mirror of
https://github.com/Mintplex-Labs/langchain-python.git
synced 2026-08-27 19:29:56 -04:00
Ankush Gola
82baecc892
This PR adds * `ZeroShotAgent.as_sql_agent`, which returns an agent for interacting with a sql database. This builds off of `SQLDatabaseChain`. The main advantages are 1) answering general questions about the db, 2) access to a tool for double checking queries, and 3) recovering from errors * `ZeroShotAgent.as_json_agent` which returns an agent for interacting with json blobs. * Several examples in notebooks --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
"""Json agent."""
|
|
from typing import Any, List, Optional
|
|
|
|
from langchain.agents.agent import AgentExecutor
|
|
from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
|
|
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
|
|
from langchain.agents.mrkl.base import ZeroShotAgent
|
|
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
|
|
from langchain.callbacks.base import BaseCallbackManager
|
|
from langchain.chains.llm import LLMChain
|
|
from langchain.llms.base import BaseLLM
|
|
|
|
|
|
def create_json_agent(
|
|
llm: BaseLLM,
|
|
toolkit: JsonToolkit,
|
|
callback_manager: Optional[BaseCallbackManager] = None,
|
|
prefix: str = JSON_PREFIX,
|
|
suffix: str = JSON_SUFFIX,
|
|
format_instructions: str = FORMAT_INSTRUCTIONS,
|
|
input_variables: Optional[List[str]] = None,
|
|
verbose: bool = False,
|
|
**kwargs: Any,
|
|
) -> AgentExecutor:
|
|
"""Construct a json agent from an LLM and tools."""
|
|
tools = toolkit.get_tools()
|
|
prompt = ZeroShotAgent.create_prompt(
|
|
tools,
|
|
prefix=prefix,
|
|
suffix=suffix,
|
|
format_instructions=format_instructions,
|
|
input_variables=input_variables,
|
|
)
|
|
llm_chain = LLMChain(
|
|
llm=llm,
|
|
prompt=prompt,
|
|
callback_manager=callback_manager,
|
|
)
|
|
tool_names = [tool.name for tool in tools]
|
|
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
|
|
return AgentExecutor.from_agent_and_tools(
|
|
agent=agent, tools=toolkit.get_tools(), verbose=verbose
|
|
)
|