add qdrant_client

qdrant_client.py

@@ -0,0 +1,36 @@
+from qdrant_client import QdrantClient
+from qdrant_client.http import models
+from langchain_ollama import OllamaEmbeddings
+
+class QdrantStore:
+    def __init__(self, url="http://localhost:6333", collection_name="rag_collection"):
+        self.client = QdrantClient(url=url)
+        self.collection_name = collection_name
+        self._ensure_collection()
+
+    def _ensure_collection(self):
+        if self.collection_name not in self.client.get_collections().collections:
+            self.client.recreate_collection(
+                collection_name=self.collection_name,
+                vectors_config=models.VectorParams(size=384, distance=models.Distance.COSINE),
+            )
+
+    def add_documents(self, documents, titles):
+        embeddings = OllamaEmbeddings(model="nomic-embed-text")
+        vectors = embeddings.embed_documents(documents)
+        payload = [{"title": t} for t in titles]
+        self.client.upsert(
+            collection_name=self.collection_name,
+            points=models.Batch(points=[models.PointStruct(id=i, vector=v, payload=p) for i, (v, p) in enumerate(zip(vectors, payload))])
+        )
+
+    def search(self, query, limit=5):
+        embeddings = OllamaEmbeddings(model="nomic-embed-text")
+        query_vector = embeddings.embed_query(query)
+        results = self.client.search(
+            collection_name=self.collection_name,
+            query_vector=query_vector,
+            limit=limit,
+            with_payload=True,
+        )
+        return [r.payload for r in results]