65 lines
2.2 KiB
JavaScript
65 lines
2.2 KiB
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.XMLAgentOutputParser = void 0;
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const output_parsers_1 = require("@langchain/core/output_parsers");
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const types_js_1 = require("../types.cjs");
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/**
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* @example
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* ```typescript
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* const prompt = ChatPromptTemplate.fromMessages([
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* HumanMessagePromptTemplate.fromTemplate(AGENT_INSTRUCTIONS),
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* new MessagesPlaceholder("agent_scratchpad"),
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* ]);
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* const runnableAgent = RunnableSequence.from([
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* ...rest of runnable
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* prompt,
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* new ChatAnthropic({ modelName: "claude-2", temperature: 0 }).bind({
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* stop: ["</tool_input>", "</final_answer>"],
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* }),
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* new XMLAgentOutputParser(),
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* ]);
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* const result = await executor.invoke({
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* input: "What is the weather in Honolulu?",
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* tools: [],
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* });
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* ```
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*/
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class XMLAgentOutputParser extends types_js_1.AgentActionOutputParser {
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constructor() {
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super(...arguments);
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Object.defineProperty(this, "lc_namespace", {
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enumerable: true,
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configurable: true,
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writable: true,
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value: ["langchain", "agents", "xml"]
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});
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}
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static lc_name() {
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return "XMLAgentOutputParser";
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}
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/**
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* Parses the output text from the agent and returns an AgentAction or
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* AgentFinish object.
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* @param text The output text from the agent.
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* @returns An AgentAction or AgentFinish object.
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*/
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async parse(text) {
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if (text.includes("</tool>")) {
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const [tool, toolInput] = text.split("</tool>");
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const _tool = tool.split("<tool>")[1];
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const _toolInput = toolInput.split("<tool_input>")[1];
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return { tool: _tool, toolInput: _toolInput, log: text };
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}
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else if (text.includes("<final_answer>")) {
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const [, answer] = text.split("<final_answer>");
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return { returnValues: { output: answer }, log: text };
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}
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else {
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throw new output_parsers_1.OutputParserException(`Could not parse LLM output: ${text}`);
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}
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}
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getFormatInstructions() {
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throw new Error("getFormatInstructions not implemented inside OpenAIFunctionsAgentOutputParser.");
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}
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}
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exports.XMLAgentOutputParser = XMLAgentOutputParser;
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