update rag examples to use fresh faiss index every time

meta-llama · Feb 7, 2025 · 3e7f682 · 3e7f682
1 parent c79cc92
commit 3e7f682
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Showing 3 changed files with 9 additions and 7 deletions.
diff --git a/docs/getting_started.ipynb b/docs/getting_started.ipynb
@@ -89,7 +89,7 @@
         "# install a branch of llama stack\n",
         "import os\n",
         "os.environ[\"UV_SYSTEM_PYTHON\"] = \"1\"\n",
-        "!pip install uv \n",
+        "!pip install uv\n",
         "!uv pip install llama-stack"
       ]
     },
@@ -691,7 +691,7 @@
         "    from google.colab import userdata\n",
         "    os.environ['TOGETHER_API_KEY'] = userdata.get('TOGETHER_API_KEY')\n",
         "    os.environ['TAVILY_SEARCH_API_KEY'] = userdata.get('TAVILY_SEARCH_API_KEY')\n",
-        "except ImportError:    \n",
+        "except ImportError:\n",
         "    print(\"Not in Google Colab environment\")\n",
         "\n",
         "for key in ['TOGETHER_API_KEY', 'TAVILY_SEARCH_API_KEY']:\n",
@@ -1656,6 +1656,7 @@
         }
       ],
       "source": [
+        "import uuid\n",
         "from llama_stack_client.lib.agents.agent import Agent\n",
         "from llama_stack_client.lib.agents.event_logger import EventLogger\n",
         "from llama_stack_client.types.agent_create_params import AgentConfig\n",
@@ -1673,7 +1674,7 @@
         "    for i, url in enumerate(urls)\n",
         "]\n",
         "\n",
-        "vector_db_id = \"test-vector-db\"\n",
+        "vector_db_id = f\"test-vector-db-{uuid.uuid4().hex}\"\n",
         "client.vector_dbs.register(\n",
         "    vector_db_id=vector_db_id,\n",
         "    embedding_model=\"all-MiniLM-L6-v2\",\n",
@@ -3098,7 +3099,7 @@
         }
       ],
       "source": [
-        "# NBVAL_SKIP    \n",
+        "# NBVAL_SKIP\n",
         "print(f\"Getting traces for session_id={session_id}\")\n",
         "import json\n",
         "\n",

diff --git a/docs/source/getting_started/index.md b/docs/source/getting_started/index.md
@@ -173,6 +173,7 @@ Here is an example of a simple RAG (Retrieval Augmented Generation) chatbot agen
 
 ```python
 import os
+import uuid
 from termcolor import cprint
 
 from llama_stack_client.lib.agents.agent import Agent
@@ -214,7 +215,7 @@ documents = [
 ]
 
 # Register a vector database
-vector_db_id = "test-vector-db"
+vector_db_id = f"test-vector-db-{uuid.uuid4().hex}"
 client.vector_dbs.register(
     vector_db_id=vector_db_id,
     embedding_model="all-MiniLM-L6-v2",

diff --git a/tests/client-sdk/agents/test_agents.py b/tests/client-sdk/agents/test_agents.py
@@ -297,7 +297,7 @@ def test_override_system_message_behavior(llama_stack_client, agent_config):
     You are an expert in composing functions. You are given a question and a set of possible functions.
     Based on the question, you may or may not need to make one or more function/tool calls to achieve the purpose.
     If none of the function can be used, don't return [], instead answer the question directly without using functions. If the given question lacks the parameters required by the function,
-    also point it out. 
+    also point it out.
 
     {{ function_description }}
     """
@@ -414,7 +414,7 @@ def test_rag_and_code_agent(llama_stack_client, agent_config):
         )
         for i, url in enumerate(urls)
     ]
-    vector_db_id = "test-vector-db"
+    vector_db_id = f"test-vector-db-{uuid4()}"
     llama_stack_client.vector_dbs.register(
         vector_db_id=vector_db_id,
         embedding_model="all-MiniLM-L6-v2",