retriever
A component that searches a data source and returns the information relevant to a query. Often used to provide context for a language model.
Also written: retrievers
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 DESCThe 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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31 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
Hands-on GenAI Workshop4 mentions
- AgentsA tour of AuraGraphRAG flow: your question goes to the agent, the LLM picks a retriever, the retriever writes a Cypher query that runs on your graph, and the answer is generated from the returned facts, with4 mentions
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