@alvinveroy/codecompass
Version:
AI-powered MCP server for codebase navigation and LLM prompt optimization
146 lines (145 loc) • 8.23 kB
JavaScript
;
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.checkOllama = checkOllama;
exports.checkOllamaModel = checkOllamaModel;
exports.generateEmbedding = generateEmbedding;
const axios_1 = __importDefault(require("axios")); // axios will be used by OllamaProvider.generateText
const config_service_1 = require("./config-service");
const text_utils_1 = require("../utils/text-utils");
const retry_utils_1 = require("../utils/retry-utils");
/**
* Check if Ollama server is running and accessible
* @returns Promise<boolean> - True if Ollama is accessible, false otherwise
*/
async function checkOllama() {
const host = config_service_1.configService.OLLAMA_HOST;
config_service_1.logger.info(`Checking Ollama at ${host}`);
try {
await (0, retry_utils_1.withRetry)(async () => {
const response = await axios_1.default.get(host, { timeout: 10000 }); // Specific timeout for check
config_service_1.logger.info(`Ollama status: ${response.status}`);
});
return true;
}
catch (error) {
const err = error instanceof Error ? error : new Error(String(error));
config_service_1.logger.error(`Failed to connect to Ollama: ${err.message}`);
return false;
}
}
/**
* Check if a specific Ollama model is available
* @param model - The model name to check
* @param isEmbeddingModel - Whether this is an embedding model
* @returns Promise<boolean> - True if model is available, false otherwise
*/
async function checkOllamaModel(model, isEmbeddingModel) {
const host = config_service_1.configService.OLLAMA_HOST;
config_service_1.logger.info(`Checking Ollama model: ${model}`);
try {
if (isEmbeddingModel) {
const response = await axios_1.default.post(`${host}/api/embeddings`, { model, prompt: "test" }, { timeout: 10000 });
if (response.status === 200 && response.data.embedding) {
config_service_1.logger.info(`Ollama model ${model} is available`);
return true;
}
// If not returned true, it means it's not functional for this path
config_service_1.logger.warn(`Ollama embedding model ${model} did not return expected data structure.`);
return false; // Explicitly return false
}
else {
const response = await axios_1.default.post(// Add type argument here
`${host}/api/generate`, { model, prompt: "test", stream: false }, { timeout: 10000 });
if (response.status === 200 && response.data && typeof response.data.response === 'string') { // Check response.data and its type
config_service_1.logger.info(`Ollama model ${model} is available`);
return true;
}
// If not returned true, it means it's not functional for this path
config_service_1.logger.warn(`Ollama generation model ${model} did not return expected data structure.`);
return false; // Explicitly return false
}
// The logic above now ensures a boolean is always returned from the try block's success paths.
// The original fall-through to throw an error is no longer needed here,
// as we explicitly return false if the checks fail.
}
catch (error) {
const err = error instanceof Error ? error : new Error(String(error));
const errorDetails = { message: err.message };
if (axios_1.default.isAxiosError(error)) {
const axiosError = error;
errorDetails.response = axiosError.response
? {
status: axiosError.response.status,
data: axiosError.response.data,
}
: undefined;
}
config_service_1.logger.error(`Ollama model check error for ${model}`, errorDetails);
// Instead of throwing, we should return false as per the function's Promise<boolean> signature
// The caller can decide if this constitutes a critical failure.
return false;
}
}
/**
* Generate embeddings for text using Ollama
* @param text - The text to generate embeddings for
* @returns Promise<number[]> - The embedding vector
*/
async function generateEmbedding(text) {
const processedText = (0, text_utils_1.preprocessText)(text);
const truncatedText = processedText.length > config_service_1.configService.MAX_INPUT_LENGTH ? processedText.slice(0, config_service_1.configService.MAX_INPUT_LENGTH) : processedText;
try {
const host = config_service_1.configService.OLLAMA_HOST;
const model = config_service_1.configService.EMBEDDING_MODEL; // Use configured embedding model
const response = await (0, retry_utils_1.withRetry)(async () => {
config_service_1.logger.info(`Generating embedding for text (length: ${truncatedText.length}, snippet: "${truncatedText.slice(0, 100)}...")`);
const res = await axios_1.default.post(`${host}/api/embeddings`, { model: model, prompt: truncatedText }, { timeout: config_service_1.configService.REQUEST_TIMEOUT });
if (!res.data.embedding || !Array.isArray(res.data.embedding)) {
throw new Error("Invalid embedding response from Ollama API");
}
// Ensure the embedding is an array of numbers and has the expected dimension
if (!Array.isArray(res.data.embedding) || res.data.embedding.some(isNaN)) {
config_service_1.logger.error(`Ollama API returned an invalid embedding vector (not an array of numbers or contains NaN) for model ${model}. Length: ${res.data.embedding?.length}`);
throw new Error(`Ollama API returned an invalid embedding vector (contains NaN or not an array of numbers) for model ${model}.`);
}
// ADD THIS CHECK:
const expectedDimension = config_service_1.configService.EMBEDDING_DIMENSION;
if (res.data.embedding.some(v => !isFinite(v))) {
config_service_1.logger.error(`Ollama API returned an embedding vector with non-finite values (Infinity or -Infinity) for model ${model}.`);
throw new Error(`Ollama API returned an embedding vector with non-finite values for model ${model}.`);
}
if (res.data.embedding.length !== expectedDimension) {
config_service_1.logger.error(`Ollama API returned an embedding vector with unexpected dimension for model ${model}. Expected: ${expectedDimension}, Actual: ${res.data.embedding.length}.`);
throw new Error(`Ollama API returned an embedding vector with unexpected dimension. Expected: ${expectedDimension}, Actual: ${res.data.embedding.length}`);
}
return res.data; // Return the whole OllamaEmbeddingResponse object
});
return response.embedding;
}
catch (error) {
const err = error instanceof Error ? error : new Error(String(error));
const errorLogDetails = {
message: err.message,
inputLength: truncatedText.length,
inputSnippet: truncatedText.slice(0, 100),
};
if (axios_1.default.isAxiosError(error)) {
const axiosError = error;
errorLogDetails.code = axiosError.code;
errorLogDetails.response = axiosError.response
? {
status: axiosError.response.status,
data: axiosError.response.data,
}
: undefined;
}
config_service_1.logger.error("Ollama embedding error", errorLogDetails);
// It's critical to re-throw here so that the calling function (e.g., in repository.ts)
// knows that embedding failed and can skip adding a point with a bad/missing vector.
// Or, return a specific marker like null/undefined if the caller is designed to handle it.
throw new Error(`Failed to generate embedding with Ollama model ${config_service_1.configService.EMBEDDING_MODEL}: ${err.message}`);
}
}