// =============================================================================
// PROVIDER FACTORIES
// =============================================================================
import { openai } from "@ai-sdk/openai";
import type { LanguageModel } from "ai";
import type { EmbeddingConfig, LLMConfig } from "./config";
// =============================================================================
// LLM PROVIDERS
// =============================================================================
/**
* Create LLM provider based on config
*/
export function createLLM(config: LLMConfig): LanguageModel {
switch (config.provider) {
case "openai":
return openai(config.model);
case "ollama":
// TODO: Ollama LLM support planned for Phase 3
// Will use @ai-sdk/openai-compatible or custom provider
throw new Error(
"Ollama LLM is not yet implemented. Use OpenAI for now (requires OPENAI_API_KEY).",
);
default:
throw new Error(`Unknown LLM provider: ${config.provider}`);
}
}
// =============================================================================
// EMBEDDING PROVIDERS
// =============================================================================
/**
* Embedder interface for generating embeddings
*/
export interface Embedder {
/** Generate embedding for a single text */
embed(text: string): Promise<number[]>;
/** Generate embeddings for multiple texts */
embedBatch(texts: string[]): Promise<number[][]>;
/** Get the embedding dimensions */
readonly dimensions: number;
}
// Known OpenAI embedding model dimensions
const OPENAI_EMBEDDING_DIMENSIONS: Record<string, number> = {
"text-embedding-3-small": 1536,
"text-embedding-3-large": 3072,
"text-embedding-ada-002": 1536,
};
/**
* OpenAI Embedder using the OpenAI API
*/
export class OpenAIEmbedder implements Embedder {
private model: string;
readonly dimensions: number;
constructor(model = "text-embedding-3-small", dimensions?: number) {
this.model = model;
// Use explicit dimensions, known dimensions, or fallback
this.dimensions =
dimensions ??
OPENAI_EMBEDDING_DIMENSIONS[model] ??
(model.includes("large") ? 3072 : 1536);
}
async embed(text: string): Promise<number[]> {
const result = await this.embedBatch([text]);
return result[0];
}
async embedBatch(texts: string[]): Promise<number[][]> {
const apiKey = process.env.OPENAI_API_KEY;
if (!apiKey) {
throw new Error("OPENAI_API_KEY environment variable is required");
}
const response = await fetch("https://api.openai.com/v1/embeddings", {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: `Bearer ${apiKey}`,
},
body: JSON.stringify({
model: this.model,
input: texts,
}),
});
if (!response.ok) {
const error = await response.text();
throw new Error(`OpenAI embedding error: ${response.status} - ${error}`);
}
const data = (await response.json()) as {
data: Array<{ embedding: number[]; index: number }>;
};
// Sort by index to maintain order
return data.data
.sort((a, b) => a.index - b.index)
.map((item) => item.embedding);
}
}
// Known Ollama embedding model dimensions
const OLLAMA_EMBEDDING_DIMENSIONS: Record<string, number> = {
"nomic-embed-text": 768,
"mxbai-embed-large": 1024,
"all-minilm": 384,
"snowflake-arctic-embed": 1024,
};
/**
* Ollama Embedder using local Ollama server
*/
export class OllamaEmbedder implements Embedder {
private model: string;
private baseUrl: string;
readonly dimensions: number;
constructor(
model = "nomic-embed-text",
baseUrl = "http://localhost:11434",
dimensions?: number,
) {
this.model = model;
this.baseUrl = baseUrl;
// Use explicit dimensions, known dimensions, or fallback
this.dimensions =
dimensions ??
OLLAMA_EMBEDDING_DIMENSIONS[model] ??
(model.includes("large") ? 1024 : 768);
}
async embed(text: string): Promise<number[]> {
const response = await fetch(`${this.baseUrl}/api/embeddings`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({
model: this.model,
prompt: text,
}),
});
if (!response.ok) {
const error = await response.text();
throw new Error(`Ollama embedding error: ${response.status} - ${error}`);
}
const data = (await response.json()) as { embedding: number[] };
return data.embedding;
}
async embedBatch(texts: string[]): Promise<number[][]> {
// Ollama doesn't support batch embeddings natively, so we parallelize
return Promise.all(texts.map((text) => this.embed(text)));
}
}
/**
* Create embedder based on config
*/
export function createEmbedder(config: EmbeddingConfig): Embedder {
switch (config.provider) {
case "openai":
return new OpenAIEmbedder(config.model);
case "ollama":
// TODO: Ollama embeddings support planned for Phase 3
// The OllamaEmbedder class exists but needs testing
throw new Error(
"Ollama embeddings are not yet implemented. Use OpenAI for now (requires OPENAI_API_KEY).",
);
default:
throw new Error(`Unknown embedding provider: ${config.provider}`);
}
}
// =============================================================================
// PROVIDER VALIDATION
// =============================================================================
/**
* Validate that required environment variables are set
*/
export function validateProviderConfig(config: {
llm: LLMConfig;
embedding: EmbeddingConfig;
}): void {
if (
config.llm.provider === "openai" ||
config.embedding.provider === "openai"
) {
if (!process.env.OPENAI_API_KEY) {
throw new Error(
"OPENAI_API_KEY environment variable is required for OpenAI provider",
);
}
}
}
/**
* Check if Ollama is available
*/
export async function isOllamaAvailable(
baseUrl = "http://localhost:11434",
): Promise<boolean> {
try {
const response = await fetch(`${baseUrl}/api/tags`, {
method: "GET",
signal: AbortSignal.timeout(2000),
});
return response.ok;
} catch {
return false;
}
}
