Amazon Bedrock - Doconda

npm install ai @ai-sdk/amazon-bedrock @doconda/sdk

Con el AI SDK

Las credenciales salen de tu entorno de AWS (AWS_REGION, AWS_ACCESS_KEY_ID…), como en el resto de tu código.

import { bedrock } from "@ai-sdk/amazon-bedrock"
import { generateText, isStepCount, jsonSchema, tool } from "ai"
import { Doconda } from "@doconda/sdk"

const doconda = new Doconda()
const tools = Object.fromEntries(
  doconda.tools().map((t) => [
    t.name,
    tool({ description: t.description, inputSchema: jsonSchema(t.inputSchema), execute: (input) => t.run(input as Record<string, unknown>) }),
  ]),
)

const { text } = await generateText({
  model: bedrock(process.env.BEDROCK_MODEL_ID!), // cualquier modelo activado en tu cuenta de Bedrock
  tools,
  stopWhen: isStepCount(5),
  prompt: "Crea una presentación de 5 diapositivas sobre nuestros resultados del trimestre",
})

console.log(text)

Con la API Converse (@aws-sdk/client-bedrock-runtime)

npm install @aws-sdk/client-bedrock-runtime @doconda/sdk

import { BedrockRuntimeClient, ConverseCommand, type Message } from "@aws-sdk/client-bedrock-runtime"
import { Doconda } from "@doconda/sdk"

const bedrock = new BedrockRuntimeClient({})
const tools = new Doconda().tools()
const toolConfig = { tools: tools.map((t) => ({ toolSpec: { name: t.name, description: t.description, inputSchema: { json: t.inputSchema } } })) }
const messages: Message[] = [{ role: "user", content: [{ text: "Hazme un Excel con el presupuesto del evento" }] }]

while (true) {
  const res = await bedrock.send(new ConverseCommand({ modelId: process.env.BEDROCK_MODEL_ID, messages, toolConfig }))
  const message = res.output?.message
  if (!message) break
  messages.push(message)
  if (res.stopReason !== "tool_use") break
  const content = await Promise.all(
    message.content!.filter((b) => b.toolUse).map(async ({ toolUse }) => {
      const result = await tools.find((t) => t.name === toolUse!.name)!.run(toolUse!.input as Record<string, unknown>)
      return { toolResult: { toolUseId: toolUse!.toolUseId, content: [{ text: JSON.stringify(result) }] } }
    }),
  )
  messages.push({ role: "user", content })
}

console.log(messages.at(-1)?.content?.map((b) => b.text ?? "").join(""))

Con Python

pip install boto3 doconda

import json
import os

import boto3
from doconda import Doconda

bedrock = boto3.client("bedrock-runtime")
tools = Doconda().tools()
tool_config = {"tools": [{"toolSpec": {"name": t.name, "description": t.description, "inputSchema": {"json": t.input_schema}}} for t in tools]}
messages = [{"role": "user", "content": [{"text": "Hazme un Excel con el presupuesto del evento"}]}]

while True:
    res = bedrock.converse(modelId=os.environ["BEDROCK_MODEL_ID"], messages=messages, toolConfig=tool_config)
    message = res["output"]["message"]
    messages.append(message)
    if res["stopReason"] != "tool_use":
        break
    results = []
    for block in message["content"]:
        if "toolUse" in block:
            use = block["toolUse"]
            out = next(t for t in tools if t.name == use["name"]).run(use["input"])
            results.append({"toolResult": {"toolUseId": use["toolUseId"], "content": [{"text": json.dumps(out)}]}})
    messages.append({"role": "user", "content": results})

print("".join(block.get("text", "") for block in message["content"]))

Con el SDK de Python. BEDROCK_MODEL_ID es cualquier modelo activado en tu cuenta de Bedrock.