course
Graph RAG with LangChain and Neo4j
विकसितकौशल स्तर
अद्यतन 03/2026कोर्स मुफ्त में शुरू करें
इसमें शामिल हैअधिमूल्य or टीमें
PythonArtificial Intelligence3 घंटा11 videos37 exercises3,100 एक्सपीउपलब्धि का कथन
अपना निःशुल्क खाता बनाएँ
या
जारी रखने पर, आप हमारी उपयोग की शर्तें, हमारी गोपनीयता नीति को स्वीकार करते हैं और यह भी कि आपका डेटा संयुक्त राज्य अमेरिका में संग्रहीत किया जाता है।हजारों कंपनियों में कार्यरत शिक्षार्थियों द्वारा पसंद किया जाता है
दो या दो से अधिक लोगों को प्रशिक्षण देना?
DataCamp for Business को आज़माएँपाठ्यक्रम विवरण
Level-Up Your RAG Applications with Graphs
Are you bored of vectors, embeddings, and vector RAG applications yet? Look no further! In this course, you'll discover how Graph RAG can greatly improve the accuracy and reliability of RAG applications by storing and querying information in the form of nodes and relationships. Combine Neo4j graph databases with LangChain and you get a truly awesome way of retrieving and integrating external data with LLMs.Create Neo4j Graph Databases from Unstructured Text
What if my dataset is messy unstructured text rather than a graph? Don't panic—you'll learn how to use LLMs with structured outputs to extract entities and relationships from text, and create new nodes and relationships for your graph database. You'll utilize the Pydantic library to define strict data structures for your LLM to populate with extracted text data.Combine Vectors and Graphs for Hybrid RAG
You don't have to choose between vectors vs. graphs—you can have the best of both worlds! Discover how you can retrieve from both data sources in a single workflow and carefully construct prompts to integrate them into a hybrid RAG application.Integrate Long-Term Chatbot Memory
Graph databases like Neo4j aren't only useful as knowledge bases for retrieval, you can also store long-term information like user facts and preferences as graphs! This long-term memory can then be queried just like any other graph database to integrate these preferences and personalize your applications.आवश्यक शर्तें
Retrieval Augmented Generation (RAG) with LangChain1
Getting Started with Graph RAG and Neo4j
Learn how Graph RAG can improve the accuracy and reliability of RAG applications! Store information as nodes and edges in a Neo4j database, and give your LLM the ability to query it so it can retrieve entity and relational information to provide informed answers.
2
Graph Models and Hybrid RAG
Text-to-Cypher applications work well in many circumstances, but we can do better than that! Discover how to construct graph databases using different graph models including lexical and domain graphs. Create Neo4j vector indexes so that you can have the best of both worlds and run graph and vector retrieval simultaneously!
3
Improving Retrieval Quality
Although Graph RAG applications are generally more reliable than vector RAG, they aren't totally infallible. In this chapter, you'll learn to evaluate your Graph RAG applications, spot incorrect or duplicate graph nodes, and integrate long-term memory so user preferences can be learned over time.
Graph RAG with LangChain and Neo4j
कोर्स पूरा
उपलब्धि प्रमाण पत्र अर्जित करें
इस क्रेडेंशियल को अपने लिंक्डइन प्रोफाइल, रिज्यूमे या सीवी में जोड़ें।इसे सोशल मीडिया पर और अपनी परफॉर्मेंस रिव्यू में साझा करें।
इसमें शामिल हैअधिमूल्य or टीमें
अभी दाखिला लेंजुड़ें 19 मिलियन शिक्षार्थी और आज ही Graph RAG with LangChain and Neo4j शुरू करें!
अपना निःशुल्क खाता बनाएँ
या
जारी रखने पर, आप हमारी उपयोग की शर्तें, हमारी गोपनीयता नीति को स्वीकार करते हैं और यह भी कि आपका डेटा संयुक्त राज्य अमेरिका में संग्रहीत किया जाता है।