Local GraphRAG + Langchain + Local LLM = Easy AI/Chat for your Docs
Science & Technology
Introduction
In this article, we will walk you through the process of creating an AI chatbot leveraging the advancements made in Local Graph RAG (Retriever-Augmented Generation) technology, along with Langchain and a local Large Language Model (LLM). This powerful combination allows users to create a chatbot capable of efficiently answering queries related to PDF documents, all while having full control over the data and customization options.
Understanding Graph RAG
First, let's revisit what Graph RAG brings to the table. Unlike traditional RAG models, which might struggle with complex inquiries, Graph RAG combines the capabilities of a Knowledge Graph with generative AI. In our previous discussions, we established that via an API, a Knowledge Graph could be formed. This time, however, we are focused on developing an advanced algorithm that grants us complete control over our data. This flexibility allows for enhanced customization and optimization of applications to meet specific user needs.
How the Chatbot Works
To showcase the capabilities of our chatbot, let's look at a simple example: issuing a query like, "What is RAG Checker?" The chatbot utilizes the local Graph RAG to respond effectively. The process begins when a user's question is transformed into a vector representation. The system then searches through stored vectors to identify the most relevant information chunks.
Employing a sophisticated algorithm known as Dijkstra’s Algorithm, which determines the shortest path between nodes, our chatbot explores the Knowledge Graph. Starting from the nodes that are most closely related to the user's inquiry, it constructs a context by collecting relevant information. If the initial search doesn't yield a complete answer, the system iterates through neighboring nodes, adjusting their importance based on connection strength until a comprehensive response is formulated.
Should the Knowledge Graph fail to provide a complete answer, the system resorts to a Large Language Model to generate a detailed response based on the context it has accumulated. The resulting Knowledge Graph provides a visual representation of interconnected information, where nodes denote chunks of text and edges illustrate their relationships. The edges are represented in light blue, conveying the strength of these connections, and the paths taken to obtain answers are highlighted in distinct colors.
The Development Process
Building an AI chatbot necessitates a comprehensive understanding of various technologies. Over the span of a few weeks, I explored numerous libraries including Langchain, Spacy, and Natural Language Toolkit (NLTK). These tools offer a robust framework for defining and interacting with diverse abstractions to create powerful chatbots.
The first step in building the chatbot involved defining a document processor responsible for splitting documents into digestible parts, generating embeddings, and subsequently comparing these embeddings for similarities. A vector store is created during this process, ensuring that every chunk of text is paired with its corresponding embedding, streamlining the search and comparison processes for various texts.
In scenarios where a substantial amount of text needs processing, breaking it into manageable batches and creating embeddings for each batch helps optimize performance. The similarity between embeddings is computed through cosine similarity—a mathematical metric for evaluating how close two vectors are, where a value approaching one indicates high similarity.
The Knowledge Graph acts as an organizing framework, mapping the relationships between concepts and how they relate to one another. This enables users to visualize various ideas as nodes, linking them with edges to indicate their similarities.
Key Components Implemented
- Graph Structure: Utilizes the NetworkX library for managing graphs.
- Concept Cache: Stores previously identified concepts to prevent redundancy and enhance operational speed.
- Edge Thresholds: Determines the necessary similarity score for connecting concepts, significantly contributing to the graph's efficiency.
- Query Engine: Combines a vector store, Knowledge Graph, and LLM components to effectively respond to user queries.
Subsequently, we develop functions for creating and visualizing the graph, handling context expansion, and updating edges based on concepts shared between document sections. The visual representation of the Knowledge Graph facilitates a clearer understanding of complex relationships between different chunks of information.
Upon completion of the chatbot, users can interactively engage with the system via a Streamlit application. This interface allows for seamless uploading and processing of PDF documents, wherein users can enter queries related to the content and receive coherent responses in real-time.
Conclusion
The fusion of Graph RAG technology with a Local LLM has transformed the paradigm of answering complex questions, enhancing both efficiency and accuracy. This advancement is particularly beneficial for individual users, researchers, and businesses striving to create powerful and engaging chatbots.
Keywords
- Local Graph RAG
- Langchain
- Local LLM
- AI chatbot
- PDF document processing
- Knowledge Graph
- Dijkstra’s Algorithm
- Cosine similarity
- Vector store
- Document processor
FAQ
Q1: What is Local Graph RAG?
A1: Local Graph RAG is an innovative approach that combines generative AI and Knowledge Graph technology, enhancing the capability to address complex inquiries.
Q2: How does the chatbot utilize embeddings?
A2: The chatbot generates embeddings from document sections, allowing it to compute similarities and establish connections between different pieces of information in the Knowledge Graph.
Q3: What technologies are used in building this chatbot?
A3: Technologies used include Langchain, Spacy, Natural Language Toolkit (NLTK), and NetworkX, amongst others.
Q4: How can users interact with the chatbot?
A4: Users can interact with the chatbot through a Streamlit application, allowing them to upload PDF documents and ask queries about the content.
Q5: What are the benefits of using this chatbot for businesses?
A5: The chatbot improves information retrieval, enhances customer engagement, and offers quick answers to complex queries, which can significantly boost operational efficiency.