Combine doccombined_docs = [doc.page_content for doc in docs]text = " ".join(combined_docs)# Split themtext_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)splits = text_splitter.split_text(text)# Build an indexembeddings = OpenAIEmbeddings()vectordb = FAISS.from_texts(splits, embeddings)# Build a QA chainqa_chain = RetrievalQA.from_chain_type(    llm=ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0),    chain_type="stuff",    retriever=vectordb.as_retriever(),)# Ask a question!query = "Why do we need to zero out the gradient before backprop at each step?"qa_chain.run(query)    "We need to zero out the gradient before backprop at each step because the backward pass accumulates gradients in the grad attribute of each parameter. If we don't reset the grad to zero before each backward pass, the gradients will accumulate and add up, leading to incorrect updates and slower convergence. By resetting the grad to zero before each backward pass, we ensure that the gradients are calculated correctly and that the optimization process works as intended."query = "What is the difference between an encoder and decoder?"qa_chain.run(query)    'In the context of transformers, an encoder is a component that reads in a sequence of input tokens and generates a sequence of hidden representations. On the other hand, a decoder is a component that takes in a sequence of hidden representations and generates a sequence of output tokens. The main difference between the two is that the encoder is used to encode the input sequence into a fixed-length representation, while the decoder is used to decode the fixed-length representation into an output sequence. In machine translation, for example, the encoder reads in the source language sentence and generates a fixed-length representation, which is then used by the decoder to generate the target language sentence.'query = "For any token, what are x, k, v, and q?"qa_chain.run(query)    'For any token, x is the
Building chat or QA applications on YouTube videos is a topic of high interest.
Building chat or QA applications on YouTube videos is a topic of high interest. ->: Combine doccombined_docs = [doc.page_content for doc in docs]text = " ".join(combined_docs)# Split themtext_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)splits = text_splitter.split_text(text)# Build an indexembeddings = OpenAIEmbeddings()vectordb = FAISS.from_texts(splits, embeddings)# Build a QA chainqa_chain = RetrievalQA.from_chain_type(    llm=ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0),    chain_type="stuff",    retriever=vectordb.as_retriever(),)# Ask a question!query = "Why do we need to zero out the gradient before backprop at each step?"qa_chain.run(query)    "We need to zero out the gradient before backprop at each step because the backward pass accumulates gradients in the grad attribute of each parameter. If we don't reset the grad to zero before each backward pass, the gradients will accumulate and add up, leading to incorrect updates and slower convergence. By resetting the grad to zero before each backward pass, we ensure that the gradients are calculated correctly and that the optimization process works as intended."query = "What is the difference between an encoder and decoder?"qa_chain.run(query)    'In the context of transformers, an encoder is a component that reads in a sequence of input tokens and generates a sequence of hidden representations. On the other hand, a decoder is a component that takes in a sequence of hidden representations and generates a sequence of output tokens. The main difference between the two is that the encoder is used to encode the input sequence into a fixed-length representation, while the decoder is used to decode the fixed-length representation into an output sequence. In machine translation, for example, the encoder reads in the source language sentence and generates a fixed-length representation, which is then used by the decoder to generate the target language sentence.'query = "For any token, what are x, k, v, and q?"qa_chain.run(query)    'For any token, x is the