Add AWS bedrock support for LLM + agents (#1935)

add AWS bedrock support for LLM + agents
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Timothy Carambat 2024-07-23 16:35:37 -07:00 committed by GitHub
parent cd597a361e
commit 9366e69d88
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22 changed files with 1838 additions and 3 deletions

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@ -75,6 +75,7 @@ Some cool features of AnythingLLM
- [OpenAI](https://openai.com)
- [OpenAI (Generic)](https://openai.com)
- [Azure OpenAI](https://azure.microsoft.com/en-us/products/ai-services/openai-service)
- [AWS Bedrock](https://aws.amazon.com/bedrock/)
- [Anthropic](https://www.anthropic.com/)
- [Google Gemini Pro](https://ai.google.dev/)
- [Hugging Face (chat models)](https://huggingface.co/)

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@ -94,6 +94,13 @@ GID='1000'
# COHERE_API_KEY=
# COHERE_MODEL_PREF='command-r'
# LLM_PROVIDER='bedrock'
# AWS_BEDROCK_LLM_ACCESS_KEY_ID=
# AWS_BEDROCK_LLM_ACCESS_KEY=
# AWS_BEDROCK_LLM_REGION=us-west-2
# AWS_BEDROCK_LLM_MODEL_PREFERENCE=meta.llama3-1-8b-instruct-v1:0
# AWS_BEDROCK_LLM_MODEL_TOKEN_LIMIT=8191
###########################################
######## Embedding API SElECTION ##########
###########################################

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@ -0,0 +1,122 @@
import { ArrowSquareOut, Info } from "@phosphor-icons/react";
import { AWS_REGIONS } from "./regions";
export default function AwsBedrockLLMOptions({ settings }) {
return (
<div className="w-full flex flex-col">
{!settings?.credentialsOnly && (
<div className="flex flex-col md:flex-row md:items-center gap-x-2 text-white mb-4 bg-blue-800/30 w-fit rounded-lg px-4 py-2">
<div className="gap-x-2 flex items-center">
<Info size={40} />
<p className="text-base">
You should use a properly defined IAM user for inferencing.
<br />
<a
href="https://docs.useanything.com/setup/llm-configuration/cloud/aws-bedrock"
target="_blank"
className="underline flex gap-x-1 items-center"
>
Read more on how to use AWS Bedrock in AnythingLLM
<ArrowSquareOut size={14} />
</a>
</p>
</div>
</div>
)}
<div className="w-full flex items-center gap-[36px] my-1.5">
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-3">
AWS Bedrock IAM Access ID
</label>
<input
type="password"
name="AwsBedrockLLMAccessKeyId"
className="border-none bg-zinc-900 text-white placeholder:text-white/20 text-sm rounded-lg focus:outline-primary-button active:outline-primary-button outline-none block w-full p-2.5"
placeholder="AWS Bedrock IAM User Access ID"
defaultValue={
settings?.AwsBedrockLLMAccessKeyId ? "*".repeat(20) : ""
}
required={true}
autoComplete="off"
spellCheck={false}
/>
</div>
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-3">
AWS Bedrock IAM Access Key
</label>
<input
type="password"
name="AwsBedrockLLMAccessKey"
className="border-none bg-zinc-900 text-white placeholder:text-white/20 text-sm rounded-lg focus:outline-primary-button active:outline-primary-button outline-none block w-full p-2.5"
placeholder="AWS Bedrock IAM User Access Key"
defaultValue={
settings?.AwsBedrockLLMAccessKey ? "*".repeat(20) : ""
}
required={true}
autoComplete="off"
spellCheck={false}
/>
</div>
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-3">
AWS region
</label>
<select
name="AwsBedrockLLMRegion"
defaultValue={settings?.AwsBedrockLLMRegion || "us-west-2"}
required={true}
className="border-none bg-zinc-900 border-gray-500 text-white text-sm rounded-lg block w-full p-2.5"
>
{AWS_REGIONS.map((region) => {
return (
<option key={region.code} value={region.code}>
{region.name} ({region.code})
</option>
);
})}
</select>
</div>
</div>
<div className="w-full flex items-center gap-[36px] my-1.5">
{!settings?.credentialsOnly && (
<>
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-3">
Model ID
</label>
<input
type="text"
name="AwsBedrockLLMModel"
className="border-none bg-zinc-900 text-white placeholder:text-white/20 text-sm rounded-lg focus:outline-primary-button active:outline-primary-button outline-none block w-full p-2.5"
placeholder="Model id from AWS eg: meta.llama3.1-v0.1"
defaultValue={settings?.AwsBedrockLLMModel}
required={true}
autoComplete="off"
spellCheck={false}
/>
</div>
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-3">
Model context window
</label>
<input
type="number"
name="AwsBedrockLLMTokenLimit"
className="border-none bg-zinc-900 text-white placeholder:text-white/20 text-sm rounded-lg focus:outline-primary-button active:outline-primary-button outline-none block w-full p-2.5"
placeholder="Content window limit (eg: 4096)"
min={1}
onScroll={(e) => e.target.blur()}
defaultValue={settings?.AwsBedrockLLMTokenLimit}
required={true}
autoComplete="off"
/>
</div>
</>
)}
</div>
</div>
);
}

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@ -0,0 +1,204 @@
export const AWS_REGIONS = [
{
name: "N. Virginia",
full_name: "US East (N. Virginia)",
code: "us-east-1",
public: true,
zones: [
"us-east-1a",
"us-east-1b",
"us-east-1c",
"us-east-1d",
"us-east-1e",
"us-east-1f",
],
},
{
name: "Ohio",
full_name: "US East (Ohio)",
code: "us-east-2",
public: true,
zones: ["us-east-2a", "us-east-2b", "us-east-2c"],
},
{
name: "N. California",
full_name: "US West (N. California)",
code: "us-west-1",
public: true,
zone_limit: 2,
zones: ["us-west-1a", "us-west-1b", "us-west-1c"],
},
{
name: "Oregon",
full_name: "US West (Oregon)",
code: "us-west-2",
public: true,
zones: ["us-west-2a", "us-west-2b", "us-west-2c", "us-west-2d"],
},
{
name: "GovCloud West",
full_name: "AWS GovCloud (US)",
code: "us-gov-west-1",
public: false,
zones: ["us-gov-west-1a", "us-gov-west-1b", "us-gov-west-1c"],
},
{
name: "GovCloud East",
full_name: "AWS GovCloud (US-East)",
code: "us-gov-east-1",
public: false,
zones: ["us-gov-east-1a", "us-gov-east-1b", "us-gov-east-1c"],
},
{
name: "Canada",
full_name: "Canada (Central)",
code: "ca-central-1",
public: true,
zones: ["ca-central-1a", "ca-central-1b", "ca-central-1c", "ca-central-1d"],
},
{
name: "Stockholm",
full_name: "EU (Stockholm)",
code: "eu-north-1",
public: true,
zones: ["eu-north-1a", "eu-north-1b", "eu-north-1c"],
},
{
name: "Ireland",
full_name: "EU (Ireland)",
code: "eu-west-1",
public: true,
zones: ["eu-west-1a", "eu-west-1b", "eu-west-1c"],
},
{
name: "London",
full_name: "EU (London)",
code: "eu-west-2",
public: true,
zones: ["eu-west-2a", "eu-west-2b", "eu-west-2c"],
},
{
name: "Paris",
full_name: "EU (Paris)",
code: "eu-west-3",
public: true,
zones: ["eu-west-3a", "eu-west-3b", "eu-west-3c"],
},
{
name: "Frankfurt",
full_name: "EU (Frankfurt)",
code: "eu-central-1",
public: true,
zones: ["eu-central-1a", "eu-central-1b", "eu-central-1c"],
},
{
name: "Milan",
full_name: "EU (Milan)",
code: "eu-south-1",
public: true,
zones: ["eu-south-1a", "eu-south-1b", "eu-south-1c"],
},
{
name: "Cape Town",
full_name: "Africa (Cape Town)",
code: "af-south-1",
public: true,
zones: ["af-south-1a", "af-south-1b", "af-south-1c"],
},
{
name: "Tokyo",
full_name: "Asia Pacific (Tokyo)",
code: "ap-northeast-1",
public: true,
zone_limit: 3,
zones: [
"ap-northeast-1a",
"ap-northeast-1b",
"ap-northeast-1c",
"ap-northeast-1d",
],
},
{
name: "Seoul",
full_name: "Asia Pacific (Seoul)",
code: "ap-northeast-2",
public: true,
zones: [
"ap-northeast-2a",
"ap-northeast-2b",
"ap-northeast-2c",
"ap-northeast-2d",
],
},
{
name: "Osaka",
full_name: "Asia Pacific (Osaka-Local)",
code: "ap-northeast-3",
public: true,
zones: ["ap-northeast-3a", "ap-northeast-3b", "ap-northeast-3c"],
},
{
name: "Singapore",
full_name: "Asia Pacific (Singapore)",
code: "ap-southeast-1",
public: true,
zones: ["ap-southeast-1a", "ap-southeast-1b", "ap-southeast-1c"],
},
{
name: "Sydney",
full_name: "Asia Pacific (Sydney)",
code: "ap-southeast-2",
public: true,
zones: ["ap-southeast-2a", "ap-southeast-2b", "ap-southeast-2c"],
},
{
name: "Jakarta",
full_name: "Asia Pacific (Jakarta)",
code: "ap-southeast-3",
public: true,
zones: ["ap-southeast-3a", "ap-southeast-3b", "ap-southeast-3c"],
},
{
name: "Hong Kong",
full_name: "Asia Pacific (Hong Kong)",
code: "ap-east-1",
public: true,
zones: ["ap-east-1a", "ap-east-1b", "ap-east-1c"],
},
{
name: "Mumbai",
full_name: "Asia Pacific (Mumbai)",
code: "ap-south-1",
public: true,
zones: ["ap-south-1a", "ap-south-1b", "ap-south-1c"],
},
{
name: "São Paulo",
full_name: "South America (São Paulo)",
code: "sa-east-1",
public: true,
zone_limit: 2,
zones: ["sa-east-1a", "sa-east-1b", "sa-east-1c"],
},
{
name: "Bahrain",
full_name: "Middle East (Bahrain)",
code: "me-south-1",
public: true,
zones: ["me-south-1a", "me-south-1b", "me-south-1c"],
},
{
name: "Beijing",
full_name: "China (Beijing)",
code: "cn-north-1",
public: false,
zones: ["cn-north-1a", "cn-north-1b", "cn-north-1c"],
},
{
name: "Ningxia",
full_name: "China (Ningxia)",
code: "cn-northwest-1",
public: false,
zones: ["cn-northwest-1a", "cn-northwest-1b", "cn-northwest-1c"],
},
];

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@ -8,6 +8,7 @@ export const DISABLED_PROVIDERS = [
"native",
"textgenwebui",
"generic-openai",
"bedrock",
];
const PROVIDER_DEFAULT_MODELS = {
openai: [],
@ -48,6 +49,7 @@ const PROVIDER_DEFAULT_MODELS = {
],
textgenwebui: [],
"generic-openai": [],
bedrock: [],
};
// For togetherAi, which has a large model list - we subgroup the options

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@ -23,6 +23,8 @@ import KoboldCPPLogo from "@/media/llmprovider/koboldcpp.png";
import TextGenWebUILogo from "@/media/llmprovider/text-generation-webui.png";
import CohereLogo from "@/media/llmprovider/cohere.png";
import LiteLLMLogo from "@/media/llmprovider/litellm.png";
import AWSBedrockLogo from "@/media/llmprovider/bedrock.png";
import PreLoader from "@/components/Preloader";
import OpenAiOptions from "@/components/LLMSelection/OpenAiOptions";
import GenericOpenAiOptions from "@/components/LLMSelection/GenericOpenAiOptions";
@ -43,6 +45,7 @@ import CohereAiOptions from "@/components/LLMSelection/CohereAiOptions";
import KoboldCPPOptions from "@/components/LLMSelection/KoboldCPPOptions";
import TextGenWebUIOptions from "@/components/LLMSelection/TextGenWebUIOptions";
import LiteLLMOptions from "@/components/LLMSelection/LiteLLMOptions";
import AWSBedrockLLMOptions from "@/components/LLMSelection/AwsBedrockLLMOptions";
import LLMItem from "@/components/LLMSelection/LLMItem";
import { CaretUpDown, MagnifyingGlass, X } from "@phosphor-icons/react";
@ -211,6 +214,19 @@ export const AVAILABLE_LLM_PROVIDERS = [
"GenericOpenAiKey",
],
},
{
name: "AWS Bedrock",
value: "bedrock",
logo: AWSBedrockLogo,
options: (settings) => <AWSBedrockLLMOptions settings={settings} />,
description: "Run powerful foundation models privately with AWS Bedrock.",
requiredConfig: [
"AwsBedrockLLMAccessKeyId",
"AwsBedrockLLMAccessKey",
"AwsBedrockLLMRegion",
"AwsBedrockLLMModel",
],
},
{
name: "Native",
value: "native",

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@ -18,6 +18,7 @@ import GroqLogo from "@/media/llmprovider/groq.png";
import KoboldCPPLogo from "@/media/llmprovider/koboldcpp.png";
import TextGenWebUILogo from "@/media/llmprovider/text-generation-webui.png";
import LiteLLMLogo from "@/media/llmprovider/litellm.png";
import AWSBedrockLogo from "@/media/llmprovider/bedrock.png";
import CohereLogo from "@/media/llmprovider/cohere.png";
import ZillizLogo from "@/media/vectordbs/zilliz.png";
@ -179,6 +180,13 @@ export const LLM_SELECTION_PRIVACY = {
],
logo: LiteLLMLogo,
},
bedrock: {
name: "AWS Bedrock",
description: [
"You model and chat contents are subject to the agreed EULA for AWS and the model provider on aws.amazon.com",
],
logo: AWSBedrockLogo,
},
};
export const VECTOR_DB_PRIVACY = {

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@ -18,6 +18,7 @@ import GroqLogo from "@/media/llmprovider/groq.png";
import KoboldCPPLogo from "@/media/llmprovider/koboldcpp.png";
import TextGenWebUILogo from "@/media/llmprovider/text-generation-webui.png";
import LiteLLMLogo from "@/media/llmprovider/litellm.png";
import AWSBedrockLogo from "@/media/llmprovider/bedrock.png";
import CohereLogo from "@/media/llmprovider/cohere.png";
import OpenAiOptions from "@/components/LLMSelection/OpenAiOptions";
@ -39,6 +40,7 @@ import CohereAiOptions from "@/components/LLMSelection/CohereAiOptions";
import KoboldCPPOptions from "@/components/LLMSelection/KoboldCPPOptions";
import TextGenWebUIOptions from "@/components/LLMSelection/TextGenWebUIOptions";
import LiteLLMOptions from "@/components/LLMSelection/LiteLLMOptions";
import AWSBedrockLLMOptions from "@/components/LLMSelection/AwsBedrockLLMOptions";
import LLMItem from "@/components/LLMSelection/LLMItem";
import System from "@/models/system";
@ -182,6 +184,13 @@ const LLMS = [
description:
"Connect to any OpenAi-compatible service via a custom configuration",
},
{
name: "AWS Bedrock",
value: "bedrock",
logo: AWSBedrockLogo,
options: (settings) => <AWSBedrockLLMOptions settings={settings} />,
description: "Run powerful foundation models privately with AWS Bedrock.",
},
{
name: "Native",
value: "native",

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@ -21,6 +21,7 @@ const ENABLED_PROVIDERS = [
"perplexity",
"textgenwebui",
"generic-openai",
"bedrock",
// TODO: More agent support.
// "cohere", // Has tool calling and will need to build explicit support
// "huggingface" // Can be done but already has issues with no-chat templated. Needs to be tested.

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@ -10,7 +10,12 @@ import paths from "@/utils/paths";
// Some providers can only be associated with a single model.
// In that case there is no selection to be made so we can just move on.
const NO_MODEL_SELECTION = ["default", "huggingface", "generic-openai"];
const NO_MODEL_SELECTION = [
"default",
"huggingface",
"generic-openai",
"bedrock",
];
const DISABLED_PROVIDERS = ["azure", "lmstudio", "native"];
const LLM_DEFAULT = {
name: "System default",

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@ -465,6 +465,12 @@ const SystemSettings = {
GenericOpenAiKey: !!process.env.GENERIC_OPEN_AI_API_KEY,
GenericOpenAiMaxTokens: process.env.GENERIC_OPEN_AI_MAX_TOKENS,
AwsBedrockLLMAccessKeyId: !!process.env.AWS_BEDROCK_LLM_ACCESS_KEY_ID,
AwsBedrockLLMAccessKey: !!process.env.AWS_BEDROCK_LLM_ACCESS_KEY,
AwsBedrockLLMRegion: process.env.AWS_BEDROCK_LLM_REGION,
AwsBedrockLLMModel: process.env.AWS_BEDROCK_LLM_MODEL_PREFERENCE,
AwsBedrockLLMTokenLimit: process.env.AWS_BEDROCK_LLM_MODEL_TOKEN_LIMIT,
// Cohere API Keys
CohereApiKey: !!process.env.COHERE_API_KEY,
CohereModelPref: process.env.COHERE_MODEL_PREF,

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@ -28,6 +28,7 @@
"@ladjs/graceful": "^3.2.2",
"@lancedb/lancedb": "0.5.2",
"@langchain/anthropic": "0.1.16",
"@langchain/aws": "^0.0.5",
"@langchain/community": "0.0.53",
"@langchain/core": "0.1.61",
"@langchain/openai": "0.0.28",

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@ -0,0 +1,217 @@
const { StringOutputParser } = require("@langchain/core/output_parsers");
const {
writeResponseChunk,
clientAbortedHandler,
} = require("../../helpers/chat/responses");
const { NativeEmbedder } = require("../../EmbeddingEngines/native");
// Docs: https://js.langchain.com/v0.2/docs/integrations/chat/bedrock_converse
class AWSBedrockLLM {
constructor(embedder = null, modelPreference = null) {
if (!process.env.AWS_BEDROCK_LLM_ACCESS_KEY_ID)
throw new Error("No AWS Bedrock LLM profile id was set.");
if (!process.env.AWS_BEDROCK_LLM_ACCESS_KEY)
throw new Error("No AWS Bedrock LLM access key was set.");
if (!process.env.AWS_BEDROCK_LLM_REGION)
throw new Error("No AWS Bedrock LLM region was set.");
this.model =
modelPreference || process.env.AWS_BEDROCK_LLM_MODEL_PREFERENCE;
this.limits = {
history: this.promptWindowLimit() * 0.15,
system: this.promptWindowLimit() * 0.15,
user: this.promptWindowLimit() * 0.7,
};
this.embedder = embedder ?? new NativeEmbedder();
this.defaultTemp = 0.7;
}
#bedrockClient({ temperature = 0.7 }) {
const { ChatBedrockConverse } = require("@langchain/aws");
return new ChatBedrockConverse({
model: process.env.AWS_BEDROCK_LLM_MODEL_PREFERENCE,
region: process.env.AWS_BEDROCK_LLM_REGION,
credentials: {
accessKeyId: process.env.AWS_BEDROCK_LLM_ACCESS_KEY_ID,
secretAccessKey: process.env.AWS_BEDROCK_LLM_ACCESS_KEY,
},
temperature,
});
}
// For streaming we use Langchain's wrapper to handle weird chunks
// or otherwise absorb headaches that can arise from Ollama models
#convertToLangchainPrototypes(chats = []) {
const {
HumanMessage,
SystemMessage,
AIMessage,
} = require("@langchain/core/messages");
const langchainChats = [];
const roleToMessageMap = {
system: SystemMessage,
user: HumanMessage,
assistant: AIMessage,
};
for (const chat of chats) {
if (!roleToMessageMap.hasOwnProperty(chat.role)) continue;
const MessageClass = roleToMessageMap[chat.role];
langchainChats.push(new MessageClass({ content: chat.content }));
}
return langchainChats;
}
#appendContext(contextTexts = []) {
if (!contextTexts || !contextTexts.length) return "";
return (
"\nContext:\n" +
contextTexts
.map((text, i) => {
return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
})
.join("")
);
}
streamingEnabled() {
return "streamGetChatCompletion" in this;
}
// Ensure the user set a value for the token limit
// and if undefined - assume 4096 window.
promptWindowLimit() {
const limit = process.env.AWS_BEDROCK_LLM_MODEL_TOKEN_LIMIT || 8191;
if (!limit || isNaN(Number(limit)))
throw new Error("No valid token context limit was set.");
return Number(limit);
}
async isValidChatCompletionModel(_ = "") {
return true;
}
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
}) {
// AWS Mistral models do not support system prompts
if (this.model.startsWith("mistral"))
return [...chatHistory, { role: "user", content: userPrompt }];
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
}
async getChatCompletion(messages = null, { temperature = 0.7 }) {
const model = this.#bedrockClient({ temperature });
const textResponse = await model
.pipe(new StringOutputParser())
.invoke(this.#convertToLangchainPrototypes(messages))
.catch((e) => {
throw new Error(
`AWSBedrock::getChatCompletion failed to communicate with Ollama. ${e.message}`
);
});
if (!textResponse || !textResponse.length)
throw new Error(`AWSBedrock::getChatCompletion text response was empty.`);
return textResponse;
}
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
const model = this.#bedrockClient({ temperature });
const stream = await model
.pipe(new StringOutputParser())
.stream(this.#convertToLangchainPrototypes(messages));
return stream;
}
handleStream(response, stream, responseProps) {
const { uuid = uuidv4(), sources = [] } = responseProps;
return new Promise(async (resolve) => {
let fullText = "";
// Establish listener to early-abort a streaming response
// in case things go sideways or the user does not like the response.
// We preserve the generated text but continue as if chat was completed
// to preserve previously generated content.
const handleAbort = () => clientAbortedHandler(resolve, fullText);
response.on("close", handleAbort);
try {
for await (const chunk of stream) {
if (chunk === undefined)
throw new Error(
"Stream returned undefined chunk. Aborting reply - check model provider logs."
);
const content = chunk.hasOwnProperty("content")
? chunk.content
: chunk;
fullText += content;
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: content,
close: false,
error: false,
});
}
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
response.removeListener("close", handleAbort);
resolve(fullText);
} catch (error) {
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: "",
close: true,
error: `AWSBedrock:streaming - could not stream chat. ${
error?.cause ?? error.message
}`,
});
response.removeListener("close", handleAbort);
}
});
}
// Simple wrapper for dynamic embedder & normalize interface for all LLM implementations
async embedTextInput(textInput) {
return await this.embedder.embedTextInput(textInput);
}
async embedChunks(textChunks = []) {
return await this.embedder.embedChunks(textChunks);
}
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageArrayCompressor } = require("../../helpers/chat");
const messageArray = this.constructPrompt(promptArgs);
return await messageArrayCompressor(this, messageArray, rawHistory);
}
}
module.exports = {
AWSBedrockLLM,
};

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@ -775,6 +775,8 @@ ${this.getHistory({ to: route.to })
return new Providers.PerplexityProvider({ model: config.model });
case "textgenwebui":
return new Providers.TextWebGenUiProvider({});
case "bedrock":
return new Providers.AWSBedrockProvider({});
default:
throw new Error(

View File

@ -12,6 +12,7 @@
const { ChatOpenAI } = require("@langchain/openai");
const { ChatAnthropic } = require("@langchain/anthropic");
const { ChatBedrockConverse } = require("@langchain/aws");
const { ChatOllama } = require("@langchain/community/chat_models/ollama");
const { toValidNumber } = require("../../../http");
@ -113,6 +114,16 @@ class Provider {
),
...config,
});
case "bedrock":
return new ChatBedrockConverse({
model: process.env.AWS_BEDROCK_LLM_MODEL_PREFERENCE,
region: process.env.AWS_BEDROCK_LLM_REGION,
credentials: {
accessKeyId: process.env.AWS_BEDROCK_LLM_ACCESS_KEY_ID,
secretAccessKey: process.env.AWS_BEDROCK_LLM_ACCESS_KEY,
},
...config,
});
// OSS Model Runners
// case "anythingllm_ollama":

View File

@ -0,0 +1,136 @@
const Provider = require("./ai-provider.js");
const InheritMultiple = require("./helpers/classes.js");
const UnTooled = require("./helpers/untooled.js");
const { ChatBedrockConverse } = require("@langchain/aws");
const {
HumanMessage,
SystemMessage,
AIMessage,
} = require("@langchain/core/messages");
/**
* The agent provider for the AWS Bedrock provider.
*/
class AWSBedrockProvider extends InheritMultiple([Provider, UnTooled]) {
model;
constructor(_config = {}) {
super();
const model = process.env.AWS_BEDROCK_LLM_MODEL_PREFERENCE ?? null;
const client = new ChatBedrockConverse({
region: process.env.AWS_BEDROCK_LLM_REGION,
credentials: {
accessKeyId: process.env.AWS_BEDROCK_LLM_ACCESS_KEY_ID,
secretAccessKey: process.env.AWS_BEDROCK_LLM_ACCESS_KEY,
},
model,
});
this._client = client;
this.model = model;
this.verbose = true;
}
get client() {
return this._client;
}
// For streaming we use Langchain's wrapper to handle weird chunks
// or otherwise absorb headaches that can arise from Ollama models
#convertToLangchainPrototypes(chats = []) {
const langchainChats = [];
const roleToMessageMap = {
system: SystemMessage,
user: HumanMessage,
assistant: AIMessage,
};
for (const chat of chats) {
if (!roleToMessageMap.hasOwnProperty(chat.role)) continue;
const MessageClass = roleToMessageMap[chat.role];
langchainChats.push(new MessageClass({ content: chat.content }));
}
return langchainChats;
}
async #handleFunctionCallChat({ messages = [] }) {
const response = await this.client
.invoke(this.#convertToLangchainPrototypes(messages))
.then((res) => res)
.catch((e) => {
console.error(e);
return null;
});
return response?.content;
}
/**
* Create a completion based on the received messages.
*
* @param messages A list of messages to send to the API.
* @param functions
* @returns The completion.
*/
async complete(messages, functions = null) {
try {
let completion;
if (functions.length > 0) {
const { toolCall, text } = await this.functionCall(
messages,
functions,
this.#handleFunctionCallChat.bind(this)
);
if (toolCall !== null) {
this.providerLog(`Valid tool call found - running ${toolCall.name}.`);
this.deduplicator.trackRun(toolCall.name, toolCall.arguments);
return {
result: null,
functionCall: {
name: toolCall.name,
arguments: toolCall.arguments,
},
cost: 0,
};
}
completion = { content: text };
}
if (!completion?.content) {
this.providerLog(
"Will assume chat completion without tool call inputs."
);
const response = await this.client.invoke(
this.#convertToLangchainPrototypes(this.cleanMsgs(messages))
);
completion = response;
}
// The UnTooled class inherited Deduplicator is mostly useful to prevent the agent
// from calling the exact same function over and over in a loop within a single chat exchange
// _but_ we should enable it to call previously used tools in a new chat interaction.
this.deduplicator.reset("runs");
return {
result: completion.content,
cost: 0,
};
} catch (error) {
throw error;
}
}
/**
* Get the cost of the completion.
*
* @param _usage The completion to get the cost for.
* @returns The cost of the completion.
* Stubbed since KoboldCPP has no cost basis.
*/
getCost(_usage) {
return 0;
}
}
module.exports = AWSBedrockProvider;

View File

@ -12,6 +12,7 @@ const MistralProvider = require("./mistral.js");
const GenericOpenAiProvider = require("./genericOpenAi.js");
const PerplexityProvider = require("./perplexity.js");
const TextWebGenUiProvider = require("./textgenwebui.js");
const AWSBedrockProvider = require("./bedrock.js");
module.exports = {
OpenAIProvider,
@ -28,4 +29,5 @@ module.exports = {
GenericOpenAiProvider,
PerplexityProvider,
TextWebGenUiProvider,
AWSBedrockProvider,
};

View File

@ -143,6 +143,17 @@ class AgentHandler {
"TextWebGenUI API base path must be provided to use agents."
);
break;
case "bedrock":
if (
!process.env.AWS_BEDROCK_LLM_ACCESS_KEY_ID ||
!process.env.AWS_BEDROCK_LLM_ACCESS_KEY ||
!process.env.AWS_BEDROCK_LLM_REGION ||
!process.env.AWS_BEDROCK_LLM_MODEL_PREFERENCE
)
throw new Error(
"AWS Bedrock Access Keys, model and region must be provided to use agents."
);
break;
default:
throw new Error(
@ -183,6 +194,8 @@ class AgentHandler {
return "sonar-small-online";
case "textgenwebui":
return null;
case "bedrock":
return null;
default:
return "unknown";
}
@ -210,7 +223,7 @@ class AgentHandler {
}
#providerSetupAndCheck() {
this.provider = this.invocation.workspace.agentProvider || "openai";
this.provider = this.invocation.workspace.agentProvider;
this.model = this.#fetchModel();
this.log(`Start ${this.#invocationUUID}::${this.provider}:${this.model}`);
this.#checkSetup();

View File

@ -140,6 +140,9 @@ function getLLMProvider({ provider = null, model = null } = {}) {
case "generic-openai":
const { GenericOpenAiLLM } = require("../AiProviders/genericOpenAi");
return new GenericOpenAiLLM(embedder, model);
case "bedrock":
const { AWSBedrockLLM } = require("../AiProviders/bedrock");
return new AWSBedrockLLM(embedder, model);
default:
throw new Error(
`ENV: No valid LLM_PROVIDER value found in environment! Using ${process.env.LLM_PROVIDER}`

View File

@ -208,6 +208,28 @@ const KEY_MAPPING = {
checks: [nonZero],
},
// AWS Bedrock LLM InferenceSettings
AwsBedrockLLMAccessKeyId: {
envKey: "AWS_BEDROCK_LLM_ACCESS_KEY_ID",
checks: [isNotEmpty],
},
AwsBedrockLLMAccessKey: {
envKey: "AWS_BEDROCK_LLM_ACCESS_KEY",
checks: [isNotEmpty],
},
AwsBedrockLLMRegion: {
envKey: "AWS_BEDROCK_LLM_REGION",
checks: [isNotEmpty],
},
AwsBedrockLLMModel: {
envKey: "AWS_BEDROCK_LLM_MODEL_PREFERENCE",
checks: [isNotEmpty],
},
AwsBedrockLLMTokenLimit: {
envKey: "AWS_BEDROCK_LLM_MODEL_TOKEN_LIMIT",
checks: [nonZero],
},
EmbeddingEngine: {
envKey: "EMBEDDING_ENGINE",
checks: [supportedEmbeddingModel],
@ -541,6 +563,7 @@ function supportedLLM(input = "") {
"cohere",
"litellm",
"generic-openai",
"bedrock",
].includes(input);
return validSelection ? null : `${input} is not a valid LLM provider.`;
}

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