> ## Documentation Index
> Fetch the complete documentation index at: https://opensource.weam.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# LLM Query Flow
Streamlined processing pipeline handling user queries through LangGraph-powered intelligent routing in Node.js.
The architecture uses a single socket event for all AI operations, with backend intelligence determining the appropriate processing flow.
## Unified Processing Pipeline
### Single Entry Point
1. **Socket Event Emission** - Frontend emits single event to Node.js server
2. **LangGraph Router** - Backend analyzes request and determines operation type
3. **Operation Execution** - Appropriate handler processes request
4. **Response Streaming** - Real-time results streamed back via Socket.IO
5. **Frontend Display** - UI updates with streamed response
## Operation Types
### 1. Normal Chat Flow
**Process:**
* Frontend emits socket event with user query
* LangGraph analyzes query and calls appropriate LLM
* Response streams back to frontend
* **LLM Calls:** 1 call (no tools needed)
**Tool Integration:**
* **Web Search (SearxNG):** Automatically triggered for search queries
* **Image Generation (DALL·E):** Activated for image requests
* **Vision Processing:** Handles uploaded images with vision-enabled models
**Model Support:**
* Web search supported for all models except: GPT-4o latest, DeepSeek, Qwen
* Image generation available for OpenAI models
* Vision processing for compatible models
### 2. Document Chat Flow
**File Upload Process:**
* Event streaming for optimized uploads to S3
* Parallel processing (2 chunks at a time):
* Chunk 1: S3 upload
* Chunk 2: Vector embedding (Pinecone)
* Significantly faster than sequential processing
**Query Process:**
* User asks document-related question
* Relevant vectors fetched from Pinecone
* Vectors sent to LLM as context
* Response generated and streamed
* **LLM Calls:** 1 call with document context
### 3. Agent Chat Flow
**Process:**
* User selects agent from interface
* Agent data fetched from database
* Agent's system prompt included with user query
* LLM generates response using agent context
* **LLM Calls:** 1 call with agent prompt
### 4. Agent + Document Flow
**Combined Context:**
* Agent data retrieved from database
* Relevant document vectors fetched from Pinecone
* Both contexts merged for LLM
* Response incorporates both agent expertise and document knowledge
* **LLM Calls:** 1 call with combined context
## Intelligent Backend Routing
### LangGraph Decision Making
The backend automatically determines:
* **Operation Type:** Chat, search, document, agent, or combination
* **Tool Requirements:** Web search, image generation, vision processing
* **Model Capabilities:** Ensures model supports requested features
* **Context Assembly:** Combines appropriate data sources
### Tool Activation Logic
```
Query Analysis
↓
Contains search intent? → Activate SearxNG
Contains image request? → Activate DALL·E
Has uploaded image? → Use vision model
Has document context? → Fetch Pinecone vectors
Has agent selected? → Load agent prompt
↓
Assemble final context → LLM call → Stream response
```
## Web Search Independence
### SearxNG Integration
* Self-hosted SearxNG metasearch engine
* Works with all supported models (except GPT-4o latest, DeepSeek, Qwen)
* Complete control over search configuration
* Privacy-focused with no external dependencies
## Frontend
* Single socket event for all operations
* Backend handles all decision-making
* Simplified frontend code
* Unified error handling
## Response Streaming
### Real-time Delivery
```
LLM Generation
↓
Token-by-token streaming
↓
Socket.IO transmission
↓
Frontend progressive rendering
↓
Immediate user feedback
```
## Error Handling
### Unified Error Management
| Error Type | Handling |
| -------------------- | ---------------------------------------------- |
| Model Unavailable | Automatic fallback to alternative model |
| Token Limit Exceeded | Context truncation with user notification |
| Tool Failure | Graceful degradation, continue without tool |
| Network Issues | Retry logic with exponential backoff |
| Invalid Input | Validation at entry point with clear messaging |
## Monitoring & Logging
### Operation Tracking
* Socket event logging
* LangGraph decision logging
* LLM call metrics
* Response time tracking
* Error rate monitoring
* Token usage analytics
### Performance Metrics
* Average response time per operation type
* Tool activation frequency
* Model selection distribution
* Error rates by category
* User satisfaction indicators