101 lines
3.8 KiB
JavaScript
101 lines
3.8 KiB
JavaScript
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"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.OpenAIToolsAgentOutputParser = void 0;
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const messages_1 = require("@langchain/core/messages");
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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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* ["ai", "You are a helpful assistant"],
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* ["human", "{input}"],
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* new MessagesPlaceholder("agent_scratchpad"),
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* ]);
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*
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* const runnableAgent = RunnableSequence.from([
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* {
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* input: (i: { input: string; steps: ToolsAgentStep[] }) => i.input,
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* agent_scratchpad: (i: { input: string; steps: ToolsAgentStep[] }) =>
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* formatToOpenAIToolMessages(i.steps),
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* },
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* prompt,
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* new ChatOpenAI({
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* modelName: "gpt-3.5-turbo-1106",
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* temperature: 0,
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* }).bind({ tools: tools.map((tool) => convertToOpenAITool(tool)) }),
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* new OpenAIToolsAgentOutputParser(),
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* ]).withConfig({ runName: "OpenAIToolsAgent" });
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*
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* const result = await runnableAgent.invoke({
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* input:
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* "What is the sum of the current temperature in San Francisco, New York, and Tokyo?",
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* });
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* ```
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*/
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class OpenAIToolsAgentOutputParser extends types_js_1.AgentMultiActionOutputParser {
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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", "openai"]
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});
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}
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static lc_name() {
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return "OpenAIToolsAgentOutputParser";
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}
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async parse(text) {
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throw new Error(`OpenAIFunctionsAgentOutputParser can only parse messages.\nPassed input: ${text}`);
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}
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async parseResult(generations) {
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if ("message" in generations[0] && (0, messages_1.isBaseMessage)(generations[0].message)) {
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return this.parseAIMessage(generations[0].message);
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}
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throw new Error("parseResult on OpenAIFunctionsAgentOutputParser only works on ChatGeneration output");
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}
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/**
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* Parses the output message into a ToolsAgentAction[] or AgentFinish
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* object.
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* @param message The BaseMessage to parse.
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* @returns A ToolsAgentAction[] or AgentFinish object.
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*/
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parseAIMessage(message) {
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if (message.content && typeof message.content !== "string") {
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throw new Error("This agent cannot parse non-string model responses.");
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}
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if (message.additional_kwargs.tool_calls) {
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const toolCalls = message.additional_kwargs.tool_calls;
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try {
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return toolCalls.map((toolCall, i) => {
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const toolInput = toolCall.function.arguments
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? JSON.parse(toolCall.function.arguments)
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: {};
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const messageLog = i === 0 ? [message] : [];
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return {
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tool: toolCall.function.name,
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toolInput,
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toolCallId: toolCall.id,
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log: `Invoking "${toolCall.function.name}" with ${toolCall.function.arguments ?? "{}"}\n${message.content}`,
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messageLog,
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};
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});
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}
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catch (error) {
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throw new output_parsers_1.OutputParserException(`Failed to parse tool arguments from chat model response. Text: "${JSON.stringify(toolCalls)}". ${error}`);
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}
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}
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else {
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return {
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returnValues: { output: message.content },
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log: message.content,
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};
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}
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}
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getFormatInstructions() {
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throw new Error("getFormatInstructions not implemented inside OpenAIToolsAgentOutputParser.");
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}
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}
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exports.OpenAIToolsAgentOutputParser = OpenAIToolsAgentOutputParser;
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