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[Feat] Query mode to return no-result when no context found (#601)

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* Query mode to return no-result when no context found

* update default error for sync chat

* remove unnecessary type conversion
This commit is contained in:
Timothy Carambat 2024-01-16 09:32:51 -08:00 committed by GitHub
parent 7aaa4b38e7
commit bd158ce7b1
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7 changed files with 61 additions and 117 deletions
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15
frontend/src/components/WorkspaceChat/ChatContainer/index.jsx
View File

@ -71,21 +71,6 @@ export default function ChatContainer({ workspace, knownHistory = [] }) {
return false;
}
// TODO: Delete this snippet once we have streaming stable.
// const chatResult = await Workspace.sendChat(
// workspace,
// promptMessage.userMessage,
// window.localStorage.getItem(`workspace_chat_mode_${workspace.slug}`) ??
// "chat",
// )
// handleChat(
// chatResult,
// setLoadingResponse,
// setChatHistory,
// remHistory,
// _chatHistory
// )
await Workspace.streamChat(
workspace,
promptMessage.userMessage,

16
frontend/src/models/workspace.js
View File

@ -168,22 +168,6 @@ const Workspace = {
const data = await response.json();
return { response, data };
},
// TODO: Deprecated and should be removed from frontend.
sendChat: async function ({ slug }, message, mode = "query") {
const chatResult = await fetch(`${API_BASE}/workspace/${slug}/chat`, {
method: "POST",
body: JSON.stringify({ message, mode }),
headers: baseHeaders(),
})
.then((res) => res.json())
.catch((e) => {
console.error(e);
return null;
});
return chatResult;
},
};
export default Workspace;

11
server/endpoints/api/workspace/index.js
View File

@ -196,10 +196,11 @@ function apiWorkspaceEndpoints(app) {
return;
}
await WorkspaceChats.delete({ workspaceId: Number(workspace.id) });
await DocumentVectors.deleteForWorkspace(Number(workspace.id));
await Document.delete({ workspaceId: Number(workspace.id) });
await Workspace.delete({ id: Number(workspace.id) });
const workspaceId = Number(workspace.id);
await WorkspaceChats.delete({ workspaceId: workspaceId });
await DocumentVectors.deleteForWorkspace(workspaceId);
await Document.delete({ workspaceId: workspaceId });
await Workspace.delete({ id: workspaceId });
try {
await VectorDb["delete-namespace"]({ namespace: slug });
} catch (e) {
@ -441,7 +442,7 @@ function apiWorkspaceEndpoints(app) {
#swagger.tags = ['Workspaces']
#swagger.description = 'Execute a chat with a workspace'
#swagger.requestBody = {
description: 'prompt to send to the workspace and the type of conversation (query or chat).',
description: 'Send a prompt to the workspace and the type of conversation (query or chat).<br/><b>Query:</b> Will not use LLM unless there are relevant sources from vectorDB & does not recall chat history.<br/><b>Chat:</b> Uses LLM general knowledge w/custom embeddings to produce output, uses rolling chat history.',
required: true,
type: 'object',
content: {

80
server/endpoints/chat.js
View File

@ -1,7 +1,6 @@
const { v4: uuidv4 } = require("uuid");
const { reqBody, userFromSession, multiUserMode } = require("../utils/http");
const { Workspace } = require("../models/workspace");
const { chatWithWorkspace } = require("../utils/chats");
const { validatedRequest } = require("../utils/middleware/validatedRequest");
const { WorkspaceChats } = require("../models/workspaceChats");
const { SystemSettings } = require("../models/systemSettings");
@ -95,85 +94,6 @@ function chatEndpoints(app) {
}
}
);
app.post(
"/workspace/:slug/chat",
[validatedRequest],
async (request, response) => {
try {
const user = await userFromSession(request, response);
const { slug } = request.params;
const { message, mode = "query" } = reqBody(request);
const workspace = multiUserMode(response)
? await Workspace.getWithUser(user, { slug })
: await Workspace.get({ slug });
if (!workspace) {
response.sendStatus(400).end();
return;
}
if (multiUserMode(response) && user.role !== "admin") {
const limitMessagesSetting = await SystemSettings.get({
label: "limit_user_messages",
});
const limitMessages = limitMessagesSetting?.value === "true";
if (limitMessages) {
const messageLimitSetting = await SystemSettings.get({
label: "message_limit",
});
const systemLimit = Number(messageLimitSetting?.value);
if (!!systemLimit) {
const currentChatCount = await WorkspaceChats.count({
user_id: user.id,
createdAt: {
gte: new Date(new Date() - 24 * 60 * 60 * 1000),
},
});
if (currentChatCount >= systemLimit) {
response.status(500).json({
id: uuidv4(),
type: "abort",
textResponse: null,
sources: [],
close: true,
error: `You have met your maximum 24 hour chat quota of ${systemLimit} chats set by the instance administrators. Try again later.`,
});
return;
}
}
}
}
const result = await chatWithWorkspace(workspace, message, mode, user);
await Telemetry.sendTelemetry(
"sent_chat",
{
multiUserMode: multiUserMode(response),
LLMSelection: process.env.LLM_PROVIDER || "openai",
Embedder: process.env.EMBEDDING_ENGINE || "inherit",
VectorDbSelection: process.env.VECTOR_DB || "pinecone",
},
user?.id
);
response.status(200).json({ ...result });
} catch (e) {
console.error(e);
response.status(500).json({
id: uuidv4(),
type: "abort",
textResponse: null,
sources: [],
close: true,
error: e.message,
});
}
}
);
}
module.exports = { chatEndpoints };

2
server/swagger/openapi.json
View File

@ -1598,7 +1598,7 @@
}
},
"requestBody": {
"description": "prompt to send to the workspace and the type of conversation (query or chat).",
"description": "Send a prompt to the workspace and the type of conversation (query or chat).<br/><b>Query:</b> Will not use LLM unless there are relevant sources from vectorDB & does not recall chat history.<br/><b>Chat:</b> Uses LLM general knowledge w/custom embeddings to produce output, uses rolling chat history.",
"required": true,
"type": "object",
"content": {

26
server/utils/chats/index.js
View File

@ -91,6 +91,18 @@ async function chatWithWorkspace(
const hasVectorizedSpace = await VectorDb.hasNamespace(workspace.slug);
const embeddingsCount = await VectorDb.namespaceCount(workspace.slug);
if (!hasVectorizedSpace || embeddingsCount === 0) {
if (chatMode === "query") {
return {
id: uuid,
type: "textResponse",
sources: [],
close: true,
error: null,
textResponse:
"There is no relevant information in this workspace to answer your query.",
};
}
// If there are no embeddings - chat like a normal LLM chat interface.
return await emptyEmbeddingChat({
uuid,
@ -131,6 +143,20 @@ async function chatWithWorkspace(
};
}
// If in query mode and no sources are found, do not
// let the LLM try to hallucinate a response or use general knowledge
if (chatMode === "query" && sources.length === 0) {
return {
id: uuid,
type: "textResponse",
sources: [],
close: true,
error: null,
textResponse:
"There is no relevant information in this workspace to answer your query.",
};
}
// Compress message to ensure prompt passes token limit with room for response
// and build system messages based on inputs and history.
const messages = await LLMConnector.compressMessages(

28
server/utils/chats/stream.js
View File

@ -50,6 +50,19 @@ async function streamChatWithWorkspace(
const hasVectorizedSpace = await VectorDb.hasNamespace(workspace.slug);
const embeddingsCount = await VectorDb.namespaceCount(workspace.slug);
if (!hasVectorizedSpace || embeddingsCount === 0) {
if (chatMode === "query") {
writeResponseChunk(response, {
id: uuid,
type: "textResponse",
textResponse:
"There is no relevant information in this workspace to answer your query.",
sources: [],
close: true,
error: null,
});
return;
}
// If there are no embeddings - chat like a normal LLM chat interface.
return await streamEmptyEmbeddingChat({
response,
@ -93,6 +106,21 @@ async function streamChatWithWorkspace(
return;
}
// If in query mode and no sources are found, do not
// let the LLM try to hallucinate a response or use general knowledge
if (chatMode === "query" && sources.length === 0) {
writeResponseChunk(response, {
id: uuid,
type: "textResponse",
textResponse:
"There is no relevant information in this workspace to answer your query.",
sources: [],
close: true,
error: null,
});
return;
}
// Compress message to ensure prompt passes token limit with room for response
// and build system messages based on inputs and history.
const messages = await LLMConnector.compressMessages(