Implementing Chat with Document System using Llama-Index
Learn how to implement chat with your document system using Llama-Index and improve document Q&A performance.
00:00:00 Learn how to implement chat with your document system using Llama-Index. Connect different data sources and improve document Q&A performance with fine-tuned embedding models.
🔍 Lama index is an alternative to Link Chain and allows for building applications based on large language models.
💡 Lama index can be used to implement a chat with your document system in just four lines of code.
📚 The process involves loading and dividing documents, computing embeddings, creating a semantic index, and performing a semantic search.
00:02:33 Learn how to implement document Q&A using Llama-Index. Install required packages, import dependencies, and load documents for chatbot functionality.
🔍 The video demonstrates how to use Llama-Index and OpenAI to implement a document question-and-answer system.
🔑 The process can be divided into four steps, and the code implementation only requires four lines of code.
📚 The example uses a specific document titled 'What I worked on' by Algram when he was working with Y Combinator.
00:05:07 Create a vector store index from documents, customize options, create a query engine, compute embeddings, perform semantic search, and generate an answer.
📂 Create a folder called 'data' and load the documents
🗂️ Divide documents into chunks, compute embeddings, and store in a vector store
❓ Create a query engine to get response based on user questions
00:07:41 The video demonstrates how to use Llama-Index to create a document Q&A system using code. It also explains how to customize the system and persist the index for future use.
💡 The author of the video worked on writing and programming outside of school before college.
⚙️ The video shows how to customize different parts of the diagram and the vector store.
📚 The video explains how to persist the index on disk and load it for future use.
00:10:16 Learn how to interact with different stores in Llama-Index and customize the default values using service context.
📚 The video discusses the Vector Store, which contains embeddings computed for each chunk of text and allows for retrieval of chunks based on embeddings.
🔍 The Index Store determines which embeddings belong to each chunk, and during the retrieval process, the Llm has access to both the embeddings and the retrieved chunks.
⚙️ Customizations can be made to the Llm, such as changing the default model to GPT 3.5 Turbo, by using the service context and creating a new Llm based on the desired model.
00:12:49 Learn how to utilize different parameters to customize chat bot models, including changing the default value, setting chunk size, and using open source models.
📝 You can import and use the Palm LLM from Llama-Index to change the default value.
🔢 You can modify the chunk size and chunk overlap parameters to optimize the performance of chat Bots for your documents.
🌍 You can set the global Service context to apply default values throughout your code.
🗒️ You can use open-source LLMs from Hugging Face by importing the LLM class and setting parameters like context window and temperature.
00:15:23 Learn how to build a document Q&A system using Llama-Index. Discover the powerful features of Llama-Index and explore advanced tutorials in this video series.
🔍 Learn how to build a document Q&A system using Llama-Index.
💡 Set various parameters like model name, tokens, GPUs, and stopping IDs.
⚡ Discover the powerful features of Llama-Index, including fine-tuning embedding models.
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