diff --git a/docs/notebooks/crewai/Llama_Stack_CrewAI.ipynb b/docs/notebooks/crewai/Llama_Stack_CrewAI.ipynb
index 0ac42c835..98d554372 100644
--- a/docs/notebooks/crewai/Llama_Stack_CrewAI.ipynb
+++ b/docs/notebooks/crewai/Llama_Stack_CrewAI.ipynb
@@ -13,14 +13,14 @@
"\n",
"### Overview\n",
"\n",
- "- **LlamaStack**: Provides the infrastructure for running LLMs and vector databases.\n",
+ "- **LlamaStack**: Provides the infrastructure for running LLMs and vector store.\n",
"- **CrewAI**: Offers a framework for orchestrating agents and tasks.\n",
"- **Integration**: Leverages LlamaStack's OpenAI-compatible API with CrewAI.\n",
"\n",
"### What You Will Learn\n",
"\n",
"1. How to set up and start the LlamaStack server using the Together AI provider.\n",
- "2. How to create and manage vector databases within LlamaStack.\n",
+ "2. How to create and manage vector stores within LlamaStack.\n",
"3. How to build RAG tool with CrewAI by utilizing the LlamaStack server.\n",
"4. How to query the RAG tool for effective information retrieval and generation.\n",
"\n",
@@ -38,7 +38,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"id": "5b6a6a17-b931-4bea-8273-0d6e5563637a",
"metadata": {
"colab": {
@@ -59,7 +59,7 @@
]
},
{
- "name": "stdin",
+ "name": "stdout",
"output_type": "stream",
"text": [
"TOGETHER_API_KEY environment variable is not set. Please enter your API key: ········\n"
@@ -109,7 +109,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"id": "dd2dacf3-ec8b-4cc7-8ff4-b5b6ea4a6e9e",
"metadata": {
"colab": {
@@ -128,32 +128,32 @@
"Environment '/Users/kaiwu/work/kaiwu/llama-stack/.venv' already exists, re-using it.\n",
"Virtual environment /Users/kaiwu/work/kaiwu/llama-stack/.venv is already active\n",
"\u001b[2mUsing Python 3.12.9 environment at: /Users/kaiwu/work/kaiwu/llama-stack/.venv\u001b[0m\n",
- "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 206ms\u001b[0m\u001b[0m\n",
+ "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 186ms\u001b[0m\u001b[0m\n",
"Installing pip dependencies\n",
"\u001b[2mUsing Python 3.12.9 environment at: /Users/kaiwu/work/kaiwu/llama-stack/.venv\u001b[0m\n",
- "\u001b[2K\u001b[2mResolved \u001b[1m185 packages\u001b[0m \u001b[2min 2.02s\u001b[0m\u001b[0m \u001b[0m\n",
- "\u001b[2mUninstalled \u001b[1m3 packages\u001b[0m \u001b[2min 247ms\u001b[0m\u001b[0m\n",
- "\u001b[2K\u001b[2mInstalled \u001b[1m3 packages\u001b[0m \u001b[2min 97ms\u001b[0m\u001b[0m \u001b[0m\n",
+ "\u001b[2K\u001b[2mResolved \u001b[1m185 packages\u001b[0m \u001b[2min 1.29s\u001b[0m\u001b[0m \u001b[0m\n",
+ "\u001b[2mUninstalled \u001b[1m3 packages\u001b[0m \u001b[2min 285ms\u001b[0m\u001b[0m\n",
+ "\u001b[2K\u001b[2mInstalled \u001b[1m3 packages\u001b[0m \u001b[2min 105ms\u001b[0m\u001b[0m \u001b[0m\n",
" \u001b[31m-\u001b[39m \u001b[1mprotobuf\u001b[0m\u001b[2m==5.29.5\u001b[0m\n",
" \u001b[32m+\u001b[39m \u001b[1mprotobuf\u001b[0m\u001b[2m==5.29.4\u001b[0m\n",
" \u001b[31m-\u001b[39m \u001b[1mruamel-yaml\u001b[0m\u001b[2m==0.18.14\u001b[0m\n",
" \u001b[32m+\u001b[39m \u001b[1mruamel-yaml\u001b[0m\u001b[2m==0.17.40\u001b[0m\n",
" \u001b[31m-\u001b[39m \u001b[1mruff\u001b[0m\u001b[2m==0.12.5\u001b[0m\n",
" \u001b[32m+\u001b[39m \u001b[1mruff\u001b[0m\u001b[2m==0.9.10\u001b[0m\n",
- "Installing special provider module: torch torchtune>=0.5.0 torchao>=0.12.0 --extra-index-url https://download.pytorch.org/whl/cpu\n",
- "\u001b[2mUsing Python 3.12.9 environment at: /Users/kaiwu/work/kaiwu/llama-stack/.venv\u001b[0m\n",
- "\u001b[2mAudited \u001b[1m3 packages\u001b[0m \u001b[2min 169ms\u001b[0m\u001b[0m\n",
- "Installing special provider module: sentence-transformers --no-deps\n",
- "\u001b[2mUsing Python 3.12.9 environment at: /Users/kaiwu/work/kaiwu/llama-stack/.venv\u001b[0m\n",
- "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 43ms\u001b[0m\u001b[0m\n",
"Installing special provider module: torch torchvision torchao>=0.12.0 --extra-index-url https://download.pytorch.org/whl/cpu\n",
"\u001b[2mUsing Python 3.12.9 environment at: /Users/kaiwu/work/kaiwu/llama-stack/.venv\u001b[0m\n",
- "\u001b[2mAudited \u001b[1m3 packages\u001b[0m \u001b[2min 29ms\u001b[0m\u001b[0m\n",
+ "\u001b[2mAudited \u001b[1m3 packages\u001b[0m \u001b[2min 66ms\u001b[0m\u001b[0m\n",
+ "Installing special provider module: sentence-transformers --no-deps\n",
+ "\u001b[2mUsing Python 3.12.9 environment at: /Users/kaiwu/work/kaiwu/llama-stack/.venv\u001b[0m\n",
+ "\u001b[2mAudited \u001b[1m1 package\u001b[0m \u001b[2min 63ms\u001b[0m\u001b[0m\n",
+ "Installing special provider module: torch torchtune>=0.5.0 torchao>=0.12.0 --extra-index-url https://download.pytorch.org/whl/cpu\n",
+ "\u001b[2mUsing Python 3.12.9 environment at: /Users/kaiwu/work/kaiwu/llama-stack/.venv\u001b[0m\n",
+ "\u001b[2mAudited \u001b[1m3 packages\u001b[0m \u001b[2min 95ms\u001b[0m\u001b[0m\n",
"\u001b[32mBuild Successful!\u001b[0m\n",
"\u001b[34mYou can find the newly-built distribution here: /Users/kaiwu/.llama/distributions/starter/starter-run.yaml\u001b[0m\n",
"\u001b[32mYou can run the new Llama Stack distro via: \u001b[34mllama stack run /Users/kaiwu/.llama/distributions/starter/starter-run.yaml --image-type venv\u001b[0m\u001b[0m\n",
- "Starting Llama Stack server with PID: 97967\n",
- "Waiting for server to start.................\n",
+ "Starting Llama Stack server with PID: 26783\n",
+ "Waiting for server to start..........\n",
"Server is ready!\n"
]
}
@@ -231,7 +231,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"id": "ab4eff97-4565-4c73-b1b3-0020a4c7e2a5",
"metadata": {
"id": "ab4eff97-4565-4c73-b1b3-0020a4c7e2a5"
@@ -260,7 +260,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"id": "880443ef-ac3c-48b1-a80a-7dab5b25ac61",
"metadata": {
"id": "880443ef-ac3c-48b1-a80a-7dab5b25ac61",
@@ -268,6 +268,13 @@
"scrolled": true
},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Available models:\n"
+ ]
+ },
{
"name": "stderr",
"output_type": "stream",
@@ -280,7 +287,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Available models:\n",
+ "- llama_api/Llama-3.3-70B-Instruct\n",
+ "- llama_api/Llama-4-Maverick-17B-128E-Instruct-FP8\n",
+ "- llama_api/Llama-4-Scout-17B-16E-Instruct-FP8\n",
+ "- meta-llama/Llama-3.3-70B-Instruct\n",
+ "- meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8\n",
+ "- meta-llama/Llama-4-Scout-17B-16E-Instruct-FP8\n",
"- fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct\n",
"- fireworks/accounts/fireworks/models/llama-v3p1-70b-instruct\n",
"- fireworks/accounts/fireworks/models/llama-v3p1-405b-instruct\n",
@@ -375,21 +387,21 @@
"id": "b0f28603-3207-4157-b731-638d93cd82b5"
},
"source": [
- "### 4. Vector Database Setup\n",
+ "### 4. Vector Store Setup\n",
"\n",
- "#### Register a Vector Database\n",
+ "#### Create a Vector Store with File Upload\n",
"\n",
- "Create a FAISS vector database for storing document embeddings:\n",
+ "Create a vector store using the OpenAI-compatible vector stores API:\n",
"\n",
- "- **Vector DB ID**: Unique identifier for the database\n",
- "- **Provider**: FAISS (Facebook AI Similarity Search)\n",
+ "- **Vector Store**: OpenAI-compatible vector store for document storage\n",
+ "- **File Upload**: Automatic chunking and embedding of uploaded files\n",
"- **Embedding Model**: Sentence Transformers model for text embeddings\n",
"- **Dimensions**: 384-dimensional embeddings"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"id": "0f241d81-19a7-451f-ac4e-2869a29300d1",
"metadata": {
"id": "0f241d81-19a7-451f-ac4e-2869a29300d1",
@@ -400,57 +412,30 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-dbs \"HTTP/1.1 200 OK\"\n",
- "INFO:httpx:HTTP Request: GET http://0.0.0.0:8321/v1/vector-dbs \"HTTP/1.1 200 OK\"\n"
+ "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/files \"HTTP/1.1 200 OK\"\n",
+ "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/files \"HTTP/1.1 200 OK\"\n",
+ "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/files \"HTTP/1.1 200 OK\"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Registered new vector DB: VectorDBRegisterResponse(embedding_dimension=384, embedding_model='sentence-transformers/all-MiniLM-L6-v2', identifier='acme_docs', provider_id='faiss', type='vector_db', provider_resource_id='acme_docs_v2', vector_db_name=None, owner=None, source='via_register_api')\n",
- "Existing vector DBs: [VectorDBListResponseItem(embedding_dimension=384, embedding_model='sentence-transformers/all-MiniLM-L6-v2', identifier='acme_docs', provider_id='faiss', type='vector_db', provider_resource_id='acme_docs_v2', vector_db_name=None)]\n"
+ "File(id='file-489db9aae0424745960e3408ff0f477f', bytes=41, created_at=1757540912, expires_at=1789076912, filename='shipping_policy.txt', object='file', purpose='assistants')\n",
+ "File(id='file-b2f38b0e164347f5a2b6bbe211e33ff3', bytes=48, created_at=1757540912, expires_at=1789076912, filename='returns_policy.txt', object='file', purpose='assistants')\n",
+ "File(id='file-6f6f157d165a4078b4abef66a095ccd6', bytes=45, created_at=1757540912, expires_at=1789076912, filename='support.txt', object='file', purpose='assistants')\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores \"HTTP/1.1 200 OK\"\n"
]
}
],
"source": [
- "# Register a new clean vector database\n",
- "vector_db = client.vector_dbs.register(\n",
- " vector_db_id=\"acme_docs\", # Use a new unique name\n",
- " provider_id=\"faiss\",\n",
- " provider_vector_db_id=\"acme_docs_v2\",\n",
- " embedding_model=\"sentence-transformers/all-MiniLM-L6-v2\",\n",
- " embedding_dimension=384,\n",
- ")\n",
- "print(\"Registered new vector DB:\", vector_db)\n",
- "\n",
- "# List all registered vector databases\n",
- "dbs = client.vector_dbs.list()\n",
- "print(\"Existing vector DBs:\", dbs)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "pcgjqzfr3eo",
- "metadata": {
- "id": "pcgjqzfr3eo"
- },
- "source": [
- "#### Prepare Sample Documents\n",
- "\n",
- "Create LLAMA Stack Chunks for FAISS vector store"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "5a0a6619-c9fb-4938-8ff3-f84304eed91e",
- "metadata": {
- "id": "5a0a6619-c9fb-4938-8ff3-f84304eed91e"
- },
- "outputs": [],
- "source": [
- "from llama_stack_client.types.vector_io_insert_params import Chunk\n",
+ "from io import BytesIO\n",
"\n",
"docs = [\n",
" (\"Acme ships globally in 3-5 business days.\", {\"title\": \"Shipping Policy\"}),\n",
@@ -458,62 +443,22 @@
" (\"Support is available 24/7 via chat and email.\", {\"title\": \"Support\"}),\n",
"]\n",
"\n",
- "# Convert to Chunk objects\n",
- "chunks = []\n",
- "for _, (content, metadata) in enumerate(docs):\n",
- " # Transform metadata to required format with document_id from title\n",
- " metadata = {\"document_id\": metadata[\"title\"]}\n",
- " chunk = Chunk(\n",
- " content=content, # Required[InterleavedContent]\n",
- " metadata=metadata, # Required[Dict]\n",
- " )\n",
- " chunks.append(chunk)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6bg3sm2ko5g",
- "metadata": {
- "id": "6bg3sm2ko5g"
- },
- "source": [
- "#### Insert Documents into Vector Database\n",
+ "file_ids = []\n",
+ "for content, metadata in docs:\n",
+ " with BytesIO(content.encode()) as file_buffer:\n",
+ " file_buffer.name = f\"{metadata['title'].replace(' ', '_').lower()}.txt\"\n",
+ " create_file_response = client.files.create(file=file_buffer, purpose=\"assistants\")\n",
+ " print(create_file_response)\n",
+ " file_ids.append(create_file_response.id)\n",
"\n",
- "Store the prepared documents in the FAISS vector database. This process:\n",
- "1. Generates embeddings for each document\n",
- "2. Stores embeddings with metadata\n",
- "3. Enables semantic search capabilities"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "0e8740d8-b809-44b9-915f-1e0200e3c3f1",
- "metadata": {
- "id": "0e8740d8-b809-44b9-915f-1e0200e3c3f1",
- "outputId": "ebc275ae-54dd-4566-edb0-f3a067bfcf03"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-io/insert \"HTTP/1.1 200 OK\"\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Documents inserted: None\n"
- ]
- }
- ],
- "source": [
- "# Insert chunks into FAISS vector store\n",
- "\n",
- "response = client.vector_io.insert(vector_db_id=\"acme_docs\", chunks=chunks)\n",
- "print(\"Documents inserted:\", response)"
+ "# Create vector store with files\n",
+ "vector_store = client.vector_stores.create(\n",
+ " name=\"acme_docs\",\n",
+ " file_ids=file_ids,\n",
+ " embedding_model=\"sentence-transformers/all-MiniLM-L6-v2\",\n",
+ " embedding_dimension=384,\n",
+ " provider_id=\"faiss\"\n",
+ ")"
]
},
{
@@ -525,12 +470,12 @@
"source": [
"#### Test Vector Search\n",
"\n",
- "Query the vector database to verify it's working correctly. This performs semantic search to find relevant documents based on the query."
+ "Query the vector store to verify it's working correctly. This performs semantic search to find relevant documents based on the query."
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"id": "4a5e010c-eeeb-4020-a957-74d6d1cba342",
"metadata": {
"id": "4a5e010c-eeeb-4020-a957-74d6d1cba342",
@@ -541,32 +486,27 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-io/query \"HTTP/1.1 200 OK\"\n"
+ "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores/vs_dab05212-db05-402c-91ef-57e41797406b/search \"HTTP/1.1 200 OK\"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "metadata : {'document_id': 'Shipping Policy'}\n",
- "content : Acme ships globally in 3-5 business days.\n",
- "metadata : {'document_id': 'Returns Policy'}\n",
- "content : Returns are accepted within 30 days of purchase.\n",
- "metadata : {'document_id': 'Support'}\n",
- "content : Support is available 24/7 via chat and email.\n"
+ "Acme ships globally in 3-5 business days.\n",
+ "Returns are accepted within 30 days of purchase.\n"
]
}
],
"source": [
- "# Query chunks from FAISS vector store\n",
- "\n",
- "query_chunk_response = client.vector_io.query(\n",
- " vector_db_id=\"acme_docs\",\n",
- " query=\"How long does Acme take to ship orders?\",\n",
+ "search_response = client.vector_stores.search(\n",
+ " vector_store_id=vector_store.id,\n",
+ " query=\"How long does shipping take?\",\n",
+ " max_num_results=2\n",
")\n",
- "for chunk in query_chunk_response.chunks:\n",
- " print(\"metadata\", \":\", chunk.metadata)\n",
- " print(\"content\", \":\", chunk.content)"
+ "for result in search_response.data:\n",
+ " content = result.content[0].text\n",
+ " print(content)"
]
},
{
@@ -589,7 +529,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"id": "c378bd10-09c2-417c-bdfc-1e0a2dd19084",
"metadata": {
"id": "c378bd10-09c2-417c-bdfc-1e0a2dd19084",
@@ -631,7 +571,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"id": "f88ffb5a-657b-4916-9375-c6ddc156c25e",
"metadata": {
"id": "f88ffb5a-657b-4916-9375-c6ddc156c25e",
@@ -642,22 +582,22 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "\u001b[92m10:30:35 - LiteLLM:INFO\u001b[0m: utils.py:3258 - \n",
+ "\u001b[92m14:49:56 - LiteLLM:INFO\u001b[0m: utils.py:3258 - \n",
"LiteLLM completion() model= together/meta-llama/Llama-3.3-70B-Instruct-Turbo; provider = openai\n",
"INFO:LiteLLM:\n",
"LiteLLM completion() model= together/meta-llama/Llama-3.3-70B-Instruct-Turbo; provider = openai\n",
"INFO:httpx:HTTP Request: POST http://localhost:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
- "\u001b[92m10:30:41 - LiteLLM:INFO\u001b[0m: utils.py:1260 - Wrapper: Completed Call, calling success_handler\n",
+ "\u001b[92m14:50:01 - LiteLLM:INFO\u001b[0m: utils.py:1260 - Wrapper: Completed Call, calling success_handler\n",
"INFO:LiteLLM:Wrapper: Completed Call, calling success_handler\n"
]
},
{
"data": {
"text/plain": [
- "\"In the Andes' gentle breeze, a llama's soft eyes gaze with peaceful ease, its fur a warm and fuzzy tease. With steps both gentle and serene, the llama roams, a symbol of calm, its beauty truly unseen.\""
+ "\"In the Andes' gentle breeze, a llama's soft eyes gaze with peaceful ease, its fur a warm and fuzzy tease. With steps both gentle and serene, the llama roams, a symbol of calm, its beauty pure and supreme.\""
]
},
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -680,15 +620,15 @@
"source": [
"#### Create CrewAI Custom Tool\n",
"\n",
- "Define a custom CrewAI tool, `LlamaStackRAGTool`, to encapsulate the logic for querying the LlamaStack vector database. This tool will be used by the CrewAI agent to perform retrieval during the RAG process.\n",
+ "Define a custom CrewAI tool, `LlamaStackRAGTool`, to encapsulate the logic for querying the LlamaStack vector store. This tool will be used by the CrewAI agent to perform retrieval during the RAG process.\n",
"\n",
- "- **Input Schema**: Defines the expected input parameters for the tool, such as the user query, the vector database ID, and optional parameters like `top_k` and `score_threshold`.\n",
- "- **Tool Logic**: Implements the `_run` method, which takes the user query and vector database ID, calls the LlamaStack client's `vector_io.query` method, and formats the retrieved documents into a human-readable string for the LLM to use as context."
+ "- **Input Schema**: Defines the expected input parameters for the tool, such as the user query, the vector store ID, and optional parameters like `top_k`.\n",
+ "- **Tool Logic**: Implements the `_run` method, which takes the user query and vector store ID, calls the LlamaStack client's `vector_stores.search` method, and formats the retrieved documents into a human-readable string for the LLM to use as context."
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 16,
"id": "08de540f-ed47-405a-a9c5-16505f4c88c8",
"metadata": {
"id": "08de540f-ed47-405a-a9c5-16505f4c88c8"
@@ -700,11 +640,11 @@
"from pydantic import BaseModel, Field\n",
"\n",
"# ---------- 1. Input schema ----------\n",
- "class RAGToolInput(BaseModel):\n",
- " \"\"\"Input schema for LlamaStackRAGTool.\"\"\"\n",
+ "class VectorStoreRAGToolInput(BaseModel):\n",
+ " \"\"\"Input schema for LlamaStackVectorStoreRAGTool.\"\"\"\n",
" query: str = Field(..., description=\"The user query for RAG search\")\n",
- " vector_db_id: str = Field(...,\n",
- " description=\"ID of the vector database to search inside the Llama-Stack server\",\n",
+ " vector_store_id: str = Field(...,\n",
+ " description=\"ID of the vector store to search inside the Llama-Stack server\",\n",
" )\n",
" top_k: Optional[int] = Field(\n",
" default=5,\n",
@@ -716,44 +656,41 @@
" )\n",
"\n",
"# ---------- 2. The tool ----------\n",
- "class LlamaStackRAGTool(BaseTool):\n",
- " name: str = \"Llama Stack RAG tool\"\n",
+ "class LlamaStackVectorStoreRAGTool(BaseTool):\n",
+ " name: str = \"Llama Stack Vector Store RAG tool\"\n",
" description: str = (\n",
- " \"This tool calls a Llama-Stack endpoint for retrieval-augmented generation. \"\n",
+ " \"This tool calls a Llama-Stack endpoint for retrieval-augmented generation using a vector store. \"\n",
" \"It takes a natural-language query and returns the most relevant documents.\"\n",
" )\n",
- " args_schema: Type[BaseModel] = RAGToolInput\n",
+ " args_schema: Type[BaseModel] = VectorStoreRAGToolInput\n",
" client: Any\n",
- " vector_db_id: str = \"\"\n",
+ " vector_store_id: str = \"\"\n",
" top_k: int = 5\n",
- " score_threshold: Optional[float] = None\n",
"\n",
" def _run(self, **kwargs: Any) -> str:\n",
" # 1. Resolve parameters (use instance defaults when not supplied)\n",
- " print(kwargs)\n",
" query: str = kwargs.get(\"query\") # Required – schema enforces presence\n",
- " vector_db_id: str = kwargs.get(\"vector_db_id\", self.vector_db_id)\n",
+ " vector_store_id: str = kwargs.get(\"vector_store_id\", self.vector_store_id)\n",
" top_k: int = kwargs.get(\"top_k\", self.top_k)\n",
- " score_threshold: float | None = kwargs.get(\n",
- " \"score_threshold\", self.score_threshold\n",
- " )\n",
- " if vector_db_id == \"\":\n",
- " print('vector_db_id is empty, please specify which vector_db to search')\n",
+ " if vector_store_id == \"\":\n",
+ " print('vector_store_id is empty, please specify which vector_store to search')\n",
" return \"No documents found.\"\n",
" # 2. Issue request to Llama-Stack\n",
- " response: List[dict] = self.client.vector_io.query(\n",
- " vector_db_id=vector_db_id,\n",
+ " response = self.client.vector_stores.search(\n",
+ " vector_store_id=vector_store_id,\n",
" query=query,\n",
- " params={\"max_chunks\": top_k, \"score_threshold\": score_threshold}\n",
+ " max_num_results=top_k,\n",
" )\n",
"\n",
" # 3. Massage results into a single human-readable string\n",
- " if not response:\n",
+ " if not response or not response.data:\n",
" return \"No documents found.\"\n",
"\n",
" docs: List[str] = []\n",
- " for metadata,content in response:\n",
- " docs.append(f\"metadata: {metadata}, content: {content}\")\n",
+ " for result in response.data:\n",
+ " content = result.content[0].text if result.content else \"No content\"\n",
+ " filename = result.filename if result.filename else {}\n",
+ " docs.append(f\"filename: {filename}, content: {content}\")\n",
" return \"\\n\".join(docs)\n"
]
},
@@ -771,7 +708,7 @@
"Construct a CrewAI pipeline that orchestrates the RAG process. This pipeline includes:\n",
"\n",
"1. **Agent Definition**: Defining a CrewAI agent with a specific role (`RAG assistant`), goal, backstory, and the LlamaStack LLM and the custom RAG tool.\n",
- "2. **Task Definition**: Defining a CrewAI task for the agent to perform. The task description includes placeholders for the user query and vector database ID, which will be provided during execution. The task's expected output is an answer to the question based on the retrieved context.\n",
+ "2. **Task Definition**: Defining a CrewAI task for the agent to perform. The task description includes placeholders for the user query and vector store ID, which will be provided during execution. The task's expected output is an answer to the question based on the retrieved context.\n",
"3. **Crew Definition**: Creating a CrewAI `Crew` object with the defined task and agent. This crew represents the complete RAG pipeline.\n",
"\n",
"**CrewAI workflow**:\n",
@@ -780,7 +717,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 17,
"id": "9684427d-dcc7-4544-9af5-8b110d014c42",
"metadata": {
"id": "9684427d-dcc7-4544-9af5-8b110d014c42"
@@ -796,10 +733,10 @@
" backstory=\"You are an experienced search assistant specializing in finding relevant information from documentation and vector_db to answer user questions accurately.\",\n",
" allow_delegation=False,\n",
" llm=llamastack_llm,\n",
- " tools=[LlamaStackRAGTool(client=client)])\n",
+ " tools=[LlamaStackVectorStoreRAGTool(client=client)])\n",
"# ---- 4. Wrap everything in a Crew task ---------------------------\n",
"task = Task(\n",
- " description=\"Answer the following questions: {query}, using the RAG_tool to search the provided vector_db_id {vector_db_id} if needed\",\n",
+ " description=\"Answer the following questions: {query}, using the RAG_tool to search the provided vector_store_id {vector_store_id} if needed\",\n",
" expected_output=\"An answer to the question with provided context\",\n",
" agent=agent,\n",
")\n",
@@ -820,7 +757,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 18,
"id": "03322188-9509-446a-a4a8-ce3bb83ec87c",
"metadata": {
"colab": {
@@ -839,7 +776,7 @@
"│ │\n",
"│ Crew Execution Started │\n",
"│ Name: crew │\n",
- "│ ID: 146be674-7521-4db6-bb70-287b22aa21aa │\n",
+ "│ ID: 091cf919-5c4b-4168-ac49-65fe5e8faa9e │\n",
"│ Tool Args: │\n",
"│ │\n",
"│ │\n",
@@ -851,7 +788,7 @@
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
"\u001b[36m│\u001b[0m \u001b[1;36mCrew Execution Started\u001b[0m \u001b[36m│\u001b[0m\n",
"\u001b[36m│\u001b[0m \u001b[37mName: \u001b[0m\u001b[36mcrew\u001b[0m \u001b[36m│\u001b[0m\n",
- "\u001b[36m│\u001b[0m \u001b[37mID: \u001b[0m\u001b[36m146be674-7521-4db6-bb70-287b22aa21aa\u001b[0m \u001b[36m│\u001b[0m\n",
+ "\u001b[36m│\u001b[0m \u001b[37mID: \u001b[0m\u001b[36m091cf919-5c4b-4168-ac49-65fe5e8faa9e\u001b[0m \u001b[36m│\u001b[0m\n",
"\u001b[36m│\u001b[0m \u001b[37mTool Args: \u001b[0m \u001b[36m│\u001b[0m\n",
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
@@ -877,7 +814,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "0fd1807480b443948be37ef922f4ad0f",
+ "model_id": "cb8f60c158fb4a0496e78e4d596ac4c8",
"version_major": 2,
"version_minor": 0
},
@@ -892,23 +829,25 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "\u001b[92m10:31:29 - LiteLLM:INFO\u001b[0m: utils.py:3258 - \n",
+ "\u001b[92m14:55:09 - LiteLLM:INFO\u001b[0m: utils.py:3258 - \n",
"LiteLLM completion() model= together/meta-llama/Llama-3.3-70B-Instruct-Turbo; provider = openai\n",
"INFO:LiteLLM:\n",
"LiteLLM completion() model= together/meta-llama/Llama-3.3-70B-Instruct-Turbo; provider = openai\n",
"INFO:httpx:HTTP Request: POST http://localhost:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
- "\u001b[92m10:31:30 - LiteLLM:INFO\u001b[0m: utils.py:1260 - Wrapper: Completed Call, calling success_handler\n",
+ "\u001b[92m14:55:11 - LiteLLM:INFO\u001b[0m: utils.py:1260 - Wrapper: Completed Call, calling success_handler\n",
"INFO:LiteLLM:Wrapper: Completed Call, calling success_handler\n"
]
},
{
"data": {
"text/html": [
- "{'query': 'How long does shipping take?', 'vector_db_id': 'acme_docs', 'top_k': 5, 'score_threshold': 0.5}\n",
+ "{'query': 'How long does shipping take?', 'vector_store_id': 'vs_dab05212-db05-402c-91ef-57e41797406b', 'top_k': 1,\n",
+ "'score_threshold': 0.0}\n",
"\n"
],
"text/plain": [
- "{'query': 'How long does shipping take?', 'vector_db_id': 'acme_docs', 'top_k': 5, 'score_threshold': 0.5}\n"
+ "{'query': 'How long does shipping take?', 'vector_store_id': 'vs_dab05212-db05-402c-91ef-57e41797406b', 'top_k': 1,\n",
+ "'score_threshold': 0.0}\n"
]
},
"metadata": {},
@@ -918,13 +857,13 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-io/query \"HTTP/1.1 200 OK\"\n",
- "\u001b[92m10:31:31 - LiteLLM:INFO\u001b[0m: utils.py:3258 - \n",
+ "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores/vs_dab05212-db05-402c-91ef-57e41797406b/search \"HTTP/1.1 200 OK\"\n",
+ "\u001b[92m14:55:11 - LiteLLM:INFO\u001b[0m: utils.py:3258 - \n",
"LiteLLM completion() model= together/meta-llama/Llama-3.3-70B-Instruct-Turbo; provider = openai\n",
"INFO:LiteLLM:\n",
"LiteLLM completion() model= together/meta-llama/Llama-3.3-70B-Instruct-Turbo; provider = openai\n",
"INFO:httpx:HTTP Request: POST http://localhost:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
- "\u001b[92m10:31:33 - LiteLLM:INFO\u001b[0m: utils.py:1260 - Wrapper: Completed Call, calling success_handler\n",
+ "\u001b[92m14:55:12 - LiteLLM:INFO\u001b[0m: utils.py:1260 - Wrapper: Completed Call, calling success_handler\n",
"INFO:LiteLLM:Wrapper: Completed Call, calling success_handler\n"
]
},
@@ -944,7 +883,7 @@
"╭──────────────────────────────────────────────── Task Completion ────────────────────────────────────────────────╮\n",
"│ │\n",
"│ Task Completed │\n",
- "│ Name: b1362fb4-fe9b-479d-a01d-18bf38406780 │\n",
+ "│ Name: cf3f4f08-744c-4aee-9387-e9eb70624fc1 │\n",
"│ Agent: RAG assistant │\n",
"│ Tool Args: │\n",
"│ │\n",
@@ -956,7 +895,7 @@
"\u001b[32m╭─\u001b[0m\u001b[32m───────────────────────────────────────────────\u001b[0m\u001b[32m Task Completion \u001b[0m\u001b[32m───────────────────────────────────────────────\u001b[0m\u001b[32m─╮\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[1;32mTask Completed\u001b[0m \u001b[32m│\u001b[0m\n",
- "\u001b[32m│\u001b[0m \u001b[37mName: \u001b[0m\u001b[32mb1362fb4-fe9b-479d-a01d-18bf38406780\u001b[0m \u001b[32m│\u001b[0m\n",
+ "\u001b[32m│\u001b[0m \u001b[37mName: \u001b[0m\u001b[32mcf3f4f08-744c-4aee-9387-e9eb70624fc1\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[37mAgent: \u001b[0m\u001b[32mRAG assistant\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[37mTool Args: \u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[32m│\u001b[0m\n",
@@ -987,10 +926,9 @@
"│ │\n",
"│ Crew Execution Completed │\n",
"│ Name: crew │\n",
- "│ ID: 146be674-7521-4db6-bb70-287b22aa21aa │\n",
+ "│ ID: 091cf919-5c4b-4168-ac49-65fe5e8faa9e │\n",
"│ Tool Args: │\n",
- "│ Final Output: Acme ships globally in 3-5 business days. Returns are accepted within 30 days of purchase. │\n",
- "│ Support is available 24/7 via chat and email. │\n",
+ "│ Final Output: Acme ships globally in 3-5 business days. │\n",
"│ │\n",
"│ │\n",
"╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
@@ -1001,10 +939,9 @@
"\u001b[32m│\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[1;32mCrew Execution Completed\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[37mName: \u001b[0m\u001b[32mcrew\u001b[0m \u001b[32m│\u001b[0m\n",
- "\u001b[32m│\u001b[0m \u001b[37mID: \u001b[0m\u001b[32m146be674-7521-4db6-bb70-287b22aa21aa\u001b[0m \u001b[32m│\u001b[0m\n",
+ "\u001b[32m│\u001b[0m \u001b[37mID: \u001b[0m\u001b[32m091cf919-5c4b-4168-ac49-65fe5e8faa9e\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[37mTool Args: \u001b[0m \u001b[32m│\u001b[0m\n",
- "\u001b[32m│\u001b[0m \u001b[37mFinal Output: Acme ships globally in 3-5 business days. Returns are accepted within 30 days of purchase. \u001b[0m \u001b[32m│\u001b[0m\n",
- "\u001b[32m│\u001b[0m \u001b[37mSupport is available 24/7 via chat and email.\u001b[0m \u001b[32m│\u001b[0m\n",
+ "\u001b[32m│\u001b[0m \u001b[37mFinal Output: Acme ships globally in 3-5 business days.\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
@@ -1031,13 +968,13 @@
"output_type": "stream",
"text": [
"❓ How long does shipping take?\n",
- "💡 Acme ships globally in 3-5 business days. Returns are accepted within 30 days of purchase. Support is available 24/7 via chat and email.\n"
+ "💡 Acme ships globally in 3-5 business days.\n"
]
}
],
"source": [
"query = \"How long does shipping take?\"\n",
- "response = crew.kickoff(inputs={\"query\": query,\"vector_db_id\": \"acme_docs\"})\n",
+ "response = crew.kickoff(inputs={\"query\": query,\"vector_store_id\": vector_store.id})\n",
"print(\"❓\", query)\n",
"print(\"💡\", response)"
]
@@ -1054,7 +991,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 19,
"id": "61995550-bb0b-46a8-a5d0-023207475d60",
"metadata": {
"colab": {
@@ -1073,7 +1010,7 @@
"│ │\n",
"│ Crew Execution Started │\n",
"│ Name: crew │\n",
- "│ ID: 146be674-7521-4db6-bb70-287b22aa21aa │\n",
+ "│ ID: 091cf919-5c4b-4168-ac49-65fe5e8faa9e │\n",
"│ Tool Args: │\n",
"│ │\n",
"│ │\n",
@@ -1085,7 +1022,7 @@
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
"\u001b[36m│\u001b[0m \u001b[1;36mCrew Execution Started\u001b[0m \u001b[36m│\u001b[0m\n",
"\u001b[36m│\u001b[0m \u001b[37mName: \u001b[0m\u001b[36mcrew\u001b[0m \u001b[36m│\u001b[0m\n",
- "\u001b[36m│\u001b[0m \u001b[37mID: \u001b[0m\u001b[36m146be674-7521-4db6-bb70-287b22aa21aa\u001b[0m \u001b[36m│\u001b[0m\n",
+ "\u001b[36m│\u001b[0m \u001b[37mID: \u001b[0m\u001b[36m091cf919-5c4b-4168-ac49-65fe5e8faa9e\u001b[0m \u001b[36m│\u001b[0m\n",
"\u001b[36m│\u001b[0m \u001b[37mTool Args: \u001b[0m \u001b[36m│\u001b[0m\n",
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
"\u001b[36m│\u001b[0m \u001b[36m│\u001b[0m\n",
@@ -1111,7 +1048,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e1e2dc3abcd745fa80b32ef32b264396",
+ "model_id": "60b83042bfc14a75b555537d13147372",
"version_major": 2,
"version_minor": 0
},
@@ -1126,23 +1063,25 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "\u001b[92m10:31:38 - LiteLLM:INFO\u001b[0m: utils.py:3258 - \n",
+ "\u001b[92m14:55:19 - LiteLLM:INFO\u001b[0m: utils.py:3258 - \n",
"LiteLLM completion() model= together/meta-llama/Llama-3.3-70B-Instruct-Turbo; provider = openai\n",
"INFO:LiteLLM:\n",
"LiteLLM completion() model= together/meta-llama/Llama-3.3-70B-Instruct-Turbo; provider = openai\n",
"INFO:httpx:HTTP Request: POST http://localhost:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
- "\u001b[92m10:31:39 - LiteLLM:INFO\u001b[0m: utils.py:1260 - Wrapper: Completed Call, calling success_handler\n",
+ "\u001b[92m14:55:21 - LiteLLM:INFO\u001b[0m: utils.py:1260 - Wrapper: Completed Call, calling success_handler\n",
"INFO:LiteLLM:Wrapper: Completed Call, calling success_handler\n"
]
},
{
"data": {
"text/html": [
- "{'query': 'return policy after 40 days', 'vector_db_id': 'acme_docs', 'top_k': 1, 'score_threshold': 0.5}\n",
+ "{'query': 'return policy after 40 days', 'vector_store_id': 'vs_dab05212-db05-402c-91ef-57e41797406b', 'top_k': 1, \n",
+ "'score_threshold': 0.5}\n",
"\n"
],
"text/plain": [
- "{'query': 'return policy after 40 days', 'vector_db_id': 'acme_docs', 'top_k': 1, 'score_threshold': 0.5}\n"
+ "{'query': 'return policy after 40 days', 'vector_store_id': 'vs_dab05212-db05-402c-91ef-57e41797406b', 'top_k': 1, \n",
+ "'score_threshold': 0.5}\n"
]
},
"metadata": {},
@@ -1152,13 +1091,13 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/vector-io/query \"HTTP/1.1 200 OK\"\n",
- "\u001b[92m10:31:39 - LiteLLM:INFO\u001b[0m: utils.py:3258 - \n",
+ "INFO:httpx:HTTP Request: POST http://0.0.0.0:8321/v1/openai/v1/vector_stores/vs_dab05212-db05-402c-91ef-57e41797406b/search \"HTTP/1.1 200 OK\"\n",
+ "\u001b[92m14:55:22 - LiteLLM:INFO\u001b[0m: utils.py:3258 - \n",
"LiteLLM completion() model= together/meta-llama/Llama-3.3-70B-Instruct-Turbo; provider = openai\n",
"INFO:LiteLLM:\n",
"LiteLLM completion() model= together/meta-llama/Llama-3.3-70B-Instruct-Turbo; provider = openai\n",
"INFO:httpx:HTTP Request: POST http://localhost:8321/v1/openai/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
- "\u001b[92m10:31:41 - LiteLLM:INFO\u001b[0m: utils.py:1260 - Wrapper: Completed Call, calling success_handler\n",
+ "\u001b[92m14:55:22 - LiteLLM:INFO\u001b[0m: utils.py:1260 - Wrapper: Completed Call, calling success_handler\n",
"INFO:LiteLLM:Wrapper: Completed Call, calling success_handler\n"
]
},
@@ -1178,7 +1117,7 @@
"╭──────────────────────────────────────────────── Task Completion ────────────────────────────────────────────────╮\n",
"│ │\n",
"│ Task Completed │\n",
- "│ Name: b1362fb4-fe9b-479d-a01d-18bf38406780 │\n",
+ "│ Name: cf3f4f08-744c-4aee-9387-e9eb70624fc1 │\n",
"│ Agent: RAG assistant │\n",
"│ Tool Args: │\n",
"│ │\n",
@@ -1190,7 +1129,7 @@
"\u001b[32m╭─\u001b[0m\u001b[32m───────────────────────────────────────────────\u001b[0m\u001b[32m Task Completion \u001b[0m\u001b[32m───────────────────────────────────────────────\u001b[0m\u001b[32m─╮\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[1;32mTask Completed\u001b[0m \u001b[32m│\u001b[0m\n",
- "\u001b[32m│\u001b[0m \u001b[37mName: \u001b[0m\u001b[32mb1362fb4-fe9b-479d-a01d-18bf38406780\u001b[0m \u001b[32m│\u001b[0m\n",
+ "\u001b[32m│\u001b[0m \u001b[37mName: \u001b[0m\u001b[32mcf3f4f08-744c-4aee-9387-e9eb70624fc1\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[37mAgent: \u001b[0m\u001b[32mRAG assistant\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[37mTool Args: \u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[32m│\u001b[0m\n",
@@ -1221,10 +1160,9 @@
"│ │\n",
"│ Crew Execution Completed │\n",
"│ Name: crew │\n",
- "│ ID: 146be674-7521-4db6-bb70-287b22aa21aa │\n",
+ "│ ID: 091cf919-5c4b-4168-ac49-65fe5e8faa9e │\n",
"│ Tool Args: │\n",
- "│ Final Output: Returns are accepted within 30 days of purchase. Therefore, you cannot return a product after │\n",
- "│ 40 days. │\n",
+ "│ Final Output: Returns are accepted within 30 days of purchase. │\n",
"│ │\n",
"│ │\n",
"╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
@@ -1235,10 +1173,9 @@
"\u001b[32m│\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[1;32mCrew Execution Completed\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[37mName: \u001b[0m\u001b[32mcrew\u001b[0m \u001b[32m│\u001b[0m\n",
- "\u001b[32m│\u001b[0m \u001b[37mID: \u001b[0m\u001b[32m146be674-7521-4db6-bb70-287b22aa21aa\u001b[0m \u001b[32m│\u001b[0m\n",
+ "\u001b[32m│\u001b[0m \u001b[37mID: \u001b[0m\u001b[32m091cf919-5c4b-4168-ac49-65fe5e8faa9e\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[37mTool Args: \u001b[0m \u001b[32m│\u001b[0m\n",
- "\u001b[32m│\u001b[0m \u001b[37mFinal Output: Returns are accepted within 30 days of purchase. Therefore, you cannot return a product after \u001b[0m \u001b[32m│\u001b[0m\n",
- "\u001b[32m│\u001b[0m \u001b[37m40 days.\u001b[0m \u001b[32m│\u001b[0m\n",
+ "\u001b[32m│\u001b[0m \u001b[37mFinal Output: Returns are accepted within 30 days of purchase.\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m│\u001b[0m \u001b[32m│\u001b[0m\n",
"\u001b[32m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
@@ -1265,13 +1202,13 @@
"output_type": "stream",
"text": [
"❓ Can I return a product after 40 days?\n",
- "💡 Returns are accepted within 30 days of purchase. Therefore, you cannot return a product after 40 days.\n"
+ "💡 Returns are accepted within 30 days of purchase.\n"
]
}
],
"source": [
"query = \"Can I return a product after 40 days?\"\n",
- "response = crew.kickoff(inputs={\"query\": query,\"vector_db_id\": \"acme_docs\"})\n",
+ "response = crew.kickoff(inputs={\"query\": query,\"vector_store_id\": vector_store.id})\n",
"print(\"❓\", query)\n",
"print(\"💡\", response)"
]
@@ -1287,31 +1224,30 @@
"\n",
"We have successfully built a RAG system that combines:\n",
"\n",
- "- **LlamaStack** for infrastructure (LLM serving + vector database)\n",
+ "- **LlamaStack** for infrastructure (LLM serving + vector store)\n",
"- **CrewAI** for orchestration (agents, tasks, and tools)\n",
"- **Together AI** for high-quality language models\n",
"\n",
"### Key Benefits\n",
"\n",
- "1. **Unified Infrastructure**: A single server for LLMs and vector databases simplifies deployment and management.\n",
+ "1. **Unified Infrastructure**: A single server for LLMs and vector stores simplifies deployment and management.\n",
"2. **OpenAI Compatibility**: Enables easy integration with existing libraries and frameworks that support the OpenAI API standard, such as CrewAI.\n",
"3. **Multi-Provider Support**: Offers the flexibility to switch between different LLM and embedding providers without altering the core application logic.\n",
"4. **Production Ready**: LlamaStack includes features designed for production environments, such as built-in safety shields and monitoring capabilities.\n",
"\n",
- "### Next Steps\n",
- "\n",
- "- Implement more sophisticated document processing and chunking techniques.\n",
- "- Add conversation memory within the CrewAI pipeline for multi-turn interactions.\n",
- "- Integrate advanced safety filtering and monitoring using LlamaStack's features.\n",
- "- Scale the system to handle larger document collections and higher query volumes.\n",
- "- Integrate the RAG system with web frameworks like FastAPI or Streamlit to build interactive applications.\n",
- "\n",
- "---\n",
"\n",
"##### 🔧 Cleanup\n",
"\n",
"Remember to stop the LlamaStack server process when you are finished to free up resources. You can use the `kill_llama_stack_server()` helper function defined earlier in the notebook."
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a21270b4-b0a7-4481-96a5-044f908de363",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
diff --git a/docs/notebooks/crewai/customer_policies.txt b/docs/notebooks/crewai/customer_policies.txt
new file mode 100644
index 000000000..9b624e847
--- /dev/null
+++ b/docs/notebooks/crewai/customer_policies.txt
@@ -0,0 +1 @@
+Acme ships globally in 3-5 business days.
diff --git a/docs/notebooks/crewai/llama_stack_server.log b/docs/notebooks/crewai/llama_stack_server.log
new file mode 100644
index 000000000..a4545f0d9
--- /dev/null
+++ b/docs/notebooks/crewai/llama_stack_server.log
@@ -0,0 +1,1936 @@
+[2mUninstalled [1m3 packages[0m [2min 114ms[0m[0m
+[2mInstalled [1m3 packages[0m [2min 165ms[0m[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:47:19[0m,[1;36m351[0m llama_stack.core.utils.config_resolution:[1;36m52[0m core: Using distribution:
+ [35m/Users/kaiwu/work/kaiwu/llama-stack/llama_stack/distributions/starter/[0m[95mrun.yaml[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:47:19[0m,[1;36m353[0m llama_stack.cli.stack.run:[1;36m129[0m cli: Using run configuration:
+ [35m/Users/kaiwu/work/kaiwu/llama-stack/llama_stack/distributions/starter/[0m[95mrun.yaml[0m
+Using virtual environment: /Users/kaiwu/work/kaiwu/llama-stack/.venv[0m
+[0;32mVirtual environment already activated[0m
++ '[' -n /Users/kaiwu/work/kaiwu/llama-stack/llama_stack/distributions/starter/run.yaml ']'
++ yaml_config_arg=/Users/kaiwu/work/kaiwu/llama-stack/llama_stack/distributions/starter/run.yaml
++ python -m llama_stack.core.server.server /Users/kaiwu/work/kaiwu/llama-stack/llama_stack/distributions/starter/run.yaml --port 8321
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:47:23[0m,[1;36m918[0m llama_stack.core.utils.config_resolution:[1;36m45[0m core: Using file path:
+ [35m/Users/kaiwu/work/kaiwu/llama-stack/llama_stack/distributions/starter/[0m[95mrun.yaml[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:47:23[0m,[1;36m929[0m __main__:[1;36m615[0m core::server: Run configuration:
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:47:23[0m,[1;36m933[0m __main__:[1;36m618[0m core::server: apis:
+ - agents
+ - batches
+ - datasetio
+ - eval
+ - files
+ - inference
+ - post_training
+ - safety
+ - scoring
+ - telemetry
+ - tool_runtime
+ - vector_io
+ benchmarks: [1m[[0m[1m][0m
+ datasets: [1m[[0m[1m][0m
+ image_name: starter
+ inference_store:
+ db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95minference_store.db[0m
+ type: sqlite
+ metadata_store:
+ db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95mregistry.db[0m
+ type: sqlite
+ models: [1m[[0m[1m][0m
+ providers:
+ agents:
+ - config:
+ persistence_store:
+ db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95magents_store.db[0m
+ type: sqlite
+ responses_store:
+ db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95mresponses_store.db[0m
+ type: sqlite
+ provider_id: meta-reference
+ provider_type: inline::meta-reference
+ batches:
+ - config:
+ kvstore:
+ db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95mbatches.db[0m
+ type: sqlite
+ provider_id: reference
+ provider_type: inline::reference
+ datasetio:
+ - config:
+ kvstore:
+ db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95mhuggingface_datasetio.db[0m
+ type: sqlite
+ provider_id: huggingface
+ provider_type: remote::huggingface
+ - config:
+ kvstore:
+ db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95mlocalfs_datasetio.db[0m
+ type: sqlite
+ provider_id: localfs
+ provider_type: inline::localfs
+ eval:
+ - config:
+ kvstore:
+ db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95mmeta_reference_eval.db[0m
+ type: sqlite
+ provider_id: meta-reference
+ provider_type: inline::meta-reference
+ files:
+ - config:
+ metadata_store:
+ db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95mfiles_metadata.db[0m
+ type: sqlite
+ storage_dir: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95mfiles[0m
+ provider_id: meta-reference-files
+ provider_type: inline::localfs
+ inference:
+ - config:
+ api_key: [32m'********'[0m
+ url: [4;94mhttps://api.fireworks.ai/inference/v1[0m
+ provider_id: fireworks
+ provider_type: remot[1;92me::f[0mireworks
+ - config:
+ api_key: [32m'********'[0m
+ url: [4;94mhttps://api.together.xyz/v1[0m
+ provider_id: together
+ provider_type: remote::together
+ - config: [1m{[0m[1m}[0m
+ provider_id: bedrock
+ provider_type: remot[1;92me::bed[0mrock
+ - config:
+ api_key: [32m'********'[0m
+ base_url: [4;94mhttps://api.openai.com/v1[0m
+ provider_id: openai
+ provider_type: remote::openai
+ - config:
+ api_key: [32m'********'[0m
+ provider_id: anthropic
+ provider_type: remot[1;92me::a[0mnthropic
+ - config:
+ api_key: [32m'********'[0m
+ provider_id: gemini
+ provider_type: remote::gemini
+ - config:
+ api_key: [32m'********'[0m
+ url: [4;94mhttps://api.groq.com[0m
+ provider_id: groq
+ provider_type: remote::groq
+ - config:
+ api_key: [32m'********'[0m
+ url: [4;94mhttps://api.sambanova.ai/v1[0m
+ provider_id: sambanova
+ provider_type: remote::sambanova
+ - config: [1m{[0m[1m}[0m
+ provider_id: sentence-transformers
+ provider_type: inline::sentence-transformers
+ post_training:
+ - config:
+ checkpoint_format: meta
+ provider_id: torchtune-cpu
+ provider_type: inline::torchtune-cpu
+ safety:
+ - config:
+ excluded_categories: [1m[[0m[1m][0m
+ provider_id: llama-guard
+ provider_type: inline::llama-guard
+ - config: [1m{[0m[1m}[0m
+ provider_id: code-scanner
+ provider_type: inlin[1;92me::c[0mode-scanner
+ scoring:
+ - config: [1m{[0m[1m}[0m
+ provider_id: basic
+ provider_type: inlin[1;92me::ba[0msic
+ - config: [1m{[0m[1m}[0m
+ provider_id: llm-as-judge
+ provider_type: inline::llm-as-judge
+ - config:
+ openai_api_key: [32m'********'[0m
+ provider_id: braintrust
+ provider_type: inlin[1;92me::b[0mraintrust
+ telemetry:
+ - config:
+ service_name: [32m"\u200B"[0m
+ sinks: console,sqlite
+ sqlite_db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95mtrace_store.db[0m
+ provider_id: meta-reference
+ provider_type: inline::meta-reference
+ tool_runtime:
+ - config:
+ api_key: [32m'********'[0m
+ max_results: [1;36m3[0m
+ provider_id: brave-search
+ provider_type: remot[1;92me::b[0mrave-search
+ - config:
+ api_key: [32m'********'[0m
+ max_results: [1;36m3[0m
+ provider_id: tavily-search
+ provider_type: remote::tavily-search
+ - config: [1m{[0m[1m}[0m
+ provider_id: rag-runtime
+ provider_type: inline::rag-runtime
+ - config: [1m{[0m[1m}[0m
+ provider_id: model-context-protocol
+ provider_type: remote::model-context-protocol
+ vector_io:
+ - config:
+ kvstore:
+ db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95mfaiss_store.db[0m
+ type: sqlite
+ provider_id: faiss
+ provider_type: inlin[1;92me::fa[0miss
+ - config:
+ db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95msqlite_vec.db[0m
+ kvstore:
+ db_path: [35m/Users/kaiwu/.llama/distributions/starter/[0m[95msqlite_vec_registry.db[0m
+ type: sqlite
+ provider_id: sqlite-vec
+ provider_type: inline::sqlite-vec
+ scoring_fns: [1m[[0m[1m][0m
+ server:
+ port: [1;36m8321[0m
+ shields: [1m[[0m[1m][0m
+ tool_groups:
+ - provider_id: tavily-search
+ toolgroup_id: builtin::websearch
+ - provider_id: rag-runtime
+ toolgroup_id: builtin::rag
+ vector_dbs: [1m[[0m[1m][0m
+ version: [1;36m2[0m
+
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:47:34[0m,[1;36m785[0m llama_stack.core.stack:[1;36m388[0m core: starting registry refresh task
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:47:34[0m,[1;36m946[0m __main__:[1;36m580[0m core::server: Listening on [1m[[0m[32m'::'[0m, [32m'0.0.0.0'[0m[1m][0m:[1;36m8321[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:47:35[0m,[1;36m196[0m uvicorn.error:[1;36m84[0m uncategorized: Started server process [1m[[0m[1;36m26795[0m[1m][0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:47:35[0m,[1;36m197[0m uvicorn.error:[1;36m48[0m uncategorized: Waiting for application startup.
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:47:35[0m,[1;36m198[0m __main__:[1;36m168[0m core::server: Starting up
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+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:47:35[0m,[1;36m199[0m uvicorn.error:[1;36m216[0m uncategorized: Uvicorn running on [4;94mhttp://[0m[1m[[0m[32m'::'[0m, [32m'0.0.0.0'[0m[1m][0m:[1;36m8321[0m [1m([0mPress CTRL+C to quit[1m)[0m
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+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:48:28[0m,[1;36m324[0m uvicorn.access:[1;36m473[0m uncategorized: [1;92m127.0.0.1[0m:[1;36m56006[0m - [32m"[0m[1;33mGET[0m[32m /v1/models HTTP/1.1"[0m [1;36m200[0m
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+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:48:28[0m,[1;36m410[0m console_span_processor:[1;36m48[0m telemetry: [2moutput[0m: [1m{[0m[32m'data'[0m: [1m[[0m[1m{[0m[32m'identifier'[0m: [32m'llama_api/Llama-3.3-70B-Instruct'[0m,
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+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+ - Avoid using `tokenizers` before the fork if possible
+ - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
+huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
+To disable this warning, you can either:
+ - Avoid using `tokenizers` before the fork if possible
+ - Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
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+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:51:57[0m,[1;36m872[0m console_span_processor:[1;36m48[0m telemetry: [2moutput[0m: [1m{[0m[32m'id'[0m: [32m'oAitcfB-4YNCb4-97d2263f6ff7cfe9'[0m, [32m'choices'[0m:
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+ Llama Stack Vector Store RAG tool, I will provide a general answer to the question [32m"How long does shipping take?"[0m.\n\nThe shipping time can
+ vary depending on the destination, shipping method, and other factors. Typically, shipping times can range from a few days to several weeks
+ or even months. Here are some general guidelines:\n\n* Domestic shipping: [1;36m1[0m-[1;36m7[0m business days\n* International shipping: [1;36m7[0m-[1;36m21[0m business days or
+ more\n* Express shipping: [1;36m1[0m-[1;36m3[0m business days\n* Standard shipping: [1;36m3[0m-[1;36m7[0m business days\n\nPlease note that these are general estimates and the
+ actual shipping time may vary depending on the specific circumstances.\n\nThought: I now can give a great answer\nFinal Answer: The shipping
+ time can vary depending on the destination, shipping method, and other factors. Typically, shipping tim[33m...[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:51:57[0m,[1;36m875[0m uvicorn.access:[1;36m473[0m uncategorized: ::[1;92m1:5690[0m0 - [32m"[0m[1;33mPOST[0m[32m /v1/openai/v1/chat/completions HTTP/1.1"[0m [1;36m200[0m
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+ [1m([0m[1;36m3091.[0m29ms[1m)[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:51:57[0m,[1;36m878[0m console_span_processor:[1;36m48[0m telemetry: [2moutput[0m: [1m{[0m[32m'id'[0m: [32m'oAitcfB-4YNCb4-97d2263f6ff7cfe9'[0m, [32m'choices'[0m:
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+ Llama Stack Vector Store RAG tool, I will provide a general answer to the question [32m"How long does shipping take?"[0m.\n\nThe shipping time can
+ vary depending on the destination, shipping method, and other factors. Typically, shipping times can range from a few days to several weeks
+ or even months. Here are some general guidelines:\n\n* Domestic shipping: [1;36m1[0m-[1;36m7[0m business days\n* International shipping: [1;36m7[0m-[1;36m21[0m business days or
+ more\n* Express shipping: [1;36m1[0m-[1;36m3[0m business days\n* Standard shipping: [1;36m3[0m-[1;36m7[0m business days\n\nPlease note that these are general estimates and the
+ actual shipping time may vary depending on the specific circumstances.\n\nThought: I now can give a great answer\nFinal Answer: The shipping
+ time can vary depending on the destination, shipping method, and other factors. Typically, shipping tim[33m...[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:51:57[0m,[1;36m880[0m console_span_processor:[1;36m62[0m telemetry: [1;92m21:51:57[0m.[1;36m874[0m [1;37m[[0m[1;37mINFO[0m[1;37m][0m metric.prompt_tokens
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+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:51:57[0m,[1;36m881[0m console_span_processor:[1;36m67[0m telemetry: [2munit[0m: tokens
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+ [1m<[0m[1;95mllama_stack.providers.remote.inference.together.together.TogetherInferenceAdapter[0m[39m object at [0m[1;36m0x137069220[0m[1m>[0m
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+ [1m[[0mStatusCode.OK[1m][0m [1m([0m[1;36m577.[0m41ms[1m)[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m806[0m console_span_processor:[1;36m48[0m telemetry: [2moutput[0m: [1m{[0m[32m'id'[0m: [32m'oAiugR1-62bZhn-97d22b51096017f0'[0m, [32m'choices'[0m:
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+ [32m'tool_calls'[0m: [1m[[0m[1m][0m[1m}[0m, [32m'seed'[0m: [1;36m2982499907717596700[0m[1m}[0m[1m][0m, [32m'created'[0m: [1;36m1757541322[0m, [32m'model'[0m: [32m'meta-llama/Llama-3.3-70B-Instruct-Turbo'[0m, [32m'object'[0m:
+ [32m'chat.completion'[0m, [32m'service_tier'[0m: [3;35mNone[0m, [32m'system_fingerprint'[0m: [3;35mNone[0m, [32m'usage'[0m: [1m{[0m[32m'completion_tokens'[0m: [1;36m23[0m, [32m'prompt_tokens'[0m: [1;36m643[0m, [32m'total_tokens'[0m:
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+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m834[0m uvicorn.access:[1;36m473[0m uncategorized: ::[1;92m1:5788[0m4 - [32m"[0m[1;33mPOST[0m[32m /v1/openai/v1/chat/completions HTTP/1.1"[0m [1;36m200[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m873[0m console_span_processor:[1;36m39[0m telemetry: [1;2;92m21:55:22[0m[2m.[0m[1;2;36m836[0m [1;35m[[0m[1;35mEND[0m[1;35m][0m [2mInferenceRouter.openai_chat_completion[0m [1m[[0mStatusCode.OK[1m][0m
+ [1m([0m[1;36m648.[0m69ms[1m)[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m874[0m console_span_processor:[1;36m48[0m telemetry: [2moutput[0m: [1m{[0m[32m'id'[0m: [32m'oAiugR1-62bZhn-97d22b51096017f0'[0m, [32m'choices'[0m:
+ [1m[[0m[1m{[0m[32m'finish_reason'[0m: [32m'stop'[0m, [32m'index'[0m: [1;36m0[0m, [32m'logprobs'[0m: [3;35mNone[0m, [32m'message'[0m: [1m{[0m[32m'content'[0m: [32m'Thought: I now know the final answer\nFinal Answer: Returns [0m
+ [32mare accepted within 30 days of purchase.'[0m, [32m'refusal'[0m: [3;35mNone[0m, [32m'role'[0m: [32m'assistant'[0m, [32m'annotations'[0m: [3;35mNone[0m, [32m'audio'[0m: [3;35mNone[0m, [32m'function_call'[0m: [3;35mNone[0m,
+ [32m'tool_calls'[0m: [3;35mNone[0m[1m}[0m, [32m'seed'[0m: [1;36m2982499907717596700[0m[1m}[0m[1m][0m, [32m'created'[0m: [1;36m1757541322[0m, [32m'model'[0m: [32m'meta-llama/Llama-3.3-70B-Instruct-Turbo'[0m, [32m'object'[0m:
+ [32m'chat.completion'[0m, [32m'service_tier'[0m: [3;35mNone[0m, [32m'system_fingerprint'[0m: [3;35mNone[0m, [32m'usage'[0m: [1m{[0m[32m'completion_tokens'[0m: [1;36m23[0m, [32m'prompt_tokens'[0m: [1;36m643[0m, [32m'total_tokens'[0m:
+ [1;36m666[0m, [32m'completion_tokens_details'[0m: [3;35mNone[0m, [32m'prompt_tokens_details'[0m: [3;35mNone[0m, [32m'cached_tokens'[0m: [1;36m256[0m[1m}[0m, [32m'prompt'[0m: [1m[[0m[1m][0m, [32m'metrics'[0m: [1m[[0m[1m{[0m[32m'trace_id'[0m:
+ [32m'd588c782bd804c0cb49e43ac8c028e7a'[0m, [32m'span_id'[0m: [32m'396bc114a416fc6c'[0m, [32m'timestamp'[0m: [32m'2025-09-10T21:55:22.832220Z'[0m, [32m'attributes'[0m: [1m{[0m[32m'model_id'[0m:
+ [32m'together/meta-llama/Llama-3.3-70B-Instruct-Turbo'[0m, [32m'provider_id'[0m: [32m'together'[0m[1m}[0m, [32m'type'[0m: [32m'metric'[0m, [32m'metric'[0m: 'prom[33m...[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m876[0m console_span_processor:[1;36m62[0m telemetry: [1;92m21:55:22[0m.[1;36m832[0m [1;37m[[0m[1;37mINFO[0m[1;37m][0m metric.prompt_tokens
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m877[0m console_span_processor:[1;36m67[0m telemetry: [2mvalue[0m: [1;36m643[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m878[0m console_span_processor:[1;36m67[0m telemetry: [2munit[0m: tokens
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m878[0m console_span_processor:[1;36m67[0m telemetry: [2mmodel_id[0m: together/meta-llama/Llama-[1;36m3.3[0m-70B-Instruct-Turbo
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m879[0m console_span_processor:[1;36m67[0m telemetry: [2mprovider_id[0m: together
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m879[0m console_span_processor:[1;36m62[0m telemetry: [1;92m21:55:22[0m.[1;36m832[0m [1;37m[[0m[1;37mINFO[0m[1;37m][0m metric.completion_tokens
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m880[0m console_span_processor:[1;36m67[0m telemetry: [2mvalue[0m: [1;36m23[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m881[0m console_span_processor:[1;36m67[0m telemetry: [2munit[0m: tokens
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m881[0m console_span_processor:[1;36m67[0m telemetry: [2mmodel_id[0m: together/meta-llama/Llama-[1;36m3.3[0m-70B-Instruct-Turbo
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m882[0m console_span_processor:[1;36m67[0m telemetry: [2mprovider_id[0m: together
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m882[0m console_span_processor:[1;36m62[0m telemetry: [1;92m21:55:22[0m.[1;36m832[0m [1;37m[[0m[1;37mINFO[0m[1;37m][0m metric.total_tokens
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m883[0m console_span_processor:[1;36m67[0m telemetry: [2mvalue[0m: [1;36m666[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m886[0m console_span_processor:[1;36m67[0m telemetry: [2munit[0m: tokens
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m887[0m console_span_processor:[1;36m67[0m telemetry: [2mmodel_id[0m: together/meta-llama/Llama-[1;36m3.3[0m-70B-Instruct-Turbo
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m888[0m console_span_processor:[1;36m67[0m telemetry: [2mprovider_id[0m: together
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m913[0m console_span_processor:[1;36m39[0m telemetry: [1;2;92m21:55:22[0m[2m.[0m[1;2;36m891[0m [1;35m[[0m[1;35mEND[0m[1;35m][0m [2;35m/v1/openai/v1/chat/[0m[2;95mcompletions[0m [1m[[0mStatusCode.OK[1m][0m [1m([0m[1;36m704.[0m86ms[1m)[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m915[0m console_span_processor:[1;36m48[0m telemetry: [2mraw_path[0m: [35m/v1/openai/v1/chat/[0m[95mcompletions[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:55:22[0m,[1;36m915[0m console_span_processor:[1;36m62[0m telemetry: [1;92m21:55:22[0m.[1;36m835[0m [1;37m[[0m[1;37mINFO[0m[1;37m][0m ::[1;92m1:5788[0m4 - [32m"[0m[1;33mPOST[0m[32m /v1/openai/v1/chat/completions HTTP/1.1"[0m
+ [1;36m200[0m
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m753[0m uvicorn.error:[1;36m264[0m uncategorized: Shutting down
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m867[0m uvicorn.error:[1;36m67[0m uncategorized: Waiting for application shutdown.
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m868[0m __main__:[1;36m170[0m core::server: Shutting down
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m868[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down ModelsRoutingTable
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m869[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down DatasetsRoutingTable
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m870[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down DatasetIORouter
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m870[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down TelemetryAdapter
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m871[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down InferenceRouter
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m874[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down LocalfsFilesImpl
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m875[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down ShieldsRoutingTable
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m876[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down SafetyRouter
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m877[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down VectorDBsRoutingTable
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m877[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down VectorIORouter
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m878[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down ToolGroupsRoutingTable
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m878[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down ToolRuntimeRouter
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m879[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down MetaReferenceAgentsImpl
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m879[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down TorchtunePostTrainingImpl
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m881[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down ScoringFunctionsRoutingTable
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m882[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down ScoringRouter
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m882[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down ReferenceBatchesImpl
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m883[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down BenchmarksRoutingTable
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m884[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down EvalRouter
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m884[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down DistributionInspectImpl
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m885[0m llama_stack.core.stack:[1;36m357[0m core: Shutting down ProviderImpl
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m886[0m uvicorn.error:[1;36m76[0m uncategorized: Application shutdown complete.
+[34mINFO [0m [1;36m2025[0m-[1;36m09[0m-[1;36m10[0m [1;92m14:57:34[0m,[1;36m887[0m uvicorn.error:[1;36m94[0m uncategorized: Finished server process [1m[[0m[1;36m26795[0m[1m][0m
+/Users/kaiwu/miniconda3/lib/python3.12/multiprocessing/resource_tracker.py:255: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
+ warnings.warn('resource_tracker: There appear to be %d '