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feat(agent): Refactor agent architecture to enhance content routing and file search capabilities

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- Introduced ContentRouterAgent to determine the next step in information gathering (file search, web search, or analysis) based on task relevance and focus mode.
- Added FileSearchAgent to handle searching through attached files, processing file content into searchable documents.
- Updated SynthesizerAgent to utilize a prompt template for generating comprehensive responses based on context and user queries.
- Enhanced TaskManagerAgent to consider file context when creating tasks.
- Improved AnalyzerAgent to assess the sufficiency of context, including file and web documents.
- Implemented utility functions for processing files and ranking documents based on similarity to queries.
- Updated prompts to include new instructions for handling file context and routing decisions.
- Adjusted agent search workflow to integrate new agents and support file handling.
This commit is contained in:
Willie Zutz 2025-06-28 14:48:08 -06:00
parent 7b47d3dacb
commit de3d26fb15
20 changed files with 1044 additions and 96 deletions
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src/lib/agents/fileSearchAgent.ts Normal file
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import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import { AIMessage } from '@langchain/core/messages';
import { Command, END } from '@langchain/langgraph';
import { EventEmitter } from 'events';
import { Document } from 'langchain/document';
import { AgentState } from './agentState';
import { Embeddings } from '@langchain/core/embeddings';
import { processFilesToDocuments, getRankedDocs } from '../utils/fileProcessing';
export class FileSearchAgent {
private llm: BaseChatModel;
private emitter: EventEmitter;
private systemInstructions: string;
private signal: AbortSignal;
private embeddings: Embeddings;
constructor(
llm: BaseChatModel,
emitter: EventEmitter,
systemInstructions: string,
signal: AbortSignal,
embeddings: Embeddings,
) {
this.llm = llm;
this.emitter = emitter;
this.systemInstructions = systemInstructions;
this.signal = signal;
this.embeddings = embeddings;
}
/**
* File search agent node
*/
async execute(state: typeof AgentState.State): Promise<Command> {
try {
// Determine current task to process
const currentTask =
state.tasks && state.tasks.length > 0
? state.tasks[state.currentTaskIndex || 0]
: state.query;
console.log(
`Processing file search for task ${(state.currentTaskIndex || 0) + 1} of ${state.tasks?.length || 1}: "${currentTask}"`,
);
// Check if we have file IDs to process
if (!state.fileIds || state.fileIds.length === 0) {
console.log('No files attached for search');
return new Command({
goto: 'analyzer',
update: {
messages: [new AIMessage('No files attached to search.')],
},
});
}
// Emit consulting attached files event
this.emitter.emit('agent_action', {
type: 'agent_action',
data: {
action: 'CONSULTING_ATTACHED_FILES',
message: `Consulting attached files...`,
details: {
query: state.query,
currentTask: currentTask,
taskIndex: (state.currentTaskIndex || 0) + 1,
totalTasks: state.tasks?.length || 1,
fileCount: state.fileIds.length,
documentCount: state.relevantDocuments.length,
},
},
});
// Process files to documents
const fileDocuments = await processFilesToDocuments(state.fileIds);
if (fileDocuments.length === 0) {
console.log('No processable file content found');
return new Command({
goto: 'analyzer',
update: {
messages: [new AIMessage('No searchable content found in attached files.')],
},
});
}
console.log(`Processed ${fileDocuments.length} file documents for search`);
// Emit searching file content event
this.emitter.emit('agent_action', {
type: 'agent_action',
data: {
action: 'SEARCHING_FILE_CONTENT',
message: `Searching through ${fileDocuments.length} file sections for relevant information`,
details: {
query: state.query,
currentTask: currentTask,
taskIndex: (state.currentTaskIndex || 0) + 1,
totalTasks: state.tasks?.length || 1,
fileDocumentCount: fileDocuments.length,
documentCount: state.relevantDocuments.length,
},
},
});
// Generate query embedding for similarity search
const queryEmbedding = await this.embeddings.embedQuery(
state.originalQuery + ' ' + currentTask,
);
// Perform similarity search over file documents
const rankedDocuments = getRankedDocs(
queryEmbedding,
fileDocuments,
12, // maxDocs
0.3, // similarity threshold
);
console.log(`Found ${rankedDocuments.length} relevant file sections`);
if (rankedDocuments.length === 0) {
// Emit no relevant content event
this.emitter.emit('agent_action', {
type: 'agent_action',
data: {
action: 'NO_RELEVANT_FILE_CONTENT',
message: `No relevant content found in attached files for the current task`,
details: {
query: state.query,
currentTask: currentTask,
taskIndex: (state.currentTaskIndex || 0) + 1,
totalTasks: state.tasks?.length || 1,
searchedDocuments: fileDocuments.length,
documentCount: state.relevantDocuments.length,
},
},
});
return new Command({
goto: 'analyzer',
update: {
messages: [new AIMessage('No relevant content found in attached files for the current task.')],
},
});
}
// Emit file content found event
this.emitter.emit('agent_action', {
type: 'agent_action',
data: {
action: 'FILE_CONTENT_FOUND',
message: `Found ${rankedDocuments.length} relevant sections in attached files`,
details: {
query: state.query,
currentTask: currentTask,
taskIndex: (state.currentTaskIndex || 0) + 1,
totalTasks: state.tasks?.length || 1,
relevantSections: rankedDocuments.length,
searchedDocuments: fileDocuments.length,
documentCount: state.relevantDocuments.length + rankedDocuments.length,
},
},
});
const responseMessage = `File search completed. Found ${rankedDocuments.length} relevant sections in attached files.`;
console.log(responseMessage);
return new Command({
goto: 'analyzer', // Route back to analyzer to process the results
update: {
messages: [new AIMessage(responseMessage)],
relevantDocuments: rankedDocuments,
},
});
} catch (error) {
console.error('File search error:', error);
const errorMessage = new AIMessage(
`File search failed: ${error instanceof Error ? error.message : 'Unknown error'}`,
);
return new Command({
goto: END,
update: {
messages: [errorMessage],
},
});
}
}
/**
* Perform a similarity search over file documents
* @param state The current agent state
* @returns Ranked documents relevant to the current task
*/
async search(state: typeof AgentState.State): Promise<Document[]> {
if (!state.fileIds || state.fileIds.length === 0) {
return [];
}
// Process files to documents
const fileDocuments = await processFilesToDocuments(state.fileIds);
if (fileDocuments.length === 0) {
return [];
}
// Determine current task to search for
const currentTask =
state.tasks && state.tasks.length > 0
? state.tasks[state.currentTaskIndex || 0]
: state.query;
// Generate query embedding for similarity search
const queryEmbedding = await this.embeddings.embedQuery(
state.originalQuery + ' ' + currentTask,
);
// Perform similarity search and return ranked documents
return getRankedDocs(
queryEmbedding,
fileDocuments,
8, // maxDocs
0.3, // similarity threshold
);
}
}