microsoft/promptbase
Promptbase
Promptbase este un framework de prompt engineering conceput pentru proiectarea, testarea și optimizarea prompt-urilor pentru modele de limbaj mari. Oferă un sistem pentru măsurarea acurateței și performanței modelului printr-un toolkit de evaluare care compară output-urile cu seturi de date de referință (ground-truth). Proiectul include, de asemenea, un pipeline de orchestrare pentru automatizarea sarcinilor de machine learning multi-component pe endpoint-uri bazate pe cloud și un utilitar pentru pregătirea seturilor de date de tip retrieval-augmented generation.
Framework-ul se distinge prin optimizarea avansată a calității răspunsului, utilizând generatoare de tip „chain-of-thought” pentru a produce pași de raționament intermediari și preluarea dinamică a exemplelor „few-shot” folosind căutarea semantică bazată pe embedding. Implementează metode de ansamblu pentru a crește acuratețea predictivă, folosind rutarea interogărilor bazată pe complexitate și agregarea prin vot majoritar a mai multor variații de modele.
Sistemul acoperă capabilități mai largi în gestionarea datelor și automatizare, inclusiv formatarea datelor externe în fișiere structurate pentru antrenare și orchestrarea pipeline-urilor de execuție a modelelor prin utilitare de linie de comandă.
Features
- Prompt Engineering Frameworks - Offers a comprehensive system for designing, testing, and optimizing prompts using structured evaluation pipelines.
- Prompt Evaluation Tools - Measures the accuracy and performance of AI model outputs by comparing them against ground truth datasets.
- AI Model Benchmarking - Provides frameworks for running standardized tests to assess the performance and reliability of various prompting strategies.
- Automated Chain-of-Thought - Automatically generates step-by-step reasoning chains for training data by instructing models to think logically.
- Chain-of-Thought Prompting - Produces intermediate logic for training examples by prompting models to explain their own thought processes.
- Few-Shot Optimizers - Selects semantically similar training samples using embedding space clustering to provide dynamic context for model inputs.
- Answer Accuracy Evaluators - Implements automated metrics to score the semantic alignment between model responses and ground-truth reference answers.
- LLM Workflow Orchestrations - Automates the flow of data through cloud environments to orchestrate complex machine learning workflows and model calls.
- Machine Learning Pipelines - Automates the execution of multi-component machine learning tasks across cloud-based AI endpoints.
- Multi-Stage Pipeline Orchestrators - Provides a CLI tool for automating cloud environment setup and program uploads to execute multi-component ML workflows.
- Machine Learning Pipelines - Automates the deployment and execution of datasets through cloud endpoints via structured machine learning workflows.
- LLM Evaluation - Provides a toolkit for measuring model accuracy and performance by comparing outputs against ground-truth datasets.
- Response Quality Optimization - Employs dynamic few-shot selection and chain-of-thought strategies to elicit more accurate answers from foundation models.
- Prompt Strategy Routing - Analyzes query complexity to dynamically select the most effective prompting technique or reasoning path for a given input.
- RAG Dataset Formatters - Fetches external data and formats it into JSONL files for training and retrieval-augmented generation.
- Majority-Vote Ensembles - Increases predictive accuracy by combining outputs from multiple model variations using a majority-vote aggregation method.
- Ensemble Aggregations - Provides strategies for combining predictions from multiple model calls or prompt variations into a single output via majority voting.
- Dynamic Routing Strategies - Implements logic to evaluate request complexity and dynamically weight different prompting strategies within an ensemble.
- Technique Selection Systems - Selects specific prompting techniques based on query complexity to maintain high performance across diverse topics.
- Training Dataset Preparation - Formats external data into structured files and generates synthetic reasoning steps for model training.
- Semantic Example Retrieval - Finds semantically similar training samples in vector space to provide relevant context for few-shot prompting.
- Ensemble Prompt Routing - Aggregates results from multiple prompt variations and routes queries based on complexity to increase predictive accuracy.
