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LocalAI for embeddings (#361)

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* feature: add localAi as embedding provider

* chore: add LocalAI image

* chore: add localai embedding examples to docker .env.example

* update setting env
pull models from localai API

* update comments on embedder
Dont show cost estimation on UI

---------

Co-authored-by: timothycarambat <rambat1010@gmail.com>
This commit is contained in:
Tobias Landenberger 2023-11-14 22:49:31 +01:00 committed by GitHub
parent f269124760
commit a96a9d41a3
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GPG Key ID: 4AEE18F83AFDEB23
10 changed files with 358 additions and 5 deletions
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9
docker/.env.example
View File

@ -35,6 +35,15 @@ CACHE_VECTORS="true"
# EMBEDDING_ENGINE='openai'
# OPEN_AI_KEY=sk-xxxx
# EMBEDDING_ENGINE='azure'
# AZURE_OPENAI_ENDPOINT=
# AZURE_OPENAI_KEY=
# EMBEDDING_MODEL_PREF='my-embedder-model' # This is the "deployment" on Azure you want to use for embeddings. Not the base model. Valid base model is text-embedding-ada-002
# EMBEDDING_ENGINE='localai'
# EMBEDDING_BASE_PATH='https://localhost:8080/v1'
# EMBEDDING_MODEL_PREF='text-embedding-ada-002'
###########################################
######## Vector Database Selection ########
###########################################

17
frontend/src/components/Modals/MangeWorkspace/Documents/index.jsx
View File

@ -8,7 +8,11 @@ import WorkspaceDirectory from "./WorkspaceDirectory";
const COST_PER_TOKEN = 0.0004;
export default function DocumentSettings({ workspace, fileTypes }) {
export default function DocumentSettings({
workspace,
fileTypes,
systemSettings,
}) {
const [highlightWorkspace, setHighlightWorkspace] = useState(false);
const [availableDocs, setAvailableDocs] = useState([]);
const [loading, setLoading] = useState(true);
@ -135,8 +139,15 @@ export default function DocumentSettings({ workspace, fileTypes }) {
}
});
const dollarAmount = (totalTokenCount / 1000) * COST_PER_TOKEN;
setEmbeddingsCost(dollarAmount);
// Do not do cost estimation unless the embedding engine is OpenAi.
if (
!systemSettings?.EmbeddingEngine ||
systemSettings.EmbeddingEngine === "openai"
) {
const dollarAmount = (totalTokenCount / 1000) * COST_PER_TOKEN;
setEmbeddingsCost(dollarAmount);
}
setMovedItems([...movedItems, ...newMovedItems]);
let newAvailableDocs = JSON.parse(JSON.stringify(availableDocs));

9
frontend/src/components/Modals/MangeWorkspace/index.jsx
View File

@ -15,11 +15,14 @@ const ManageWorkspace = ({ hideModal = noop, providedSlug = null }) => {
const [selectedTab, setSelectedTab] = useState("documents");
const [workspace, setWorkspace] = useState(null);
const [fileTypes, setFileTypes] = useState(null);
const [settings, setSettings] = useState({});
useEffect(() => {
async function checkSupportedFiletypes() {
const acceptedTypes = await System.acceptedDocumentTypes();
const _settings = await System.keys();
setFileTypes(acceptedTypes ?? {});
setSettings(_settings ?? {});
}
checkSupportedFiletypes();
}, []);
@ -104,7 +107,11 @@ const ManageWorkspace = ({ hideModal = noop, providedSlug = null }) => {
</div>
<Suspense fallback={<div>Loading...</div>}>
<div className={selectedTab === "documents" ? "" : "hidden"}>
<DocumentSettings workspace={workspace} fileTypes={fileTypes} />
<DocumentSettings
workspace={workspace}
fileTypes={fileTypes}
systemSettings={settings}
/>
</div>
<div className={selectedTab === "settings" ? "" : "hidden"}>
<WorkspaceSettings workspace={workspace} fileTypes={fileTypes} />

114
frontend/src/pages/GeneralSettings/EmbeddingPreference/index.jsx
View File

@ -7,6 +7,7 @@ import System from "../../../models/system";
import showToast from "../../../utils/toast";
import OpenAiLogo from "../../../media/llmprovider/openai.png";
import AzureOpenAiLogo from "../../../media/llmprovider/azure.png";
import LocalAiLogo from "../../../media/llmprovider/localai.png";
import PreLoader from "../../../components/Preloader";
import LLMProviderOption from "../../../components/LLMSelection/LLMProviderOption";
@ -16,6 +17,8 @@ export default function GeneralEmbeddingPreference() {
const [embeddingChoice, setEmbeddingChoice] = useState("openai");
const [settings, setSettings] = useState(null);
const [loading, setLoading] = useState(true);
const [basePathValue, setBasePathValue] = useState("");
const [basePath, setBasePath] = useState("");
const handleSubmit = async (e) => {
e.preventDefault();
@ -38,11 +41,17 @@ export default function GeneralEmbeddingPreference() {
setHasChanges(true);
};
function updateBasePath() {
setBasePath(basePathValue);
}
useEffect(() => {
async function fetchKeys() {
const _settings = await System.keys();
setSettings(_settings);
setEmbeddingChoice(_settings?.EmbeddingEngine || "openai");
setBasePath(_settings?.EmbeddingBasePath || "");
setBasePathValue(_settings?.EmbeddingBasePath || "");
setLoading(false);
}
fetchKeys();
@ -136,6 +145,15 @@ export default function GeneralEmbeddingPreference() {
image={AzureOpenAiLogo}
onClick={updateChoice}
/>
<LLMProviderOption
name="LocalAI"
value="localai"
link="localai.io"
description="Self hosted LocalAI embedding engine."
checked={embeddingChoice === "localai"}
image={LocalAiLogo}
onClick={updateChoice}
/>
</div>
<div className="mt-10 flex flex-wrap gap-4 max-w-[800px]">
{embeddingChoice === "openai" && (
@ -215,6 +233,32 @@ export default function GeneralEmbeddingPreference() {
</div>
</>
)}
{embeddingChoice === "localai" && (
<>
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-4">
LocalAI Base URL
</label>
<input
type="url"
name="EmbeddingBasePath"
className="bg-zinc-900 text-white placeholder-white placeholder-opacity-60 text-sm rounded-lg focus:border-white block w-full p-2.5"
placeholder="http://localhost:8080/v1"
defaultValue={settings?.EmbeddingBasePath}
onChange={(e) => setBasePathValue(e.target.value)}
onBlur={updateBasePath}
required={true}
autoComplete="off"
spellCheck={false}
/>
</div>
<LocalAIModelSelection
settings={settings}
basePath={basePath}
/>
</>
)}
</div>
</>
)}
@ -225,3 +269,73 @@ export default function GeneralEmbeddingPreference() {
</div>
);
}
function LocalAIModelSelection({ settings, basePath = null }) {
const [customModels, setCustomModels] = useState([]);
const [loading, setLoading] = useState(true);
useEffect(() => {
async function findCustomModels() {
if (!basePath || !basePath.includes("/v1")) {
setCustomModels([]);
setLoading(false);
return;
}
setLoading(true);
const { models } = await System.customModels("localai", null, basePath);
setCustomModels(models || []);
setLoading(false);
}
findCustomModels();
}, [basePath]);
if (loading || customModels.length == 0) {
return (
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-4">
Embedding Model Name
</label>
<select
name="EmbeddingModelPref"
disabled={true}
className="bg-zinc-900 border border-gray-500 text-white text-sm rounded-lg block w-full p-2.5"
>
<option disabled={true} selected={true}>
{basePath?.includes("/v1")
? "-- loading available models --"
: "-- waiting for URL --"}
</option>
</select>
</div>
);
}
return (
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-4">
Embedding Model Name
</label>
<select
name="EmbeddingModelPref"
required={true}
className="bg-zinc-900 border border-gray-500 text-white text-sm rounded-lg block w-full p-2.5"
>
{customModels.length > 0 && (
<optgroup label="Your loaded models">
{customModels.map((model) => {
return (
<option
key={model.id}
value={model.id}
selected={settings?.EmbeddingModelPref === model.id}
>
{model.id}
</option>
);
})}
</optgroup>
)}
</select>
</div>
);
}

113
frontend/src/pages/OnboardingFlow/OnboardingModal/Steps/EmbeddingSelection/index.jsx
View File

@ -1,6 +1,7 @@
import React, { memo, useEffect, useState } from "react";
import OpenAiLogo from "../../../../../media/llmprovider/openai.png";
import AzureOpenAiLogo from "../../../../../media/llmprovider/azure.png";
import LocalAiLogo from "../../../../../media/llmprovider/localai.png";
import System from "../../../../../models/system";
import PreLoader from "../../../../../components/Preloader";
import LLMProviderOption from "../../../../../components/LLMSelection/LLMProviderOption";
@ -9,16 +10,23 @@ function EmbeddingSelection({ nextStep, prevStep, currentStep }) {
const [embeddingChoice, setEmbeddingChoice] = useState("openai");
const [settings, setSettings] = useState(null);
const [loading, setLoading] = useState(true);
const [basePathValue, setBasePathValue] = useState("");
const [basePath, setBasePath] = useState("");
const updateChoice = (selection) => {
setEmbeddingChoice(selection);
};
function updateBasePath() {
setBasePath(basePathValue);
}
useEffect(() => {
async function fetchKeys() {
const _settings = await System.keys();
setSettings(_settings);
setEmbeddingChoice(_settings?.EmbeddingEngine || "openai");
setBasePathValue(_settings?.EmbeddingBasePath || "");
setLoading(false);
}
fetchKeys();
@ -77,6 +85,15 @@ function EmbeddingSelection({ nextStep, prevStep, currentStep }) {
image={AzureOpenAiLogo}
onClick={updateChoice}
/>
<LLMProviderOption
name="LocalAI"
value="localai"
link="localai.io"
description="Self hosted LocalAI embedding engine."
checked={embeddingChoice === "localai"}
image={LocalAiLogo}
onClick={updateChoice}
/>
</div>
<div className="mt-10 flex flex-wrap gap-4 max-w-[800px]">
{embeddingChoice === "openai" && (
@ -152,6 +169,32 @@ function EmbeddingSelection({ nextStep, prevStep, currentStep }) {
</div>
</>
)}
{embeddingChoice === "localai" && (
<>
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-4">
LocalAI Base URL
</label>
<input
type="url"
name="EmbeddingBasePath"
className="bg-zinc-900 text-white placeholder-white placeholder-opacity-60 text-sm rounded-lg focus:border-white block w-full p-2.5"
placeholder="http://localhost:8080/v1"
defaultValue={settings?.EmbeddingBasePath}
onChange={(e) => setBasePathValue(e.target.value)}
onBlur={updateBasePath}
required={true}
autoComplete="off"
spellCheck={false}
/>
</div>
<LocalAIModelSelection
settings={settings}
basePath={basePath}
/>
</>
)}
</div>
</div>
<div className="flex w-full justify-between items-center p-6 space-x-2 border-t rounded-b border-gray-500/50">
@ -174,4 +217,74 @@ function EmbeddingSelection({ nextStep, prevStep, currentStep }) {
);
}
function LocalAIModelSelection({ settings, basePath = null }) {
const [customModels, setCustomModels] = useState([]);
const [loading, setLoading] = useState(true);
useEffect(() => {
async function findCustomModels() {
if (!basePath || !basePath.includes("/v1")) {
setCustomModels([]);
setLoading(false);
return;
}
setLoading(true);
const { models } = await System.customModels("localai", null, basePath);
setCustomModels(models || []);
setLoading(false);
}
findCustomModels();
}, [basePath]);
if (loading || customModels.length == 0) {
return (
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-4">
Embedding Model Name
</label>
<select
name="EmbeddingModelPref"
disabled={true}
className="bg-zinc-900 border border-gray-500 text-white text-sm rounded-lg block w-full p-2.5"
>
<option disabled={true} selected={true}>
{basePath?.includes("/v1")
? "-- loading available models --"
: "-- waiting for URL --"}
</option>
</select>
</div>
);
}
return (
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-4">
Embedding Model Name
</label>
<select
name="EmbeddingModelPref"
required={true}
className="bg-zinc-900 border border-gray-500 text-white text-sm rounded-lg block w-full p-2.5"
>
{customModels.length > 0 && (
<optgroup label="Your loaded models">
{customModels.map((model) => {
return (
<option
key={model.id}
value={model.id}
selected={settings.EmbeddingModelPref === model.id}
>
{model.id}
</option>
);
})}
</optgroup>
)}
</select>
</div>
);
}
export default memo(EmbeddingSelection);

9
server/.env.example
View File

@ -35,6 +35,15 @@ JWT_SECRET="my-random-string-for-seeding" # Please generate random string at lea
# EMBEDDING_ENGINE='openai'
# OPEN_AI_KEY=sk-xxxx
# EMBEDDING_ENGINE='azure'
# AZURE_OPENAI_ENDPOINT=
# AZURE_OPENAI_KEY=
# EMBEDDING_MODEL_PREF='my-embedder-model' # This is the "deployment" on Azure you want to use for embeddings. Not the base model. Valid base model is text-embedding-ada-002
# EMBEDDING_ENGINE='localai'
# EMBEDDING_BASE_PATH='https://localhost:8080/v1'
# EMBEDDING_MODEL_PREF='text-embedding-ada-002'
###########################################
######## Vector Database Selection ########
###########################################

2
server/models/systemSettings.js
View File

@ -25,6 +25,8 @@ const SystemSettings = {
MultiUserMode: await this.isMultiUserMode(),
VectorDB: vectorDB,
EmbeddingEngine: process.env.EMBEDDING_ENGINE,
EmbeddingBasePath: process.env.EMBEDDING_BASE_PATH,
EmbeddingModelPref: process.env.EMBEDDING_MODEL_PREF,
...(vectorDB === "pinecone"
? {
PineConeEnvironment: process.env.PINECONE_ENVIRONMENT,

77
server/utils/EmbeddingEngines/localAi/index.js Normal file
View File

@ -0,0 +1,77 @@
const { toChunks } = require("../../helpers");
class LocalAiEmbedder {
constructor() {
const { Configuration, OpenAIApi } = require("openai");
if (!process.env.EMBEDDING_BASE_PATH)
throw new Error("No embedding base path was set.");
if (!process.env.EMBEDDING_MODEL_PREF)
throw new Error("No embedding model was set.");
const config = new Configuration({
basePath: process.env.EMBEDDING_BASE_PATH,
});
this.openai = new OpenAIApi(config);
// Arbitrary limit to ensure we stay within reasonable POST request size.
this.embeddingChunkLimit = 1_000;
}
async embedTextInput(textInput) {
const result = await this.embedChunks(textInput);
return result?.[0] || [];
}
async embedChunks(textChunks = []) {
const embeddingRequests = [];
for (const chunk of toChunks(textChunks, this.embeddingChunkLimit)) {
embeddingRequests.push(
new Promise((resolve) => {
this.openai
.createEmbedding({
model: process.env.EMBEDDING_MODEL_PREF,
input: chunk,
})
.then((res) => {
resolve({ data: res.data?.data, error: null });
})
.catch((e) => {
resolve({ data: [], error: e?.error });
});
})
);
}
const { data = [], error = null } = await Promise.all(
embeddingRequests
).then((results) => {
// If any errors were returned from LocalAI abort the entire sequence because the embeddings
// will be incomplete.
const errors = results
.filter((res) => !!res.error)
.map((res) => res.error)
.flat();
if (errors.length > 0) {
return {
data: [],
error: `(${errors.length}) Embedding Errors! ${errors
.map((error) => `[${error.type}]: ${error.message}`)
.join(", ")}`,
};
}
return {
data: results.map((res) => res?.data || []).flat(),
error: null,
};
});
if (!!error) throw new Error(`LocalAI Failed to embed: ${error}`);
return data.length > 0 &&
data.every((embd) => embd.hasOwnProperty("embedding"))
? data.map((embd) => embd.embedding)
: null;
}
}
module.exports = {
LocalAiEmbedder,
};

3
server/utils/helpers/index.js
View File

@ -59,6 +59,9 @@ function getEmbeddingEngineSelection() {
AzureOpenAiEmbedder,
} = require("../EmbeddingEngines/azureOpenAi");
return new AzureOpenAiEmbedder();
case "localai":
const { LocalAiEmbedder } = require("../EmbeddingEngines/localAi");
return new LocalAiEmbedder();
default:
return null;
}

10
server/utils/helpers/updateENV.js
View File

@ -72,6 +72,14 @@ const KEY_MAPPING = {
envKey: "EMBEDDING_ENGINE",
checks: [supportedEmbeddingModel],
},
EmbeddingBasePath: {
envKey: "EMBEDDING_BASE_PATH",
checks: [isNotEmpty, validLLMExternalBasePath],
},
EmbeddingModelPref: {
envKey: "EMBEDDING_MODEL_PREF",
checks: [isNotEmpty],
},
// Vector Database Selection Settings
VectorDB: {
@ -191,7 +199,7 @@ function validAnthropicModel(input = "") {
}
function supportedEmbeddingModel(input = "") {
const supported = ["openai", "azure"];
const supported = ["openai", "azure", "localai"];
return supported.includes(input)
? null
: `Invalid Embedding model type. Must be one of ${supported.join(", ")}.`;