ankn
a5f8fb49ab
Bahn: aisupport, Analyse-O2C-C2S, awesome-bahn-mcp-servers, beam-mcp,
Confluence_Bot, db-planet-mcp-server, O2C-Harness, project-audit,
Projekt-KIQ-HP, teamlandkarte-mcp
Dhive: Jury-Voting
Privat: CV, NoteGraph (NOTE: NoteGraph needs complete redo after consolidation)
Shared: AI-Orchestrator, OrgMyLife, power_skills_and_more
Shared/references: symphony (read-only)
Bahn repos remain available as independent remotes - this monorepo
pulls them in via subtree, the originals are untouched.
113 lines
3.2 KiB
Python
113 lines
3.2 KiB
Python
"""LiteLLM wrapper for model-agnostic LLM access.
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Supports OpenAI, Anthropic, Google, Mistral, and Ollama via a unified interface.
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Implements retry logic with exponential backoff for transient failures.
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"""
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import logging
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import time
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from typing import Any
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from pydantic import BaseModel
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from ingestion.config import IngestionConfig
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logger = logging.getLogger(__name__)
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# Retry configuration
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MAX_RETRIES = 3
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BASE_DELAY_SECONDS = 1.0
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MAX_DELAY_SECONDS = 30.0
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BACKOFF_FACTOR = 2.0
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class LLMError(Exception):
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"""Raised when an LLM call fails after all retries."""
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def __init__(self, provider: str, model: str, message: str):
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self.provider = provider
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self.model = model
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super().__init__(
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f"LLM call failed [{provider}/{model}]: {message}"
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)
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class LLMClient:
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"""Model-agnostic LLM client using LiteLLM."""
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def __init__(self, config: IngestionConfig):
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self.model = config.llm_model_string
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self.provider = config.llm_provider
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self.config = config
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def complete(
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self,
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messages: list[dict[str, str]],
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response_format: type[BaseModel] | None = None,
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) -> str:
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"""Send a completion request with retry logic.
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Args:
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messages: List of message dicts with 'role' and 'content' keys.
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response_format: Optional Pydantic model for structured output.
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Returns:
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The assistant's response content as a string.
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Raises:
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LLMError: If all retries are exhausted.
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"""
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import litellm
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last_error: Exception | None = None
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for attempt in range(1, MAX_RETRIES + 1):
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try:
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kwargs: dict[str, Any] = {
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"model": self.model,
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"messages": messages,
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}
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if response_format is not None:
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kwargs["response_format"] = response_format
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response = litellm.completion(**kwargs)
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content = response.choices[0].message.content
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return content or ""
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except (
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litellm.RateLimitError,
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litellm.Timeout,
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litellm.ServiceUnavailableError,
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litellm.InternalServerError,
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) as e:
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last_error = e
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delay = min(
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BASE_DELAY_SECONDS * (BACKOFF_FACTOR ** (attempt - 1)),
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MAX_DELAY_SECONDS,
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)
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logger.warning(
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"LLM call attempt %d/%d failed (%s: %s), retrying in %.1fs",
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attempt,
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MAX_RETRIES,
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type(e).__name__,
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str(e),
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delay,
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)
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time.sleep(delay)
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except Exception as e:
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# Non-retryable error
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raise LLMError(
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provider=self.provider,
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model=self.model,
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message=f"{type(e).__name__}: {e}",
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) from e
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# All retries exhausted
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raise LLMError(
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provider=self.provider,
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model=self.model,
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message=f"All {MAX_RETRIES} retries exhausted. Last error: {last_error}",
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)
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