Update neo4j template readmes (#12540)

templates/neo4j-cypher-ft/README.md

@@ -1,9 +1,22 @@
 # Neo4j Knowledge Graph: Enhanced mapping from text to database using a full-text index
 
 This template allows you to chat with Neo4j graph database in natural language, using an OpenAI LLM.
+Its primary purpose is to convert a natural language question into a Cypher query (which is used to query Neo4j databases), 
+execute the query, and then provide a natural language response based on the query's results.
 The addition of the full-text index ensures efficient mapping of values from text to database for more precise Cypher statement generation.
+In this example, full-text index is used to map names of people and movies from the user's query with corresponding database entries.
 
-## Set up Environment
+## Neo4j database
+
+There are a number of ways to set up a Neo4j database.
+
+### Neo4j Aura
+
+Neo4j AuraDB is a fully managed cloud graph database service.
+Create a free instance on [Neo4j Aura](https://neo4j.com/cloud/platform/aura-graph-database?utm_source=langchain&utm_content=langserve).
+When you initiate a free database instance, you'll receive credentials to access the database.
+
+##  Environment variables
 
 You need to define the following environment variables
 
@@ -14,9 +27,16 @@ NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
 NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
 ```
 
+## Populating with data
+
+If you want to populate the DB with some example data, you can run `python ingest.py`.
+This script will populate the database with sample movie data.
+Additionally, it will create an full-text index named `entity`, which is used to 
+map person and movies from user input to database values for precise Cypher statement generation.
+
 ## Installation
 
 ```bash
 # from inside your LangServe instance
-poe add neo4j-cypher
+poe add neo4j-cypher-ft
 ```

templates/neo4j-cypher/README.md

@@ -1,8 +1,20 @@
 # Neo4j Knowledge Graph with OpenAI LLMs
 
 This template allows you to chat with Neo4j graph database in natural language, using an OpenAI LLM.
+Its primary purpose is to convert a natural language question into a Cypher query (which is used to query Neo4j databases), 
+execute the query, and then provide a natural language response based on the query's results.
 
-## Set up Environment
+## Neo4j database
+
+There are a number of ways to set up a Neo4j database.
+
+### Neo4j Aura
+
+Neo4j AuraDB is a fully managed cloud graph database service.
+Create a free instance on [Neo4j Aura](https://neo4j.com/cloud/platform/aura-graph-database?utm_source=langchain&utm_content=langserve).
+When you initiate a free database instance, you'll receive credentials to access the database.
+
+##  Environment variables
 
 You need to define the following environment variables
 
@@ -13,6 +25,11 @@ NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
 NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
 ```
 
+## Populating with data
+
+If you want to populate the DB with some example data, you can run `python ingest.py`.
+This script will populate the database with sample movie data.
+
 ## Installation
 
 ```bash

templates/neo4j-generation/README.md

@@ -1,10 +1,29 @@
 # Graph Generation Chain for Neo4j Knowledge Graph
 
-Harness the power of natural language understanding and convert plain text into structured knowledge graphs with the Graph Generation Chain.
-This system integrates with the Neo4j graph database using OpenAI's LLM.
-By leveraging OpenAI Functions capabilities, the Graph Generation Chain efficiently extracts graph structure from text.
+Harness the power of natural language understanding of LLMs and convert plain text into structured knowledge graphs with the Graph Generation Chain.
+This chain uses OpenAI's LLM to construct a knowledge graph in Neo4j.
+Leveraging OpenAI Functions capabilities, the Graph Generation Chain efficiently extracts structured information from text.
+The chain has the following input parameters:
 
-## Set up Environment
+* text (str): The input text from which the information will be extracted to construct the graph.
+* allowed_nodes (Optional[List[str]]): A list of node labels to guide the extraction process.
+                                If not provided, extraction won't have specific restriction on node labels.
+* allowed_relationships (Optional[List[str]]): A list of relationship types to guide the extraction process.
+                                If not provided, extraction won't have specific restriction on relationship types.
+
+Find more details in [this blog post](https://blog.langchain.dev/constructing-knowledge-graphs-from-text-using-openai-functions/).
+
+## Neo4j database
+
+There are a number of ways to set up a Neo4j database.
+
+### Neo4j Aura
+
+Neo4j AuraDB is a fully managed cloud graph database service.
+Create a free instance on [Neo4j Aura](https://neo4j.com/cloud/platform/aura-graph-database?utm_source=langchain&utm_content=langserve).
+When you initiate a free database instance, you'll receive credentials to access the database.
+
+##  Environment variables
 
 You need to define the following environment variables
 

templates/neo4j-parent/README.md

@@ -1,8 +1,19 @@
 # Parent Document Retriever with Neo4j Vector Index
 
 This template allows you to balance precise embeddings and context retention by splitting documents into smaller chunks and retrieving their original or larger text information.
+Using a Neo4j vector index, the template queries child nodes using vector similarity search and retrieves the corresponding parent's text by defining an appropriate `retrieval_query` parameter.
 
-## Set up Environment
+## Neo4j database
+
+There are a number of ways to set up a Neo4j database.
+
+### Neo4j Aura
+
+Neo4j AuraDB is a fully managed cloud graph database service.
+Create a free instance on [Neo4j Aura](https://neo4j.com/cloud/platform/aura-graph-database?utm_source=langchain&utm_content=langserve).
+When you initiate a free database instance, you'll receive credentials to access the database.
+
+##  Environment variables
 
 You need to define the following environment variables
 
@@ -13,6 +24,14 @@ NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
 NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
 ```
 
+## Populating with data
+
+If you want to populate the DB with some example data, you can run `python ingest.py`.
+The script process and stores sections of the text from the file `dune.txt` into a Neo4j graph database.
+First, the text is divided into larger chunks ("parents") and then further subdivided into smaller chunks ("children"), where both parent and child chunks overlap slightly to maintain context.
+After storing these chunks in the database, embeddings for the child nodes are computed using OpenAI's embeddings and stored back in the graph for future retrieval or analysis.
+Additionally, a vector index named `retrieval` is created for efficient querying of these embeddings.
+
 ## Installation
 
 ```bash

templates/neo4j-parent/neo4j_parent/chain.py

@@ -8,6 +8,7 @@ from langchain.vectorstores import Neo4jVector
 
 retrieval_query = """
 MATCH (node)-[:HAS_PARENT]->(parent)
+WITH parent, max(score) AS score // deduplicate parents
 RETURN parent.text AS text, score, {} AS metadata
 """