Interact with various Large Language Models to generate text-based responses for a wide range of tasks.

LLM Query Block

This block allows you to interact with various Large Language Models (LLMs) to generate text-based responses for a wide range of tasks.

Overview

The LLM Query block provides a unified interface for interacting with different Large Language Models. It supports various LLM providers and can handle text generation, completion, summarization, translation, and other natural language processing tasks.

Configuration Options

Model Selection

Choose the LLM model to use:

OpenAI Models: GPT-3.5, GPT-4, and other OpenAI models
Anthropic Models: Claude models for various tasks
Open Source Models: Local and cloud-based open source models
Custom Models: Use custom trained or fine-tuned models
Model Comparison: Compare responses from multiple models

Query Types

Configure the type of LLM interaction:

Text Generation: Generate new text based on prompts
Text Completion: Complete partial text or sentences
Text Summarization: Summarize long texts
Text Translation: Translate text between languages
Question Answering: Answer questions based on context
Code Generation: Generate code based on specifications

Parameters

Temperature: Control randomness in responses (0.0 to 1.0)
Max Tokens: Maximum number of tokens in response
Top P: Nucleus sampling parameter
Frequency Penalty: Reduce repetition in responses
Presence Penalty: Encourage new topics in responses

How It Works

The LLM Query block:

Receives Input: Gets text prompt and parameters from input message
Sends Query: Sends query to selected LLM model
Processes Response: Receives and processes LLM response
Returns Results: Sends generated text with metadata

LLM Query Flow

Text Prompt → Model Selection → LLM Processing → Generated Response

Use Cases

Content Generation

Generate various types of content:

content request → LLM Query → generated content → content management

Text Summarization

Summarize long documents or articles:

long document → LLM Query (summarize) → summary → document processing

Language Translation

Translate text between different languages:

source text → LLM Query (translate) → translated text → multilingual system

Code Generation

Generate code based on specifications:

code specification → LLM Query (code generation) → generated code → development

Common Patterns

Basic Text Generation

// Configuration
Model: GPT-4
Query Type: Text Generation
Temperature: 0.7
Max Tokens: 500
Output Format: Plain Text

// Input: "Write a short story about a robot"
// Output: {
//   text: "Generated story content...",
//   model: "gpt-4",
//   tokens_used: 150,
//   finish_reason: "stop"
// }

Text Summarization

// Configuration
Model: Claude
Query Type: Text Summarization
Temperature: 0.3
Max Tokens: 200
Output Format: Structured

// Input: Long document text
// Output: {
//   summary: "Concise summary of the document...",
//   key_points: ["point1", "point2", "point3"],
//   word_count: 150
// }

Code Generation

// Configuration
Model: Codex
Query Type: Code Generation
Temperature: 0.2
Max Tokens: 1000
Language: Python

// Input: "Create a function to sort a list"
// Output: {
//   code: "def sort_list(lst):\n    return sorted(lst)",
//   language: "python",
//   explanation: "Function that sorts a list using built-in sorted()"
// }

Advanced Features

Multi-Model Comparison

Compare responses from multiple models:

Model Selection: Choose multiple models for comparison
Response Analysis: Analyze differences between model responses
Quality Metrics: Evaluate response quality and relevance
Best Response Selection: Automatically select the best response

Context Management

Handle conversation context and history:

Conversation Memory: Maintain conversation history
Context Window: Manage context window size
Context Optimization: Optimize context for better responses
Context Persistence: Persist context across sessions

Custom Prompts

Create and manage custom prompt templates:

Prompt Templates: Define reusable prompt templates
Variable Substitution: Use variables in prompt templates
Prompt Optimization: Optimize prompts for better results
Prompt Versioning: Track and manage prompt versions

Configuration Examples

Content Creation System

// Configuration
Model: GPT-4
Query Type: Text Generation
Temperature: 0.8
Max Tokens: 1000
Output Format: Markdown

// Use case: Generate blog posts and articles

Customer Support

// Configuration
Model: Claude
Query Type: Question Answering
Temperature: 0.3
Max Tokens: 300
Context: Customer support knowledge base

// Use case: Automated customer support responses

Code Assistant

// Configuration
Model: Codex
Query Type: Code Generation
Temperature: 0.2
Max Tokens: 800
Language: Multiple languages

// Use case: Code generation and assistance

Tips

Choose Appropriate Models: Select models that match your specific use case
Optimize Prompts: Craft clear and specific prompts for better results
Adjust Parameters: Fine-tune temperature and other parameters for desired output
Handle Context: Manage conversation context for better responses
Monitor Usage: Track token usage and costs
Validate Responses: Always validate LLM responses for accuracy and relevance

Common Issues

Poor Response Quality

Issue: LLM responses are not relevant or accurate
Solution: Improve prompt quality and adjust model parameters

High Token Usage

Issue: Excessive token usage and costs
Solution: Optimize prompts and adjust max token limits

Slow Response Times

Issue: LLM queries taking too long
Solution: Use faster models or optimize query parameters

Context Limitations

Issue: Responses not using available context
Solution: Optimize context management and prompt structure

Performance Considerations

Model Selection

Speed vs Quality: Balance between response speed and quality
Cost Optimization: Consider token costs and usage limits
Resource Requirements: Consider computational requirements
Availability: Ensure model availability and reliability

Optimization Strategies

Prompt Engineering: Optimize prompts for better results
Parameter Tuning: Fine-tune model parameters
Caching: Cache responses for repeated queries
Batch Processing: Process multiple queries together when possible

LLM Judge - For evaluating LLM response quality
Hallucination Detector - For detecting hallucinations in responses
Text Processor - For processing LLM responses
debug - For monitoring LLM query results

LLM Query

On this page