From the course: Hands-On AI: Building LLM-Powered Apps

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Solution: Putting it all together

Solution: Putting it all together - Python Tutorial

From the course: Hands-On AI: Building LLM-Powered Apps

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Solution: Putting it all together

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- [Instructor] Welcome back to the solution portion of our lab. Let's navigate to app/app.py where we need to implement our chain. Because we want to retrieve with sources, so we will use the RetrievalQAWithSourcesChain. We will initialize it from chain_type, and we will pass in a large language model. And the large language model is the model we define here. We will want to set the temperature to zero, specifically because temperature ranges between zero to two, where zero means the model is not going to be very imaginative, and when temperature is two, it means model can produce more creative answers. Since we are talking to a PDF, we do not need the model to be too creative. Then, we will set the chain type to "stuff". This is the default type for chain and stuff means we will send all of the retrieved documents into the context. And for retriever we will set it to the search engine we have built previously, and we use it as retriever, and we will set the max_token_limit to 4097…

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