这是indexloc提供的服务，不要输入任何密码
Skip to content

Patch Ollama Streaming chunk issues #500

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Dec 28, 2023
Merged

Patch Ollama Streaming chunk issues #500

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions .vscode/settings.json
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
{
"cSpell.words": [
"Dockerized",
"Langchain",
"Ollama",
"openai",
"Qdrant",
Expand Down
175 changes: 74 additions & 101 deletions server/utils/AiProviders/ollama/index.js
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
const { chatPrompt } = require("../../chats");
const { StringOutputParser } = require("langchain/schema/output_parser");

// Docs: https://github.com/jmorganca/ollama/blob/main/docs/api.md
class OllamaAILLM {
Expand All @@ -21,6 +22,42 @@ class OllamaAILLM {
this.embedder = embedder;
}

#ollamaClient({ temperature = 0.07 }) {
const { ChatOllama } = require("langchain/chat_models/ollama");
return new ChatOllama({
baseUrl: this.basePath,
model: this.model,
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/schema");
const langchainChats = [];
for (const chat of chats) {
switch (chat.role) {
case "system":
langchainChats.push(new SystemMessage({ content: chat.content }));
break;
case "user":
langchainChats.push(new HumanMessage({ content: chat.content }));
break;
case "assistant":
langchainChats.push(new AIMessage({ content: chat.content }));
break;
default:
break;
}
}
Comment on lines +43 to +57
Copy link

@review-agent-prime review-agent-prime bot Dec 28, 2023

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Replaced the switch statement in the #convertToLangchainPrototypes method with a map object for better readability and performance.

Suggested change
for (const chat of chats) {
switch (chat.role) {
case "system":
langchainChats.push(new SystemMessage({ content: chat.content }));
break;
case "user":
langchainChats.push(new HumanMessage({ content: chat.content }));
break;
case "assistant":
langchainChats.push(new AIMessage({ content: chat.content }));
break;
default:
break;
}
}
const roleToMessageMap = {
'system': SystemMessage,
'user': HumanMessage,
'assistant': AIMessage
};
for (const chat of chats) {
const MessageClass = roleToMessageMap[chat.role];
if (MessageClass) {
langchainChats.push(new MessageClass({ content: chat.content }));
}
}

return langchainChats;
}

streamingEnabled() {
return "streamChat" in this && "streamGetChatCompletion" in this;
}
Expand Down Expand Up @@ -63,37 +100,21 @@ Context:
}

async sendChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
const textResponse = await fetch(`${this.basePath}/api/chat`, {
method: "POST",
headers: {
"Content-Type": "application/json",
const messages = await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
body: JSON.stringify({
model: this.model,
stream: false,
options: {
temperature: Number(workspace?.openAiTemp ?? 0.7),
},
messages: await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
),
}),
})
.then((res) => {
if (!res.ok)
throw new Error(`Ollama:sendChat ${res.status} ${res.statusText}`);
return res.json();
})
.then((data) => data?.message?.content)
.catch((e) => {
console.error(e);
throw new Error(`Ollama::sendChat failed with: ${error.message}`);
});
rawHistory
);

const model = this.#ollamaClient({
temperature: Number(workspace?.openAiTemp ?? 0.7),
});
const textResponse = await model
.pipe(new StringOutputParser())
.invoke(this.#convertToLangchainPrototypes(messages));

if (!textResponse.length)
throw new Error(`Ollama::sendChat text response was empty.`);
Expand All @@ -102,63 +123,29 @@ Context:
}

async streamChat(chatHistory = [], prompt, workspace = {}, rawHistory = []) {
const response = await fetch(`${this.basePath}/api/chat`, {
method: "POST",
headers: {
"Content-Type": "application/json",
const messages = await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
body: JSON.stringify({
model: this.model,
stream: true,
options: {
temperature: Number(workspace?.openAiTemp ?? 0.7),
},
messages: await this.compressMessages(
{
systemPrompt: chatPrompt(workspace),
userPrompt: prompt,
chatHistory,
},
rawHistory
),
}),
}).catch((e) => {
console.error(e);
throw new Error(`Ollama:streamChat ${error.message}`);
});
rawHistory
);

return { type: "ollamaStream", response };
const model = this.#ollamaClient({
temperature: Number(workspace?.openAiTemp ?? 0.7),
});
const stream = await model
.pipe(new StringOutputParser())
.stream(this.#convertToLangchainPrototypes(messages));
return stream;
}

async getChatCompletion(messages = null, { temperature = 0.7 }) {
const textResponse = await fetch(`${this.basePath}/api/chat`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({
model: this.model,
messages,
stream: false,
options: {
temperature,
},
}),
})
.then((res) => {
if (!res.ok)
throw new Error(
`Ollama:getChatCompletion ${res.status} ${res.statusText}`
);
return res.json();
})
.then((data) => data?.message?.content)
.catch((e) => {
console.error(e);
throw new Error(
`Ollama::getChatCompletion failed with: ${error.message}`
);
});
const model = this.#ollamaClient({ temperature });
const textResponse = await model
.pipe(new StringOutputParser())
.invoke(this.#convertToLangchainPrototypes(messages));

if (!textResponse.length)
throw new Error(`Ollama::getChatCompletion text response was empty.`);
Expand All @@ -167,25 +154,11 @@ Context:
}

async streamGetChatCompletion(messages = null, { temperature = 0.7 }) {
const response = await fetch(`${this.basePath}/api/chat`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({
model: this.model,
stream: true,
messages,
options: {
temperature,
},
}),
}).catch((e) => {
console.error(e);
throw new Error(`Ollama:streamGetChatCompletion ${error.message}`);
});

return { type: "ollamaStream", response };
const model = this.#ollamaClient({ temperature });
const stream = await model
.pipe(new StringOutputParser())
.stream(this.#convertToLangchainPrototypes(messages));
return stream;
}

// Simple wrapper for dynamic embedder & normalize interface for all LLM implementations
Expand Down
35 changes: 4 additions & 31 deletions server/utils/chats/stream.js
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -232,46 +232,19 @@ function handleStreamResponses(response, stream, responseProps) {
});
}

if (stream?.type === "ollamaStream") {
return new Promise(async (resolve) => {
let fullText = "";
for await (const dataChunk of stream.response.body) {
const chunk = JSON.parse(Buffer.from(dataChunk).toString());
fullText += chunk.message.content;
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: chunk.message.content,
close: false,
error: false,
});
}

writeResponseChunk(response, {
uuid,
sources,
type: "textResponseChunk",
textResponse: "",
close: true,
error: false,
});
resolve(fullText);
});
}

// If stream is not a regular OpenAI Stream (like if using native model)
// If stream is not a regular OpenAI Stream (like if using native model, Ollama, or most LangChain interfaces)
// we can just iterate the stream content instead.
if (!stream.hasOwnProperty("data")) {
return new Promise(async (resolve) => {
let fullText = "";
for await (const chunk of stream) {
fullText += chunk.content;
const content = chunk.hasOwnProperty("content") ? chunk.content : chunk;
fullText += content;
writeResponseChunk(response, {
uuid,
sources: [],
type: "textResponseChunk",
textResponse: chunk.content,
textResponse: content,
close: false,
error: false,
});
Expand Down