Added graphrag-python page

modules/genai-ecosystem/nav.adoc

@@ -7,6 +7,7 @@
 **** xref:neoconverse.adoc[NeoConverse]
 **** xref:genai-stack.adoc[GenAI Stack]
 *** Neo4j GenAI Features
+**** xref:graphrag-python.adoc[GraphRAG Python Package]
 **** xref:vector-search.adoc[Vector Index and Search]
 **** xref:apoc-genai.adoc[APOC GenAI]
 *** Cloud Examples

modules/genai-ecosystem/pages/_developer_survey.adoc

@@ -0,0 +1,7 @@
+// Developer survey
+:page-ad-icon: ~
+:page-ad-title: Neo4j Developer Survey
+:page-ad-description: Your input matters! Share your Feedback
+:page-ad-underline-role: button
+:page-ad-underline: Start Here
+:page-ad-link: https://neo4j.typeform.com/to/E6yOZ2Py?utm_source=GA&utm_medium=blurb&utm_campaign=survey

modules/genai-ecosystem/pages/genai-stack.adoc

@@ -7,13 +7,7 @@ include::_graphacademy_llm.adoc[]
 :neo4j-versions: 5.x
 :page-pagination:
 :page-product: genai-stack
-// Developer survey
-:page-ad-icon: ~
-:page-ad-title: Neo4j Developer Survey
-:page-ad-description: Your input matters! Share your Feedback
-:page-ad-underline-role: button
-:page-ad-underline: Start Here
-:page-ad-link: https://neo4j.typeform.com/to/E6yOZ2Py?utm_source=GA&utm_medium=blurb&utm_campaign=survey
+include::_developer_survey.adoc[]
 
 
 image::https://dist.neo4j.com/wp-content/uploads/20231005063102/import-embed-data-stack-overflow.png[width=800]

modules/genai-ecosystem/pages/graphrag-python.adoc

@@ -0,0 +1,239 @@
+= Neo4j GraphRAG Python Package
+include::_graphacademy_llm.adoc[]
+:slug: graphrag-python
+:author: 
+:category: genai-ecosystem
+:tags: graphrag, knowledgegraph, embedding, vectorsearch, neo4j, python
+:neo4j-versions: 5.23+
+:page-pagination:
+:page-product: neo4j
+
+
+The Neo4j GraphRAG package is a comprehensive Python library that allows building GenAI applications.
+It supports knowledge graph creation through a pipeline that extracts entities from unstructured text, generates embeddings, and creates a graph in Neo4j.
+The package also provides a number of retrievers, for graph search, vector search and integration with vector databases.
+
+== Functionality Includes
+
+* Knowledge Graph Construction Pipeline
+* Neo4j Vector Retriever
+* Vector Cypher Retriever
+* Vector Database Retriever
+
+image::https://cdn.graphacademy.neo4j.com/assets/img/courses/banners/genai-workshop-graphrag.png[width=800,link="https://graphacademy.neo4j.com/courses/genai-workshop-graphrag/"]
+
+== Usage - Examples for a BioMedical Knowledge Graph
+
+First Knowlege Graph Construction using the SimpleKGPipeline
+
+image::https://dist.neo4j.com/wp-content/uploads/20241015075828/simplekgpipeline-1.png[]
+
+Setup of Neo4j connection, schema and foundation models (LLM, Eebeddings) and extraction prompt template.
+
+[source,python]
+----
+# Neo4j Driver
+import neo4j
+
+neo4j_driver = neo4j.GraphDatabase.driver(NEO4J_URI,
+                auth=(NEO4J_USERNAME, NEO4J_PASSWORD))
+
+# LLM and Embedding Model
+from neo4j_graphrag.llm import OpenAILLM
+from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings
+
+llm=OpenAILLM(
+   model_name="gpt-4o-mini",
+   model_params={
+       "response_format": {"type": "json_object"}, # use json_object formatting for best results
+       "temperature": 0 # turning temperature down for more deterministic results
+   }
+)
+
+# Graph Schema Setup
+basic_node_labels = ["Object", "Entity", "Group", "Person", "Organization", "Place"]
+
+academic_node_labels = ["ArticleOrPaper", "PublicationOrJournal"]
+
+medical_node_labels = ["Anatomy", "BiologicalProcess", "Cell", "CellularComponent",
+                      "CellType", "Condition", "Disease", "Drug",
+                      "EffectOrPhenotype", "Exposure", "GeneOrProtein", "Molecule",
+                      "MolecularFunction", "Pathway"]
+
+node_labels = basic_node_labels + academic_node_labels + medical_node_labels
+
+# define relationship types
+rel_types = ["ACTIVATES", "AFFECTS", "ASSESSES", "ASSOCIATED_WITH", "AUTHORED",
+   "BIOMARKER_FOR", …]
+
+#create text embedder
+embedder = OpenAIEmbeddings()
+
+# define prompt template
+prompt_template = '''
+You are a medical researcher tasks with extracting information from papers 
+and structuring it in a property graph to inform further medical and research Q&A.
+
+Extract the entities (nodes) and specify their type from the following Input text.
+Also extract the relationships between these nodes. the relationship direction goes from the start node to the end node. 
+
+
+Return result as JSON using the following format:
+{{"nodes": [ {{"id": "0", "label": "the type of entity", "properties": {{"name": "name of entity" }} }}],
+  "relationships": [{{"type": "TYPE_OF_RELATIONSHIP", "start_node_id": "0", "end_node_id": "1", "properties": {{"details": "Description of the relationship"}} }}] }}
+
+...
+
+Use only fhe following nodes and relationships:
+{schema}
+
+Assign a unique ID (string) to each node, and reuse it to define relationships.
+Do respect the source and target node types for relationship and the relationship direction.
+
+Do not return any additional information other than the JSON in it.
+
+Examples:
+{examples}
+
+Input text:
+
+{text}
+'''
+----
+
+Knowledge Graph Pipeline Setup and Execution with example PDFs
+
+[source,python]
+----
+# Knowledge Graph Builder
+from neo4j_graphrag.experimental.components.text_splitters.fixed_size_splitter import FixedSizeSplitter
+from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline
+
+kg_builder_pdf = SimpleKGPipeline(
+   llm=ex_llm,
+   driver=driver,
+   text_splitter=FixedSizeSplitter(chunk_size=500, chunk_overlap=100),
+   embedder=embedder,
+   entities=node_labels,
+   relations=rel_types,
+   prompt_template=prompt_template,
+   from_pdf=True
+)
+
+pdf_file_paths = ['truncated-pdfs/biomolecules-11-00928-v2-trunc.pdf',
+            'truncated-pdfs/GAP-between-patients-and-clinicians_2023_Best-Practice-trunc.pdf',
+            'truncated-pdfs/pgpm-13-39-trunc.pdf']
+
+for path in pdf_file_paths:
+    print(f"Processing : {path}")
+    pdf_result = await kg_builder_pdf.run_async(file_path=path)
+    print(f"Result: {pdf_result}")
+----
+
+image::https://dist.neo4j.com/wp-content/uploads/20241015075652/document-chunk-entity.png[width=800]
+
+Then running the GraphRAG Search with the VectorCypher Retriever.
+
+[source,python]
+----
+from neo4j_graphrag.indexes import create_vector_index
+
+create_vector_index(driver, name="text_embeddings", label="Chunk",
+                   embedding_property="embedding", dimensions=1536, similarity_fn="cosine")
+
+# Vector Retriever
+from neo4j_graphrag.retrievers import VectorRetriever
+
+vector_retriever = VectorRetriever(
+   driver,
+   index_name="text_embeddings",
+   embedder=embedder,
+   return_properties=["text"],
+)
+
+# GraphRAG Vector Cypher Retriever
+from neo4j_graphrag.retrievers import VectorCypherRetriever
+
+graph_retriever = VectorCypherRetriever(
+    driver,
+    index_name="text_embeddings",
+    embedder=embedder,
+    retrieval_query="""
+//1) Go out 2-3 hops in the entity graph and get relationships
+WITH node AS chunk
+MATCH (chunk)<-[:FROM_CHUNK]-(entity)-[relList:!FROM_CHUNK]-{1,2}(nb)
+UNWIND relList AS rel
+
+//2) collect relationships and text chunks
+WITH collect(DISTINCT chunk) AS chunks, collect(DISTINCT rel) AS rels
+
+//3) format and return context
+RETURN apoc.text.join([c in chunks | c.text], '\n') + 
+  apoc.text.join([r in rels | 
+  startNode(r).name+' - '+type(r)+' '+r.details+' -> '+endNode(r).name],  
+  '\n') AS info
+"""
+)
+
+llm = LLM(model_name="gpt-4o",  model_params={"temperature": 0.0})
+
+rag_template = RagTemplate(template='''Answer the Question using the following Context. Only respond with information mentioned in the Context. Do not inject any speculative information not mentioned. 
+
+# Question:
+{query_text}
+ 
+# Context:
+{context}
+
+# Answer:
+''', expected_inputs=['query_text', 'context'])
+
+vector_rag  = GraphRAG(llm=llm, retriever=vector_retriever, prompt_template=rag_template)
+
+graph_rag = GraphRAG(llm=llm, retriever=graph_retriever, prompt_template=rag_template)
+
+q = "Can you summarize systemic lupus erythematosus (SLE)? including common effects, biomarkers, and treatments? Provide in detailed list format."
+
+vector_rag.search(q, retriever_config={'top_k':5}).answer
+graph_rag.search(q, retriever_config={'top_k':5}).answer
+----
+
+image::https://dist.neo4j.com/wp-content/uploads/20241128072906/Bildschirmfoto-2024-11-19-um-17.31.45.png[]
+
+== Documentation
+
+[cols="1,4"]
+|===
+| icon:book[] Documentation | https://neo4j.com/docs/neo4j-graphrag-python/current/
+| icon:book[] Guides | https://neo4j.com/docs/neo4j-graphrag-python/current/user_guide_rag.html[RAG & GraphRAG^]
+| icon:book[] Guides | https://neo4j.com/docs/neo4j-graphrag-python/current/user_guide_kg_builder.html[Guide Knowledge Graph Builder^]
+
+|===
+
+== Relevant Links
+
+[cols="1,4"]
+|===
+| icon:user[] Authors | Neo4j Engineering
+| icon:github[] Repository | https://github.com/neo4j/neo4j-graphrag-python[GitHub]
+| icon:github[] Issues | https://github.com/neo4j/neo4j-graphrag-python/issues
+|===
+
+
+== Videos & Tutorials
+
+++++
+<iframe width="560" height="315" src="https://www.youtube.com/embed/hDJlruy60AM?si=TEFW1mj91qrQnaeX" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+++++
+
+++++
+<iframe width="560" height="315" src="https://www.youtube.com/embed/OALrsghrP_I?si=Yw08z6fiCp3y_L0j" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+++++
+
+== Highlighted Articles
+
+* https://neo4j.com/blog/graphrag-python-package/[GraphRAG Python Package: Accelerating GenAI With Knowledge Graphs^]
+* https://neo4j.com/developer-blog/get-started-graphrag-python-package/[Getting Started With the Neo4j GraphRAG Python Package^]
+* https://neo4j.com/developer-blog/graph-traversal-graphrag-python-package/[Vector Search With Graph Traversal the Using Neo4j GraphRAG Package^]
+* https://neo4j.com/developer-blog/hybrid-retrieval-graphrag-python-package/[Hybrid Retrieval Using the Neo4j GraphRAG Package for Python^]
+* https://neo4j.com/developer-blog/enhancing-hybrid-retrieval-graphrag-python-package/[Enhancing Hybrid Retrieval With Graph Traversal: Neo4j GraphRAG Python^]

modules/genai-ecosystem/pages/index.adoc

@@ -8,13 +8,7 @@ include::_graphacademy_llm.adoc[]
 :neo4j-versions: 5.X
 :page-pagination:
 :page-product: GenAI Ecosystem
-// Developer survey
-:page-ad-icon: ~
-:page-ad-title: Neo4j Developer Survey
-:page-ad-description: Your input matters! Share your Feedback
-:page-ad-underline-role: button
-:page-ad-underline: Start Here
-:page-ad-link: https://neo4j.typeform.com/to/E6yOZ2Py?utm_source=GA&utm_medium=blurb&utm_campaign=survey
+include::_developer_survey.adoc[]
 
 
 image::https://dist.neo4j.com/wp-content/uploads/20231030151119/genai-art-diagram-1.svg[width=800]
@@ -79,6 +73,7 @@ Neo4j has worked with the main cloud providers to create GenAI integrations and
 
 Neo4j added a number of features to make it easier to build GenAI applications and integrate LLMs with knowledge graphs.
 
+* xref:graphrag-python.adoc[GraphRAG Package (Python)]
 * xref:vector-search.adoc[Vector Index & Search]
 * xref:apoc-genai.adoc[APOC GenAI Procedures]
 

modules/genai-ecosystem/pages/langchain.adoc

@@ -7,13 +7,7 @@ include::_graphacademy_llm.adoc[]
 :neo4j-versions: 5.x
 :page-pagination:
 :page-product: langchain
-// Developer survey
-:page-ad-icon: ~
-:page-ad-title: Neo4j Developer Survey
-:page-ad-description: Your input matters! Share your Feedback
-:page-ad-underline-role: button
-:page-ad-underline: Start Here
-:page-ad-link: https://neo4j.typeform.com/to/E6yOZ2Py?utm_source=GA&utm_medium=blurb&utm_campaign=survey
+include::_developer_survey.adoc[]
 
 
 image::https://dist.neo4j.com/wp-content/uploads/20230615211357/1AH05dvGA_7db_EMySc9AAw.png[width=800]

modules/genai-ecosystem/pages/llamaindex.adoc

@@ -6,14 +6,7 @@
 :neo4j-versions: 5.x
 :page-pagination:
 :page-product: llamaindex
-// Developer survey
-:page-ad-icon: ~
-:page-ad-title: Neo4j Developer Survey
-:page-ad-description: Your input matters! Share your Feedback
-:page-ad-underline-role: button
-:page-ad-underline: Start Here
-:page-ad-link: https://neo4j.typeform.com/to/E6yOZ2Py?utm_source=GA&utm_medium=blurb&utm_campaign=survey
-
+include::_developer_survey.adoc[]
 
 // image::todo.png[width=800]
 

modules/genai-ecosystem/pages/llm-graph-builder-deployment.adoc

@@ -171,7 +171,7 @@ uvicorn score:app --reload
 | Env Variable Name | Mandatory/Optional | Default Value | Description
 | VITE_BACKEND_API_URL | Optional | http://localhost:8000 | URL for backend API
 | VITE_SOURCES | Optional | local,youtube,wiki,s3 | List of input sources that will be available
-| VITE_BLOOM_URL | Optional | https://workspace-preview.neo4j.io/workspace/explore?connectURL={CONNECT_URL}&search=Show+me+a+graph | URL for Bloom visualization
+| VITE_BLOOM_URL | Optional | https://workspace-preview.neo4j.io/workspace/explore?connectURL=\{CONNECT_URL\}&search=Show+me+a+graph | URL for Bloom visualization
 | VITE_LLM_MODELS | Mandatory | diffbot,gpt-4o,gpt-4o-mini | Comma separated list of LLM Model names to show in the selector
 |===
 

modules/genai-ecosystem/pages/llm-graph-builder.adoc

@@ -9,13 +9,7 @@ include::_graphacademy_llm.adoc[]
 :page-pagination:
 :page-product: llm-graph-builder
 :imagesdir: https://dev.assets.neo4j.com/wp-content/uploads/2024/
-// Developer survey
-:page-ad-icon: ~
-:page-ad-title: Neo4j Developer Survey
-:page-ad-description: Your input matters! Share your Feedback
-:page-ad-underline-role: button
-:page-ad-underline: Start Here
-:page-ad-link: https://neo4j.typeform.com/to/E6yOZ2Py?utm_source=GA&utm_medium=blurb&utm_campaign=survey
+include::_developer_survey.adoc[]
 
 
 // image::llm-graph-builder.png[width=600, align=center]

modules/genai-ecosystem/pages/rag-demo.adoc

@@ -7,13 +7,8 @@ include::_graphacademy_llm.adoc[]
 :neo4j-versions: 5.x
 :page-pagination:
 :page-product: rag-demo
-// Developer survey
-:page-ad-icon: ~
-:page-ad-title: Neo4j Developer Survey
-:page-ad-description: Your input matters! Share your Feedback
-:page-ad-underline-role: button
-:page-ad-underline: Start Here
-:page-ad-link: https://neo4j.typeform.com/to/E6yOZ2Py?utm_source=GA&utm_medium=blurb&utm_campaign=survey
+
+include::_developer_survey.adoc[]
 
 
 image::https://res.cloudinary.com/dk0tizgdn/image/upload/v1707842287/rag-demo-short_vwezew.gif[width=600, align=center]

modules/genai-ecosystem/pages/vector-search.adoc

@@ -1,19 +1,13 @@
 = Neo4j Vector Index and Search
 include::_graphacademy_llm.adoc[]
+include::_developer_survey.adoc[]
 :slug: vector-search
 :author: 
 :category: genai-ecosystem
 :tags: vector index, vector search, embeddings, neo4j
 :neo4j-versions: 5.11+
 :page-pagination:
 :page-product: neo4j
-// Developer survey
-:page-ad-icon: ~
-:page-ad-title: Neo4j Developer Survey
-:page-ad-description: Your input matters! Share your Feedback
-:page-ad-underline-role: button
-:page-ad-underline: Start Here
-:page-ad-link: https://neo4j.typeform.com/to/E6yOZ2Py?utm_source=GA&utm_medium=blurb&utm_campaign=survey
 
 
 image::https://dist.neo4j.com/wp-content/uploads/20230821135317/Grounding-LLM-Responses-with-Implicit-and-Explicit-Search-Through-Neo4js-Knowledge-Graph-2048x1152.png[width=800]

modules/neodash/pages/index.adoc

@@ -5,7 +5,7 @@ For users of the supported NeoDash offering, refer to https://neo4j.com/docs/neo
 
 ====
 
-= NeoDash - Dashboard Builder for Neo4j
+== NeoDash - Dashboard Builder for Neo4j
 :imagesdir: https://s3.amazonaws.com/dev.assets.neo4j.com/wp-content/uploads
 :slug: neodash
 :author: Niels de Jong