Creating text chunks

The text from Romeo and Juliet has been loaded into a lexical graph with a structure of Acts and Scenes, but the text is still too large to generate meaningful embeddings. Complete the code to split the text from Act I, Scene I into smaller chunks for each line.

The visualize_graph() function will display the nodes and relationships in the graph document.

This exercise is part of the course

Graph RAG with LangChain and Neo4j

Exercise instructions

Append each line_node to the graph_document object.
Create HAS_LINE and SPOKEN_BY relationships between each scene and line_node, and line_node and character, respectively, and add them to graph_document.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

graph_document = GraphDocument(nodes=[scene], relationships=[])
characters = {}

for i, line in enumerate(lines[20:40]): 
    character, text = get_character_and_line(line)

    line_node = Node(type="Line", id=f"{scene.id}-line-{i}", properties={"raw": line,"character": character, "text": text, "caption": f"{text[:15]}..."})

    if character not in characters:
        character_node = Node(type="Character", id=character, properties={"name": character})
        characters[character] = character_node
        graph_document.nodes.append(character_node)

    # Add nodes to the array
    graph_document.____.append(____)

    # Add relationship to the array
    graph_document.____.extend(
        [Relationship(source=scene, target=line_node, type="____"),
        Relationship(source=line_node, target=characters[character], type="SPOKEN_BY")]
    )

visualize_graph(graph_document)

Edit and Run Code

This exercise is part of the course

Graph RAG with LangChain and Neo4j

AdvancedSkill Level

4.9+

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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.

Exercise 1: Graphs and RAG Exercise 2: Creating nodes Exercise 3: Creating relationships Exercise 4: Saving graph documents Exercise 5: Querying a knowledge graph Exercise 6: Writing Cypher statements Exercise 7: Running Cypher statements Exercise 8: The MERGE clause Exercise 9: Text-to-Cypher Graph RAG with Neo4j Exercise 10: Building a text-to-Cypher chain Exercise 11: Text-to-Cypher retrieval chain

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!

Exercise 1: Lexical graphs Exercise 2: Elements of a lexical graph Exercise 3: Splitting the play into Acts Exercise 4: Creating a hierarchical lexical graph Exercise 5: Combining lexical graphs with vector search Exercise 6: Creating text chunks

Current Exercise

Exercise 7: Creating a vector index Exercise 8: Domain graphs Exercise 9: Creating a structured output Exercise 10: Requesting a structured output Exercise 11: Providing few-shot examples Exercise 12: Building a hybrid retrieval chain Exercise 13: Runnable lambdas Exercise 14: Assigning additional values to an input Exercise 15: The final link in the chain

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.

Exercise 1: Entity resolution Exercise 2: Using extracted entities Exercise 3: Graph-based entity resolution Exercise 4: Evaluating Graph RAG with RAGAS Exercise 5: Creating a Ragas evaluation Exercise 6: Evaluating context retrieval with Ragas Exercise 7: Memory graphs Exercise 8: Saving conversation memory in the graph Exercise 9: Extracting facts from conversation histories Exercise 10: Using extracted conversation facts Exercise 11: Congratulations!