phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-08-06 10:42:39 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

test

Browse source
This commit is contained in:
Xi Yan 2025-03-20 16:10:59 -07:00
parent 1f04ca357b
commit 0b1e71718c
2 changed files with 448 additions and 156 deletions
Download patch file Download diff file Expand all files Collapse all files

193
docs/notebooks/RAG_as_attchements.ipynb Normal file
View file

@ -0,0 +1,193 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from llama_stack_client import LlamaStackClient\n",
"from llama_stack_client.types import Document\n",
"from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n",
"from llama_stack_client.types.agent_create_params import AgentConfig\n",
"from llama_stack_client.lib.agents.agent import Agent\n",
"from rich.pretty import pprint\n",
"import json\n",
"import uuid\n",
"from pydantic import BaseModel\n",
"import rich\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"MODEL_ID = \"meta-llama/Llama-3.3-70B-Instruct\"\n",
"\n",
"client = LlamaStackClient(\n",
" base_url=\"http://localhost:8321\",\n",
" provider_data={\n",
" \"fireworks_api_key\": os.environ[\"FIREWORKS_API_KEY\"]\n",
" }\n",
")\n",
"\n",
"urls = [\n",
" \"memory_optimizations.rst\",\n",
" \"chat.rst\",\n",
" \"llama3.rst\",\n",
" \"datasets.rst\",\n",
" \"qat_finetune.rst\",\n",
" \"lora_finetune.rst\",\n",
"]\n",
"\n",
"attachments = [\n",
" {\n",
" \"content\": f\"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}\",\n",
" \"mime_type\": \"text/plain\",\n",
" }\n",
"\n",
" for i, url in enumerate(urls)\n",
"]\n",
"\n",
"simple_agent = Agent(client, model=MODEL_ID, \n",
" instructions=\"You are a helpful assistant that can answer questions about the Torchtune project.\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Turn</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">input_messages</span>=<span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">UserMessage</span><span style=\"font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'What precision formats does torchtune support?'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">role</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'user'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">context</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-style: italic\">None</span><span style=\"font-weight: bold\">)</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">]</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">output_message</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">CompletionMessage</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Torchtune supports the following precision formats:\\n\\n* FP32 (32-bit floating point)\\n* FP16 (16-bit floating point)\\n* INT8 (8-bit integer)\\n* BF16 (Brain Floating Point 16, a 16-bit floating point format)\\n\\nThese precision formats can be used for model weights, activations, and gradients, allowing for flexible and efficient tuning of models for various hardware and performance requirements.'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">role</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'assistant'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">stop_reason</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'end_of_turn'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">tool_calls</span>=<span style=\"font-weight: bold\">[]</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">)</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">session_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'1c23c79b-3945-4e99-bda6-7922b6b4e91c'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">started_at</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">datetime</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">.datetime</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2025</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">175804</span>, <span style=\"color: #808000; text-decoration-color: #808000\">tzinfo</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">TzInfo</span><span style=\"font-weight: bold\">(</span>UTC<span style=\"font-weight: bold\">))</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">steps</span>=<span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">InferenceStep</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">api_model_response</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">CompletionMessage</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">content</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'Torchtune supports the following precision formats:\\n\\n* FP32 (32-bit floating point)\\n* FP16 (16-bit floating point)\\n* INT8 (8-bit integer)\\n* BF16 (Brain Floating Point 16, a 16-bit floating point format)\\n\\nThese precision formats can be used for model weights, activations, and gradients, allowing for flexible and efficient tuning of models for various hardware and performance requirements.'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">role</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'assistant'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">stop_reason</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'end_of_turn'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">tool_calls</span>=<span style=\"font-weight: bold\">[]</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"font-weight: bold\">)</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">step_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'bf452f18-8fae-470e-9e97-b1af60628fc1'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">step_type</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'inference'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">turn_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'efb92c6d-d482-4dd2-ad4b-3250c1e9a231'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">completed_at</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">datetime</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">.datetime</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2025</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">618765</span>, <span style=\"color: #808000; text-decoration-color: #808000\">tzinfo</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">TzInfo</span><span style=\"font-weight: bold\">(</span>UTC<span style=\"font-weight: bold\">))</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">started_at</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">datetime</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">.datetime</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2025</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">175855</span>, <span style=\"color: #808000; text-decoration-color: #808000\">tzinfo</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">TzInfo</span><span style=\"font-weight: bold\">(</span>UTC<span style=\"font-weight: bold\">))</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">)</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">]</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">turn_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'efb92c6d-d482-4dd2-ad4b-3250c1e9a231'</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">completed_at</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">datetime</span><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">.datetime</span><span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2025</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">631357</span>, <span style=\"color: #808000; text-decoration-color: #808000\">tzinfo</span>=<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">TzInfo</span><span style=\"font-weight: bold\">(</span>UTC<span style=\"font-weight: bold\">))</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">output_attachments</span>=<span style=\"font-weight: bold\">[]</span>\n",
"<span style=\"font-weight: bold\">)</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1;35mTurn\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[33minput_messages\u001b[0m=\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1;35mUserMessage\u001b[0m\u001b[1m(\u001b[0m\u001b[33mcontent\u001b[0m=\u001b[32m'What precision formats does torchtune support?'\u001b[0m, \u001b[33mrole\u001b[0m=\u001b[32m'user'\u001b[0m, \u001b[33mcontext\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33moutput_message\u001b[0m=\u001b[1;35mCompletionMessage\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[33mcontent\u001b[0m=\u001b[32m'Torchtune supports the following precision formats:\\n\\n* FP32 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m32-bit floating point\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* FP16 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m16-bit floating point\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* INT8 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m8-bit integer\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* BF16 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBrain Floating Point 16, a 16-bit floating point format\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nThese precision formats can be used for model weights, activations, and gradients, allowing for flexible and efficient tuning of models for various hardware and performance requirements.'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[33mrole\u001b[0m=\u001b[32m'assistant'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[33mstop_reason\u001b[0m=\u001b[32m'end_of_turn'\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[33mtool_calls\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m)\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33msession_id\u001b[0m=\u001b[32m'1c23c79b-3945-4e99-bda6-7922b6b4e91c'\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mstarted_at\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2025\u001b[0m, \u001b[1;36m3\u001b[0m, \u001b[1;36m20\u001b[0m, \u001b[1;36m22\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m175804\u001b[0m, \u001b[33mtzinfo\u001b[0m=\u001b[1;35mTzInfo\u001b[0m\u001b[1m(\u001b[0mUTC\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33msteps\u001b[0m=\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1;35mInferenceStep\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mapi_model_response\u001b[0m=\u001b[1;35mCompletionMessage\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mcontent\u001b[0m=\u001b[32m'Torchtune supports the following precision formats:\\n\\n* FP32 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m32-bit floating point\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* FP16 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m16-bit floating point\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* INT8 \u001b[0m\u001b[32m(\u001b[0m\u001b[32m8-bit integer\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n* BF16 \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBrain Floating Point 16, a 16-bit floating point format\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\n\\nThese precision formats can be used for model weights, activations, and gradients, allowing for flexible and efficient tuning of models for various hardware and performance requirements.'\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mrole\u001b[0m=\u001b[32m'assistant'\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mstop_reason\u001b[0m=\u001b[32m'end_of_turn'\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mtool_calls\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[1m)\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mstep_id\u001b[0m=\u001b[32m'bf452f18-8fae-470e-9e97-b1af60628fc1'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mstep_type\u001b[0m=\u001b[32m'inference'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mturn_id\u001b[0m=\u001b[32m'efb92c6d-d482-4dd2-ad4b-3250c1e9a231'\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mcompleted_at\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2025\u001b[0m, \u001b[1;36m3\u001b[0m, \u001b[1;36m20\u001b[0m, \u001b[1;36m22\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m618765\u001b[0m, \u001b[33mtzinfo\u001b[0m=\u001b[1;35mTzInfo\u001b[0m\u001b[1m(\u001b[0mUTC\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mstarted_at\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2025\u001b[0m, \u001b[1;36m3\u001b[0m, \u001b[1;36m20\u001b[0m, \u001b[1;36m22\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m0\u001b[0m, \u001b[1;36m175855\u001b[0m, \u001b[33mtzinfo\u001b[0m=\u001b[1;35mTzInfo\u001b[0m\u001b[1m(\u001b[0mUTC\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mturn_id\u001b[0m=\u001b[32m'efb92c6d-d482-4dd2-ad4b-3250c1e9a231'\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mcompleted_at\u001b[0m=\u001b[1;35mdatetime\u001b[0m\u001b[1;35m.datetime\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2025\u001b[0m, \u001b[1;36m3\u001b[0m, \u001b[1;36m20\u001b[0m, \u001b[1;36m22\u001b[0m, \u001b[1;36m41\u001b[0m, \u001b[1;36m1\u001b[0m, \u001b[1;36m631357\u001b[0m, \u001b[33mtzinfo\u001b[0m=\u001b[1;35mTzInfo\u001b[0m\u001b[1m(\u001b[0mUTC\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33moutput_attachments\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"\u001b[1m)\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"simple_session_id = simple_agent.create_session(session_name=f\"simple_session_{uuid.uuid4()}\")\n",
"response = simple_agent.create_turn(\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"What precision formats does torchtune support?\"\n",
" }\n",
" ],\n",
" session_id=simple_session_id,\n",
" stream=False\n",
" )\n",
"\n",
"pprint(response)\n",
"\n",
"session_response = client.agents.session.retrieve(agent_id=simple_agent.agent_id, session_id=simple_session_id)\n",
"pprint(session_response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "master",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

411
llama_stack/providers/inline/agents/meta_reference/agent_instance.py
View file

@ -40,10 +40,10 @@ from llama_stack.apis.agents import (
Turn, Turn,
) )
from llama_stack.apis.common.content_types import ( from llama_stack.apis.common.content_types import (
URL,
TextContentItem, TextContentItem,
ToolCallDelta, ToolCallDelta,
ToolCallParseStatus, ToolCallParseStatus,
URL,
) )
from llama_stack.apis.inference import ( from llama_stack.apis.inference import (
ChatCompletionResponseEventType, ChatCompletionResponseEventType,
@ -80,7 +80,9 @@ from .safety import SafetyException, ShieldRunnerMixin
def make_random_string(length: int = 8): def make_random_string(length: int = 8):
return "".join(secrets.choice(string.ascii_letters + string.digits) for _ in range(length)) return "".join(
secrets.choice(string.ascii_letters + string.digits) for _ in range(length)
)
TOOLS_ATTACHMENT_KEY_REGEX = re.compile(r"__tools_attachment__=(\{.*?\})") TOOLS_ATTACHMENT_KEY_REGEX = re.compile(r"__tools_attachment__=(\{.*?\})")
@ -179,7 +181,9 @@ class ChatAgent(ShieldRunnerMixin):
messages.extend(self.turn_to_messages(turn)) messages.extend(self.turn_to_messages(turn))
return messages return messages
async def create_and_execute_turn(self, request: AgentTurnCreateRequest) -> AsyncGenerator: async def create_and_execute_turn(
self, request: AgentTurnCreateRequest
) -> AsyncGenerator:
await self._initialize_tools(request.toolgroups) await self._initialize_tools(request.toolgroups)
async with tracing.span("create_and_execute_turn") as span: async with tracing.span("create_and_execute_turn") as span:
span.set_attribute("session_id", request.session_id) span.set_attribute("session_id", request.session_id)
@ -220,13 +224,16 @@ class ChatAgent(ShieldRunnerMixin):
messages = await self.get_messages_from_turns(turns) messages = await self.get_messages_from_turns(turns)
if is_resume: if is_resume:
tool_response_messages = [ tool_response_messages = [
ToolResponseMessage(call_id=x.call_id, content=x.content) for x in request.tool_responses ToolResponseMessage(call_id=x.call_id, content=x.content)
for x in request.tool_responses
] ]
messages.extend(tool_response_messages) messages.extend(tool_response_messages)
last_turn = turns[-1] last_turn = turns[-1]
last_turn_messages = self.turn_to_messages(last_turn) last_turn_messages = self.turn_to_messages(last_turn)
last_turn_messages = [ last_turn_messages = [
x for x in last_turn_messages if isinstance(x, UserMessage) or isinstance(x, ToolResponseMessage) x
for x in last_turn_messages
if isinstance(x, UserMessage) or isinstance(x, ToolResponseMessage)
] ]
last_turn_messages.extend(tool_response_messages) last_turn_messages.extend(tool_response_messages)
@ -236,17 +243,31 @@ class ChatAgent(ShieldRunnerMixin):
# mark tool execution step as complete # mark tool execution step as complete
# if there's no tool execution in progress step (due to storage, or tool call parsing on client), # if there's no tool execution in progress step (due to storage, or tool call parsing on client),
# we'll create a new tool execution step with current time # we'll create a new tool execution step with current time
in_progress_tool_call_step = await self.storage.get_in_progress_tool_call_step( in_progress_tool_call_step = (
request.session_id, request.turn_id await self.storage.get_in_progress_tool_call_step(
request.session_id, request.turn_id
)
) )
now = datetime.now(timezone.utc).isoformat() now = datetime.now(timezone.utc).isoformat()
tool_execution_step = ToolExecutionStep( tool_execution_step = ToolExecutionStep(
step_id=(in_progress_tool_call_step.step_id if in_progress_tool_call_step else str(uuid.uuid4())), step_id=(
in_progress_tool_call_step.step_id
if in_progress_tool_call_step
else str(uuid.uuid4())
),
turn_id=request.turn_id, turn_id=request.turn_id,
tool_calls=(in_progress_tool_call_step.tool_calls if in_progress_tool_call_step else []), tool_calls=(
in_progress_tool_call_step.tool_calls
if in_progress_tool_call_step
else []
),
tool_responses=request.tool_responses, tool_responses=request.tool_responses,
completed_at=now, completed_at=now,
started_at=(in_progress_tool_call_step.started_at if in_progress_tool_call_step else now), started_at=(
in_progress_tool_call_step.started_at
if in_progress_tool_call_step
else now
),
) )
steps.append(tool_execution_step) steps.append(tool_execution_step)
yield AgentTurnResponseStreamChunk( yield AgentTurnResponseStreamChunk(
@ -280,9 +301,14 @@ class ChatAgent(ShieldRunnerMixin):
output_message = chunk output_message = chunk
continue continue
assert isinstance(chunk, AgentTurnResponseStreamChunk), f"Unexpected type {type(chunk)}" assert isinstance(
chunk, AgentTurnResponseStreamChunk
), f"Unexpected type {type(chunk)}"
event = chunk.event event = chunk.event
if event.payload.event_type == AgentTurnResponseEventType.step_complete.value: if (
event.payload.event_type
== AgentTurnResponseEventType.step_complete.value
):
steps.append(event.payload.step_details) steps.append(event.payload.step_details)
yield chunk yield chunk
@ -440,6 +466,18 @@ class ChatAgent(ShieldRunnerMixin):
) )
span.set_attribute("output", "no violations") span.set_attribute("output", "no violations")
async def get_raw_document_text(self, document: Document) -> str:
if isinstance(document.content, URL):
return await load_data_from_url(document.content)
elif isinstance(document.content, str):
return document.content
elif isinstance(document.content, TextContentItem):
return document.content.text
else:
raise ValueError(
f"Unexpected document content type: {type(document.content)}"
)
async def _run( async def _run(
self, self,
session_id: str, session_id: str,
@ -449,8 +487,23 @@ class ChatAgent(ShieldRunnerMixin):
stream: bool = False, stream: bool = False,
documents: Optional[List[Document]] = None, documents: Optional[List[Document]] = None,
) -> AsyncGenerator: ) -> AsyncGenerator:
# if documents:
# await self.handle_documents(session_id, documents, input_messages)
# if document is passed in a turn, we parse the raw text of the document
# and sent it as a user message
if documents: if documents:
await self.handle_documents(session_id, documents, input_messages) contexts = []
for document in documents:
raw_document_text = await self.get_raw_document_text(document)
contexts.append(TextContentItem(text=raw_document_text))
# modify the last user message to include the document
input_messages.append(
ToolResponseMessage(
call_id=str(uuid.uuid4()),
content=contexts,
)
)
session_info = await self.storage.get_session_info(session_id) session_info = await self.storage.get_session_info(session_id)
# if the session has a memory bank id, let the memory tool use it # if the session has a memory bank id, let the memory tool use it
@ -458,13 +511,19 @@ class ChatAgent(ShieldRunnerMixin):
for tool_name in self.tool_name_to_args.keys(): for tool_name in self.tool_name_to_args.keys():
if tool_name == MEMORY_QUERY_TOOL: if tool_name == MEMORY_QUERY_TOOL:
if "vector_db_ids" not in self.tool_name_to_args[tool_name]: if "vector_db_ids" not in self.tool_name_to_args[tool_name]:
self.tool_name_to_args[tool_name]["vector_db_ids"] = [session_info.vector_db_id] self.tool_name_to_args[tool_name]["vector_db_ids"] = [
session_info.vector_db_id
]
else: else:
self.tool_name_to_args[tool_name]["vector_db_ids"].append(session_info.vector_db_id) self.tool_name_to_args[tool_name]["vector_db_ids"].append(
session_info.vector_db_id
)
output_attachments = [] output_attachments = []
n_iter = await self.storage.get_num_infer_iters_in_turn(session_id, turn_id) or 0 n_iter = (
await self.storage.get_num_infer_iters_in_turn(session_id, turn_id) or 0
)
# Build a map of custom tools to their definitions for faster lookup # Build a map of custom tools to their definitions for faster lookup
client_tools = {} client_tools = {}
@ -487,6 +546,9 @@ class ChatAgent(ShieldRunnerMixin):
stop_reason = None stop_reason = None
async with tracing.span("inference") as span: async with tracing.span("inference") as span:
from rich.pretty import pprint
pprint(input_messages)
async for chunk in await self.inference_api.chat_completion( async for chunk in await self.inference_api.chat_completion(
self.agent_config.model, self.agent_config.model,
input_messages, input_messages,
@ -542,12 +604,16 @@ class ChatAgent(ShieldRunnerMixin):
span.set_attribute("stop_reason", stop_reason) span.set_attribute("stop_reason", stop_reason)
span.set_attribute( span.set_attribute(
"input", "input",
json.dumps([json.loads(m.model_dump_json()) for m in input_messages]), json.dumps(
[json.loads(m.model_dump_json()) for m in input_messages]
),
) )
output_attr = json.dumps( output_attr = json.dumps(
{ {
"content": content, "content": content,
"tool_calls": [json.loads(t.model_dump_json()) for t in tool_calls], "tool_calls": [
json.loads(t.model_dump_json()) for t in tool_calls
],
} }
) )
span.set_attribute("output", output_attr) span.set_attribute("output", output_attr)
@ -611,7 +677,9 @@ class ChatAgent(ShieldRunnerMixin):
message.content = [message.content] + output_attachments message.content = [message.content] + output_attachments
yield message yield message
else: else:
logger.debug(f"completion message with EOM (iter: {n_iter}): {str(message)}") logger.debug(
f"completion message with EOM (iter: {n_iter}): {str(message)}"
)
input_messages = input_messages + [message] input_messages = input_messages + [message]
else: else:
input_messages = input_messages + [message] input_messages = input_messages + [message]
@ -660,7 +728,9 @@ class ChatAgent(ShieldRunnerMixin):
"input": message.model_dump_json(), "input": message.model_dump_json(),
}, },
) as span: ) as span:
tool_execution_start_time = datetime.now(timezone.utc).isoformat() tool_execution_start_time = datetime.now(
timezone.utc
).isoformat()
tool_result = await self.execute_tool_call_maybe( tool_result = await self.execute_tool_call_maybe(
session_id, session_id,
tool_call, tool_call,
@ -709,7 +779,9 @@ class ChatAgent(ShieldRunnerMixin):
# TODO: add tool-input touchpoint and a "start" event for this step also # TODO: add tool-input touchpoint and a "start" event for this step also
# but that needs a lot more refactoring of Tool code potentially # but that needs a lot more refactoring of Tool code potentially
if (type(result_message.content) is str) and ( if (type(result_message.content) is str) and (
out_attachment := _interpret_content_as_attachment(result_message.content) out_attachment := _interpret_content_as_attachment(
result_message.content
)
): ):
# NOTE: when we push this message back to the model, the model may ignore the # NOTE: when we push this message back to the model, the model may ignore the
# attached file path etc. since the model is trained to only provide a user message # attached file path etc. since the model is trained to only provide a user message
@ -746,16 +818,24 @@ class ChatAgent(ShieldRunnerMixin):
toolgroups_for_turn: Optional[List[AgentToolGroup]] = None, toolgroups_for_turn: Optional[List[AgentToolGroup]] = None,
) -> None: ) -> None:
toolgroup_to_args = {} toolgroup_to_args = {}
for toolgroup in (self.agent_config.toolgroups or []) + (toolgroups_for_turn or []): for toolgroup in (self.agent_config.toolgroups or []) + (
toolgroups_for_turn or []
):
if isinstance(toolgroup, AgentToolGroupWithArgs): if isinstance(toolgroup, AgentToolGroupWithArgs):
tool_group_name, _ = self._parse_toolgroup_name(toolgroup.name) tool_group_name, _ = self._parse_toolgroup_name(toolgroup.name)
toolgroup_to_args[tool_group_name] = toolgroup.args toolgroup_to_args[tool_group_name] = toolgroup.args
# Determine which tools to include # Determine which tools to include
tool_groups_to_include = toolgroups_for_turn or self.agent_config.toolgroups or [] tool_groups_to_include = (
toolgroups_for_turn or self.agent_config.toolgroups or []
)
agent_config_toolgroups = [] agent_config_toolgroups = []
for toolgroup in tool_groups_to_include: for toolgroup in tool_groups_to_include:
name = toolgroup.name if isinstance(toolgroup, AgentToolGroupWithArgs) else toolgroup name = (
toolgroup.name
if isinstance(toolgroup, AgentToolGroupWithArgs)
else toolgroup
)
if name not in agent_config_toolgroups: if name not in agent_config_toolgroups:
agent_config_toolgroups.append(name) agent_config_toolgroups.append(name)
@ -781,20 +861,32 @@ class ChatAgent(ShieldRunnerMixin):
}, },
) )
for toolgroup_name_with_maybe_tool_name in agent_config_toolgroups: for toolgroup_name_with_maybe_tool_name in agent_config_toolgroups:
toolgroup_name, input_tool_name = self._parse_toolgroup_name(toolgroup_name_with_maybe_tool_name) toolgroup_name, input_tool_name = self._parse_toolgroup_name(
toolgroup_name_with_maybe_tool_name
)
tools = await self.tool_groups_api.list_tools(toolgroup_id=toolgroup_name) tools = await self.tool_groups_api.list_tools(toolgroup_id=toolgroup_name)
if not tools.data: if not tools.data:
available_tool_groups = ", ".join( available_tool_groups = ", ".join(
[t.identifier for t in (await self.tool_groups_api.list_tool_groups()).data] [
t.identifier
for t in (await self.tool_groups_api.list_tool_groups()).data
]
) )
raise ValueError(f"Toolgroup {toolgroup_name} not found, available toolgroups: {available_tool_groups}") raise ValueError(
if input_tool_name is not None and not any(tool.identifier == input_tool_name for tool in tools.data): f"Toolgroup {toolgroup_name} not found, available toolgroups: {available_tool_groups}"
)
if input_tool_name is not None and not any(
tool.identifier == input_tool_name for tool in tools.data
):
raise ValueError( raise ValueError(
f"Tool {input_tool_name} not found in toolgroup {toolgroup_name}. Available tools: {', '.join([tool.identifier for tool in tools.data])}" f"Tool {input_tool_name} not found in toolgroup {toolgroup_name}. Available tools: {', '.join([tool.identifier for tool in tools.data])}"
) )
for tool_def in tools.data: for tool_def in tools.data:
if toolgroup_name.startswith("builtin") and toolgroup_name != RAG_TOOL_GROUP: if (
toolgroup_name.startswith("builtin")
and toolgroup_name != RAG_TOOL_GROUP
):
identifier: str | BuiltinTool | None = tool_def.identifier identifier: str | BuiltinTool | None = tool_def.identifier
if identifier == "web_search": if identifier == "web_search":
identifier = BuiltinTool.brave_search identifier = BuiltinTool.brave_search
@ -823,11 +915,18 @@ class ChatAgent(ShieldRunnerMixin):
for param in tool_def.parameters for param in tool_def.parameters
}, },
) )
tool_name_to_args[tool_def.identifier] = toolgroup_to_args.get(toolgroup_name, {}) tool_name_to_args[tool_def.identifier] = toolgroup_to_args.get(
toolgroup_name, {}
)
self.tool_defs, self.tool_name_to_args = list(tool_name_to_def.values()), tool_name_to_args self.tool_defs, self.tool_name_to_args = (
list(tool_name_to_def.values()),
tool_name_to_args,
)
def _parse_toolgroup_name(self, toolgroup_name_with_maybe_tool_name: str) -> tuple[str, Optional[str]]: def _parse_toolgroup_name(
self, toolgroup_name_with_maybe_tool_name: str
) -> tuple[str, Optional[str]]:
"""Parse a toolgroup name into its components. """Parse a toolgroup name into its components.
Args: Args:
@ -863,7 +962,9 @@ class ChatAgent(ShieldRunnerMixin):
else: else:
tool_name_str = tool_name tool_name_str = tool_name
logger.info(f"executing tool call: {tool_name_str} with args: {tool_call.arguments}") logger.info(
f"executing tool call: {tool_name_str} with args: {tool_call.arguments}"
)
result = await self.tool_runtime_api.invoke_tool( result = await self.tool_runtime_api.invoke_tool(
tool_name=tool_name_str, tool_name=tool_name_str,
kwargs={ kwargs={
@ -876,144 +977,142 @@ class ChatAgent(ShieldRunnerMixin):
logger.debug(f"tool call {tool_name_str} completed with result: {result}") logger.debug(f"tool call {tool_name_str} completed with result: {result}")
return result return result
async def handle_documents( # async def handle_documents(
self, # self,
session_id: str, # session_id: str,
documents: List[Document], # documents: List[Document],
input_messages: List[Message], # input_messages: List[Message],
) -> None: # ) -> None:
memory_tool = any(tool_def.tool_name == MEMORY_QUERY_TOOL for tool_def in self.tool_defs) # memory_tool = any(tool_def.tool_name == MEMORY_QUERY_TOOL for tool_def in self.tool_defs)
code_interpreter_tool = any(tool_def.tool_name == BuiltinTool.code_interpreter for tool_def in self.tool_defs) # code_interpreter_tool = any(tool_def.tool_name == BuiltinTool.code_interpreter for tool_def in self.tool_defs)
content_items = [] # content_items = []
url_items = [] # url_items = []
pattern = re.compile("^(https?://|file://|data:)") # pattern = re.compile("^(https?://|file://|data:)")
for d in documents: # for d in documents:
if isinstance(d.content, URL): # if isinstance(d.content, URL):
url_items.append(d.content) # url_items.append(d.content)
elif pattern.match(d.content): # elif pattern.match(d.content):
url_items.append(URL(uri=d.content)) # url_items.append(URL(uri=d.content))
else: # else:
content_items.append(d) # content_items.append(d)
# Save the contents to a tempdir and use its path as a URL if code interpreter is present # # Save the contents to a tempdir and use its path as a URL if code interpreter is present
if code_interpreter_tool: # if code_interpreter_tool:
for c in content_items: # for c in content_items:
temp_file_path = os.path.join(self.tempdir, f"{make_random_string()}.txt") # temp_file_path = os.path.join(self.tempdir, f"{make_random_string()}.txt")
with open(temp_file_path, "w") as temp_file: # with open(temp_file_path, "w") as temp_file:
temp_file.write(c.content) # temp_file.write(c.content)
url_items.append(URL(uri=f"file://{temp_file_path}")) # url_items.append(URL(uri=f"file://{temp_file_path}"))
if memory_tool and code_interpreter_tool: # if memory_tool and code_interpreter_tool:
# if both memory and code_interpreter are available, we download the URLs # # if both memory and code_interpreter are available, we download the URLs
# and attach the data to the last message. # # and attach the data to the last message.
await attachment_message(self.tempdir, url_items, input_messages[-1]) # await attachment_message(self.tempdir, url_items, input_messages[-1])
# Since memory is present, add all the data to the memory bank # # Since memory is present, add all the data to the memory bank
await self.add_to_session_vector_db(session_id, documents) # await self.add_to_session_vector_db(session_id, documents)
elif code_interpreter_tool: # elif code_interpreter_tool:
# if only code_interpreter is available, we download the URLs to a tempdir # # if only code_interpreter is available, we download the URLs to a tempdir
# and attach the path to them as a message to inference with the # # and attach the path to them as a message to inference with the
# assumption that the model invokes the code_interpreter tool with the path # # assumption that the model invokes the code_interpreter tool with the path
await attachment_message(self.tempdir, url_items, input_messages[-1]) # await attachment_message(self.tempdir, url_items, input_messages[-1])
elif memory_tool: # elif memory_tool:
# if only memory is available, we load the data from the URLs and content items to the memory bank # # if only memory is available, we load the data from the URLs and content items to the memory bank
await self.add_to_session_vector_db(session_id, documents) # await self.add_to_session_vector_db(session_id, documents)
else: # else:
# if no memory or code_interpreter tool is available, # # if no memory or code_interpreter tool is available,
# we try to load the data from the URLs and content items as a message to inference # # we try to load the data from the URLs and content items as a message to inference
# and add it to the last message's context # # and add it to the last message's context
input_messages[-1].context = "\n".join( # input_messages[-1].context = "\n".join(
[doc.content for doc in content_items] + await load_data_from_urls(url_items) # [doc.content for doc in content_items] + await load_data_from_urls(url_items)
) # )
async def _ensure_vector_db(self, session_id: str) -> str: # async def _ensure_vector_db(self, session_id: str) -> str:
session_info = await self.storage.get_session_info(session_id) # session_info = await self.storage.get_session_info(session_id)
if session_info is None: # if session_info is None:
raise ValueError(f"Session {session_id} not found") # raise ValueError(f"Session {session_id} not found")
if session_info.vector_db_id is None: # if session_info.vector_db_id is None:
vector_db_id = f"vector_db_{session_id}" # vector_db_id = f"vector_db_{session_id}"
# TODO: the semantic for registration is definitely not "creation" # # TODO: the semantic for registration is definitely not "creation"
# so we need to fix it if we expect the agent to create a new vector db # # so we need to fix it if we expect the agent to create a new vector db
# for each session # # for each session
await self.vector_io_api.register_vector_db( # await self.vector_io_api.register_vector_db(
vector_db_id=vector_db_id, # vector_db_id=vector_db_id,
embedding_model="all-MiniLM-L6-v2", # embedding_model="all-MiniLM-L6-v2",
) # )
await self.storage.add_vector_db_to_session(session_id, vector_db_id) # await self.storage.add_vector_db_to_session(session_id, vector_db_id)
else: # else:
vector_db_id = session_info.vector_db_id # vector_db_id = session_info.vector_db_id
return vector_db_id # return vector_db_id
async def add_to_session_vector_db(self, session_id: str, data: List[Document]) -> None: # async def add_to_session_vector_db(
vector_db_id = await self._ensure_vector_db(session_id) # self, session_id: str, data: List[Document]
documents = [ # ) -> None:
RAGDocument( # vector_db_id = await self._ensure_vector_db(session_id)
document_id=str(uuid.uuid4()), # documents = [
content=a.content, # RAGDocument(
mime_type=a.mime_type, # document_id=str(uuid.uuid4()),
metadata={}, # content=a.content,
) # mime_type=a.mime_type,
for a in data # metadata={},
] # )
await self.tool_runtime_api.rag_tool.insert( # for a in data
documents=documents, # ]
vector_db_id=vector_db_id, # await self.tool_runtime_api.rag_tool.insert(
chunk_size_in_tokens=512, # documents=documents,
) # vector_db_id=vector_db_id,
# chunk_size_in_tokens=512,
# )
async def load_data_from_urls(urls: List[URL]) -> List[str]: async def load_data_from_url(url: URL) -> str:
data = [] uri = url.uri
for url in urls: if uri.startswith("http"):
uri = url.uri async with httpx.AsyncClient() as client:
if uri.startswith("file://"): r = await client.get(uri)
filepath = uri[len("file://") :] resp = r.text
with open(filepath, "r") as f: return resp
data.append(f.read()) return ""
elif uri.startswith("http"):
async with httpx.AsyncClient() as client:
r = await client.get(uri)
resp = r.text
data.append(resp)
return data
async def attachment_message(tempdir: str, urls: List[URL], message: UserMessage) -> None: # async def attachment_message(
contents = [] # tempdir: str, urls: List[URL], message: UserMessage
# ) -> None:
# contents = []
for url in urls: # for url in urls:
uri = url.uri # uri = url.uri
if uri.startswith("file://"): # if uri.startswith("file://"):
filepath = uri[len("file://") :] # filepath = uri[len("file://") :]
elif uri.startswith("http"): # elif uri.startswith("http"):
path = urlparse(uri).path # path = urlparse(uri).path
basename = os.path.basename(path) # basename = os.path.basename(path)
filepath = f"{tempdir}/{make_random_string() + basename}" # filepath = f"{tempdir}/{make_random_string() + basename}"
logger.info(f"Downloading {url} -> {filepath}") # logger.info(f"Downloading {url} -> {filepath}")
async with httpx.AsyncClient() as client: # async with httpx.AsyncClient() as client:
r = await client.get(uri) # r = await client.get(uri)
resp = r.text # resp = r.text
with open(filepath, "w") as fp: # with open(filepath, "w") as fp:
fp.write(resp) # fp.write(resp)
else: # else:
raise ValueError(f"Unsupported URL {url}") # raise ValueError(f"Unsupported URL {url}")
contents.append( # contents.append(
TextContentItem( # TextContentItem(
text=f'# User provided a file accessible to you at "{filepath}"\nYou can use code_interpreter to load and inspect it.' # text=f'# User provided a file accessible to you at "{filepath}"\nYou can use code_interpreter to load and inspect it.'
) # )
) # )
if isinstance(message.content, list): # if isinstance(message.content, list):
message.content.extend(contents) # message.content.extend(contents)
else: # else:
if isinstance(message.content, str): # if isinstance(message.content, str):
message.content = [TextContentItem(text=message.content)] + contents # message.content = [TextContentItem(text=message.content)] + contents
else: # else:
message.content = [message.content] + contents # message.content = [message.content] + contents
def _interpret_content_as_attachment( def _interpret_content_as_attachment(