Update Hypothetical Question Retriever

Author: jexp

src/content/docs/reference/graphrag/hypothetical-question-retriever.md

@@ -7,16 +7,38 @@ tags: ["Basic"]
 ## Required Graph Shape
 
 [Lexical Graph with Hypothetical Questions](/reference/knowledge-graph/lexical-graph-hypothetical-questions)
+![Lexical Graph with Hypothetical Questions](../../../../assets/images/knowledge-graph-lexical-graph-hypothetical-questions.svg)
+
+## Context: 
+
+The vector similarity between a question’s embedding and the text embedding of an appropriate answer or text source might be quite low. 
+If we have question-chunk pairs available, we can execute a vector similarity search on the question embeddings, which will probably deliver much better results than a vector similarity search on the original text chunk.
 
 ## Description
 
-The user question is embedded using the same embedder that has been used before to create the question embeddings. A vector similarity search is executed on the previously generated questions. k (number previously configured by developer / user) most similar questions are found and their related Chunks are retrieved.
+The user question is embedded using the same embedder that has been used before to create the question embeddings. 
+A vector similarity search is executed on the previously generated questions. `k` (number previously configured by developer / user) most similar questions are found and their related Chunks are retrieved.
 
 ## Usage
 
-This pattern can yield better results in the vector similarity search than a question-to-chunk similarity search as used in e.g. [Basic Retrievers](/reference/graphrag/basic-retriever) or [Parent-Child Retrievers](/reference/graphrag/parent-child-retriever). However, it also requires more pre-processing effort and cost in LLM calls for the question generation.
+This pattern can yield better results in the vector similarity search than a question-to-chunk similarity search as used in e.g. [Basic Retrievers](/reference/graphrag/basic-retriever) or [Parent-Child Retrievers](/reference/graphrag/parent-child-retriever). 
+However, it also requires more pre-processing effort and cost in LLM calls for the question generation.
+
+## Required pre-processing
+
+Use an LLM to generate hypothetical questions answered within the chunks. 
+Embed the question using an embedding model. 
+Record the relationship between questions and the chunk that contains their answer.
+
+## Retrieval Query
 
-## Further reading
+```cypher
+MATCH (node)<-[:HAS_QUESTION]-(chunk)
+WITH chunk, max(score) AS score // deduplicate chunks
+RETURN chunk.text AS text, score, {} AS metadata
+```
+
+## Resources
 
 - [Implementing advanced RAG strategies with Neo4j](https://blog.langchain.dev/implementing-advanced-retrieval-rag-strategies-with-neo4j/) (November 2023)
 
@@ -27,3 +49,12 @@ This pattern can yield better results in the vector similarity search than a que
 ## Example Implementations
 
 - [Langchain Templates: Neo4j Advanced RAG](https://github.com/langchain-ai/langchain/blob/master/templates/neo4j-advanced-rag/neo4j_advanced_rag/retrievers.py)
+
+
+## Notes
+
+The *Hypothetical Question Retriever* is in a way quite similar to the Hypothetical Document Embeddings (HyDE) Retriever (see [RAG using LangChain: Part 5 — Hypothetical Document Embeddings](https://jayant017.medium.com/rag-using-langchain-part-5-hypothetical-document-embeddings-hyde-050f57dfc252)). 
+The main idea behind them is to increase the similarity between the user question and the available text by moving them into a similar region of the vector space. 
+In the Hypothetical Question Retriever, we generate hypothetical questions that the user question is matched against
+Whereas in the HyDE retriever, the LLM generates a hypothetical answer to the user question (without using the grounding database) and, subsequently, the hypothetical answer is matched against the actual chunks in the database to find the best fit. 
+We aren’t looking at the HyDE retriever in more detail since it lives in the pre-processing RAG phase, rather than in the retrieval phase, and also does not require a specific kind of underlying graph pattern.