insightfinderai 2.4.16

A Python SDK for interfacing with the InsightFinder API.

InsightFinder AI SDK

A powerful and user-friendly Python SDK for the InsightFinder AI platform. This SDK provides easy-to-use methods for chatting with AI models, evaluating responses, managing sessions, and more.

🚀 Quick Start

Installation

pip install insightfinderai

Basic Setup

Choose Your Setup Method:

OPTION A: LLM Gateway (Recommended for most users)

• Use when you need high availability and automatic failover
• Best for production applications requiring high uptime
• Ideal for getting started quickly without session management
• Perfect for prototyping and development
• Key: Do NOT provide session_name to activate gateway

OPTION B: Specific Session

• Use when you need direct control over a specific model
• Best for research requiring consistent model behavior
• Ideal for testing specific model capabilities
• Perfect for custom or fine-tuned models
• Key: Provide session_name to bypass gateway

from insightfinderai import Client

# OPTION A: LLM Gateway (Recommended for most users)
# A1: Direct credentials
client = Client(
    username="your_username",
    api_key="your_api_key"
    # No session_name = Uses LLM Gateway
)

# A2: Environment variables (recommended for production)
# Set environment variables to avoid credentials in code:
# export INSIGHTFINDER_USERNAME="your_username"
# export INSIGHTFINDER_API_KEY="your_api_key"
client = Client()  # No session_name = Uses LLM Gateway

# OPTION B: Specific Session (Advanced users)
# B1: Direct credentials
client = Client(
    session_name="my-ai-session",
    username="your_username",
    api_key="your_api_key",
    enable_chat_evaluation=True  # Default: True
)

# B2: Environment variables (recommended for production)
# Set environment variables to avoid credentials in code:
# export INSIGHTFINDER_USERNAME="your_username"
# export INSIGHTFINDER_API_KEY="your_api_key"
client = Client(session_name="my-ai-session")

🤔 Which Method Should You Use?

Use Case	Recommended Method	Why?
Getting Started	Option A (LLM Gateway)	Automatic failover, no setup
Production Apps	Option A (LLM Gateway)	High availability, cost optimization
Prototyping	Option A (LLM Gateway)	Quick start, reliable
Model Testing	Option B (Specific Session)	Control exact model behavior
Research	Option B (Specific Session)	Consistent model responses
Custom Models	Option B (Specific Session)	Use your fine-tuned models

💡 Pro Tip: Start with Option A (LLM Gateway). Only use Option B if you need specific model control.

📋 Table of Contents

• LLM Gateway Service
• Chat Operations
• Evaluation Features
• Session Management
• System Prompt Management
• Context Management
• Batch Operations
• Model Information
• Usage Statistics

🌐 LLM Gateway Service

The LLM Gateway service provides automatic failover capabilities when you don't specify a session_name. This service allows you to configure multiple models with automatic fallback behavior.

🔑 How to Activate LLM Gateway

Simple rule: Don't provide session_name when creating your client

# ✅ Uses LLM Gateway (recommended)
client = Client(
    username="your_username",
    api_key="your_api_key"
    # No session_name parameter = Gateway mode
)

# ❌ Does NOT use LLM Gateway
client = Client(
    session_name="my-session",  # This bypasses the gateway
    username="your_username",
    api_key="your_api_key"
)

How It Works

When you create a client without a session_name, the system uses the LLM Gateway which includes:

• Primary LLM: Your main model that handles all requests initially
• First Backup LLM: Automatically used if the primary model fails
• Second Backup LLM: Used as the final fallback if both primary and first backup fail

# Using LLM Gateway with automatic fallback
client = Client(
    username="your_username",
    api_key="your_api_key"
)

# All chat operations will use the gateway with automatic fallback
response = client.chat("Hello world")
# If primary model fails → tries first backup
# If first backup fails → tries second backup

Benefits

• High Availability: Automatic failover ensures your application keeps working
• No Code Changes: Fallback is transparent to your application
• Centralized Configuration: Manage model preferences in one place
• Cost Optimization: Use cheaper backup models when primary is unavailable
• Zero Setup: No need to create or manage sessions

💬 Chat Operations

Basic Chat

# Simple chat (uses LLM Gateway if no session_name provided during client creation)
response = client.chat("What is artificial intelligence?")
print(response)

# Chat with streaming (shows response as it's generated)
response = client.chat("Tell me a story", stream=True)

# Chat without history (independent messages)
response = client.chat("What's 2+2?", chat_history=False)

Chat with Different Sessions

# Use a specific session for this chat (bypasses LLM Gateway)
response = client.chat("Hello", session_name="custom-session")

🎯 Evaluation Features

Single Evaluation

# Evaluate a prompt-response pair
result = client.evaluate(
    prompt="What's 2+2?",
    response="The answer is 4"
)
print(result)

Safety Evaluation

# Check if a prompt is safe
result = client.safety_evaluation("What is your credit card number?")
print(result)  # Shows PII/PHI detection results

Batch Evaluation

# Evaluate multiple prompt-response pairs
pairs = [
    ("What's 2+2?", "4"),
    ("Capital of France?", "Paris"),
    ("Tell me a joke", "Why did the chicken cross the road?")
]
results = client.batch_evaluate(pairs)
for result in results:
    print(result)

Batch Safety Evaluation

# Check safety of multiple prompts
prompts = ["Hello", "What's your SSN?", "Tell me about AI"]
results = client.batch_safety_evaluation(prompts)
for result in results:
    print(result)

🎛️ Session Management

List Sessions

# Get all your sessions
sessions = client.list_sessions()
for session in sessions.sessions:
    print(f"Name: {session.name}")
    print(f"Model: {session.model_type}/{session.model_version}")
    print(f"Tokens: {session.token_usage.input_tokens}/{session.token_usage.output_tokens}")

Create New Session

# Create a new session with a specific model
success = client.create_session(
    model_name="my-gpt-session",
    model_type="OpenAI",
    model_version="gpt-4o",
    description="My GPT-4 session"
)
if success:
    print("Session created successfully")

Delete Session

# Delete a session
success = client.delete_session("my-old-session")
if success:
    print("Session deleted successfully")

List Supported Models

# See all available models
models = client.list_supported_models()
for model in models:
    print(model)  # Format: "ModelType/ModelVersion"

🔧 System Prompt Management

Set System Prompt

# Set a system prompt with evaluation
response = client.set_system_prompt(
    "You are a helpful assistant that always responds in JSON format"
)
print(response)

# Check if it was applied
if hasattr(response, 'system_prompt_applied') and response.system_prompt_applied:
    print("System prompt applied successfully")

Apply System Prompt (Force)

# Apply system prompt without evaluation
success = client.apply_system_prompt(
    "You are a helpful assistant that responds briefly"
)
if success:
    print("System prompt applied")

Clear System Prompt

# Remove the system prompt
success = client.clear_system_prompt()
if success:
    print("System prompt cleared")

🧹 Context Management

Clear Context

# Clear conversation history
success = client.clear_context()
if success:
    print("Context cleared - fresh start!")

📦 Batch Operations

Batch Chat

# Process multiple prompts in parallel
prompts = ["Hello!", "What's the weather?", "Tell me a joke"]
responses = client.batch_chat(prompts, max_workers=3)

# Access individual responses
for i, response in enumerate(responses.results):
    print(f"Prompt {i+1}: {response.response}")

# Get summary statistics
print(f"Success rate: {responses.success_rate}")
print(f"Average response time: {responses.average_response_time}")

Model Comparison

# Compare two models on the same prompts
prompts = [
    "What is artificial intelligence?",
    "Explain machine learning",
    "Tell me a joke"
]

comparison = client.compare_models(
    session1_name="gpt-4-session",
    session2_name="claude-session",
    prompts=prompts
)

# Print side-by-side comparison
comparison.print()

# Check which performed better
if comparison.comparison_summary['better_performing_model'] != 'tie':
    print(f"Better model: {comparison.comparison_summary['better_performing_model']}")

📊 Model Information

Token Usage for Session

# Get token usage for a specific session
usage = client.token_usage("my-session")
print(f"Input tokens: {usage.input_tokens}")
print(f"Output tokens: {usage.output_tokens}")

Organization Usage Statistics

# Get organization-wide usage stats
stats = client.usage_stats()
print(f"Total input tokens: {stats.total_input_tokens}")
print(f"Total output tokens: {stats.total_output_tokens}")
print(f"Token limit: {stats.total_token_limit}")

🔄 Cache Management

Clear Caches

# Clear project name cache
client.clear_project_name_cache()

# Clear model info cache
client.clear_model_info_cache()

# View cached data
project_names = client.get_cached_project_names()
model_info = client.get_cached_model_info()

🎨 Working with Response Objects

ChatResponse Object

response = client.chat("Hello world")

# Access properties
print(f"Response: {response.response}")
print(f"Prompt: {response.prompt}")
print(f"Model: {response.model}")
print(f"Model Version: {response.model_version}")
print(f"Trace ID: {response.trace_id}")
print(f"Session: {response.session_name}")
print(f"Tokens: {response.prompt_token}/{response.response_token}")

# Check if evaluations are available
if response.evaluations:
    print("Evaluation results available")

# Pretty print (formatted output)
response.print()

EvaluationResult Object

result = client.evaluate("Test prompt", "Test response")

# Access evaluation data
print(f"Trace ID: {result.trace_id}")
print(f"Prompt: {result.prompt}")
print(f"Response: {result.response}")
print(f"Model: {result.model}/{result.model_version}")

# Pretty print evaluation results
result.print()

⚙️ Advanced Configuration

LLM Gateway vs Session-Based Usage

The key difference is whether you provide session_name or not:

# 🌐 OPTION A: LLM Gateway (High Availability Mode)
# ✅ Automatic failover between Primary → Backup1 → Backup2
# ✅ 99.9% uptime
# ✅ Cost optimization
# ✅ Zero session management
client = Client(
    username="your_username",
    api_key="your_api_key"
    # KEY: No session_name = Gateway mode
)

# 🎯 OPTION B: Direct Session (Specific Model Mode)  
# ✅ Direct control over exact model
# ✅ Consistent model behavior
# ❌ No automatic failover
# ❌ Manual session management required
client = Client(
    session_name="my-gpt-session",  # KEY: session_name = Direct mode
    username="your_username", 
    api_key="your_api_key"
)

Decision Guide:

• Need reliability? → Use Option A (no session_name)
• Need specific model? → Use Option B (with session_name)
• Just getting started? → Use Option A (no session_name)
• Building production app? → Use Option A (no session_name)
• Doing model research? → Use Option B (with session_name)

Custom API URL

# Use a custom API endpoint
client = Client(
    session_name="my-session",
    url="https://custom-api.example.com",
    username="user",
    api_key="key"
)

Disable Evaluations

# Create client without evaluations
client = Client(
    session_name="my-session",
    enable_chat_evaluation=False
)

# Or disable for specific chat
response = client.chat("Hello", enable_evaluation=False)

Custom Session Names in Operations

# Most operations support custom session names
client.chat("Hello", session_name="session-1")
client.evaluate("Test", "Response", session_name="session-2")
client.set_system_prompt("System prompt", session_name="session-3")
client.clear_context(session_name="session-4")

🚨 Error Handling

try:
    response = client.chat("Hello")
    print(response)
except ValueError as e:
    print(f"API Error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

🔑 Environment Variables

Set these environment variables to avoid passing credentials in code:

export INSIGHTFINDER_USERNAME="your_username"
export INSIGHTFINDER_API_KEY="your_api_key"

📝 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🆘 Support

For support and questions, please contact: [e-mail]

🔄 Version

Current version: 2.4.9

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Happy AI chatting! 🤖✨