DataHour: LlamaIndex QA System with Private Data and Effective Evaluation

Introduction

Datahour is an online 1-hour web series by Analytics Vidhya, where industry experts share their knowledge and experience in data science and artificial intelligence. In one such session, Ravi Theja, an accomplished Data Scientist at Glance-Inmobi, shared his expertise in building and deploying cutting-edge machine learning models for recommender systems, NLP applications, and Generative AI. With a Master’s degree in Computer Science from IIIT-Bangalore, Ravi has solidified his foundation in data science and artificial intelligence. The session revolves around LlamaIndex and how it can build QA systems with private data and evaluate QA systems. In this blog post, we will discuss the key takeaways from the session and provide a detailed explanation of the Llama Index and its applications.

What is the Llama Index?

The Llama Index is a solution that acts as an interface between external data sources and a query engine. It has three components: a data engine, indexing or data success, and a query interface. The data connectors provided by Llama index allow for easy data ingestion from various sources, including PDFs, audio files, and CRM systems. The index stores and indexes the data for different use cases, and the query interface pulls up the required information to answer a question. The Llama index is helpful for various applications, including sales, marketing, recruitment, legal, and finance.

Challenges of Dealing with Large Amounts of Text Data

The session discusses the challenges of dealing with large amounts of text data and how to extract the right information to answer a given question. Private data is available from various sources, and one way to use it is to fine-tune LLMs by training your data. However, this requires a lot of data preparation effort and lacks transparency. Another way is to use prompts with a context to answer questions, but there is a token limitation.

Llama Index Structure

The Llama index structure involves creating an overview of data through indexing documents. The indexing process involves chunking the text document into different nodes, each with an embedding. A retriever helps retrieve documents for a given query, and a query engine manages retrieval and census. The Llama index has different types of indexes, with the vector store index being the simplest. To generate a response using the sales model, the system divides the document into nodes and creates an embedding for each node to store. Querying involves retrieving the query embedding and the top nodes similar to the query. The sales model utilizes these nodes to generate a response. Llama is free and integrates with the collapse.

Generating a Response Given a Query on Indexes

The speaker discusses generating a response given a query on indexes. The author explains that the default value of the test store indexing is set to one, meaning that using a vector for indexing will only take the first node to generate an answer. However, use the list index if the LLM will iterate over all nodes to generate a response. The author also explains the create and refine framework used to generate responses, where the LLM regenerates the answer based on the previous answer, query, and node information. The speaker mentions that this process is helpful for semantic search and achieve with just a few lines of code.

Querying and Summarizing Documents Using a Specific Response Mode

The speaker discusses how to query and summarize documents using a specific response mode called “3 summarize” provided by the Mindex tool. The process involves importing necessary libraries, loading data from various sources such as web pages, PDFs, and Google Drive, and creating a vector store index from the documents. The text also mentions a simple UI system that can be created using the tool. The response mode allows for querying documents and providing summaries of the article. The speaker also mentions using source notes and similarity support for answering questions.

Indexing CSV Files and How They Can be Retrieved for Queries?

The text discusses indexing CSV files and how they can be retrieved for queries. If a CSV file is indexed, it can be retrieved for a query, but if it is indexed with one row having one data point with different columns, some information may be lost. For CSV files, it is recommended to ingest the data into a WSL database and use a wrapper on top of any SQL database to perform text U SQL. One document can be divided into multiple chunks; each is represented as one node, embedding, and text. The text is split based on different texts, such as cars, computers, and sentences.

Use Different Textures and Data Sources in Creating Indexes and Query Engines

You can utilize different textures and data sources when creating indexes and query engines. By creating indexes from each source and combining them into a composite graph, retrieve the relevant nodes from both indexes when querying, even if the data sources are in different stories. The query engine can also split a query into multiple questions to generate a meaningful answer. The notebook provides an example of how to use these techniques.

Evaluation Framework for a Question & Answer System

The Lamb index system has both service context and storage context. Service context helps define different LLM models or embedding models, while storage context stores notes and chunks of documents. The system reads and indexes documents, creates an object for query transformation and uses a multi-step query engine to answer questions about the author. The system splits complex questions into multiple queries and generates a final answer based on the answers from the intermediate queries. However, evaluating the system’s responses is critical, especially when dealing with large enterprise-level data sources. Creating questions and answers for each document is not feasible, so evaluation becomes crucial.

The evaluation framework discussed in the text aims to simplify the process of generating questions and evaluating answers. The framework has two components: a question generator and a response evaluator. The question generator creates questions from a given document, and the response evaluator checks whether the system’s answers are correct. The response evaluator also checks whether the source node information matches the response text and the query. If all three are in line, the answer is correct. The framework aims to reduce the time and cost associated with manual labeling and evaluation.

Conclusion

In conclusion, the Llama Index is a powerful tool that builds systems with private data and evaluates QA systems. It provides an interface between external data sources and a query engine, making it easy to ingest data from various sources and retrieve the required information to answer a question. The Llama index is helpful for various applications, including sales, marketing, recruitment, legal, and finance. The evaluation framework discussed in the text simplifies the process of generating questions and evaluating answers, reducing the time and cost associated with manual labeling and evaluation.

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