9e5a066d26
Adds a proper 'none' provider option so users can run Hindsight as a chunk store with semantic search but without any LLM dependency, replacing the hacky workaround of setting provider to 'mock'. When HINDSIGHT_API_LLM_PROVIDER=none: - Retain automatically uses chunks mode (no fact extraction) - Recall works normally (semantic search, BM25, graph retrieval) - Reflect returns HTTP 400 with clear error message - Consolidation/observations are disabled - Mental model refresh returns HTTP 400 - No API key required
78 lines
2.7 KiB
Python
78 lines
2.7 KiB
Python
"""
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No-op LLM provider for chunk-only storage mode.
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When the LLM provider is set to "none", the system operates without any LLM dependency.
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Retain uses chunks mode (no fact extraction), and reflect/consolidation are disabled.
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This provider acts as a safety net — if any code path unexpectedly tries to call the LLM,
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it raises a clear error instead of a confusing connection failure.
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"""
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import logging
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from typing import Any
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from ..llm_interface import LLMInterface
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from ..response_models import LLMToolCallResult
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logger = logging.getLogger(__name__)
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class LLMNotAvailableError(Exception):
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"""Raised when an operation requires an LLM but the provider is set to 'none'."""
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pass
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class NoneLLM(LLMInterface):
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"""
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No-op LLM provider that rejects all LLM calls.
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Used when HINDSIGHT_API_LLM_PROVIDER=none to run Hindsight as a chunk store
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with semantic search but without LLM-based features (fact extraction, reflect,
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consolidation).
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"""
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async def verify_connection(self) -> None:
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"""No-op — no LLM connection to verify."""
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logger.debug("NoneLLM: no LLM connection to verify (provider=none)")
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async def call(
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self,
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messages: list[dict[str, str]],
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response_format: Any | None = None,
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max_completion_tokens: int | None = None,
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temperature: float | None = None,
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scope: str = "memory",
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max_retries: int = 10,
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initial_backoff: float = 1.0,
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max_backoff: float = 60.0,
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skip_validation: bool = False,
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strict_schema: bool = False,
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return_usage: bool = False,
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) -> Any:
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"""Raise LLMNotAvailableError — no LLM is configured."""
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raise LLMNotAvailableError(
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"LLM provider is set to 'none'. This operation requires an LLM. "
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"Set HINDSIGHT_API_LLM_PROVIDER to a real provider (e.g., openai, anthropic, gemini)."
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)
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async def call_with_tools(
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self,
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messages: list[dict[str, Any]],
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tools: list[dict[str, Any]],
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max_completion_tokens: int | None = None,
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temperature: float | None = None,
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scope: str = "tools",
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max_retries: int = 5,
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initial_backoff: float = 1.0,
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max_backoff: float = 30.0,
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tool_choice: str | dict[str, Any] = "auto",
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) -> LLMToolCallResult:
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"""Raise LLMNotAvailableError — no LLM is configured."""
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raise LLMNotAvailableError(
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"LLM provider is set to 'none'. This operation requires an LLM. "
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"Set HINDSIGHT_API_LLM_PROVIDER to a real provider (e.g., openai, anthropic, gemini)."
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)
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async def cleanup(self) -> None:
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"""No-op — nothing to clean up."""
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pass
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