set up more reasonable context window and dimension sizes

db/migrations/0229__ai_llm_options.sql

@@ -27,3 +27,4 @@ INSERT INTO options (name, value, isSynced, utcDateModified) VALUES ('embeddings
 INSERT INTO options (name, value, isSynced, utcDateModified) VALUES ('enableAutomaticIndexing', 'true', 1, strftime('%Y-%m-%dT%H:%M:%fZ', 'now'));
 INSERT INTO options (name, value, isSynced, utcDateModified) VALUES ('embeddingSimilarityThreshold', '0.65', 1, strftime('%Y-%m-%dT%H:%M:%fZ', 'now'));
 INSERT INTO options (name, value, isSynced, utcDateModified) VALUES ('maxNotesPerLlmQuery', '10', 1, strftime('%Y-%m-%dT%H:%M:%fZ', 'now')); 
+INSERT INTO options (name, value, isSynced, utcDateModified) VALUES ('embeddingBatchSize', '10', 1, strftime('%Y-%m-%dT%H:%M:%fZ', 'now')); 

src/routes/api/llm.ts

@@ -41,6 +41,37 @@ export const LLM_CONSTANTS = {
         }
     },
 
+    // Model-specific embedding dimensions for Ollama models
+    OLLAMA_MODEL_DIMENSIONS: {
+        "llama3": 4096,
+        "llama3.1": 4096,
+        "mistral": 4096,
+        "nomic": 768,
+        "mxbai": 1024,
+        "nomic-embed-text": 768,
+        "mxbai-embed-large": 1024,
+        "default": 384
+    },
+
+    // Model-specific context windows for Ollama models
+    OLLAMA_MODEL_CONTEXT_WINDOWS: {
+        "llama3": 8192,
+        "mistral": 8192,
+        "nomic": 32768,
+        "mxbai": 32768,
+        "nomic-embed-text": 32768,
+        "mxbai-embed-large": 32768,
+        "default": 4096
+    },
+
+    // Batch size configuration
+    BATCH_SIZE: {
+        OPENAI: 10,     // OpenAI can handle larger batches efficiently
+        ANTHROPIC: 5,   // More conservative for Anthropic
+        OLLAMA: 1,      // Ollama processes one at a time
+        DEFAULT: 5      // Conservative default
+    },
+
     // Chunking parameters
     CHUNKING: {
         DEFAULT_SIZE: 1500,

src/services/llm/embeddings/base_embeddings.ts

@@ -1,22 +1,212 @@
 import type { EmbeddingProvider, EmbeddingConfig, NoteEmbeddingContext } from './embeddings_interface.js';
+import log from "../../log.js";
+import { LLM_CONSTANTS } from "../../../routes/api/llm.js";
+import options from "../../options.js";
 
 /**
  * Base class that implements common functionality for embedding providers
  */
 export abstract class BaseEmbeddingProvider implements EmbeddingProvider {
-    abstract name: string;
+    name: string = "base";
     protected config: EmbeddingConfig;
+    protected apiKey?: string;
+    protected baseUrl: string;
+    protected modelInfoCache = new Map<string, any>();
 
     constructor(config: EmbeddingConfig) {
         this.config = config;
+        this.apiKey = config.apiKey;
+        this.baseUrl = config.baseUrl || "";
     }
 
     getConfig(): EmbeddingConfig {
-        return this.config;
+        return { ...this.config };
     }
 
+    getDimension(): number {
+        return this.config.dimension;
+    }
+
+    async initialize(): Promise<void> {
+        // Default implementation does nothing
+        return;
+    }
+
+    /**
+     * Generate embeddings for a single text
+     */
     abstract generateEmbeddings(text: string): Promise<Float32Array>;
-    abstract generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]>;
+
+    /**
+     * Get the appropriate batch size for this provider
+     * Override in provider implementations if needed
+     */
+    protected async getBatchSize(): Promise<number> {
+        // Try to get the user-configured batch size
+        let configuredBatchSize: number | null = null;
+
+        try {
+            const batchSizeStr = await options.getOption('embeddingBatchSize');
+            if (batchSizeStr) {
+                configuredBatchSize = parseInt(batchSizeStr, 10);
+            }
+        } catch (error) {
+            log.error(`Error getting batch size from options: ${error}`);
+        }
+
+        // If user has configured a specific batch size, use that
+        if (configuredBatchSize && !isNaN(configuredBatchSize) && configuredBatchSize > 0) {
+            return configuredBatchSize;
+        }
+
+        // Otherwise use the provider-specific default from constants
+        return this.config.batchSize ||
+               LLM_CONSTANTS.BATCH_SIZE[this.name.toUpperCase() as keyof typeof LLM_CONSTANTS.BATCH_SIZE] ||
+               LLM_CONSTANTS.BATCH_SIZE.DEFAULT;
+    }
+
+    /**
+     * Process a batch of texts with adaptive handling
+     * This method will try to process the batch and reduce batch size if encountering errors
+     */
+    protected async processWithAdaptiveBatch<T>(
+        items: T[],
+        processFn: (batch: T[]) => Promise<any[]>,
+        isBatchSizeError: (error: any) => boolean
+    ): Promise<any[]> {
+        const results: any[] = [];
+        const failures: { index: number, error: string }[] = [];
+        let currentBatchSize = await this.getBatchSize();
+        let lastError: Error | null = null;
+
+        // Process items in batches
+        for (let i = 0; i < items.length;) {
+            const batch = items.slice(i, i + currentBatchSize);
+
+            try {
+                // Process the current batch
+                const batchResults = await processFn(batch);
+                results.push(...batchResults);
+                i += batch.length;
+            }
+            catch (error: any) {
+                lastError = error;
+                const errorMessage = error.message || 'Unknown error';
+
+                // Check if this is a batch size related error
+                if (isBatchSizeError(error) && currentBatchSize > 1) {
+                    // Reduce batch size and retry
+                    const newBatchSize = Math.max(1, Math.floor(currentBatchSize / 2));
+                    console.warn(`Batch size error detected, reducing batch size from ${currentBatchSize} to ${newBatchSize}: ${errorMessage}`);
+                    currentBatchSize = newBatchSize;
+                }
+                else if (currentBatchSize === 1) {
+                    // If we're already at batch size 1, we can't reduce further, so log the error and skip this item
+                    log.error(`Error processing item at index ${i} with batch size 1: ${errorMessage}`);
+                    failures.push({ index: i, error: errorMessage });
+                    i++; // Move to the next item
+                }
+                else {
+                    // For other errors, retry with a smaller batch size as a precaution
+                    const newBatchSize = Math.max(1, Math.floor(currentBatchSize / 2));
+                    console.warn(`Error processing batch, reducing batch size from ${currentBatchSize} to ${newBatchSize} as a precaution: ${errorMessage}`);
+                    currentBatchSize = newBatchSize;
+                }
+            }
+        }
+
+        // If all items failed and we have a last error, throw it
+        if (results.length === 0 && failures.length > 0 && lastError) {
+            throw lastError;
+        }
+
+        // If some items failed but others succeeded, log the summary
+        if (failures.length > 0) {
+            console.warn(`Processed ${results.length} items successfully, but ${failures.length} items failed`);
+        }
+
+        return results;
+    }
+
+    /**
+     * Detect if an error is related to batch size limits
+     * Override in provider-specific implementations
+     */
+    protected isBatchSizeError(error: any): boolean {
+        const errorMessage = error?.message || '';
+        const batchSizeErrorPatterns = [
+            'batch size', 'too many items', 'too many inputs',
+            'input too large', 'payload too large', 'context length',
+            'token limit', 'rate limit', 'request too large'
+        ];
+
+        return batchSizeErrorPatterns.some(pattern =>
+            errorMessage.toLowerCase().includes(pattern.toLowerCase())
+        );
+    }
+
+    /**
+     * Generate embeddings for multiple texts
+     * Default implementation processes texts one by one
+     */
+    async generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]> {
+        if (texts.length === 0) {
+            return [];
+        }
+
+        try {
+            return await this.processWithAdaptiveBatch(
+                texts,
+                async (batch) => {
+                    const batchResults = await Promise.all(
+                        batch.map(text => this.generateEmbeddings(text))
+                    );
+                    return batchResults;
+                },
+                this.isBatchSizeError
+            );
+        }
+        catch (error: any) {
+            const errorMessage = error.message || "Unknown error";
+            log.error(`Batch embedding error for provider ${this.name}: ${errorMessage}`);
+            throw new Error(`${this.name} batch embedding error: ${errorMessage}`);
+        }
+    }
+
+    /**
+     * Generate embeddings for a note with its context
+     */
+    async generateNoteEmbeddings(context: NoteEmbeddingContext): Promise<Float32Array> {
+        const text = [context.title || "", context.content || ""].filter(Boolean).join(" ");
+        return this.generateEmbeddings(text);
+    }
+
+    /**
+     * Generate embeddings for multiple notes with their contexts
+     */
+    async generateBatchNoteEmbeddings(contexts: NoteEmbeddingContext[]): Promise<Float32Array[]> {
+        if (contexts.length === 0) {
+            return [];
+        }
+
+        try {
+            return await this.processWithAdaptiveBatch(
+                contexts,
+                async (batch) => {
+                    const batchResults = await Promise.all(
+                        batch.map(context => this.generateNoteEmbeddings(context))
+                    );
+                    return batchResults;
+                },
+                this.isBatchSizeError
+            );
+        }
+        catch (error: any) {
+            const errorMessage = error.message || "Unknown error";
+            log.error(`Batch note embedding error for provider ${this.name}: ${errorMessage}`);
+            throw new Error(`${this.name} batch note embedding error: ${errorMessage}`);
+        }
+    }
 
     /**
      * Cleans and normalizes text for embeddings by removing excessive whitespace
@@ -157,20 +347,4 @@ export abstract class BaseEmbeddingProvider implements EmbeddingProvider {
 
         return result;
     }
-
-    /**
-     * Default implementation that converts note context to text and generates embeddings
-     */
-    async generateNoteEmbeddings(context: NoteEmbeddingContext): Promise<Float32Array> {
-        const text = this.generateNoteContextText(context);
-        return this.generateEmbeddings(text);
-    }
-
-    /**
-     * Default implementation that processes notes in batch
-     */
-    async generateBatchNoteEmbeddings(contexts: NoteEmbeddingContext[]): Promise<Float32Array[]> {
-        const texts = contexts.map(ctx => this.generateNoteContextText(ctx));
-        return this.generateBatchEmbeddings(texts);
-    }
 }

src/services/llm/embeddings/embeddings_interface.ts

@@ -36,6 +36,14 @@ export interface NoteEmbeddingContext {
     templateTitles?: string[];
 }
 
+/**
+ * Information about an embedding model's capabilities
+ */
+export interface EmbeddingModelInfo {
+    dimension: number;
+    contextWindow: number;
+}
+
 /**
  * Configuration for how embeddings should be generated
  */
@@ -46,6 +54,8 @@ export interface EmbeddingConfig {
     normalize?: boolean;
     batchSize?: number;
     contextWindowSize?: number;
+    apiKey?: string;
+    baseUrl?: string;
 }
 
 /**

src/services/llm/embeddings/providers/anthropic.ts

@@ -1,25 +1,117 @@
-import { BaseEmbeddingProvider } from "../base_embeddings.js";
-import type { EmbeddingConfig } from "../embeddings_interface.js";
 import axios from "axios";
 import log from "../../../log.js";
+import { BaseEmbeddingProvider } from "../base_embeddings.js";
+import type { EmbeddingConfig, EmbeddingModelInfo } from "../embeddings_interface.js";
+import { LLM_CONSTANTS } from "../../../../routes/api/llm.js";
 
-interface AnthropicEmbeddingConfig extends EmbeddingConfig {
+// Anthropic model context window sizes - as of current API version
-    apiKey: string;
+const ANTHROPIC_MODEL_CONTEXT_WINDOWS: Record<string, number> = {
-    baseUrl: string;
+    "claude-3-opus-20240229": 200000,
-}
+    "claude-3-sonnet-20240229": 180000,
+    "claude-3-haiku-20240307": 48000,
+    "claude-2.1": 200000,
+    "claude-2.0": 100000,
+    "claude-instant-1.2": 100000,
+    "default": 100000
+};
 
 /**
- * Anthropic (Claude) embedding provider implementation
+ * Anthropic embedding provider implementation
  */
 export class AnthropicEmbeddingProvider extends BaseEmbeddingProvider {
     name = "anthropic";
-    private apiKey: string;
-    private baseUrl: string;
 
-    constructor(config: AnthropicEmbeddingConfig) {
+    constructor(config: EmbeddingConfig) {
         super(config);
-        this.apiKey = config.apiKey;
+    }
-        this.baseUrl = config.baseUrl;
+
+    /**
+     * Initialize the provider by detecting model capabilities
+     */
+    async initialize(): Promise<void> {
+        const modelName = this.config.model || "claude-3-haiku-20240307";
+        try {
+            // Detect model capabilities
+            const modelInfo = await this.getModelInfo(modelName);
+
+            // Update the config dimension
+            this.config.dimension = modelInfo.dimension;
+
+            log.info(`Anthropic model ${modelName} initialized with dimension ${this.config.dimension} and context window ${modelInfo.contextWindow}`);
+        } catch (error: any) {
+            log.error(`Error initializing Anthropic provider: ${error.message}`);
+        }
+    }
+
+    /**
+     * Try to determine Anthropic model capabilities
+     * Note: Anthropic doesn't have a public endpoint for model metadata, so we use a combination
+     * of known values and detection by test embeddings
+     */
+    private async fetchModelCapabilities(modelName: string): Promise<EmbeddingModelInfo | null> {
+        // Anthropic doesn't have a model info endpoint, but we can look up known context sizes
+        // and detect embedding dimensions by making a test request
+
+        try {
+            // Get context window size from our local registry of known models
+            const modelBase = Object.keys(ANTHROPIC_MODEL_CONTEXT_WINDOWS).find(
+                model => modelName.startsWith(model)
+            ) || "default";
+
+            const contextWindow = ANTHROPIC_MODEL_CONTEXT_WINDOWS[modelBase];
+
+            // For embedding dimension, we'll return null and let getModelInfo detect it
+            return {
+                dimension: 0, // Will be detected by test embedding
+                contextWindow
+            };
+        } catch (error) {
+            log.info(`Could not determine capabilities for Anthropic model ${modelName}: ${error}`);
+            return null;
+        }
+    }
+
+    /**
+     * Get model information including embedding dimensions
+     */
+    async getModelInfo(modelName: string): Promise<EmbeddingModelInfo> {
+        // Check cache first
+        if (this.modelInfoCache.has(modelName)) {
+            return this.modelInfoCache.get(modelName);
+        }
+
+        // Try to determine model capabilities
+        const capabilities = await this.fetchModelCapabilities(modelName);
+        const contextWindow = capabilities?.contextWindow || LLM_CONSTANTS.CONTEXT_WINDOW.ANTHROPIC;
+
+        // For Anthropic, we need to detect embedding dimension with a test call
+        try {
+            // Detect dimension with a test embedding
+            const testEmbedding = await this.generateEmbeddings("Test");
+            const dimension = testEmbedding.length;
+
+            const modelInfo: EmbeddingModelInfo = {
+                dimension,
+                contextWindow
+            };
+
+            this.modelInfoCache.set(modelName, modelInfo);
+            this.config.dimension = dimension;
+
+            log.info(`Detected Anthropic model ${modelName} with dimension ${dimension} (context: ${contextWindow})`);
+            return modelInfo;
+        } catch (error: any) {
+            // If detection fails, use defaults
+            const dimension = LLM_CONSTANTS.EMBEDDING_DIMENSIONS.ANTHROPIC.DEFAULT;
+
+            log.info(`Using default parameters for Anthropic model ${modelName}: dimension ${dimension}, context ${contextWindow}`);
+
+            const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
+            this.modelInfoCache.set(modelName, modelInfo);
+            this.config.dimension = dimension;
+
+            return modelInfo;
+        }
     }
 
     /**
@@ -27,11 +119,23 @@ export class AnthropicEmbeddingProvider extends BaseEmbeddingProvider {
      */
     async generateEmbeddings(text: string): Promise<Float32Array> {
         try {
+            if (!text.trim()) {
+                return new Float32Array(this.config.dimension);
+            }
+
+            // Get model info to check context window
+            const modelName = this.config.model || "claude-3-haiku-20240307";
+            const modelInfo = await this.getModelInfo(modelName);
+
+            // Trim text if it might exceed context window (rough character estimate)
+            const charLimit = modelInfo.contextWindow * 4; // Rough estimate: avg 4 chars per token
+            const trimmedText = text.length > charLimit ? text.substring(0, charLimit) : text;
+
             const response = await axios.post(
                 `${this.baseUrl}/embeddings`,
                 {
-                    model: this.config.model || "claude-3-haiku-20240307",
+                    model: modelName,
-                    input: text,
+                    input: trimmedText,
                     encoding_format: "float"
                 },
                 {
@@ -44,8 +148,7 @@ export class AnthropicEmbeddingProvider extends BaseEmbeddingProvider {
             );
 
             if (response.data && response.data.embedding) {
-                const embedding = response.data.embedding;
+                return new Float32Array(response.data.embedding);
-                return new Float32Array(embedding);
             } else {
                 throw new Error("Unexpected response structure from Anthropic API");
             }
@@ -56,23 +159,60 @@ export class AnthropicEmbeddingProvider extends BaseEmbeddingProvider {
         }
     }
 
+    /**
+     * More specific implementation of batch size error detection for Anthropic
+     */
+    protected isBatchSizeError(error: any): boolean {
+        const errorMessage = error?.message || error?.response?.data?.error?.message || '';
+        const anthropicBatchSizeErrorPatterns = [
+            'batch size', 'too many inputs', 'context length exceeded',
+            'token limit', 'rate limit', 'limit exceeded',
+            'too long', 'request too large', 'content too large'
+        ];
+
+        return anthropicBatchSizeErrorPatterns.some(pattern =>
+            errorMessage.toLowerCase().includes(pattern.toLowerCase())
+        );
+    }
+
     /**
      * Generate embeddings for multiple texts in a single batch
      *
      * Note: Anthropic doesn't currently support batch embedding, so we process each text individually
+     * but using the adaptive batch processor to handle errors and retries
      */
     async generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]> {
         if (texts.length === 0) {
             return [];
         }
 
+        try {
+            return await this.processWithAdaptiveBatch(
+                texts,
+                async (batch) => {
                     const results: Float32Array[] = [];
 
-        for (const text of texts) {
+                    // For Anthropic, we have to process one at a time
+                    for (const text of batch) {
+                        // Skip empty texts
+                        if (!text.trim()) {
+                            results.push(new Float32Array(this.config.dimension));
+                            continue;
+                        }
+
                         const embedding = await this.generateEmbeddings(text);
                         results.push(embedding);
                     }
 
                     return results;
+                },
+                this.isBatchSizeError
+            );
+        }
+        catch (error: any) {
+            const errorMessage = error.message || "Unknown error";
+            log.error(`Anthropic batch embedding error: ${errorMessage}`);
+            throw new Error(`Anthropic batch embedding error: ${errorMessage}`);
+        }
     }
 }

src/services/llm/embeddings/providers/ollama.ts

@@ -1,30 +1,17 @@
-import { BaseEmbeddingProvider } from "../base_embeddings.js";
-import type { EmbeddingConfig } from "../embeddings_interface.js";
 import axios from "axios";
 import log from "../../../log.js";
-
+import { BaseEmbeddingProvider } from "../base_embeddings.js";
-interface OllamaEmbeddingConfig extends EmbeddingConfig {
+import type { EmbeddingConfig, EmbeddingModelInfo } from "../embeddings_interface.js";
-    baseUrl: string;
+import { LLM_CONSTANTS } from "../../../../routes/api/llm.js";
-}
-
-// Model-specific embedding dimensions
-interface EmbeddingModelInfo {
-    dimension: number;
-    contextWindow: number;
-}
 
 /**
  * Ollama embedding provider implementation
  */
 export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
     name = "ollama";
-    private baseUrl: string;
-    // Cache for model dimensions to avoid repeated API calls
-    private modelInfoCache = new Map<string, EmbeddingModelInfo>();
 
-    constructor(config: OllamaEmbeddingConfig) {
+    constructor(config: EmbeddingConfig) {
         super(config);
-        this.baseUrl = config.baseUrl;
     }
 
     /**
@@ -33,43 +20,129 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
     async initialize(): Promise<void> {
         const modelName = this.config.model || "llama3";
         try {
-            await this.getModelInfo(modelName);
+            // Detect model capabilities
-            log.info(`Ollama embedding provider initialized with model ${modelName}`);
+            const modelInfo = await this.getModelInfo(modelName);
+
+            // Update the config dimension
+            this.config.dimension = modelInfo.dimension;
+
+            log.info(`Ollama model ${modelName} initialized with dimension ${this.config.dimension} and context window ${modelInfo.contextWindow}`);
         } catch (error: any) {
-            log.error(`Failed to initialize Ollama embedding provider: ${error.message}`);
+            log.error(`Error initializing Ollama provider: ${error.message}`);
-            // Still continue with default dimensions
         }
     }
 
     /**
-     * Get model information including embedding dimensions
+     * Fetch detailed model information from Ollama API
+     * @param modelName The name of the model to fetch information for
+     */
+    private async fetchModelCapabilities(modelName: string): Promise<EmbeddingModelInfo | null> {
+        try {
+            // First try the /api/show endpoint which has detailed model information
+            const showResponse = await axios.get(
+                `${this.baseUrl}/api/show`,
+                {
+                    params: { name: modelName },
+                    headers: { "Content-Type": "application/json" },
+                    timeout: 10000
+                }
+            );
+
+            if (showResponse.data && showResponse.data.parameters) {
+                const params = showResponse.data.parameters;
+                // Extract context length from parameters (different models might use different parameter names)
+                const contextWindow = params.context_length ||
+                                     params.num_ctx ||
+                                     params.context_window ||
+                                     (LLM_CONSTANTS.OLLAMA_MODEL_CONTEXT_WINDOWS as Record<string, number>).default;
+
+                // Some models might provide embedding dimensions
+                const embeddingDimension = params.embedding_length || params.dim || null;
+
+                log.info(`Fetched Ollama model info from API for ${modelName}: context window ${contextWindow}`);
+
+                return {
+                    dimension: embeddingDimension || 0, // We'll detect this separately if not provided
+                    contextWindow: contextWindow
+                };
+            }
+        } catch (error: any) {
+            log.info(`Could not fetch model info from Ollama show API: ${error.message}. Will try embedding test.`);
+            // We'll fall back to embedding test if this fails
+        }
+
+        return null;
+    }
+
+    /**
+     * Get model information by probing the API
      */
     async getModelInfo(modelName: string): Promise<EmbeddingModelInfo> {
         // Check cache first
         if (this.modelInfoCache.has(modelName)) {
-            return this.modelInfoCache.get(modelName)!;
+            return this.modelInfoCache.get(modelName);
         }
 
-        // Default dimensions for common embedding models
+        // Try to fetch model capabilities from API
-        const defaultDimensions: Record<string, number> = {
+        const apiModelInfo = await this.fetchModelCapabilities(modelName);
-            "nomic-embed-text": 768,
+        if (apiModelInfo) {
-            "mxbai-embed-large": 1024,
+            // If we have context window but no embedding dimension, we need to detect the dimension
-            "llama3": 4096,
+            if (apiModelInfo.contextWindow && !apiModelInfo.dimension) {
-            "all-minilm": 384,
-            "default": 4096
-        };
-
-        // Default context windows
-        const defaultContextWindows: Record<string, number> = {
-            "nomic-embed-text": 8192,
-            "mxbai-embed-large": 8192,
-            "llama3": 8192,
-            "all-minilm": 4096,
-            "default": 4096
-        };
-
                 try {
-            // Try to detect if this is an embedding model
+                    // Detect dimension with a test embedding
+                    const dimension = await this.detectEmbeddingDimension(modelName);
+                    apiModelInfo.dimension = dimension;
+                } catch (error) {
+                    // If dimension detection fails, fall back to defaults
+                    const baseModelName = modelName.split(':')[0];
+                    apiModelInfo.dimension = (LLM_CONSTANTS.OLLAMA_MODEL_DIMENSIONS as Record<string, number>)[baseModelName] ||
+                                           (LLM_CONSTANTS.OLLAMA_MODEL_DIMENSIONS as Record<string, number>).default;
+                }
+            }
+
+            // Cache and return the API-provided info
+            this.modelInfoCache.set(modelName, apiModelInfo);
+            this.config.dimension = apiModelInfo.dimension;
+            return apiModelInfo;
+        }
+
+        // If API info fetch fails, fall back to test embedding
+        try {
+            const dimension = await this.detectEmbeddingDimension(modelName);
+            const baseModelName = modelName.split(':')[0];
+            const contextWindow = (LLM_CONSTANTS.OLLAMA_MODEL_CONTEXT_WINDOWS as Record<string, number>)[baseModelName] ||
+                                (LLM_CONSTANTS.OLLAMA_MODEL_CONTEXT_WINDOWS as Record<string, number>).default;
+
+            const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
+            this.modelInfoCache.set(modelName, modelInfo);
+            this.config.dimension = dimension;
+
+            log.info(`Detected Ollama model ${modelName} with dimension ${dimension} (context: ${contextWindow})`);
+            return modelInfo;
+        } catch (error: any) {
+            log.error(`Error detecting Ollama model capabilities: ${error.message}`);
+
+            // If all detection fails, use defaults based on model name
+            const baseModelName = modelName.split(':')[0];
+            const dimension = (LLM_CONSTANTS.OLLAMA_MODEL_DIMENSIONS as Record<string, number>)[baseModelName] ||
+                            (LLM_CONSTANTS.OLLAMA_MODEL_DIMENSIONS as Record<string, number>).default;
+            const contextWindow = (LLM_CONSTANTS.OLLAMA_MODEL_CONTEXT_WINDOWS as Record<string, number>)[baseModelName] ||
+                                (LLM_CONSTANTS.OLLAMA_MODEL_CONTEXT_WINDOWS as Record<string, number>).default;
+
+            log.info(`Using default parameters for model ${modelName}: dimension ${dimension}, context ${contextWindow}`);
+
+            const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
+            this.modelInfoCache.set(modelName, modelInfo);
+            this.config.dimension = dimension;
+
+            return modelInfo;
+        }
+    }
+
+    /**
+     * Detect embedding dimension by making a test API call
+     */
+    private async detectEmbeddingDimension(modelName: string): Promise<number> {
         const testResponse = await axios.post(
             `${this.baseUrl}/api/embeddings`,
             {
@@ -82,46 +155,11 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
             }
         );
 
-            let dimension = 0;
-            let contextWindow = 0;
-
         if (testResponse.data && Array.isArray(testResponse.data.embedding)) {
-                dimension = testResponse.data.embedding.length;
+            return testResponse.data.embedding.length;
-
-                // Set context window based on model name if we have it
-                const baseModelName = modelName.split(':')[0];
-                contextWindow = defaultContextWindows[baseModelName] || defaultContextWindows.default;
-
-                log.info(`Detected Ollama model ${modelName} with dimension ${dimension}`);
         } else {
             throw new Error("Could not detect embedding dimensions");
         }
-
-            const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
-            this.modelInfoCache.set(modelName, modelInfo);
-
-            // Update the provider config dimension
-            this.config.dimension = dimension;
-
-            return modelInfo;
-        } catch (error: any) {
-            log.error(`Error detecting Ollama model capabilities: ${error.message}`);
-
-            // If detection fails, use defaults based on model name
-            const baseModelName = modelName.split(':')[0];
-            const dimension = defaultDimensions[baseModelName] || defaultDimensions.default;
-            const contextWindow = defaultContextWindows[baseModelName] || defaultContextWindows.default;
-
-            log.info(`Using default dimension ${dimension} for model ${modelName}`);
-
-            const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
-            this.modelInfoCache.set(modelName, modelInfo);
-
-            // Update the provider config dimension
-            this.config.dimension = dimension;
-
-            return modelInfo;
-        }
     }
 
     /**
@@ -136,6 +174,10 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
      */
     async generateEmbeddings(text: string): Promise<Float32Array> {
         try {
+            if (!text.trim()) {
+                return new Float32Array(this.config.dimension);
+            }
+
             const modelName = this.config.model || "llama3";
 
             // Ensure we have model info
@@ -173,29 +215,60 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
         }
     }
 
+    /**
+     * More specific implementation of batch size error detection for Ollama
+     */
+    protected isBatchSizeError(error: any): boolean {
+        const errorMessage = error?.message || '';
+        const ollamaBatchSizeErrorPatterns = [
+            'context length', 'token limit', 'out of memory',
+            'too large', 'overloaded', 'prompt too long',
+            'too many tokens', 'maximum size'
+        ];
+
+        return ollamaBatchSizeErrorPatterns.some(pattern =>
+            errorMessage.toLowerCase().includes(pattern.toLowerCase())
+        );
+    }
+
     /**
      * Generate embeddings for multiple texts
      *
      * Note: Ollama API doesn't support batch embedding, so we process them sequentially
+     * but using the adaptive batch processor to handle rate limits and retries
      */
     async generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]> {
         if (texts.length === 0) {
             return [];
         }
 
+        try {
+            return await this.processWithAdaptiveBatch(
+                texts,
+                async (batch) => {
                     const results: Float32Array[] = [];
 
-        for (const text of texts) {
+                    // For Ollama, we have to process one at a time
-            try {
+                    for (const text of batch) {
+                        // Skip empty texts
+                        if (!text.trim()) {
+                            results.push(new Float32Array(this.config.dimension));
+                            continue;
+                        }
+
                         const embedding = await this.generateEmbeddings(text);
                         results.push(embedding);
-            } catch (error: any) {
+                    }
-                const errorMessage = error.response?.data?.error?.message || error.message || "Unknown error";
+
+                    return results;
+                },
+                this.isBatchSizeError
+            );
+        }
+        catch (error: any) {
+            const errorMessage = error.message || "Unknown error";
             log.error(`Ollama batch embedding error: ${errorMessage}`);
             throw new Error(`Ollama batch embedding error: ${errorMessage}`);
         }
     }
-
-        return results;
-    }
 }

src/services/llm/embeddings/providers/openai.ts

@@ -1,25 +1,165 @@
-import { BaseEmbeddingProvider } from "../base_embeddings.js";
-import type { EmbeddingConfig } from "../embeddings_interface.js";
 import axios from "axios";
 import log from "../../../log.js";
-
+import { BaseEmbeddingProvider } from "../base_embeddings.js";
-interface OpenAIEmbeddingConfig extends EmbeddingConfig {
+import type { EmbeddingConfig, EmbeddingModelInfo } from "../embeddings_interface.js";
-    apiKey: string;
+import { LLM_CONSTANTS } from "../../../../routes/api/llm.js";
-    baseUrl: string;
-}
 
 /**
  * OpenAI embedding provider implementation
  */
 export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
     name = "openai";
-    private apiKey: string;
-    private baseUrl: string;
 
-    constructor(config: OpenAIEmbeddingConfig) {
+    constructor(config: EmbeddingConfig) {
         super(config);
-        this.apiKey = config.apiKey;
+    }
-        this.baseUrl = config.baseUrl;
+
+    /**
+     * Initialize the provider by detecting model capabilities
+     */
+    async initialize(): Promise<void> {
+        const modelName = this.config.model || "text-embedding-3-small";
+        try {
+            // Detect model capabilities
+            const modelInfo = await this.getModelInfo(modelName);
+
+            // Update the config dimension
+            this.config.dimension = modelInfo.dimension;
+
+            log.info(`OpenAI model ${modelName} initialized with dimension ${this.config.dimension} and context window ${modelInfo.contextWindow}`);
+        } catch (error: any) {
+            log.error(`Error initializing OpenAI provider: ${error.message}`);
+        }
+    }
+
+    /**
+     * Fetch model information from the OpenAI API
+     */
+    private async fetchModelCapabilities(modelName: string): Promise<EmbeddingModelInfo | null> {
+        if (!this.apiKey) {
+            return null;
+        }
+
+        try {
+            // First try to get model details from the models API
+            const response = await axios.get(
+                `${this.baseUrl}/models/${modelName}`,
+                {
+                    headers: {
+                        "Authorization": `Bearer ${this.apiKey}`,
+                        "Content-Type": "application/json"
+                    },
+                    timeout: 10000
+                }
+            );
+
+            if (response.data) {
+                // Different model families may have different ways of exposing context window
+                let contextWindow = 0;
+                let dimension = 0;
+
+                // Extract context window if available
+                if (response.data.context_window) {
+                    contextWindow = response.data.context_window;
+                } else if (response.data.limits && response.data.limits.context_window) {
+                    contextWindow = response.data.limits.context_window;
+                } else if (response.data.limits && response.data.limits.context_length) {
+                    contextWindow = response.data.limits.context_length;
+                }
+
+                // Extract embedding dimensions if available
+                if (response.data.dimensions) {
+                    dimension = response.data.dimensions;
+                } else if (response.data.embedding_dimension) {
+                    dimension = response.data.embedding_dimension;
+                }
+
+                // If we didn't get all the info, use defaults for missing values
+                if (!contextWindow) {
+                    // Set default context window based on model name patterns
+                    if (modelName.includes('ada') || modelName.includes('embedding-ada')) {
+                        contextWindow = LLM_CONSTANTS.CONTEXT_WINDOW.OPENAI;
+                    } else if (modelName.includes('davinci')) {
+                        contextWindow = 8192;
+                    } else if (modelName.includes('embedding-3')) {
+                        contextWindow = 8191;
+                    } else {
+                        contextWindow = LLM_CONSTANTS.CONTEXT_WINDOW.OPENAI;
+                    }
+                }
+
+                if (!dimension) {
+                    // Set default dimensions based on model name patterns
+                    if (modelName.includes('ada') || modelName.includes('embedding-ada')) {
+                        dimension = LLM_CONSTANTS.EMBEDDING_DIMENSIONS.OPENAI.ADA;
+                    } else if (modelName.includes('embedding-3-small')) {
+                        dimension = 1536;
+                    } else if (modelName.includes('embedding-3-large')) {
+                        dimension = 3072;
+                    } else {
+                        dimension = LLM_CONSTANTS.EMBEDDING_DIMENSIONS.OPENAI.DEFAULT;
+                    }
+                }
+
+                log.info(`Fetched OpenAI model info for ${modelName}: context window ${contextWindow}, dimension ${dimension}`);
+
+                return {
+                    dimension,
+                    contextWindow
+                };
+            }
+        } catch (error: any) {
+            log.info(`Could not fetch model info from OpenAI API: ${error.message}. Will try embedding test.`);
+        }
+
+        return null;
+    }
+
+    /**
+     * Get model information including embedding dimensions
+     */
+    async getModelInfo(modelName: string): Promise<EmbeddingModelInfo> {
+        // Check cache first
+        if (this.modelInfoCache.has(modelName)) {
+            return this.modelInfoCache.get(modelName);
+        }
+
+        // Try to fetch model capabilities from API
+        const apiModelInfo = await this.fetchModelCapabilities(modelName);
+        if (apiModelInfo) {
+            // Cache and return the API-provided info
+            this.modelInfoCache.set(modelName, apiModelInfo);
+            this.config.dimension = apiModelInfo.dimension;
+            return apiModelInfo;
+        }
+
+        // If API info fetch fails, try to detect embedding dimension with a test call
+        try {
+            const testEmbedding = await this.generateEmbeddings("Test");
+            const dimension = testEmbedding.length;
+
+            // Use default context window
+            let contextWindow = LLM_CONSTANTS.CONTEXT_WINDOW.OPENAI;
+
+            const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
+            this.modelInfoCache.set(modelName, modelInfo);
+            this.config.dimension = dimension;
+
+            log.info(`Detected OpenAI model ${modelName} with dimension ${dimension} (context: ${contextWindow})`);
+            return modelInfo;
+        } catch (error: any) {
+            // If detection fails, use defaults
+            const dimension = LLM_CONSTANTS.EMBEDDING_DIMENSIONS.OPENAI.DEFAULT;
+            const contextWindow = LLM_CONSTANTS.CONTEXT_WINDOW.OPENAI;
+
+            log.info(`Using default parameters for OpenAI model ${modelName}: dimension ${dimension}, context ${contextWindow}`);
+
+            const modelInfo: EmbeddingModelInfo = { dimension, contextWindow };
+            this.modelInfoCache.set(modelName, modelInfo);
+            this.config.dimension = dimension;
+
+            return modelInfo;
+        }
     }
 
     /**
@@ -27,6 +167,10 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
      */
     async generateEmbeddings(text: string): Promise<Float32Array> {
         try {
+            if (!text.trim()) {
+                return new Float32Array(this.config.dimension);
+            }
+
             const response = await axios.post(
                 `${this.baseUrl}/embeddings`,
                 {
@@ -43,8 +187,7 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
             );
 
             if (response.data && response.data.data && response.data.data[0] && response.data.data[0].embedding) {
-                const embedding = response.data.data[0].embedding;
+                return new Float32Array(response.data.data[0].embedding);
-                return new Float32Array(embedding);
             } else {
                 throw new Error("Unexpected response structure from OpenAI API");
             }
@@ -56,24 +199,33 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
     }
 
     /**
-     * Generate embeddings for multiple texts in a single batch
+     * More specific implementation of batch size error detection for OpenAI
      */
-    async generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]> {
+    protected isBatchSizeError(error: any): boolean {
+        const errorMessage = error?.message || error?.response?.data?.error?.message || '';
+        const openAIBatchSizeErrorPatterns = [
+            'batch size', 'too many inputs', 'context length exceeded',
+            'maximum context length', 'token limit', 'rate limit exceeded',
+            'tokens in the messages', 'reduce the length', 'too long'
+        ];
+
+        return openAIBatchSizeErrorPatterns.some(pattern =>
+            errorMessage.toLowerCase().includes(pattern.toLowerCase())
+        );
+    }
+
+    /**
+     * Custom implementation for batched OpenAI embeddings
+     */
+    async generateBatchEmbeddingsWithAPI(texts: string[]): Promise<Float32Array[]> {
         if (texts.length === 0) {
             return [];
         }
 
-        const batchSize = this.config.batchSize || 10;
-        const results: Float32Array[] = [];
-
-        // Process in batches to avoid API limits
-        for (let i = 0; i < texts.length; i += batchSize) {
-            const batch = texts.slice(i, i + batchSize);
-            try {
         const response = await axios.post(
             `${this.baseUrl}/embeddings`,
             {
-                        input: batch,
+                input: texts,
                 model: this.config.model || "text-embedding-3-small",
                 encoding_format: "float"
             },
@@ -91,17 +243,49 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
                 .sort((a: any, b: any) => a.index - b.index)
                 .map((item: any) => new Float32Array(item.embedding));
 
-                    results.push(...sortedEmbeddings);
+            return sortedEmbeddings;
         } else {
             throw new Error("Unexpected response structure from OpenAI API");
         }
-            } catch (error: any) {
+    }
-                const errorMessage = error.response?.data?.error?.message || error.message || "Unknown error";
+
+    /**
+     * Generate embeddings for multiple texts in a single batch
+     * OpenAI API supports batch embedding, so we implement a custom version
+     */
+    async generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]> {
+        if (texts.length === 0) {
+            return [];
+        }
+
+        try {
+            return await this.processWithAdaptiveBatch(
+                texts,
+                async (batch) => {
+                    // Filter out empty texts and use the API batch functionality
+                    const filteredBatch = batch.filter(text => text.trim().length > 0);
+
+                    if (filteredBatch.length === 0) {
+                        // If all texts are empty after filtering, return empty embeddings
+                        return batch.map(() => new Float32Array(this.config.dimension));
+                    }
+
+                    if (filteredBatch.length === 1) {
+                        // If only one text, use the single embedding endpoint
+                        const embedding = await this.generateEmbeddings(filteredBatch[0]);
+                        return [embedding];
+                    }
+
+                    // Use the batch API endpoint
+                    return this.generateBatchEmbeddingsWithAPI(filteredBatch);
+                },
+                this.isBatchSizeError
+            );
+        }
+        catch (error: any) {
+            const errorMessage = error.message || "Unknown error";
             log.error(`OpenAI batch embedding error: ${errorMessage}`);
             throw new Error(`OpenAI batch embedding error: ${errorMessage}`);
         }
     }
-
-        return results;
-    }
 }