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https://github.com/hwchase17/langchain.git
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core[patch], community[patch], langchain[patch], docs: Update SQL chains/agents/docs (#16168)
Revamp SQL use cases docs. In the process update SQL chains and agents.
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GitHub
@@ -1,11 +1,11 @@
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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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import warnings
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from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Sequence, Union
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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 import BasePromptTemplate, PromptTemplate
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from langchain_core.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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@@ -15,22 +15,29 @@ from langchain_core.prompts.chat import (
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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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from langchain_community.tools.sql_database.tool import (
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InfoSQLDatabaseTool,
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ListSQLDatabaseTool,
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)
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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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from langchain_core.callbacks import BaseCallbackManager
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.tools import BaseTool
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from langchain_community.utilities.sql_database import SQLDatabase
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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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toolkit: Optional[SQLDatabaseToolkit] = None,
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agent_type: Optional[Union[AgentType, Literal["openai-tools"]]] = None,
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callback_manager: Optional[BaseCallbackManager] = None,
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prefix: str = SQL_PREFIX,
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prefix: Optional[str] = None,
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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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@@ -41,62 +48,165 @@ def create_sql_agent(
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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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*,
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db: Optional[SQLDatabase] = None,
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prompt: Optional[BasePromptTemplate] = None,
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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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"""Construct a SQL agent from an LLM and toolkit or database.
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Args:
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llm: Language model to use for the agent.
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toolkit: SQLDatabaseToolkit for the agent to use. Must provide exactly one of
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'toolkit' or 'db'. Specify 'toolkit' if you want to use a different model
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for the agent and the toolkit.
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agent_type: One of "openai-tools", "openai-functions", or
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"zero-shot-react-description". Defaults to "zero-shot-react-description".
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"openai-tools" is recommended over "openai-functions".
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callback_manager: DEPRECATED. Pass "callbacks" key into 'agent_executor_kwargs'
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instead to pass constructor callbacks to AgentExecutor.
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prefix: Prompt prefix string. Must contain variables "top_k" and "dialect".
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suffix: Prompt suffix string. Default depends on agent type.
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format_instructions: Formatting instructions to pass to
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ZeroShotAgent.create_prompt() when 'agent_type' is
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"zero-shot-react-description". Otherwise ignored.
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input_variables: DEPRECATED. Input variables to explicitly specify as part of
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ZeroShotAgent.create_prompt() when 'agent_type' is
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"zero-shot-react-description". Otherwise ignored.
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top_k: Number of rows to query for by default.
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max_iterations: Passed to AgentExecutor init.
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max_execution_time: Passed to AgentExecutor init.
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early_stopping_method: Passed to AgentExecutor init.
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verbose: AgentExecutor verbosity.
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agent_executor_kwargs: Arbitrary additional AgentExecutor args.
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extra_tools: Additional tools to give to agent on top of the ones that come with
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SQLDatabaseToolkit.
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db: SQLDatabase from which to create a SQLDatabaseToolkit. Toolkit is created
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using 'db' and 'llm'. Must provide exactly one of 'db' or 'toolkit'.
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prompt: Complete agent prompt. prompt and {prefix, suffix, format_instructions,
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input_variables} are mutually exclusive.
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**kwargs: DEPRECATED. Not used, kept for backwards compatibility.
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Returns:
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An AgentExecutor with the specified agent_type agent.
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Example:
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.. code-block:: python
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from langchain_openai import ChatOpenAI
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from langchain_community.agent_toolkits import create_sql_agent
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from langchain_community.utilities import SQLDatabase
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db = SQLDatabase.from_uri("sqlite:///Chinook.db")
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llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
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agent_executor = create_sql_agent(llm, db=db, agent_type="openai-tools", verbose=True)
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""" # noqa: E501
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from langchain.agents import (
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create_openai_functions_agent,
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create_openai_tools_agent,
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create_react_agent,
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)
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from langchain.agents.agent import (
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AgentExecutor,
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RunnableAgent,
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RunnableMultiActionAgent,
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)
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from langchain.agents.agent_types import AgentType
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if toolkit is None and db is None:
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raise ValueError(
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"Must provide exactly one of 'toolkit' or 'db'. Received neither."
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)
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if toolkit and db:
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raise ValueError(
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"Must provide exactly one of 'toolkit' or 'db'. Received both."
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)
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if kwargs:
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warnings.warn(
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f"Received additional kwargs {kwargs} which are no longer supported."
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)
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toolkit = toolkit or SQLDatabaseToolkit(llm=llm, db=db)
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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 prompt is None:
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prefix = prefix or SQL_PREFIX
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prefix = prefix.format(dialect=toolkit.dialect, top_k=top_k)
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else:
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if "top_k" in prompt.input_variables:
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prompt = prompt.partial(top_k=str(top_k))
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if "dialect" in prompt.input_variables:
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prompt = prompt.partial(dialect=toolkit.dialect)
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db_context = toolkit.get_context()
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if "table_info" in prompt.input_variables:
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prompt = prompt.partial(table_info=db_context["table_info"])
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tools = [
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tool for tool in tools if not isinstance(tool, InfoSQLDatabaseTool)
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]
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if "table_names" in prompt.input_variables:
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prompt = prompt.partial(table_names=db_context["table_names"])
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tools = [
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tool for tool in tools if not isinstance(tool, ListSQLDatabaseTool)
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]
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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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if prompt is None:
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from langchain.agents.mrkl import prompt as react_prompt
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format_instructions = (
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format_instructions or react_prompt.FORMAT_INSTRUCTIONS
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)
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template = "\n\n".join(
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[
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react_prompt.PREFIX,
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"{tools}",
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format_instructions,
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react_prompt.SUFFIX,
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]
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)
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prompt = PromptTemplate.from_template(template)
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agent = RunnableAgent(
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runnable=create_react_agent(llm, tools, prompt),
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input_keys_arg=["input"],
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return_keys_arg=["output"],
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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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if prompt is None:
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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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prompt = ChatPromptTemplate.from_messages(messages)
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agent = RunnableAgent(
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runnable=create_openai_functions_agent(llm, tools, prompt),
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input_keys_arg=["input"],
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return_keys_arg=["output"],
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)
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elif agent_type == "openai-tools":
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if prompt is None:
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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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prompt = ChatPromptTemplate.from_messages(messages)
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agent = RunnableMultiActionAgent(
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runnable=create_openai_tools_agent(llm, tools, prompt),
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input_keys_arg=["input"],
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return_keys_arg=["output"],
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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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return AgentExecutor(
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name="SQL Agent Executor",
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agent=agent,
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tools=tools,
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callback_manager=callback_manager,
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