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LlamaIndex is an impressive data framework designed to support application development using LLMs (Large Language Models). It offers a wide range of essential tools that simplify tasks such as data capture, organization, retrieval and integration with various application frameworks. The range of capabilities provided by LlamaIndex is extensive and of great value to developers looking to use LLMs in their applications.
LlamaIndex has tools to help you connect and fetch data from different sources like API, PDF, 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 query to LLM and LlamaIndex will provide you with relevant information and improved results with more knowledge. In addition, LlamaIndex is easily integrated with external application frameworks such as LangChain, Flask, Docker, ChatGPT and more, so you can work seamlessly with your favorite tools and technologies.
In this blog, we’ll explore using LlamaIndex to answer document-based queries. Let’s learn the step-by-step process of creating a Q&A system with LlamaIndex.
Loading the document
The first step is to load the document to perform the Q&A using LlamaIndex. For this we can use the “SimpleDirectoryReader” function provided by LlamaIndex. We need to gather all the document files or one document that we want to respond to and put them in one folder. Next, we need to pass the path of this folder to the “SimpleDirectoryReader” function. It will read and collect all the data from the documents.
Split the document into pieces
In this step, we partition the data to overcome the sign limit imposed by the LLM models. This step is crucial for effective data management.
To achieve this, we can use the “NodeParser” class provided by LlamaIndex. By passing the previously read document to the “NodeParser”, the method will split the document into chunks of the desired length.
Index construction
Now that we have created the document fragments, we can proceed to create the index using LlamaIndex. LlamaIndex offers a variety of indexes suitable for different tasks. For more detailed information about the available indexes, you can visit the following link:
https://gpt-index.readthedocs.io/en/latest/core_modules/data_modules/index/root.html
To create a data index, LlamaIndex uses the LLM model to create vectors for the database. These vectors are then stored as an index on disk, allowing them to be used later. The default embedding model used for this process is “text-embedding-ada-002”. However, you also have the option of using a custom model to generate the index. For more guidance on using custom embeds, you can refer to this link.
In our case, we’ll use a simple vector store index to convert chunks of data into an index. To achieve this, we pass chunks of data to the Vector Store Index method. This method will call the LLM model to create embeddings for parts and create an index.
question
Now we can proceed with the document index query. To do this, we first need to initialize the query engine. After the query engine is initialized, we can use its “query” method to enter our query.
The query process involves several steps. First, the query engine creates a query vector representation of the input we provide. It then matches this vector with the chunk vectors of the indexed data stored in the index, determining the most relevant chunks based on our query. Next, the selected part along with our question is passed to the LLM model to generate the answers.
In addition, we can customize our query system according to our specific needs. By default, the query engine returns the two most relevant parts. However, we can change this value to adjust the number of parts returned. Additionally, we can also change the query mode used by the engine, providing further customization options.
For more information on customizing the query engine, you can refer to this link.
In addition, we have the ability to tailor the LLM model to our specific requirements. By default, LlamaIndex uses the “text-davinci-003” LLM model to generate responses. However, we can also use other HuggingFace models. Additionally, we can change the values of LLM model parameters such as top_p, temperature, and max_tokens to affect the output.
For more information on customizing the LLM model, you should refer to the link below:
https://gpt-index.readthedocs.io/en/latest/core_modules/model_modules/llms/usage_custom.html
Please refer to the given link for a demo that you can evaluate.
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