Semantic kernel memory connector#69

examples/semantic_kernel_vector_store.py

@@ -0,0 +1,89 @@
+"""Use dynavec as a Semantic Kernel VectorStore.
+
+pip install "dynavec[sentence-transformers,semantic-kernel]"
+python examples/semantic_kernel_vector_store.py
+"""
+
+import asyncio
+
+from semantic_kernel.data.vector import (
+    FieldTypes,
+    VectorStoreCollectionDefinition,
+    VectorStoreField,
+)
+
+from dynavec import Dynavec, DynavecConfig
+from dynavec.embeddings import SentenceTransformerEmbedder
+from dynavec.integrations.semantic_kernel import DynavecStore
+
+
+async def main():
+    embedder = SentenceTransformerEmbedder(model="all-MiniLM-L6-v2")
+
+    cfg = DynavecConfig(
+        vector_bucket="dynavec-demo",
+        index="sk-vector-store",
+        table="dynavec_sk_vector_store",
+        dimension=embedder.dimension,
+        region="us-east-1",
+        auto_provision=True,
+    )
+
+    db = Dynavec(cfg, embedder=embedder)
+    store = DynavecStore(db)
+
+    definition = VectorStoreCollectionDefinition(
+        fields=[
+            VectorStoreField(field_type=FieldTypes.KEY, name="id"),
+            VectorStoreField(field_type=FieldTypes.DATA, name="text"),
+            VectorStoreField(field_type=FieldTypes.DATA, name="category"),
+            VectorStoreField(
+                field_type=FieldTypes.VECTOR,
+                name="embedding",
+                dimensions=embedder.dimension,
+            ),
+        ],
+    )
+
+    collection = store.get_collection(
+        record_type=dict,
+        definition=definition,
+        collection_name="kb",
+    )
+
+    texts = [
+        "Semantic Kernel is a framework for building AI applications.",
+        "Retrieval-augmented generation grounds answers in your documents.",
+    ]
+    vectors = embedder.embed_documents(texts)
+
+    await collection.upsert(
+        [
+            {
+                "id": "sk-1",
+                "text": texts[0],
+                "category": "ai",
+                "embedding": vectors[0],
+            },
+            {
+                "id": "sk-2",
+                "text": texts[1],
+                "category": "rag",
+                "embedding": vectors[1],
+            },
+        ]
+    )
+
+    query_vector = embedder.embed_query("How can I build an AI application?")
+
+    results = await collection.search(
+        vector=query_vector,
+        top=2,
+    )
+
+    async for result in results.results:
+        print(result.score, "-", result.record["text"])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

pyproject.toml

@@ -52,6 +52,7 @@ ingest = ["pypdf>=4.0", "python-docx>=1.1", "python-pptx>=0.6", "openpyxl>=3.1",
 langchain = ["langchain-core>=0.3"]
 llamaindex = ["llama-index-core>=0.11"]
 crewai = ["crewai>=0.70,!=1.14.0; python_version >= '3.10'"]
+semantic-kernel = ["semantic-kernel>=1.44"]
 haystack = ["haystack-ai>=2.0"]
 dspy = ["dspy>=3.3; python_version >= '3.10'"]
 all = [
@@ -67,6 +68,7 @@ all = [
     "llama-index-core>=0.11",
     "haystack-ai>=2.0",
     "crewai>=0.70,!=1.14.0; python_version >= '3.10'",
+    "semantic-kernel>=1.44",
     "dspy>=3.3; python_version >= '3.10'",
 ]