Updated basic retriever

src/content/docs/concepts/intro-to-graphrag.md

@@ -116,4 +116,4 @@ In the above example, a vector similarity search is executed on the existing ind
 
 * [Neo4j GraphAcademy: Build a Neo4j-backed Chatbot using Python](https://graphacademy.neo4j.com/courses/llm-chatbot-python/) 
 * [Integrating Neo4j into the LangChain ecosystem](https://towardsdatascience.com/integrating-neo4j-into-the-langchain-ecosystem-df0e988344d2)
-* [Neo4j GraphAcademy: Mastering Retrieval-Augmented Generation (RAG)]https://graphacademy.neo4j.com/courses/genai-workshop-graphrag/
+* [Neo4j GraphAcademy: Mastering Retrieval-Augmented Generation (RAG)](https://graphacademy.neo4j.com/courses/genai-workshop-graphrag/)

src/content/docs/reference/graphrag/basic-retriever.md

@@ -6,21 +6,43 @@ tags: ["Basic"]
 
 ## Alternative Names
 
+- Vector Retriever
 - Naive Retriever
 - Baseline RAG
+- Basic RAG
 - Typical RAG
 
 ## Required Graph Shape
 
 [Lexical Graph](/reference/knowledge-graph/lexical-graph)
 
+![](../../../../assets/images/knowledge-graph-lexical-graph.svg)
+
+## Context 
+
+It’s useful to chunk large documents into smaller pieces when creating embeddings. 
+An embedding is a text’s semantic representation capturing the meaning of what the text is about. 
+If the given text is long and contains too many diverse subjects, the informative value of its embedding deteriorates.
+
 ## Description
 
-The user question is embedded using the same embedder that has been used before to create the chunk embeddings. A vector similarity search is executed on the chunk embeddings to retrieve k (number previously configured by developer / user) most similar chunks.
+The user question is embedded using the same embedder that has been used before to create the chunk embeddings. 
+A vector similarity search is executed on the chunk embeddings to retrieve k (number previously configured by developer / user) most similar chunks.
 
 ## Usage
 
-This pattern is useful if the user asks for specific information about a topic that exists in one or more (but not too many) chunks. The question should not require complex aggregations or knowledge about the whole dataset. Since the pattern only contains a vector similarity search it is easy to understand, implement and get started with.
+This pattern is useful if the user asks for specific information about a topic that exists in one or more (but not too many) chunks. 
+The question should not require complex aggregations or knowledge about the whole dataset. 
+Since the pattern only contains a vector similarity search it is easy to understand, implement and get started with.
+
+## Required pre-processing
+
+Split documents into chunks and use an embedding model to embed the text content of the chunks.
+See [chunking](/guides/chunking).
+
+## Retrieval Query
+
+No additional query is necessary since the Neo4j Vector retriever retrieves similar chunks by default.
 
 ## Further reading
 
@@ -29,6 +51,7 @@ This pattern is useful if the user asks for specific information about a topic t
 
 ## Existing Implementations
 
+- [Neo4j GraphRAG - Vector Retriever](https://neo4j.com/docs/neo4j-graphrag-python/current/user_guide_rag.html#vector-retriever)
 - [Langchain Retrievers: Vector store-backed retriever](https://python.langchain.com/v0.1/docs/modules/data_connection/retrievers/vectorstore/)
 - [Langchain: Neo4jVector](https://python.langchain.com/v0.2/docs/integrations/vectorstores/neo4jvector/)