Revolutionary Chatbot Powered by Knowledge Graph - NODES 2023
Table of Contents
- Introduction
- The Limitations of Large Language Models (LLMs)
- 2.1 Knowledge Cutoff
- 2.2 Hallucinations
- 2.3 Privacy Concerns
- Overcoming the Limitations with Retrievable Augmented Generation
- 3.1 Supervised Fine-Tuning vs. Retrieval Augmented Generation
- 3.2 Architecture of Retrieval Augmented Generation
- Chat with Your PDF Applications
- 4.1 Embedding and Indexing PDF Text Chunks
- 4.2 Retrieving Relevant Documents with Vector Similarity Search
- 4.3 Generating Answers with LLMs
- Leveraging Knowledge Graphs for Structured Data
- 5.1 Knowledge Graphs as a Representation of Complex Structured Data
- 5.2 The Difference between Vector Similarity Search and Cypher Query Language
- 5.3 Generating Cypher Statements for Information Retrieval
- 5.4 Improving Cypher Generation Accuracy
- Use Cases for Retrieval Augmented Generation
- 6.1 Multihub Question Answering
- 6.2 Real-time Analytics
- The Future of Augmented Generation Applications
The Power of Knowledge Graphs in Retrieval Augmented Generation
In recent years, large language models (LLMs) have gained significant popularity for their ability to generate coherent and contextually relevant text. However, these models have limitations that hinder their accuracy and performance. This article explores the concept of retrieval augmented generation and how knowledge graphs can enhance the capabilities of LLMs for retrieving and generating information.
1. Introduction
Large language models have revolutionized the field of natural language generation. They can generate text that is almost indistinguishable from human-written content. However, LLMs suffer from several limitations, such as a knowledge cutoff, hallucinations, and privacy concerns. To overcome these limitations, researchers and developers have started incorporating retrieval augmented generation techniques, leveraging the power of knowledge graphs.
2. The Limitations of Large Language Models (LLMs)
2.1 Knowledge Cutoff
LLMs have a knowledge cutoff, meaning they are only aware of the events that occurred during their training period. If You ask an LLM about an event that took place after its training data, it won't be able to provide an accurate answer. This limited knowledge makes LLMs less reliable for real-time and future-focused questions.
2.2 Hallucinations
Hallucinations are a common issue with LLMs. These models are trained to provide assertive answers, even when they lack the necessary knowledge. For example, if you ask an LLM to provide a URL or cite a scientific paper, it may generate a non-existent link or reference. Hallucinations can undermine the credibility of the generated content and mislead users.
2.3 Privacy Concerns
LLMs are not aware of private or confidential information during their training. Therefore, when asked sensitive questions, such as those related to a company's proprietary data, LLMs may not possess the necessary information or provide accurate answers. This lack of awareness limits their applicability in industries that deal with sensitive data.
3. Overcoming the Limitations with Retrieval Augmented Generation
To address the limitations of LLMs, retrieval augmented generation techniques have emerged as promising solutions. Rather than relying solely on fine-tuning LLMs, this approach involves presenting relevant Context to the LLMs during the generation process. By providing additional information, LLMs can overcome knowledge cutoff, reduce hallucinations, and enhance the accuracy of generated content.
3.1 Supervised Fine-Tuning vs. Retrieval Augmented Generation
Initially, supervised fine-tuning was considered a popular approach to customize LLMs for domain-specific use cases. However, this method proved to be inefficient for introducing additional information to LLMs. Instead, retrieval augmented generation, which involves feeding relevant context to LLMs during the prompt, has shown better results. This shift in approach emphasizes the importance of contextual information in generating accurate answers.
3.2 Architecture of Retrieval Augmented Generation
The architecture of retrieval augmented generation involves multiple components: the user interface, a smart search model, a knowledge base, and the LLM. When a user asks a question, the smart search model retrieves relevant documents from the knowledge base. These documents are then passed to the LLM along with the original question. The LLM utilizes the provided context to generate an accurate and up-to-date answer.
4. Chat with Your PDF Applications
One practical application of retrieval augmented generation is the "Chat with Your PDF" concept. This application allows users to Interact with the content of PDF documents using natural language queries. By splitting the text into manageable chunks, embedding them in a vector index, and utilizing vector similarity search, users can retrieve precise information from PDFs and generate informative responses using LLMs.
4.1 Embedding and Indexing PDF Text Chunks
Due to the limited token space of LLMs, PDF text chunks must be split and indexed for efficient retrieval. By leveraging frameworks like Clank Chain, the text can be processed, split, and stored in a vector index. This indexing process enables quick and accurate information retrieval from the PDF documents.
4.2 Retrieving Relevant Documents with Vector Similarity Search
To ensure the retrieval of relevant documents, vector similarity search is employed. The user's question is converted into a vector representation, which is then compared to the vector representations of indexed documents. By calculating the Cosine similarity, the most similar documents are retrieved for further processing.
4.3 Generating Answers with LLMs
Once the relevant documents are obtained, they are provided as context to the LLM. The LLM generates an answer Based on the question and the information from the documents. Additionally, the source of the information can be transparently provided, ensuring the credibility and verifiability of the generated answer.
5. Leveraging Knowledge Graphs for Structured Data
While retrieval augmented generation with unstructured text has its benefits, structured data can greatly enhance the capabilities of these applications. Knowledge graphs, with their ability to represent complex and interconnected data, offer a powerful way to incorporate structured information into retrieval augmented generation.
5.1 Knowledge Graphs as a Representation of Complex Structured Data
Knowledge graphs provide an ideal representation for complex and interconnected structured data. By linking entities and their relationships, knowledge graphs capture the semantic connections between different elements. For example, a knowledge graph may represent the relationship between a painting (Mona Lisa), its artist (Da Vinci), and its location (Paris).
5.2 The Difference between Vector Similarity Search and Cypher Query Language
While vector similarity search is effective for unstructured text retrieval, Cypher query language enables precise retrieval of structured data from knowledge graphs. By translating natural language queries into Cypher statements, relevant information can be fetched from the database. This allows for accurate and targeted information retrieval.
5.3 Generating Cypher Statements for Information Retrieval
Generating Cypher statements for information retrieval can be achieved through different approaches. One method involves providing the LLM with the schema information of the knowledge graph, enabling the generation of Cypher statements based on the provided schema. Another approach utilizes examples to guide the LLM in translating natural language into Cypher expressions, thereby improving the accuracy and relevance of the generated statements.
5.4 Improving Cypher Generation Accuracy
To enhance the accuracy of Cypher statement generation, several techniques can be employed. Providing multiple relevant examples dynamically at query time can improve the precision of generated statements. Additionally, mapping user text entities to database values through full-text indexing or keyword search can ensure the relevance of the generated Cypher statements.
6. Use Cases for Retrieval Augmented Generation
Retrieval augmented generation has proven useful in various applications. Two notable use cases are multihub question answering and real-time analytics.
6.1 Multihub Question Answering
Multihub question answering refers to situations where information needs to be retrieved from multiple documents or where a question can be broken down into subquestions. Retrieval augmented generation with knowledge graphs offers a more efficient and accurate approach for answering such questions. By building a knowledge graph from relevant documents, complex queries can be Simplified into a single Cypher statement, eliminating the need for multiple steps and improving reliability.
6.2 Real-time Analytics
Real-time analytics heavily rely on the ability to efficiently aggregate, transform, and analyze data. While LLMs alone struggle to handle such tasks, retrieval augmented generation with knowledge graphs can facilitate real-time analytics. By leveraging the structured data in knowledge graphs, complex queries and operations can be executed efficiently, enabling Timely and accurate analytics.
7. The Future of Augmented Generation Applications
The future of retrieval augmented generation lies in the combination of unstructured and structured data. While unstructured text excels in certain use cases, structured data enriches the capabilities of LLMs and enables more accurate and comprehensive information retrieval. The integration of these two data types will unlock the full potential of retrieval augmented generation applications.
In conclusion, retrieval augmented generation, powered by knowledge graphs, overcomes the limitations of large language models and enhances their capabilities. By incorporating relevant context and leveraging structured data, retrieval augmented generation enables accurate and reliable information retrieval and generation. This approach opens up new possibilities for various industries, from content generation to data analytics, making retrieval augmented generation a promising field for the future.
Highlights
- Retrieval augmented generation combines the power of large language models (LLMs) with the context provided by knowledge graphs.
- LLMs have limitations such as knowledge cutoff, hallucinations, and privacy concerns that can be overcome with retrieval augmented generation.
- Chat with Your PDF applications allow users to interact with PDF documents using natural language queries.
- Knowledge graphs are ideal for representing complex structured data and enhancing retrieval augmented generation applications.
- Cypher query language enables precise retrieval of information from knowledge graphs, complementing vector similarity search for unstructured text retrieval.
- Multihub question answering and real-time analytics are two use cases where retrieval augmented generation excels.
- The future of augmented generation lies in the combination of unstructured and structured data, unlocking the full potential of retrieval augmented generation applications.