mirror of
https://github.com/Mintplex-Labs/anything-llm.git
synced 2024-11-05 06:20:10 +01:00
655ebd9479
* Implement use of native embedder (all-Mini-L6-v2) stop showing prisma queries during dev * Add native embedder as an available embedder selection * wrap model loader in try/catch * print progress on download * add built-in LLM support (expiermental) * Update to progress output for embedder * move embedder selection options to component * saftey checks for modelfile * update ref * Hide selection when on hosted subdomain * update documentation hide localLlama when on hosted * saftey checks for storage of models * update dockerfile to pre-build Llama.cpp bindings * update lockfile * add langchain doc comment * remove extraneous --no-metal option * Show data handling for private LLM * persist model in memory for N+1 chats * update import update dev comment on token model size * update primary README * chore: more readme updates and remove screenshots - too much to maintain, just use the app! * remove screeshot link
85 lines
2.7 KiB
JavaScript
85 lines
2.7 KiB
JavaScript
function getVectorDbClass() {
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const vectorSelection = process.env.VECTOR_DB || "pinecone";
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switch (vectorSelection) {
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case "pinecone":
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const { Pinecone } = require("../vectorDbProviders/pinecone");
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return Pinecone;
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case "chroma":
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const { Chroma } = require("../vectorDbProviders/chroma");
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return Chroma;
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case "lancedb":
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const { LanceDb } = require("../vectorDbProviders/lance");
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return LanceDb;
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case "weaviate":
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const { Weaviate } = require("../vectorDbProviders/weaviate");
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return Weaviate;
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case "qdrant":
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const { QDrant } = require("../vectorDbProviders/qdrant");
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return QDrant;
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default:
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throw new Error("ENV: No VECTOR_DB value found in environment!");
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}
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}
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function getLLMProvider() {
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const vectorSelection = process.env.LLM_PROVIDER || "openai";
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const embedder = getEmbeddingEngineSelection();
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switch (vectorSelection) {
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case "openai":
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const { OpenAiLLM } = require("../AiProviders/openAi");
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return new OpenAiLLM(embedder);
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case "azure":
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const { AzureOpenAiLLM } = require("../AiProviders/azureOpenAi");
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return new AzureOpenAiLLM(embedder);
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case "anthropic":
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const { AnthropicLLM } = require("../AiProviders/anthropic");
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return new AnthropicLLM(embedder);
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case "lmstudio":
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const { LMStudioLLM } = require("../AiProviders/lmStudio");
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return new LMStudioLLM(embedder);
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case "localai":
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const { LocalAiLLM } = require("../AiProviders/localAi");
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return new LocalAiLLM(embedder);
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case "native":
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const { NativeLLM } = require("../AiProviders/native");
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return new NativeLLM(embedder);
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default:
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throw new Error("ENV: No LLM_PROVIDER value found in environment!");
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}
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}
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function getEmbeddingEngineSelection() {
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const engineSelection = process.env.EMBEDDING_ENGINE;
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switch (engineSelection) {
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case "openai":
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const { OpenAiEmbedder } = require("../EmbeddingEngines/openAi");
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return new OpenAiEmbedder();
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case "azure":
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const {
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AzureOpenAiEmbedder,
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} = require("../EmbeddingEngines/azureOpenAi");
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return new AzureOpenAiEmbedder();
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case "localai":
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const { LocalAiEmbedder } = require("../EmbeddingEngines/localAi");
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return new LocalAiEmbedder();
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case "native":
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const { NativeEmbedder } = require("../EmbeddingEngines/native");
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return new NativeEmbedder();
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default:
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return null;
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}
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}
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function toChunks(arr, size) {
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return Array.from({ length: Math.ceil(arr.length / size) }, (_v, i) =>
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arr.slice(i * size, i * size + size)
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);
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}
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module.exports = {
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getEmbeddingEngineSelection,
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getVectorDbClass,
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getLLMProvider,
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toChunks,
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};
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