Realtime Powerful RAG Pipeline using Neo4j(Knowledge Graph Db) and Langchain #rag

Summary

In this informative video, Savita demonstrates how to implement a Retrieval-Augmented Generation (RAG) pipeline using Neo4j, a powerful graph database. She guides viewers through setting up a cloud instance of Neo4j, installing necessary libraries like Langchain, and utilizing the GPU-enabled Colab for smooth execution. Savita explains the RAG architecture, detailing the process of data retrieval from a single database through keyword search, vector search, and graph search. She further illustrates how to store and retrieve data using Neo4j and generate responses with the help of the LLM (Language Model). The video serves as an educational resource for those interested in exploring RAG systems and fine-tuning techniques within the realm of Knowledge Graph applications.

Takeaways

• 🌟 The video is a tutorial on implementing a RAI (Retrieval-Augmented Indexing) pipeline using Neo4j, a graph database.
• 🔍 The presenter, Savita, aims to create a RAI application with a Knowledge Graph using Neo4j's cloud instance, Neo4j Aura.
• 📚 The process involves using different variants of the LangChain library, which is divided into 'core', 'community', and 'experimental' versions for various functionalities.
• 💾 The data for the RAI system is sourced from Wikipedia and is intended to be stored in Neo4j after processing.
• 🔑 To connect with Neo4j, certain parameters like URI, username, password, and instance ID are required, which can be obtained from the Neo4j Aura platform.
• 📈 The architecture of the project is explained, detailing how data is retrieved based on keywords, vectors, and graph search, then combined and passed to an LLM (Large Language Model) for response generation.
• 📝 The importance of setting up environment variables for the API key and other sensitive information is highlighted for secure access to services.
• 📚 The script covers the theoretical aspects of NoSQL databases, explaining document stores, key-value stores, column family stores, and graph stores, with examples like MongoDB, Cassandra, and Neo4j.
• 🤖 The use of an 'LLM Graph Transformer' is introduced to automate the creation of graph structures from documents, leveraging the power of AI to understand and relate data.
• 🔄 The video demonstrates the process of data retrieval from Neo4j and its conversion into a usable format for the RAI system, emphasizing the accuracy and efficiency of the process.
• 🔗 The final part of the script focuses on the retrieval process, showing how information is extracted from the graph database and combined with user queries to generate precise answers.

Q & A

What is the main topic of the video?

The main topic of the video is the implementation of a RAG (Retrieval-Augmented Generation) pipeline using Neo4j, a graph database.

What is the purpose of using Neo4j in the RAG pipeline?

Neo4j is used in the RAG pipeline to create a knowledge graph that aids in the retrieval process by performing keyword search, vector similarity search, and graph search to gather context for generating responses.

What is the role of Langchain in the video?

Langchain is used as a framework to build the RAG application, providing different variants for various tasks such as the core language model, community integrations, and experimental features.

How does the video cover the RAG architecture?

The video explains the RAG architecture by detailing the process of converting data into embeddings, storing them in a database, performing retrieval operations based on user queries, and then using the retrieved context to generate responses with an LLM (Large Language Model).

What is the significance of using different variants of Langchain?

Different variants of Langchain are used to cater to different requirements of the project. For instance, Langchain Core contains the latest features, Langchain Community offers third-party integrations, and Langchain provides the older package with traditional agent algorithms.

How does the video address the installation of necessary libraries for the project?

The video demonstrates the installation of required libraries such as Langchain Community, Langchain OpenAI, and others, using commands that emphasize upgrading to the latest version and installing quietly to minimize output.

What is the process of connecting to Neo4j as shown in the video?

The process involves creating an instance of Neo4j Aura, obtaining necessary credentials like URI, username, password, and instance ID, and then using these credentials to establish a connection within the notebook environment.

How is data fetched from Wikipedia in the video?

The video uses the `bigy_pedia_loader` from Langchain to fetch data from Wikipedia. It demonstrates fetching data about Elizabeth I and converting the raw document into tokens and chunks for storage in Neo4j.

What is the importance of storing data as embeddings in the database?

Storing data as embeddings in the database allows for efficient retrieval of information based on similarity searches. Embeddings represent the data in a format that can be easily compared and searched within the context of a RAG system.

How does the video explain the concept of NoSQL databases?

The video provides a brief overview of NoSQL databases, categorizing them into document stores, key-value stores, column family stores, and graph stores, with examples like MongoDB, Cassandra, and Neo4j, respectively.

What is the final step shown in the video for implementing the RAG pipeline?

The final step shown in the video is the retrieval of data from Neo4j and its use in generating responses with an LLM. It involves creating a prompt template, performing a hybrid search using keyword and vector similarity, and combining the retrieved information to generate accurate responses.