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Add Open AI client example for LLAMAExtensions
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Swapna Lekkala
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docs/notebooks/OpenAIClient_with_LLAMAStackExtensions.ipynb
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270
docs/notebooks/OpenAIClient_with_LLAMAStackExtensions.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "notebook-title",
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"metadata": {},
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"source": [
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"# OpenAI Client with LLAMA Stack Extensions\n",
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"\n",
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"This notebook demonstrates how to use the **OpenAI Python client** with **LLAMA Stack server extensions**, allowing you to access LLAMA Stack-specific APIs through familiar OpenAI client patterns.\n",
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"\n",
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"## What You'll Learn\n",
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"\n",
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"1. 🔌 **Connect to LLAMA Stack** using the OpenAI client with a custom base URL\n",
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"2. 🗄️ **Create and manage vector databases** using LLAMA Stack's vector-db API\n",
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"3. 📄 **Insert and query vector data** for semantic search capabilities\n",
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"4. 🌐 **Use low-level HTTP requests** to access LLAMA Stack-specific endpoints\n",
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"\n",
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"## Prerequisites\n",
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"\n",
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"- ✅ LLAMA Stack server running on `localhost:8321`\n",
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"- ✅ Python packages: `pip install openai llama-stack-client`"
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]
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},
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{
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"cell_type": "markdown",
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"id": "setup-section",
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"metadata": {},
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"source": [
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"## 🔧 Setup: Connect OpenAI Client to LLAMA Stack\n",
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"\n",
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"We'll use the OpenAI client but point it to our local LLAMA Stack server instead of OpenAI's servers."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a42d6950-65c2-445e-96f9-6301d36d3a0f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from openai import OpenAI\n",
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"\n",
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"client = OpenAI(base_url=\"http://localhost:8321/v1\", api_key=\"dummy-key\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "vector-db-section",
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"metadata": {},
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"source": [
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"## 🗄️ Create a Vector Database\n",
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"\n",
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"The code above creates a vector database using LLAMA Stack's vector-db API. We're using:\n",
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"- **FAISS** as the backend provider\n",
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"- **sentence-transformers/all-MiniLM-L6-v2** for embeddings (384 dimensions)\n",
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"- A unique identifier `acme_docs_v2` for this database"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e45da4ef-03d9-48f7-a48f-8b5c09f2a46f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Using a low-level request for llama-stack specific API\n",
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"resp = client._client.request(\n",
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" \"POST\",\n",
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" \"/vector-dbs\",\n",
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" json={\n",
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" \"vector_db_id\": \"acme_docs\", # Use a new unique name\n",
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" \"provider_id\": \"faiss\",\n",
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" \"provider_vector_db_id\": \"acme_docs_v2\",\n",
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" \"embedding_model\": \"sentence-transformers/all-MiniLM-L6-v2\",\n",
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" \"embedding_dimension\": 384,\n",
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" },\n",
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")\n",
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"\n",
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"print(resp.json())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "list-dbs-section",
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"metadata": {},
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"source": [
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"## 📋 List All Vector Databases\n",
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"\n",
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"This lists all vector databases registered in the LLAMA Stack server, allowing us to verify our database was created successfully."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "94e3ca3c-3a8a-4e95-91a6-e87aa4221629",
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"metadata": {},
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"outputs": [],
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"source": [
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"resp = client._client.request(\n",
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" \"GET\",\n",
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" \"/vector-dbs\"\n",
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")\n",
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"\n",
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"print(resp.json())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "get-db-section",
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"metadata": {},
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"source": [
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"## 🔍 Retrieve Specific Database Info\n",
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"\n",
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"Get detailed information about our specific vector database, including its configuration and metadata."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e67a8ba3-7785-4087-b0c8-442596fbdf92",
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"metadata": {},
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"outputs": [],
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"source": [
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"resp = client._client.request(\n",
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" \"GET\",\n",
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" \"/vector-dbs/acme_docs_v2\"\n",
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")\n",
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"\n",
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"print(resp.json())"
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]
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},
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{
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"cell_type": "markdown",
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"id": "prepare-data-section",
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"metadata": {},
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"source": [
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"## 📄 Prepare Documents for Vector Storage\n",
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"\n",
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"We create sample company policy documents and convert them into **Chunk** objects that LLAMA Stack can process. Each chunk contains:\n",
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"- **content**: The actual text content\n",
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"- **metadata**: Searchable metadata with a `document_id`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3f08461a-7f9b-439e-bd38-72b68ee9a430",
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"metadata": {},
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"outputs": [],
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"source": [
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"from llama_stack_client.types.vector_io_insert_params import Chunk\n",
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"\n",
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"docs = [\n",
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" (\"Acme ships globally in 3-5 business days.\", {\"title\": \"Shipping Policy\"}),\n",
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" (\"Returns are accepted within 30 days of purchase.\", {\"title\": \"Returns Policy\"}),\n",
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" (\"Support is available 24/7 via chat and email.\", {\"title\": \"Support\"}),\n",
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"]\n",
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"\n",
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"# Convert to Chunk objects\n",
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"chunks = []\n",
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"for _, (content, metadata) in enumerate(docs):\n",
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" # Transform metadata to required format with document_id from title\n",
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" metadata = {\"document_id\": metadata[\"title\"]}\n",
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" chunk = Chunk(\n",
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" content=content, # Required[InterleavedContent]\n",
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" metadata=metadata, # Required[Dict]\n",
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" )\n",
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" chunks.append(chunk)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "insert-data-section",
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"metadata": {},
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"source": [
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"## 📤 Insert Documents into Vector Database\n",
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"\n",
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"Insert our prepared chunks into the vector database. LLAMA Stack will automatically:\n",
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"- Generate embeddings using the specified model\n",
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"- Store the vectors in the FAISS index\n",
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"- Set a TTL (time-to-live) of 1 hour for the chunks"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0fb7f7a7-6b8e-4af4-af93-ecc4fb3e1696",
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"metadata": {},
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"outputs": [],
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"source": [
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"resp = client._client.request(\n",
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" \"POST\",\n",
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" \"/vector-io/insert\",\n",
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" json={\n",
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" \"vector_db_id\": \"acme_docs_v2\",\n",
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" \"chunks\": chunks,\n",
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" \"ttl_seconds\": 3600, # optional\n",
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" }\n",
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")\n",
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"\n",
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"print(resp.status_code)\n",
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"print(resp.json()) # might be empty if API returns None"
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]
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},
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{
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"cell_type": "markdown",
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"id": "search-section",
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"metadata": {},
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"source": [
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"## 🔍 Semantic Search Query\n",
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"\n",
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"Perform a **semantic search** on our documents. The query \"How long does Acme take to ship orders?\" will be converted to an embedding and matched against stored document embeddings to find the most relevant content.\n",
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"\n",
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"The results show the most relevant chunks ranked by semantic similarity, with metadata and content for each match."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "faa7e0ab-aef9-496f-a2bc-90b4eb8bc860",
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"How long does Acme take to ship orders?\"\n",
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"\n",
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"resp = client._client.request(\n",
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" \"POST\",\n",
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" \"/vector-io/query\", # endpoint for vector queries\n",
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" json={\n",
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" \"vector_db_id\": \"acme_docs_v2\",\n",
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" \"query\": query,\n",
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" \"top_k\": 5 # optional, number of results to return\n",
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" }\n",
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")\n",
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"\n",
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"# Convert response to Python dictionary\n",
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"data = resp.json()\n",
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"\n",
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"# Loop through returned chunks\n",
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"for i, chunk in enumerate(data.get(\"chunks\", []), start=1):\n",
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" print(f\"Chunk {i}:\")\n",
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" print(\" Metadata:\", chunk.get(\"metadata\"))\n",
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" print(\" Content :\", chunk.get(\"content\"))\n",
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" print(\"-\" * 50)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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