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update rag examples to use fresh faiss index every time
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3 changed files with 9 additions and 7 deletions
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@ -1656,6 +1656,7 @@
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}
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],
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"source": [
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"import uuid\n",
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"from llama_stack_client.lib.agents.agent import Agent\n",
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"from llama_stack_client.lib.agents.event_logger import EventLogger\n",
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"from llama_stack_client.types.agent_create_params import AgentConfig\n",
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@ -1673,7 +1674,7 @@
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" for i, url in enumerate(urls)\n",
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"]\n",
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"\n",
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"vector_db_id = \"test-vector-db\"\n",
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"vector_db_id = f\"test-vector-db-{uuid.uuid4().hex}\"\n",
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"client.vector_dbs.register(\n",
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" vector_db_id=vector_db_id,\n",
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" embedding_model=\"all-MiniLM-L6-v2\",\n",
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@ -173,6 +173,7 @@ Here is an example of a simple RAG (Retrieval Augmented Generation) chatbot agen
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```python
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import os
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import uuid
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from termcolor import cprint
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from llama_stack_client.lib.agents.agent import Agent
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@ -214,7 +215,7 @@ documents = [
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]
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# Register a vector database
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vector_db_id = "test-vector-db"
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vector_db_id = f"test-vector-db-{uuid.uuid4().hex}"
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client.vector_dbs.register(
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vector_db_id=vector_db_id,
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embedding_model="all-MiniLM-L6-v2",
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@ -414,7 +414,7 @@ def test_rag_and_code_agent(llama_stack_client, agent_config):
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)
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for i, url in enumerate(urls)
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]
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vector_db_id = "test-vector-db"
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vector_db_id = f"test-vector-db-{uuid4()}"
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llama_stack_client.vector_dbs.register(
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vector_db_id=vector_db_id,
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embedding_model="all-MiniLM-L6-v2",
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