From 1ec7216c3ff669eff09f570c1da6bfe1c1283478 Mon Sep 17 00:00:00 2001 From: ehhuang Date: Tue, 21 Oct 2025 11:33:23 -0700 Subject: [PATCH] chore: update quick_start (#3878) # What does this PR do? ## Test Plan --- docs/quick_start.ipynb | 111 ++++++++++++++++++++++++++--------------- 1 file changed, 72 insertions(+), 39 deletions(-) diff --git a/docs/quick_start.ipynb b/docs/quick_start.ipynb index 4ddde693f..0d5ad227c 100644 --- a/docs/quick_start.ipynb +++ b/docs/quick_start.ipynb @@ -126,17 +126,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "J2kGed0R5PSf", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "collapsed": true, "id": "J2kGed0R5PSf", "outputId": "2478ea60-8d35-48a1-b011-f233831740c5" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2mUsing Python 3.12.12 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/test\u001b[0m\n", + "\u001b[2mAudited \u001b[1m52 packages\u001b[0m \u001b[2min 1.56s\u001b[0m\u001b[0m\n", + "\u001b[2mUsing Python 3.12.12 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/test\u001b[0m\n", + "\u001b[2mAudited \u001b[1m3 packages\u001b[0m \u001b[2min 122ms\u001b[0m\u001b[0m\n", + "\u001b[2mUsing Python 3.12.12 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/test\u001b[0m\n", + "\u001b[2mAudited \u001b[1m3 packages\u001b[0m \u001b[2min 197ms\u001b[0m\u001b[0m\n", + "\u001b[2mUsing Python 3.12.12 environment at: /opt/homebrew/Caskroom/miniconda/base/envs/test\u001b[0m\n", + "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 11ms\u001b[0m\u001b[0m\n" + ] + } + ], "source": [ "import os\n", "import subprocess\n", @@ -150,7 +164,7 @@ "def run_llama_stack_server_background():\n", " log_file = open(\"llama_stack_server.log\", \"w\")\n", " process = subprocess.Popen(\n", - " f\"OLLAMA_URL=http://localhost:11434 uv run --with llama-stack llama stack run starter\n", + " f\"OLLAMA_URL=http://localhost:11434 uv run --with llama-stack llama stack run starter\",\n", " shell=True,\n", " stdout=log_file,\n", " stderr=log_file,\n", @@ -200,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "id": "f779283d", "metadata": {}, "outputs": [ @@ -208,8 +222,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Starting Llama Stack server with PID: 787100\n", - "Waiting for server to start\n", + "Starting Llama Stack server with PID: 20778\n", + "Waiting for server to start........\n", "Server is ready!\n" ] } @@ -229,65 +243,84 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "id": "7da71011", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/models \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/files \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector_stores \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/conversations \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/responses \"HTTP/1.1 200 OK\"\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "rag_tool> Ingesting document: https://www.paulgraham.com/greatwork.html\n", "prompt> How do you do great work?\n", - "\u001b[33minference> \u001b[0m\u001b[33m[k\u001b[0m\u001b[33mnowledge\u001b[0m\u001b[33m_search\u001b[0m\u001b[33m(query\u001b[0m\u001b[33m=\"\u001b[0m\u001b[33mWhat\u001b[0m\u001b[33m is\u001b[0m\u001b[33m the\u001b[0m\u001b[33m key\u001b[0m\u001b[33m to\u001b[0m\u001b[33m doing\u001b[0m\u001b[33m great\u001b[0m\u001b[33m work\u001b[0m\u001b[33m\")]\u001b[0m\u001b[97m\u001b[0m\n", - "\u001b[32mtool_execution> Tool:knowledge_search Args:{'query': 'What is the key to doing great work'}\u001b[0m\n", - "\u001b[32mtool_execution> Tool:knowledge_search Response:[TextContentItem(text='knowledge_search tool found 5 chunks:\\nBEGIN of knowledge_search tool results.\\n', type='text'), TextContentItem(text=\"Result 1:\\nDocument_id:docum\\nContent: work. Doing great work means doing something important\\nso well that you expand people's ideas of what's possible. But\\nthere's no threshold for importance. It's a matter of degree, and\\noften hard to judge at the time anyway.\\n\", type='text'), TextContentItem(text=\"Result 2:\\nDocument_id:docum\\nContent: work. Doing great work means doing something important\\nso well that you expand people's ideas of what's possible. But\\nthere's no threshold for importance. It's a matter of degree, and\\noften hard to judge at the time anyway.\\n\", type='text'), TextContentItem(text=\"Result 3:\\nDocument_id:docum\\nContent: work. Doing great work means doing something important\\nso well that you expand people's ideas of what's possible. But\\nthere's no threshold for importance. It's a matter of degree, and\\noften hard to judge at the time anyway.\\n\", type='text'), TextContentItem(text=\"Result 4:\\nDocument_id:docum\\nContent: work. Doing great work means doing something important\\nso well that you expand people's ideas of what's possible. But\\nthere's no threshold for importance. It's a matter of degree, and\\noften hard to judge at the time anyway.\\n\", type='text'), TextContentItem(text=\"Result 5:\\nDocument_id:docum\\nContent: work. Doing great work means doing something important\\nso well that you expand people's ideas of what's possible. But\\nthere's no threshold for importance. It's a matter of degree, and\\noften hard to judge at the time anyway.\\n\", type='text'), TextContentItem(text='END of knowledge_search tool results.\\n', type='text'), TextContentItem(text='The above results were retrieved to help answer the user\\'s query: \"What is the key to doing great work\". Use them as supporting information only in answering this query.\\n', type='text')]\u001b[0m\n", - "\u001b[33minference> \u001b[0m\u001b[33mDoing\u001b[0m\u001b[33m great\u001b[0m\u001b[33m work\u001b[0m\u001b[33m means\u001b[0m\u001b[33m doing\u001b[0m\u001b[33m something\u001b[0m\u001b[33m important\u001b[0m\u001b[33m so\u001b[0m\u001b[33m well\u001b[0m\u001b[33m that\u001b[0m\u001b[33m you\u001b[0m\u001b[33m expand\u001b[0m\u001b[33m people\u001b[0m\u001b[33m's\u001b[0m\u001b[33m ideas\u001b[0m\u001b[33m of\u001b[0m\u001b[33m what\u001b[0m\u001b[33m's\u001b[0m\u001b[33m possible\u001b[0m\u001b[33m.\u001b[0m\u001b[33m However\u001b[0m\u001b[33m,\u001b[0m\u001b[33m there\u001b[0m\u001b[33m's\u001b[0m\u001b[33m no\u001b[0m\u001b[33m threshold\u001b[0m\u001b[33m for\u001b[0m\u001b[33m importance\u001b[0m\u001b[33m,\u001b[0m\u001b[33m and\u001b[0m\u001b[33m it\u001b[0m\u001b[33m's\u001b[0m\u001b[33m often\u001b[0m\u001b[33m hard\u001b[0m\u001b[33m to\u001b[0m\u001b[33m judge\u001b[0m\u001b[33m at\u001b[0m\u001b[33m the\u001b[0m\u001b[33m time\u001b[0m\u001b[33m anyway\u001b[0m\u001b[33m.\u001b[0m\u001b[33m Great\u001b[0m\u001b[33m work\u001b[0m\u001b[33m is\u001b[0m\u001b[33m a\u001b[0m\u001b[33m matter\u001b[0m\u001b[33m of\u001b[0m\u001b[33m degree\u001b[0m\u001b[33m,\u001b[0m\u001b[33m and\u001b[0m\u001b[33m it\u001b[0m\u001b[33m can\u001b[0m\u001b[33m be\u001b[0m\u001b[33m difficult\u001b[0m\u001b[33m to\u001b[0m\u001b[33m determine\u001b[0m\u001b[33m whether\u001b[0m\u001b[33m someone\u001b[0m\u001b[33m has\u001b[0m\u001b[33m done\u001b[0m\u001b[33m great\u001b[0m\u001b[33m work\u001b[0m\u001b[33m until\u001b[0m\u001b[33m after\u001b[0m\u001b[33m the\u001b[0m\u001b[33m fact\u001b[0m\u001b[33m.\u001b[0m\u001b[97m\u001b[0m\n", - "\u001b[30m\u001b[0m" + "🤔 Doing great work involves a combination of skills, habits, and mindsets. Here are some key principles:\n", + "\n", + "1. **Set Clear Goals**: Start with a clear vision of what you want to achieve. Define specific, measurable, achievable, relevant, and time-bound (SMART) goals.\n", + "\n", + "2. **Plan and Prioritize**: Break your goals into smaller, manageable tasks. Prioritize these tasks based on their importance and urgency.\n", + "\n", + "3. **Focus on Quality**: Aim for high-quality outcomes rather than just finishing tasks. Pay attention to detail, and ensure your work meets or exceeds standards.\n", + "\n", + "4. **Stay Organized**: Keep your workspace, both physical and digital, organized to help you stay focused and efficient.\n", + "\n", + "5. **Manage Your Time**: Use time management techniques such as the Pomodoro Technique, time blocking, or the Eisenhower Box to maximize productivity.\n", + "\n", + "6. **Seek Feedback and Learn**: Regularly seek feedback from peers, mentors, or supervisors. Use constructive criticism to improve continuously.\n", + "\n", + "7. **Innovate and Improve**: Look for ways to improve processes or introduce new ideas. Be open to change and willing to adapt.\n", + "\n", + "8. **Stay Motivated and Persistent**: Keep your end goals in mind to stay motivated. Overcome setbacks with resilience and persistence.\n", + "\n", + "9. **Balance and Rest**: Ensure you maintain a healthy work-life balance. Take breaks and manage stress to sustain long-term productivity.\n", + "\n", + "10. **Reflect and Adjust**: Regularly assess your progress and adjust your strategies as needed. Reflect on what works well and what doesn't.\n", + "\n", + "By incorporating these elements, you can consistently produce high-quality work and achieve excellence in your endeavors.\n" ] } ], "source": [ "from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient\n", + "import requests\n", "\n", - "vector_db_id = \"my_demo_vector_db\"\n", + "vector_store_id = \"my_demo_vector_db\"\n", "client = LlamaStackClient(base_url=\"http://0.0.0.0:8321\")\n", "\n", "models = client.models.list()\n", "\n", "# Select the first ollama and first ollama's embedding model\n", "model_id = next(m for m in models if m.model_type == \"llm\" and m.provider_id == \"ollama\").identifier\n", - "embedding_model = next(m for m in models if m.model_type == \"embedding\" and m.provider_id == \"ollama\")\n", - "embedding_model_id = embedding_model.identifier\n", - "embedding_dimension = embedding_model.metadata[\"embedding_dimension\"]\n", "\n", - "_ = client.vector_dbs.register(\n", - " vector_db_id=vector_db_id,\n", - " embedding_model=embedding_model_id,\n", - " embedding_dimension=embedding_dimension,\n", - " provider_id=\"faiss\",\n", - ")\n", + "\n", "source = \"https://www.paulgraham.com/greatwork.html\"\n", - "print(\"rag_tool> Ingesting document:\", source)\n", - "document = RAGDocument(\n", - " document_id=\"document_1\",\n", - " content=source,\n", - " mime_type=\"text/html\",\n", - " metadata={},\n", + "response = requests.get(source)\n", + "file = client.files.create(\n", + " file=response.content,\n", + " purpose='assistants'\n", ")\n", - "client.tool_runtime.rag_tool.insert(\n", - " documents=[document],\n", - " vector_db_id=vector_db_id,\n", - " chunk_size_in_tokens=50,\n", + "vector_store = client.vector_stores.create(\n", + " name=vector_store_id,\n", + " file_ids=[file.id],\n", ")\n", + "\n", "agent = Agent(\n", " client,\n", " model=model_id,\n", " instructions=\"You are a helpful assistant\",\n", " tools=[\n", " {\n", - " \"name\": \"builtin::rag/knowledge_search\",\n", - " \"args\": {\"vector_db_ids\": [vector_db_id]},\n", + " \"type\": \"file_search\",\n", + " \"vector_store_ids\": [vector_store_id],\n", " }\n", " ],\n", ")\n", @@ -302,7 +335,7 @@ ")\n", "\n", "for log in AgentEventLogger().log(response):\n", - " log.print()" + " print(log, end=\"\")" ] }, { @@ -344,7 +377,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -358,7 +391,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.12.12" } }, "nbformat": 4,