79 lines
2.9 KiB
TypeScript
79 lines
2.9 KiB
TypeScript
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import { ChatPromptTemplate } from "@langchain/core/prompts";
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import { StructuredToolInterface } from "@langchain/core/tools";
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import { LanguageModelLike, ToolDefinition } from "@langchain/core/language_models/base";
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import { AgentRunnableSequence } from "../agent.js";
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import { ToolsAgentStep } from "./output_parser.js";
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/**
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* Params used by the createOpenAIToolsAgent function.
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*/
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export type CreateToolCallingAgentParams = {
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/**
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* LLM to use as the agent. Should work with OpenAI tool calling,
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* so must either be an OpenAI model that supports that or a wrapper of
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* a different model that adds in equivalent support.
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*/
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llm: LanguageModelLike;
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/** Tools this agent has access to. */
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tools: StructuredToolInterface[] | ToolDefinition[];
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/** The prompt to use, must have an input key of `agent_scratchpad`. */
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prompt: ChatPromptTemplate;
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/**
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* Whether to invoke the underlying model in streaming mode,
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* allowing streaming of intermediate steps. Defaults to true.
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*/
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streamRunnable?: boolean;
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};
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/**
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* Create an agent that uses tools.
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* @param params Params required to create the agent. Includes an LLM, tools, and prompt.
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* @returns A runnable sequence representing an agent. It takes as input all the same input
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* variables as the prompt passed in does. It returns as output either an
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* AgentAction or AgentFinish.
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* @example
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* ```typescript
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* import { ChatAnthropic } from "@langchain/anthropic";
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* import { ChatPromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts";
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* import { AgentExecutor, createToolCallingAgent } from "langchain/agents";
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*
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* const prompt = ChatPromptTemplate.fromMessages(
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* [
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* ["system", "You are a helpful assistant"],
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* ["placeholder", "{chat_history}"],
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* ["human", "{input}"],
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* ["placeholder", "{agent_scratchpad}"],
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* ]
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* );
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*
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*
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* const llm = new ChatAnthropic({
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* modelName: "claude-3-opus-20240229",
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* temperature: 0,
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* });
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*
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* // Define the tools the agent will have access to.
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* const tools = [...];
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*
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* const agent = createToolCallingAgent({ llm, tools, prompt });
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*
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* const agentExecutor = new AgentExecutor({ agent, tools });
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*
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* const result = await agentExecutor.invoke({input: "what is LangChain?"});
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*
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* // Using with chat history
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* import { AIMessage, HumanMessage } from "@langchain/core/messages";
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*
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* const result2 = await agentExecutor.invoke(
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* {
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* input: "what's my name?",
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* chat_history: [
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* new HumanMessage({content: "hi! my name is bob"}),
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* new AIMessage({content: "Hello Bob! How can I assist you today?"}),
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* ],
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* }
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* );
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* ```
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*/
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export declare function createToolCallingAgent({ llm, tools, prompt, streamRunnable, }: CreateToolCallingAgentParams): AgentRunnableSequence<{
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steps: ToolsAgentStep[];
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}, import("@langchain/core/agents").AgentFinish | import("@langchain/core/agents").AgentAction[]>;
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