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.

πŸ” Lama index is an alternative to Link Chain and allows for building applications based on large language models.

πŸ’‘ Lama index can be used to implement a chat with your document system in just four lines of code.

πŸ“š 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.

πŸ” The video demonstrates how to use Llama-Index and OpenAI to implement a document question-and-answer system.

πŸ”‘ The process can be divided into four steps, and the code implementation only requires four lines of code.

πŸ“š 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.

πŸ“‚ Create a folder called 'data' and load the documents

πŸ—‚οΈ 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.

πŸ’‘ The author of the video worked on writing and programming outside of school before college.

βš™οΈ The video shows how to customize different parts of the diagram and the vector store.

πŸ“š 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.

πŸ“š The video discusses the Vector Store, which contains embeddings computed for each chunk of text and allows for retrieval of chunks based on embeddings.

πŸ” 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.

βš™οΈ 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.

πŸ“ You can import and use the Palm LLM from Llama-Index to change the default value.

πŸ”’ 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.

πŸ—’οΈ 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.

πŸ” Learn how to build a document Q&A system using Llama-Index.

πŸ’‘ Set various parameters like model name, tokens, GPUs, and stopping IDs.

⚑ Discover the powerful features of Llama-Index, including fine-tuning embedding models.

Summary of a video "Talk to Your Documents, Powered by Llama-Index" by Prompt Engineering on YouTube.

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