PickYourLLM 0.3

PickYourLLM Framework

- This framework helps you automatically select the most suitable Large Language Model (LLM) for a given business or technical use case.

- It analyzes use case requirements (e.g., cost, latency, reasoning quality, context window, provider constraints), matches them against available LLMs, and ranks the best candidates based on weighted scoring.

Features

• Use Case–Driven Selection: Takes a natural-language description of a use case and extracts structured constraints and priorities.
• Constraint Extraction: Uses advanced LLM models to normalize requirements into a standardized schema (provider, latency, cost, openness, tool calling, languages, etc.).
• Model Matching: Filters candidate LLMs based on hard constraints such as provider restrictions, deployment type, language support, context window, and cost thresholds.
• Weighted Recommendation Engine: Scores models using weighted dimensions such as cost, latency, reasoning, quality, throughput, tool-calling capability, and openness.
• Transparent Ranking: Produces ranked recommendations with clear rationales explaining why each model was selected.

How It Works

The pipeline runs in sequential steps:

• Use Case Selection
  Choose from predefined scenarios (customer assistant, travel agent assistant, multilingual chatbot, internal copilot, etc.) or provide your own description.

• Requirement Extraction (LLM Agent)
  the use case is parsed into structured metadata, including:
  Provider constraints
  Deployment preferences
  Latency and cost requirements
  Language support
  Reasoning / quality expectations
  Tool-calling or multimodal needs
  Priority weights across decision criteria

• Model Filtering
  Candidate LLMs from the model catalog are filtered according to the extracted hard constraints.

• Scoring & Ranking
  The remaining models are scored using a weighted recommendation engine across the most relevant dimensions for the use case.

• Export
  Ranked recommendations are exported to CSV, along with the extracted use case metadata in JSON format for inspection.

Usage

To use the tool, follow these steps:

pip install PickYourLLM

PickYourLLM