anything-llm/server/utils/AiProviders/anthropic/index.js

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const { v4 } = require("uuid");
const { chatPrompt } = require("../../chats");
const {
writeResponseChunk,
clientAbortedHandler,
} = require("../../helpers/chat/responses");
class AnthropicLLM {
constructor(embedder = null, modelPreference = null) {
if (!process.env.ANTHROPIC_API_KEY)
throw new Error("No Anthropic API key was set.");
// Docs: https://www.npmjs.com/package/@anthropic-ai/sdk
const AnthropicAI = require("@anthropic-ai/sdk");
const anthropic = new AnthropicAI({
apiKey: process.env.ANTHROPIC_API_KEY,
});
this.anthropic = anthropic;
this.model =
modelPreference || process.env.ANTHROPIC_MODEL_PREF || "claude-2.0";
this.limits = {
history: this.promptWindowLimit() * 0.15,
system: this.promptWindowLimit() * 0.15,
user: this.promptWindowLimit() * 0.7,
};
if (!embedder)
throw new Error(
"INVALID ANTHROPIC SETUP. No embedding engine has been set. Go to instance settings and set up an embedding interface to use Anthropic as your LLM."
);
this.embedder = embedder;
this.answerKey = v4().split("-")[0];
this.defaultTemp = 0.7;
}
streamingEnabled() {
return "streamChat" in this && "streamGetChatCompletion" in this;
}
promptWindowLimit() {
switch (this.model) {
case "claude-instant-1.2":
return 100_000;
case "claude-2.0":
return 100_000;
case "claude-2.1":
return 200_000;
case "claude-3-opus-20240229":
return 200_000;
case "claude-3-sonnet-20240229":
return 200_000;
default:
return 100_000; // assume a claude-instant-1.2 model
}
}
isValidChatCompletionModel(modelName = "") {
const validModels = [
"claude-instant-1.2",
"claude-2.0",
"claude-2.1",
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
];
return validModels.includes(modelName);
}
// Moderation can be done with Anthropic, but its not really "exact" so we skip it
// https://docs.anthropic.com/claude/docs/content-moderation
async isSafe(_input = "") {
// Not implemented so must be stubbed
return { safe: true, reasons: [] };
}
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
}) {
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
}
async getChatCompletion(messages = null, { temperature = 0.7 }) {
if (!this.isValidChatCompletionModel(this.model))
throw new Error(
`Anthropic chat: ${this.model} is not valid for chat completion!`
);
try {
const response = await this.anthropic.messages.create({
model: this.model,
max_tokens: 4096,
system: messages[0].content, // Strip out the system message
messages: messages.slice(1), // Pop off the system message
temperature: Number(temperature ?? this.defaultTemp),
});
return response.content[0].text;
} catch (error) {
console.log(error);
return error;
}
}
async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
if (!this.isValidChatCompletionModel(this.model))
throw new Error(
`Anthropic chat: ${this.model} is not valid for chat completion!`
);
const messages = await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
);
const streamRequest = await this.anthropic.messages.stream({
model: this.model,
max_tokens: 4096,
system: messages[0].content, // Strip out the system message
messages: messages.slice(1), // Pop off the system message
temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
});
return streamRequest;
}
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
if (!this.isValidChatCompletionModel(this.model))
throw new Error(
`OpenAI chat: ${this.model} is not valid for chat completion!`
);
const streamRequest = await this.anthropic.messages.stream({
model: this.model,
max_tokens: 4096,
system: messages[0].content, // Strip out the system message
messages: messages.slice(1), // Pop off the system message
temperature: Number(temperature ?? this.defaultTemp),
});
return streamRequest;
}
handleStream(response, stream, responseProps) {
return new Promise((resolve) => {
let fullText = "";
const { uuid = v4(), sources = [] } = responseProps;
// 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);
stream.on("streamEvent", (message) => {
const data = message;
if (
data.type === "content_block_delta" &&
data.delta.type === "text_delta"
) {
const text = data.delta.text;
fullText += text;
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: text,
close: false,
error: false,
});
}
if (
message.type === "message_stop" ||
(data.stop_reason && data.stop_reason === "end_turn")
) {
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
response.removeListener("close", handleAbort);
resolve(fullText);
}
});
});
}
#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("")
);
}
async compressMessages(promptArgs = {}, rawHistory = []) {
const { messageStringCompressor } = require("../../helpers/chat");
const compressedPrompt = await messageStringCompressor(
this,
promptArgs,
rawHistory
);
return compressedPrompt;
}
// 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);
}
}
module.exports = {
AnthropicLLM,
};