Initiaal push

pyproject.toml

@@ -23,7 +23,9 @@ classifiers = [
 
 [tool.poetry.dependencies]
 python = "^3.8.1"
+sentence_transformers = "*"
 transformers = "*"
+qdrant_client = "*"
 openai = "0.28.0"
 langchain = "*"
 asyncio = "*"

swarms/memory/qdrant.py

@@ -1,6 +1,59 @@
-"""
+from httpx import RequestError
-QDRANT MEMORY CLASS
+from qdrant_client import QdrantClient
+from qdrant_client.http.models import Distance, VectorParams, PointStruct
+from sentence_transformers import SentenceTransformer
+class Qdrant:
+    def __init__(self,api_key, host, port=6333, collection_name="qdrant", model_name="BAAI/bge-small-en-v1.5", https=True ):
+        self.client = QdrantClient(url=host, port=port, api_key=api_key) #, port=port, api_key=api_key, https=False
+        self.collection_name = collection_name
+        self._load_embedding_model(model_name)
+        self._setup_collection()
 
+    def _load_embedding_model(self, model_name):
+        # Load the embedding model
+        self.model = SentenceTransformer(model_name)
 
+    def _setup_collection(self):
+        # Check if the collection already exists
+        try:
+            exists = self.client.get_collection(self.collection_name)
+            return
+        except Exception:
+            # Collection does not exist, create it
+            self.client.create_collection(
+                collection_name=self.collection_name,
+                vectors_config=VectorParams(size=self.model.get_sentence_embedding_dimension(), distance=Distance.DOT),
+            )
+            print(f"Collection '{self.collection_name}' created.")
+        else:
+            print(f"Collection '{self.collection_name}' already exists.")
 
-"""
+    def add_vectors(self, docs):
+        # Add vectors with payloads to the collection
+        points = []
+        for i, doc in enumerate(docs):
+            if doc.page_content:
+                embedding = self.model.encode(doc.page_content, normalize_embeddings=True)
+                points.append(PointStruct(id=i + 1, vector=embedding, payload={"content":doc.page_content}))
+            else:
+                print(f"Document at index {i} is missing 'text' or 'payload' key")
+
+        operation_info = self.client.upsert(
+            collection_name=self.collection_name,
+            wait=True,
+            points=points,
+        )
+        print(operation_info)
+    def search_vectors(self, query, limit=3):
+        query_vector= self.model.encode(query, normalize_embeddings=True)
+        # Search for similar vectors
+        search_result = self.client.search(
+            collection_name=self.collection_name, 
+            query_vector=query_vector, 
+            limit=limit
+        )
+        return search_result
+
+
+
+#TODO, use kwargs in constructor, have search result be text

swarms/memory/qdrant/usage.py

@@ -0,0 +1,18 @@
+from langchain.document_loaders import CSVLoader
+from swarms.memory import qdrant
+
+loader = CSVLoader(file_path="../document_parsing/aipg/aipg.csv", encoding='utf-8-sig')
+docs = loader.load()
+
+
+# Initialize the Qdrant instance
+# See qdrant documentation on how to run locally
+qdrant_client = qdrant.Qdrant(host ="https://697ea26c-2881-4e17-8af4-817fcb5862e8.europe-west3-0.gcp.cloud.qdrant.io", collection_name="qdrant", api_key="BhG2_yINqNU-aKovSEBadn69Zszhbo5uaqdJ6G_qDkdySjAljvuPqQ")
+qdrant_client.add_vectors(docs)
+
+# Perform a search
+search_query = "Who is jojo"
+search_results = qdrant_client.search_vectors(search_query)
+print("Search Results:")
+for result in search_results:
+    print(result)