feat: upgrade langchain to 0.0.311

api/core/agent/agent/openai_function_call.py

 from typing import List, Tuple, Any, Union, Sequence, Optional
 
 from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent
-from langchain.agents.openai_functions_agent.base import _parse_ai_message, \
-    _format_intermediate_steps
+from langchain.agents.format_scratchpad import format_to_openai_functions
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import Callbacks
 from langchain.chat_models.openai import _convert_message_to_dict, _import_tiktoken
@@ -94,6 +94,7 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixi
             self,
             intermediate_steps: List[Tuple[AgentAction, str]],
             callbacks: Callbacks = None,
+            with_functions: bool = True,
             **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
         """Given input, decided what to do.
@@ -105,7 +106,7 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixi
         Returns:
             Action specifying what tool to use.
         """
-        agent_scratchpad = _format_intermediate_steps(intermediate_steps)
+        agent_scratchpad = format_to_openai_functions(intermediate_steps)
         selected_inputs = {
             k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
         }

api/core/agent/agent/structed_multi_dataset_router_agent.py

 import re
 from typing import List, Tuple, Any, Union, Sequence, Optional, cast
 
-from langchain import BasePromptTemplate, PromptTemplate
 from langchain.agents import StructuredChatAgent, AgentOutputParser, Agent
 from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import Callbacks
 from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
-from langchain.schema import AgentAction, AgentFinish, OutputParserException
+from langchain.schema import AgentAction, AgentFinish, OutputParserException, BasePromptTemplate
 from langchain.tools import BaseTool
 from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
 

api/core/agent/agent/structured_chat.py

 import re
 from typing import List, Tuple, Any, Union, Sequence, Optional, cast
 
-from langchain import BasePromptTemplate, PromptTemplate
 from langchain.agents import StructuredChatAgent, AgentOutputParser, Agent
 from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
 from langchain.callbacks.base import BaseCallbackManager
@@ -9,7 +8,7 @@ from langchain.callbacks.manager import Callbacks
 from langchain.memory.prompt import SUMMARY_PROMPT
 from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
 from langchain.schema import AgentAction, AgentFinish, AIMessage, HumanMessage, OutputParserException, BaseMessage, \
-    get_buffer_string
+    get_buffer_string, BasePromptTemplate
 from langchain.tools import BaseTool
 from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
 

api/core/callback_handler/agent_loop_gather_callback_handler.py

@@ -4,9 +4,9 @@ import time
 
 from typing import Any, Dict, List, Union, Optional
 
-from langchain.agents import openai_functions_agent, openai_functions_multi_agent
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.schema import AgentAction, AgentFinish, LLMResult, ChatGeneration, BaseMessage
+from langchain.schema.agent import AgentActionMessageLog
 
 from core.callback_handler.entity.agent_loop import AgentLoop
 from core.conversation_message_task import ConversationMessageTask
@@ -134,8 +134,8 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         tool_input = json.dumps({"query": action.tool_input}
                                 if isinstance(action.tool_input, str) else action.tool_input)
         completion = None
-        if isinstance(action, openai_functions_agent.base._FunctionsAgentAction) \
-                or isinstance(action, openai_functions_multi_agent.base._FunctionsAgentAction):
+        if (isinstance(action, AgentActionMessageLog) and len(action.message_log) > 0
+                and 'function_call' in action.message_log[0].additional_kwargs):
             thought = action.log.strip()
             completion = json.dumps({'function_call': action.message_log[0].additional_kwargs['function_call']})
         else:

api/core/model_providers/models/llm/replicate_model.py

-import decimal
-from functools import wraps
 from typing import List, Optional, Any
 
 from langchain.callbacks.manager import Callbacks
@@ -29,7 +27,7 @@ class ReplicateModel(BaseLLM):
 
         return EnhanceReplicate(
             model=self.name + ':' + self.credentials.get('model_version'),
-            input=provider_model_kwargs,
+            model_kwargs=provider_model_kwargs,
             streaming=self.streaming,
             replicate_api_token=self.credentials.get('replicate_api_token'),
             callbacks=self.callbacks,
@@ -60,9 +58,9 @@ class ReplicateModel(BaseLLM):
 
         # The maximum length the generated tokens can have.
         # Corresponds to the length of the input prompt + max_new_tokens.
-        if 'max_length' in self._client.input:
-            self._client.input['max_length'] = min(
-                self._client.input['max_length'] + self.get_num_tokens(messages),
+        if 'max_length' in self._client.model_kwargs:
+            self._client.model_kwargs['max_length'] = min(
+                self._client.model_kwargs['max_length'] + self.get_num_tokens(messages),
                 self.model_rules.max_tokens.max
             )
 
@@ -83,7 +81,7 @@ class ReplicateModel(BaseLLM):
 
     def _set_model_kwargs(self, model_kwargs: ModelKwargs):
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs)
-        self.client.input = provider_model_kwargs
+        self.client.model_kwargs = provider_model_kwargs
 
     def handle_exceptions(self, ex: Exception) -> Exception:
         if isinstance(ex, (ModelError, ReplicateError)):

api/core/orchestrator_rule_parser.py

@@ -2,11 +2,10 @@ import json
 import threading
 from typing import Optional, List
 
-from flask import Flask
-from langchain import WikipediaAPIWrapper
 from langchain.callbacks.manager import Callbacks
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.tools import BaseTool, Tool, WikipediaQueryRun
+from langchain.utilities.wikipedia import WikipediaAPIWrapper
 from pydantic import BaseModel, Field
 
 from core.agent.agent.multi_dataset_router_agent import MultiDatasetRouterAgent

api/core/third_party/langchain/llms/anthropic_llm.py

 from typing import Dict
 
 from langchain.chat_models import ChatAnthropic
+from langchain.llms.anthropic import _to_secret
 from langchain.schema import ChatMessage, BaseMessage, HumanMessage, AIMessage, SystemMessage
 from langchain.utils import get_from_dict_or_env, check_package_version
 from pydantic import root_validator
@@ -10,8 +11,8 @@ class AnthropicLLM(ChatAnthropic):
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
         """Validate that api key and python package exists in environment."""
-        values["anthropic_api_key"] = get_from_dict_or_env(
-            values, "anthropic_api_key", "ANTHROPIC_API_KEY"
+        values["anthropic_api_key"] = _to_secret(
+            get_from_dict_or_env(values, "anthropic_api_key", "ANTHROPIC_API_KEY")
         )
         # Get custom api url from environment.
         values["anthropic_api_url"] = get_from_dict_or_env(
@@ -27,13 +28,13 @@ class AnthropicLLM(ChatAnthropic):
             check_package_version("anthropic", gte_version="0.3")
             values["client"] = anthropic.Anthropic(
                 base_url=values["anthropic_api_url"],
-                api_key=values["anthropic_api_key"],
+                api_key=values["anthropic_api_key"].get_secret_value(),
                 timeout=values["default_request_timeout"],
                 max_retries=0
             )
             values["async_client"] = anthropic.AsyncAnthropic(
                 base_url=values["anthropic_api_url"],
-                api_key=values["anthropic_api_key"],
+                api_key=values["anthropic_api_key"].get_secret_value(),
                 timeout=values["default_request_timeout"],
             )
             values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT

api/core/third_party/langchain/llms/azure_chat_open_ai.py

 from typing import Dict, Any, Optional, List, Tuple, Union
 
+from langchain.adapters.openai import convert_dict_to_message
 from langchain.callbacks.manager import CallbackManagerForLLMRun
 from langchain.chat_models import AzureChatOpenAI
-from langchain.chat_models.openai import _convert_dict_to_message
 from langchain.schema import ChatResult, BaseMessage, ChatGeneration
 from pydantic import root_validator
 
@@ -79,7 +79,7 @@ class EnhanceAzureChatOpenAI(AzureChatOpenAI):
                             function_call["arguments"] += _function_call["arguments"]
                     if run_manager:
                         run_manager.on_llm_new_token(token)
-            message = _convert_dict_to_message(
+            message = convert_dict_to_message(
                 {
                     "content": inner_completion,
                     "role": role,

api/core/third_party/langchain/llms/huggingface_hub_llm.py

 from typing import Dict, Optional, List, Any
 
 from huggingface_hub import HfApi, InferenceApi
-from langchain import HuggingFaceHub
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.huggingface_hub import VALID_TASKS
+from langchain.llms.huggingface_hub import VALID_TASKS, HuggingFaceHub
 from pydantic import root_validator
 
 from langchain.utils import get_from_dict_or_env

api/core/third_party/langchain/llms/open_ai.py

 import os
 
 from typing import Dict, Any, Mapping, Optional, Union, Tuple, List, Iterator
-from langchain import OpenAI
 from langchain.callbacks.manager import CallbackManagerForLLMRun
-from langchain.llms.openai import completion_with_retry, _stream_response_to_generation_chunk
+from langchain.llms.openai import completion_with_retry, _stream_response_to_generation_chunk, OpenAI
 from langchain.schema.output import GenerationChunk
 from pydantic import root_validator
 

api/core/third_party/langchain/llms/replicate_llm.py

@@ -47,7 +47,7 @@ class EnhanceReplicate(Replicate):
             key=lambda item: item[1].get("x-order", 0),
         )
         first_input_name = input_properties[0][0]
-        inputs = {first_input_name: prompt, **self.input}
+        inputs = {first_input_name: prompt, **self.model_kwargs}
 
         prediction = client.predictions.create(
             version=version, input={**inputs, **kwargs}

api/core/tool/serpapi_wrapper.py

-from langchain import SerpAPIWrapper
+from langchain.utilities.serpapi import SerpAPIWrapper
 from pydantic import Field, BaseModel
 
 

api/requirements.txt

@@ -10,7 +10,7 @@ flask-session2==1.3.1
 flask-cors==3.0.10
 gunicorn~=21.2.0
 gevent~=22.10.2
-langchain==0.0.250
+langchain==0.0.311
 openai~=0.28.0
 psycopg2-binary~=2.9.6
 pycryptodome==3.17
@@ -24,7 +24,7 @@ tenacity==8.2.2
 cachetools~=5.3.0
 weaviate-client~=3.21.0
 mailchimp-transactional~=1.0.50
-scikit-learn==1.2.2
+scikit-learn==1.3.1
 sentry-sdk[flask]~=1.21.1
 jieba==0.42.1
 celery==5.2.7