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https://github.com/Mintplex-Labs/anything-llm.git
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Patch Ollama Streaming chunk issues (#500)
Replace stream/sync chats with Langchain interface for now connect #499 ref: https://github.com/Mintplex-Labs/anything-llm/issues/495#issuecomment-1871476091
This commit is contained in:
Timothy Carambat
GitHub
parent
d7481671ba
commit
2a1202de54
1
.vscode/settings.json
vendored
1
.vscode/settings.json
vendored
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@ -1,6 +1,7 @@
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{
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"cSpell.words": [
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"Dockerized",
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"Langchain",
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"Ollama",
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"openai",
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"Qdrant",
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@ -1,4 +1,5 @@
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const { chatPrompt } = require("../../chats");
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const { StringOutputParser } = require("langchain/schema/output_parser");
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// Docs: https://github.com/jmorganca/ollama/blob/main/docs/api.md
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class OllamaAILLM {
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@ -21,6 +22,42 @@ class OllamaAILLM {
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this.embedder = embedder;
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}
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#ollamaClient({ temperature = 0.07 }) {
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const { ChatOllama } = require("langchain/chat_models/ollama");
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return new ChatOllama({
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baseUrl: this.basePath,
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model: this.model,
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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 Ollama 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/schema");
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const langchainChats = [];
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for (const chat of chats) {
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switch (chat.role) {
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case "system":
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langchainChats.push(new SystemMessage({ content: chat.content }));
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break;
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case "user":
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langchainChats.push(new HumanMessage({ content: chat.content }));
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break;
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case "assistant":
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langchainChats.push(new AIMessage({ content: chat.content }));
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break;
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default:
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break;
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}
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}
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return langchainChats;
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}
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streamingEnabled() {
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return "streamChat" in this && "streamGetChatCompletion" in this;
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}
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@ -63,37 +100,21 @@ Context:
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}
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async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
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const textResponse = await fetch(`${this.basePath}/api/chat`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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const messages = await this.compressMessages(
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{
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systemPrompt: chatPrompt(workspace),
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userPrompt: prompt,
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chatHistory,
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},
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body: JSON.stringify({
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model: this.model,
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stream: false,
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options: {
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temperature: Number(workspace?.openAiTemp ?? 0.7),
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},
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messages: await this.compressMessages(
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{
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systemPrompt: chatPrompt(workspace),
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userPrompt: prompt,
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chatHistory,
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},
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rawHistory
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),
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}),
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})
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.then((res) => {
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if (!res.ok)
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throw new Error(`Ollama:sendChat ${res.status} ${res.statusText}`);
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return res.json();
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})
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.then((data) => data?.message?.content)
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.catch((e) => {
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console.error(e);
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throw new Error(`Ollama::sendChat failed with: ${error.message}`);
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});
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rawHistory
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);
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const model = this.#ollamaClient({
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temperature: Number(workspace?.openAiTemp ?? 0.7),
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});
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const textResponse = await model
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.pipe(new StringOutputParser())
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.invoke(this.#convertToLangchainPrototypes(messages));
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if (!textResponse.length)
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throw new Error(`Ollama::sendChat text response was empty.`);
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@ -102,63 +123,29 @@ Context:
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}
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async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
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const response = await fetch(`${this.basePath}/api/chat`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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const messages = await this.compressMessages(
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{
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systemPrompt: chatPrompt(workspace),
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userPrompt: prompt,
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chatHistory,
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},
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body: JSON.stringify({
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model: this.model,
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stream: true,
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options: {
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temperature: Number(workspace?.openAiTemp ?? 0.7),
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},
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messages: await this.compressMessages(
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{
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systemPrompt: chatPrompt(workspace),
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userPrompt: prompt,
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chatHistory,
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},
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rawHistory
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),
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}),
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}).catch((e) => {
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console.error(e);
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throw new Error(`Ollama:streamChat ${error.message}`);
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});
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rawHistory
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);
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return { type: "ollamaStream", response };
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const model = this.#ollamaClient({
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temperature: Number(workspace?.openAiTemp ?? 0.7),
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});
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const stream = await model
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.pipe(new StringOutputParser())
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.stream(this.#convertToLangchainPrototypes(messages));
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return stream;
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}
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async getChatCompletion(messages = null, { temperature = 0.7 }) {
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const textResponse = await fetch(`${this.basePath}/api/chat`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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model: this.model,
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messages,
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stream: false,
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options: {
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temperature,
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},
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}),
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})
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.then((res) => {
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if (!res.ok)
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throw new Error(
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`Ollama:getChatCompletion ${res.status} ${res.statusText}`
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);
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return res.json();
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})
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.then((data) => data?.message?.content)
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.catch((e) => {
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console.error(e);
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throw new Error(
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`Ollama::getChatCompletion failed with: ${error.message}`
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);
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});
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const model = this.#ollamaClient({ temperature });
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const textResponse = await model
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.pipe(new StringOutputParser())
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.invoke(this.#convertToLangchainPrototypes(messages));
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if (!textResponse.length)
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throw new Error(`Ollama::getChatCompletion text response was empty.`);
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@ -167,25 +154,11 @@ Context:
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}
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async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
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const response = await fetch(`${this.basePath}/api/chat`, {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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model: this.model,
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stream: true,
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messages,
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options: {
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temperature,
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},
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}),
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}).catch((e) => {
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console.error(e);
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throw new Error(`Ollama:streamGetChatCompletion ${error.message}`);
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});
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return { type: "ollamaStream", response };
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const model = this.#ollamaClient({ temperature });
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const stream = await model
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.pipe(new StringOutputParser())
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.stream(this.#convertToLangchainPrototypes(messages));
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return stream;
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}
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// Simple wrapper for dynamic embedder & normalize interface for all LLM implementations
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@ -232,46 +232,19 @@ function handleStreamResponses(response, stream, responseProps) {
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});
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}
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if (stream?.type === "ollamaStream") {
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return new Promise(async (resolve) => {
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let fullText = "";
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for await (const dataChunk of stream.response.body) {
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const chunk = JSON.parse(Buffer.from(dataChunk).toString());
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fullText += chunk.message.content;
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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: chunk.message.content,
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close: false,
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error: false,
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});
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}
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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: "",
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close: true,
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error: false,
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});
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resolve(fullText);
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});
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}
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// If stream is not a regular OpenAI Stream (like if using native model)
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// If stream is not a regular OpenAI Stream (like if using native model, Ollama, or most LangChain interfaces)
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// we can just iterate the stream content instead.
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if (!stream.hasOwnProperty("data")) {
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return new Promise(async (resolve) => {
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let fullText = "";
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for await (const chunk of stream) {
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fullText += chunk.content;
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const content = chunk.hasOwnProperty("content") ? chunk.content : chunk;
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fullText += content;
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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: chunk.content,
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textResponse: content,
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close: false,
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error: false,
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});
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