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https://github.com/hwchase17/langchain.git
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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
This commit is contained in:
108
libs/community/langchain_community/agent_toolkits/sql/base.py
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108
libs/community/langchain_community/agent_toolkits/sql/base.py
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"""SQL agent."""
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence
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from langchain_core.callbacks import BaseCallbackManager
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.messages import AIMessage, SystemMessage
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from langchain_core.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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MessagesPlaceholder,
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)
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from langchain_community.agent_toolkits.sql.prompt import (
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SQL_FUNCTIONS_SUFFIX,
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SQL_PREFIX,
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SQL_SUFFIX,
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)
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from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
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from langchain_community.tools import BaseTool
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if TYPE_CHECKING:
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from langchain.agents.agent import AgentExecutor
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from langchain.agents.agent_types import AgentType
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def create_sql_agent(
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llm: BaseLanguageModel,
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toolkit: SQLDatabaseToolkit,
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agent_type: Optional[AgentType] = None,
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callback_manager: Optional[BaseCallbackManager] = None,
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prefix: str = SQL_PREFIX,
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suffix: Optional[str] = None,
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format_instructions: Optional[str] = None,
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input_variables: Optional[List[str]] = None,
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top_k: int = 10,
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max_iterations: Optional[int] = 15,
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max_execution_time: Optional[float] = None,
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early_stopping_method: str = "force",
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verbose: bool = False,
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agent_executor_kwargs: Optional[Dict[str, Any]] = None,
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extra_tools: Sequence[BaseTool] = (),
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**kwargs: Any,
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) -> AgentExecutor:
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"""Construct an SQL agent from an LLM and tools."""
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from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
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from langchain.agents.agent_types import AgentType
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from langchain.agents.mrkl.base import ZeroShotAgent
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from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
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from langchain.chains.llm import LLMChain
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agent_type = agent_type or AgentType.ZERO_SHOT_REACT_DESCRIPTION
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tools = toolkit.get_tools() + list(extra_tools)
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prefix = prefix.format(dialect=toolkit.dialect, top_k=top_k)
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agent: BaseSingleActionAgent
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if agent_type == AgentType.ZERO_SHOT_REACT_DESCRIPTION:
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prompt_params = (
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{"format_instructions": format_instructions}
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if format_instructions is not None
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else {}
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)
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prompt = ZeroShotAgent.create_prompt(
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tools,
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prefix=prefix,
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suffix=suffix or SQL_SUFFIX,
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input_variables=input_variables,
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**prompt_params,
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)
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llm_chain = LLMChain(
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llm=llm,
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prompt=prompt,
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callback_manager=callback_manager,
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)
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tool_names = [tool.name for tool in tools]
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agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
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elif agent_type == AgentType.OPENAI_FUNCTIONS:
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messages = [
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SystemMessage(content=prefix),
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HumanMessagePromptTemplate.from_template("{input}"),
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AIMessage(content=suffix or SQL_FUNCTIONS_SUFFIX),
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MessagesPlaceholder(variable_name="agent_scratchpad"),
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]
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input_variables = ["input", "agent_scratchpad"]
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_prompt = ChatPromptTemplate(input_variables=input_variables, messages=messages)
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agent = OpenAIFunctionsAgent(
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llm=llm,
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prompt=_prompt,
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tools=tools,
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callback_manager=callback_manager,
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**kwargs,
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)
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else:
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raise ValueError(f"Agent type {agent_type} not supported at the moment.")
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return AgentExecutor.from_agent_and_tools(
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agent=agent,
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tools=tools,
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callback_manager=callback_manager,
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verbose=verbose,
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max_iterations=max_iterations,
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max_execution_time=max_execution_time,
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early_stopping_method=early_stopping_method,
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**(agent_executor_kwargs or {}),
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)
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