How Chain-of-Verification (CoVe) works and how to implement it using Python 🐍 + Langchain 🔗 + OpenAI 🦾 + Search Tool 🔍

📄 Article: I highly recommend reading this article before diving into the code.

1. Clone the Repository
2. Install Dependencies:

   python3 -m pip install -r requirements.txt

3. Set Up OpenAI API Key:

   export OPENAI_API_KEY='sk-...'

4. Run the Program:

   cd src/
   python3 main.py --question "Who are some politicians born in Boston?"

python3 main.py --question "Who are some politicians born in Boston?" --llm-name "gpt-3.5-turbo-0613" --temperature 0.1 --max-tokens 500 --show-intermediate-steps

• --question: This is the original query/question asked by the user
• --llm-name: The OpenAI model name the user wants to use
• --temperature: You know it 😉
• --max-tokens: Tou know it as well 😉
• --show-intermediate-steps: Activating this will alow printing of the intermediate results such as baseline response, verification questions and answers.

This implementation provides a comprehensive guide for you to modify according to your need and use case. Although below are some of the ideas you can employ to make it more robust and effective.

1. Prompt Engineering: One of the major ways to improve performances of any LLM powered applications is through prompt engineering and prompt optimizations. You can check all the prompts used in the prompts.py file. Try your own prompt engineering and experiment in your use case.
2. External Tools: As the final output highly depends on the answers of the verification questions, based on different use cases you can try out different tools. For factual questions & answering you can use advanced search tools like google search or serp API etc. For custom use cases you can always use RAG methods or other retrieval techniques for answering the verification questions.
3. More Chains: I have implemented three chains according to the three question types (Wiki Data, Mutli-Span QA & Long-Form QA) the authors have used for their research. Depending on your use case you can create other chains which can handle other types of QA methods to increase the variabilty.
4. Human In Loop (HIL): HIL is one of the important steps in many LLM powered applications. In your specific applications, the whole pipeline can be designed to incorporate HIL either for generating proper verification questions or answering verification questions to further improve the overall CoVe pipeline.

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Happy coding!