Update LLM framework docs (#79)

modules/genai-ecosystem/pages/langchain-js.adoc

@@ -135,6 +135,12 @@ const res = await chain.run("Who acted in Pulp Fiction?");
 // Bruce Willis acted in Pulp Fiction.
 ----
 
+=== Knowledge Graph Construction
+
+Creating a Knowledge Graph from unstructured data like PDF documents used to be a complex and time-consuming task that required training and using dedicated, large NLP models.
+
+The https://js.langchain.com/docs/use_cases/graph/construction[Graph Transformers^] are tools that allows you to extract structured data from unstructured documents and transform it into a Knowledge Graph.
+
 == Documentation
 
 * https://js.langchain.com/docs/modules/data_connection/experimental/graph_databases/neo4j[Neo4jGraph^]

modules/genai-ecosystem/pages/langchain.adoc

@@ -42,6 +42,7 @@ The Neo4j Vector integration supports a number of operations
 * query vector with additional graph retrieval Cypher query
 * construct vector instance from existing graph data
 * hybrid search
+* metadata filtering
 
 // todo include
 [source,python]
@@ -106,6 +107,25 @@ query = "What did the president say about Ketanji Brown Jackson"
 docs_with_score = db.similarity_search_with_score(query, k=2)
 ----
 
+==== Metadata filtering
+
+Metadata filtering enhances vector search by allowing searches to be refined based on specific node properties.
+This integrated approach ensures more precise and relevant search results by leveraging both the vector similarities and the contextual attributes of the nodes.
+
+[source,python]
+----
+db = Neo4jVector.from_documents(
+    docs, 
+    OpenAIEmbeddings(), 
+    url=url, username=username, password=password
+)
+
+query = "What did the president say about Ketanji Brown Jackson"
+filter = {"name": {"$eq": "adam"}}
+
+docs = db.similarity_search(query, filter=filter)
+----
+
 === Neo4j Graph
 
 The Neo4j Graph integration is a wrapper for the Neo4j Python driver. 

modules/genai-ecosystem/pages/llamaindex.adoc

@@ -16,7 +16,107 @@ LlamaIndex provides a lot of detailed examples for GenAI application development
 
 The Neo4j integration covers both the vector store as well as query generation from natural language and knowledge graph construction.
 
-== Example Usage
+== Functionality Includes
+
+=== Neo4jVector
+
+The Neo4j Vector integration supports a number of operations
+
+* create vector from langchain documents
+* query vector
+* query vector with additional graph retrieval Cypher query
+* construct vector instance from existing graph data
+* hybrid search
+* metadata filtering
+
+[source,python]
+----
+%pip install llama-index-llms-openai
+%pip install llama-index-vector-stores-neo4jvector
+%pip install llama-index-embeddings-openai
+%pip install neo4j
+
+import os
+import openai
+from llama_index.vector_stores.neo4jvector import Neo4jVectorStore
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+
+os.environ["OPENAI_API_KEY"] = "OPENAI_API_KEY"
+openai.api_key = os.environ["OPENAI_API_KEY"]
+username = "neo4j"
+password = "pleaseletmein"
+url = "bolt://localhost:7687"
+embed_dim = 1536
+
+neo4j_vector = Neo4jVectorStore(username, password, url, embed_dim)
+# load documents
+documents = SimpleDirectoryReader("./data/paul_graham").load_data()
+from llama_index.core import StorageContext
+
+storage_context = StorageContext.from_defaults(vector_store=neo4j_vector)
+index = VectorStoreIndex.from_documents(
+    documents, storage_context=storage_context
+)
+
+query_engine = index.as_query_engine()
+response = query_engine.query("What happened at interleaf?")
+----
+
+==== Hybrid search
+
+Hybrid search combines vector search with fulltext search with re-ranking and de-duplication of the results.
+
+[source,python]
+----
+neo4j_vector_hybrid = Neo4jVectorStore(
+    username, password, url, embed_dim, hybrid_search=True
+)
+
+storage_context = StorageContext.from_defaults(
+    vector_store=neo4j_vector_hybrid
+)
+index = VectorStoreIndex.from_documents(
+    documents, storage_context=storage_context
+)
+query_engine = index.as_query_engine()
+response = query_engine.query("What happened at interleaf?")
+----
+
+==== Metadata filtering
+
+Metadata filtering enhances vector search by allowing searches to be refined based on specific node properties.
+This integrated approach ensures more precise and relevant search results by leveraging both the vector similarities and the contextual attributes of the nodes.
+
+[source,python]
+----
+from llama_index.core.vector_stores import (
+    MetadataFilter,
+    MetadataFilters,
+    FilterOperator,
+)
+
+filters = MetadataFilters(
+    filters=[
+        MetadataFilter(
+            key="theme", operator=FilterOperator.EQ, value="Fiction"
+        ),
+    ]
+)
+
+retriever = index.as_retriever(filters=filters)
+retriever.retrieve("What is inception about?")
+----
+
+=== Neo4jGraphStore
+
+The Neo4j Graph Store integration is a wrapper for the Neo4j Python driver. 
+It allows querying and updating the Neo4j database in a simplified manner from LlamaIndex.
+Many integrations allow you to use the Neo4j Graph Store as a source of data for LlamaIndex.
+
+==== Knowledge graph index
+
+Knowledge graph index can be used to extract graph representation of information from text and use it to construct a knowledge graph.
+The graph information can then be retrieved in a RAG application for more accurate responses.
 
 [source,python]
 ----
@@ -68,6 +168,41 @@ response = query_engine.query(
 )
 ----
 
+==== Knowledge graph query engine
+
+The Knowledge Graph Query Engine generated Cypher statements based on natural language input to retrieve information from the knowledge graph.
+
+[source,python]
+----
+%pip install llama-index-llms-openai
+%pip install llama-index-graph-stores-neo4j
+%pip install llama-index-embeddings-openai
+%pip install neo4j
+
+from llama_index.llms.openai import OpenAI
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.query_engine import KnowledgeGraphQueryEngine
+from llama_index.graph_stores.neo4j import Neo4jGraphStore
+
+llm=OpenAI(model_name="gpt-3.5-turbo")
+service_context = ServiceContext.from_defaults(llm=llm, chunk_size=256)
+graph_store = Neo4jGraphStore(username=username,password=password,
+    url=url,database=database)
+
+storage_context = StorageContext.from_defaults(graph_store=graph_store)
+query_engine = KnowledgeGraphQueryEngine(
+    storage_context=storage_context,
+    service_context=service_context,
+    llm=llm,
+    verbose=True,
+    refresh_schema=True
+)
+
+response = query_engine.query(
+    "Tell me more about what the author worked on at Interleaf",
+)
+----
+
 == Documentation
 
 * https://docs.llamaindex.ai/en/latest/examples/index_structs/knowledge_graph/Neo4jKGIndexDemo.html[Neo4jKGIndexDemo^]
@@ -115,3 +250,5 @@ This https://llamahub.ai/l/llama_packs-neo4j_query_engine[Neo4j Query Engine Lla
 * https://blog.llamaindex.ai/enriching-llamaindex-models-from-graphql-and-graph-databases-bcaecec262d7[Enriching LlamaIndex Models from GraphQL and Graph Databases^]
 
 * https://levelup.gitconnected.com/a-simpler-way-to-query-neo4j-knowledge-graphs-99c0a8bbf1d7[A Simpler Way to Query Neo4j Knowledge Graphs^]
+
+* https://medium.com/@yu-joshua/using-llamaparse-for-knowledge-graph-creation-from-documents-3bd1e1849754[Using LlamaParse for Knowledge Graph Creation from Documents^]