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fix hardcoded values part 5

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perf3ct 2025-04-15 18:03:35 +00:00
parent 67a45333b3
commit 374975eafc
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10 changed files with 57 additions and 56 deletions
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1
src/services/llm/constants/search_constants.ts
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@ -47,6 +47,7 @@ export const SEARCH_CONSTANTS = {
// Token/char limits
LIMITS: {
DEFAULT_NOTE_SUMMARY_LENGTH: 500,
DEFAULT_MAX_TOKENS: 4096,
RELATIONSHIP_TOOL_MAX_TOKENS: 50,
VECTOR_SEARCH_MAX_TOKENS: 500,
QUERY_PROCESSOR_MAX_TOKENS: 300,

16
src/services/llm/context_extractors/note_navigator_tool.ts
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@ -289,7 +289,7 @@ export class NoteNavigatorTool {
/**
* Search for notes by title
*/
searchNotesByTitle(searchTerm: string, limit: number = 10): NoteInfo[] {
searchNotesByTitle(searchTerm: string, limit: number = SEARCH_CONSTANTS.HIERARCHY.MAX_NOTES_PER_QUERY): NoteInfo[] {
try {
if (!searchTerm || searchTerm.trim().length === 0) {
return [];
@ -369,14 +369,14 @@ export class NoteNavigatorTool {
if (paths.length > 1) {
result += `This note appears in ${paths.length} different locations:\n`;
// Show max 3 paths to avoid overwhelming context
for (let i = 0; i < Math.min(3, paths.length); i++) {
// Show max paths to avoid overwhelming context
for (let i = 0; i < Math.min(SEARCH_CONSTANTS.HIERARCHY.MAX_PATHS_TO_SHOW, paths.length); i++) {
const path = paths[i];
result += `${i+1}. ${path.notePathTitles.join(' > ')}\n`;
}
if (paths.length > 3) {
result += `... and ${paths.length - 3} more locations\n`;
if (paths.length > SEARCH_CONSTANTS.HIERARCHY.MAX_PATHS_TO_SHOW) {
result += `... and ${paths.length - SEARCH_CONSTANTS.HIERARCHY.MAX_PATHS_TO_SHOW} more locations\n`;
}
} else {
// Just one path
@ -385,7 +385,7 @@ export class NoteNavigatorTool {
}
// Children info using the async function
const children = await this.getChildNotes(noteId, 5);
const children = await this.getChildNotes(noteId, SEARCH_CONSTANTS.CONTEXT.MAX_POINTS);
if (children.length > 0) {
result += `\nContains ${note.children.length} child notes`;
@ -520,7 +520,7 @@ export class NoteNavigatorTool {
/**
* Get child notes of a specified note
*/
async getChildNotes(noteId: string, limit: number = 10): Promise<Array<{noteId: string, title: string}>> {
async getChildNotes(noteId: string, limit: number = SEARCH_CONSTANTS.CONTEXT.MAX_CHILDREN): Promise<Array<{noteId: string, title: string}>> {
try {
const note = becca.notes[noteId];
@ -564,7 +564,7 @@ export class NoteNavigatorTool {
/**
* Find notes linked to/from the specified note
*/
async getLinkedNotes(noteId: string, limit: number = 10): Promise<Array<{noteId: string, title: string, direction: 'from'|'to'}>> {
async getLinkedNotes(noteId: string, limit: number = SEARCH_CONSTANTS.CONTEXT.MAX_LINKS): Promise<Array<{noteId: string, title: string, direction: 'from'|'to'}>> {
try {
const note = becca.notes[noteId];

9
src/services/llm/embeddings/chunking/chunking_processor.ts
View File

@ -5,6 +5,7 @@ import becca from "../../../../becca/becca.js";
import cls from "../../../../services/cls.js";
import type { NoteEmbeddingContext } from "../types.js";
import { LLM_CONSTANTS } from "../../../llm/constants/provider_constants.js";
import { EMBEDDING_PROCESSING } from '../../constants/search_constants.js';
// Define error categories for better handling
const ERROR_CATEGORIES = {
@ -27,14 +28,14 @@ const ERROR_CATEGORIES = {
};
// Maximum time (in milliseconds) allowed for the entire chunking process
const MAX_TOTAL_PROCESSING_TIME = 5 * 60 * 1000; // 5 minutes
const MAX_TOTAL_PROCESSING_TIME = EMBEDDING_PROCESSING.MAX_TOTAL_PROCESSING_TIME;
// Maximum number of retry attempts per chunk
const MAX_CHUNK_RETRY_ATTEMPTS = 2;
const MAX_CHUNK_RETRY_ATTEMPTS = EMBEDDING_PROCESSING.MAX_CHUNK_RETRY_ATTEMPTS;
// Maximum time per chunk processing (to prevent individual chunks from hanging)
const DEFAULT_MAX_CHUNK_PROCESSING_TIME = 60 * 1000; // 1 minute
const OLLAMA_MAX_CHUNK_PROCESSING_TIME = 120 * 1000; // 2 minutes
const DEFAULT_MAX_CHUNK_PROCESSING_TIME = EMBEDDING_PROCESSING.DEFAULT_MAX_CHUNK_PROCESSING_TIME;
const OLLAMA_MAX_CHUNK_PROCESSING_TIME = EMBEDDING_PROCESSING.OLLAMA_MAX_CHUNK_PROCESSING_TIME;
/**
* Categorize an error as temporary or permanent based on its message

35
src/services/llm/embeddings/providers/openai.ts
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@ -5,6 +5,7 @@ import { NormalizationStatus } from "../embeddings_interface.js";
import { LLM_CONSTANTS } from "../../constants/provider_constants.js";
import type { EmbeddingModelInfo } from "../../interfaces/embedding_interfaces.js";
import OpenAI from "openai";
import { PROVIDER_EMBEDDING_CAPABILITIES } from '../../constants/search_constants.js';
/**
* OpenAI embedding provider implementation using the official SDK
@ -40,7 +41,7 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
if (!this.client && this.apiKey) {
this.initClient();
}
// Detect model capabilities
const modelInfo = await this.getModelInfo(modelName);
@ -64,7 +65,7 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
try {
// Get model details using the SDK
const model = await this.client.models.retrieve(modelName);
if (model) {
// Different model families may have different ways of exposing context window
let contextWindow = 0;
@ -72,7 +73,7 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
// Extract context window if available from the response
const modelData = model as any;
if (modelData.context_window) {
contextWindow = modelData.context_window;
} else if (modelData.limits && modelData.limits.context_window) {
@ -90,15 +91,11 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
// 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;
// Set contextWindow based on model name patterns
if (modelName.includes('embedding-3')) {
contextWindow = PROVIDER_EMBEDDING_CAPABILITIES.OPENAI.MODELS['text-embedding-3-small'].contextWindow;
} else {
contextWindow = LLM_CONSTANTS.CONTEXT_WINDOW.OPENAI;
contextWindow = PROVIDER_EMBEDDING_CAPABILITIES.OPENAI.MODELS.default.contextWindow;
}
}
@ -107,11 +104,11 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
if (modelName.includes('ada') || modelName.includes('embedding-ada')) {
dimension = LLM_CONSTANTS.EMBEDDING_DIMENSIONS.OPENAI.ADA;
} else if (modelName.includes('embedding-3-small')) {
dimension = 1536;
dimension = PROVIDER_EMBEDDING_CAPABILITIES.OPENAI.MODELS['text-embedding-3-small'].dimension;
} else if (modelName.includes('embedding-3-large')) {
dimension = 3072;
dimension = PROVIDER_EMBEDDING_CAPABILITIES.OPENAI.MODELS['text-embedding-3-large'].dimension;
} else {
dimension = LLM_CONSTANTS.EMBEDDING_DIMENSIONS.OPENAI.DEFAULT;
dimension = PROVIDER_EMBEDDING_CAPABILITIES.OPENAI.MODELS.default.dimension;
}
}
@ -155,7 +152,7 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
const dimension = testEmbedding.length;
// Use default context window
let contextWindow = LLM_CONSTANTS.CONTEXT_WINDOW.OPENAI;
let contextWindow = PROVIDER_EMBEDDING_CAPABILITIES.OPENAI.MODELS.default.contextWindow;
const modelInfo: EmbeddingModelInfo = {
name: modelName,
@ -170,8 +167,8 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
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;
const dimension = PROVIDER_EMBEDDING_CAPABILITIES.OPENAI.MODELS.default.dimension;
const contextWindow = PROVIDER_EMBEDDING_CAPABILITIES.OPENAI.MODELS.default.contextWindow;
log.info(`Using default parameters for OpenAI model ${modelName}: dimension ${dimension}, context ${contextWindow}`);
@ -209,7 +206,7 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
input: text,
encoding_format: "float"
});
if (response && response.data && response.data[0] && response.data[0].embedding) {
return new Float32Array(response.data[0].embedding);
} else {
@ -258,7 +255,7 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
input: texts,
encoding_format: "float"
});
if (response && response.data) {
// Sort the embeddings by index to ensure they match the input order
const sortedEmbeddings = response.data

23
src/services/llm/embeddings/providers/voyage.ts
View File

@ -51,21 +51,17 @@ export class VoyageEmbeddingProvider extends BaseEmbeddingProvider {
*/
private async fetchModelCapabilities(modelName: string): Promise<EmbeddingModelInfo | null> {
try {
// Get context window size from our local registry of known models
const modelBase = Object.keys(VOYAGE_MODEL_CONTEXT_WINDOWS).find(
// Find the closest matching model
const modelMapKey = Object.keys(PROVIDER_EMBEDDING_CAPABILITIES.VOYAGE.MODELS).find(
model => modelName.startsWith(model)
) || "default";
const modelInfo = VOYAGE_MODEL_CONTEXT_WINDOWS[modelBase as keyof typeof VOYAGE_MODEL_CONTEXT_WINDOWS];
const contextWindow = modelInfo.contextWidth;
// Get dimension from our registry of known models
const dimension = VOYAGE_MODEL_DIMENSIONS[modelBase as keyof typeof VOYAGE_MODEL_DIMENSIONS] ||
VOYAGE_MODEL_DIMENSIONS["default"];
// Use as keyof to tell TypeScript this is a valid key
const modelInfo = PROVIDER_EMBEDDING_CAPABILITIES.VOYAGE.MODELS[modelMapKey as keyof typeof PROVIDER_EMBEDDING_CAPABILITIES.VOYAGE.MODELS];
return {
dimension,
contextWidth: contextWindow,
dimension: modelInfo.dimension,
contextWidth: modelInfo.contextWidth,
name: modelName,
type: 'float32'
};
@ -86,8 +82,9 @@ export class VoyageEmbeddingProvider extends BaseEmbeddingProvider {
// Try to determine model capabilities
const capabilities = await this.fetchModelCapabilities(modelName);
const contextWindow = capabilities?.contextWidth || 8192; // Default context window for Voyage
const knownDimension = capabilities?.dimension || 1024; // Default dimension for Voyage models
const defaults = PROVIDER_EMBEDDING_CAPABILITIES.VOYAGE.MODELS.default;
const contextWindow = capabilities?.contextWidth || defaults.contextWidth;
const knownDimension = capabilities?.dimension || defaults.dimension;
// For Voyage, we can use known dimensions or detect with a test call
try {
@ -166,7 +163,7 @@ export class VoyageEmbeddingProvider extends BaseEmbeddingProvider {
const modelInfo = await this.getModelInfo(modelName);
// Trim text if it might exceed context window (rough character estimate)
const charLimit = (modelInfo.contextWidth || 8192) * 4; // Rough estimate: avg 4 chars per token
const charLimit = (modelInfo.contextWidth || PROVIDER_EMBEDDING_CAPABILITIES.VOYAGE.MODELS.default.contextWidth) * 4; // Rough estimate: avg 4 chars per token
const trimmedText = text.length > charLimit ? text.substring(0, charLimit) : text;
const response = await fetch(`${this.baseUrl}/embeddings`, {

7
src/services/llm/embeddings/storage.ts
View File

@ -7,6 +7,7 @@ import type { EmbeddingResult } from "./types.js";
import entityChangesService from "../../../services/entity_changes.js";
import type { EntityChange } from "../../../services/entity_changes_interface.js";
import { EMBEDDING_CONSTANTS } from "../constants/embedding_constants.js";
import { SEARCH_CONSTANTS } from '../constants/search_constants.js';
/**
* Creates or updates an embedding for a note
*/
@ -139,14 +140,14 @@ export async function findSimilarNotes(
embedding: Float32Array,
providerId: string,
modelId: string,
limit = 10,
limit = SEARCH_CONSTANTS.VECTOR_SEARCH.DEFAULT_MAX_RESULTS,
threshold?: number, // Made optional to use constants
useFallback = true // Whether to try other providers if no embeddings found
): Promise<{noteId: string, similarity: number, contentType?: string}[]> {
// Import constants dynamically to avoid circular dependencies
const llmModule = await import('../../../routes/api/llm.js');
// Use a default threshold of 0.65 if not provided
const actualThreshold = threshold || 0.65;
// Use default threshold if not provided
const actualThreshold = threshold || SEARCH_CONSTANTS.VECTOR_SEARCH.EXACT_MATCH_THRESHOLD;
try {
log.info(`Finding similar notes with provider: ${providerId}, model: ${modelId}, dimension: ${embedding.length}, threshold: ${actualThreshold}`);

8
src/services/llm/embeddings/vector_utils.ts
View File

@ -1,3 +1,5 @@
import { SEARCH_CONSTANTS } from '../constants/search_constants.js';
/**
* Computes the cosine similarity between two vectors
* If dimensions don't match, automatically adapts using the enhanced approach
@ -549,9 +551,9 @@ export function ensembleSimilarity(
): number {
// Default weights if not provided
const weights = options.ensembleWeights ?? {
[SimilarityMetric.COSINE]: 0.6,
[SimilarityMetric.HYBRID]: 0.3,
[SimilarityMetric.DIM_AWARE]: 0.1
[SimilarityMetric.COSINE]: SEARCH_CONSTANTS.VECTOR_SEARCH.SIMILARITY_THRESHOLD.COSINE,
[SimilarityMetric.HYBRID]: SEARCH_CONSTANTS.VECTOR_SEARCH.SIMILARITY_THRESHOLD.HYBRID,
[SimilarityMetric.DIM_AWARE]: SEARCH_CONSTANTS.VECTOR_SEARCH.SIMILARITY_THRESHOLD.DIM_AWARE
};
let totalWeight = 0;

3
src/services/llm/providers/anthropic_service.ts
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@ -6,6 +6,7 @@ import type { AnthropicOptions } from './provider_options.js';
import { getAnthropicOptions } from './providers.js';
import log from '../../log.js';
import Anthropic from '@anthropic-ai/sdk';
import { SEARCH_CONSTANTS } from '../constants/search_constants.js';
export class AnthropicService extends BaseAIService {
private client: any = null;
@ -78,7 +79,7 @@ export class AnthropicService extends BaseAIService {
model: providerOptions.model,
messages: anthropicMessages,
system: systemPrompt,
max_tokens: providerOptions.max_tokens || 4096,
max_tokens: providerOptions.max_tokens || SEARCH_CONSTANTS.LIMITS.DEFAULT_MAX_TOKENS,
temperature: providerOptions.temperature,
top_p: providerOptions.top_p,
stream: !!providerOptions.stream

4
src/services/llm/rest_chat_service.ts
View File

@ -355,7 +355,7 @@ class RestChatService {
createdAt: now,
lastActive: now,
metadata: {
temperature: 0.7,
temperature: SEARCH_CONSTANTS.TEMPERATURE.DEFAULT,
maxTokens: undefined,
model: undefined,
provider: undefined
@ -1645,7 +1645,7 @@ class RestChatService {
lastActive: now,
noteContext: options.contextNoteId,
metadata: {
temperature: options.temperature,
temperature: SEARCH_CONSTANTS.TEMPERATURE.DEFAULT,
maxTokens: options.maxTokens,
model: options.model,
provider: options.provider,

7
src/services/llm/tools/note_summarization_tool.ts
View File

@ -8,6 +8,7 @@ import type { Tool, ToolHandler } from './tool_interfaces.js';
import log from '../../log.js';
import becca from '../../../becca/becca.js';
import aiServiceManager from '../ai_service_manager.js';
import { SEARCH_CONSTANTS } from '../constants/search_constants.js';
/**
* Definition of the note summarization tool
@ -59,7 +60,7 @@ export class NoteSummarizationTool implements ToolHandler {
focus?: string
}): Promise<string | object> {
try {
const { noteId, maxLength = 500, format = 'paragraph', focus } = args;
const { noteId, maxLength = SEARCH_CONSTANTS.LIMITS.DEFAULT_NOTE_SUMMARY_LENGTH, format = 'paragraph', focus } = args;
log.info(`Executing summarize_note tool - NoteID: "${noteId}", MaxLength: ${maxLength}, Format: ${format}`);
@ -134,8 +135,8 @@ export class NoteSummarizationTool implements ToolHandler {
{ role: 'system', content: 'You are a skilled summarizer. Create concise, accurate summaries while preserving the key information.' },
{ role: 'user', content: prompt }
], {
temperature: 0.3, // Lower temperature for more focused summaries
maxTokens: 1000 // Enough tokens for the summary
temperature: SEARCH_CONSTANTS.TEMPERATURE.VECTOR_SEARCH, // Lower temperature for more focused summaries
maxTokens: SEARCH_CONSTANTS.LIMITS.VECTOR_SEARCH_MAX_TOKENS // Enough tokens for the summary
});
const summaryDuration = Date.now() - summaryStartTime;