Overhaul AWS Bedrock provider (#3537)
* Patch AWS Bedrock provider for newer models and performance * patch prompt constructor
This commit is contained in:
Timothy Carambat
GitHub
parent
c29f7669c4
commit
78c83383d8
@ -20,6 +20,7 @@
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},
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"dependencies": {
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"@anthropic-ai/sdk": "^0.39.0",
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"@aws-sdk/client-bedrock-runtime": "^3.775.0",
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"@azure/openai": "1.0.0-beta.10",
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"@datastax/astra-db-ts": "^0.1.3",
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"@google/generative-ai": "^0.7.1",
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@ -1,15 +1,18 @@
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const { StringOutputParser } = require("@langchain/core/output_parsers");
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const {
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BedrockRuntimeClient,
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ConverseCommand,
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ConverseStreamCommand,
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} = require("@aws-sdk/client-bedrock-runtime");
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const {
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writeResponseChunk,
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clientAbortedHandler,
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formatChatHistory,
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} = require("../../helpers/chat/responses");
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const { NativeEmbedder } = require("../../EmbeddingEngines/native");
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const {
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LLMPerformanceMonitor,
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} = require("../../helpers/chat/LLMPerformanceMonitor");
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const { v4: uuidv4 } = require("uuid");
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// Docs: https://js.langchain.com/v0.2/docs/integrations/chat/bedrock_converse
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class AWSBedrockLLM {
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/**
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* These models do not support system prompts
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@ -23,6 +26,7 @@ class AWSBedrockLLM {
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"amazon.titan-text-lite-v1",
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"cohere.command-text-v14",
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"cohere.command-light-text-v14",
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"us.deepseek.r1-v1:0",
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];
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constructor(embedder = null, modelPreference = null) {
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@ -51,6 +55,17 @@ class AWSBedrockLLM {
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user: this.promptWindowLimit() * 0.7,
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};
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this.bedrockClient = new BedrockRuntimeClient({
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region: process.env.AWS_BEDROCK_LLM_REGION,
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credentials: {
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accessKeyId: process.env.AWS_BEDROCK_LLM_ACCESS_KEY_ID,
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secretAccessKey: process.env.AWS_BEDROCK_LLM_ACCESS_KEY,
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...(this.authMethod === "sessionToken"
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? { sessionToken: process.env.AWS_BEDROCK_LLM_SESSION_TOKEN }
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: {}),
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},
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});
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this.embedder = embedder ?? new NativeEmbedder();
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this.defaultTemp = 0.7;
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this.#log(
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@ -69,62 +84,6 @@ class AWSBedrockLLM {
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return method;
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}
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#bedrockClient({ temperature = 0.7 }) {
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const { ChatBedrockConverse } = require("@langchain/aws");
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return new ChatBedrockConverse({
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model: this.model,
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region: process.env.AWS_BEDROCK_LLM_REGION,
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credentials: {
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accessKeyId: process.env.AWS_BEDROCK_LLM_ACCESS_KEY_ID,
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secretAccessKey: process.env.AWS_BEDROCK_LLM_ACCESS_KEY,
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...(this.authMethod === "sessionToken"
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? { sessionToken: process.env.AWS_BEDROCK_LLM_SESSION_TOKEN }
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: {}),
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},
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temperature,
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});
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}
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// For streaming we use Langchain's wrapper to handle weird chunks
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// or otherwise absorb headaches that can arise from Bedrock models
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#convertToLangchainPrototypes(chats = []) {
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const {
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HumanMessage,
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SystemMessage,
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AIMessage,
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} = require("@langchain/core/messages");
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const langchainChats = [];
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const roleToMessageMap = {
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system: SystemMessage,
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user: HumanMessage,
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assistant: AIMessage,
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};
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for (const chat of chats) {
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if (!roleToMessageMap.hasOwnProperty(chat.role)) continue;
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// When a model does not support system prompts, we need to handle it.
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// We will add a new message that simulates the system prompt via a user message and AI response.
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// This will allow the model to respond without crashing but we can still inject context.
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if (
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this.noSystemPromptModels.includes(this.model) &&
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chat.role === "system"
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) {
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this.#log(
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`Model does not support system prompts! Simulating system prompt via Human/AI message pairs.`
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);
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langchainChats.push(new HumanMessage({ content: chat.content }));
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langchainChats.push(new AIMessage({ content: "Okay." }));
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continue;
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}
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const MessageClass = roleToMessageMap[chat.role];
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langchainChats.push(new MessageClass({ content: chat.content }));
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}
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return langchainChats;
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}
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#appendContext(contextTexts = []) {
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if (!contextTexts || !contextTexts.length) return "";
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return (
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@ -167,22 +126,21 @@ class AWSBedrockLLM {
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/**
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* Generates appropriate content array for a message + attachments.
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* TODO: Implement this - attachments are not supported yet for Bedrock.
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* @param {{userPrompt:string, attachments: import("../../helpers").Attachment[]}}
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* @returns {string|object[]}
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*/
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#generateContent({ userPrompt, attachments = [] }) {
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if (!attachments.length) {
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return { content: userPrompt };
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}
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if (!attachments.length) return [{ text: userPrompt }];
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const content = [{ type: "text", text: userPrompt }];
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for (let attachment of attachments) {
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content.push({
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type: "image_url",
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image_url: attachment.contentString,
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});
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}
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return { content: content.flat() };
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// const content = [{ type: "text", text: userPrompt }];
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// for (let attachment of attachments) {
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// content.push({
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// type: "image_url",
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// image_url: attachment.contentString,
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// });
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// }
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// return { content: content.flat() };
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}
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/**
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@ -195,72 +153,125 @@ class AWSBedrockLLM {
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contextTexts = [],
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chatHistory = [],
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userPrompt = "",
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attachments = [],
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_attachments = [],
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}) {
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// AWS Mistral models do not support system prompts
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if (this.model.startsWith("mistral"))
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return [
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...formatChatHistory(chatHistory, this.#generateContent, "spread"),
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let prompt = [
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{
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role: "user",
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...this.#generateContent({ userPrompt, attachments }),
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role: "system",
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content: [
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{ text: `${systemPrompt}${this.#appendContext(contextTexts)}` },
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],
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},
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];
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const prompt = {
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role: "system",
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content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
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};
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return [
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prompt,
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...formatChatHistory(chatHistory, this.#generateContent, "spread"),
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// If the model does not support system prompts, we need to add a user message and assistant message
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if (this.noSystemPromptModels.includes(this.model)) {
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prompt = [
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{
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role: "user",
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...this.#generateContent({ userPrompt, attachments }),
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content: [
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{ text: `${systemPrompt}${this.#appendContext(contextTexts)}` },
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],
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},
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{
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role: "assistant",
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content: [{ text: "Okay." }],
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},
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];
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}
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return [
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...prompt,
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...chatHistory.map((msg) => ({
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role: msg.role,
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content: this.#generateContent({
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userPrompt: msg.content,
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attachments: msg.attachments,
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}),
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})),
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{
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role: "user",
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content: this.#generateContent({
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userPrompt: userPrompt,
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attachments: [],
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}),
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},
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];
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}
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/**
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* Parses and prepends reasoning from the response and returns the full text response.
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* @param {Object} response
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* @returns {string}
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*/
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#parseReasoningFromResponse({ content = [] }) {
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let textResponse = content[0]?.text;
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if (
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!!content?.[1]?.reasoningContent &&
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content?.[1]?.reasoningContent?.reasoningText?.text?.trim().length > 0
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)
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textResponse = `<think>${content?.[1]?.reasoningContent?.reasoningText?.text}</think>${textResponse}`;
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return textResponse;
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}
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async getChatCompletion(messages = null, { temperature = 0.7 }) {
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const model = this.#bedrockClient({ temperature });
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const hasSystem = messages[0]?.role === "system";
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const [system, ...history] = hasSystem ? messages : [null, ...messages];
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const result = await LLMPerformanceMonitor.measureAsyncFunction(
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model
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.pipe(new StringOutputParser())
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.invoke(this.#convertToLangchainPrototypes(messages))
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this.bedrockClient
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.send(
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new ConverseCommand({
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modelId: this.model,
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messages: history,
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inferenceConfig: {
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maxTokens: this.promptWindowLimit(),
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temperature,
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},
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system: !!system ? system.content : undefined,
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})
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)
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.catch((e) => {
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throw new Error(
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`AWSBedrock::getChatCompletion failed to communicate with Bedrock client. ${e.message}`
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);
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})
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}),
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messages,
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false
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);
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if (!result.output || result.output.length === 0) return null;
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// Langchain does not return the usage metrics in the response so we estimate them
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const promptTokens = LLMPerformanceMonitor.countTokens(messages);
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const completionTokens = LLMPerformanceMonitor.countTokens([
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{ content: result.output },
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]);
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const response = result.output;
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if (!response || !response?.output) return null;
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return {
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textResponse: result.output,
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textResponse: this.#parseReasoningFromResponse(response.output?.message),
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metrics: {
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prompt_tokens: promptTokens,
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completion_tokens: completionTokens,
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total_tokens: promptTokens + completionTokens,
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outputTps: completionTokens / result.duration,
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prompt_tokens: response?.usage?.inputTokens,
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completion_tokens: response?.usage?.outputTokens,
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total_tokens: response?.usage?.totalTokens,
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outputTps:
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response?.usage?.outputTokens / (response?.metrics?.latencyMs / 1000),
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duration: result.duration,
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},
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};
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}
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async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
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const model = this.#bedrockClient({ temperature });
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const hasSystem = messages[0]?.role === "system";
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const [system, ...history] = hasSystem ? messages : [null, ...messages];
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const measuredStreamRequest = await LLMPerformanceMonitor.measureStream(
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model
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.pipe(new StringOutputParser())
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.stream(this.#convertToLangchainPrototypes(messages)),
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messages
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this.bedrockClient.send(
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new ConverseStreamCommand({
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modelId: this.model,
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messages: history,
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inferenceConfig: { maxTokens: this.promptWindowLimit(), temperature },
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system: !!system ? system.content : undefined,
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})
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),
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messages,
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false
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);
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return measuredStreamRequest;
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}
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@ -275,12 +286,15 @@ class AWSBedrockLLM {
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*/
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handleStream(response, stream, responseProps) {
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const { uuid = uuidv4(), sources = [] } = responseProps;
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let hasUsageMetrics = false;
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let usage = {
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prompt_tokens: 0,
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completion_tokens: 0,
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};
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return new Promise(async (resolve) => {
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let fullText = "";
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let usage = {
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completion_tokens: 0,
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};
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let reasoningText = "";
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// Establish listener to early-abort a streaming response
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// in case things go sideways or the user does not like the response.
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@ -293,25 +307,82 @@ class AWSBedrockLLM {
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response.on("close", handleAbort);
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try {
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for await (const chunk of stream) {
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for await (const chunk of stream.stream) {
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if (chunk === undefined)
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throw new Error(
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"Stream returned undefined chunk. Aborting reply - check model provider logs."
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);
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const content = chunk.hasOwnProperty("content")
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? chunk.content
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: chunk;
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fullText += content;
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if (!!content) usage.completion_tokens++; // Dont count empty chunks
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const action = Object.keys(chunk)[0];
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if (action === "metadata") {
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hasUsageMetrics = true;
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usage.prompt_tokens = chunk.metadata?.usage?.inputTokens ?? 0;
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usage.completion_tokens = chunk.metadata?.usage?.outputTokens ?? 0;
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usage.total_tokens = chunk.metadata?.usage?.totalTokens ?? 0;
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}
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if (action === "contentBlockDelta") {
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const token = chunk.contentBlockDelta?.delta?.text;
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const reasoningToken =
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chunk.contentBlockDelta?.delta?.reasoningContent?.text;
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// Reasoning models will always return the reasoning text before the token text.
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if (reasoningToken) {
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// If the reasoning text is empty (''), we need to initialize it
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// and send the first chunk of reasoning text.
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if (reasoningText.length === 0) {
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writeResponseChunk(response, {
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uuid,
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sources: [],
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type: "textResponseChunk",
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textResponse: content,
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textResponse: `<think>${reasoningToken}`,
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close: false,
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error: false,
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});
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reasoningText += `<think>${reasoningToken}`;
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continue;
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} else {
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writeResponseChunk(response, {
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uuid,
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sources: [],
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type: "textResponseChunk",
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textResponse: reasoningToken,
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close: false,
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error: false,
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});
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reasoningText += reasoningToken;
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}
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}
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// If the reasoning text is not empty, but the reasoning token is empty
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// and the token text is not empty we need to close the reasoning text and begin sending the token text.
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if (!!reasoningText && !reasoningToken && token) {
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writeResponseChunk(response, {
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uuid,
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sources: [],
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type: "textResponseChunk",
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textResponse: `</think>`,
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close: false,
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error: false,
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});
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fullText += `${reasoningText}</think>`;
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reasoningText = "";
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}
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if (token) {
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fullText += token;
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// If we never saw a usage metric, we can estimate them by number of completion chunks
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if (!hasUsageMetrics) usage.completion_tokens++;
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writeResponseChunk(response, {
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uuid,
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sources: [],
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type: "textResponseChunk",
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textResponse: token,
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close: false,
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error: false,
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});
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}
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}
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}
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writeResponseChunk(response, {
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@ -326,18 +397,18 @@ class AWSBedrockLLM {
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stream?.endMeasurement(usage);
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resolve(fullText);
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} catch (error) {
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console.log(`\x1b[43m\x1b[34m[STREAMING ERROR]\x1b[0m ${e.message}`);
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writeResponseChunk(response, {
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uuid,
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type: "abort",
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textResponse: null,
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sources: [],
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type: "textResponseChunk",
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textResponse: "",
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close: true,
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error: `AWSBedrock:streaming - could not stream chat. ${
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error?.cause ?? error.message
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}`,
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error: `AWSBedrock:streaming - could not stream chat. ${error?.cause ?? error.message}`,
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});
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response.removeListener("close", handleAbort);
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stream?.endMeasurement(usage);
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resolve(fullText); // Return what we currently have - if anything.
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}
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});
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}
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848
server/yarn.lock
848
server/yarn.lock
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