Add dspy integration (#72)

* Add dspy integration

* fix tags

modules/genai-ecosystem/pages/dspy.adoc

@@ -0,0 +1,46 @@
+= DSPy Neo4j Integration
+:slug: dspy
+:author: Tomaz Bratanic
+:category: labs
+:tags: dspy, rag, vector search, neo4j
+:neo4j-versions: 5.x
+:page-pagination:
+:page-product: dspy
+
+
+DSPy is a framework for algorithmically optimizing LM prompts and weights, especially when LMs are used one or more times within a pipeline.
+
+The Neo4j integration allows for vector search.
+
+Here is an overview of the https://dspy-docs.vercel.app/api/retrieval_model_clients/Neo4jRM[DSPy Integrations^].
+
+== Installation
+
+[source,shell]
+----
+pip install dspy neo4j
+----
+== Functionality Includes
+
+* `Neo4jRM` - is a typical retriever component which can be used to query vector store index and find related Documents.
+
+[source,python]
+----
+from dspy.retrieve.neo4j_rm import Neo4jRM
+import os
+
+os.environ["NEO4J_URI"] = 'bolt://localhost:7687'
+os.environ["NEO4J_USERNAME"] = 'neo4j'
+os.environ["NEO4J_PASSWORD"] = 'password'
+os.environ["OPENAI_API_KEY"] = 'sk-'
+
+retriever_model = Neo4jRM(
+    index_name="vector",
+    text_node_property="text"
+)
+
+results = retriever_model("Explore the significance of quantum computing", k=3)
+
+for passage in results:
+    print("Document:", passage, "\n")
+----

modules/genai-ecosystem/pages/index.adoc

@@ -54,6 +54,8 @@ image::https://cdn.graphacademy.neo4j.com/assets/img/courses/banners/llm-fundame
 * xref:langchain4j.adoc[LangChain4j]
 * xref:haystack.adoc[Haystack]
 * xref:semantic-kernel.adoc[Semantic Kernel]
+* xref:dspy.adoc[DSPy]
+
 
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