pydantic model langchain.agents.MRKLChain[source]#
Chain that implements the MRKL system.
Example
from langchain import OpenAI, MRKLChain
from langchain.chains.mrkl.base import ChainConfig
llm = OpenAI(temperature=0)
prompt = PromptTemplate(...)
chains = [...]
mrkl = MRKLChain.from_chains(llm=llm, prompt=prompt)
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_return_direct_tool » all fields
validate_tools » all fields
classmethod from_chains(llm: langchain.base_language.BaseLanguageModel, chains: List[langchain.agents.mrkl.base.ChainConfig], **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
User friendly way to initialize the MRKL chain.
This is intended to be an easy way to get up and running with the
MRKL chain.
Parameters
llm – The LLM to use as the agent LLM.
chains – The chains the MRKL system has access to.
**kwargs – parameters to be passed to initialization.
Returns
An initialized MRKL chain.
Example
from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, MRKLChain
from langchain.chains.mrkl.base import ChainConfig
llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
llm_math_chain = LLMMathChain(llm=llm)
chains = [
    ChainConfig(
        action_name = "Search",
        action=search.search,
        action_description="useful for searching"
    ),
    ChainConfig(
        action_name="Calculator",
        action=llm_math_chain.run,
        action_description="useful for doing math"
    )
]
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html