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fix: undo recent changes to handle matching (#5808)

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This commit is contained in:
Sarah Wooders
2025-10-28 21:50:35 -07:00
committed by Caren Thomas
parent 655c9489d8
commit f37197f00d
1 changed files with 62 additions and 37 deletions
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99
letta/server/server.py
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@@ -1034,67 +1034,92 @@ class SyncServer(object):
max_reasoning_tokens: Optional[int] = None,
enable_reasoner: Optional[bool] = None,
) -> LLMConfig:
"""String match the `handle` to the available configs"""
matched_llm_config = None
available_handles = []
# Get all enabled providers (including BYOK providers from database)
providers = await self.get_enabled_providers_async(actor=actor)
for provider in providers:
llm_configs = await self.list_llm_models_async(actor=actor)
for llm_config in llm_configs:
available_handles.append(llm_config.handle)
if llm_config.handle == handle:
matched_llm_config = llm_config
break
if not matched_llm_config:
raise HandleNotFoundError(handle, available_handles)
try:
provider_name, model_name = handle.split("/", 1)
provider = await self.get_provider_from_name_async(provider_name, actor)
all_llm_configs = await provider.list_llm_models_async()
llm_configs = [config for config in all_llm_configs if config.handle == handle]
if not llm_configs:
llm_configs = [config for config in all_llm_configs if config.model == model_name]
if not llm_configs:
available_handles = [config.handle for config in all_llm_configs]
raise HandleNotFoundError(handle, available_handles)
except ValueError as e:
llm_configs = [config for config in self.get_local_llm_configs() if config.handle == handle]
if not llm_configs:
llm_configs = [config for config in self.get_local_llm_configs() if config.model == model_name]
if not llm_configs:
raise e
if len(llm_configs) == 1:
llm_config = llm_configs[0]
elif len(llm_configs) > 1:
raise LettaInvalidArgumentError(
f"Multiple LLM models with name {model_name} supported by {provider_name}", argument_name="model_name"
)
else:
llm_config = llm_configs[0]
if context_window_limit is not None:
if context_window_limit > matched_llm_config.context_window:
if context_window_limit > llm_config.context_window:
raise LettaInvalidArgumentError(
f"Context window limit ({context_window_limit}) is greater than maximum of ({matched_llm_config.context_window})",
f"Context window limit ({context_window_limit}) is greater than maximum of ({llm_config.context_window})",
argument_name="context_window_limit",
)
matched_llm_config.context_window = context_window_limit
llm_config.context_window = context_window_limit
else:
matched_llm_config.context_window = min(matched_llm_config.context_window, model_settings.global_max_context_window_limit)
llm_config.context_window = min(llm_config.context_window, model_settings.global_max_context_window_limit)
if max_tokens is not None:
matched_llm_config.max_tokens = max_tokens
llm_config.max_tokens = max_tokens
if max_reasoning_tokens is not None:
if not max_tokens or max_reasoning_tokens > max_tokens:
raise LettaInvalidArgumentError(
f"Max reasoning tokens ({max_reasoning_tokens}) must be less than max tokens ({max_tokens})",
argument_name="max_reasoning_tokens",
)
matched_llm_config.max_reasoning_tokens = max_reasoning_tokens
llm_config.max_reasoning_tokens = max_reasoning_tokens
if enable_reasoner is not None:
matched_llm_config.enable_reasoner = enable_reasoner
if enable_reasoner and matched_llm_config.model_endpoint_type == "anthropic":
matched_llm_config.put_inner_thoughts_in_kwargs = False
llm_config.enable_reasoner = enable_reasoner
if enable_reasoner and llm_config.model_endpoint_type == "anthropic":
llm_config.put_inner_thoughts_in_kwargs = False
return matched_llm_config
return llm_config
@trace_method
async def get_embedding_config_from_handle_async(
self, actor: User, handle: str, embedding_chunk_size: int = constants.DEFAULT_EMBEDDING_CHUNK_SIZE
) -> EmbeddingConfig:
matched_embedding_config = None
available_handles = []
# Get all enabled providers (including BYOK providers from database)
providers = await self.get_enabled_providers_async(actor=actor)
for provider in providers:
embedding_configs = await self.list_embedding_models_async(actor=actor)
for embedding_config in embedding_configs:
available_handles.append(embedding_config.handle)
if embedding_config.handle == handle:
matched_embedding_config = embedding_config
break
try:
provider_name, model_name = handle.split("/", 1)
provider = await self.get_provider_from_name_async(provider_name, actor)
all_embedding_configs = await provider.list_embedding_models_async()
embedding_configs = [config for config in all_embedding_configs if config.handle == handle]
if not embedding_configs:
raise LettaInvalidArgumentError(
f"Embedding model {model_name} is not supported by {provider_name}", argument_name="model_name"
)
except LettaInvalidArgumentError as e:
# search local configs
embedding_configs = [config for config in self.get_local_embedding_configs() if config.handle == handle]
if not embedding_configs:
raise e
if len(embedding_configs) == 1:
embedding_config = embedding_configs[0]
elif len(embedding_configs) > 1:
raise LettaInvalidArgumentError(
f"Multiple embedding models with name {model_name} supported by {provider_name}", argument_name="model_name"
)
else:
embedding_config = embedding_configs[0]
if embedding_chunk_size:
matched_embedding_config.embedding_chunk_size = embedding_chunk_size
embedding_config.embedding_chunk_size = embedding_chunk_size
return matched_embedding_config
return embedding_config
async def get_provider_from_name_async(self, provider_name: str, actor: User) -> Provider:
all_providers = await self.get_enabled_providers_async(actor)