parts.append(f"\n{content}\n\n") - elif role == "assistant": - parts.append(f"\n{content}\n\n") - else: - parts.append(content) - return "\n".join(parts) - - def _strip_markdown_code_block(self, content: str) -> str: - """Strip markdown code block wrappers from content. - - Claude often returns JSON wrapped in ```json ... ``` blocks. - This strips those wrappers to return clean content. - """ - import re - - content = content.strip() - # Match ```json, ```JSON, or just ``` at start, and ``` at end - pattern = r"^```(?:json|JSON)?\s*\n?(.*?)\n?```$" - match = re.match(pattern, content, re.DOTALL) - if match: - content = match.group(1).strip() - - # Claude sometimes returns Python dict syntax with single quotes - # Try to fix simple cases for JSON parsing - if content.startswith("{") and "'" in content: - try: - # Try parsing as-is first - json.loads(content) - except json.JSONDecodeError: - # Convert Python dict syntax to JSON (single → double quotes) - # This handles simple cases like {'key': 'value'} - import ast - - try: - # ast.literal_eval safely parses Python literals - parsed = ast.literal_eval(content) - if isinstance(parsed, dict): - content = json.dumps(parsed) - except (ValueError, SyntaxError): - pass # Keep original if parsing fails - - return content - - @property - def name(self) -> str: - return "claudecode" - - def complete( - self, - messages: list[dict[str, str]], - temperature: float = 0.7, - max_tokens: int | None = None, - json_mode: bool = False, - json_schema: dict[str, Any] | None = None, - ) -> CompletionResult: - self._verify_cli() - # Note: ClaudeCode CLI doesn't support json_schema - - prompt = self._format_prompt(messages) - if json_mode: - prompt += "\n\nRespond with valid JSON only." - - resolved_model = self._resolve_model(self.config.model) - - # Use stdin for prompt to avoid Windows command line length limit - cmd = [ - "claude", - "--model", - resolved_model, - "--output-format", - "json", - ] - if max_tokens: - cmd.extend(["--max-tokens", str(max_tokens)]) - - try: - # shell=True needed on Windows where claude is a .cmd script - # Pass prompt via stdin to avoid command line length limits - # encoding='utf-8' required for Unicode characters on Windows - result = subprocess.run( - cmd, - input=prompt, - capture_output=True, - text=True, - encoding="utf-8", - timeout=self.config.timeout, - check=False, - shell=(sys.platform == "win32"), - ) - - if result.returncode != 0: - error_msg = result.stderr.strip() or result.stdout.strip() - raise ProviderError(f"Claude CLI error: {error_msg}") - - # Parse JSON output - try: - output = json.loads(result.stdout) - except json.JSONDecodeError: - return CompletionResult( - content=result.stdout.strip(), - finish_reason="stop", - ) - - # Extract content from JSON output - content = "" - if isinstance(output, dict): - content = output.get("result", output.get("content", str(output))) - if isinstance(content, dict): - content = content.get("text", json.dumps(content)) - elif isinstance(output, str): - content = output - else: - content = json.dumps(output) - - # Strip markdown code blocks if present (Claude often wraps JSON in ```json ... ```) - content = self._strip_markdown_code_block(content) - - usage = None - if isinstance(output, dict) and ( - output.get("cost_usd") or output.get("duration_ms") - ): - usage = { - "cost_usd": output.get("cost_usd"), - "duration_ms": output.get("duration_ms"), - } - - return CompletionResult( - content=content, - usage=usage, - finish_reason="stop", - raw_response=output if isinstance(output, dict) else None, - markdown_stripped=True, # We already stripped markdown above - ) - - except subprocess.TimeoutExpired as exc: - raise ProviderError( - f"Claude CLI timed out after {self.config.timeout}s" - ) from exc - except ProviderError: - raise - except Exception as e: - raise ProviderError(f"Claude Code request failed: {e}") from e - - -def create_provider(provider_name: str, config: ProviderConfig) -> LLMProvider: - """ - Factory function to create a provider instance. - - Args: - provider_name: Name of the provider (azure, openai, anthropic, ollama) - config: Provider configuration - - Returns: - LLMProvider instance - - Raises: - ValueError: If provider name is unknown - """ - providers: dict[str, type[BaseProvider]] = { - "azure": AzureOpenAIProvider, - "openai": OpenAIProvider, - "anthropic": AnthropicProvider, - "ollama": OllamaProvider, - "lmstudio": LMStudioProvider, - "claudecode": ClaudeCodeProvider, - "mistral": MistralProvider, - } - - provider_class = providers.get(provider_name.lower()) - if not provider_class: - available = ", ".join(providers.keys()) - raise ValueError(f"Unknown provider: {provider_name}. Available: {available}") - - return provider_class(config) diff --git a/pyproject.toml b/pyproject.toml index 3710bdd..13a5975 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "Deriva" -version = "0.6.7" +version = "0.6.8" description = "Deriva is a research project aimed at enabling digital architects by automating the derivation of enterprise architecture models. Deriva uses dual knowledge graphs, heuristics and LLM's." readme = "README.md" requires-python = ">=3.14" @@ -35,9 +35,9 @@ classifiers = [ dependencies = [ # Core "pydantic>=2.12.5", + "pydantic-ai>=0.1.0", "python-dotenv>=1.2.1", "pyyaml>=6.0.3", - "requests>=2.32.5", "rich>=14.2.0", # Database & Graph "duckdb>=1.4.3", @@ -65,7 +65,7 @@ dev = [ "pytest>=9.0.2", "pytest-cov>=7.0.0", "ruff>=0.14.11", - "ty>=0.0.11", + "ty>=0.0.12", "types-lxml>=2025.3.30", ] diff --git a/tests/test_adapters/llm/test_manager.py b/tests/test_adapters/llm/test_manager.py index a035b84..1a6837b 100644 --- a/tests/test_adapters/llm/test_manager.py +++ b/tests/test_adapters/llm/test_manager.py @@ -1,8 +1,7 @@ -"""Tests for LLM Manager.""" +"""Tests for LLM Manager with PydanticAI.""" from __future__ import annotations -import json from unittest.mock import MagicMock, patch import pytest @@ -18,9 +17,9 @@ ConfigurationError, FailedResponse, LiveResponse, - ValidationError, ) + # ============================================================================= # load_benchmark_models() Tests # ============================================================================= @@ -134,40 +133,6 @@ def test_converts_name_to_lowercase_with_hyphens(self, monkeypatch): assert "my-custom-model" in result - def test_loads_structured_output_flag(self, monkeypatch): - """Should load STRUCTURED_OUTPUT flag from env vars.""" - env_vars = { - "LLM_AZURE_GPT4_PROVIDER": "azure", - "LLM_AZURE_GPT4_MODEL": "gpt-4", - "LLM_AZURE_GPT4_URL": "https://example.azure.com", - "LLM_AZURE_GPT4_KEY": "sk-test-key", - "LLM_AZURE_GPT4_STRUCTURED_OUTPUT": "true", - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - result = load_benchmark_models() - - assert "azure-gpt4" in result - assert result["azure-gpt4"].structured_output is True - - def test_structured_output_defaults_to_false(self, monkeypatch): - """Should default structured_output to False when not set.""" - env_vars = { - "LLM_AZURE_GPT4_PROVIDER": "azure", - "LLM_AZURE_GPT4_MODEL": "gpt-4", - "LLM_AZURE_GPT4_URL": "https://example.azure.com", - "LLM_AZURE_GPT4_KEY": "sk-test-key", - # No STRUCTURED_OUTPUT set - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - result = load_benchmark_models() - - assert "azure-gpt4" in result - assert result["azure-gpt4"].structured_output is False - # ============================================================================= # LLMManager.__init__ Tests @@ -207,8 +172,8 @@ def test_init_creates_cache_manager(self, tmp_path, monkeypatch): assert manager.cache is not None - def test_init_creates_provider(self, tmp_path, monkeypatch): - """Should create the appropriate provider.""" + def test_init_creates_pydantic_model(self, tmp_path, monkeypatch): + """Should create PydanticAI model.""" env_vars = { "LLM_PROVIDER": "ollama", "LLM_OLLAMA_MODEL": "llama3", @@ -219,8 +184,7 @@ def test_init_creates_provider(self, tmp_path, monkeypatch): with patch.dict("os.environ", env_vars, clear=True): manager = LLMManager() - assert manager.provider is not None - assert manager.provider.name == "ollama" + assert manager._pydantic_model is not None def test_init_sets_default_values(self, tmp_path, monkeypatch): """Should set default values for optional config.""" @@ -337,23 +301,6 @@ def test_from_config_validates_config(self, tmp_path, monkeypatch): with pytest.raises(ConfigurationError): LLMManager.from_config(config, cache_dir=str(tmp_path / "cache")) - def test_from_config_passes_structured_output_flag(self, tmp_path, monkeypatch): - """Should pass structured_output flag from config to manager.""" - config = BenchmarkModelConfig( - name="test-model", - provider="ollama", - model="llama3", - api_url="http://localhost:11434/api/chat", - structured_output=True, - ) - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", {}, clear=True): - manager = LLMManager.from_config(config, cache_dir=str(tmp_path / "cache")) - - assert manager.structured_output is True - assert manager.config["structured_output"] is True - # ============================================================================= # LLMManager._load_config_from_env Tests @@ -443,6 +390,22 @@ def test_loads_lmstudio_provider_config(self, tmp_path, monkeypatch): assert manager.config["provider"] == "lmstudio" assert "localhost:1234" in manager.config["api_url"] + def test_loads_mistral_provider_config(self, tmp_path, monkeypatch): + """Should load Mistral provider configuration.""" + env_vars = { + "LLM_PROVIDER": "mistral", + "LLM_MISTRAL_API_KEY": "test-key", + "LLM_MISTRAL_MODEL": "mistral-large", + "LLM_CACHE_DIR": str(tmp_path / "cache"), + } + + with patch("deriva.adapters.llm.manager.load_dotenv"): + with patch.dict("os.environ", env_vars, clear=True): + manager = LLMManager() + + assert manager.config["provider"] == "mistral" + assert "mistral.ai" in manager.config["api_url"] + def test_raises_for_unknown_provider(self, tmp_path, monkeypatch): """Should raise ConfigurationError for unknown provider.""" env_vars = { @@ -516,226 +479,7 @@ def test_max_tokens_none_when_not_set(self, tmp_path, monkeypatch): # ============================================================================= -# LLMManager._validate_config Tests -# ============================================================================= - - -class TestValidateConfig: - """Tests for _validate_config method.""" - - def test_passes_for_valid_config(self, tmp_path, monkeypatch): - """Should not raise for valid configuration.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - # Should not raise - manager = LLMManager() - assert manager is not None - - def test_raises_for_missing_api_url(self, tmp_path, monkeypatch): - """Should raise ConfigurationError when api_url is missing.""" - env_vars = { - "LLM_PROVIDER": "azure", - "LLM_AZURE_API_KEY": "test-key", - "LLM_AZURE_MODEL": "gpt-4", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - with pytest.raises(ConfigurationError, match="api_url"): - LLMManager() - - -# ============================================================================= -# LLMManager._validate_prompt Tests -# ============================================================================= - - -class TestValidatePrompt: - """Tests for _validate_prompt method.""" - - def test_raises_for_empty_prompt(self, tmp_path, monkeypatch): - """Should raise ValidationError for empty prompt.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - - with pytest.raises(ValidationError): - manager._validate_prompt("") - - def test_raises_for_whitespace_only_prompt(self, tmp_path, monkeypatch): - """Should raise ValidationError for whitespace-only prompt.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - - with pytest.raises(ValidationError): - manager._validate_prompt(" ") - - def test_raises_for_none_prompt(self, tmp_path, monkeypatch): - """Should raise ValidationError for None prompt.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - - with pytest.raises(ValidationError): - manager._validate_prompt(None) # type: ignore[arg-type] - - def test_passes_for_valid_prompt(self, tmp_path, monkeypatch): - """Should not raise for valid prompt.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - - # Should not raise - manager._validate_prompt("Hello, world!") - - -# ============================================================================= -# LLMManager._build_messages Tests -# ============================================================================= - - -class TestBuildMessages: - """Tests for _build_messages method.""" - - def test_builds_user_message_only(self, tmp_path, monkeypatch): - """Should build message list with user message only.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - - messages = manager._build_messages("Hello") - - assert len(messages) == 1 - assert messages[0]["role"] == "user" - assert messages[0]["content"] == "Hello" - - def test_builds_messages_with_system_prompt(self, tmp_path, monkeypatch): - """Should include system message when provided.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - - messages = manager._build_messages("Hello", system_prompt="You are helpful") - - assert len(messages) == 2 - assert messages[0]["role"] == "system" - assert messages[0]["content"] == "You are helpful" - assert messages[1]["role"] == "user" - - -# ============================================================================= -# LLMManager._augment_prompt_for_schema Tests -# ============================================================================= - - -class TestAugmentPromptForSchema: - """Tests for _augment_prompt_for_schema method.""" - - def test_returns_original_prompt_when_no_schema(self, tmp_path, monkeypatch): - """Should return original prompt when no schema provided.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - - result = manager._augment_prompt_for_schema("Hello") - - assert result == "Hello" - - def test_augments_with_raw_schema(self, tmp_path, monkeypatch): - """Should augment prompt with raw JSON schema.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - - schema = {"type": "object", "properties": {"name": {"type": "string"}}} - result = manager._augment_prompt_for_schema("Hello", schema=schema) - - assert "Hello" in result - assert "JSON" in result - assert '"type": "object"' in result - - def test_augments_with_response_model(self, tmp_path, monkeypatch): - """Should augment prompt with Pydantic model schema.""" - - class TestModel(BaseModel): - name: str - value: int - - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - - result = manager._augment_prompt_for_schema("Hello", response_model=TestModel) - - assert "Hello" in result - assert "JSON" in result - assert "name" in result - - -# ============================================================================= -# LLMManager.query Tests +# LLMManager.query Tests (with PydanticAI mocking) # ============================================================================= @@ -752,16 +496,15 @@ def test_query_returns_live_response(self, tmp_path, monkeypatch): } mock_result = MagicMock() - mock_result.content = "Hello back!" - mock_result.usage = {"total_tokens": 10} - mock_result.finish_reason = "stop" + mock_result.output = "Hello back!" + mock_result.usage = None with patch("deriva.adapters.llm.manager.load_dotenv"): with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - manager.provider.complete = MagicMock(return_value=mock_result) # type: ignore[method-assign] - - response = manager.query("Hello") + with patch("deriva.adapters.llm.manager.Agent") as mock_agent_class: + mock_agent_class.return_value.run_sync.return_value = mock_result + manager = LLMManager() + response = manager.query("Hello") assert isinstance(response, LiveResponse) assert response.content == "Hello back!" @@ -785,7 +528,7 @@ def test_query_returns_cached_response(self, tmp_path, monkeypatch): with patch("deriva.adapters.llm.manager.load_dotenv"): with patch.dict("os.environ", env_vars, clear=True): manager = LLMManager() - manager.cache.get = MagicMock(return_value=cached_data) # type: ignore[method-assign] + manager.cache.get = MagicMock(return_value=cached_data) response = manager.query("Hello") @@ -803,7 +546,6 @@ def test_query_returns_failed_response_on_validation_error(self, tmp_path, monke with patch("deriva.adapters.llm.manager.load_dotenv"): with patch.dict("os.environ", env_vars, clear=True): manager = LLMManager() - response = manager.query("") assert isinstance(response, FailedResponse) @@ -823,74 +565,19 @@ class ResponseModel(BaseModel): } mock_result = MagicMock() - mock_result.content = '{"message": "Hello!"}' - mock_result.usage = {"total_tokens": 10} - mock_result.finish_reason = "stop" + mock_result.output = ResponseModel(message="Hello!") + mock_result.usage = None with patch("deriva.adapters.llm.manager.load_dotenv"): with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - manager.provider.complete = MagicMock(return_value=mock_result) # type: ignore[method-assign] - - response = manager.query("Hello", response_model=ResponseModel) + with patch("deriva.adapters.llm.manager.Agent") as mock_agent_class: + mock_agent_class.return_value.run_sync.return_value = mock_result + manager = LLMManager() + response = manager.query("Hello", response_model=ResponseModel) assert isinstance(response, ResponseModel) assert response.message == "Hello!" - def test_query_retries_on_json_error(self, tmp_path, monkeypatch): - """Should retry when JSON parsing fails.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - "LLM_NOCACHE": "true", - "LLM_MAX_RETRIES": "2", - } - - mock_result_bad = MagicMock() - mock_result_bad.content = "not valid json" - mock_result_bad.usage = {"total_tokens": 10} - mock_result_bad.finish_reason = "stop" - - mock_result_good = MagicMock() - mock_result_good.content = '{"key": "value"}' - mock_result_good.usage = {"total_tokens": 10} - mock_result_good.finish_reason = "stop" - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - manager.provider.complete = MagicMock(side_effect=[mock_result_bad, mock_result_good]) # type: ignore[method-assign] - - response = manager.query("Hello", schema={"type": "object"}) - - assert isinstance(response, LiveResponse) - assert '"key"' in response.content - - def test_query_uses_custom_temperature(self, tmp_path, monkeypatch): - """Should use provided temperature override.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - "LLM_NOCACHE": "true", - } - - mock_result = MagicMock() - mock_result.content = "Response" - mock_result.usage = {"total_tokens": 10} - mock_result.finish_reason = "stop" - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - manager.provider.complete = MagicMock(return_value=mock_result) # type: ignore[method-assign] - - manager.query("Hello", temperature=0.1) - - call_args = manager.provider.complete.call_args - assert call_args.kwargs["temperature"] == 0.1 - def test_query_caches_successful_response(self, tmp_path, monkeypatch): """Should cache successful responses when caching enabled.""" env_vars = { @@ -900,57 +587,21 @@ def test_query_caches_successful_response(self, tmp_path, monkeypatch): } mock_result = MagicMock() - mock_result.content = "Response" - mock_result.usage = {"total_tokens": 10} - mock_result.finish_reason = "stop" + mock_result.output = "Response" + mock_result.usage = None with patch("deriva.adapters.llm.manager.load_dotenv"): with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - manager.cache.get = MagicMock(return_value=None) # type: ignore[method-assign] - manager.cache.set_response = MagicMock() # type: ignore[method-assign] - manager.provider.complete = MagicMock(return_value=mock_result) # type: ignore[method-assign] + with patch("deriva.adapters.llm.manager.Agent") as mock_agent_class: + mock_agent_class.return_value.run_sync.return_value = mock_result + manager = LLMManager() + manager.cache.get = MagicMock(return_value=None) + manager.cache.set_response = MagicMock() - manager.query("Hello") + manager.query("Hello") manager.cache.set_response.assert_called_once() - def test_query_skips_cached_error_and_retries(self, tmp_path, monkeypatch): - """Should skip cached errors and retry API call instead.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - "LLM_NOCACHE": "true", - } - - cached_error = { - "prompt": "Hello", - "model": "llama3", - "error": "Previous error", - "error_type": "APIError", - "is_error": True, - } - - mock_result = MagicMock() - mock_result.content = "Fresh response" - mock_result.usage = {"total_tokens": 10} - mock_result.finish_reason = "stop" - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - # Cache returns an error, but we should skip it and call API - manager.cache.get = MagicMock(return_value=cached_error) # type: ignore[method-assign] - manager.provider.complete = MagicMock(return_value=mock_result) # type: ignore[method-assign] - - response = manager.query("Hello") - - # Should have made a fresh API call instead of returning cached error - assert isinstance(response, LiveResponse) - assert response.content == "Fresh response" - manager.provider.complete.assert_called_once() - # ============================================================================= # LLMManager utility methods Tests @@ -985,7 +636,7 @@ def test_clear_cache(self, tmp_path, monkeypatch): with patch("deriva.adapters.llm.manager.load_dotenv"): with patch.dict("os.environ", env_vars, clear=True): manager = LLMManager() - manager.cache.clear_all = MagicMock() # type: ignore[method-assign] + manager.cache.clear_all = MagicMock() manager.clear_cache() @@ -1004,7 +655,7 @@ def test_get_cache_stats(self, tmp_path, monkeypatch): with patch("deriva.adapters.llm.manager.load_dotenv"): with patch.dict("os.environ", env_vars, clear=True): manager = LLMManager() - manager.cache.get_cache_stats = MagicMock(return_value=mock_stats) # type: ignore[method-assign] + manager.cache.get_cache_stats = MagicMock(return_value=mock_stats) stats = manager.get_cache_stats() @@ -1027,67 +678,3 @@ def test_repr(self, tmp_path, monkeypatch): assert "LLMManager" in repr_str assert "ollama" in repr_str assert "llama3" in repr_str - - -# ============================================================================= -# LLMManager._cache_error Tests -# ============================================================================= - - -class TestCacheError: - """Tests for _cache_error method.""" - - def test_cache_error_writes_file(self, tmp_path, monkeypatch): - """Should write error to cache file.""" - cache_dir = tmp_path / "cache" - cache_dir.mkdir() - - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(cache_dir), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - - manager._cache_error( - cache_key="test-error-key", - prompt="Test prompt", - error="Test error", - error_type="TestError", - ) - - cache_file = cache_dir / "test-error-key.json" - assert cache_file.exists() - - with open(cache_file, encoding="utf-8") as f: - data = json.load(f) - - assert data["is_error"] is True - assert data["error"] == "Test error" - assert data["error_type"] == "TestError" - - def test_cache_error_handles_write_failure(self, tmp_path, monkeypatch, caplog): - """Should log warning when cache write fails.""" - env_vars = { - "LLM_PROVIDER": "ollama", - "LLM_OLLAMA_MODEL": "llama3", - "LLM_CACHE_DIR": str(tmp_path / "cache"), - } - - with patch("deriva.adapters.llm.manager.load_dotenv"): - with patch.dict("os.environ", env_vars, clear=True): - manager = LLMManager() - # Make cache_dir a file instead of directory to cause write error - manager.cache.cache_dir = tmp_path / "not_a_dir" - (tmp_path / "not_a_dir").write_text("fake file") - - # Should not raise, just log warning - manager._cache_error( - cache_key="test-key", - prompt="Test", - error="Error", - error_type="TestError", - ) diff --git a/tests/test_adapters/llm/test_models.py b/tests/test_adapters/llm/test_models.py index 421e57b..aaaf3f5 100644 --- a/tests/test_adapters/llm/test_models.py +++ b/tests/test_adapters/llm/test_models.py @@ -11,7 +11,6 @@ LiveResponse, LLMResponse, ResponseType, - StructuredOutputMixin, ) @@ -138,85 +137,6 @@ def test_failed_response_is_llm_response(self): assert isinstance(response, BaseResponse) -class TestStructuredOutputMixin: - """Tests for StructuredOutputMixin.""" - - def test_to_prompt_schema(self): - """Should generate prompt-friendly schema.""" - - class TestModel(StructuredOutputMixin): - name: str = Field(description="The name") - count: int = Field(description="The count") - - schema = TestModel.to_prompt_schema() - - assert '"name"' in schema - assert '"count"' in schema - assert "The name" in schema - assert "The count" in schema - - def test_model_json_schema(self): - """Should generate valid JSON schema.""" - - class TestModel(StructuredOutputMixin): - name: str - value: int - - schema = TestModel.model_json_schema() - - assert schema["type"] == "object" - assert "name" in schema["properties"] - assert "value" in schema["properties"] - assert schema["properties"]["name"]["type"] == "string" - assert schema["properties"]["value"]["type"] == "integer" - - def test_extra_forbid(self): - """Should reject extra fields.""" - - class StrictModel(StructuredOutputMixin): - name: str - - with pytest.raises(Exception): # Pydantic ValidationError - StrictModel.model_validate({"name": "test", "extra_field": "not allowed"}) - - def test_nested_objects_in_schema(self): - """Should handle nested objects in schema.""" - - class Inner(BaseModel): - value: str - - class Outer(StructuredOutputMixin): - inner: Inner = Field(description="Nested object") - - schema = Outer.to_prompt_schema() - # Should not crash and should include the field - assert '"inner"' in schema - - def test_array_type_in_schema(self): - """Should handle array types in schema.""" - - class WithArray(StructuredOutputMixin): - items: list[str] = Field(description="List of items") - - schema = WithArray.to_prompt_schema() - assert '"items"' in schema - assert "List of items" in schema - - def test_enum_type_in_schema(self): - """Should handle enum types in schema.""" - from enum import Enum - - class Status(str, Enum): - ACTIVE = "active" - INACTIVE = "inactive" - - class WithEnum(StructuredOutputMixin): - status: Status = Field(description="Current status") - - schema = WithEnum.to_prompt_schema() - assert '"status"' in schema - - class TestBenchmarkModelConfig: """Tests for BenchmarkModelConfig dataclass.""" @@ -258,6 +178,24 @@ def test_creates_with_ollama_provider(self): ) assert config.provider == "ollama" + def test_creates_with_mistral_provider(self): + """Should accept mistral provider.""" + config = BenchmarkModelConfig( + name="test-mistral", + provider="mistral", + model="mistral-large", + ) + assert config.provider == "mistral" + + def test_creates_with_lmstudio_provider(self): + """Should accept lmstudio provider.""" + config = BenchmarkModelConfig( + name="test-lmstudio", + provider="lmstudio", + model="local-model", + ) + assert config.provider == "lmstudio" + def test_rejects_invalid_provider(self): """Should raise ValueError for invalid provider.""" with pytest.raises(ValueError) as exc_info: @@ -348,6 +286,15 @@ def test_get_api_url_ollama_default(self): ) assert "localhost:11434" in config.get_api_url() + def test_get_api_url_mistral_default(self): + """Should return Mistral default URL.""" + config = BenchmarkModelConfig( + name="test", + provider="mistral", + model="mistral-large", + ) + assert "mistral.ai" in config.get_api_url() + def test_get_api_url_azure_no_default(self): """Azure has no default URL (requires custom endpoint).""" config = BenchmarkModelConfig( diff --git a/tests/test_adapters/llm/test_providers.py b/tests/test_adapters/llm/test_providers.py deleted file mode 100644 index de2c0c2..0000000 --- a/tests/test_adapters/llm/test_providers.py +++ /dev/null @@ -1,876 +0,0 @@ -"""Tests for managers.llm.providers module.""" - -import json -from unittest.mock import MagicMock, patch - -import pytest - -from deriva.adapters.llm.providers import ( - AnthropicProvider, - AzureOpenAIProvider, - ClaudeCodeProvider, - CompletionResult, - LMStudioProvider, - OllamaProvider, - OpenAIProvider, - ProviderConfig, - ProviderError, - create_provider, -) - - -class TestProviderConfig: - """Tests for ProviderConfig.""" - - def test_config_defaults(self): - """Should use default timeout.""" - config = ProviderConfig( - api_url="https://api.example.com", - api_key="test-key", - model="gpt-4", - ) - assert config.timeout == 60 - - def test_config_custom_timeout(self): - """Should accept custom timeout.""" - config = ProviderConfig( - api_url="https://api.example.com", - api_key="test-key", - model="gpt-4", - timeout=120, - ) - assert config.timeout == 120 - - -class TestCreateProvider: - """Tests for create_provider factory function.""" - - @pytest.fixture - def config(self): - return ProviderConfig( - api_url="https://api.example.com", - api_key="test-key", - model="gpt-4", - ) - - def test_create_azure_provider(self, config): - """Should create Azure provider.""" - provider = create_provider("azure", config) - assert isinstance(provider, AzureOpenAIProvider) - assert provider.name == "azure" - - def test_create_openai_provider(self, config): - """Should create OpenAI provider.""" - provider = create_provider("openai", config) - assert isinstance(provider, OpenAIProvider) - assert provider.name == "openai" - - def test_create_anthropic_provider(self, config): - """Should create Anthropic provider.""" - provider = create_provider("anthropic", config) - assert isinstance(provider, AnthropicProvider) - assert provider.name == "anthropic" - - def test_create_ollama_provider(self, config): - """Should create Ollama provider.""" - provider = create_provider("ollama", config) - assert isinstance(provider, OllamaProvider) - assert provider.name == "ollama" - - def test_create_lmstudio_provider(self, config): - """Should create LM Studio provider.""" - provider = create_provider("lmstudio", config) - assert isinstance(provider, LMStudioProvider) - assert provider.name == "lmstudio" - - @patch("deriva.adapters.llm.providers.subprocess.run") - def test_create_claudecode_provider(self, mock_run, config): - """Should create ClaudeCode provider.""" - # Mock successful CLI verification - mock_run.return_value = MagicMock(returncode=0) - provider = create_provider("claudecode", config) - assert isinstance(provider, ClaudeCodeProvider) - assert provider.name == "claudecode" - - def test_create_provider_case_insensitive(self, config): - """Should handle case-insensitive provider names.""" - provider = create_provider("AZURE", config) - assert isinstance(provider, AzureOpenAIProvider) - - def test_create_unknown_provider_raises(self, config): - """Should raise ValueError for unknown provider.""" - with pytest.raises(ValueError) as exc_info: - create_provider("unknown", config) - assert "Unknown provider" in str(exc_info.value) - assert "claudecode" in str(exc_info.value) - - -class TestAzureOpenAIProvider: - """Tests for AzureOpenAIProvider.""" - - @pytest.fixture - def provider(self): - config = ProviderConfig( - api_url="https://azure.openai.com/test", - api_key="test-key", - model="gpt-4", - ) - return AzureOpenAIProvider(config) - - def test_name(self, provider): - """Should return 'azure' as name.""" - assert provider.name == "azure" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_success(self, mock_post, provider): - """Should parse Azure OpenAI response correctly.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [ - { - "message": {"content": "Hello, world!"}, - "finish_reason": "stop", - } - ], - "usage": {"prompt_tokens": 10, "completion_tokens": 5}, - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - result = provider.complete( - messages=[{"role": "user", "content": "Hi"}], - temperature=0.5, - ) - - assert isinstance(result, CompletionResult) - assert result.content == "Hello, world!" - assert result.finish_reason == "stop" - assert result.usage == {"prompt_tokens": 10, "completion_tokens": 5} - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_with_json_mode(self, mock_post, provider): - """Should include response_format when json_mode is True.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [{"message": {"content": "{}"}, "finish_reason": "stop"}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - provider.complete( - messages=[{"role": "user", "content": "Hi"}], - json_mode=True, - ) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - assert body["response_format"] == {"type": "json_object"} - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_with_json_schema(self, mock_post, provider): - """Should use structured output with json_schema parameter.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [{"message": {"content": '{"answer": "4"}'}, "finish_reason": "stop"}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - test_schema = { - "name": "test_response", - "strict": True, - "schema": { - "type": "object", - "properties": {"answer": {"type": "string"}}, - "required": ["answer"], - }, - } - - provider.complete( - messages=[{"role": "user", "content": "Hi"}], - json_schema=test_schema, - ) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - assert body["response_format"]["type"] == "json_schema" - assert body["response_format"]["json_schema"] == test_schema - - @patch("deriva.adapters.llm.providers.requests.post") - def test_json_schema_takes_precedence_over_json_mode(self, mock_post, provider): - """json_schema should take precedence over json_mode.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [{"message": {"content": '{"answer": "4"}'}, "finish_reason": "stop"}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - test_schema = {"name": "test", "schema": {"type": "object"}} - - provider.complete( - messages=[{"role": "user", "content": "Hi"}], - json_mode=True, - json_schema=test_schema, - ) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - # Should use json_schema, not json_object - assert body["response_format"]["type"] == "json_schema" - - -class TestOpenAIProvider: - """Tests for OpenAIProvider.""" - - @pytest.fixture - def provider(self): - config = ProviderConfig( - api_url="https://api.openai.com/v1/chat/completions", - api_key="test-key", - model="gpt-4", - ) - return OpenAIProvider(config) - - def test_name(self, provider): - """Should return 'openai' as name.""" - assert provider.name == "openai" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_includes_model_in_body(self, mock_post, provider): - """Should include model in request body (OpenAI requires it).""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [{"message": {"content": "Hi"}, "finish_reason": "stop"}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - provider.complete(messages=[{"role": "user", "content": "Hi"}]) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - assert body["model"] == "gpt-4" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_uses_bearer_auth(self, mock_post, provider): - """Should use Bearer token authentication.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [{"message": {"content": "Hi"}, "finish_reason": "stop"}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - provider.complete(messages=[{"role": "user", "content": "Hi"}]) - - call_args = mock_post.call_args - headers = call_args.kwargs["headers"] - assert headers["Authorization"] == "Bearer test-key" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_with_json_schema(self, mock_post, provider): - """Should use structured output with json_schema parameter.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [{"message": {"content": '{"answer": "4"}'}, "finish_reason": "stop"}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - test_schema = { - "name": "test_response", - "schema": {"type": "object", "properties": {"answer": {"type": "string"}}}, - } - - provider.complete( - messages=[{"role": "user", "content": "Hi"}], - json_schema=test_schema, - ) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - assert body["response_format"]["type"] == "json_schema" - # OpenAI provider normalizes schema: adds strict=True and additionalProperties=false - json_schema = body["response_format"]["json_schema"] - assert json_schema["name"] == "test_response" - assert json_schema["strict"] is True - assert json_schema["schema"]["type"] == "object" - assert json_schema["schema"]["additionalProperties"] is False - - -class TestAnthropicProvider: - """Tests for AnthropicProvider.""" - - @pytest.fixture - def provider(self): - config = ProviderConfig( - api_url="https://api.anthropic.com/v1/messages", - api_key="test-key", - model="claude-3-sonnet", - ) - return AnthropicProvider(config) - - def test_name(self, provider): - """Should return 'anthropic' as name.""" - assert provider.name == "anthropic" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_parses_anthropic_format(self, mock_post, provider): - """Should parse Anthropic's response format.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "content": [{"type": "text", "text": "Hello from Claude!"}], - "usage": {"input_tokens": 10, "output_tokens": 5}, - "stop_reason": "end_turn", - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - result = provider.complete(messages=[{"role": "user", "content": "Hi"}]) - - assert result.content == "Hello from Claude!" - assert result.usage == { - "prompt_tokens": 10, - "completion_tokens": 5, - "total_tokens": 15, - } - assert result.finish_reason == "end_turn" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_extracts_system_message(self, mock_post, provider): - """Should extract system message and send separately.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "content": [{"type": "text", "text": "Hi"}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - provider.complete( - messages=[ - {"role": "system", "content": "You are helpful"}, - {"role": "user", "content": "Hi"}, - ] - ) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - assert body["system"] == "You are helpful" - assert len(body["messages"]) == 1 - assert body["messages"][0]["role"] == "user" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_with_json_schema(self, mock_post, provider): - """Should use structured output with json_schema parameter.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "content": [{"type": "text", "text": '{"answer": "4"}'}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - test_schema = { - "name": "test_response", - "schema": {"type": "object", "properties": {"answer": {"type": "string"}}}, - } - - provider.complete( - messages=[{"role": "user", "content": "Hi"}], - json_schema=test_schema, - ) - - call_args = mock_post.call_args - headers = call_args.kwargs["headers"] - body = call_args.kwargs["json"] - # Anthropic uses beta header for structured output - assert "anthropic-beta" in headers - assert "structured-outputs" in headers["anthropic-beta"] - # Anthropic uses output_format (not response_format) - assert body["output_format"]["type"] == "json_schema" - - -class TestOllamaProvider: - """Tests for OllamaProvider.""" - - @pytest.fixture - def provider(self): - config = ProviderConfig( - api_url="http://localhost:11434/api/chat", - api_key=None, - model="llama3.2", - ) - return OllamaProvider(config) - - def test_name(self, provider): - """Should return 'ollama' as name.""" - assert provider.name == "ollama" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_parses_ollama_format(self, mock_post, provider): - """Should parse Ollama's response format.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "message": {"content": "Hello from Llama!"}, - "done": True, - "prompt_eval_count": 10, - "eval_count": 5, - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - result = provider.complete(messages=[{"role": "user", "content": "Hi"}]) - - assert result.content == "Hello from Llama!" - assert result.usage == { - "prompt_tokens": 10, - "completion_tokens": 5, - "total_tokens": 15, - } - assert result.finish_reason == "stop" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_uses_stream_false(self, mock_post, provider): - """Should set stream to false.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "message": {"content": "Hi"}, - "done": True, - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - provider.complete(messages=[{"role": "user", "content": "Hi"}]) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - assert body["stream"] is False - - @patch("deriva.adapters.llm.providers.requests.post") - def test_json_mode_uses_format(self, mock_post, provider): - """Should use 'format' key for JSON mode.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "message": {"content": "{}"}, - "done": True, - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - provider.complete( - messages=[{"role": "user", "content": "Hi"}], - json_mode=True, - ) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - assert body["format"] == "json" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_with_json_schema(self, mock_post, provider): - """Should use structured output with json_schema parameter.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "message": {"content": '{"answer": "4"}'}, - "done": True, - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - test_schema = { - "type": "object", - "properties": {"answer": {"type": "string"}}, - "required": ["answer"], - } - - provider.complete( - messages=[{"role": "user", "content": "Hi"}], - json_schema=test_schema, - ) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - # Ollama passes schema directly to format parameter - assert body["format"] == test_schema - - @patch("deriva.adapters.llm.providers.requests.post") - def test_json_schema_extracts_nested_schema(self, mock_post, provider): - """Should extract schema from nested structure.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "message": {"content": '{"answer": "4"}'}, - "done": True, - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - # Schema wrapped in OpenAI-style structure - nested_schema = { - "name": "test_response", - "schema": { - "type": "object", - "properties": {"answer": {"type": "string"}}, - }, - } - - provider.complete( - messages=[{"role": "user", "content": "Hi"}], - json_schema=nested_schema, - ) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - # Should extract the inner schema - assert body["format"] == nested_schema["schema"] - - -class TestLMStudioProvider: - """Tests for LMStudioProvider.""" - - @pytest.fixture - def provider(self): - config = ProviderConfig( - api_url="http://localhost:1234/v1/chat/completions", - api_key=None, - model="local-model", - ) - return LMStudioProvider(config) - - def test_name(self, provider): - """Should return 'lmstudio' as name.""" - assert provider.name == "lmstudio" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_success(self, mock_post, provider): - """Should parse OpenAI-compatible response correctly.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [ - { - "message": {"content": "Hello from LM Studio!"}, - "finish_reason": "stop", - } - ], - "usage": {"prompt_tokens": 10, "completion_tokens": 5}, - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - result = provider.complete( - messages=[{"role": "user", "content": "Hi"}], - temperature=0.5, - ) - - assert isinstance(result, CompletionResult) - assert result.content == "Hello from LM Studio!" - assert result.finish_reason == "stop" - assert result.usage == {"prompt_tokens": 10, "completion_tokens": 5} - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_includes_model_in_body(self, mock_post, provider): - """Should include model in request body.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [{"message": {"content": "Hi"}, "finish_reason": "stop"}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - provider.complete(messages=[{"role": "user", "content": "Hi"}]) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - assert body["model"] == "local-model" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_no_auth_header(self, mock_post, provider): - """Should not include Authorization header (local provider).""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [{"message": {"content": "Hi"}, "finish_reason": "stop"}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - provider.complete(messages=[{"role": "user", "content": "Hi"}]) - - call_args = mock_post.call_args - headers = call_args.kwargs["headers"] - assert "Authorization" not in headers - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_with_json_mode(self, mock_post, provider): - """Should include response_format when json_mode is True.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [{"message": {"content": "{}"}, "finish_reason": "stop"}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - provider.complete( - messages=[{"role": "user", "content": "Hi"}], - json_mode=True, - ) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - # LM Studio uses json_schema format (not json_object like OpenAI) - assert body["response_format"]["type"] == "json_schema" - assert body["response_format"]["json_schema"]["name"] == "response" - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_with_max_tokens(self, mock_post, provider): - """Should include max_tokens when specified.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [{"message": {"content": "Hi"}, "finish_reason": "stop"}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - provider.complete( - messages=[{"role": "user", "content": "Hi"}], - max_tokens=100, - ) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - assert body["max_tokens"] == 100 - - @patch("deriva.adapters.llm.providers.requests.post") - def test_complete_with_json_schema(self, mock_post, provider): - """Should use structured output with json_schema parameter.""" - mock_response = MagicMock() - mock_response.json.return_value = { - "choices": [{"message": {"content": '{"answer": "4"}'}, "finish_reason": "stop"}], - } - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - test_schema = { - "name": "test_response", - "schema": {"type": "object", "properties": {"answer": {"type": "string"}}}, - } - - provider.complete( - messages=[{"role": "user", "content": "Hi"}], - json_schema=test_schema, - ) - - call_args = mock_post.call_args - body = call_args.kwargs["json"] - assert body["response_format"]["type"] == "json_schema" - assert body["response_format"]["json_schema"]["name"] == "test_response" - assert body["response_format"]["json_schema"]["strict"] is True - - -class TestClaudeCodeProvider: - """Tests for ClaudeCodeProvider.""" - - @pytest.fixture - def provider(self): - config = ProviderConfig( - api_url="cli", - api_key=None, - model="haiku", - timeout=60, - ) - return ClaudeCodeProvider(config) - - def test_name(self, provider): - """Should return 'claudecode' as name.""" - assert provider.name == "claudecode" - - def test_model_aliases(self, provider): - """Should resolve model aliases.""" - assert provider._resolve_model("haiku") == "haiku" - assert provider._resolve_model("sonnet") == "sonnet" - assert provider._resolve_model("opus") == "opus" - assert provider._resolve_model("claude-haiku-4-5-20251001") == "haiku" - - def test_format_prompt_user_only(self, provider): - """Should format user message.""" - messages = [{"role": "user", "content": "Hello"}] - result = provider._format_prompt(messages) - assert result == "Hello" - - def test_format_prompt_with_system(self, provider): - """Should wrap system message in tags.""" - messages = [ - {"role": "system", "content": "Be helpful"}, - {"role": "user", "content": "Hi"}, - ] - result = provider._format_prompt(messages) - assert "" in result - assert "Be helpful" in result - assert "Hi" in result - - @patch("deriva.adapters.llm.providers.subprocess.run") - def test_verify_cli_success(self, mock_run, provider): - """Should verify CLI is available.""" - mock_run.return_value = MagicMock(returncode=0) - provider._verify_cli() - assert provider._cli_verified is True - - @patch("deriva.adapters.llm.providers.subprocess.run") - def test_verify_cli_not_found(self, mock_run, provider): - """Should raise ProviderError when CLI not found.""" - mock_run.side_effect = FileNotFoundError() - with pytest.raises(ProviderError) as exc_info: - provider._verify_cli() - assert "not found" in str(exc_info.value).lower() - - @patch("deriva.adapters.llm.providers.subprocess.run") - def test_complete_success(self, mock_run, provider): - """Should parse CLI JSON output correctly.""" - # First call for verification, second for completion - mock_run.side_effect = [ - MagicMock(returncode=0), # verify - MagicMock( - returncode=0, - stdout='{"result": "Hello from Claude!"}', - stderr="", - ), - ] - - result = provider.complete( - messages=[{"role": "user", "content": "Hi"}], - ) - - assert isinstance(result, CompletionResult) - assert result.content == "Hello from Claude!" - assert result.finish_reason == "stop" - - @patch("deriva.adapters.llm.providers.subprocess.run") - def test_complete_plain_text_fallback(self, mock_run, provider): - """Should handle plain text output.""" - mock_run.side_effect = [ - MagicMock(returncode=0), # verify - MagicMock( - returncode=0, - stdout="Plain text response", - stderr="", - ), - ] - - result = provider.complete( - messages=[{"role": "user", "content": "Hi"}], - ) - - assert result.content == "Plain text response" - - @patch("deriva.adapters.llm.providers.subprocess.run") - def test_complete_cli_error(self, mock_run, provider): - """Should raise ProviderError on CLI error.""" - mock_run.side_effect = [ - MagicMock(returncode=0), # verify - MagicMock( - returncode=1, - stdout="", - stderr="CLI error message", - ), - ] - - with pytest.raises(ProviderError) as exc_info: - provider.complete(messages=[{"role": "user", "content": "Hi"}]) - - assert "CLI error" in str(exc_info.value) - - def test_strip_markdown_json_block(self, provider): - """Should strip markdown code blocks from JSON content.""" - content = '```json\n{"name": "test"}\n```' - result = provider._strip_markdown_code_block(content) - assert result == '{"name": "test"}' - - def test_strip_markdown_plain_block(self, provider): - """Should strip plain markdown code blocks.""" - content = '```\n{"name": "test"}\n```' - result = provider._strip_markdown_code_block(content) - assert result == '{"name": "test"}' - - def test_strip_preserves_non_markdown(self, provider): - """Should preserve content without markdown blocks.""" - content = '{"name": "test"}' - result = provider._strip_markdown_code_block(content) - assert result == '{"name": "test"}' - - def test_strip_converts_python_dict_to_json(self, provider): - """Should convert Python dict syntax to JSON.""" - content = "{'name': 'test'}" - result = provider._strip_markdown_code_block(content) - assert result == '{"name": "test"}' - - def test_strip_handles_nested_python_dict(self, provider): - """Should handle nested Python dict syntax.""" - content = "{'outer': {'inner': 'value'}}" - result = provider._strip_markdown_code_block(content) - assert result == '{"outer": {"inner": "value"}}' - - @patch("deriva.adapters.llm.providers.subprocess.run") - def test_complete_strips_markdown_from_result(self, mock_run, provider): - """Should strip markdown from CLI result field.""" - mock_run.side_effect = [ - MagicMock(returncode=0), # verify - MagicMock( - returncode=0, - stdout='{"result": "```json\\n{\\"name\\": \\"test\\"}\\n```"}', - stderr="", - ), - ] - - result = provider.complete( - messages=[{"role": "user", "content": "Hi"}], - ) - - assert result.content == '{"name": "test"}' - - -class TestProviderErrors: - """Tests for provider error handling.""" - - @pytest.fixture - def provider(self): - config = ProviderConfig( - api_url="https://api.example.com", - api_key="test-key", - model="gpt-4", - ) - return AzureOpenAIProvider(config) - - @patch("deriva.adapters.llm.providers.requests.post") - def test_timeout_raises_provider_error(self, mock_post, provider): - """Should raise ProviderError on timeout.""" - import requests - - mock_post.side_effect = requests.exceptions.Timeout() - - with pytest.raises(ProviderError) as exc_info: - provider.complete(messages=[{"role": "user", "content": "Hi"}]) - - assert "timed out" in str(exc_info.value) - - @patch("deriva.adapters.llm.providers.requests.post") - def test_request_error_raises_provider_error(self, mock_post, provider): - """Should raise ProviderError on request failure.""" - import requests - - mock_post.side_effect = requests.exceptions.ConnectionError("Connection failed") - - with pytest.raises(ProviderError) as exc_info: - provider.complete(messages=[{"role": "user", "content": "Hi"}]) - - assert "request failed" in str(exc_info.value) - - @patch("deriva.adapters.llm.providers.requests.post") - def test_invalid_json_raises_provider_error(self, mock_post, provider): - """Should raise ProviderError on invalid JSON response.""" - mock_response = MagicMock() - mock_response.json.side_effect = json.JSONDecodeError("", "", 0) - mock_response.raise_for_status = MagicMock() - mock_post.return_value = mock_response - - with pytest.raises(ProviderError) as exc_info: - provider.complete(messages=[{"role": "user", "content": "Hi"}]) - - assert "invalid JSON" in str(exc_info.value) diff --git a/uv.lock b/uv.lock index 40ec083..7e93bf5 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,90 @@ version = 1 requires-python = ">=3.14" 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