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>
90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
"""Toolkit for interacting with a vector store."""
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from typing import List
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from pydantic import BaseModel, Field
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from langchain.agents.agent_toolkits.base import BaseToolkit
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from langchain.llms.base import BaseLLM
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from langchain.llms.openai import OpenAI
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from langchain.tools import BaseTool
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from langchain.tools.vectorstore.tool import (
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VectorStoreQATool,
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VectorStoreQAWithSourcesTool,
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)
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from langchain.vectorstores.base import VectorStore
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class VectorStoreInfo(BaseModel):
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"""Information about a vectorstore."""
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vectorstore: VectorStore = Field(exclude=True)
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name: str
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description: str
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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class VectorStoreToolkit(BaseToolkit):
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"""Toolkit for interacting with a vector store."""
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vectorstore_info: VectorStoreInfo = Field(exclude=True)
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llm: BaseLLM = Field(default_factory=lambda: OpenAI(temperature=0))
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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def get_tools(self) -> List[BaseTool]:
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"""Get the tools in the toolkit."""
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description = VectorStoreQATool.get_description(
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self.vectorstore_info.name, self.vectorstore_info.description
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)
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qa_tool = VectorStoreQATool(
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name=self.vectorstore_info.name,
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description=description,
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vectorstore=self.vectorstore_info.vectorstore,
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llm=self.llm,
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)
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description = VectorStoreQAWithSourcesTool.get_description(
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self.vectorstore_info.name, self.vectorstore_info.description
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)
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qa_with_sources_tool = VectorStoreQAWithSourcesTool(
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name=f"{self.vectorstore_info.name}_with_sources",
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description=description,
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vectorstore=self.vectorstore_info.vectorstore,
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llm=self.llm,
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)
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return [qa_tool, qa_with_sources_tool]
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class VectorStoreRouterToolkit(BaseToolkit):
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"""Toolkit for routing between vectorstores."""
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vectorstores: List[VectorStoreInfo] = Field(exclude=True)
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llm: BaseLLM = Field(default_factory=lambda: OpenAI(temperature=0))
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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def get_tools(self) -> List[BaseTool]:
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"""Get the tools in the toolkit."""
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tools: List[BaseTool] = []
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for vectorstore_info in self.vectorstores:
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description = VectorStoreQATool.get_description(
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vectorstore_info.name, vectorstore_info.description
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)
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qa_tool = VectorStoreQATool(
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name=vectorstore_info.name,
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description=description,
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vectorstore=vectorstore_info.vectorstore,
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llm=self.llm,
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)
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tools.append(qa_tool)
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return tools
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