agsamantha/node_modules/langchain/dist/agents/structured_chat/index.d.ts
2024-10-02 15:15:21 -05:00

172 lines
7.3 KiB
TypeScript

import type { StructuredToolInterface } from "@langchain/core/tools";
import { type BaseLanguageModelInterface, type ToolDefinition } from "@langchain/core/language_models/base";
import type { BasePromptTemplate } from "@langchain/core/prompts";
import { BaseMessagePromptTemplate, ChatPromptTemplate } from "@langchain/core/prompts";
import { AgentStep } from "@langchain/core/agents";
import { Optional } from "../../types/type-utils.js";
import { Agent, AgentArgs, AgentRunnableSequence, OutputParserArgs } from "../agent.js";
import { AgentInput } from "../types.js";
import { StructuredChatOutputParserWithRetries } from "./outputParser.js";
/**
* Interface for arguments used to create a prompt for a
* StructuredChatAgent.
*/
export interface StructuredChatCreatePromptArgs {
/** String to put after the list of tools. */
suffix?: string;
/** String to put before the list of tools. */
prefix?: string;
/** String to use directly as the human message template. */
humanMessageTemplate?: string;
/** List of input variables the final prompt will expect. */
inputVariables?: string[];
/** List of historical prompts from memory. */
memoryPrompts?: BaseMessagePromptTemplate[];
}
/**
* Type for input data for creating a StructuredChatAgent, with the
* 'outputParser' property made optional.
*/
export type StructuredChatAgentInput = Optional<AgentInput, "outputParser">;
/**
* Agent that interoperates with Structured Tools using React logic.
* @augments Agent
* @deprecated Use the {@link https://api.js.langchain.com/functions/langchain.agents.createStructuredChatAgent.html | createStructuredChatAgent method instead}.
*/
export declare class StructuredChatAgent extends Agent {
static lc_name(): string;
lc_namespace: string[];
constructor(input: StructuredChatAgentInput);
_agentType(): "structured-chat-zero-shot-react-description";
observationPrefix(): string;
llmPrefix(): string;
_stop(): string[];
/**
* Validates that all provided tools have a description. Throws an error
* if any tool lacks a description.
* @param tools Array of StructuredTool instances to validate.
*/
static validateTools(tools: StructuredToolInterface[]): void;
/**
* Returns a default output parser for the StructuredChatAgent. If an LLM
* is provided, it creates an output parser with retry logic from the LLM.
* @param fields Optional fields to customize the output parser. Can include an LLM and a list of tool names.
* @returns An instance of StructuredChatOutputParserWithRetries.
*/
static getDefaultOutputParser(fields?: OutputParserArgs & {
toolNames: string[];
}): StructuredChatOutputParserWithRetries;
/**
* Constructs the agent's scratchpad from a list of steps. If the agent's
* scratchpad is not empty, it prepends a message indicating that the
* agent has not seen any previous work.
* @param steps Array of AgentStep instances to construct the scratchpad from.
* @returns A Promise that resolves to a string representing the agent's scratchpad.
*/
constructScratchPad(steps: AgentStep[]): Promise<string>;
/**
* Creates a string representation of the schemas of the provided tools.
* @param tools Array of StructuredTool instances to create the schemas string from.
* @returns A string representing the schemas of the provided tools.
*/
static createToolSchemasString(tools: StructuredToolInterface[]): string;
/**
* Create prompt in the style of the agent.
*
* @param tools - List of tools the agent will have access to, used to format the prompt.
* @param args - Arguments to create the prompt with.
* @param args.suffix - String to put after the list of tools.
* @param args.prefix - String to put before the list of tools.
* @param args.inputVariables List of input variables the final prompt will expect.
* @param args.memoryPrompts List of historical prompts from memory.
*/
static createPrompt(tools: StructuredToolInterface[], args?: StructuredChatCreatePromptArgs): ChatPromptTemplate<any, any>;
/**
* Creates a StructuredChatAgent from an LLM and a list of tools.
* Validates the tools, creates a prompt, and sets up an LLM chain for the
* agent.
* @param llm BaseLanguageModel instance to create the agent from.
* @param tools Array of StructuredTool instances to create the agent from.
* @param args Optional arguments to customize the creation of the agent. Can include arguments for creating the prompt and AgentArgs.
* @returns A new instance of StructuredChatAgent.
*/
static fromLLMAndTools(llm: BaseLanguageModelInterface, tools: StructuredToolInterface[], args?: StructuredChatCreatePromptArgs & AgentArgs): StructuredChatAgent;
}
/**
* Params used by the createStructuredChatAgent function.
*/
export type CreateStructuredChatAgentParams = {
/** LLM to use as the agent. */
llm: BaseLanguageModelInterface;
/** Tools this agent has access to. */
tools: (StructuredToolInterface | ToolDefinition)[];
/**
* The prompt to use. Must have input keys for
* `tools`, `tool_names`, and `agent_scratchpad`.
*/
prompt: BasePromptTemplate;
/**
* Whether to invoke the underlying model in streaming mode,
* allowing streaming of intermediate steps. Defaults to true.
*/
streamRunnable?: boolean;
};
/**
* Create an agent aimed at supporting tools with multiple inputs.
* @param params Params required to create the agent. Includes an LLM, tools, and prompt.
* @returns A runnable sequence representing an agent. It takes as input all the same input
* variables as the prompt passed in does. It returns as output either an
* AgentAction or AgentFinish.
*
* @example
* ```typescript
* import { AgentExecutor, createStructuredChatAgent } from "langchain/agents";
* import { pull } from "langchain/hub";
* import type { ChatPromptTemplate } from "@langchain/core/prompts";
* import { AIMessage, HumanMessage } from "@langchain/core/messages";
*
* import { ChatOpenAI } from "@langchain/openai";
*
* // Define the tools the agent will have access to.
* const tools = [...];
*
* // Get the prompt to use - you can modify this!
* // If you want to see the prompt in full, you can at:
* // https://smith.langchain.com/hub/hwchase17/structured-chat-agent
* const prompt = await pull<ChatPromptTemplate>(
* "hwchase17/structured-chat-agent"
* );
*
* const llm = new ChatOpenAI({
* temperature: 0,
* modelName: "gpt-3.5-turbo-1106",
* });
*
* const agent = await createStructuredChatAgent({
* llm,
* tools,
* prompt,
* });
*
* const agentExecutor = new AgentExecutor({
* agent,
* tools,
* });
*
* const result = await agentExecutor.invoke({
* input: "what is LangChain?",
* });
*
* // With chat history
* const result2 = await agentExecutor.invoke({
* input: "what's my name?",
* chat_history: [
* new HumanMessage("hi! my name is cob"),
* new AIMessage("Hello Cob! How can I assist you today?"),
* ],
* });
* ```
*/
export declare function createStructuredChatAgent({ llm, tools, prompt, streamRunnable, }: CreateStructuredChatAgentParams): Promise<AgentRunnableSequence<{
steps: AgentStep[];
}, import("@langchain/core/agents").AgentAction | import("@langchain/core/agents").AgentFinish>>;