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[FEAT] Add LiteLLM embedding provider support (#1579)

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* add liteLLM embedding provider support

* update tooltip id

---------

Co-authored-by: timothycarambat <rambat1010@gmail.com>
This commit is contained in:
Sean Hatfield 2024-06-06 12:43:34 -07:00 committed by GitHub
parent 5578e567ce
commit d29292ebd2
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6
docker/.env.example
View File

@ -128,6 +128,12 @@ GID='1000'
# VOYAGEAI_API_KEY=
# EMBEDDING_MODEL_PREF='voyage-large-2-instruct'
# EMBEDDING_ENGINE='litellm'
# EMBEDDING_MODEL_PREF='text-embedding-ada-002'
# EMBEDDING_MODEL_MAX_CHUNK_LENGTH=8192
# LITE_LLM_BASE_PATH='http://127.0.0.1:4000'
# LITE_LLM_API_KEY='sk-123abc'
###########################################
######## Vector Database Selection ########
###########################################

186
frontend/src/components/EmbeddingSelection/LiteLLMOptions/index.jsx Normal file
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@ -0,0 +1,186 @@
import { useEffect, useState } from "react";
import System from "@/models/system";
import { Warning } from "@phosphor-icons/react";
import { Tooltip } from "react-tooltip";
export default function LiteLLMOptions({ settings }) {
const [basePathValue, setBasePathValue] = useState(settings?.LiteLLMBasePath);
const [basePath, setBasePath] = useState(settings?.LiteLLMBasePath);
const [apiKeyValue, setApiKeyValue] = useState(settings?.LiteLLMAPIKey);
const [apiKey, setApiKey] = useState(settings?.LiteLLMAPIKey);
return (
<div className="w-full flex flex-col gap-y-4">
<div className="w-full flex items-center gap-4">
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-4">
Base URL
</label>
<input
type="url"
name="LiteLLMBasePath"
className="bg-zinc-900 text-white placeholder:text-white/20 text-sm rounded-lg focus:border-white block w-full p-2.5"
placeholder="http://127.0.0.1:4000"
defaultValue={settings?.LiteLLMBasePath}
required={true}
autoComplete="off"
spellCheck={false}
onChange={(e) => setBasePathValue(e.target.value)}
onBlur={() => setBasePath(basePathValue)}
/>
</div>
<LiteLLMModelSelection
settings={settings}
basePath={basePath}
apiKey={apiKey}
/>
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-4">
Max embedding chunk length
</label>
<input
type="number"
name="EmbeddingModelMaxChunkLength"
className="bg-zinc-900 text-white placeholder-white/20 text-sm rounded-lg focus:border-white block w-full p-2.5"
placeholder="8192"
min={1}
onScroll={(e) => e.target.blur()}
defaultValue={settings?.EmbeddingModelMaxChunkLength}
required={false}
autoComplete="off"
/>
</div>
</div>
<div className="w-full flex items-center gap-4">
<div className="flex flex-col w-60">
<div className="flex flex-col gap-y-1 mb-4">
<label className="text-white text-sm font-semibold flex items-center gap-x-2">
API Key <p className="!text-xs !italic !font-thin">optional</p>
</label>
</div>
<input
type="password"
name="LiteLLMAPIKey"
className="bg-zinc-900 text-white placeholder:text-white/20 text-sm rounded-lg focus:border-white block w-full p-2.5"
placeholder="sk-mysecretkey"
defaultValue={settings?.LiteLLMAPIKey ? "*".repeat(20) : ""}
autoComplete="off"
spellCheck={false}
onChange={(e) => setApiKeyValue(e.target.value)}
onBlur={() => setApiKey(apiKeyValue)}
/>
</div>
</div>
</div>
);
}
function LiteLLMModelSelection({ settings, basePath = null, apiKey = null }) {
const [customModels, setCustomModels] = useState([]);
const [loading, setLoading] = useState(true);
useEffect(() => {
async function findCustomModels() {
if (!basePath) {
setCustomModels([]);
setLoading(false);
return;
}
setLoading(true);
const { models } = await System.customModels(
"litellm",
typeof apiKey === "boolean" ? null : apiKey,
basePath
);
setCustomModels(models || []);
setLoading(false);
}
findCustomModels();
}, [basePath, apiKey]);
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 Selection
</label>
<select
name="EmbeddingModelPref"
disabled={true}
className="bg-zinc-900 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">
<div className="flex items-center">
<label className="text-white text-sm font-semibold block mb-4">
Embedding Model Selection
</label>
<EmbeddingModelTooltip />
</div>
<select
name="EmbeddingModelPref"
required={true}
className="bg-zinc-900 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>
);
}
function EmbeddingModelTooltip() {
return (
<div className="flex items-center justify-center -mt-3 ml-1">
<Warning
size={14}
className="ml-1 text-orange-500 cursor-pointer"
data-tooltip-id="model-tooltip"
data-tooltip-place="right"
/>
<Tooltip
delayHide={300}
id="model-tooltip"
className="max-w-xs"
clickable={true}
>
<p className="text-sm">
Be sure to select a valid embedding model. Chat models are not
embedding models. See{" "}
<a
href="https://litellm.vercel.app/docs/embedding/supported_embedding"
target="_blank"
rel="noreferrer"
className="underline"
>
this page
</a>{" "}
for more information.
</p>
</Tooltip>
</div>
);
}

9
frontend/src/pages/GeneralSettings/EmbeddingPreference/index.jsx
View File

@ -11,6 +11,7 @@ import OllamaLogo from "@/media/llmprovider/ollama.png";
import LMStudioLogo from "@/media/llmprovider/lmstudio.png";
import CohereLogo from "@/media/llmprovider/cohere.png";
import VoyageAiLogo from "@/media/embeddingprovider/voyageai.png";
import LiteLLMLogo from "@/media/llmprovider/litellm.png";
import PreLoader from "@/components/Preloader";
import ChangeWarningModal from "@/components/ChangeWarning";
@ -22,6 +23,7 @@ import OllamaEmbeddingOptions from "@/components/EmbeddingSelection/OllamaOption
import LMStudioEmbeddingOptions from "@/components/EmbeddingSelection/LMStudioOptions";
import CohereEmbeddingOptions from "@/components/EmbeddingSelection/CohereOptions";
import VoyageAiOptions from "@/components/EmbeddingSelection/VoyageAiOptions";
import LiteLLMOptions from "@/components/EmbeddingSelection/LiteLLMOptions";
import EmbedderItem from "@/components/EmbeddingSelection/EmbedderItem";
import { CaretUpDown, MagnifyingGlass, X } from "@phosphor-icons/react";
@ -88,6 +90,13 @@ const EMBEDDERS = [
options: (settings) => <VoyageAiOptions settings={settings} />,
description: "Run powerful embedding models from Voyage AI.",
},
{
name: "LiteLLM",
value: "litellm",
logo: LiteLLMLogo,
options: (settings) => <LiteLLMOptions settings={settings} />,
description: "Run powerful embedding models from LiteLLM.",
},
];
export default function GeneralEmbeddingPreference() {

7
frontend/src/pages/OnboardingFlow/Steps/DataHandling/index.jsx
View File

@ -301,6 +301,13 @@ export const EMBEDDING_ENGINE_PRIVACY = {
],
logo: VoyageAiLogo,
},
litellm: {
name: "LiteLLM",
description: [
"Your document text is only accessible on the server running LiteLLM and to the providers you configured in LiteLLM.",
],
logo: LiteLLMLogo,
},
};
export default function DataHandling({ setHeader, setForwardBtn, setBackBtn }) {

6
server/.env.example
View File

@ -125,6 +125,12 @@ JWT_SECRET="my-random-string-for-seeding" # Please generate random string at lea
# VOYAGEAI_API_KEY=
# EMBEDDING_MODEL_PREF='voyage-large-2-instruct'
# EMBEDDING_ENGINE='litellm'
# EMBEDDING_MODEL_PREF='text-embedding-ada-002'
# EMBEDDING_MODEL_MAX_CHUNK_LENGTH=8192
# LITE_LLM_BASE_PATH='http://127.0.0.1:4000'
# LITE_LLM_API_KEY='sk-123abc'
###########################################
######## Vector Database Selection ########
###########################################

93
server/utils/EmbeddingEngines/liteLLM/index.js Normal file
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@ -0,0 +1,93 @@
const { toChunks, maximumChunkLength } = require("../../helpers");
class LiteLLMEmbedder {
constructor() {
const { OpenAI: OpenAIApi } = require("openai");
if (!process.env.LITE_LLM_BASE_PATH)
throw new Error(
"LiteLLM must have a valid base path to use for the api."
);
this.basePath = process.env.LITE_LLM_BASE_PATH;
this.openai = new OpenAIApi({
baseURL: this.basePath,
apiKey: process.env.LITE_LLM_API_KEY ?? null,
});
this.model = process.env.EMBEDDING_MODEL_PREF || "text-embedding-ada-002";
// Limit of how many strings we can process in a single pass to stay with resource or network limits
this.maxConcurrentChunks = 500;
this.embeddingMaxChunkLength = maximumChunkLength();
}
async embedTextInput(textInput) {
const result = await this.embedChunks(
Array.isArray(textInput) ? textInput : [textInput]
);
return result?.[0] || [];
}
async embedChunks(textChunks = []) {
// Because there is a hard POST limit on how many chunks can be sent at once to LiteLLM (~8mb)
// we concurrently execute each max batch of text chunks possible.
// Refer to constructor maxConcurrentChunks for more info.
const embeddingRequests = [];
for (const chunk of toChunks(textChunks, this.maxConcurrentChunks)) {
embeddingRequests.push(
new Promise((resolve) => {
this.openai.embeddings
.create({
model: this.model,
input: chunk,
})
.then((result) => {
resolve({ data: result?.data, error: null });
})
.catch((e) => {
e.type =
e?.response?.data?.error?.code ||
e?.response?.status ||
"failed_to_embed";
e.message = e?.response?.data?.error?.message || e.message;
resolve({ data: [], error: e });
});
})
);
}
const { data = [], error = null } = await Promise.all(
embeddingRequests
).then((results) => {
// If any errors were returned from LiteLLM 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) {
let uniqueErrors = new Set();
errors.map((error) =>
uniqueErrors.add(`[${error.type}]: ${error.message}`)
);
return {
data: [],
error: Array.from(uniqueErrors).join(", "),
};
}
return {
data: results.map((res) => res?.data || []).flat(),
error: null,
};
});
if (!!error) throw new Error(`LiteLLM Failed to embed: ${error}`);
return data.length > 0 &&
data.every((embd) => embd.hasOwnProperty("embedding"))
? data.map((embd) => embd.embedding)
: null;
}
}
module.exports = {
LiteLLMEmbedder,
};

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

@ -128,6 +128,9 @@ function getEmbeddingEngineSelection() {
case "voyageai":
const { VoyageAiEmbedder } = require("../EmbeddingEngines/voyageAi");
return new VoyageAiEmbedder();
case "litellm":
const { LiteLLMEmbedder } = require("../EmbeddingEngines/liteLLM");
return new LiteLLMEmbedder();
default:
return new NativeEmbedder();
}

1
server/utils/helpers/updateENV.js
View File

@ -577,6 +577,7 @@ function supportedEmbeddingModel(input = "") {
"lmstudio",
"cohere",
"voyageai",
"litellm",
];
return supported.includes(input)
? null