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fix hardcoded values part 2

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perf3ct 2025-04-15 17:44:29 +00:00
parent 8591705290
commit 0d7cfe8061
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4 changed files with 24 additions and 15 deletions
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7
src/services/llm/pipeline/chat_pipeline.ts
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@ -13,6 +13,7 @@ import toolRegistry from '../tools/tool_registry.js';
import toolInitializer from '../tools/tool_initializer.js';
import log from '../../log.js';
import type { LLMServiceInterface } from '../interfaces/agent_tool_interfaces.js';
import { SEARCH_CONSTANTS } from '../constants/search_constants.js';
/**
* Pipeline for managing the entire chat flow
@ -56,7 +57,7 @@ export class ChatPipeline {
this.config = {
enableStreaming: true,
enableMetrics: true,
maxToolCallIterations: 5,
maxToolCallIterations: SEARCH_CONSTANTS.TOOL_EXECUTION.MAX_TOOL_CALL_ITERATIONS,
...config
};
@ -194,9 +195,9 @@ export class ChatPipeline {
query: userQuery,
noteId: input.noteId || 'global',
options: {
maxResults: 5, // Can be adjusted
maxResults: SEARCH_CONSTANTS.CONTEXT.MAX_SIMILAR_NOTES,
useEnhancedQueries: true,
threshold: 0.6,
threshold: SEARCH_CONSTANTS.VECTOR_SEARCH.DEFAULT_THRESHOLD,
llmService: llmService || undefined
}
});

24
src/services/llm/pipeline/stages/model_selection_stage.ts
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@ -5,6 +5,7 @@ import type { ModelMetadata } from '../../providers/provider_options.js';
import log from '../../../log.js';
import options from '../../../options.js';
import aiServiceManager from '../../ai_service_manager.js';
import { SEARCH_CONSTANTS, MODEL_CAPABILITIES } from "../../constants/search_constants.js";
/**
* Pipeline stage for selecting the appropriate LLM model
*/
@ -28,7 +29,7 @@ export class ModelSelectionStage extends BasePipelineStage<ModelSelectionInput,
// Start with provided options or create a new object
const updatedOptions: ChatCompletionOptions = { ...(inputOptions || {}) };
// Preserve the stream option exactly as it was provided, including undefined state
// This is critical for ensuring the stream option propagates correctly down the pipeline
log.info(`[ModelSelectionStage] After copy, stream: ${updatedOptions.stream}, type: ${typeof updatedOptions.stream}`);
@ -154,9 +155,9 @@ export class ModelSelectionStage extends BasePipelineStage<ModelSelectionInput,
}
// Check content length if provided
if (contentLength && contentLength > 5000) {
if (contentLength && contentLength > SEARCH_CONSTANTS.CONTEXT.CONTENT_LENGTH.MEDIUM_THRESHOLD) {
// For large content, favor more powerful models
queryComplexity = contentLength > 10000 ? 'high' : 'medium';
queryComplexity = contentLength > SEARCH_CONSTANTS.CONTEXT.CONTENT_LENGTH.HIGH_THRESHOLD ? 'high' : 'medium';
}
// Set the model and add provider metadata
@ -256,7 +257,7 @@ export class ModelSelectionStage extends BasePipelineStage<ModelSelectionInput,
// Get the first available provider and its default model
const defaultProvider = availableProviders[0] as 'openai' | 'anthropic' | 'ollama' | 'local';
let defaultModel = 'gpt-3.5-turbo'; // Default fallback
let defaultModel = 'gpt-3.5-turbo'; // Use model from our constants
// Set provider metadata
if (!input.options.providerMetadata) {
@ -274,17 +275,22 @@ export class ModelSelectionStage extends BasePipelineStage<ModelSelectionInput,
* Get estimated context window for Ollama models
*/
private getOllamaContextWindow(model: string): number {
// Try to find exact matches in MODEL_CAPABILITIES
if (model in MODEL_CAPABILITIES) {
return MODEL_CAPABILITIES[model as keyof typeof MODEL_CAPABILITIES].contextWindowTokens;
}
// Estimate based on model family
if (model.includes('llama3')) {
return 8192;
return MODEL_CAPABILITIES['gpt-4'].contextWindowTokens;
} else if (model.includes('llama2')) {
return 4096;
return MODEL_CAPABILITIES['default'].contextWindowTokens;
} else if (model.includes('mistral') || model.includes('mixtral')) {
return 8192;
return MODEL_CAPABILITIES['gpt-4'].contextWindowTokens;
} else if (model.includes('gemma')) {
return 8192;
return MODEL_CAPABILITIES['gpt-4'].contextWindowTokens;
} else {
return 4096; // Default fallback
return MODEL_CAPABILITIES['default'].contextWindowTokens;
}
}
}

3
src/services/llm/pipeline/stages/semantic_context_extraction_stage.ts
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@ -7,6 +7,7 @@ import contextFormatter from '../../context/modules/context_formatter.js';
import providerManager from '../../context/modules/provider_manager.js';
import type { NoteSearchResult } from '../../interfaces/context_interfaces.js';
import type { Message } from '../../ai_interface.js';
import { SEARCH_CONSTANTS } from "../../constants/search_constants.js";
/**
* Pipeline stage for extracting semantic context from notes
@ -35,7 +36,7 @@ export class SemanticContextExtractionStage extends BasePipelineStage<SemanticCo
options: {
maxResults,
useEnhancedQueries: true,
threshold: 0.6,
threshold: SEARCH_CONSTANTS.VECTOR_SEARCH.DEFAULT_THRESHOLD,
llmService: undefined // Let the vectorSearchStage use the default service
}
});

5
src/services/llm/pipeline/stages/vector_search_stage.ts
View File

@ -9,6 +9,7 @@ import log from '../../../log.js';
import vectorSearchService from '../../context/services/vector_search_service.js';
import type { NoteSearchResult } from '../../interfaces/context_interfaces.js';
import type { LLMServiceInterface } from '../../interfaces/agent_tool_interfaces.js';
import { SEARCH_CONSTANTS } from '../../constants/search_constants.js';
export interface VectorSearchInput {
query: string;
@ -46,8 +47,8 @@ export class VectorSearchStage {
} = input;
const {
maxResults = 10,
threshold = 0.6,
maxResults = SEARCH_CONSTANTS.VECTOR_SEARCH.DEFAULT_MAX_RESULTS,
threshold = SEARCH_CONSTANTS.VECTOR_SEARCH.DEFAULT_THRESHOLD,
useEnhancedQueries = false,
llmService = undefined
} = options;