anything-llm/server/utils/AiProviders/openRouter/index.js
Sean Hatfield 633f425206
[FEAT] OpenRouter integration (#784)
* WIP openrouter integration

* add OpenRouter options to onboarding flow and data handling

* add todo to fix headers for rankings

* OpenRouter LLM support complete

* Fix hanging response stream with OpenRouter
update tagline
update comment

* update timeout comment

* wait for first chunk to start timer

* sort OpenRouter models by organization

* uppercase first letter of organization

* sort grouped models by org

---------

Co-authored-by: timothycarambat <rambat1010@gmail.com>
2024-02-23 17:18:58 -08:00

335 lines
10 KiB
JavaScript

const { NativeEmbedder } = require("../../EmbeddingEngines/native");
const { chatPrompt } = require("../../chats");
const { v4: uuidv4 } = require("uuid");
const { writeResponseChunk } = require("../../helpers/chat/responses");
function openRouterModels() {
const { MODELS } = require("./models.js");
return MODELS || {};
}
class OpenRouterLLM {
constructor(embedder = null, modelPreference = null) {
const { Configuration, OpenAIApi } = require("openai");
if (!process.env.OPENROUTER_API_KEY)
throw new Error("No OpenRouter API key was set.");
const config = new Configuration({
basePath: "https://openrouter.ai/api/v1",
apiKey: process.env.OPENROUTER_API_KEY,
baseOptions: {
headers: {
"HTTP-Referer": "https://useanything.com",
"X-Title": "AnythingLLM",
},
},
});
this.openai = new OpenAIApi(config);
this.model =
modelPreference || process.env.OPENROUTER_MODEL_PREF || "openrouter/auto";
this.limits = {
history: this.promptWindowLimit() * 0.15,
system: this.promptWindowLimit() * 0.15,
user: this.promptWindowLimit() * 0.7,
};
this.embedder = !embedder ? new NativeEmbedder() : embedder;
this.defaultTemp = 0.7;
}
#appendContext(contextTexts = []) {
if (!contextTexts || !contextTexts.length) return "";
return (
"\nContext:\n" +
contextTexts
.map((text, i) => {
return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
})
.join("")
);
}
allModelInformation() {
return openRouterModels();
}
streamingEnabled() {
return "streamChat" in this && "streamGetChatCompletion" in this;
}
promptWindowLimit() {
const availableModels = this.allModelInformation();
return availableModels[this.model]?.maxLength || 4096;
}
async isValidChatCompletionModel(model = "") {
const availableModels = this.allModelInformation();
return availableModels.hasOwnProperty(model);
}
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
}) {
const prompt = {
role: "system",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
return [prompt, ...chatHistory, { role: "user", content: userPrompt }];
}
async isSafe(_input = "") {
// Not implemented so must be stubbed
return { safe: true, reasons: [] };
}
async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`OpenRouter chat: ${this.model} is not valid for chat completion!`
);
const textResponse = await this.openai
.createChatCompletion({
model: this.model,
temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
n: 1,
messages: await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
),
})
.then((json) => {
const res = json.data;
if (!res.hasOwnProperty("choices"))
throw new Error("OpenRouter chat: No results!");
if (res.choices.length === 0)
throw new Error("OpenRouter chat: No results length!");
return res.choices[0].message.content;
})
.catch((error) => {
throw new Error(
`OpenRouter::createChatCompletion failed with: ${error.message}`
);
});
return textResponse;
}
async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`OpenRouter chat: ${this.model} is not valid for chat completion!`
);
const streamRequest = await this.openai.createChatCompletion(
{
model: this.model,
stream: true,
temperature: Number(workspace?.openAiTemp ?? this.defaultTemp),
n: 1,
messages: await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
),
},
{ responseType: "stream" }
);
return streamRequest;
}
async getChatCompletion(messages = null, { temperature = 0.7 }) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`OpenRouter chat: ${this.model} is not valid for chat completion!`
);
const { data } = await this.openai
.createChatCompletion({
model: this.model,
messages,
temperature,
})
.catch((e) => {
throw new Error(e.response.data.error.message);
});
if (!data.hasOwnProperty("choices")) return null;
return data.choices[0].message.content;
}
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
if (!(await this.isValidChatCompletionModel(this.model)))
throw new Error(
`OpenRouter chat: ${this.model} is not valid for chat completion!`
);
const streamRequest = await this.openai.createChatCompletion(
{
model: this.model,
stream: true,
messages,
temperature,
},
{ responseType: "stream" }
);
return streamRequest;
}
handleStream(response, stream, responseProps) {
const timeoutThresholdMs = 500;
const { uuid = uuidv4(), sources = [] } = responseProps;
return new Promise((resolve) => {
let fullText = "";
let chunk = "";
let lastChunkTime = null; // null when first token is still not received.
// NOTICE: Not all OpenRouter models will return a stop reason
// which keeps the connection open and so the model never finalizes the stream
// like the traditional OpenAI response schema does. So in the case the response stream
// never reaches a formal close state we maintain an interval timer that if we go >=timeoutThresholdMs with
// no new chunks then we kill the stream and assume it to be complete. OpenRouter is quite fast
// so this threshold should permit most responses, but we can adjust `timeoutThresholdMs` if
// we find it is too aggressive.
const timeoutCheck = setInterval(() => {
if (lastChunkTime === null) return;
const now = Number(new Date());
const diffMs = now - lastChunkTime;
if (diffMs >= timeoutThresholdMs) {
console.log(
`OpenRouter stream did not self-close and has been stale for >${timeoutThresholdMs}ms. Closing response stream.`
);
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
clearInterval(timeoutCheck);
resolve(fullText);
}
}, 500);
stream.data.on("data", (data) => {
const lines = data
?.toString()
?.split("\n")
.filter((line) => line.trim() !== "");
for (const line of lines) {
let validJSON = false;
const message = chunk + line.replace(/^data: /, "");
// JSON chunk is incomplete and has not ended yet
// so we need to stitch it together. You would think JSON
// chunks would only come complete - but they don't!
try {
JSON.parse(message);
validJSON = true;
} catch {}
if (!validJSON) {
// It can be possible that the chunk decoding is running away
// and the message chunk fails to append due to string length.
// In this case abort the chunk and reset so we can continue.
// ref: https://github.com/Mintplex-Labs/anything-llm/issues/416
try {
chunk += message;
} catch (e) {
console.error(`Chunk appending error`, e);
chunk = "";
}
continue;
} else {
chunk = "";
}
if (message == "[DONE]") {
lastChunkTime = Number(new Date());
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
clearInterval(timeoutCheck);
resolve(fullText);
} else {
let finishReason = null;
let token = "";
try {
const json = JSON.parse(message);
token = json?.choices?.[0]?.delta?.content;
finishReason = json?.choices?.[0]?.finish_reason || null;
} catch {
continue;
}
if (token) {
fullText += token;
lastChunkTime = Number(new Date());
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: token,
close: false,
error: false,
});
}
if (finishReason !== null) {
lastChunkTime = Number(new Date());
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
clearInterval(timeoutCheck);
resolve(fullText);
}
}
}
});
});
}
// 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 = {
OpenRouterLLM,
openRouterModels,
};