mirror of
https://github.com/zadam/trilium.git
synced 2025-11-03 20:06:08 +01:00
tool calling is close to working
getting closer to calling tools... we definitely need this closer to tool execution... agentic tool calling is...kind of working?
This commit is contained in:
perf3ct
@@ -7,6 +7,10 @@ import { MessagePreparationStage } from './stages/message_preparation_stage.js';
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import { ModelSelectionStage } from './stages/model_selection_stage.js';
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import { LLMCompletionStage } from './stages/llm_completion_stage.js';
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import { ResponseProcessingStage } from './stages/response_processing_stage.js';
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import { ToolCallingStage } from './stages/tool_calling_stage.js';
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import { VectorSearchStage } from './stages/vector_search_stage.js';
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import toolRegistry from '../tools/tool_registry.js';
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import toolInitializer from '../tools/tool_initializer.js';
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import log from '../../log.js';
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/**
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@@ -22,6 +26,8 @@ export class ChatPipeline {
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modelSelection: ModelSelectionStage;
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llmCompletion: LLMCompletionStage;
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responseProcessing: ResponseProcessingStage;
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toolCalling: ToolCallingStage;
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vectorSearch: VectorSearchStage;
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};
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config: ChatPipelineConfig;
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@@ -40,7 +46,9 @@ export class ChatPipeline {
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messagePreparation: new MessagePreparationStage(),
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modelSelection: new ModelSelectionStage(),
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llmCompletion: new LLMCompletionStage(),
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responseProcessing: new ResponseProcessingStage()
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responseProcessing: new ResponseProcessingStage(),
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toolCalling: new ToolCallingStage(),
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vectorSearch: new VectorSearchStage()
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};
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// Set default configuration values
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@@ -87,6 +95,34 @@ export class ChatPipeline {
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contentLength += message.content.length;
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}
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// Initialize tools if needed
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try {
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const toolCount = toolRegistry.getAllTools().length;
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// If there are no tools registered, initialize them
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if (toolCount === 0) {
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log.info('No tools found in registry, initializing tools...');
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await toolInitializer.initializeTools();
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log.info(`Tools initialized, now have ${toolRegistry.getAllTools().length} tools`);
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} else {
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log.info(`Found ${toolCount} tools already registered`);
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}
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} catch (error: any) {
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log.error(`Error checking/initializing tools: ${error.message || String(error)}`);
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}
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// First, select the appropriate model based on query complexity and content length
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const modelSelectionStartTime = Date.now();
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const modelSelection = await this.stages.modelSelection.execute({
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options: input.options,
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query: input.query,
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contentLength
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});
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this.updateStageMetrics('modelSelection', modelSelectionStartTime);
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// Determine if we should use tools or semantic context
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const useTools = modelSelection.options.enableTools === true;
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// Determine which pipeline flow to use
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let context: string | undefined;
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@@ -102,27 +138,63 @@ export class ChatPipeline {
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});
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context = agentContext.context;
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this.updateStageMetrics('agentToolsContext', contextStartTime);
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} else {
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// Get semantic context for regular queries
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} else if (!useTools) {
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// Only get semantic context if tools are NOT enabled
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// When tools are enabled, we'll let the LLM request context via tools instead
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log.info('Getting semantic context for note using pipeline stages');
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// First use the vector search stage to find relevant notes
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const vectorSearchStartTime = Date.now();
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log.info(`Executing vector search stage for query: "${input.query?.substring(0, 50)}..."`);
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const vectorSearchResult = await this.stages.vectorSearch.execute({
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query: input.query || '',
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noteId: input.noteId,
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options: {
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maxResults: 10,
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useEnhancedQueries: true,
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threshold: 0.6
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}
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});
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this.updateStageMetrics('vectorSearch', vectorSearchStartTime);
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log.info(`Vector search found ${vectorSearchResult.searchResults.length} relevant notes`);
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// Then pass to the semantic context stage to build the formatted context
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const semanticContext = await this.stages.semanticContextExtraction.execute({
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noteId: input.noteId,
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query: input.query,
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messages: input.messages
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});
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context = semanticContext.context;
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this.updateStageMetrics('semanticContextExtraction', contextStartTime);
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} else {
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log.info('Tools are enabled - using minimal direct context to avoid race conditions');
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// Get context from current note directly without semantic search
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if (input.noteId) {
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try {
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const contextExtractor = new (await import('../../llm/context/index.js')).ContextExtractor();
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// Just get the direct content of the current note
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context = await contextExtractor.extractContext(input.noteId, {
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includeContent: true,
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includeParents: true,
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includeChildren: true,
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includeLinks: true,
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includeSimilar: false // Skip semantic search to avoid race conditions
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});
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log.info(`Direct context extracted (${context.length} chars) without semantic search`);
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} catch (error: any) {
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log.error(`Error extracting direct context: ${error.message}`);
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context = ""; // Fallback to empty context if extraction fails
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}
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} else {
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context = ""; // No note ID, so no context
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}
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}
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}
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// Select the appropriate model based on query complexity and content length
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const modelSelectionStartTime = Date.now();
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const modelSelection = await this.stages.modelSelection.execute({
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options: input.options,
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query: input.query,
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contentLength
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});
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this.updateStageMetrics('modelSelection', modelSelectionStartTime);
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// Prepare messages with context and system prompt
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const messagePreparationStartTime = Date.now();
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const preparedMessages = await this.stages.messagePreparation.execute({
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@@ -167,17 +239,106 @@ export class ChatPipeline {
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});
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}
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// For non-streaming responses, process the full response
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// Process any tool calls in the response
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let currentMessages = preparedMessages.messages;
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let currentResponse = completion.response;
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let needsFollowUp = false;
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let toolCallIterations = 0;
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const maxToolCallIterations = this.config.maxToolCallIterations;
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// Check if tools were enabled in the options
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const toolsEnabled = modelSelection.options.enableTools !== false;
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log.info(`========== TOOL CALL PROCESSING ==========`);
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log.info(`Tools enabled: ${toolsEnabled}`);
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log.info(`Tool calls in response: ${currentResponse.tool_calls ? currentResponse.tool_calls.length : 0}`);
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log.info(`Current response format: ${typeof currentResponse}`);
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log.info(`Response keys: ${Object.keys(currentResponse).join(', ')}`);
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// Detailed tool call inspection
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if (currentResponse.tool_calls) {
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currentResponse.tool_calls.forEach((tool, idx) => {
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log.info(`Tool call ${idx+1}: ${JSON.stringify(tool)}`);
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});
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}
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// Process tool calls if present and tools are enabled
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if (toolsEnabled && currentResponse.tool_calls && currentResponse.tool_calls.length > 0) {
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log.info(`Response contains ${currentResponse.tool_calls.length} tool calls, processing...`);
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// Start tool calling loop
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log.info(`Starting tool calling loop with max ${maxToolCallIterations} iterations`);
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do {
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log.info(`Tool calling iteration ${toolCallIterations + 1}`);
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// Execute tool calling stage
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const toolCallingStartTime = Date.now();
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const toolCallingResult = await this.stages.toolCalling.execute({
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response: currentResponse,
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messages: currentMessages,
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options: modelSelection.options
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});
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this.updateStageMetrics('toolCalling', toolCallingStartTime);
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// Update state for next iteration
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currentMessages = toolCallingResult.messages;
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needsFollowUp = toolCallingResult.needsFollowUp;
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// Make another call to the LLM if needed
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if (needsFollowUp) {
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log.info(`Tool execution completed, making follow-up LLM call (iteration ${toolCallIterations + 1})...`);
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// Generate a new LLM response with the updated messages
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const followUpStartTime = Date.now();
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log.info(`Sending follow-up request to LLM with ${currentMessages.length} messages (including tool results)`);
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const followUpCompletion = await this.stages.llmCompletion.execute({
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messages: currentMessages,
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options: modelSelection.options
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});
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this.updateStageMetrics('llmCompletion', followUpStartTime);
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// Update current response for next iteration
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currentResponse = followUpCompletion.response;
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// Check for more tool calls
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const hasMoreToolCalls = !!(currentResponse.tool_calls && currentResponse.tool_calls.length > 0);
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if (hasMoreToolCalls) {
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log.info(`Follow-up response contains ${currentResponse.tool_calls?.length || 0} more tool calls`);
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} else {
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log.info(`Follow-up response contains no more tool calls - completing tool loop`);
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}
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// Continue loop if there are more tool calls
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needsFollowUp = hasMoreToolCalls;
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}
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// Increment iteration counter
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toolCallIterations++;
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} while (needsFollowUp && toolCallIterations < maxToolCallIterations);
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// If we hit max iterations but still have tool calls, log a warning
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if (toolCallIterations >= maxToolCallIterations && needsFollowUp) {
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log.error(`Reached maximum tool call iterations (${maxToolCallIterations}), stopping`);
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}
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log.info(`Completed ${toolCallIterations} tool call iterations`);
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}
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// For non-streaming responses, process the final response
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const processStartTime = Date.now();
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const processed = await this.stages.responseProcessing.execute({
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response: completion.response,
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response: currentResponse,
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options: input.options
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});
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this.updateStageMetrics('responseProcessing', processStartTime);
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// Combine response with processed text, using accumulated text if streamed
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const finalResponse: ChatResponse = {
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...completion.response,
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...currentResponse,
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text: accumulatedText || processed.text
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};
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