Method predict

packages/llm/src/langchain.ts:34–97

({
    query,
    typesenseApiKey,
    communityName,
    summarize,
    accountId,
    threadId,
  }: {
    query: string;
    typesenseApiKey: string;
    communityName: string;
    accountId: string;
    summarize: boolean;
    threadId: string;
  })

export default class LangChain {
  static async predict({
    query,
    typesenseApiKey,
    communityName,
    summarize,
    accountId,
    threadId,
  }: {
    query: string;
    typesenseApiKey: string;
    communityName: string;
    accountId: string;
    summarize: boolean;
    threadId: string;
  }) {
    const body = await Typesense.queryThreads({
      query,
      apiKey: typesenseApiKey,
    });

    if (summarize) {
      for await (let item of body) {
        item.body = await this.summarize(item.body);
      }
    }
    const typesenseResults = body
      .filter((t) => t.id !== threadId)
      .map(
        (b) =>
          new Document({
            pageContent: b.body,
            metadata: { source: b.id },
          })
      );

    const directory = `.db/${communityName}`;
    const fileStore = await FaissStore.load(directory, embeddingsModel);
    const fileResults = await fileStore.similaritySearch(query, env.CHUNKS);

    const prismaVectorStore = PrismaVectorStore.withModel<database.embeddings>(
      database.prisma
    ).create(embeddingsModel, {
      prisma: database.Prisma,
      tableName: 'embeddings',
      vectorColumnName: 'embedding',
      columns: {
        threadId: PrismaVectorStore.IdColumn,
        value: PrismaVectorStore.ContentColumn,
      },
      filter: {
        accountId: { equals: accountId },
        confidence: { equals: 100 },
      },
    });
    const vectorResults = await prismaVectorStore.similaritySearch(
      query,
      env.CHUNKS
    );