This is a lightweight evaluation tool for question-answering using Langchain to:

• Ask the user to input a set of documents of interest

• Use an LLM (GPT-3.5-turbo) to auto-generate question``answer pairs from these docs

• Generate a question-answering chain with a specified set of UI-chosen configurations

• Use the chain to generate a response to each question

• Use an LLM (GPT-3.5-turbo) to score the response relative to the answer

• Explore scoring across various chain configurations

Run as Streamlit app

pip install -r requirements.txt

streamlit run auto-evaluator.py

Inputs

num_eval_questions - Number of question to auto-generate (if the user does not supply an eval set)

split_method - Method for text splitting

chunk_chars - Chunk size for text splitting

overlap - Chunk overlap for text splitting

embeddings - Embedding method for chunks

retriever_type - Chunk retrival method

num_neighbors - Neighbors for retrivial

model - LLM for summarization of retrived chunks

grade_prompt - Promp choice for model self-grading

Blog

https://blog.langchain.dev/auto-eval-of-question-answering-tasks/

UI

Disclaimer

You will need an OpenAI API key with with access to `GPT-4` and an Anthropic API key to take advantage of all of the default dashboard model settings. However, additional models (e.g., from Hugging Face) can be easily added to the app.