AI Agents and RAG

export const userWorkflow = inngest.createFunction(
  fnOptions,
  fnListener,
  async ({ event, step }) => {
    const similar = await step.run("query-vectordb", async () => {
      // Query a vectorDB for similar results given input
      const embedding = createEmbedding(event.data.input)
      return await index.query({ vector: embedding, topK: 3 }).matches
    })

    const response = await step.run("generate-llm-response", async () => {
      // Inject our prompt given similar search results and event.data.input
      const prompt = createAgentPrompt(similar, event.data.input)
      return await llm.createCompletion({
        model: "gpt-3.5-turbo",
        prompt,
      })
    })

    const entities = await step.run("extract-entities-hf", async () => {
      // Extract entities from the generated response using Hugging Face's named entity recognition model
      let pipe = await pipeline(
        "entity-extraction",
        "Xenova/bert-base-multilingual-uncased-sentiment"
      )
      return await pipe(response)
    })

    const summary = await step.run("generate-summary-anthropic", async () => {
      // Generate a summary document using the extracted entities and the Anthropic API
      const anthropic = new Anthropic()

      const anthropicPrompt = `The following entities were mentioned in the response: ${entities.join(
        ", "
      )}. Please generate a summary document based on these entities and the original response:\n\nResponse: ${response}`

      return await anthropic.messages.create({
        model: "claude-3-opus-20240229",
        max_tokens: 1024,
        messages: [{ role: "user", content: anthropicPrompt }],
      })
    })

    await step.run("save-to-db", async () => {
      // Save the generated response, extracted entities, and summary to the database
      await db.summaries.create({
        requestID: event.data.requestID,
        response,
        entities,
        summary,
      })
    })
  }
)