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>
56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
"""VectorStore agent."""
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from typing import Any, Optional
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from langchain.agents.agent import AgentExecutor
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from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX, ROUTER_PREFIX
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from langchain.agents.agent_toolkits.vectorstore.toolkit import (
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VectorStoreRouterToolkit,
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VectorStoreToolkit,
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)
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from langchain.agents.mrkl.base import ZeroShotAgent
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from langchain.callbacks.base import BaseCallbackManager
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from langchain.chains.llm import LLMChain
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from langchain.llms.base import BaseLLM
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def create_vectorstore_agent(
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llm: BaseLLM,
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toolkit: VectorStoreToolkit,
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callback_manager: Optional[BaseCallbackManager] = None,
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prefix: str = PREFIX,
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verbose: bool = False,
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**kwargs: Any,
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) -> AgentExecutor:
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"""Construct a vectorstore agent from an LLM and tools."""
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tools = toolkit.get_tools()
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prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
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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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return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=verbose)
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def create_vectorstore_router_agent(
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llm: BaseLLM,
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toolkit: VectorStoreRouterToolkit,
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callback_manager: Optional[BaseCallbackManager] = None,
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prefix: str = ROUTER_PREFIX,
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verbose: bool = False,
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**kwargs: Any,
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) -> AgentExecutor:
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"""Construct a vectorstore router agent from an LLM and tools."""
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tools = toolkit.get_tools()
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prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
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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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return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=verbose)
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