Retrieval-augmented generation (RAG) has revolutionized the way we interact with data, offering unparalleled performance in similarity searches. It excels at retrieving relevant information based on simple queries. However, RAG often falls short when handling more complex tasks, such as time-based queries or intricate relational database queries. This is because RAG is primarily designed for augmented text generation with relevant information from external sources, rather than performing exact, condition-based retrievals. These limitations restrict its application in scenarios requiring precise and conditional data retrieval.
Our advanced RAG model, based on a SQL vector database, will efficiently manage various query types. It not only handles simple similarity searches but also excels in time-based queries and complex relational queries.
Let’s discuss how we overcome these RAG limitations by creating an AI assistant using MyScale and LangChain, enhancing both the accuracy and efficiency of the data retrieval process. We will scrape the latest stories from Hacker News while guiding you through the process to demonstrate how your RAG application can be enhanced with advanced SQL vector queries.
Tools and Technologies
We’ll use several tools, including MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop this useful application.
- MyScaleDB: MyScale is a SQL vector database that stores and processes both structured and unstructured data efficiently.
- OpenAI: We’ll use OpenAI’s chat model for generating text-to-SQL queries.
- LangChain: LangChain will help build the workflow and seamlessly integrate with MyScale and OpenAI.
- Hugging Face: We’ll use Hugging Face’s embedding model to obtain text embeddings, which will be stored in MyScale for further analysis.
- HackerNews API: This API will fetch real-time data from HackerNews for processing and analysis.
Preparation
Setting Up the Environment
Before we start writing the code, we must ensure all the necessary libraries and dependencies are installed. You can install these using pip:
This pip command should install all the dependencies required in this project.
Import Libraries and Define Helper Functions
First, we’ll import the necessary libraries and define the helper functions that will be used to fetch and process data from Hacker News.
These functions fetch story IDs, get details of specific items, fetch comments recursively and convert comments into a single string.
Fetch and Process Stories
Next, we fetch the latest and top stories from Hacker News and process them to extract relevant data.
We fetch the latest and top stories from Hacker News using the above-defined helper functions. We process the fetched stories to extract relevant information like title, URL, score, time, writer and comments. We also convert the list of comments into a single string.
Initialize the Hugging Face Model for Embedding
We will now generate embeddings for the story titles and comments using a pretrained model. This step is crucial for creating a RAG system.
We’ll load a pretrained model for generating embeddings using the Hugging Face transformers library and generate embeddings for the story titles and comments.
Handling Long Comments
To handle long comments that exceed the model’s maximum token length, we’ll split them into manageable parts.
This function splits long comments into parts that fit within the model’s maximum token length.
Process Stories for Embeddings
Finally, we’ll process each story to generate embeddings for titles and comments and create a final Pandas DataFrame.
In this step, we process each story to generate embeddings for titles and comments, handle long comments if necessary and create a final DataFrame with all the processed data.
Connecting to MyScaleDB And Creating the Table
MyScaleDB is an advanced SQL vector database that enhances RAG models by efficiently handling complex queries and similarity searches such as full-text search and filtered vector search.
We will connect to MyScaleDB using clickhouse-connect and create a table to store the scraped stories.
This code imports the clickhouse-connect library and establishes a connection to MyScaleDB using the provided credentials. It drops the existing table default.posts if it exists, and creates a new table with the specified schema.
Note: MyScaleDB provides a free pod for vector storage of 5 million vectors. So, you can start using MyScaleDB in your RAG application without any initial payments.
Inserting Data and Creating a Vector Index
Now, we insert the processed data into the MyScaleDB table and create an index to enable efficient retrieval of data.
This code inserts the data into the default.posts table in batches to manage large amounts of data efficiently. The vector index is created on the Title_Embedding column.
Setting Up the Prompt Template for Query Generation
We’ll set up a prompt template to convert natural language queries into MyScaleDB SQL queries.
This code sets up a prompt template that guides the LLM to generate correct MyScaleDB queries based on the input questions.
Setting Query Parameters
We’ll set up the parameters for the query generation.
This code sets the number of top results to retrieve (top_k), defines the table information (table_info) and sets an empty input string (input) for the question.
Setting Up the Model
In this step, we will set up the OpenAI model for converting user inputs into SQL queries.
Convert Text to SQL
This method first generates a final prompt based on user input and table information, then uses the OpenAI model to convert the text to a SQL vector query.
After this step, we will get a query like this:
But MyScaleDB DISTANCE expects DISTANCE(column, array) . So, we need to convert the Embeddings(\'AI domain\') part to vector embeddings.
Processing and Replacing Embeddings in a Query String
This method will be used to replace Embeddings(“Extracted keywords”) with an array of float32.
This method takes the query as input and returns the updated query if there is any Embeddings method present in the query string.
Executing a Query
Finally, we’ll execute a query to retrieve the relevant stories from the vector database.
Furthermore, you can take the query returned by the model, extract the specified columns and use them to fetch columns, as shown above. These results can then be passed back to a chat model, creating a complete AI chat assistant. This way, the assistant can dynamically respond to user queries with relevant data extracted directly from the results, ensuring a seamless and interactive experience.
Conclusion
Simple RAG has limited usage due to its focus on straightforward similarity searches. However, when combined with advanced tools like MyScaleDB, LangChain, etc., the RAG applications can not only meet but exceed the demands of large-scale big data management. They can handle a broader range of queries, including time-based and complex relational queries, significantly improving the performance and efficiency of your current systems.
If you have any suggestions, please reach out to us through X/Twitter or Discord.
The post Enhance Your RAG Application With Advanced SQL Vector Queries appeared first on The New Stack.
Overcome RAG limitations by creating an AI assistant using MyScale and LangChain to enhance accuracy and efficiency of the data retrieval process.