What is retriever?

Example

A retriever that searches plots, then reads the graph around each film it finds.

cypher

WITH ai.text.embed(
    "A mysterious spaceship lands Earth",
    "OpenAI",
    { token: "sk-...", model: "text-embedding-ada-002" }
) AS myMoviePlot
 
MATCH (node:Movie)
SEARCH node IN (
  VECTOR INDEX moviePlots
  FOR myMoviePlot
  LIMIT 6
) SCORE AS score
 
MATCH (node)<-[r:RATED]-()
RETURN
  node.title AS title, node.plot AS plot, score AS similarityScore,
  collect { MATCH (node)-[:IN_GENRE]->(g) RETURN g.name } as genres,
  collect { MATCH (node)<-[:ACTED_IN]->(a) RETURN a.name } as actors,
  avg(r.rating) as userRating
ORDER BY userRating DESC

The example query here performs graph-enhanced vector retrieval. First it embeds the prompt, then finds a list of nearest neighbours by semantic similarity. Finally, it traverses a single relationship to gather rating properties.

A retriever is not required to use embeddings or graph to be defined as a retriever.

Lessons that use this term

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30 lessons use this term. Results are ordered by term density.

Neo4j & GenerativeAI Fundamentals43 mentions

Vector RetrieverIntegrating Neo4j with Generative AIthis lesson, you will create a vector retriever to retrieve relevant data from12 mentions
RAG PipelineIntegrating Neo4j with Generative AIcan use a retriever as part of a RAG (Retrieval-Augmented Generation) pipeline to provide context to a8 mentions
Text to Cypher RetrieverIntegrating Neo4j with Generative AIand full text retrievers are great for finding relevant data based on semantic similarity or keyword9 mentions
What is RAG?Retrieval Augmented Generation (RAG)Retrieval A retriever searches external data sources (such as documents, databases, or knowledge graphs) to find relevant information based6 mentions
Graph-Enhanced Vector RetrieverIntegrating Neo4j with Generative AItake advantage of the relationships in the graph, you can create a retriever that uses both vector search and graph traversal to find relevant5 mentions
GraphRAG for PythonIntegrating Neo4j with Generative AIRetrievers GraphRAG pipelines Knowledge graph2 mentions
Vector RAGRetrieval Augmented Generation (RAG)you learned about Retrieval Augmented Generation (RAG) and the role of retrievers in finding relevant1 mention

Using Neo4j with LangChain16 mentions

RetrieverText to Cypherthis lesson, you will use the GraphCypherQAChain to add a text to Cypher retriever to the LangChain6 mentions
Graph RetrievalVectorscan enhance a vector retriever using GraphRAG to include additional5 mentions
Vector RetrieverVectorsthis lesson, you will update the LangChain agent to use a vector retriever that will allow you to search for movies based on3 mentions
Additional DataVectorsoptional challenge, you can explore how adding additional data to the GraphRAG retriever can improve the agent's responses to more complex1 mention
Neo4jGraphNeo4j and LangChainthe connection to the database when using other LangChain components, such as retrievers and1 mention

Neo4j and Generative AI Workshop35 mentions

Search lesson contentAgentswill enhance your agent by adding a search lesson tool using a vector + cypher retriever6 mentions
Text to Cypher retrieverRetrievalThe Text2CypherRetriever retriever allows you to create GraphRAG pipelines that can answer natural language questions by generating and executing Cypher5 mentions
Vector + Cypher retrieverRetrievalthis lesson, you will create a vector retriever that uses these embeddings to find the most relevant chunks for a given7 mentions
Query databaseAgentstool will use a TextToCypherRetriever retriever to convert user queries into Cypher statements and return the results as4 mentions
What is an Agent?Agentsare typically retrievers that2 mentions
GraphRAG for PythonGenerative AIRetrievers GraphRAG pipelines Knowledge graph2 mentions
Create an agentAgentsCypher queries against the graph and return the results Vector + Cypher retrievers to semantically search the knowledge1 mention
GraphRAG?Generative AIA retriever searches external data sources (such as documents, databases, or knowledge graphs) to find relevant information based6 mentions
Extracting a schema from textKnowledge Graph Constructionyou will use the neo4j_graphrag package to build a knowledge graph and retrievers to extract information from the graph using1 mention
Vector RAGRetrievallearned about Retrieval Augmented Generation (RAG) and the role of retrievers in finding relevant1 mention

Constructing Knowledge Graphs with Neo4j GraphRAG for Python11 mentions

Text to Cypher retrieverRetrievalretriever allows you to create GraphRAG pipelines that can answer natural language questions by generating and executing Cypher3 mentions
Vector + Cypher retrieverRetrievalthis lesson you will create a vector retriever that uses these embeddings to find the most relevant chunks for a given6 mentions
Extracting a schema from textKnowledge Graph Pipelineyou will use the neo4j_graphrag package to build a knowledge graph and retrievers to extract information from the graph using1 mention
Set up your development environmentIntroductiongraph from unstructured and structured data Create vector and text to Cypher retrievers that use the knowledge graph to provide context to an1 mention

AI on Your Lakehouse: Context Comes in Shapes, Not Queries2 mentions

Port the PatternPort the Patterncourses - Neo4j & GenAI Fundamentals for retrievers and GraphRAG, Community Detection to go deeper on Leiden and1 mention
The Estate QuestionsPut It Together - the finalea top-k retriever for "patterns across all our notices" and it hands back the nearest handful - it samples, and it cannot tell you what1 mention

Building Knowledge Graphs with LLMs2 mentions

Querying using CypherExploring your knowledge graph| --- | | Neo4J | 10 | | vector search | 10 | | LLM | 9 | | GraphRAG | 9 | | retriever | 72 mentions

Zero to Production Hands-On Workshop1 mention

AgentsAccess your dataare typically retrievers that1 mention

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