refactor: replace litellm with native openai + anthropic SDKs
- Remove litellm dependency entirely (supply chain risk mitigation) - Add AnthropicProvider (native SDK) and OpenAICompatProvider (unified) - Merge CustomProvider into OpenAICompatProvider, delete custom_provider.py - Add ProviderSpec.backend field for declarative provider routing - Remove _resolve_model, find_gateway, find_by_model (dead heuristics) - Pass resolved spec directly into provider — zero internal lookups - Stub out litellm-dependent model database (cli/models.py) - Add anthropic>=0.45.0 to dependencies, remove litellm - 593 tests passed, net -1034 lines
This commit is contained in:
Xubin Ren
Xubin Ren
+7
-207
@@ -1,229 +1,29 @@
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"""Model information helpers for the onboard wizard.
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Provides model context window lookup and autocomplete suggestions using litellm.
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Model database / autocomplete is temporarily disabled while litellm is
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being replaced. All public function signatures are preserved so callers
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continue to work without changes.
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"""
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from __future__ import annotations
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from functools import lru_cache
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from typing import Any
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def _litellm():
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"""Lazy accessor for litellm (heavy import deferred until actually needed)."""
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import litellm as _ll
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return _ll
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@lru_cache(maxsize=1)
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def _get_model_cost_map() -> dict[str, Any]:
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"""Get litellm's model cost map (cached)."""
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return getattr(_litellm(), "model_cost", {})
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@lru_cache(maxsize=1)
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def get_all_models() -> list[str]:
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"""Get all known model names from litellm.
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"""
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models = set()
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# From model_cost (has pricing info)
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cost_map = _get_model_cost_map()
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for k in cost_map.keys():
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if k != "sample_spec":
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models.add(k)
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# From models_by_provider (more complete provider coverage)
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for provider_models in getattr(_litellm(), "models_by_provider", {}).values():
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if isinstance(provider_models, (set, list)):
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models.update(provider_models)
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return sorted(models)
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def _normalize_model_name(model: str) -> str:
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"""Normalize model name for comparison."""
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return model.lower().replace("-", "_").replace(".", "")
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return []
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def find_model_info(model_name: str) -> dict[str, Any] | None:
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"""Find model info with fuzzy matching.
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Args:
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model_name: Model name in any common format
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Returns:
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Model info dict or None if not found
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"""
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cost_map = _get_model_cost_map()
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if not cost_map:
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return None
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# Direct match
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if model_name in cost_map:
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return cost_map[model_name]
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# Extract base name (without provider prefix)
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base_name = model_name.split("/")[-1] if "/" in model_name else model_name
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base_normalized = _normalize_model_name(base_name)
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candidates = []
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for key, info in cost_map.items():
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if key == "sample_spec":
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continue
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key_base = key.split("/")[-1] if "/" in key else key
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key_base_normalized = _normalize_model_name(key_base)
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# Score the match
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score = 0
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# Exact base name match (highest priority)
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if base_normalized == key_base_normalized:
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score = 100
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# Base name contains model
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elif base_normalized in key_base_normalized:
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score = 80
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# Model contains base name
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elif key_base_normalized in base_normalized:
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score = 70
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# Partial match
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elif base_normalized[:10] in key_base_normalized:
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score = 50
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if score > 0:
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# Prefer models with max_input_tokens
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if info.get("max_input_tokens"):
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score += 10
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candidates.append((score, key, info))
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if not candidates:
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return None
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# Return the best match
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candidates.sort(key=lambda x: (-x[0], x[1]))
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return candidates[0][2]
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def get_model_context_limit(model: str, provider: str = "auto") -> int | None:
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"""Get the maximum input context tokens for a model.
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Args:
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model: Model name (e.g., "claude-3.5-sonnet", "gpt-4o")
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provider: Provider name for informational purposes (not yet used for filtering)
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Returns:
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Maximum input tokens, or None if unknown
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Note:
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The provider parameter is currently informational only. Future versions may
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use it to prefer provider-specific model variants in the lookup.
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"""
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# First try fuzzy search in model_cost (has more accurate max_input_tokens)
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info = find_model_info(model)
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if info:
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# Prefer max_input_tokens (this is what we want for context window)
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max_input = info.get("max_input_tokens")
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if max_input and isinstance(max_input, int):
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return max_input
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# Fall back to litellm's get_max_tokens (returns max_output_tokens typically)
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try:
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result = _litellm().get_max_tokens(model)
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if result and result > 0:
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return result
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except (KeyError, ValueError, AttributeError):
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# Model not found in litellm's database or invalid response
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pass
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# Last resort: use max_tokens from model_cost
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if info:
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max_tokens = info.get("max_tokens")
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if max_tokens and isinstance(max_tokens, int):
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return max_tokens
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return None
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@lru_cache(maxsize=1)
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def _get_provider_keywords() -> dict[str, list[str]]:
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"""Build provider keywords mapping from nanobot's provider registry.
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Returns:
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Dict mapping provider name to list of keywords for model filtering.
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"""
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try:
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from nanobot.providers.registry import PROVIDERS
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mapping = {}
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for spec in PROVIDERS:
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if spec.keywords:
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mapping[spec.name] = list(spec.keywords)
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return mapping
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except ImportError:
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return {}
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def get_model_context_limit(model: str, provider: str = "auto") -> int | None:
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return None
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def get_model_suggestions(partial: str, provider: str = "auto", limit: int = 20) -> list[str]:
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"""Get autocomplete suggestions for model names.
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Args:
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partial: Partial model name typed by user
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provider: Provider name for filtering (e.g., "openrouter", "minimax")
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limit: Maximum number of suggestions to return
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Returns:
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List of matching model names
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"""
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all_models = get_all_models()
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if not all_models:
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return []
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partial_lower = partial.lower()
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partial_normalized = _normalize_model_name(partial)
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# Get provider keywords from registry
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provider_keywords = _get_provider_keywords()
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# Filter by provider if specified
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allowed_keywords = None
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if provider and provider != "auto":
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allowed_keywords = provider_keywords.get(provider.lower())
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matches = []
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for model in all_models:
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model_lower = model.lower()
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# Apply provider filter
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if allowed_keywords:
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if not any(kw in model_lower for kw in allowed_keywords):
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continue
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# Match against partial input
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if not partial:
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matches.append(model)
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continue
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if partial_lower in model_lower:
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# Score by position of match (earlier = better)
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pos = model_lower.find(partial_lower)
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score = 100 - pos
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matches.append((score, model))
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elif partial_normalized in _normalize_model_name(model):
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score = 50
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matches.append((score, model))
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# Sort by score if we have scored matches
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if matches and isinstance(matches[0], tuple):
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matches.sort(key=lambda x: (-x[0], x[1]))
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matches = [m[1] for m in matches]
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else:
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matches.sort()
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return matches[:limit]
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return []
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def format_token_count(tokens: int) -> str:
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