MultiRetrievalQAChain: Enhancing Router Chains with Multiple Prompts Selection

Langchain Chains offer a powerful way to manage and optimize conversational AI applications. One of the key components of Langchain Chains is the Router Chain, which helps in managing the flow of user input to appropriate models. In this article, we will explore how to use MultiRetrievalQAChain to select from multiple prompts and improve the performance of your Router Chains.

What is MultiRetrievalQAChain?

MultiRetrievalQAChain is a component of Langchain Chains that allows you to select from multiple prompts while passing the user input to a model. This can be particularly helpful when you want to optimize the performance of your Router Chains by considering various prompt options.

How does MultiRetrievalQAChain work?

When using MultiRetrievalQAChain, it takes a list of prompts and generates a score for each prompt based on the user input. The score is determined using various metrics such as:

1. Cosine similarity between the user input and the prompt
2. The relevance of the prompt to the user input
3. The quality of the prompt itself

After calculating the scores, the MultiRetrievalQAChain selects the prompt with the highest score and passes it to the next chain in the Router Chain.

Implementing MultiRetrievalQAChain

To implement MultiRetrievalQAChain in your Router Chain, follow these steps:

1. Import the required module: Import the MultiRetrievalQAChain module from Langchain Chains:

from langchain_chains import MultiRetrievalQAChain

2. Define the prompts: Create a list of prompts that you want to use with the MultiRetrievalQAChain. For example:

prompts = [
    "What is the capital of {country}?",
    "Tell me the capital city of {country}.",
    "Which city is the capital of {country}?"
]

3. Initialize the MultiRetrievalQAChain: Instantiate the MultiRetrievalQAChain object with the list of prompts:

multi_retrieval_qa_chain = MultiRetrievalQAChain(prompts)

4. Use the MultiRetrievalQAChain in your Router Chain: Add the MultiRetrievalQAChain object to your Router Chain and use it to select the best prompt for the user input:

router_chain.add_chain(multi_retrieval_qa_chain)

Benefits of MultiRetrievalQAChain

Using MultiRetrievalQAChain in your Router Chains offers several advantages:

1. Improved accuracy: By selecting the most appropriate prompt for the user input, the model can generate more accurate and relevant responses.
2. Reduced model complexity: Instead of using multiple models to address different prompts, you can use a single model with MultiRetrievalQAChain to handle various prompts.
3. Adaptability: Easily add, remove, or modify prompts in the MultiRetrievalQAChain to cater to changing requirements or to experiment with different prompt options.

In conclusion, MultiRetrievalQAChain is a powerful tool to enhance your Router Chains in Langchain Chains. By selecting the best prompt from multiple options, it offers improved accuracy and adaptability for your conversational AI applications.