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Merge branch 'master' of github.com:Mintplex-Labs/anything-llm

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timothycarambat 2024-06-24 09:32:53 -07:00
commit 158ba83cab
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6
docker/.env.example
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@ -136,6 +136,12 @@ GID='1000'
# LITE_LLM_BASE_PATH='http://127.0.0.1:4000'
# LITE_LLM_API_KEY='sk-123abc'
# EMBEDDING_ENGINE='generic-openai'
# EMBEDDING_MODEL_PREF='text-embedding-ada-002'
# EMBEDDING_MODEL_MAX_CHUNK_LENGTH=8192
# EMBEDDING_BASE_PATH='http://127.0.0.1:4000'
# GENERIC_OPEN_AI_EMBEDDING_API_KEY='sk-123abc'
###########################################
######## Vector Database Selection ########
###########################################

74
frontend/src/components/EmbeddingSelection/GenericOpenAiOptions/index.jsx Normal file
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@ -0,0 +1,74 @@
export default function GenericOpenAiEmbeddingOptions({ settings }) {
return (
<div className="w-full flex flex-col gap-y-4">
<div className="w-full flex items-center gap-4 flex-wrap">
<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="EmbeddingBasePath"
className="bg-zinc-900 text-white placeholder:text-white/20 text-sm rounded-lg focus:border-white block w-full p-2.5"
placeholder="https://api.openai.com/v1"
defaultValue={settings?.EmbeddingBasePath}
required={true}
autoComplete="off"
spellCheck={false}
/>
</div>
<div className="flex flex-col w-60">
<label className="text-white text-sm font-semibold block mb-4">
Embedding Model
</label>
<input
type="text"
name="EmbeddingModelPref"
className="bg-zinc-900 text-white placeholder:text-white/20 text-sm rounded-lg focus:border-white block w-full p-2.5"
placeholder="text-embedding-ada-002"
defaultValue={settings?.EmbeddingModelPref}
required={true}
autoComplete="off"
spellCheck={false}
/>
</div>
<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="GenericOpenAiEmbeddingApiKey"
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?.GenericOpenAiEmbeddingApiKey ? "*".repeat(20) : ""
}
autoComplete="off"
spellCheck={false}
/>
</div>
</div>
</div>
);
}

11
frontend/src/pages/GeneralSettings/EmbeddingPreference/index.jsx
View File

@ -12,6 +12,7 @@ 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 GenericOpenAiLogo from "@/media/llmprovider/generic-openai.png";
import PreLoader from "@/components/Preloader";
import ChangeWarningModal from "@/components/ChangeWarning";
@ -24,6 +25,7 @@ import LMStudioEmbeddingOptions from "@/components/EmbeddingSelection/LMStudioOp
import CohereEmbeddingOptions from "@/components/EmbeddingSelection/CohereOptions";
import VoyageAiOptions from "@/components/EmbeddingSelection/VoyageAiOptions";
import LiteLLMOptions from "@/components/EmbeddingSelection/LiteLLMOptions";
import GenericOpenAiEmbeddingOptions from "@/components/EmbeddingSelection/GenericOpenAiOptions";
import EmbedderItem from "@/components/EmbeddingSelection/EmbedderItem";
import { CaretUpDown, MagnifyingGlass, X } from "@phosphor-icons/react";
@ -98,6 +100,15 @@ const EMBEDDERS = [
options: (settings) => <LiteLLMOptions settings={settings} />,
description: "Run powerful embedding models from LiteLLM.",
},
{
name: "Generic OpenAI",
value: "generic-openai",
logo: GenericOpenAiLogo,
options: (settings) => (
<GenericOpenAiEmbeddingOptions settings={settings} />
),
description: "Run embedding models from any OpenAI compatible API service.",
},
];
export default function GeneralEmbeddingPreference() {

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

@ -308,6 +308,13 @@ export const EMBEDDING_ENGINE_PRIVACY = {
],
logo: LiteLLMLogo,
},
"generic-openai": {
name: "Generic OpenAI compatible service",
description: [
"Data is shared according to the terms of service applicable with your generic endpoint provider.",
],
logo: GenericOpenAiLogo,
},
};
export default function DataHandling({ setHeader, setForwardBtn, setBackBtn }) {

6
server/.env.example
View File

@ -133,6 +133,12 @@ SIG_SALT='salt' # Please generate random string at least 32 chars long.
# LITE_LLM_BASE_PATH='http://127.0.0.1:4000'
# LITE_LLM_API_KEY='sk-123abc'
# EMBEDDING_ENGINE='generic-openai'
# EMBEDDING_MODEL_PREF='text-embedding-ada-002'
# EMBEDDING_MODEL_MAX_CHUNK_LENGTH=8192
# EMBEDDING_BASE_PATH='http://127.0.0.1:4000'
# GENERIC_OPEN_AI_EMBEDDING_API_KEY='sk-123abc'
###########################################
######## Vector Database Selection ########
###########################################

2
server/models/systemSettings.js
View File

@ -149,6 +149,8 @@ const SystemSettings = {
EmbeddingModelPref: process.env.EMBEDDING_MODEL_PREF,
EmbeddingModelMaxChunkLength:
process.env.EMBEDDING_MODEL_MAX_CHUNK_LENGTH,
GenericOpenAiEmbeddingApiKey:
!!process.env.GENERIC_OPEN_AI_EMBEDDING_API_KEY,
// --------------------------------------------------------
// VectorDB Provider Selection Settings & Configs

95
server/utils/EmbeddingEngines/genericOpenAi/index.js Normal file
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@ -0,0 +1,95 @@
const { toChunks } = require("../../helpers");
class GenericOpenAiEmbedder {
constructor() {
if (!process.env.EMBEDDING_BASE_PATH)
throw new Error(
"GenericOpenAI must have a valid base path to use for the api."
);
const { OpenAI: OpenAIApi } = require("openai");
this.basePath = process.env.EMBEDDING_BASE_PATH;
this.openai = new OpenAIApi({
baseURL: this.basePath,
apiKey: process.env.GENERIC_OPEN_AI_EMBEDDING_API_KEY ?? null,
});
this.model = process.env.EMBEDDING_MODEL_PREF ?? null;
// Limit of how many strings we can process in a single pass to stay with resource or network limits
this.maxConcurrentChunks = 500;
// Refer to your specific model and provider you use this class with to determine a valid maxChunkLength
this.embeddingMaxChunkLength = 8_191;
}
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 OpenAI (~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 OpenAI 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(`GenericOpenAI Failed to embed: ${error}`);
return data.length > 0 &&
data.every((embd) => embd.hasOwnProperty("embedding"))
? data.map((embd) => embd.embedding)
: null;
}
}
module.exports = {
GenericOpenAiEmbedder,
};

5
server/utils/helpers/index.js
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@ -131,6 +131,11 @@ function getEmbeddingEngineSelection() {
case "litellm":
const { LiteLLMEmbedder } = require("../EmbeddingEngines/liteLLM");
return new LiteLLMEmbedder();
case "generic-openai":
const {
GenericOpenAiEmbedder,
} = require("../EmbeddingEngines/genericOpenAi");
return new GenericOpenAiEmbedder();
default:
return new NativeEmbedder();
}

7
server/utils/helpers/updateENV.js
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@ -221,6 +221,12 @@ const KEY_MAPPING = {
checks: [nonZero],
},
// Generic OpenAI Embedding Settings
GenericOpenAiEmbeddingApiKey: {
envKey: "GENERIC_OPEN_AI_EMBEDDING_API_KEY",
checks: [],
},
// Vector Database Selection Settings
VectorDB: {
envKey: "VECTOR_DB",
@ -587,6 +593,7 @@ function supportedEmbeddingModel(input = "") {
"cohere",
"voyageai",
"litellm",
"generic-openai",
];
return supported.includes(input)
? null