Class ChatConversationalAgent

Agent for the MRKL chain.

⚠️ Deprecated ⚠️

Use the createStructuredChatAgent method instead.

This feature is deprecated and will be removed in the future.

It is not recommended for use.

Hierarchy (view full)

Agent
- ChatConversationalAgent

Index

Constructors

constructor

new ChatConversationalAgent(input): ChatConversationalAgent
Parameters
- input: ChatConversationalAgentInput
Returns ChatConversationalAgent
Overrides Agent.constructor
- Defined in langchain/src/agents/chat_convo/index.ts:65

Properties

ToolType

ToolType: ToolInterface

llmChain

llmChain: LLMChain<string, any>

outputParser

outputParser: undefined | AgentActionOutputParser

Accessors

allowedTools

get allowedTools(): undefined | string[]
Returns undefined | string[]
Inherited from Agent.allowedTools
- Defined in langchain/src/agents/agent.ts:490

inputKeys

get inputKeys(): string[]
Returns string[]
Inherited from Agent.inputKeys
- Defined in langchain/src/agents/agent.ts:494

returnValues

get returnValues(): string[]
Returns string[]
Inherited from Agent.returnValues
- Defined in langchain/src/agents/agent.ts:57

Methods

constructScratchPad

constructScratchPad(steps): Promise<BaseMessage[]>
Constructs the agent scratchpad based on the agent steps. It returns an array of base messages representing the thoughts of the agent.
Parameters
- steps: AgentStep[]
  The agent steps to construct the scratchpad from.
Returns Promise<BaseMessage[]>
An array of base messages representing the thoughts of the agent.
Overrides Agent.constructScratchPad
- Defined in langchain/src/agents/chat_convo/index.ts:103

finishToolName

finishToolName(): string
Name of tool to use to terminate the chain.

Returns string
Inherited from Agent.finishToolName
- Defined in langchain/src/agents/agent.ts:568

llmPrefix

llmPrefix(): string
Prefix to append the LLM call with.

Returns string
Overrides Agent.llmPrefix
- Defined in langchain/src/agents/chat_convo/index.ts:79

observationPrefix

observationPrefix(): string
Prefix to append the observation with.

Returns string
Overrides Agent.observationPrefix
- Defined in langchain/src/agents/chat_convo/index.ts:75

plan

plan(steps, inputs, callbackManager?): Promise<any>
Decide what to do given some input.
Parameters
- steps: AgentStep[]
  Steps the LLM has taken so far, along with observations from each.
- inputs: ChainValues
  User inputs.
- Optional callbackManager: any
  Callback manager to use for this call.
Returns Promise<any>
Action specifying what tool to use.
Inherited from Agent.plan
- Defined in langchain/src/agents/agent.ts:622

prepareForOutput

prepareForOutput(_returnValues, _steps): Promise<AgentFinish>
Prepare the agent for output, if needed
Parameters
- _returnValues: AgentFinish
- _steps: AgentStep[]
Returns Promise<AgentFinish>
Inherited from Agent.prepareForOutput
- Defined in langchain/src/agents/agent.ts:99

returnStoppedResponse

returnStoppedResponse(earlyStoppingMethod, steps, inputs, callbackManager?): Promise<AgentFinish>
Return response when agent has been stopped due to max iterations
Parameters
- earlyStoppingMethod: StoppingMethod
- steps: AgentStep[]
- inputs: ChainValues
- Optional callbackManager: any
Returns Promise<AgentFinish>
Inherited from Agent.returnStoppedResponse
- Defined in langchain/src/agents/agent.ts:633

`Static` createPrompt

createPrompt(tools, args?): any
Create prompt in the style of the ChatConversationAgent.
Parameters
- tools: ToolInterface[]
  List of tools the agent will have access to, used to format the prompt.
- Optional args: ChatConversationalCreatePromptArgs
  Arguments to create the prompt with.
Returns any
Overrides Agent.createPrompt
- Defined in langchain/src/agents/chat_convo/index.ts:152

`Static` deserialize

deserialize(data): Promise<Agent>
Load an agent from a json-like object describing it.
Parameters
- data: SerializedZeroShotAgent & {
  llm?: any;
  tools?: ToolInterface[];
  }
Returns Promise<Agent>
Inherited from Agent.deserialize
- Defined in langchain/src/agents/agent.ts:675

`Static` fromLLMAndTools

fromLLMAndTools(llm, tools, args?): ChatConversationalAgent
Creates an instance of the ChatConversationalAgent class from a BaseLanguageModel and a set of tools. It takes optional arguments to customize the agent.
Parameters
- llm: BaseLanguageModelInterface
  The BaseLanguageModel to create the agent from.
- tools: ToolInterface[]
  The set of tools to create the agent from.
- Optional args: ChatConversationalCreatePromptArgs & AgentArgs
  Optional arguments to customize the agent.
Returns ChatConversationalAgent
An instance of the ChatConversationalAgent class.
Overrides Agent.fromLLMAndTools
- Defined in langchain/src/agents/chat_convo/index.ts:190

`Static` getDefaultOutputParser

getDefaultOutputParser(fields?): AgentActionOutputParser
Returns the default output parser for the ChatConversationalAgent class. It takes optional fields as arguments to customize the output parser.
Parameters
- Optional fields: OutputParserArgs & {
  toolNames: string[];
  }
  Optional fields to customize the output parser.
Returns AgentActionOutputParser
The default output parser for the ChatConversationalAgent class.
Overrides Agent.getDefaultOutputParser
- Defined in langchain/src/agents/chat_convo/index.ts:125

`Static` validateTools

validateTools(tools): void
Validate that appropriate tools are passed in
Parameters
- tools: ToolInterface[]
Returns void
Overrides Agent.validateTools
- Defined in langchain/src/agents/chat_convo/index.ts:87

Class ChatConversationalAgent

⚠️ Deprecated ⚠️

Hierarchy (view full)

Index

Constructors

Properties

Accessors

Methods

Constructors

constructor

Parameters

input: ChatConversationalAgentInput

Returns ChatConversationalAgent

Properties

ToolType

llmChain

outputParser

Accessors

allowedTools

Returns undefined | string[]

inputKeys

Returns string[]

returnValues

Returns string[]

Methods

constructScratchPad

Parameters

steps: AgentStep[]

Returns Promise<BaseMessage[]>

finishToolName

Returns string

llmPrefix

Returns string

observationPrefix

Returns string

plan

Parameters

steps: AgentStep[]

inputs: ChainValues

Optional callbackManager: any

Returns Promise<any>

prepareForOutput

Parameters

_returnValues: AgentFinish

_steps: AgentStep[]

Returns Promise<AgentFinish>

returnStoppedResponse

Parameters

earlyStoppingMethod: StoppingMethod

steps: AgentStep[]

inputs: ChainValues

Optional callbackManager: any

Returns Promise<AgentFinish>

Static createPrompt

Parameters

tools: ToolInterface[]

Optional args: ChatConversationalCreatePromptArgs

Returns any

Static deserialize

Parameters

data: SerializedZeroShotAgent & { llm?: any; tools?: ToolInterface[]; }

Returns Promise<Agent>

Static fromLLMAndTools

Parameters

llm: BaseLanguageModelInterface

tools: ToolInterface[]

Optional args: ChatConversationalCreatePromptArgs & AgentArgs

Returns ChatConversationalAgent

Static getDefaultOutputParser

Parameters

Optional fields: OutputParserArgs & { toolNames: string[]; }

Returns AgentActionOutputParser

Static validateTools

Parameters

tools: ToolInterface[]

Returns void

Settings

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`Optional` callbackManager: any

`Optional` callbackManager: any

`Static` createPrompt

`Optional` args: ChatConversationalCreatePromptArgs

`Static` deserialize

data: SerializedZeroShotAgent & {
llm?: any;
tools?: ToolInterface[];
}

`Static` fromLLMAndTools

`Optional` args: ChatConversationalCreatePromptArgs & AgentArgs

`Static` getDefaultOutputParser

`Optional` fields: OutputParserArgs & {
toolNames: string[];
}

`Static` validateTools