updated with vllm based values

rh-pre-commit.version: 2.3.2
rh-pre-commit.check-secrets: ENABLED

examples/demo_script.py

@@ -0,0 +1,103 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient
+
+# vector_db_id = "my_demo_vector_db"
+vector_db_name = "my_demo_vector_db"
+
+# Initialize client with telemetry headers
+# All API calls will automatically generate traces sent to Jaeger
+client = LlamaStackClient(
+    base_url="http://localhost:8321",
+    default_headers={
+        "X-Telemetry-Service": "llama-stack-rag-demo",
+        "X-Telemetry-Version": "1.0.0",
+    }
+)
+
+print("=" * 80)
+print("🔭 Telemetry enabled: Traces will be sent to Jaeger")
+print("   View traces at: http://localhost:16686")
+print("   Service name: llama-stack-rag-demo")
+print("=" * 80)
+print()
+
+models = client.models.list()
+
+# Select the first LLM from vLLM provider and first embedding model
+model_id = next(m for m in models if m.model_type == "llm" and m.provider_id == "vllm").identifier
+embedding_model_id = (
+    em := next(m for m in models if m.model_type == "embedding")
+).identifier
+embedding_dimension = em.metadata["embedding_dimension"]
+
+# ✅ FIXED: Use vector_stores.create instead of vector_dbs.register
+vector_store = client.vector_stores.create(
+    name=vector_db_name,
+    extra_body={
+        "embedding_model": embedding_model_id,
+    },
+)
+vector_db_id = vector_store.id
+
+
+# vector_db = client.vector_dbs.register(
+#     vector_db_id=vector_db_id,
+#     embedding_model=embedding_model_id,
+#     embedding_dimension=embedding_dimension,
+#     provider_id="faiss",
+# )
+# vector_db_id = vector_db.identifier
+source = "https://www.paulgraham.com/greatwork.html"
+print("rag_tool> Ingesting document:", source)
+document = RAGDocument(
+    document_id="document_1",
+    content=source,
+    mime_type="text/html",
+    metadata={},
+)
+client.tool_runtime.rag_tool.insert(
+    documents=[document],
+    vector_db_id=vector_db_id,
+    chunk_size_in_tokens=100,
+)
+agent = Agent(
+    client,
+    model=model_id,
+    instructions="You are a helpful assistant",
+    tools=[
+        {
+            "name": "builtin::rag/knowledge_search",
+            "args": {"vector_db_ids": [vector_db_id]},
+        }
+    ],
+)
+
+prompt = "How do you do great work?"
+print("prompt>", prompt)
+
+use_stream = True
+response = agent.create_turn(
+    messages=[{"role": "user", "content": prompt}],
+    session_id=agent.create_session("rag_session"),
+    stream=use_stream,
+)
+
+# Only call `AgentEventLogger().log(response)` for streaming responses.
+if use_stream:
+    for log in AgentEventLogger().log(response):
+        log.print()
+else:
+    print(response)
+
+print()
+print("=" * 80)
+print("✅ Demo completed!")
+print("🔭 View telemetry traces in Jaeger UI: http://localhost:16686")
+print("   - Service: llama-stack-rag-demo")
+print("   - Look for traces showing RAG operations, inference calls, and tool execution")
+print("=" * 80)