前端LangChain中Chains用法

export abstract class BaseChain<RunInput extends ChainValues = ChainValues,RunOutput extends ChainValues = ChainValues> extends BaseLangChain<RunInput, RunOutput> implements ChainInputs {
  get lc_namespace(): string[] {
    return ["langchain", "chains", this._chainType()];
  }
  /** 
   * 处理存储
   */
  _selectMemoryInputs(values: ChainValues): ChainValues {
    const valuesForMemory = { ...values };
    return valuesForMemory;
  }

  /**
   * 调用执行该链
   */
  async invoke(input: RunInput, options?: RunnableConfig): Promise<RunOutput> {
    const fullValues = await this._formatValues(input);    
    let outputValues: RunOutput = this._call(fullValues as RunInput, runManager, config));
    this._selectMemoryInputs(input)
    return outputValues;
  }

  /**
   * 验证输出
   */
  private _validateOutputs(outputs: Record<string, unknown>): void {}

  /**
   * 输出的预处理
   */
  async prepOutputs( inputs: Record<string, unknown>, outputs: Record<string, unknown>, returnOnlyOutputs = false) {
    this._validateOutputs(outputs);
    return { ...inputs, ...outputs };
  }

  /**
   * 链的核心执行逻辑,集成基类后一般都是需要重写该方法
   */
  abstract _call(values: RunInput, runManager?: CallbackManagerForChainRun, config?: RunnableConfig): Promise<RunOutput>;

  /**
   * 链的唯一标识,实现的链都需要自己定义链的唯一标识
   */
  abstract _chainType(): string;

  /**
   * 格式化
   */
  protected async _formatValues(values: ChainValues & { signal?: AbortSignal; timeout?: number }) {
    const fullValues = { ...values } as typeof values;
    this._selectMemoryInputs(values)
    return fullValues;
  }

  /**
   * 序列化
   */
  serialize(): SerializedBaseChain {
    throw new Error("Method not implemented.");
  }

  /**
   * 反序列化,加载链
   */
  static async deserialize(data: SerializedBaseChain, values: LoadValues = {}): Promise<BaseChain> {}
}

import { xxxLLMAI } from "langchain/llms/xxxLLMAI";
import { PromptTemplate } from "langchain/prompts";
import { LLMChain } from "langchain/chains";

const model = new xxxLLMAI({ temperature: 0 });
const prompt = PromptTemplate.fromTemplate(
  "帮我生成一个关于 {subject} 为主题大纲?"
);
const chainA = new LLMChain({ llm: model, prompt });

const resA = await chainA.call({ subject: "动物世界的快乐" });

import { xxxLLMAI } from "langchain/llms/xxxLLMAI";
import { APIChain } from "langchain/chains";

const OPEN_METEO_DOCS = `BASE URL: https://api.open-meteo.com/

API Documentation
The API endpoint /v1/forecast accepts a geographical coordinate, a list of weather variables and responds with a JSON hourly weather forecast for 7 days. Time always starts at 0:00 today and contains 168 hours. All URL parameters are listed below:

Parameter	Format	Required	Default	Description
latitude, longitude	Floating point	Yes		Geographical WGS84 coordinate of the location
hourly	String array	No		A list of weather variables which should be returned. Values can be comma separated, or multiple &hourly= parameter in the URL can be used.
daily	String array	No		A list of daily weather variable aggregations which should be returned. Values can be comma separated, or multiple &daily= parameter in the URL can be used. If daily weather variables are specified, parameter timezone is required.
current_weather	Bool	No	false	Include current weather conditions in the JSON output.
temperature_unit	String	No	celsius	If fahrenheit is set, all temperature values are converted to Fahrenheit.
windspeed_unit	String	No	kmh	Other wind speed speed units: ms, mph and kn
precipitation_unit	String	No	mm	Other precipitation amount units: inch
timeformat	String	No	iso8601	If format unixtime is selected, all time values are returned in UNIX epoch time in seconds. Please note that all timestamp are in GMT+0! For daily values with unix timestamps, please apply utc_offset_seconds again to get the correct date.
timezone	String	No	GMT	If timezone is set, all timestamps are returned as local-time and data is returned starting at 00:00 local-time. Any time zone name from the time zone database is supported. If auto is set as a time zone, the coordinates will be automatically resolved to the local time zone.
past_days	Integer (0-2)	No	0	If past_days is set, yesterday or the day before yesterday data are also returned.
start_date
end_date	String (yyyy-mm-dd)	No		The time interval to get weather data. A day must be specified as an ISO8601 date (e.g. 2022-06-30).
models	String array	No	auto	Manually select one or more weather models. Per default, the best suitable weather models will be combined.

Variable	Valid time	Unit	Description
temperature_2m	Instant	°C (°F)	Air temperature at 2 meters above ground
snowfall	Preceding hour sum	cm (inch)	Snowfall amount of the preceding hour in centimeters. For the water equivalent in millimeter, divide by 7. E.g. 7 cm snow = 10 mm precipitation water equivalent
rain	Preceding hour sum	mm (inch)	Rain from large scale weather systems of the preceding hour in millimeter
showers	Preceding hour sum	mm (inch)	Showers from convective precipitation in millimeters from the preceding hour
weathercode	Instant	WMO code	Weather condition as a numeric code. Follow WMO weather interpretation codes. See table below for details.
snow_depth	Instant	meters	Snow depth on the ground
freezinglevel_height	Instant	meters	Altitude above sea level of the 0°C level
visibility	Instant	meters	Viewing distance in meters. Influenced by low clouds, humidity and aerosols. Maximum visibility is approximately 24 km.`;

export async function run() {
  const model = new xxxLLMAI({ temperature: 0 });
  const chain = APIChain.fromLLMAndAPIDocs(model, OPEN_METEO_DOCS);

  const res = await chain.call({
    question:
      "What is the weather like right now in Munich, Germany in degrees Farenheit?",
  });
  console.log({ res });
}

import { xxxLLMAI } from "langchain/llms/xxxLLMAI";
import { RetrievalQAChain } from "langchain/chains";
import { HNSWLib } from "langchain/vectorstores/hnswlib";
import { xxxLLMAIEmbeddings } from "langchain/embeddings/xxxLLMAI";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import * as fs from "fs";

export const run = async () => {
  const model = new xxxLLMAI({});
  const text = fs.readFileSync("xxx.txt", "utf8");
  const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000 });
  const docs = await textSplitter.createDocuments([text]);
  const vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());
  const chain = RetrievalQAChain.fromLLM(model, vectorStore.asRetriever());
  const res = await chain.call({
    query: "我的私有库中有关于langchian的知识吗?",
  });
  console.log({ res });
};

const files = fs.readdirSync(docFilePath);
const pdfFiles = files.filter(file => path.extname(file).toLowerCase() === '.pdf');
let docs: any = [];
for (let i = 0; i < pdfFiles.length; i++) {
    const loader = new PDFLoader(docFilePath + "/" + pdfFiles[i]);
    const docsNew = await loader.load();
    docs = [...docs, ...docsNew];
}
if (this.embeddings != null && this.model != null && docs.length > 0) {
    const vectorStoreVec = await LangchainVectorStore.fromDocuments(
        docs,
        this.embeddings,
        {
            url: process.env.VECTOR_URL || "",
            username: "xxx",
            key: process.env.VECTOR_KEY,
            timeout: 10,
            databaseName: "string28996",
            collectionName: "string28996"
        }
    );

    const chain = new RetrievalQAChain({
        combineDocumentsChain: loadQAStuffChain(this.model, { prompt }),
        retriever: vectorStoreVec.asRetriever(),
    });
    chain.returnSourceDocuments = true;
    const res = await chain.call({
        query: query,
    });
    res.request = this.model.requestMessage;
    return res;
}

import { LLMChain, ChainInputs, BaseChain } from "langchain/chains"
export class xxxxChains extends BaseChain {
    //链的调用
    _call(values: ChainValues, runManager?: CallbackManagerForChainRun | undefined): Promise<ChainValues> {
        throw new Error("Method not implemented.");
    }
    //定义链的唯一标识
    _chainType(): string {
        throw new Error("Method not implemented.");
    }
    get inputKeys(): string[] {
        throw new Error("Method not implemented.");
    }
    get outputKeys(): string[] {
        throw new Error("Method not implemented.");
    }
}