250 lines
9.8 KiB
JavaScript
250 lines
9.8 KiB
JavaScript
|
import { zodToJsonSchema } from "zod-to-json-schema";
|
||
|
|
import { isOpenAITool, } from "@langchain/core/language_models/base";
|
||
|
|
import { RunnablePassthrough } from "@langchain/core/runnables";
|
||
|
|
import { ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate, PromptTemplate, } from "@langchain/core/prompts";
|
||
|
|
import { isStructuredTool } from "@langchain/core/utils/function_calling";
|
||
|
|
import { LLMChain } from "../../chains/llm_chain.js";
|
||
|
|
import { Agent, AgentRunnableSequence, } from "../agent.js";
|
||
|
|
import { StructuredChatOutputParserWithRetries } from "./outputParser.js";
|
||
|
|
import { FORMAT_INSTRUCTIONS, PREFIX, SUFFIX } from "./prompt.js";
|
||
|
|
import { renderTextDescriptionAndArgs } from "../../tools/render.js";
|
||
|
|
import { formatLogToString } from "../format_scratchpad/log.js";
|
||
|
|
/**
|
||
|
|
* 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 class StructuredChatAgent extends Agent {
|
||
|
|
static lc_name() {
|
||
|
|
return "StructuredChatAgent";
|
||
|
|
}
|
||
|
|
constructor(input) {
|
||
|
|
const outputParser = input?.outputParser ?? StructuredChatAgent.getDefaultOutputParser();
|
||
|
|
super({ ...input, outputParser });
|
||
|
|
Object.defineProperty(this, "lc_namespace", {
|
||
|
|
enumerable: true,
|
||
|
|
configurable: true,
|
||
|
|
writable: true,
|
||
|
|
value: ["langchain", "agents", "structured_chat"]
|
||
|
|
});
|
||
|
|
}
|
||
|
|
_agentType() {
|
||
|
|
return "structured-chat-zero-shot-react-description";
|
||
|
|
}
|
||
|
|
observationPrefix() {
|
||
|
|
return "Observation: ";
|
||
|
|
}
|
||
|
|
llmPrefix() {
|
||
|
|
return "Thought:";
|
||
|
|
}
|
||
|
|
_stop() {
|
||
|
|
return ["Observation:"];
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* 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) {
|
||
|
|
const descriptionlessTool = tools.find((tool) => !tool.description);
|
||
|
|
if (descriptionlessTool) {
|
||
|
|
const msg = `Got a tool ${descriptionlessTool.name} without a description.` +
|
||
|
|
` This agent requires descriptions for all tools.`;
|
||
|
|
throw new Error(msg);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* Returns a default output parser for the StructuredChatAgent. If an LLM
|
||
|
|
* is provided, it creates an output parser with retry logic from the LLM.
|
||
|
|
* @param fields Optional fields to customize the output parser. Can include an LLM and a list of tool names.
|
||
|
|
* @returns An instance of StructuredChatOutputParserWithRetries.
|
||
|
|
*/
|
||
|
|
static getDefaultOutputParser(fields) {
|
||
|
|
if (fields?.llm) {
|
||
|
|
return StructuredChatOutputParserWithRetries.fromLLM(fields.llm, {
|
||
|
|
toolNames: fields.toolNames,
|
||
|
|
});
|
||
|
|
}
|
||
|
|
return new StructuredChatOutputParserWithRetries({
|
||
|
|
toolNames: fields?.toolNames,
|
||
|
|
});
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* Constructs the agent's scratchpad from a list of steps. If the agent's
|
||
|
|
* scratchpad is not empty, it prepends a message indicating that the
|
||
|
|
* agent has not seen any previous work.
|
||
|
|
* @param steps Array of AgentStep instances to construct the scratchpad from.
|
||
|
|
* @returns A Promise that resolves to a string representing the agent's scratchpad.
|
||
|
|
*/
|
||
|
|
async constructScratchPad(steps) {
|
||
|
|
const agentScratchpad = await super.constructScratchPad(steps);
|
||
|
|
if (agentScratchpad) {
|
||
|
|
return `This was your previous work (but I haven't seen any of it! I only see what you return as final answer):\n${agentScratchpad}`;
|
||
|
|
}
|
||
|
|
return agentScratchpad;
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* 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) {
|
||
|
|
return tools
|
||
|
|
.map((tool) => `${tool.name}: ${tool.description}, args: ${JSON.stringify(zodToJsonSchema(tool.schema).properties)}`)
|
||
|
|
.join("\n");
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* 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, args) {
|
||
|
|
const { prefix = PREFIX, suffix = SUFFIX, inputVariables = ["input", "agent_scratchpad"], humanMessageTemplate = "{input}\n\n{agent_scratchpad}", memoryPrompts = [], } = args ?? {};
|
||
|
|
const template = [prefix, FORMAT_INSTRUCTIONS, suffix].join("\n\n");
|
||
|
|
const messages = [
|
||
|
|
new SystemMessagePromptTemplate(new PromptTemplate({
|
||
|
|
template,
|
||
|
|
inputVariables,
|
||
|
|
partialVariables: {
|
||
|
|
tool_schemas: StructuredChatAgent.createToolSchemasString(tools),
|
||
|
|
tool_names: tools.map((tool) => tool.name).join(", "),
|
||
|
|
},
|
||
|
|
})),
|
||
|
|
...memoryPrompts,
|
||
|
|
new HumanMessagePromptTemplate(new PromptTemplate({
|
||
|
|
template: humanMessageTemplate,
|
||
|
|
inputVariables,
|
||
|
|
})),
|
||
|
|
];
|
||
|
|
return ChatPromptTemplate.fromMessages(messages);
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* 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, tools, args) {
|
||
|
|
StructuredChatAgent.validateTools(tools);
|
||
|
|
const prompt = StructuredChatAgent.createPrompt(tools, args);
|
||
|
|
const outputParser = args?.outputParser ??
|
||
|
|
StructuredChatAgent.getDefaultOutputParser({
|
||
|
|
llm,
|
||
|
|
toolNames: tools.map((tool) => tool.name),
|
||
|
|
});
|
||
|
|
const chain = new LLMChain({
|
||
|
|
prompt,
|
||
|
|
llm,
|
||
|
|
callbacks: args?.callbacks,
|
||
|
|
});
|
||
|
|
return new StructuredChatAgent({
|
||
|
|
llmChain: chain,
|
||
|
|
outputParser,
|
||
|
|
allowedTools: tools.map((t) => t.name),
|
||
|
|
});
|
||
|
|
}
|
||
|
|
}
|
||
|
|
/**
|
||
|
|
* 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 async function createStructuredChatAgent({ llm, tools, prompt, streamRunnable, }) {
|
||
|
|
const missingVariables = ["tools", "tool_names", "agent_scratchpad"].filter((v) => !prompt.inputVariables.includes(v));
|
||
|
|
if (missingVariables.length > 0) {
|
||
|
|
throw new Error(`Provided prompt is missing required input variables: ${JSON.stringify(missingVariables)}`);
|
||
|
|
}
|
||
|
|
let toolNames = [];
|
||
|
|
if (tools.every(isOpenAITool)) {
|
||
|
|
toolNames = tools.map((tool) => tool.function.name);
|
||
|
|
}
|
||
|
|
else if (tools.every(isStructuredTool)) {
|
||
|
|
toolNames = tools.map((tool) => tool.name);
|
||
|
|
}
|
||
|
|
else {
|
||
|
|
throw new Error("All tools must be either OpenAI or Structured tools, not a mix.");
|
||
|
|
}
|
||
|
|
const partialedPrompt = await prompt.partial({
|
||
|
|
tools: renderTextDescriptionAndArgs(tools),
|
||
|
|
tool_names: toolNames.join(", "),
|
||
|
|
});
|
||
|
|
// TODO: Add .bind to core runnable interface.
|
||
|
|
const llmWithStop = llm.bind({
|
||
|
|
stop: ["Observation"],
|
||
|
|
});
|
||
|
|
const agent = AgentRunnableSequence.fromRunnables([
|
||
|
|
RunnablePassthrough.assign({
|
||
|
|
agent_scratchpad: (input) => formatLogToString(input.steps),
|
||
|
|
}),
|
||
|
|
partialedPrompt,
|
||
|
|
llmWithStop,
|
||
|
|
StructuredChatOutputParserWithRetries.fromLLM(llm, {
|
||
|
|
toolNames,
|
||
|
|
}),
|
||
|
|
], {
|
||
|
|
name: "StructuredChatAgent",
|
||
|
|
streamRunnable,
|
||
|
|
singleAction: true,
|
||
|
|
});
|
||
|
|
return agent;
|
||
|
|
}
|