122 lines
4.3 KiB
JavaScript
122 lines
4.3 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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case "milvus":
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const { Milvus } = require("../vectorDbProviders/milvus");
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return Milvus;
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case "zilliz":
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const { Zilliz } = require("../vectorDbProviders/zilliz");
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return Zilliz;
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case "astra":
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const { AstraDB } = require("../vectorDbProviders/astra");
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return AstraDB;
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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(modelPreference = null) {
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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, modelPreference);
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case "azure":
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const { AzureOpenAiLLM } = require("../AiProviders/azureOpenAi");
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return new AzureOpenAiLLM(embedder, modelPreference);
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case "anthropic":
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const { AnthropicLLM } = require("../AiProviders/anthropic");
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return new AnthropicLLM(embedder, modelPreference);
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case "gemini":
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const { GeminiLLM } = require("../AiProviders/gemini");
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return new GeminiLLM(embedder, modelPreference);
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case "lmstudio":
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const { LMStudioLLM } = require("../AiProviders/lmStudio");
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return new LMStudioLLM(embedder, modelPreference);
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case "localai":
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const { LocalAiLLM } = require("../AiProviders/localAi");
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return new LocalAiLLM(embedder, modelPreference);
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case "ollama":
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const { OllamaAILLM } = require("../AiProviders/ollama");
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return new OllamaAILLM(embedder, modelPreference);
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case "togetherai":
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const { TogetherAiLLM } = require("../AiProviders/togetherAi");
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return new TogetherAiLLM(embedder, modelPreference);
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case "mistral":
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const { MistralLLM } = require("../AiProviders/mistral");
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return new MistralLLM(embedder, modelPreference);
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case "native":
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const { NativeLLM } = require("../AiProviders/native");
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return new NativeLLM(embedder, modelPreference);
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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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console.log("\x1b[34m[INFO]\x1b[0m Using Native Embedder");
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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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// Some models have lower restrictions on chars that can be encoded in a single pass
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// and by default we assume it can handle 1,000 chars, but some models use work with smaller
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// chars so here we can override that value when embedding information.
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function maximumChunkLength() {
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if (
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!!process.env.EMBEDDING_MODEL_MAX_CHUNK_LENGTH &&
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!isNaN(process.env.EMBEDDING_MODEL_MAX_CHUNK_LENGTH) &&
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Number(process.env.EMBEDDING_MODEL_MAX_CHUNK_LENGTH) > 1
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)
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return Number(process.env.EMBEDDING_MODEL_MAX_CHUNK_LENGTH);
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return 1_000;
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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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maximumChunkLength,
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getVectorDbClass,
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getLLMProvider,
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toChunks,
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};
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