Supercharge AI with Knowledge Graphs: RAG System Mastery NEW

Learn how to locate and access the course source code by navigating resources, clicking the lecture, and using the source code link to view or download code.

Set up your development environment with Python and a preferred editor like VS Code, then create an OpenAI API account and key to access GPT-3 models and understand costs.

Set up your development environment with Python and VS Code, create and secure an OpenAI API key, and begin using the API after reviewing API keys, dashboard, and pricing.

Explore knowledge graphs, their motivation, advantages, and structure through entities, edges, attributes, and triples. Learn use cases like search, recommendations, and AI in data integration and retrieval, with hands-on challenges.

Understand the graph-based structure of knowledge graphs, with nodes, edges, ontology, taxonomy, labels, and properties, and learn manual, natural language processing-driven, and crowd-sourced construction for search engines and recommendations.

Explore how knowledge graphs integrate heterogeneous data into a unified framework, enable complex queries, and let machines infer knowledge for rag systems, while addressing data quality, scalability, and privacy challenges.

Build knowledge graphs with Neo4j by defining entities and relationships in Cypher, illustrated with Albert Einstein’s physics, Nobel Prize, Germany and USA.

Explore Neo4j, a graph database management system with ACID compliant transactions, enabling secure, scalable knowledge graphs through nodes, relationships, properties, labels, and the Cypher query language.

Explore the neo4j browser dashboard to view nodes and relationships, run queries, and learn how to parse data, load it into the database, and build a knowledge graph with embeddings.

Set up neo4j's web-based graph database, create a cloud instance for free, and configure programmatic access by saving the uri, username, and password in environment variables.

Install the Neo4j driver and python-dotenv, load environment variables, and create a Python script to connect to a remote graph database, run a cypher query, and return the node count.

Programmatically create entities and relationships to generate a knowledge graph using Cypher queries, merging nodes like Albert Einstein, physics, Nobel Prize, and country nodes, then linking birth and death relations.

Run a simple Cypher query to retrieve all node names from the graph, returning the name values. The results include Albert Einstein, USA, Germany, Nobel Prize, and Physics.

Build complex cypher queries to retrieve paths and relationships, such as Einstein's born and died relationships, and visualize results with a query graph for deeper graph insights.

Explore Neo4j setup, create and query a graph database, and visualize knowledge graphs in the browser while connecting code to the backend for thorough querying.

Conduct a quick check-in to encourage reviews and feedback for the course. Invite questions in the discussion board so the instructor and peers can respond and support learning.

Explore knowledge graphs and graph rag, a graph retrieval augmented generation framework that blends vector search, keyword search, and knowledge graphs to boost retrieval and generation.

Extracts data from a csv file to build a healthcare knowledge graph, creating provider, patient, specialization, and location nodes and the relationships treats, specializes in, and located at in neo4j.

Visualize the entire graph and relationships in the neo4j browser, then zoom in and move nodes to explore a health care provider knowledge graph built from CSV data.

Master querying a Neo4j knowledge graph with LangChain wrappers and Cypher. Retrieve counts, providers, patients, and locations, using wrappers to simplify graph queries.

Learn to build a rag system by integrating unstructured data into a knowledge graph, use a vector store with embeddings, and combine graph and vector searches to answer with llm.

Create and test a vector index and generate embeddings for health care providers, then populate a comprehensive embedding into the graph database using OpenAI, LangChain, and cosine similarity.

Learn to query the vector index and knowledge graph using embeddings, cypher, and top-k results to retrieve health providers in cardiology, with relevance scores guiding results.

Combine keyword search, vector search, and documents from the Neo4j vector store to enhance query relevance. Attach the knowledge graph search to deliver coherent responses through the large language model.

Integrate the knowledge graph flow with the graph retriever to combine keyword and vector searches using a lang chain, delivering flattened results via a large language model.

Build an enhanced RAG system for the Roman Empire by integrating a knowledge graph from unstructured data, performing extraction, integration, and graph retrieval with embeddings and vector search.

Set up a knowledge graph project with LangChain to load roman empire data from Wikipedia, split into 512-token chunks with overlap, and prepare a vector database and Neo4j graph.

Instantiate an LM graph transformer from Lang Chain, convert retrieved documents into graph documents, and store the graph in Neo4j with nodes linked to their sources for traceability.

Visualize the entire knowledge graph, explore relationships among Byzantine, the Roman Empire, and the Renaissance, then apply hybrid retrieval for RAG.

Integrate vector search with a knowledge graph to create a hybrid rag system, leveraging Neo4j's vector wrapper and Lang Chain prompts to extract entities and fuse results for LLM responses.

Define a full text index and a function to generate full text queries, then use a structured retriever with Cypher templates to fetch neighboring entities in the knowledge graph.

Define the rag chain by merging structured knowledge graph data with unstructured vector-store data and feeding the combined context and chat history to a language model.

Enhance the accuracy of rag systems by integrating knowledge graphs with hybrid retrieval, combining vector search and keyword search, and prompts to guide a large language model.

Celebrate your progress by summarizing fundamentals of knowledge graphs, building your first graph, mastering Cypher with Neo4j, and applying a hybrid retrieval approach to enhance rack applications with knowledge graphs.