658 lines
24 KiB
Python
658 lines
24 KiB
Python
from collections.abc import AsyncIterator, Callable
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from functools import wraps
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from typing import Any, Generic, Literal, TypeVar, cast, overload
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from anthropic import AsyncAnthropic
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from anthropic.types import TextBlock
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from anthropic.types.message import Message as AnthropicMessage
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from google import genai
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from google.genai.types import GenerateContentResponse
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from groq import AsyncGroq
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from langfuse import get_client
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from openai import AsyncOpenAI
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from openai.types.chat import ChatCompletion, ChatCompletionChunk
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from pydantic import BaseModel, Field
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from sentry_sdk.ai.monitoring import ai_track
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from tenacity import retry, stop_after_attempt, wait_exponential
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from src.config import settings
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from src.utils.types import SupportedProviders
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T = TypeVar("T")
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M = TypeVar("M", bound=BaseModel)
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lf = get_client()
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CLIENTS: dict[
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SupportedProviders,
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AsyncAnthropic | AsyncOpenAI | genai.Client | AsyncGroq,
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] = {}
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if settings.LLM.ANTHROPIC_API_KEY:
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anthropic = AsyncAnthropic(api_key=settings.LLM.ANTHROPIC_API_KEY)
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CLIENTS["anthropic"] = anthropic
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if settings.LLM.OPENAI_API_KEY:
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openai_client = AsyncOpenAI(
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api_key=settings.LLM.OPENAI_API_KEY,
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)
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CLIENTS["openai"] = openai_client
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if settings.LLM.OPENAI_COMPATIBLE_BASE_URL:
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CLIENTS["custom"] = AsyncOpenAI(
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api_key=settings.LLM.OPENAI_COMPATIBLE_API_KEY,
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base_url=settings.LLM.OPENAI_COMPATIBLE_BASE_URL,
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)
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if settings.LLM.GEMINI_API_KEY:
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google = genai.client.Client(api_key=settings.LLM.GEMINI_API_KEY)
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CLIENTS["google"] = google
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if settings.LLM.GROQ_API_KEY:
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groq = AsyncGroq(api_key=settings.LLM.GROQ_API_KEY)
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CLIENTS["groq"] = groq
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SELECTED_PROVIDERS = [
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("Dialectic", settings.DIALECTIC.PROVIDER),
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("Summary", settings.SUMMARY.PROVIDER),
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("Deriver", settings.DERIVER.PROVIDER),
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("Query Generation Provider", settings.DIALECTIC.QUERY_GENERATION_PROVIDER),
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]
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for provider_name, provider_value in SELECTED_PROVIDERS:
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if provider_value not in CLIENTS:
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raise ValueError(f"Missing client for {provider_name}: {provider_value}")
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class HonchoLLMCallResponse(BaseModel, Generic[T]):
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"""
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Response object for LLM calls.
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Args:
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content: The response content. When a response_model is provided, this will be
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the parsed object of that type. Otherwise, it will be a string.
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output_tokens: Number of tokens generated in the response.
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finish_reasons: List of finish reasons for the response.
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"""
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content: T
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output_tokens: int
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finish_reasons: list[str]
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class HonchoLLMCallStreamChunk(BaseModel):
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"""
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A single chunk in a streaming LLM response.
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Args:
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content: The text content for this chunk. Empty for chunks that only contain metadata.
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is_done: Whether this is the final chunk in the stream.
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finish_reasons: List of finish reasons if the stream is complete.
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"""
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content: str
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is_done: bool = False
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finish_reasons: list[str] = Field(default_factory=list)
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@overload
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async def honcho_llm_call(
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provider: SupportedProviders,
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model: str,
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prompt: str,
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max_tokens: int,
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track_name: str | None = None,
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*,
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response_model: type[M],
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json_mode: bool = False,
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reasoning_effort: Literal["low", "medium", "high", "minimal"]
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| None = None, # OpenAI only
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verbosity: Literal["low", "medium", "high"] | None = None, # OpenAI only
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thinking_budget_tokens: int | None = None,
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enable_retry: bool = True,
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retry_attempts: int = 3,
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stream: Literal[False] = False,
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) -> HonchoLLMCallResponse[M]: ...
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@overload
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async def honcho_llm_call(
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provider: SupportedProviders,
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model: str,
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prompt: str,
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max_tokens: int,
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track_name: str | None = None,
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response_model: None = None,
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json_mode: bool = False,
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reasoning_effort: Literal["low", "medium", "high", "minimal"]
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| None = None, # OpenAI only
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verbosity: Literal["low", "medium", "high"] | None = None, # OpenAI only
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thinking_budget_tokens: int | None = None,
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enable_retry: bool = True,
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retry_attempts: int = 3,
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stream: Literal[False] = False,
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) -> HonchoLLMCallResponse[str]: ...
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@overload
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async def honcho_llm_call(
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provider: SupportedProviders,
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model: str,
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prompt: str,
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max_tokens: int,
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track_name: str | None = None,
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response_model: type[BaseModel] | None = None,
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json_mode: bool = False,
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reasoning_effort: Literal["low", "medium", "high", "minimal"]
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| None = None, # OpenAI only
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verbosity: Literal["low", "medium", "high"] | None = None, # OpenAI only
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thinking_budget_tokens: int | None = None,
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enable_retry: bool = True,
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retry_attempts: int = 3,
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stream: Literal[True] = ...,
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) -> AsyncIterator[HonchoLLMCallStreamChunk]: ...
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async def honcho_llm_call(
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provider: SupportedProviders,
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model: str,
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prompt: str,
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max_tokens: int,
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track_name: str | None = None,
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response_model: type[BaseModel] | None = None,
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json_mode: bool = False,
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reasoning_effort: Literal["low", "medium", "high", "minimal"]
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| None = None, # OpenAI only
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verbosity: Literal["low", "medium", "high"] | None = None, # OpenAI only
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thinking_budget_tokens: int | None = None,
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enable_retry: bool = True,
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retry_attempts: int = 3,
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stream: bool = False,
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) -> HonchoLLMCallResponse[Any] | AsyncIterator[HonchoLLMCallStreamChunk]:
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client = CLIENTS.get(provider)
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if not client:
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raise ValueError(f"Missing client for {provider}")
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decorated = honcho_llm_call_inner
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# apply langfuse if enabled
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if settings.LANGFUSE_PUBLIC_KEY:
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decorated = with_langfuse(decorated)
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# apply tracking
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if track_name:
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decorated = ai_track(track_name)(decorated)
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# apply retry logic
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if enable_retry:
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decorated = retry(
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stop=stop_after_attempt(retry_attempts),
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wait=wait_exponential(multiplier=1, min=4, max=10),
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)(decorated)
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if stream:
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return await decorated(
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provider,
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model,
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prompt,
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max_tokens,
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response_model,
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json_mode,
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reasoning_effort,
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verbosity,
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thinking_budget_tokens,
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True,
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)
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else:
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return await decorated(
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provider,
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model,
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prompt,
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max_tokens,
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response_model,
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json_mode,
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reasoning_effort,
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verbosity,
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thinking_budget_tokens,
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False,
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)
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@overload
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async def honcho_llm_call_inner(
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provider: SupportedProviders,
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model: str,
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prompt: str,
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max_tokens: int,
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response_model: type[M],
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json_mode: bool = False,
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reasoning_effort: Literal["low", "medium", "high", "minimal"]
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| None = None, # OpenAI only
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verbosity: Literal["low", "medium", "high"] | None = None, # OpenAI only
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thinking_budget_tokens: int | None = None, # Anthropic only
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stream: Literal[False] = False,
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) -> HonchoLLMCallResponse[M]: ...
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@overload
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async def honcho_llm_call_inner(
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provider: SupportedProviders,
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model: str,
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prompt: str,
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max_tokens: int,
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response_model: None = None,
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json_mode: bool = False,
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reasoning_effort: Literal["low", "medium", "high", "minimal"]
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| None = None, # OpenAI only
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verbosity: Literal["low", "medium", "high"] | None = None, # OpenAI only
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thinking_budget_tokens: int | None = None, # Anthropic only
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stream: Literal[False] = False,
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) -> HonchoLLMCallResponse[str]: ...
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@overload
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async def honcho_llm_call_inner(
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provider: SupportedProviders,
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model: str,
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prompt: str,
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max_tokens: int,
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response_model: type[BaseModel] | None = None,
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json_mode: bool = False,
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reasoning_effort: Literal["low", "medium", "high", "minimal"]
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| None = None, # OpenAI only
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verbosity: Literal["low", "medium", "high"] | None = None, # OpenAI only
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thinking_budget_tokens: int | None = None, # Anthropic only
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stream: Literal[True] = ...,
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) -> AsyncIterator[HonchoLLMCallStreamChunk]: ...
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async def honcho_llm_call_inner(
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provider: SupportedProviders,
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model: str,
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prompt: str,
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max_tokens: int,
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response_model: type[BaseModel] | None = None,
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json_mode: bool = False,
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reasoning_effort: Literal["low", "medium", "high", "minimal"]
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| None = None, # OpenAI only
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verbosity: Literal["low", "medium", "high"] | None = None, # OpenAI only
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thinking_budget_tokens: int | None = None, # Anthropic only
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stream: bool = False,
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) -> HonchoLLMCallResponse[Any] | AsyncIterator[HonchoLLMCallStreamChunk]:
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# has already been validated by honcho_llm_call
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client = CLIENTS[provider]
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params: dict[str, Any] = {
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"model": model,
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"max_tokens": max_tokens,
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"messages": [{"role": "user", "content": prompt}],
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"stream": stream,
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}
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if stream:
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# Return async generator for streaming responses
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return handle_streaming_response(
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client,
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params,
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json_mode,
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thinking_budget_tokens,
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response_model,
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reasoning_effort,
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verbosity,
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)
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# Remove stream parameter for non-streaming calls as some providers don't accept it
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params.pop("stream", None)
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match client:
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case AsyncAnthropic():
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if response_model:
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raise NotImplementedError(
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"Response model is not supported for Anthropic"
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)
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anthropic_params: dict[str, Any] = {
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"model": params["model"],
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"max_tokens": params["max_tokens"],
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"messages": list(params["messages"]),
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}
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if json_mode:
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anthropic_params["messages"].append(
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{"role": "assistant", "content": "{"}
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)
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if thinking_budget_tokens:
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anthropic_params["thinking"] = {
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"type": "enabled",
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"budget_tokens": thinking_budget_tokens,
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}
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anthropic_response: AnthropicMessage = await client.messages.create( # pyright: ignore
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**anthropic_params
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)
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# Extract text content from content blocks
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text_blocks: list[str] = []
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for block in anthropic_response.content: # pyright: ignore
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if isinstance(block, TextBlock):
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text_blocks.append(block.text)
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# Safely extract usage and stop_reason
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usage = anthropic_response.usage # pyright: ignore
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stop_reason = anthropic_response.stop_reason # pyright: ignore
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return HonchoLLMCallResponse(
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content="\n".join(text_blocks),
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output_tokens=usage.output_tokens if usage else 0, # pyright: ignore
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finish_reasons=[stop_reason] if stop_reason else [],
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)
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case AsyncOpenAI():
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openai_params: dict[str, Any] = {
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"model": params["model"],
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"messages": params["messages"],
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}
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if "gpt-5" in model:
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openai_params["max_completion_tokens"] = params["max_tokens"]
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if reasoning_effort:
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openai_params["reasoning_effort"] = reasoning_effort
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if verbosity:
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openai_params["verbosity"] = verbosity
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else:
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openai_params["max_tokens"] = params["max_tokens"]
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if json_mode:
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openai_params["response_format"] = {"type": "json_object"}
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if response_model:
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openai_params["response_format"] = response_model
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response: ChatCompletion = await client.chat.completions.parse( # pyright: ignore
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**openai_params
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)
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# Extract the parsed object for structured output
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parsed_content = response.choices[0].message.parsed
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if parsed_content is None:
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raise ValueError("No parsed content in structured response")
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# Safely extract usage and finish_reason
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usage = response.usage
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finish_reason = response.choices[0].finish_reason
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return HonchoLLMCallResponse(
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content=parsed_content,
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output_tokens=usage.completion_tokens if usage else 0,
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finish_reasons=[finish_reason] if finish_reason else [],
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)
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else:
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response: ChatCompletion = await client.chat.completions.create( # pyright: ignore
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**openai_params
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)
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# Safely extract usage and finish_reason
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usage = response.usage # pyright: ignore
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finish_reason = response.choices[0].finish_reason # pyright: ignore
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return HonchoLLMCallResponse(
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content=response.choices[0].message.content or "", # pyright: ignore
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output_tokens=usage.completion_tokens if usage else 0, # pyright: ignore
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finish_reasons=[finish_reason] if finish_reason else [],
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)
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case genai.Client():
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if response_model is None:
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gemini_response: GenerateContentResponse = (
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client.models.generate_content(
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model=model,
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contents=prompt,
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config={
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"response_mime_type": "application/json"
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if json_mode
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else None,
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},
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)
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)
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# Safely extract response data
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text_content = gemini_response.text if gemini_response.text else ""
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token_count = (
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gemini_response.candidates[0].token_count or 0
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if gemini_response.candidates
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else 0
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)
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finish_reason = (
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gemini_response.candidates[0].finish_reason.name
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if gemini_response.candidates
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and gemini_response.candidates[0].finish_reason
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else "stop"
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)
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return HonchoLLMCallResponse(
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content=text_content,
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output_tokens=token_count,
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finish_reasons=[finish_reason],
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)
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else:
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gemini_response = client.models.generate_content(
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model=model,
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contents=prompt,
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config={
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"response_mime_type": "application/json",
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"response_schema": response_model,
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},
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)
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token_count = (
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gemini_response.candidates[0].token_count or 0
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if gemini_response.candidates
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else 0
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)
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finish_reason = (
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gemini_response.candidates[0].finish_reason.name
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if gemini_response.candidates
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and gemini_response.candidates[0].finish_reason
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else "stop"
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)
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return HonchoLLMCallResponse(
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content=gemini_response.parsed,
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output_tokens=token_count,
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finish_reasons=[finish_reason],
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)
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case AsyncGroq():
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groq_params: dict[str, Any] = {
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"model": params["model"],
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"max_tokens": params["max_tokens"],
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"messages": params["messages"],
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}
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if response_model:
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groq_params["response_format"] = response_model
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elif json_mode:
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groq_params["response_format"] = {"type": "json_object"}
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response: ChatCompletion = await client.chat.completions.create( # pyright: ignore
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**groq_params
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)
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if response.choices[0].message.content is None: # pyright: ignore
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raise ValueError("No content in response")
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# Safely extract usage and finish_reason
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usage = response.usage # pyright: ignore
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finish_reason = response.choices[0].finish_reason # pyright: ignore
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return HonchoLLMCallResponse(
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content=response.choices[0].message.content, # pyright: ignore
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output_tokens=usage.completion_tokens if usage else 0, # pyright: ignore
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finish_reasons=[finish_reason] if finish_reason else [],
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)
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async def handle_streaming_response(
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client: AsyncAnthropic | AsyncOpenAI | genai.Client | AsyncGroq,
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params: dict[str, Any],
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json_mode: bool,
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thinking_budget_tokens: int | None,
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response_model: type[BaseModel] | None = None,
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reasoning_effort: Literal["low", "medium", "high", "minimal"] | None = None,
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verbosity: Literal["low", "medium", "high"] | None = None,
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) -> AsyncIterator[HonchoLLMCallStreamChunk]:
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"""
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Handle streaming responses for all supported providers.
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Args:
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client: The LLM client instance
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params: Request parameters including stream=True
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json_mode: Whether to use JSON mode
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thinking_budget_tokens: Anthropic thinking budget tokens
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response_model: Pydantic model for structured output
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reasoning_effort: OpenAI reasoning effort level (GPT-5 only)
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verbosity: OpenAI verbosity level (GPT-5 only)
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Yields:
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HonchoLLMCallStreamChunk: Individual chunks of the streaming response
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"""
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match client:
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case AsyncAnthropic():
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if response_model:
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raise NotImplementedError(
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"Response model is not supported for Anthropic"
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)
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anthropic_params: dict[str, Any] = {
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"model": params["model"],
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"max_tokens": params["max_tokens"],
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"messages": list(params["messages"]),
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}
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if json_mode:
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anthropic_params["messages"].append(
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{"role": "assistant", "content": "{"}
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)
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if thinking_budget_tokens:
|
|
anthropic_params["thinking"] = {
|
|
"type": "enabled",
|
|
"budget_tokens": thinking_budget_tokens,
|
|
}
|
|
async with client.messages.stream(**anthropic_params) as anthropic_stream:
|
|
async for chunk in anthropic_stream:
|
|
if (
|
|
chunk.type == "content_block_delta"
|
|
and hasattr(chunk, "delta")
|
|
and hasattr(chunk.delta, "text")
|
|
):
|
|
text_content = getattr(chunk.delta, "text", "")
|
|
yield HonchoLLMCallStreamChunk(content=text_content)
|
|
final_message = await anthropic_stream.get_final_message()
|
|
yield HonchoLLMCallStreamChunk(
|
|
content="",
|
|
is_done=True,
|
|
finish_reasons=[final_message.stop_reason]
|
|
if final_message.stop_reason
|
|
else [],
|
|
)
|
|
|
|
case AsyncOpenAI():
|
|
openai_params: dict[str, Any] = {
|
|
"model": params["model"],
|
|
"messages": params["messages"],
|
|
"stream": True,
|
|
}
|
|
|
|
model_name = params["model"]
|
|
if "gpt-5" in model_name:
|
|
openai_params["max_completion_tokens"] = params["max_tokens"]
|
|
if reasoning_effort:
|
|
openai_params["reasoning_effort"] = reasoning_effort
|
|
if verbosity:
|
|
openai_params["verbosity"] = verbosity
|
|
else:
|
|
openai_params["max_tokens"] = params["max_tokens"]
|
|
|
|
if response_model:
|
|
openai_params["response_format"] = response_model
|
|
elif json_mode:
|
|
openai_params["response_format"] = {"type": "json_object"}
|
|
|
|
openai_stream = await client.chat.completions.create(**openai_params) # pyright: ignore
|
|
async for chunk in openai_stream: # pyright: ignore
|
|
chunk = cast(ChatCompletionChunk, chunk)
|
|
if chunk.choices and chunk.choices[0].delta.content:
|
|
yield HonchoLLMCallStreamChunk(
|
|
content=chunk.choices[0].delta.content
|
|
)
|
|
if chunk.choices and chunk.choices[0].finish_reason:
|
|
yield HonchoLLMCallStreamChunk(
|
|
content="",
|
|
is_done=True,
|
|
finish_reasons=[chunk.choices[0].finish_reason],
|
|
)
|
|
|
|
case genai.Client():
|
|
prompt_text = params["messages"][0]["content"] if params["messages"] else ""
|
|
|
|
if response_model is not None:
|
|
response_stream = await client.aio.models.generate_content_stream(
|
|
model=params["model"],
|
|
contents=prompt_text,
|
|
config={
|
|
"response_mime_type": "application/json",
|
|
"response_schema": response_model,
|
|
},
|
|
)
|
|
else:
|
|
response_stream = await client.aio.models.generate_content_stream(
|
|
model=params["model"],
|
|
contents=prompt_text,
|
|
config={
|
|
"response_mime_type": "application/json" if json_mode else None,
|
|
},
|
|
)
|
|
|
|
final_chunk = None
|
|
async for chunk in response_stream:
|
|
if chunk.text:
|
|
yield HonchoLLMCallStreamChunk(content=chunk.text)
|
|
final_chunk = chunk
|
|
|
|
finish_reason = "stop" # Default fallback
|
|
if (
|
|
final_chunk
|
|
and hasattr(final_chunk, "candidates")
|
|
and final_chunk.candidates
|
|
and hasattr(final_chunk.candidates[0], "finish_reason")
|
|
and final_chunk.candidates[0].finish_reason
|
|
):
|
|
finish_reason = final_chunk.candidates[0].finish_reason.name
|
|
|
|
yield HonchoLLMCallStreamChunk(
|
|
content="", is_done=True, finish_reasons=[finish_reason]
|
|
)
|
|
|
|
case AsyncGroq():
|
|
groq_params: dict[str, Any] = {
|
|
"model": params["model"],
|
|
"max_tokens": params["max_tokens"],
|
|
"messages": params["messages"],
|
|
"stream": True,
|
|
}
|
|
|
|
if response_model:
|
|
groq_params["response_format"] = response_model
|
|
elif json_mode:
|
|
groq_params["response_format"] = {"type": "json_object"}
|
|
|
|
groq_stream = await client.chat.completions.create(**groq_params) # pyright: ignore
|
|
async for chunk in groq_stream: # pyright: ignore
|
|
chunk = cast(ChatCompletionChunk, chunk)
|
|
if chunk.choices and chunk.choices[0].delta.content:
|
|
yield HonchoLLMCallStreamChunk(
|
|
content=chunk.choices[0].delta.content
|
|
)
|
|
if chunk.choices and chunk.choices[0].finish_reason:
|
|
yield HonchoLLMCallStreamChunk(
|
|
content="",
|
|
is_done=True,
|
|
finish_reasons=[chunk.choices[0].finish_reason],
|
|
)
|
|
|
|
|
|
def with_langfuse(func: Callable[..., Any]) -> Callable[..., Any]:
|
|
@wraps(func)
|
|
async def wrapper(*args: Any, **kwargs: Any) -> Any:
|
|
lf.start_as_current_generation(name="LLM Call")
|
|
return await func(*args, **kwargs)
|
|
|
|
return wrapper
|