Make dimensions work dynamically

trustgraph/triple_vectors.py

@@ -7,13 +7,21 @@ class TripleVectors:
 
         self.client = MilvusClient(uri=uri)
 
-        self.collection = "edges"
-        self.dimension = 384
+        # Strategy is to create collections per dimension.  Probably only
+        # going to be using 1 anyway, but that means we don't need to
+        # hard-code the dimension anywhere, and no big deal if more than
+        # one are created.
+        self.collections = {}
 
-        if not self.client.has_collection(collection_name=self.collection):
-            self.init_collection()
+#         self.collection = "edges"
+#         self.dimension = 384
 
-    def init_collection(self):
+#         if not self.client.has_collection(collection_name=self.collection):
+#             self.init_collection()
+
+    def init_collection(self, dimension):
+
+        collection_name = "triples_" + str(dimension)
 
         pkey_field = FieldSchema(
             name="id",
@@ -25,7 +33,7 @@ class TripleVectors:
         vec_field = FieldSchema(
             name="vector",
             dtype=DataType.FLOAT_VECTOR,
-            dim=self.dimension,
+            dim=dimension,
         )
 
         entity_field = FieldSchema(
@@ -40,9 +48,9 @@ class TripleVectors:
         )
 
         self.client.create_collection(
-            collection_name=self.collection,
+            collection_name=collection_name,
             schema=schema,
-            metric_type="IP",
+            metric_type="COSINE",
         )
 
         index_params = MilvusClient.prepare_index_params()
@@ -50,17 +58,24 @@ class TripleVectors:
         index_params.add_index(
             field_name="vector",
             metric_type="COSINE",
-            index_type="FLAT",  # IVF_FLAT?!
+            index_type="IVF_SQ8",
             index_name="vector_index",
             params={ "nlist": 128 }
         )
 
         self.client.create_index(
-            collection_name=self.collection,
+            collection_name=collection_name,
             index_params=index_params
         )
 
+        self.collections[dimension] = collection_name
+
     def insert(self, embeds, entity):
+
+        dim = len(embeds)
+
+        if dim not in self.collections:
+            self.init_collection(dim)
     
         data = [
             {
@@ -69,10 +84,20 @@ class TripleVectors:
             }
         ]
 
-        self.client.insert(collection_name=self.collection, data=data)
+        self.client.insert(
+            collection_name=self.collections[dim],
+            data=data
+        )
 
     def search(self, embeds, fields=["entity"], limit=10):
 
+        dim = len(embeds)
+
+        if dim not in self.collections:
+            self.init_collection(dim)
+
+        coll = self.collections[dim]
+
         search_params = {
             "metric_type": "COSINE",
             "params": {
@@ -82,20 +107,21 @@ class TripleVectors:
         }
 
         self.client.load_collection(
-            collection_name=self.collection,
-#             replica_number=1
+            collection_name=coll,
         )
 
         res = self.client.search(
-            collection_name=self.collection,
+            collection_name=coll,
             data=[embeds],
             limit=limit,
             output_fields=fields,
             search_params=search_params,
         )[0]
 
+        # FIXME: a lot of loading/unloading going on.  How about using a
+        # time window?
         self.client.release_collection(
-            collection_name=self.collection,
+            collection_name=coll,
         )
 
         return res