fix(llm): add streaming retry with exponential backoff
- Retry failed streams up to 3 times with exp backoff (8s min, 64s max) - Reset chunks on failure and retry full request - Use litellm._should_retry() for retryable error detection - Switch to async acompletion() for streaming - Refactor generate() into smaller focused methods
This commit is contained in:
0xallam
128
strix/llm/llm.py
128
strix/llm/llm.py
@@ -1,3 +1,4 @@
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import asyncio
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import logging
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import os
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from collections.abc import AsyncIterator
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@@ -24,6 +25,23 @@ from strix.prompts import load_prompt_modules
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from strix.tools import get_tools_prompt
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MAX_RETRIES = 5
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RETRY_MULTIPLIER = 8
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RETRY_MIN = 8
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RETRY_MAX = 64
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def _should_retry(exception: Exception) -> bool:
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status_code = None
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if hasattr(exception, "status_code"):
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status_code = exception.status_code
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elif hasattr(exception, "response") and hasattr(exception.response, "status_code"):
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status_code = exception.response.status_code
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if status_code is not None:
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return bool(litellm._should_retry(status_code))
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return True
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logger = logging.getLogger(__name__)
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litellm.drop_params = True
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@@ -272,12 +290,7 @@ class LLM:
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return cached_messages
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async def generate( # noqa: PLR0912, PLR0915
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self,
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conversation_history: list[dict[str, Any]],
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scan_id: str | None = None,
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step_number: int = 1,
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) -> AsyncIterator[LLMResponse]:
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def _prepare_messages(self, conversation_history: list[dict[str, Any]]) -> list[dict[str, Any]]:
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messages = [{"role": "system", "content": self.system_prompt}]
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identity_message = self._build_identity_message()
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@@ -290,13 +303,18 @@ class LLM:
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conversation_history.extend(compressed_history)
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messages.extend(compressed_history)
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cached_messages = self._prepare_cached_messages(messages)
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return self._prepare_cached_messages(messages)
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try:
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async def _stream_and_accumulate(
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self,
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messages: list[dict[str, Any]],
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scan_id: str | None,
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step_number: int,
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) -> AsyncIterator[LLMResponse]:
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accumulated_content = ""
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chunks: list[Any] = []
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async for chunk in self._stream_request(cached_messages):
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async for chunk in self._stream_request(messages):
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chunks.append(chunk)
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delta = self._extract_chunk_delta(chunk)
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if delta:
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@@ -333,51 +351,57 @@ class LLM:
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tool_invocations=tool_invocations if tool_invocations else None,
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)
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except litellm.RateLimitError as e:
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raise LLMRequestFailedError("LLM request failed: Rate limit exceeded", str(e)) from e
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except litellm.AuthenticationError as e:
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raise LLMRequestFailedError("LLM request failed: Invalid API key", str(e)) from e
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except litellm.NotFoundError as e:
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raise LLMRequestFailedError("LLM request failed: Model not found", str(e)) from e
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except litellm.ContextWindowExceededError as e:
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raise LLMRequestFailedError("LLM request failed: Context too long", str(e)) from e
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except litellm.ContentPolicyViolationError as e:
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raise LLMRequestFailedError(
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"LLM request failed: Content policy violation", str(e)
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) from e
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except litellm.ServiceUnavailableError as e:
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raise LLMRequestFailedError("LLM request failed: Service unavailable", str(e)) from e
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except litellm.Timeout as e:
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raise LLMRequestFailedError("LLM request failed: Request timed out", str(e)) from e
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except litellm.UnprocessableEntityError as e:
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raise LLMRequestFailedError("LLM request failed: Unprocessable entity", str(e)) from e
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except litellm.InternalServerError as e:
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raise LLMRequestFailedError("LLM request failed: Internal server error", str(e)) from e
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except litellm.APIConnectionError as e:
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raise LLMRequestFailedError("LLM request failed: Connection error", str(e)) from e
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except litellm.UnsupportedParamsError as e:
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raise LLMRequestFailedError("LLM request failed: Unsupported parameters", str(e)) from e
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except litellm.BudgetExceededError as e:
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raise LLMRequestFailedError("LLM request failed: Budget exceeded", str(e)) from e
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except litellm.APIResponseValidationError as e:
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raise LLMRequestFailedError(
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"LLM request failed: Response validation error", str(e)
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) from e
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except litellm.JSONSchemaValidationError as e:
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raise LLMRequestFailedError(
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"LLM request failed: JSON schema validation error", str(e)
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) from e
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except litellm.InvalidRequestError as e:
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raise LLMRequestFailedError("LLM request failed: Invalid request", str(e)) from e
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except litellm.BadRequestError as e:
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raise LLMRequestFailedError("LLM request failed: Bad request", str(e)) from e
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except litellm.APIError as e:
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raise LLMRequestFailedError("LLM request failed: API error", str(e)) from e
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except litellm.OpenAIError as e:
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raise LLMRequestFailedError("LLM request failed: OpenAI error", str(e)) from e
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except Exception as e:
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def _raise_llm_error(self, e: Exception) -> None:
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error_map: list[tuple[type, str]] = [
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(litellm.RateLimitError, "Rate limit exceeded"),
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(litellm.AuthenticationError, "Invalid API key"),
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(litellm.NotFoundError, "Model not found"),
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(litellm.ContextWindowExceededError, "Context too long"),
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(litellm.ContentPolicyViolationError, "Content policy violation"),
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(litellm.ServiceUnavailableError, "Service unavailable"),
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(litellm.Timeout, "Request timed out"),
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(litellm.UnprocessableEntityError, "Unprocessable entity"),
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(litellm.InternalServerError, "Internal server error"),
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(litellm.APIConnectionError, "Connection error"),
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(litellm.UnsupportedParamsError, "Unsupported parameters"),
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(litellm.BudgetExceededError, "Budget exceeded"),
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(litellm.APIResponseValidationError, "Response validation error"),
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(litellm.JSONSchemaValidationError, "JSON schema validation error"),
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(litellm.InvalidRequestError, "Invalid request"),
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(litellm.BadRequestError, "Bad request"),
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(litellm.APIError, "API error"),
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(litellm.OpenAIError, "OpenAI error"),
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]
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for error_type, message in error_map:
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if isinstance(e, error_type):
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raise LLMRequestFailedError(f"LLM request failed: {message}", str(e)) from e
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raise LLMRequestFailedError(f"LLM request failed: {type(e).__name__}", str(e)) from e
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async def generate(
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self,
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conversation_history: list[dict[str, Any]],
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scan_id: str | None = None,
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step_number: int = 1,
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) -> AsyncIterator[LLMResponse]:
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messages = self._prepare_messages(conversation_history)
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last_error: Exception | None = None
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for attempt in range(MAX_RETRIES):
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try:
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async for response in self._stream_and_accumulate(messages, scan_id, step_number):
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yield response
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return # noqa: TRY300
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except Exception as e: # noqa: BLE001
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last_error = e
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if not _should_retry(e) or attempt == MAX_RETRIES - 1:
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break
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wait_time = min(RETRY_MAX, RETRY_MULTIPLIER * (2**attempt))
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wait_time = max(RETRY_MIN, wait_time)
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await asyncio.sleep(wait_time)
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if last_error:
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self._raise_llm_error(last_error)
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def _extract_chunk_delta(self, chunk: Any) -> str:
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if chunk.choices and hasattr(chunk.choices[0], "delta"):
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delta = chunk.choices[0].delta
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@@ -1,31 +1,12 @@
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import asyncio
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import logging
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import os
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import threading
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import time
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from collections.abc import AsyncIterator
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from typing import Any
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import litellm
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from litellm import completion
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from litellm import acompletion
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from litellm.types.utils import ModelResponseStream
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from tenacity import retry, retry_if_exception, stop_after_attempt, wait_exponential
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logger = logging.getLogger(__name__)
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def should_retry_exception(exception: Exception) -> bool:
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status_code = None
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if hasattr(exception, "status_code"):
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status_code = exception.status_code
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elif hasattr(exception, "response") and hasattr(exception.response, "status_code"):
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status_code = exception.response.status_code
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if status_code is not None:
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return bool(litellm._should_retry(status_code))
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return True
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class LLMRequestQueue:
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@@ -60,22 +41,17 @@ class LLMRequestQueue:
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if sleep_needed > 0:
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await asyncio.sleep(sleep_needed)
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async for chunk in self._reliable_stream_request(completion_args):
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async for chunk in self._stream_request(completion_args):
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yield chunk
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finally:
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self._semaphore.release()
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@retry( # type: ignore[misc]
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stop=stop_after_attempt(3),
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wait=wait_exponential(multiplier=8, min=8, max=64),
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retry=retry_if_exception(should_retry_exception),
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reraise=True,
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)
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async def _reliable_stream_request(
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async def _stream_request(
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self, completion_args: dict[str, Any]
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) -> AsyncIterator[ModelResponseStream]:
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response = await asyncio.to_thread(completion, **completion_args, stream=True)
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for chunk in response:
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response = await acompletion(**completion_args, stream=True)
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async for chunk in response:
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yield chunk
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