LlamaIndex is an impressive data framework designed to support the development of applications utilizing LLMs (Large Language Models). It offers a wide range of essential tools that simplify tasks such as data ingestion, organization, retrieval, and integration with different application frameworks. The array of capabilities provided by LlamaIndex is extensive and holds immense value for developers seeking to leverage LLMs in their applications.

LlamaIndex has tools that help you connect and bring in data from different sources like APIs, PDFs, documents, and SQL databases. It also has ways to organize and structure your data, making it compatible with LLMs (Large Language Models). With LlamaIndex, you can use a smart interface to search and retrieve your data. Just give a prompt to an LLM, and LlamaIndex will give you related information and improved results with more knowledge. Additionally, LlamaIndex is easy to integrate with external application frameworks such as LangChain, Flask, Docker, ChatGPT, and others, so you can work smoothly with your favorite tools and technologies.

In this blog, we will learn about using LlamaIndex for document-based question answering. Let’s understand the step-by-step process of creating a question-answering system with LlamaIndex.

Load Document

The first step is to load the document for performing question-answering using LlamaIndex. To do this, we can use the “SimpleDirectoryReader” function provided by LlamaIndex. We should gather all the document files or a single document on which we want to perform question answering and place them in a single folder. Then, we need to pass the path of that folder to the “SimpleDirectoryReader” function. It will read and gather all the data from the documents.

Divide the document into chunks

In this step, we will divide the data into chunks to overcome the token limit imposed by LLM models. This step is crucial for effectively managing the data.

To accomplish this, we can utilize the “NodeParser” class provided by LlamaIndex. By passing the previously read Document into the “NodeParser,” the method will divide the document into chunks of the desired length.

Index construction

Now that we have created chunks of the document, we can proceed to create an index using LlamaIndex. LlamaIndex offers a variety of indexes suitable for different tasks. For more detailed information about the available indexes, you can refer to the following link:

https://gpt-index.readthedocs.io/en/latest/core_modules/data_modules/index/root.html

To generate an index of the data, LlamaIndex utilizes the LLM model to generate vectors for the database. These vectors are then stored as the index on the disk, enabling their later use. The default embedding model used for this process is “text-embedding-ada-002”. However, you also have the option to use a custom model for index generation. For further guidance on using custom embeddings, you can refer to this link.

In our case, we will utilize the Simple Vector Store index to convert the data chunks into an index. To achieve this, we pass the chunks of data into the method of the Vector Store Index. This method will call the LLM model to create embeddings for the chunks and generate the index.

Query

Now, we can proceed to query the document index. To do this, we first need to initialize the query engine. Once the query engine is initialized, we can use its “query” method to pass our question as input.

The query process involves several steps. First, the query engine creates a vector representation of the input question that we provided. Then, it matches this vector with the vectors of the indexed data chunks stored in the index, identifying the most relevant chunks based on our question. Next, the selected chunk along with our question is passed to the LLM model for answer generation. 

Additionally, we can customize our query engine according to our specific needs. By default, the query engine returns the two most relevant chunks. However, we can modify this value to adjust the number of chunks returned. Moreover, we can also change the query mode used by the engine, providing further customization options.

To learn more about customizing the query engine, you can refer to this link.

Furthermore, we have the option to customize the LLM model according to our specific requirements. By default, LlamaIndex uses the “text-davinci-003” LLM model for response generation. However, we can also utilize other models from HuggingFace. Additionally, we can modify the parameter values of the LLM model, such as top_p, temperature, and max_tokens, to influence the output.

For more information on customizing the LLM model, you need to refer to the below link:

https://gpt-index.readthedocs.io/en/latest/core_modules/model_modules/llms/usage_custom.html

Kindly refer to the provided this link for the demonstration that you can evaluate.

Categories: ChatGPT Natural Language Processing NLP

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