Home / File/ base.py — langchain Source File

base.py — langchain Source File

Architecture documentation for base.py, a python file in the langchain codebase. 9 imports, 0 dependents.

Entity Profile

Dependency Diagram

graph LR
  ce87a6d2_8bc8_e573_64ca_a0c97fddcca7["base.py"]
  8e2034b7_ceb8_963f_29fc_2ea6b50ef9b3["typing"]
  ce87a6d2_8bc8_e573_64ca_a0c97fddcca7 --> 8e2034b7_ceb8_963f_29fc_2ea6b50ef9b3
  b19a8b7e_fbee_95b1_65b8_509a1ed3cad7["langchain_core._api"]
  ce87a6d2_8bc8_e573_64ca_a0c97fddcca7 --> b19a8b7e_fbee_95b1_65b8_509a1ed3cad7
  7e64d143_ea36_1c73_4897_1d0ae1757b5b["langchain_core.callbacks.base"]
  ce87a6d2_8bc8_e573_64ca_a0c97fddcca7 --> 7e64d143_ea36_1c73_4897_1d0ae1757b5b
  ba43b74d_3099_7e1c_aac3_cf594720469e["langchain_core.language_models"]
  ce87a6d2_8bc8_e573_64ca_a0c97fddcca7 --> ba43b74d_3099_7e1c_aac3_cf594720469e
  e160f068_75de_4342_6673_9969b919de85["langchain_classic.agents.agent"]
  ce87a6d2_8bc8_e573_64ca_a0c97fddcca7 --> e160f068_75de_4342_6673_9969b919de85
  bdcb074b_68c9_4772_9b65_676e72d0ee6e["langchain_classic.agents.agent_toolkits.vectorstore.prompt"]
  ce87a6d2_8bc8_e573_64ca_a0c97fddcca7 --> bdcb074b_68c9_4772_9b65_676e72d0ee6e
  8bf291f5_099f_a12b_04f5_8500bc5435e5["langchain_classic.agents.agent_toolkits.vectorstore.toolkit"]
  ce87a6d2_8bc8_e573_64ca_a0c97fddcca7 --> 8bf291f5_099f_a12b_04f5_8500bc5435e5
  659d6546_9a06_6dc3_b830_ac7baac67a08["langchain_classic.agents.mrkl.base"]
  ce87a6d2_8bc8_e573_64ca_a0c97fddcca7 --> 659d6546_9a06_6dc3_b830_ac7baac67a08
  31974615_0d58_bd26_13f1_776e0a9d1413["langchain_classic.chains.llm"]
  ce87a6d2_8bc8_e573_64ca_a0c97fddcca7 --> 31974615_0d58_bd26_13f1_776e0a9d1413
  style ce87a6d2_8bc8_e573_64ca_a0c97fddcca7 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

"""VectorStore agent."""

from typing import Any

from langchain_core._api import deprecated
from langchain_core.callbacks.base import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel

from langchain_classic.agents.agent import AgentExecutor
from langchain_classic.agents.agent_toolkits.vectorstore.prompt import (
    PREFIX,
    ROUTER_PREFIX,
)
from langchain_classic.agents.agent_toolkits.vectorstore.toolkit import (
    VectorStoreRouterToolkit,
    VectorStoreToolkit,
)
from langchain_classic.agents.mrkl.base import ZeroShotAgent
from langchain_classic.chains.llm import LLMChain


@deprecated(
    since="0.2.13",
    removal="1.0",
    message=(
        "This function will continue to be supported, but it is recommended for new "
        "use cases to be built with LangGraph. LangGraph offers a more flexible and "
        "full-featured framework for building agents, including support for "
        "tool-calling, persistence of state, and human-in-the-loop workflows. "
        "See API reference for this function for a replacement implementation: "
        "https://api.python.langchain.com/en/latest/agents/langchain.agents.agent_toolkits.vectorstore.base.create_vectorstore_agent.html "  # noqa: E501
        "Read more here on how to create agents that query vector stores: "
        "https://python.langchain.com/docs/how_to/qa_chat_history_how_to/#agents"
    ),
)
def create_vectorstore_agent(
    llm: BaseLanguageModel,
    toolkit: VectorStoreToolkit,
    callback_manager: BaseCallbackManager | None = None,
    prefix: str = PREFIX,
    verbose: bool = False,  # noqa: FBT001,FBT002
    agent_executor_kwargs: dict[str, Any] | None = None,
    **kwargs: Any,
) -> AgentExecutor:
    """Construct a VectorStore agent from an LLM and tools.

    !!! note
        This class is deprecated. See below for a replacement that uses tool
        calling methods and LangGraph. Install LangGraph with:

        ```bash
        pip install -U langgraph
        ```

        ```python
        from langchain_core.tools import create_retriever_tool
        from langchain_core.vectorstores import InMemoryVectorStore
        from langchain_openai import ChatOpenAI, OpenAIEmbeddings
        from langgraph.prebuilt import create_react_agent

// ... (171 more lines)

Subdomains

Dependencies

  • langchain_classic.agents.agent
  • langchain_classic.agents.agent_toolkits.vectorstore.prompt
  • langchain_classic.agents.agent_toolkits.vectorstore.toolkit
  • langchain_classic.agents.mrkl.base
  • langchain_classic.chains.llm
  • langchain_core._api
  • langchain_core.callbacks.base
  • langchain_core.language_models
  • typing

Frequently Asked Questions

What does base.py do?
base.py is a source file in the langchain codebase, written in python. It belongs to the AgentOrchestration domain, ToolInterface subdomain.
What functions are defined in base.py?
base.py defines 2 function(s): create_vectorstore_agent, create_vectorstore_router_agent.
What does base.py depend on?
base.py imports 9 module(s): langchain_classic.agents.agent, langchain_classic.agents.agent_toolkits.vectorstore.prompt, langchain_classic.agents.agent_toolkits.vectorstore.toolkit, langchain_classic.agents.mrkl.base, langchain_classic.chains.llm, langchain_core._api, langchain_core.callbacks.base, langchain_core.language_models, and 1 more.
Where is base.py in the architecture?
base.py is located at libs/langchain/langchain_classic/agents/agent_toolkits/vectorstore/base.py (domain: AgentOrchestration, subdomain: ToolInterface, directory: libs/langchain/langchain_classic/agents/agent_toolkits/vectorstore).

Analyze Your Own Codebase

Get architecture documentation, dependency graphs, and domain analysis for your codebase in minutes.

Try Supermodel Free