Make sure you understand how data flows through your system. You have your CSV data stored in BigQuery, and you've used langchain and text-bison for generating SQL queries and getting results. You'll need to prepare your existing questions and their corresponding results in a format suitable for chat-bison to understand. This could involve formatting the data in a way that links questions to their context or results.
Incorporate the chat-bison model into your system. This might involve creating a pipeline where incoming questions are processed by chat-bison to understand context and generate appropriate responses. Ensure that your system can efficiently retrieve the relevant context or information from previous interactions as needed by chat-bison. This might involve database queries or an efficient data storage mechanism.
Depending on the complexity of your interactions, you might need to fine-tune the chat-bison model using your dataset to ensure it understands the specific context of your questions and provides relevant responses.
Test your system rigorously to ensure that it maintains context accurately and provides meaningful responses. Iterate on the system as needed based on testing results and user feedback.
Consider the scalability and performance aspects of your system. As the volume of interactions grows, ensure that the system can handle the load efficiently without compromising on response times.
Remember, integrating different models and systems can sometimes be complex, so it's important to break down the task into smaller steps and tackle them one by one. Also, ensure that you have proper error handling and monitoring in place to identify and resolve issues as they arise.
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