From 944cb552bbcd0a9236e0932dba47be3a316d3364 Mon Sep 17 00:00:00 2001
From: Lance Martin <122662504+rlancemartin@users.noreply.github.com>
Date: Tue, 31 Oct 2023 08:34:46 -0700
Subject: [PATCH] Minor updates to READMEs (#12642)

---
 templates/neo4j-generation/README.md    | 9 ++++++---
 templates/plate-chain/README.md         | 4 ++--
 templates/rag-semi-structured/README.md | 2 ++
 3 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/templates/neo4j-generation/README.md b/templates/neo4j-generation/README.md
index f4c26189f5..85de22c76d 100644
--- a/templates/neo4j-generation/README.md
+++ b/templates/neo4j-generation/README.md
@@ -1,10 +1,13 @@
+
 # neo4j-generation
 
-The neo4j-generation template is designed to convert plain text into structured knowledge graphs. 
+This template pairs LLM-based knowledge graph extraction with Neo4j AuraDB, a fully managed cloud graph database.
 
-By using OpenAI's language model, it can efficiently extract structured information from text and construct a knowledge graph in Neo4j. 
+You can create a free instance on [Neo4j Aura](https://neo4j.com/cloud/platform/aura-graph-database?utm_source=langchain&utm_content=langserve).
 
-This package is flexible and allows users to guide the extraction process by specifying a list of node labels and relationship types.
+When you initiate a free database instance, you'll receive credentials to access the database.
+
+This template is flexible and allows users to guide the extraction process by specifying a list of node labels and relationship types.
 
 For more details on the functionality and capabilities of this package, please refer to [this blog post](https://blog.langchain.dev/constructing-knowledge-graphs-from-text-using-openai-functions/).
 
diff --git a/templates/plate-chain/README.md b/templates/plate-chain/README.md
index 8c5219b110..e703e9e628 100644
--- a/templates/plate-chain/README.md
+++ b/templates/plate-chain/README.md
@@ -34,9 +34,9 @@ langchain app add plate-chain
 Then add the following code to your `server.py` file:
 
 ```python
-from plate_chain import chain as plate_chain_chain
+from plate_chain import chain as plate_chain
 
-add_routes(app, plate_chain_chain, path="/plate-chain")
+add_routes(app, plate_chain, path="/plate-chain")
 ```
 
 (Optional) For configuring LangSmith, which helps trace, monitor and debug LangChain applications, use the following code:
diff --git a/templates/rag-semi-structured/README.md b/templates/rag-semi-structured/README.md
index f1f918a88e..55e268d5f9 100644
--- a/templates/rag-semi-structured/README.md
+++ b/templates/rag-semi-structured/README.md
@@ -2,6 +2,8 @@
 
 This template performs RAG on semi-structured data, such as a PDF with text and tables.
 
+See [this cookbook](https://github.com/langchain-ai/langchain/blob/master/cookbook/Semi_Structured_RAG.ipynb) as a reference.
+
 ## Environment Setup
 
 Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.