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CogniVis AI
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  • FAQ

Providers

A complete guide for choosing and utilizing LLM providers in Cognivis

CogniVis’s Approach to LLM Providers

CogniVis is built to be model-agnostic, giving you the flexibility to select the Language Model (LLM) that best fits your requirements. This ensures that you’re not restricted to a single provider, allowing you to take advantage of different models' strengths for various tasks.

Model Overview

CogniVis supports integration with multiple leading LLM providers, with a particular focus on models from OpenAI and Anthropic. Below is an overview of key models:

OpenAI Models

GPT-3.5-Turbo

  • Strengths: Fast processing, good quality for general tasks
  • Best for: Quick queries, general information retrieval
  • Knowledge cutoff: September 2021

GPT-4

  • Strengths: Delivers high-quality responses with strong reasoning capabilities
  • Best for: In-depth analysis, creative tasks, and code generation
  • Knowledge cutoff: April 2023
  • Note: Includes support for image analysis

Below is the latest information on Anthropic models, including Claude 3.5 Sonnet and a recommended option:

Anthropic Models

Claude-3 Opus

  • Strengths: Outstanding performance in reasoning and analysis tasks
  • Best for: Solving complex problems and providing detailed explanations
  • Note: Delivers high accuracy, though it may have slower response times

Claude-3 Sonnet

  • Strengths: Balanced performance and speed
  • Best for: General tasks that require good quality and moderate speed
  • Note: A strong all-rounder for a variety of use cases

Claude 3.5 Sonnet

  • Strengths: Enhanced capabilities compared to Claude-3 Sonnet
  • Best for: Advanced general-purpose tasks with superior performance
  • Note: Recommended for most scenarios due to its balanced capabilities

Claude-3 Haiku

  • Strengths: Fast responses, optimized for simpler tasks
  • Best for: Quick queries, real-time use cases
  • Note: Prioritizes speed over complexity

For most use cases, Claude 3.5 Sonnet is the recommended choice, as it offers an exceptional balance of advanced features, performance, and speed, making it well-suited for a wide range of applications.

Custom Providers

CogniVis allows you to add custom providers by integrating any model from the LiteLLM providers list. This flexibility lets you utilize specialized or proprietary models that are tailored to meet your organization's specific needs.

Choosing the Right Model

When selecting a model, consider the following factors:

  • Task Complexity: Complex tasks benefit from advanced models such as GPT-4 or Claude-3 Opus.
  • Response Speed: For quicker replies, you might want to try faster models such as GPT-3.5-Turbo or Claude-3 Haiku.
  • Cost Considerations: More advanced models usually come with higher usage costs.
  • Data Privacy: For stricter data handling, open-source models like Llama 2, which can be self-hosted, are a good ch
  • Specific Strengths: Certain models are more effective in specific areas (e.g., coding, creativity, analysis).

It’s recommended to try different models with your typical queries to find the one that performs best for your needs.

Utilizing Model Flexibility

  • Experiment: Test various models on the same task to compare their performance.
  • Monitor Performance: Keep an eye on which models perform best for different types of queries.
  • Stay Updated: Regularly check for updates and new releases of models.
  • Custom Integration: Consider integrating specialized models from the LiteLLM providers list for specific use cases.

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  • CV’s Approach to LLM Providers
  • Model Overview
  • OpenAI Models
    • GPT-3.5-Turbo
    • GPT-4
  • Anthropic Models
    • Claude-3 Opus
    • Claude-3 Sonnet
    • Claude 3.5 Sonnet
    • Claude-3 Haiku
  • Custom Providers
  • Choosing the Right Model
  • Utilizing Model Flexibility
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