Fimeg/letta-server
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

chore: refactor not to use warnings.warn (#5730)

Browse Source 
* refactor not to use warnings.warn

* temp circular import fix maybe unecessary/bnad

* fix Deprecation warning

* fix deprecation warning and mcp thing?

* revert changes to mcp server test

* fix deprecation warning
This commit is contained in:
Kian Jones
2025-10-23 22:05:37 -07:00
committed by Caren Thomas
parent c4c9d12f42
commit 704d3b2d79
25 changed files with 127 additions and 96 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
letta/functions/schema_generator.py
View File

@@ -1,5 +1,4 @@
import inspect
import warnings
from typing import Any, Dict, List, Optional, Tuple, Type, Union, get_args, get_origin
from docstring_parser import parse
@@ -101,7 +100,7 @@ def type_to_json_schema_type(py_type) -> dict:
args = get_args(py_type)
if len(args) == 0:
# is this correct
warnings.warn("Defaulting to string type for untyped List")
logger.warning("Defaulting to string type for untyped List")
return {
"type": "array",
"items": {"type": "string"},

3
letta/llm_api/deepseek_client.py
View File

@@ -1,7 +1,6 @@
import json
import os
import re
import warnings
from typing import List, Optional
from openai import AsyncOpenAI, AsyncStream, OpenAI
@@ -130,7 +129,7 @@ def build_deepseek_chat_completions_request(
if llm_config.model:
model = llm_config.model
else:
warnings.warn(f"Model type not set in llm_config: {llm_config.model_dump_json(indent=4)}")
logger.warning(f"Model type not set in llm_config: {llm_config.model_dump_json(indent=4)}")
model = None
if use_tool_naming:
if function_call is None:

11
letta/llm_api/helpers.py
View File

@@ -1,6 +1,5 @@
import copy
import json
import warnings
from collections import OrderedDict
from typing import Any, List, Union
@@ -300,7 +299,7 @@ def unpack_inner_thoughts_from_kwargs(choice: Choice, inner_thoughts_key: str) -
if message.role == "assistant" and message.tool_calls and len(message.tool_calls) >= 1:
if len(message.tool_calls) > 1:
warnings.warn(f"Unpacking inner thoughts from more than one tool call ({len(message.tool_calls)}) is not supported")
logger.warning(f"Unpacking inner thoughts from more than one tool call ({len(message.tool_calls)}) is not supported")
# TODO support multiple tool calls
tool_call = message.tool_calls[0]
@@ -316,20 +315,20 @@ def unpack_inner_thoughts_from_kwargs(choice: Choice, inner_thoughts_key: str) -
new_choice.message.tool_calls[0].function.arguments = json_dumps(func_args)
# also replace the message content
if new_choice.message.content is not None:
warnings.warn(f"Overwriting existing inner monologue ({new_choice.message.content}) with kwarg ({inner_thoughts})")
logger.warning(f"Overwriting existing inner monologue ({new_choice.message.content}) with kwarg ({inner_thoughts})")
new_choice.message.content = inner_thoughts
# update the choice object
rewritten_choice = new_choice
else:
warnings.warn(f"Did not find inner thoughts in tool call: {str(tool_call)}")
logger.warning(f"Did not find inner thoughts in tool call: {str(tool_call)}")
except json.JSONDecodeError as e:
warnings.warn(f"Failed to strip inner thoughts from kwargs: {e}")
logger.warning(f"Failed to strip inner thoughts from kwargs: {e}")
logger.error(f"Failed to strip inner thoughts from kwargs: {e}, Tool call arguments: {tool_call.function.arguments}")
raise e
else:
warnings.warn(f"Did not find tool call in message: {str(message)}")
logger.warning(f"Did not find tool call in message: {str(message)}")
return rewritten_choice

22
letta/llm_api/openai.py
View File

@@ -1,4 +1,3 @@
import warnings
from typing import Generator, List, Optional, Union
import httpx
@@ -70,9 +69,10 @@ def openai_get_model_list(url: str, api_key: Optional[str] = None, fix_url: bool
# In Letta config the address for vLLM is w/o a /v1 suffix for simplicity
# However if we're treating the server as an OpenAI proxy we want the /v1 suffix on our model hit
import warnings
warnings.warn("The synchronous version of openai_get_model_list function is deprecated. Use the async one instead.", DeprecationWarning)
logger.warning(
"The synchronous version of openai_get_model_list function is deprecated. Use the async one instead.",
stacklevel=2,
)
if fix_url:
if not url.endswith("/v1"):
@@ -224,7 +224,7 @@ def build_openai_chat_completions_request(
if llm_config.model:
model = llm_config.model
else:
warnings.warn(f"Model type not set in llm_config: {llm_config.model_dump_json(indent=4)}")
logger.warning(f"Model type not set in llm_config: {llm_config.model_dump_json(indent=4)}")
model = None
if use_tool_naming:
@@ -285,7 +285,7 @@ def build_openai_chat_completions_request(
structured_output_version = convert_to_structured_output(tool.function.model_dump())
tool.function = FunctionSchema(**structured_output_version)
except ValueError as e:
warnings.warn(f"Failed to convert tool function to structured output, tool={tool}, error={e}")
logger.warning(f"Failed to convert tool function to structured output, tool={tool}, error={e}")
return data
@@ -377,7 +377,7 @@ def openai_chat_completions_process_stream(
):
assert isinstance(chat_completion_chunk, ChatCompletionChunkResponse), type(chat_completion_chunk)
if chat_completion_chunk.choices is None or len(chat_completion_chunk.choices) == 0:
warnings.warn(f"No choices in chunk: {chat_completion_chunk}")
logger.warning(f"No choices in chunk: {chat_completion_chunk}")
continue
# NOTE: this assumes that the tool call ID will only appear in one of the chunks during the stream
@@ -472,7 +472,7 @@ def openai_chat_completions_process_stream(
try:
accum_message.tool_calls[tool_call_delta.index].id = tool_call_delta.id
except IndexError:
warnings.warn(
logger.warning(
f"Tool call index out of range ({tool_call_delta.index})\ncurrent tool calls: {accum_message.tool_calls}\ncurrent delta: {tool_call_delta}"
)
# force index 0
@@ -486,14 +486,14 @@ def openai_chat_completions_process_stream(
tool_call_delta.index
].function.name += tool_call_delta.function.name # TODO check for parallel tool calls
except IndexError:
warnings.warn(
logger.warning(
f"Tool call index out of range ({tool_call_delta.index})\ncurrent tool calls: {accum_message.tool_calls}\ncurrent delta: {tool_call_delta}"
)
if tool_call_delta.function.arguments is not None:
try:
accum_message.tool_calls[tool_call_delta.index].function.arguments += tool_call_delta.function.arguments
except IndexError:
warnings.warn(
logger.warning(
f"Tool call index out of range ({tool_call_delta.index})\ncurrent tool calls: {accum_message.tool_calls}\ncurrent delta: {tool_call_delta}"
)
@@ -642,7 +642,7 @@ def prepare_openai_payload(chat_completion_request: ChatCompletionRequest):
# try:
# tool["function"] = convert_to_structured_output(tool["function"])
# except ValueError as e:
# warnings.warn(f"Failed to convert tool function to structured output, tool={tool}, error={e}")
# logger.warning(f"Failed to convert tool function to structured output, tool={tool}, error={e}")
if not supports_parallel_tool_calling(chat_completion_request.model):
data.pop("parallel_tool_calls", None)

7
letta/local_llm/constants.py
View File

@@ -1,9 +1,10 @@
# Import constants from settings to avoid circular import
# (settings.py imports from this module indirectly through log.py)
# Import this here to avoid circular dependency at module level
from letta.local_llm.llm_chat_completion_wrappers.chatml import ChatMLInnerMonologueWrapper
from letta.settings import DEFAULT_WRAPPER_NAME, INNER_THOUGHTS_KWARG
DEFAULT_WRAPPER = ChatMLInnerMonologueWrapper
DEFAULT_WRAPPER_NAME = "chatml"
INNER_THOUGHTS_KWARG = "thinking"
INNER_THOUGHTS_KWARG_VERTEX = "thinking"
VALID_INNER_THOUGHTS_KWARGS = ("thinking", "inner_thoughts")
INNER_THOUGHTS_KWARG_DESCRIPTION = "Deep inner monologue private to you only."

7
letta/local_llm/json_parser.py
View File

@@ -1,6 +1,9 @@
import json
import re
import warnings
from letta.log import get_logger
logger = get_logger(__name__)
from letta.errors import LLMJSONParsingError
from letta.helpers.json_helpers import json_loads
@@ -83,7 +86,7 @@ def clean_and_interpret_send_message_json(json_string):
kwarg = model_settings.inner_thoughts_kwarg
if kwarg not in VALID_INNER_THOUGHTS_KWARGS:
warnings.warn(f"INNER_THOUGHTS_KWARG is not valid: {kwarg}")
logger.warning(f"INNER_THOUGHTS_KWARG is not valid: {kwarg}")
kwarg = INNER_THOUGHTS_KWARG
# If normal parsing fails, attempt to clean and extract manually

5
letta/local_llm/utils.py
View File

@@ -1,5 +1,4 @@
import os
import warnings
from typing import List, Union
import requests
@@ -84,11 +83,11 @@ def num_tokens_from_functions(functions: List[dict], model: str = "gpt-4"):
function_tokens = len(encoding.encode(function["name"]))
if function["description"]:
if not isinstance(function["description"], str):
warnings.warn(f"Function {function['name']} has non-string description: {function['description']}")
logger.warning(f"Function {function['name']} has non-string description: {function['description']}")
else:
function_tokens += len(encoding.encode(function["description"]))
else:
warnings.warn(f"Function {function['name']} has no description, function: {function}")
logger.warning(f"Function {function['name']} has no description, function: {function}")
if "parameters" in function:
parameters = function["parameters"]

6
letta/prompts/prompt_generator.py
View File

@@ -1,6 +1,10 @@
from datetime import datetime
from typing import List, Literal, Optional
from letta.log import get_logger
logger = get_logger(__name__)
from letta.constants import IN_CONTEXT_MEMORY_KEYWORD
from letta.helpers import ToolRulesSolver
from letta.helpers.datetime_helpers import format_datetime, get_local_time_fast
@@ -137,7 +141,7 @@ class PromptGenerator:
if append_icm_if_missing:
if memory_variable_string not in system_prompt:
# In this case, append it to the end to make sure memory is still injected
# warnings.warn(f"{IN_CONTEXT_MEMORY_KEYWORD} variable was missing from system prompt, appending instead")
# logger.warning(f"{IN_CONTEXT_MEMORY_KEYWORD} variable was missing from system prompt, appending instead")
system_prompt += "\n\n" + memory_variable_string
# render the variables using the built-in templater

6
letta/schemas/memory.py
View File

@@ -4,6 +4,10 @@ from datetime import datetime
from io import StringIO
from typing import TYPE_CHECKING, List, Optional, Union
from letta.log import get_logger
logger = get_logger(__name__)
from openai.types.beta.function_tool import FunctionTool as OpenAITool
from pydantic import BaseModel, Field, field_validator
@@ -319,7 +323,7 @@ class Memory(BaseModel, validate_assignment=True):
"""Deprecated: use compile() instead."""
import warnings
warnings.warn("compile_in_thread_async is deprecated; use compile()", DeprecationWarning, stacklevel=2)
logger.warning("compile_in_thread_async is deprecated; use compile()", stacklevel=2)
return self.compile(tool_usage_rules=tool_usage_rules, sources=sources, max_files_open=max_files_open, llm_config=llm_config)
def list_block_labels(self) -> List[str]:

19
letta/schemas/message.py
View File

@@ -1,10 +1,13 @@
from __future__ import annotations
from letta.log import get_logger
logger = get_logger(__name__)
import copy
import json
import re
import uuid
import warnings
from collections import OrderedDict
from datetime import datetime, timezone
from enum import Enum
@@ -72,7 +75,7 @@ def add_inner_thoughts_to_tool_call(
updated_tool_call.function.arguments = json_dumps(ordered_args)
return updated_tool_call
except json.JSONDecodeError as e:
warnings.warn(f"Failed to put inner thoughts in kwargs: {e}")
logger.warning(f"Failed to put inner thoughts in kwargs: {e}")
raise e
@@ -510,7 +513,7 @@ class Message(BaseMessage):
)
else:
warnings.warn(f"Unrecognized content part in assistant message: {content_part}")
logger.warning(f"Unrecognized content part in assistant message: {content_part}")
return messages
@@ -1193,7 +1196,7 @@ class Message(BaseMessage):
if bool(re.match(r"^[^\s<|\\/>]+$", self.name)):
openai_message["name"] = self.name
else:
warnings.warn(f"Using OpenAI with invalid 'name' field (name={self.name} role={self.role}).")
logger.warning(f"Using OpenAI with invalid 'name' field (name={self.name} role={self.role}).")
if parse_content_parts and self.content is not None:
for content in self.content:
@@ -1260,7 +1263,7 @@ class Message(BaseMessage):
if bool(re.match(r"^[^\s<|\\/>]+$", self.name)):
user_dict["name"] = self.name
else:
warnings.warn(f"Using OpenAI with invalid 'name' field (name={self.name} role={self.role}).")
logger.warning(f"Using OpenAI with invalid 'name' field (name={self.name} role={self.role}).")
message_dicts.append(user_dict)
@@ -1597,7 +1600,7 @@ class Message(BaseMessage):
text_content = None
if self.role != "tool" and self.name is not None:
warnings.warn(f"Using Google AI with non-null 'name' field (name={self.name} role={self.role}), not yet supported.")
logger.warning(f"Using Google AI with non-null 'name' field (name={self.name} role={self.role}), not yet supported.")
if self.role == "system":
# NOTE: Gemini API doesn't have a 'system' role, use 'user' instead
@@ -1717,7 +1720,7 @@ class Message(BaseMessage):
assert self.tool_call_id is not None, vars(self)
if self.name is None:
warnings.warn("Couldn't find function name on tool call, defaulting to tool ID instead.")
logger.warning("Couldn't find function name on tool call, defaulting to tool ID instead.")
function_name = self.tool_call_id
else:
function_name = self.name
@@ -1750,7 +1753,7 @@ class Message(BaseMessage):
if "parts" not in google_ai_message or not google_ai_message["parts"]:
# If parts is empty, add a default text part
google_ai_message["parts"] = [{"text": "empty message"}]
warnings.warn(
logger.warning(
f"Empty 'parts' detected in message with role '{self.role}'. Added default empty text part. Full message:\n{vars(self)}"
)

7
letta/schemas/providers/anthropic.py
View File

@@ -1,6 +1,9 @@
import warnings
from typing import Literal
from letta.log import get_logger
logger = get_logger(__name__)
import anthropic
from pydantic import Field
@@ -149,7 +152,7 @@ class AnthropicProvider(Provider):
model["context_window"] = model_library[model["id"]]
else:
# On fallback, we can set 200k (generally safe), but we should warn the user
warnings.warn(f"Couldn't find context window size for model {model['id']}, defaulting to 200,000")
logger.warning(f"Couldn't find context window size for model {model['id']}, defaulting to 200,000")
model["context_window"] = 200000
# Optional override: enable 1M context for Sonnet 4/4.5 when flag is set

8
letta/schemas/providers/base.py
View File

@@ -1,5 +1,9 @@
from datetime import datetime
from letta.log import get_logger
logger = get_logger(__name__)
from pydantic import BaseModel, Field, model_validator
from letta.schemas.embedding_config import EmbeddingConfig
@@ -90,7 +94,7 @@ class Provider(ProviderBase):
import asyncio
import warnings
warnings.warn("list_llm_models is deprecated, use list_llm_models_async instead", DeprecationWarning, stacklevel=2)
logger.warning("list_llm_models is deprecated, use list_llm_models_async instead", stacklevel=2)
# Simplified asyncio handling - just use asyncio.run()
# This works in most contexts and avoids complex event loop detection
@@ -115,7 +119,7 @@ class Provider(ProviderBase):
import asyncio
import warnings
warnings.warn("list_embedding_models is deprecated, use list_embedding_models_async instead", DeprecationWarning, stacklevel=2)
logger.warning("list_embedding_models is deprecated, use list_embedding_models_async instead", stacklevel=2)
# Simplified asyncio handling - just use asyncio.run()
# This works in most contexts and avoids complex event loop detection

7
letta/schemas/providers/cerebras.py
View File

@@ -1,6 +1,9 @@
import warnings
from typing import Literal
from letta.log import get_logger
logger = get_logger(__name__)
from pydantic import Field
from letta.schemas.enums import ProviderCategory, ProviderType
@@ -58,7 +61,7 @@ class CerebrasProvider(OpenAIProvider):
context_window_size = self.get_model_context_window_size(model_name)
if not context_window_size:
warnings.warn(f"Couldn't find context window size for model {model_name}")
logger.warning(f"Couldn't find context window size for model {model_name}")
continue
# Cerebras supports function calling

6
letta/schemas/providers/google_gemini.py
View File

@@ -1,6 +1,10 @@
import asyncio
from typing import Literal
from letta.log import get_logger
logger = get_logger(__name__)
from pydantic import Field
from letta.constants import DEFAULT_EMBEDDING_CHUNK_SIZE, LLM_MAX_TOKENS
@@ -88,7 +92,7 @@ class GoogleAIProvider(Provider):
def get_model_context_window(self, model_name: str) -> int | None:
import warnings
warnings.warn("This is deprecated, use get_model_context_window_async when possible.", DeprecationWarning)
logger.warning("This is deprecated, use get_model_context_window_async when possible.")
from letta.llm_api.google_ai_client import google_ai_get_model_context_window
if model_name in LLM_MAX_TOKENS:

15
letta/schemas/providers/lmstudio.py
View File

@@ -1,6 +1,9 @@
import warnings
from typing import Literal
from letta.log import get_logger
logger = get_logger(__name__)
from pydantic import Field
from letta.constants import DEFAULT_EMBEDDING_CHUNK_SIZE
@@ -27,14 +30,14 @@ class LMStudioOpenAIProvider(OpenAIProvider):
response = await openai_get_model_list_async(self.model_endpoint_url)
if "data" not in response:
warnings.warn(f"LMStudio OpenAI model query response missing 'data' field: {response}")
logger.warning(f"LMStudio OpenAI model query response missing 'data' field: {response}")
return []
configs = []
for model in response["data"]:
model_type = model.get("type")
if not model_type:
warnings.warn(f"LMStudio OpenAI model missing 'type' field: {model}")
logger.warning(f"LMStudio OpenAI model missing 'type' field: {model}")
continue
if model_type not in ("vlm", "llm"):
continue
@@ -48,7 +51,7 @@ class LMStudioOpenAIProvider(OpenAIProvider):
if "compatibility_type" in model:
compatibility_type = model["compatibility_type"]
else:
warnings.warn(f"LMStudio OpenAI model missing 'compatibility_type' field: {model}")
logger.warning(f"LMStudio OpenAI model missing 'compatibility_type' field: {model}")
continue
configs.append(
@@ -72,14 +75,14 @@ class LMStudioOpenAIProvider(OpenAIProvider):
response = await openai_get_model_list_async(self.model_endpoint_url)
if "data" not in response:
warnings.warn(f"LMStudio OpenAI model query response missing 'data' field: {response}")
logger.warning(f"LMStudio OpenAI model query response missing 'data' field: {response}")
return []
configs = []
for model in response["data"]:
model_type = model.get("type")
if not model_type:
warnings.warn(f"LMStudio OpenAI model missing 'type' field: {model}")
logger.warning(f"LMStudio OpenAI model missing 'type' field: {model}")
continue
if model_type not in ("embeddings"):
continue

6
letta/schemas/providers/together.py
View File

@@ -4,6 +4,10 @@ Note: this supports completions (deprecated by openai) and chat completions via
from typing import Literal, Optional
from letta.log import get_logger
logger = get_logger(__name__)
from pydantic import Field
from letta.constants import MIN_CONTEXT_WINDOW
@@ -33,7 +37,7 @@ class TogetherProvider(OpenAIProvider):
async def list_embedding_models_async(self) -> list[EmbeddingConfig]:
import warnings
warnings.warn(
logger.warning(
"Letta does not currently support listing embedding models for Together. Please "
"contact support or reach out via GitHub or Discord to get support."
)

7
letta/schemas/providers/xai.py
View File

@@ -1,6 +1,9 @@
import warnings
from typing import Literal
from letta.log import get_logger
logger = get_logger(__name__)
from pydantic import Field
from letta.schemas.enums import ProviderCategory, ProviderType
@@ -49,7 +52,7 @@ class XAIProvider(OpenAIProvider):
context_window_size = self.get_model_context_window_size(model_name)
if not context_window_size:
warnings.warn(f"Couldn't find context window size for model {model_name}")
logger.warning(f"Couldn't find context window size for model {model_name}")
continue
configs.append(

11
letta/server/rest_api/interface.py
View File

@@ -1,7 +1,10 @@
import asyncio
import json
import queue
import warnings
from letta.log import get_logger
logger = get_logger(__name__)
from collections import deque
from datetime import datetime
from typing import AsyncGenerator, Literal, Optional, Union
@@ -503,7 +506,7 @@ class StreamingServerInterface(AgentChunkStreamingInterface):
data: {"function_return": "None", "status": "success", "date": "2024-02-29T06:07:50.847262+00:00"}
"""
if not chunk.choices or len(chunk.choices) == 0:
warnings.warn(f"No choices in chunk: {chunk}")
logger.warning(f"No choices in chunk: {chunk}")
return None
choice = chunk.choices[0]
@@ -1028,7 +1031,7 @@ class StreamingServerInterface(AgentChunkStreamingInterface):
# created=1713216662
# model='gpt-4o-mini-2024-07-18'
# object='chat.completion.chunk'
warnings.warn(f"Couldn't find delta in chunk: {chunk}")
logger.warning(f"Couldn't find delta in chunk: {chunk}")
return None
return processed_chunk
@@ -1255,7 +1258,7 @@ class StreamingServerInterface(AgentChunkStreamingInterface):
try:
func_args = parse_json(function_call.function.arguments)
except:
warnings.warn(f"Failed to parse function arguments: {function_call.function.arguments}")
logger.warning(f"Failed to parse function arguments: {function_call.function.arguments}")
func_args = {}
if (

9
letta/server/server.py
View File

@@ -2,7 +2,6 @@ import asyncio
import json
import os
import traceback
import warnings
from abc import abstractmethod
from datetime import datetime
from pathlib import Path
@@ -1149,9 +1148,9 @@ class SyncServer(object):
# llm_config = LLMConfig(**config_data)
# llm_models.append(llm_config)
# except (json.JSONDecodeError, ValueError) as e:
# warnings.warn(f"Error parsing LLM config file {filename}: {e}")
# logger.warning(f"Error parsing LLM config file {filename}: {e}")
# except Exception as e:
# warnings.warn(f"Error reading LLM configs directory: {e}")
# logger.warning(f"Error reading LLM configs directory: {e}")
return llm_models
def get_local_embedding_configs(self):
@@ -1169,9 +1168,9 @@ class SyncServer(object):
# embedding_config = EmbeddingConfig(**config_data)
# embedding_models.append(embedding_config)
# except (json.JSONDecodeError, ValueError) as e:
# warnings.warn(f"Error parsing embedding config file {filename}: {e}")
# logger.warning(f"Error parsing embedding config file {filename}: {e}")
# except Exception as e:
# warnings.warn(f"Error reading embedding configs directory: {e}")
# logger.warning(f"Error reading embedding configs directory: {e}")
return embedding_models
def add_llm_model(self, request: LLMConfig) -> LLMConfig:

3
letta/services/agent_manager.py
View File

@@ -1954,9 +1954,8 @@ class AgentManager:
"""
import warnings
warnings.warn(
logger.warning(
"list_passages_async is deprecated. Use query_source_passages_async or query_agent_passages_async instead.",
DeprecationWarning,
stacklevel=2,
)

6
letta/services/helpers/agent_manager_helper.py
View File

@@ -2,6 +2,10 @@ import uuid
from datetime import datetime
from typing import List, Literal, Optional, Set
from letta.log import get_logger
logger = get_logger(__name__)
import numpy as np
from sqlalchemy import Select, and_, asc, desc, func, literal, nulls_last, or_, select, union_all
from sqlalchemy.orm import noload
@@ -304,7 +308,7 @@ def compile_system_message(
if append_icm_if_missing:
if memory_variable_string not in system_prompt:
# In this case, append it to the end to make sure memory is still injected
# warnings.warn(f"{IN_CONTEXT_MEMORY_KEYWORD} variable was missing from system prompt, appending instead")
# logger.warning(f"{IN_CONTEXT_MEMORY_KEYWORD} variable was missing from system prompt, appending instead")
system_prompt += "\n\n" + memory_variable_string
# render the variables using the built-in templater

30
letta/services/passage_manager.py
View File

@@ -120,12 +120,8 @@ class PassageManager:
@trace_method
async def get_passage_by_id_async(self, passage_id: str, actor: PydanticUser) -> Optional[PydanticPassage]:
"""DEPRECATED: Use get_agent_passage_by_id_async() or get_source_passage_by_id_async() instead."""
import warnings
warnings.warn(
"get_passage_by_id_async is deprecated. Use get_agent_passage_by_id_async() or get_source_passage_by_id_async() instead.",
DeprecationWarning,
stacklevel=2,
logger.warning(
"get_passage_by_id_async is deprecated. Use get_agent_passage_by_id_async() or get_source_passage_by_id_async() instead."
)
async with db_registry.async_session() as session:
@@ -231,13 +227,7 @@ class PassageManager:
@trace_method
async def create_passage_async(self, pydantic_passage: PydanticPassage, actor: PydanticUser) -> PydanticPassage:
"""DEPRECATED: Use create_agent_passage_async() or create_source_passage_async() instead."""
import warnings
warnings.warn(
"create_passage_async is deprecated. Use create_agent_passage_async() or create_source_passage_async() instead.",
DeprecationWarning,
stacklevel=2,
)
logger.warning("create_passage_async is deprecated. Use create_agent_passage_async() or create_source_passage_async() instead.")
# Common fields for both passage types
passage = self._preprocess_passage_for_creation(pydantic_passage=pydantic_passage)
@@ -365,9 +355,8 @@ class PassageManager:
"""DEPRECATED: Use create_many_agent_passages() or create_many_source_passages() instead."""
import warnings
warnings.warn(
logger.warning(
"create_many_passages is deprecated. Use create_many_agent_passages() or create_many_source_passages() instead.",
DeprecationWarning,
stacklevel=2,
)
return [self.create_passage(p, actor) for p in passages]
@@ -378,9 +367,8 @@ class PassageManager:
"""DEPRECATED: Use create_many_agent_passages_async() or create_many_source_passages_async() instead."""
import warnings
warnings.warn(
logger.warning(
"create_many_passages_async is deprecated. Use create_many_agent_passages_async() or create_many_source_passages_async() instead.",
DeprecationWarning,
stacklevel=2,
)
@@ -651,9 +639,8 @@ class PassageManager:
"""DEPRECATED: Use delete_agent_passage_by_id_async() or delete_source_passage_by_id_async() instead."""
import warnings
warnings.warn(
logger.warning(
"delete_passage_by_id_async is deprecated. Use delete_agent_passage_by_id_async() or delete_source_passage_by_id_async() instead.",
DeprecationWarning,
stacklevel=2,
)
@@ -765,9 +752,8 @@ class PassageManager:
"""DEPRECATED: Use delete_agent_passages() or delete_source_passages() instead."""
import warnings
warnings.warn(
logger.warning(
"delete_passages is deprecated. Use delete_agent_passages() or delete_source_passages() instead.",
DeprecationWarning,
stacklevel=2,
)
# TODO: This is very inefficient
@@ -787,7 +773,7 @@ class PassageManager:
"""DEPRECATED: Use agent_passage_size() instead (this only counted agent passages anyway)."""
import warnings
warnings.warn("size is deprecated. Use agent_passage_size() instead.", DeprecationWarning, stacklevel=2)
logger.warning("size is deprecated. Use agent_passage_size() instead.", stacklevel=2)
return self.agent_passage_size(actor=actor, agent_id=agent_id)
@enforce_types

3
letta/services/tool_manager.py
View File

@@ -1,5 +1,4 @@
import importlib
import warnings
from typing import List, Optional, Set, Union
from sqlalchemy import and_, func, or_, select
@@ -636,7 +635,7 @@ class ToolManager:
module = importlib.import_module(module_name)
functions_to_schema.update(load_function_set(module))
except ValueError as e:
warnings.warn(f"Error loading function set '{module_name}': {e}")
logger.warning(f"Error loading function set '{module_name}': {e}")
except Exception as e:
raise e

5
letta/settings.py
View File

@@ -6,10 +6,13 @@ from typing import Optional
from pydantic import AliasChoices, Field
from pydantic_settings import BaseSettings, SettingsConfigDict
from letta.local_llm.constants import DEFAULT_WRAPPER_NAME, INNER_THOUGHTS_KWARG
from letta.schemas.enums import SandboxType
from letta.services.summarizer.enums import SummarizationMode
# Define constants here to avoid circular import with letta.log
DEFAULT_WRAPPER_NAME = "chatml"
INNER_THOUGHTS_KWARG = "thinking"
class ToolSettings(BaseSettings):
# Sandbox Configurations

11
letta/system.py
View File

@@ -1,7 +1,10 @@
import json
import warnings
from typing import Optional
from letta.log import get_logger
logger = get_logger(__name__)
from .constants import (
INITIAL_BOOT_MESSAGE,
INITIAL_BOOT_MESSAGE_SEND_MESSAGE_FIRST_MSG,
@@ -160,7 +163,7 @@ def package_system_message(system_message, timezone, message_type="system_alert"
try:
message_json = json.loads(system_message)
if "type" in message_json and message_json["type"] == message_type:
warnings.warn(f"Attempted to pack a system message that is already packed. Not packing: '{system_message}'")
logger.warning(f"Attempted to pack a system message that is already packed. Not packing: '{system_message}'")
return system_message
except:
pass # do nothing, expected behavior that the message is not JSON
@@ -251,7 +254,7 @@ def unpack_message(packed_message: str) -> str:
if "type" in message_json and message_json["type"] in ["login", "heartbeat"]:
# This is a valid user message that the ADE expects, so don't print warning
return packed_message
warnings.warn(f"Was unable to find 'message' field in packed message object: '{packed_message}'")
logger.warning(f"Was unable to find 'message' field in packed message object: '{packed_message}'")
return packed_message
else:
try:
@@ -260,6 +263,6 @@ def unpack_message(packed_message: str) -> str:
return packed_message
if message_type != "user_message":
warnings.warn(f"Expected type to be 'user_message', but was '{message_type}', so not unpacking: '{packed_message}'")
logger.warning(f"Expected type to be 'user_message', but was '{message_type}', so not unpacking: '{packed_message}'")
return packed_message
return message_json.get("message")