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base.py — langchain Source File

Architecture documentation for base.py, a python file in the langchain codebase. 21 imports, 0 dependents.

File python AgentOrchestration ActionLogic 21 imports 1 functions 1 classes

Entity Profile

Dependency Diagram

graph LR
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e["base.py"]
  67ec3255_645e_8b6e_1eff_1eb3c648ed95["re"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> 67ec3255_645e_8b6e_1eff_1eb3c648ed95
  cfe2bde5_180e_e3b0_df2b_55b3ebaca8e7["collections.abc"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> cfe2bde5_180e_e3b0_df2b_55b3ebaca8e7
  8e2034b7_ceb8_963f_29fc_2ea6b50ef9b3["typing"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> 8e2034b7_ceb8_963f_29fc_2ea6b50ef9b3
  b19a8b7e_fbee_95b1_65b8_509a1ed3cad7["langchain_core._api"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> b19a8b7e_fbee_95b1_65b8_509a1ed3cad7
  80d582c5_7cc3_ac96_2742_3dbe1cbd4e2b["langchain_core.agents"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> 80d582c5_7cc3_ac96_2742_3dbe1cbd4e2b
  f3bc7443_c889_119d_0744_aacc3620d8d2["langchain_core.callbacks"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> f3bc7443_c889_119d_0744_aacc3620d8d2
  ba43b74d_3099_7e1c_aac3_cf594720469e["langchain_core.language_models"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> ba43b74d_3099_7e1c_aac3_cf594720469e
  e6b4f61e_7b98_6666_3641_26b069517d4a["langchain_core.prompts"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> e6b4f61e_7b98_6666_3641_26b069517d4a
  e45722a2_0136_a972_1f58_7b5987500404["langchain_core.prompts.chat"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> e45722a2_0136_a972_1f58_7b5987500404
  2ceb1686_0f8c_8ae0_36d1_7c0b702fda1c["langchain_core.runnables"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> 2ceb1686_0f8c_8ae0_36d1_7c0b702fda1c
  43d88577_548b_2248_b01b_7987bae85dcc["langchain_core.tools"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> 43d88577_548b_2248_b01b_7987bae85dcc
  ca5ba58c_877f_7975_369b_ca5e40124948["langchain_core.tools.render"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> ca5ba58c_877f_7975_369b_ca5e40124948
  6e58aaea_f08e_c099_3cc7_f9567bfb1ae7["pydantic"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> 6e58aaea_f08e_c099_3cc7_f9567bfb1ae7
  91721f45_4909_e489_8c1f_084f8bd87145["typing_extensions"]
  960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e --> 91721f45_4909_e489_8c1f_084f8bd87145
  style 960fedcb_7c5c_a4a8_1c04_3d7fa9869b1e fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

import re
from collections.abc import Sequence
from typing import Any

from langchain_core._api import deprecated
from langchain_core.agents import AgentAction
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.tools.render import ToolsRenderer
from pydantic import Field
from typing_extensions import override

from langchain_classic.agents.agent import Agent, AgentOutputParser
from langchain_classic.agents.format_scratchpad import format_log_to_str
from langchain_classic.agents.output_parsers import JSONAgentOutputParser
from langchain_classic.agents.structured_chat.output_parser import (
    StructuredChatOutputParserWithRetries,
)
from langchain_classic.agents.structured_chat.prompt import (
    FORMAT_INSTRUCTIONS,
    PREFIX,
    SUFFIX,
)
from langchain_classic.chains.llm import LLMChain
from langchain_classic.tools.render import render_text_description_and_args

HUMAN_MESSAGE_TEMPLATE = "{input}\n\n{agent_scratchpad}"


@deprecated("0.1.0", alternative="create_structured_chat_agent", removal="1.0")
class StructuredChatAgent(Agent):
    """Structured Chat Agent."""

    output_parser: AgentOutputParser = Field(
        default_factory=StructuredChatOutputParserWithRetries,
    )
    """Output parser for the agent."""

    @property
    def observation_prefix(self) -> str:
        """Prefix to append the observation with."""
        return "Observation: "

    @property
    def llm_prefix(self) -> str:
        """Prefix to append the llm call with."""
        return "Thought:"

    def _construct_scratchpad(
        self,
        intermediate_steps: list[tuple[AgentAction, str]],
    ) -> str:
// ... (258 more lines)

Domain

Subdomains

Functions

Classes

Dependencies

  • collections.abc
  • langchain_classic.agents.agent
  • langchain_classic.agents.format_scratchpad
  • langchain_classic.agents.output_parsers
  • langchain_classic.agents.structured_chat.output_parser
  • langchain_classic.agents.structured_chat.prompt
  • langchain_classic.chains.llm
  • langchain_classic.tools.render
  • langchain_core._api
  • langchain_core.agents
  • langchain_core.callbacks
  • langchain_core.language_models
  • langchain_core.prompts
  • langchain_core.prompts.chat
  • langchain_core.runnables
  • langchain_core.tools
  • langchain_core.tools.render
  • pydantic
  • re
  • typing
  • typing_extensions

Source

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, ActionLogic subdomain.
What functions are defined in base.py?
base.py defines 1 function(s): create_structured_chat_agent.
What does base.py depend on?
base.py imports 21 module(s): collections.abc, langchain_classic.agents.agent, langchain_classic.agents.format_scratchpad, langchain_classic.agents.output_parsers, langchain_classic.agents.structured_chat.output_parser, langchain_classic.agents.structured_chat.prompt, langchain_classic.chains.llm, langchain_classic.tools.render, and 13 more.
Where is base.py in the architecture?
base.py is located at libs/langchain/langchain_classic/agents/structured_chat/base.py (domain: AgentOrchestration, subdomain: ActionLogic, directory: libs/langchain/langchain_classic/agents/structured_chat).

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