anything-llm/server/utils/AiProviders/huggingface/index.js
Timothy Carambat c59ab9da0a
Refactor LLM chat backend (#717)
* refactor stream/chat/embed-stram to be a single execution logic path so that it is easier to maintain and build upon

* no thread in sync chat since only api uses it
adjust import locations
2024-02-14 12:32:07 -08:00

295 lines
8.7 KiB
JavaScript

const { NativeEmbedder } = require("../../EmbeddingEngines/native");
const { OpenAiEmbedder } = require("../../EmbeddingEngines/openAi");
const { chatPrompt } = require("../../chats");
const { writeResponseChunk } = require("../../helpers/chat/responses");
class HuggingFaceLLM {
constructor(embedder = null, _modelPreference = null) {
const { Configuration, OpenAIApi } = require("openai");
if (!process.env.HUGGING_FACE_LLM_ENDPOINT)
throw new Error("No HuggingFace Inference Endpoint was set.");
if (!process.env.HUGGING_FACE_LLM_API_KEY)
throw new Error("No HuggingFace Access Token was set.");
const config = new Configuration({
basePath: `${process.env.HUGGING_FACE_LLM_ENDPOINT}/v1`,
apiKey: process.env.HUGGING_FACE_LLM_API_KEY,
});
this.openai = new OpenAIApi(config);
// When using HF inference server - the model param is not required so
// we can stub it here. HF Endpoints can only run one model at a time.
// We set to 'tgi' so that endpoint for HF can accept message format
this.model = "tgi";
this.limits = {
history: this.promptWindowLimit() * 0.15,
system: this.promptWindowLimit() * 0.15,
user: this.promptWindowLimit() * 0.7,
};
if (!embedder)
console.warn(
"No embedding provider defined for HuggingFaceLLM - falling back to Native for embedding!"
);
this.embedder = !embedder ? new OpenAiEmbedder() : new NativeEmbedder();
this.defaultTemp = 0.2;
}
#appendContext(contextTexts = []) {
if (!contextTexts || !contextTexts.length) return "";
return (
"\nContext:\n" +
contextTexts
.map((text, i) => {
return `[CONTEXT ${i}]:\n${text}\n[END CONTEXT ${i}]\n\n`;
})
.join("")
);
}
streamingEnabled() {
return "streamChat" in this && "streamGetChatCompletion" in this;
}
promptWindowLimit() {
const limit = process.env.HUGGING_FACE_LLM_TOKEN_LIMIT || 4096;
if (!limit || isNaN(Number(limit)))
throw new Error("No HuggingFace token context limit was set.");
return Number(limit);
}
async isValidChatCompletionModel(_ = "") {
return true;
}
constructPrompt({
systemPrompt = "",
contextTexts = [],
chatHistory = [],
userPrompt = "",
}) {
// System prompt it not enabled for HF model chats
const prompt = {
role: "user",
content: `${systemPrompt}${this.#appendContext(contextTexts)}`,
};
const assistantResponse = {
role: "assistant",
content: "Okay, I will follow those instructions",
};
return [
prompt,
assistantResponse,
...chatHistory,
{ role: "user", content: userPrompt },
];
}
async isSafe(_input = "") {
// Not implemented so must be stubbed
return { safe: true, reasons: [] };
}
async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
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("HuggingFace chat: No results!");
if (res.choices.length === 0)
throw new Error("HuggingFace chat: No results length!");
return res.choices[0].message.content;
})
.catch((error) => {
throw new Error(
`HuggingFace::createChatCompletion failed with: ${error.message}`
);
});
return textResponse;
}
async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
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 }) {
const { data } = await this.openai.createChatCompletion({
model: this.model,
messages,
temperature,
});
if (!data.hasOwnProperty("choices")) return null;
return data.choices[0].message.content;
}
async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
const streamRequest = await this.openai.createChatCompletion(
{
model: this.model,
stream: true,
messages,
temperature,
},
{ responseType: "stream" }
);
return streamRequest;
}
handleStream(response, stream, responseProps) {
const { uuid = uuidv4(), sources = [] } = responseProps;
return new Promise((resolve) => {
let fullText = "";
let chunk = "";
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:/, "");
if (message !== "[DONE]") {
// 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 {
console.log("Failed to parse message", message);
}
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]") {
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
resolve(fullText);
} else {
let error = null;
let finishReason = null;
let token = "";
try {
const json = JSON.parse(message);
error = json?.error || null;
token = json?.choices?.[0]?.delta?.content;
finishReason = json?.choices?.[0]?.finish_reason || null;
} catch {
continue;
}
if (!!error) {
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: null,
close: true,
error,
});
resolve("");
return;
}
if (token) {
fullText += token;
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: token,
close: false,
error: false,
});
}
if (finishReason !== null) {
writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
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 = {
HuggingFaceLLM,
};