Using extracted entities

The Dramatis Personæ section of the text describes the characters in the play and can be considered a comprehensive source of truth. This information also holds rich information on the personal relationships between the characters.

 Dramatis Personæ

ESCALUS, Prince of Verona.
MERCUTIO, kinsman to the Prince, and friend to Romeo.
PARIS, a young Nobleman, kinsman to the Prince.
Page to Paris.

Complete the code to extract the characters from the personae text using the character_chain, and then use the list of characters to build a list of relationships from the text using relationship_chain.

The visualize_families() function will display the resulting relationships in a graph.

Este ejercicio forma parte del curso

Graph RAG with LangChain and Neo4j

Instrucciones del ejercicio

Invoke character_chain, passing it personae to extract the character entities.
Invoke the relationship_chain on the character name, personae, and the extracted characters in JSON format (character_json) to extract the relationships between the three characters listed and the characters listed in personae.

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# Extract characters from the personae variable
output = character_chain.____({"personae": ____})

character_json = dumps([dict(character) for character in output.characters])

# Extract the relationships
for character_name in ["Romeo", "Juliet", "Friar Lawrence"]:
    relationship_output = relationship_chain.invoke({
        "character_name": character_name, 
        "personae": ____, 
        "characters": ____
    })
    
    for relationship in relationship_output.relationships:
        all_relationships.append(relationship)

visualize_families(all_relationships, output)

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Este ejercicio forma parte del curso

Graph RAG with LangChain and Neo4j

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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 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

Ejercicio actual

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!