Method askLLM

packages/llm/src/langchain.ts:137–166

({
    query,
    vectorStore,
  }: {
    query: string;
    vectorStore: FaissStore;
  })

  @measure
  private static async askLLM({
    query,
    vectorStore,
  }: {
    query: string;
    vectorStore: FaissStore;
  }) {
    const template = `Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, DON'T try to make up an answer.
Keep the answer as concise as possible.
DON'T crop the answer, ensure the answer is complete.
No need to say "thanks for asking!" in the answer.
Context: {context}
Question: {question}
Helpful Answer:`;

    const chain = RetrievalQAChain.fromLLM(
      model,
      vectorStore.asRetriever({ k: env.CHUNKS * 2 }),
      {
        prompt: PromptTemplate.fromTemplate(template),
        returnSourceDocuments: true,
      }
    );

    const response = await chain.call({
      query,
    });
    return response;
  }

  @measure
  private static async crawl(url: string, options: CrawlOptions) {