cookbook vectordb: Qdrant (#1515)

* add qdrant example with agentic rag under vectordb

* consistent with other vector db examples

cookbook/vectordb/qdrant_db.py

@@ -0,0 +1,30 @@
+# pip install qdrant-client 
+from phi.vectordb.qdrant import Qdrant
+from phi.agent import Agent
+from phi.knowledge.pdf import PDFUrlKnowledgeBase
+
+# run qdrant client locally
+"""
+- Run the docker image: docker pull qdrant/qdrant
+- Then, run the service:
+docker run -p 6333:6333 -p 6334:6334 \
+    -v $(pwd)/qdrant_storage:/qdrant/storage:z \
+    qdrant/qdrant
+"""
+COLLECTION_NAME = "thai-recipes"
+
+vector_db = Qdrant(
+    collection=COLLECTION_NAME,
+    url="http://localhost:6333"
+)
+
+knowledge_base = PDFUrlKnowledgeBase(
+    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    vector_db=vector_db,
+)
+
+knowledge_base.load(recreate=False)  # Comment out after first run
+
+# Create and use the agent
+agent = Agent(knowledge_base=knowledge_base, use_tools=True, show_tool_calls=True)
+agent.print_response("List down the ingredients to make Massaman Gai", markdown=True)