打造聊天GPT RAG模式:使用穷开发者的向量库
Table of Contents
- Introduction
- The Concept of Large Language Models (LLMs)
- Retrieval Augmented Generation (RAG)
- The Importance of Granular Chunks of Information
- Vector Search vs. Keyword Search
- The Challenges of Setting Up and Operating Vector Databases
- Using SQL Server for Vector Search
- Obtaining Vectors for Vector Search
- Running the Sample Application
- Exploring the Code
- Performing a Vector Search
- Creating the Prompt
- Conclusion
Introduction
In this article, we will explore the concept of large language models (LLMs) and how they can be used for natural language generation. We will focus on a technique called retrieval augmented generation (RAG), which involves retrieving and using Relevant information from external sources to generate coherent responses. Additionally, we will discuss the importance of granular chunks of information, the difference between vector search and keyword search, the challenges of setting up and operating vector databases, and the use of SQL Server for vector search. We will also provide a step-by-step guide on how to obtain vectors for vector search and run a sample application. Finally, we will Delve into the code behind the application, including performing a vector search and creating the prompt. So, let's dive in and explore the fascinating world of LLMs and RAG!
The Concept of Large Language Models (LLMs)
Large language models (LLMs) are a Type of artificial intelligence system that can generate natural language text Based on a given input. These models can complete various kinds of Prompts, such as questions, sentences, paragraphs, or stories. LLMs have significantly advanced the field of natural language processing and have numerous applications in areas like chatbots, text generation, and language translation.
Retrieval Augmented Generation (RAG)
Retrieval augmented generation (RAG) is a technique for building natural language generation systems that can retrieve and use relevant information from external sources. The concept behind RAG is to first retrieve a set of passages related to the search query and then use them to ground the prompt, incorporating the retrieved information into the generated response. This technique allows for more accurate and contextually relevant responses.
The Importance of Granular Chunks of Information
The RAG pattern requires granular chunks of information to provide accurate and Meaningful responses. These chunks should ideally be as specific as possible but also limited in number due to the constraints on the size of prompts in AI completions. By using granular chunks of information, the model can generate well-grounded and coherent responses.
Vector Search vs. Keyword Search
A vector search, as opposed to a usual keyword search, is capable of returning semantically related results to the search query. This type of search is essential for grounding the model and providing contextually relevant responses. However, implementing vector search requires a vector database, which can be complex and costly to set up and operate.
The Challenges of Setting Up and Operating Vector Databases
Vector databases are crucial for enabling vector search but come with their own unique challenges. Setting up and operating vector databases can be complex, requiring expertise and resources. Additionally, managing the scalability and performance of vector databases can be a daunting task. However, there are alternative solutions, such as using SQL Server, that can help address these challenges.
Using SQL Server for Vector Search
Using a SQL Server for vector search can simplify the process and reduce the complexity and costs associated with setting up and operating a vector database. By leveraging SQL Server's capabilities, developers can avoid the intricacies of managing a vector database and focus on implementing efficient vector search functionality.
Obtaining Vectors for Vector Search
To perform a vector search, we need to obtain the vectors necessary for comparison. This involves processing the text by calling an OpenAI model that produces embeddings, which are mathematical representations of words or phrases capturing their meaning in a high-dimensional space. These embeddings are then used to calculate vector similarities between the search query and the text chunks stored in the database.
Running the Sample Application
To run the sample application, You first need to download it from the specified Website. After opening the application in Visual Studio 2022 or higher, you'll need to Create a database in your Microsoft SQL Server called "Blazer poor person vector" and run the provided SQL script to set up the required database objects. Additionally, you'll need an OpenAI API Key and organization ID, which can be obtained by signing up on the OpenAI website. These credentials need to be entered in the app settings.json file. Once the application is set up, you can explore its functionalities.
Exploring the Code
The application's code performs various tasks, including loading data, creating chunks, obtaining embeddings, and performing vector searches. By understanding the code, developers can gain insights into the underlying mechanisms of implementing vector search using a poor developer's vector database. The code also provides methods to clean up the text and manage the search results.
Performing a Vector Search
The application allows users to enter a search query and perform a vector search against the database. The search query is converted into embeddings, and Cosine similarity is used to calculate vector similarities with the documents in the database. The top five results are returned and used as grounding for the model. The search results, as well as the response from the OpenAI completions endpoint, are displayed to the user.
Creating the Prompt
To generate a coherent response, the application constructs a prompt using the retrieved search results as knowledge. The prompt is designed to instruct the language model on how to use the knowledge to generate a response. Examples are provided to guide the model, demonstrating both sample knowledge and the expected answer. The prompt is then passed to the OpenAI completion API to obtain the final response, which is presented to the user.
Conclusion
In conclusion, large language models (LLMs) and retrieval augmented generation (RAG) have revolutionized natural language processing, enabling sophisticated text generation systems. Vector search plays a crucial role in grounding the model and producing contextually relevant responses. Despite the challenges associated with setting up and operating vector databases, using alternative solutions like SQL Server can simplify the process. By understanding the code behind the sample application, developers can gain insights into implementing vector search and creating prompts. With further advancements in LLMs and RAG, the possibilities for natural language generation are limitless. So, embrace the power of LLMs and explore the fascinating world of retrieval augmented generation!