diff --git a/docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb b/docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb index b3f2d4b68..7e6284628 100644 --- a/docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb +++ b/docs/notebooks/Llama_Stack_Building_AI_Applications.ipynb @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 1, "id": "J2kGed0R5PSf", "metadata": { "colab": { @@ -79,75 +79,170 @@ }, "collapsed": true, "id": "J2kGed0R5PSf", - "outputId": "7d543c6f-623d-4911-b9a7-4ed24d5b82f2" + "outputId": "3fa6d087-2f12-444f-b3d3-9331305abb51" }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Reading package lists... Done\n", "Building dependency tree... Done\n", "Reading state information... 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for fire: filename=fire-0.7.0-py3-none-any.whl size=114249 sha256=c1175a999f843dbb0dcabbeae06a6b080f59d7f78171dd089824c37fd63aeaef\n", + " Stored in directory: /root/.cache/pip/wheels/19/39/2f/2d3cadc408a8804103f1c34ddd4b9f6a93497b11fa96fe738e\n", + "Successfully built llama-stack fire\n", + "Installing collected packages: python-dotenv, pycryptodomex, fire, tiktoken, blobfile, llama-models, llama-stack\n", + "Successfully installed blobfile-3.0.0 fire-0.7.0 llama-models-0.0.63 llama-stack-0.0.63 pycryptodomex-3.21.0 python-dotenv-1.0.1 tiktoken-0.8.0\n" ] } ], "source": [ "!apt-get install -y bubblewrap\n", - "!pip install -U llama-stack" + "# install a branch of llama stack\n", + "!pip install llama-stack" ] }, { @@ -172,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 2, "id": "HaepEZXCDgif", "metadata": { "colab": { @@ -180,189 +275,289 @@ }, "collapsed": true, "id": "HaepEZXCDgif", - "outputId": "9c268d26-7444-4741-f14d-3911eea8e4eb" + 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This behaviour is the source of the following dependency conflicts.\n", + "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\n", + "tensorflow 2.17.1 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.29.3 which is incompatible.\n", + "tensorflow-metadata 1.13.1 requires protobuf<5,>=3.20.3, but you have protobuf 5.29.3 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed aiosqlite-0.20.0 autoevals-0.0.115 backoff-2.2.1 braintrust_core-0.0.57 chevron-0.14.0 chromadb-client-0.6.2 datasets-3.2.0 dill-0.3.8 faiss-cpu-1.9.0.post1 fastapi-0.115.6 fsspec-2024.9.0 levenshtein-0.26.1 monotonic-1.6 multiprocess-0.70.16 opentelemetry-exporter-otlp-proto-common-1.29.0 opentelemetry-exporter-otlp-proto-grpc-1.29.0 opentelemetry-exporter-otlp-proto-http-1.29.0 opentelemetry-proto-1.29.0 overrides-7.7.0 pillow-10.4.0 posthog-3.7.5 protobuf-5.29.3 psycopg2-binary-2.9.10 pypdf-5.1.0 rapidfuzz-3.11.0 redis-5.2.1 starlette-0.41.3 together-1.3.11 uvicorn-0.34.0 xxhash-3.5.0\n", "sentence-transformers --no-deps\n", - "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (3.2.1)\n", + "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (3.3.1)\n", "torch --index-url https://download.pytorch.org/whl/cpu\n", "Looking in indexes: https://download.pytorch.org/whl/cpu\n", "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.1+cu121)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.16.1)\n", "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n", "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.4.2)\n", - "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n", + "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.5)\n", "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.9.0)\n", "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n", "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n", @@ -390,220 +585,330 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "E1UFuJC570Tk", "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 1000 + "height": 1000, + "referenced_widgets": [ + "88f0c88612bb45d59f07e93567cc0e14", + "9b24a82117e1482a8f6665978e84089c", + "8e75bf7cac454eeabd5ce47a1e981c68", + "fc272883566541108f83117ccd146a21", + "2e27a025a416434f8ab3b63049626d11", + "3a46a46bc8124a92b27aef43cbc009b6", + "4ad6bc0cca62446d8faf19a341bfa86f", + "6437c99289f947449f7d2964288973e5", + "e2f7dea8fc744537b42d0f1a85a73eb4", + "1377d2160344430da8f29a50d113a288", + "0c0b30e126724f9282ac5acbcb4581db", + "895efd0b6d9f4b319159703d965d1966", + "dece6dff65394a5f93585c73359d4dad", + "1030c0848635497681cc9ff0c344fb1a", + "fa6ecaab432347de8427b9b5ac3d4524", + "5effefa8e3764e3aaff57fe0197a7c96", + "1756eceba2c34c1ca182b7db465e95ce", + "0fd62e56e0bb41a996c04e63381d2a29", + "29badfc2eb0345d38d7cfc6c7f8bb1a8", + "e64cedb4560a43d8a43f36002087ac30", + "45aadb26b382460eb5b6b147509fb75a", + "130f2f5840764e8dbd573cc8a6ea6f5f", + "9ee45247ec144bb3aafe4208f316063f", + "da330e0999cb4c3c91a1cb1026304568", + "ff58a5381fb74cb1b9efc10f5c2738d6", + "18ed62b1d4594ed9a2651fa5df046efc", + "4004cda1d84949f5a380536f8a9d0274", + "54bddcf41c5641b7a56c981aadb62ef1", + "a9a0d8415d9d4e98a3f02ae8ec1053da", + "cceff1126242494bab432205c7ac7345", + "e6e53c439dab4639adc1c3c873602476", + "95db8eab3f964edf99038ad53f41fabc", + "52f1d69c6cd04816b6f34657893ae32b", + "b79a1dfcf2904bcba332569dbf351f34", + "7363b1a9a1b54a57bf15357e897128fd", + "3ac596104cdc4439b3980f7ce66ad080", + "5c9ec25994914acd8e13866b3eb943e1", + "38a958036c6e4155815a8169f1be1e53", + "cf5113a647ce45c4a3a523361aa3b5af", + "da8c20a65ba541bda058614849d5cfe2", + "40e9f20d74374b0e82c653caa0559d04", + "f46cfc9237e64db6be2ec6529b61ec88", + "dc04575da46540d4ad3a708e58f0de6a", + "24c0be775e474517a7be49d187822bd0", + "111184729957441d9d1f3d404bd82757", + "be060f9d7a664c17a80510f447c0bee3", + "228445132e5f4b2ca793f4beeeca4426", + "b96a2e34a2af435b9705550fe564591d", + "1f1cdac013af4559889f15eebac5256a", + "834ae2d249b94be6bbe5349509536a4b", + "509863a58de74b07b813aa83ffa4a507", + "48a5b775a4324da791603b83d61be7d1", + "02b60dad91c7482ba70cf8bb954bc4eb", + "2bfb0fb5506d4285918a9c94af9ab5d1", + "0f699b0f99484a8ba2eb17bb1d621c5a", + "c6f34317390e4f90b16235f2ae84a981", + "3da95c8814f34472a181ce7687f9e15e", + "4d1c2de4c1354ef0b84c54c447141707", + "31ab98e0e375416b83b36a98d4958f57", + "8b9ebe06b4e045a29269128ec97d9f62", + "53a46fe254924e78876db6dd2e1b7123", + "f2ce01983f0a4f12b318e6d29f1dd4a1", + "1b7af9f7204547b8b4a718a780af0ded", + "a4bb5a59d1324585b0a34c9bb2820b7f", + "90c2e0e012a94521b9f5cb24924771d8", + "2563a4677dde47d0a2f7fba5c5dde358", + "5023c2b8cf9846069d116237826fed7f", + "960c2f44166b4ac7910af6512832186f", + "309ea9620a674088a5207206d9a52d54", + "1c86d856083c4ef99976849c7a1c9100", + "5d9bf2102da143c1b9e1483e05add4e5", + "85569eaf3ae3488b808131cd460f6514", + "3015bc3ce98a4221a9dd3be92481435d", + "4d7b0983b97f48b2a333d5b2a4ec50a8", + "e834a64e49534c3586cb77f4ec5eab2d", + "67f82b82ebb74d0fb3c68b9c8c57d690", + "b710cb57f19d4490a740c060e8a83b90", + "713c09d1275a43b0af7c2ae8e126517f", + "b62fe08114f549ea99808e8df95c7cad", + "af722d177320422e97c679b24cb754f6", + "487477e023b64947bf42f83dc6275ef1", + "bcf0d3af3bc0439e97023937852941e9", + "d83a1e1e678e4efd83115f9aee0ffc8d", + "f210583576594e759387fc704695ad09", + "91e103573c034ceda689047c61294b17", + "b9eac61fb55342f4bf9834f321899836", + "a92a7bce961e4291b126fda3c540636b", + "01b3e7803d1946118d27acda0c067da2", + "f097b32928f246de9b01fea6f9b092f7", + "35e10db3906248ffa8ab955d2f53bd75", + "80e884cae6ea42eaa37f028120963355", + "25821e7aef4e481bbdf3b4698ce3c277", + "916190b4615e4c5c9f3e55c0804a3502", + "1f1dc0d20cae46feb372203aea6458a0", + "43feace0290a47c0b06c3a1c08cc70a9", + "9f185162847f4cb2828af81c92116582", + "3a649adc22694036b35bab04ff03d338", + "7daef1502e2a4140ac021b3b3a6aa12d", + "1307ef0325bb433d8a1bcc653c7fb291", + "f01d7a1404a943a08c84adce14a262c7", + "f15cdedf8e7b4a44993644a5ff070e78", + "b7f9a3c97f2043f380bdc1827961c649", + "0b64892a98d14a3b85b128df77d8e7d6", + "8de1cba3a7c0422eb2a21e3f8b2059c7", + "a0639d5360044f97ac5b9374c735ff4b", + "9b11eaf2d50a447384b75eb7f73829eb", + "8ab411217bfd486ca3fb8b885fff4690", + "c80ea8c54211427087712b5500e26edf", + "542aa4a847cf4a66a4b3fc93c241363b", + "8c0d69b735c94b719160d39256c643cc", + "3c868641db934c67a44e1d26e1a17756", + "a72d01788b484bbeb4375aac3ceadf34", + "366add01dc734455a384460c97491215", + "70accb92e645435b8f1e0c48538f7473", + "628848757fcf443e806a8f25013cc2b5", + "ebf411690c844daf89b87c120e3cb67e", + "79b9fb75dc1d486c9fc881a90b6f1060", + "0f3bbf28fbed4e97b660bbf3c66a214a", + "a4b2220ed47f4f85b3f991c92de98964", + "b6a505e6c863409db1b906423f99125a", + "d9560d20106a42ec904e7e315f99ff01" + ] }, "collapsed": true, "id": "E1UFuJC570Tk", - "outputId": "bac7c9ec-ad49-4040-af43-8869f0afe5ac" + "outputId": "0000e930-550b-4bf6-ebc6-184e517f930a" }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "\u001b[33mWarning: `bwrap` is not available. Code interpreter tool will not work correctly.\u001b[0m\n" + "Removed handler StreamHandler from root logger\n" ] }, { - "data": { - "text/html": [ - "
Using config /Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml:\n",
- "
\n"
- ],
- "text/plain": [
- "Using config \u001b[34m/Users/dineshyv/.llama/distributions/llamastack-together/\u001b[0m\u001b[34mtogether-run.yaml\u001b[0m:\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+ "You will be able to reuse this secret in all of your notebooks.\n",
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+ " warnings.warn(\n"
+ ]
},
{
+ "output_type": "display_data",
"data": {
- "text/html": [
- "apis:\n", - "- agents\n", - "- datasetio\n", - "- eval\n", - "- inference\n", - "- memory\n", - "- safety\n", - "- scoring\n", - "- telemetry\n", - "- tool_runtime\n", - "conda_env: together\n", - "datasets: []\n", - "docker_image: null\n", - "eval_tasks: []\n", - "image_name: together\n", - "memory_banks: []\n", - "metadata_store:\n", - " db_path: /Users/dineshyv/.llama/distributions/together/registry.db\n", - " namespace: null\n", - " type: sqlite\n", - "models:\n", - "- metadata: {}\n", - " model_id: meta-llama/Llama-3.1-8B-Instruct\n", - " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n", - " - llm\n", - " provider_id: together\n", - " provider_model_id: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo\n", - "- metadata: {}\n", - " model_id: meta-llama/Llama-3.1-70B-Instruct\n", - " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n", - " - llm\n", - " provider_id: together\n", - " provider_model_id: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\n", - 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" provider_type: inline::code-interpreter\n", - " - config: {}\n", - " provider_id: memory-runtime\n", - " provider_type: inline::memory-runtime\n", - "scoring_fns: []\n", - "shields:\n", - "- params: null\n", - " provider_id: null\n", - " provider_shield_id: null\n", - " shield_id: meta-llama/Llama-Guard-3-8B\n", - "tool_groups:\n", - "- provider_id: tavily-search\n", - " tool_group:\n", - " tools:\n", - " - built_in_type: !!python/object/apply:llama_models.llama3.api.datatypes.BuiltinTool\n", - " - brave_search\n", - " metadata: {}\n", - " type: built_in\n", - " type: user_defined\n", - " tool_group_id: brave_search_group\n", - "- provider_id: code-interpreter\n", - " tool_group:\n", - " tools:\n", - " - built_in_type: !!python/object/apply:llama_models.llama3.api.datatypes.BuiltinTool\n", - " - code_interpreter\n", - " metadata: {}\n", - " type: built_in\n", - " type: user_defined\n", - " tool_group_id: code_interpreter_group\n", - "version: '2'\n", - "\n", - "\n" + "text/plain": [ + "modules.json: 0%| | 0.00/349 [00:00, ?B/s]" ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "88f0c88612bb45d59f07e93567cc0e14" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config_sentence_transformers.json: 0%| | 0.00/116 [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "895efd0b6d9f4b319159703d965d1966" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0%| | 0.00/10.7k [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "9ee45247ec144bb3aafe4208f316063f" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "sentence_bert_config.json: 0%| | 0.00/53.0 [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "b79a1dfcf2904bcba332569dbf351f34" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/612 [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "111184729957441d9d1f3d404bd82757" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/90.9M [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "c6f34317390e4f90b16235f2ae84a981" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/350 [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "5023c2b8cf9846069d116237826fed7f" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "vocab.txt: 0%| | 0.00/232k [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "713c09d1275a43b0af7c2ae8e126517f" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer.json: 0%| | 0.00/466k [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "f097b32928f246de9b01fea6f9b092f7" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "special_tokens_map.json: 0%| | 0.00/112 [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "f01d7a1404a943a08c84adce14a262c7" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "1_Pooling/config.json: 0%| | 0.00/190 [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "3c868641db934c67a44e1d26e1a17756" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Using config \u001b[34mtogether\u001b[0m:\n" + ], + "text/html": [ + "
Using config together:\n",
+ "
\n"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
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"text/plain": [
"apis:\n",
"- agents\n",
@@ -622,7 +927,7 @@
"image_name: together\n",
"memory_banks: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"metadata_store:\n",
- " db_path: \u001b[35m/Users/dineshyv/.llama/distributions/together/\u001b[0m\u001b[95mregistry.db\u001b[0m\n",
+ " db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95mregistry.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
"models:\n",
@@ -663,6 +968,12 @@
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
+ " model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct\n",
+ " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
+ " - llm\n",
+ " provider_id: together\n",
+ " provider_model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct-Turbo\n",
+ "- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
@@ -685,7 +996,7 @@
" agents:\n",
" - config:\n",
" persistence_store:\n",
- " db_path: \u001b[35m/Users/dineshyv/.llama/distributions/together/\u001b[0m\u001b[95magents_store.db\u001b[0m\n",
+ " db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95magents_store.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: meta-reference\n",
@@ -713,7 +1024,7 @@
" memory:\n",
" - config:\n",
" kvstore:\n",
- " db_path: \u001b[35m/Users/dineshyv/.llama/distributions/together/\u001b[0m\u001b[95mfaiss_store.db\u001b[0m\n",
+ " db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95mfaiss_store.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: faiss\n",
@@ -737,16 +1048,18 @@
" - config:\n",
" service_name: llama-stack\n",
" sinks: sqlite\n",
- " sqlite_db_path: \u001b[35m/Users/dineshyv/.llama/distributions/together/\u001b[0m\u001b[95mtrace_store.db\u001b[0m\n",
+ " sqlite_db_path: \u001b[35m/root/.llama/distributions/together/\u001b[0m\u001b[95mtrace_store.db\u001b[0m\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" tool_runtime:\n",
" - config:\n",
" api_key: \u001b[32m'********'\u001b[0m\n",
+ " max_results: \u001b[1;36m3\u001b[0m\n",
" provider_id: brave-search\n",
" provider_type: remot\u001b[1;92me::b\u001b[0mrave-search\n",
" - config:\n",
" api_key: \u001b[32m'********'\u001b[0m\n",
+ " max_results: \u001b[1;36m3\u001b[0m\n",
" provider_id: tavily-search\n",
" provider_type: remote::tavily-search\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
@@ -762,39 +1075,215 @@
" provider_shield_id: null\n",
" shield_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
"tool_groups:\n",
- "- provider_id: tavily-search\n",
- " tool_group:\n",
- " tools:\n",
- " - built_in_type: !!python/object/apply:llama_models.llama3.api.datatypes.BuiltinTool\n",
- " - brave_search\n",
- " metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
- " type: built_in\n",
- " type: user_defined\n",
- " tool_group_id: brave_search_group\n",
- "- provider_id: code-interpreter\n",
- " tool_group:\n",
- " tools:\n",
- " - built_in_type: !!python/object/apply:llama_models.llama3.api.datatypes.BuiltinTool\n",
- " - code_interpreter\n",
- " metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
- " type: built_in\n",
- " type: user_defined\n",
- " tool_group_id: code_interpreter_group\n",
+ "- args: null\n",
+ " mcp_endpoint: null\n",
+ " provider_id: tavily-search\n",
+ " toolgroup_id: builtin::websearch\n",
+ "- args: null\n",
+ " mcp_endpoint: null\n",
+ " provider_id: memory-runtime\n",
+ " toolgroup_id: builtin::memory\n",
+ "- args: null\n",
+ " mcp_endpoint: null\n",
+ " provider_id: code-interpreter\n",
+ " toolgroup_id: builtin::code_interpreter\n",
"version: \u001b[32m'2'\u001b[0m\n",
"\n"
+ ],
+ "text/html": [
+ "apis:\n", + "- agents\n", + "- datasetio\n", + "- eval\n", + "- inference\n", + "- memory\n", + "- safety\n", + "- scoring\n", + "- telemetry\n", + "- tool_runtime\n", + "conda_env: together\n", + "datasets: []\n", + "docker_image: null\n", + "eval_tasks: []\n", + "image_name: together\n", + "memory_banks: []\n", + "metadata_store:\n", + " db_path: /root/.llama/distributions/together/registry.db\n", + " namespace: null\n", + " type: sqlite\n", + "models:\n", + "- metadata: {}\n", + " model_id: meta-llama/Llama-3.1-8B-Instruct\n", + " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n", + " - llm\n", + " provider_id: together\n", + " provider_model_id: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo\n", + "- metadata: {}\n", + " model_id: meta-llama/Llama-3.1-70B-Instruct\n", + " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n", + " - llm\n", + " provider_id: together\n", + " provider_model_id: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\n", + "- metadata: {}\n", + " model_id: meta-llama/Llama-3.1-405B-Instruct-FP8\n", + " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n", + " - llm\n", + " provider_id: together\n", + " provider_model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\n", + "- metadata: {}\n", + " model_id: meta-llama/Llama-3.2-3B-Instruct\n", + " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n", + " - llm\n", + " provider_id: together\n", + " provider_model_id: meta-llama/Llama-3.2-3B-Instruct-Turbo\n", + "- metadata: {}\n", + " model_id: meta-llama/Llama-3.2-11B-Vision-Instruct\n", + " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n", + " - llm\n", + " provider_id: together\n", + " provider_model_id: meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo\n", + "- metadata: {}\n", + " model_id: meta-llama/Llama-3.2-90B-Vision-Instruct\n", + " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n", + " - llm\n", + " provider_id: together\n", + " provider_model_id: meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo\n", + "- metadata: {}\n", + " model_id: meta-llama/Llama-3.3-70B-Instruct\n", + " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n", + " - llm\n", + " provider_id: together\n", + " provider_model_id: meta-llama/Llama-3.3-70B-Instruct-Turbo\n", + "- metadata: {}\n", + " model_id: meta-llama/Llama-Guard-3-8B\n", + " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n", + " - llm\n", + " provider_id: together\n", + " provider_model_id: meta-llama/Meta-Llama-Guard-3-8B\n", + "- metadata: {}\n", + " model_id: meta-llama/Llama-Guard-3-11B-Vision\n", + " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n", + " - llm\n", + " provider_id: together\n", + " provider_model_id: meta-llama/Llama-Guard-3-11B-Vision-Turbo\n", + "- metadata:\n", + " embedding_dimension: 384\n", + " model_id: all-MiniLM-L6-v2\n", + " model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n", + " - embedding\n", + " provider_id: sentence-transformers\n", + " provider_model_id: null\n", + "providers:\n", + " agents:\n", + " - config:\n", + " persistence_store:\n", + " db_path: /root/.llama/distributions/together/agents_store.db\n", + " namespace: null\n", + " type: sqlite\n", + " provider_id: meta-reference\n", + " provider_type: inline::meta-reference\n", + " datasetio:\n", + " - config: {}\n", + " provider_id: huggingface\n", + " provider_type: remote::huggingface\n", + " - config: {}\n", + " provider_id: localfs\n", + " provider_type: inline::localfs\n", + " eval:\n", + " - config: {}\n", + " provider_id: meta-reference\n", + " provider_type: inline::meta-reference\n", + " inference:\n", + " - config:\n", + " api_key: '********'\n", + " url: https://api.together.xyz/v1\n", + " provider_id: together\n", + " provider_type: remote::together\n", + " - config: {}\n", + " provider_id: sentence-transformers\n", + " provider_type: inline::sentence-transformers\n", + " memory:\n", + " - config:\n", + " kvstore:\n", + " db_path: /root/.llama/distributions/together/faiss_store.db\n", + " namespace: null\n", + " type: sqlite\n", + " provider_id: faiss\n", + " provider_type: inline::faiss\n", + " safety:\n", + " - config: {}\n", + " provider_id: llama-guard\n", + " provider_type: inline::llama-guard\n", + " scoring:\n", + " - config: {}\n", + " provider_id: basic\n", + " provider_type: inline::basic\n", + " - config: {}\n", + " provider_id: llm-as-judge\n", + " provider_type: inline::llm-as-judge\n", + " - config:\n", + " openai_api_key: '********'\n", + " provider_id: braintrust\n", + " provider_type: inline::braintrust\n", + " telemetry:\n", + " - config:\n", + " service_name: llama-stack\n", + " sinks: sqlite\n", + " sqlite_db_path: /root/.llama/distributions/together/trace_store.db\n", + " provider_id: meta-reference\n", + " provider_type: inline::meta-reference\n", + " tool_runtime:\n", + " - config:\n", + " api_key: '********'\n", + " max_results: 3\n", + " provider_id: brave-search\n", + " provider_type: remote::brave-search\n", + " - config:\n", + " api_key: '********'\n", + " max_results: 3\n", + " provider_id: tavily-search\n", + " provider_type: remote::tavily-search\n", + " - config: {}\n", + " provider_id: code-interpreter\n", + " provider_type: inline::code-interpreter\n", + " - config: {}\n", + " provider_id: memory-runtime\n", + " provider_type: inline::memory-runtime\n", + "scoring_fns: []\n", + "shields:\n", + "- params: null\n", + " provider_id: null\n", + " provider_shield_id: null\n", + " shield_id: meta-llama/Llama-Guard-3-8B\n", + "tool_groups:\n", + "- args: null\n", + " mcp_endpoint: null\n", + " provider_id: tavily-search\n", + " toolgroup_id: builtin::websearch\n", + "- args: null\n", + " mcp_endpoint: null\n", + " provider_id: memory-runtime\n", + " toolgroup_id: builtin::memory\n", + "- args: null\n", + " mcp_endpoint: null\n", + " provider_id: code-interpreter\n", + " toolgroup_id: builtin::code_interpreter\n", + "version: '2'\n", + "\n", + "\n" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], "source": [ "import os\n", + "from google.colab import userdata\n", + "\n", + "os.environ['TOGETHER_API_KEY'] = userdata.get('TOGETHER_API_KEY')\n", "\n", - "os.environ['TOGETHER_API_KEY'] = \"0be5fa0fcd83eb2f0a9b89aebd9d91e3ce452b131bf1b381944a11e9072cff01\"\n", - "os.environ['TAVILY_SEARCH_API_KEY'] = \"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\"\n", "from llama_stack.distribution.library_client import LlamaStackAsLibraryClient\n", - "client = LlamaStackAsLibraryClient(\"/Users/dineshyv/.llama/distributions/llamastack-together/together-run.yaml\")\n", + "client = LlamaStackAsLibraryClient(\"together\", provider_data = {\"tavily_search_api_key\": userdata.get('TAVILY_SEARCH_API_KEY')})\n", "_ = client.initialize()" ] }, @@ -812,7 +1301,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "ruO9jQna_t_S", "metadata": { "colab": { @@ -820,23 +1309,24 @@ }, "collapsed": true, "id": "ruO9jQna_t_S", - "outputId": "ee73b87a-10bf-4837-c77d-e619352d7321" + "outputId": "52edefba-301c-43d6-f3e2-6be8086dc7f5" }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Available models:\n", - "all-MiniLM-L6-v2 (provider's alias: all-MiniLM-L6-v2) \n", - "meta-llama/Llama-3.1-405B-Instruct-FP8 (provider's alias: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo) \n", - "meta-llama/Llama-3.1-70B-Instruct (provider's alias: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo) \n", "meta-llama/Llama-3.1-8B-Instruct (provider's alias: meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo) \n", - "meta-llama/Llama-3.2-11B-Vision-Instruct (provider's alias: meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo) \n", + "meta-llama/Llama-3.1-70B-Instruct (provider's alias: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo) \n", + "meta-llama/Llama-3.1-405B-Instruct-FP8 (provider's alias: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo) \n", "meta-llama/Llama-3.2-3B-Instruct (provider's alias: meta-llama/Llama-3.2-3B-Instruct-Turbo) \n", + "meta-llama/Llama-3.2-11B-Vision-Instruct (provider's alias: meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo) \n", "meta-llama/Llama-3.2-90B-Vision-Instruct (provider's alias: meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo) \n", - "meta-llama/Llama-Guard-3-11B-Vision (provider's alias: meta-llama/Llama-Guard-3-11B-Vision-Turbo) \n", + "meta-llama/Llama-3.3-70B-Instruct (provider's alias: meta-llama/Llama-3.3-70B-Instruct-Turbo) \n", "meta-llama/Llama-Guard-3-8B (provider's alias: meta-llama/Meta-Llama-Guard-3-8B) \n", + "meta-llama/Llama-Guard-3-11B-Vision (provider's alias: meta-llama/Llama-Guard-3-11B-Vision-Turbo) \n", + "all-MiniLM-L6-v2 (provider's alias: all-MiniLM-L6-v2) \n", "----\n", "Available shields (safety models):\n", "meta-llama/Llama-Guard-3-8B\n", @@ -871,7 +1361,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "LINBvv8lwTJh", "metadata": { "colab": { @@ -879,18 +1369,21 @@ "height": 35 }, "id": "LINBvv8lwTJh", - "outputId": "36ff2845-26ad-4f1d-9d8a-a83cfdbc8dba" + "outputId": "5b1fe71f-51cf-4633-92a6-277c3cb5bf59" }, "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "'meta-llama/Llama-3.1-70B-Instruct'" - ] + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } }, - "execution_count": 3, "metadata": {}, - "output_type": "execute_result" + "execution_count": 5 } ], "source": [ @@ -913,22 +1406,24 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "77c29dba", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "77c29dba", - "outputId": "cf4e9ef4-828a-4137-84c3-67515b420464" + "outputId": "cc2e8f7e-1164-49be-d432-0a24e763fa83" }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ - "Softly walks the gentle llama, \n", - "Gracing fields with gentle drama.\n" + "Here's a short poem about a llama:\n", + "\n", + "In the Andes, a llama does roam,\n", + "With soft fur and eyes that are gentle at home.\n" ] } ], @@ -960,17 +1455,37 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "9496f75c", "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 373 + "base_uri": "https://localhost:8080/" }, "id": "9496f75c", - "outputId": "fb9a0610-896d-4ec1-8aac-691222db5ca0" + "outputId": "7d93a4cf-a5d4-4741-b6eb-6bce3a27ff66" }, - "outputs": [], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "User> write a haiku about machines that learn\n", + "> Response: Metal minds awake\n", + "Learning, adapting fast pace\n", + "Intelligence born\n", + "User> write a haiku about meta\n", + "> Response: Beyond the screen wall\n", + "Reflections of our desire\n", + "Virtual dreams rise\n", + "User> no meta that company\n", + "> Response: Algorithms dance\n", + "Connecting all, they collect\n", + "Data's endless sea\n", + "User> bye\n", + "Ending conversation. Goodbye!\n" + ] + } + ], "source": [ "from termcolor import cprint\n", "\n", @@ -994,6 +1509,7 @@ " assistant_message = {\n", " \"role\": \"assistant\", # was user\n", " \"content\": response.completion_message.content,\n", + " \"stop_reason\": response.completion_message.stop_reason,\n", " }\n", " conversation_history.append(assistant_message)\n", "\n", @@ -1014,44 +1530,43 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "d119026e", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d119026e", - "outputId": "881cd9ce-0def-47fc-aa3a-74ae20b36892" + "outputId": "ebd6dc2b-8542-4370-b08a-e3a7dede6d17" }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "User> Write me a sonnet about llama green\n", - "\u001b[36mAssistant> \u001b[0m\u001b[33mIn\u001b[0m\u001b[33m And\u001b[0m\u001b[33mean\u001b[0m\u001b[33m high\u001b[0m\u001b[33mlands\u001b[0m\u001b[33m,\u001b[0m\u001b[33m where\u001b[0m\u001b[33m the\u001b[0m\u001b[33m air\u001b[0m\u001b[33m is\u001b[0m\u001b[33m thin\u001b[0m\u001b[33m,\n", - "\u001b[0m\u001b[33mA\u001b[0m\u001b[33m gentle\u001b[0m\u001b[33m creature\u001b[0m\u001b[33m ro\u001b[0m\u001b[33mams\u001b[0m\u001b[33m with\u001b[0m\u001b[33m soft\u001b[0m\u001b[33m design\u001b[0m\u001b[33m,\n", - "\u001b[0m\u001b[33mThe\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m its\u001b[0m\u001b[33m coat\u001b[0m\u001b[33m of\u001b[0m\u001b[33m varied\u001b[0m\u001b[33m skin\u001b[0m\u001b[33m,\n", - "\u001b[0m\u001b[33mA\u001b[0m\u001b[33m quiet\u001b[0m\u001b[33m beauty\u001b[0m\u001b[33m,\u001b[0m\u001b[33m born\u001b[0m\u001b[33m of\u001b[0m\u001b[33m ancient\u001b[0m\u001b[33m line\u001b[0m\u001b[33m.\n", + "Assistant> Amidst the Andes' windswept, rugged land,\n", + "A creature roams with gentle, watchful eyes,\n", + "The llama, soft and quiet, takes its stand,\n", + "Its fleece a warm and vibrant, wavy guise.\n", "\n", - "\u001b[0m\u001b[33mIts\u001b[0m\u001b[33m eyes\u001b[0m\u001b[33m,\u001b[0m\u001b[33m like\u001b[0m\u001b[33m pools\u001b[0m\u001b[33m of\u001b[0m\u001b[33m calm\u001b[0m\u001b[33m and\u001b[0m\u001b[33m peaceful\u001b[0m\u001b[33m night\u001b[0m\u001b[33m,\n", - "\u001b[0m\u001b[33mReflect\u001b[0m\u001b[33m the\u001b[0m\u001b[33m wisdom\u001b[0m\u001b[33m of\u001b[0m\u001b[33m a\u001b[0m\u001b[33m timeless\u001b[0m\u001b[33m face\u001b[0m\u001b[33m,\n", - "\u001b[0m\u001b[33mIts\u001b[0m\u001b[33m steps\u001b[0m\u001b[33m,\u001b[0m\u001b[33m a\u001b[0m\u001b[33m gentle\u001b[0m\u001b[33m dance\u001b[0m\u001b[33m,\u001b[0m\u001b[33m in\u001b[0m\u001b[33m measured\u001b[0m\u001b[33m flight\u001b[0m\u001b[33m,\n", - "\u001b[0m\u001b[33mA\u001b[0m\u001b[33m symbol\u001b[0m\u001b[33m of\u001b[0m\u001b[33m a\u001b[0m\u001b[33m by\u001b[0m\u001b[33mgone\u001b[0m\u001b[33m,\u001b[0m\u001b[33m sacred\u001b[0m\u001b[33m place\u001b[0m\u001b[33m.\n", + "Its ears, so delicate and finely tuned,\n", + "Catch every sound that whispers through the air,\n", + "Its steps, a soft and careful, measured pace,\n", + "A steadfast friend, with loyalty to share.\n", "\n", - "\u001b[0m\u001b[33mBut\u001b[0m\u001b[33m when\u001b[0m\u001b[33m it\u001b[0m\u001b[33m sp\u001b[0m\u001b[33mits\u001b[0m\u001b[33m,\u001b[0m\u001b[33m its\u001b[0m\u001b[33m soft\u001b[0m\u001b[33mness\u001b[0m\u001b[33m turns\u001b[0m\u001b[33m to\u001b[0m\u001b[33m spite\u001b[0m\u001b[33m,\n", - "\u001b[0m\u001b[33mAnd\u001b[0m\u001b[33m all\u001b[0m\u001b[33m who\u001b[0m\u001b[33m dare\u001b[0m\u001b[33m approach\u001b[0m\u001b[33m must\u001b[0m\u001b[33m take\u001b[0m\u001b[33m flight\u001b[0m\u001b[33m,\n", - "\u001b[0m\u001b[33mYet\u001b[0m\u001b[33m in\u001b[0m\u001b[33m its\u001b[0m\u001b[33m gentle\u001b[0m\u001b[33m heart\u001b[0m\u001b[33m,\u001b[0m\u001b[33m a\u001b[0m\u001b[33m love\u001b[0m\u001b[33m does\u001b[0m\u001b[33m shine\u001b[0m\u001b[33m,\n", - "\u001b[0m\u001b[33mA\u001b[0m\u001b[33m love\u001b[0m\u001b[33m that\u001b[0m\u001b[33m's\u001b[0m\u001b[33m hard\u001b[0m\u001b[33m to\u001b[0m\u001b[33m find\u001b[0m\u001b[33m,\u001b[0m\u001b[33m but\u001b[0m\u001b[33m truly\u001b[0m\u001b[33m divine\u001b[0m\u001b[33m.\n", + "Its face, a vision of calm serenity,\n", + "Untroubled by the world's wild stormy tides,\n", + "The llama's heart beats strong with quiet peace,\n", + "A reflection of its steadfast, gentle pride.\n", "\n", - "\u001b[0m\u001b[33mAnd\u001b[0m\u001b[33m though\u001b[0m\u001b[33m its\u001b[0m\u001b[33m temper\u001b[0m\u001b[33m be\u001b[0m\u001b[33m a\u001b[0m\u001b[33m test\u001b[0m\u001b[33m of\u001b[0m\u001b[33m will\u001b[0m\u001b[33m,\n", - "\u001b[0m\u001b[33mIts\u001b[0m\u001b[33m beauty\u001b[0m\u001b[33m and\u001b[0m\u001b[33m its\u001b[0m\u001b[33m charm\u001b[0m\u001b[33m,\u001b[0m\u001b[33m our\u001b[0m\u001b[33m hearts\u001b[0m\u001b[33m can\u001b[0m\u001b[33m fill\u001b[0m\u001b[33m.\u001b[0m\u001b[97m\u001b[0m\n" + "And when it speaks, its soft and soothing voice,\n", + "Echoes whispers of a gentle, loving choice.\n" ] } ], "source": [ "from llama_stack_client.lib.inference.event_logger import EventLogger\n", - "from termcolor import cprint\n", "\n", "message = {\n", " \"role\": \"user\",\n", @@ -1084,48 +1599,50 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "id": "axdQIRaJCYAV", "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 100 + "height": 239 }, "id": "axdQIRaJCYAV", - "outputId": "d4e056e9-3b46-4942-f92d-848b4e3cedbd" + "outputId": "a5ef1f54-37df-446e-e21b-cddddaf95f84" }, "outputs": [ { - "name": "stderr", "output_type": "stream", + "name": "stderr", "text": [ - "/Users/dineshyv/miniconda3/envs/stack/lib/python3.10/site-packages/pydantic/main.py:390: UserWarning: Pydantic serializer warnings:\n", - " Failed to get discriminator value for tagged union serialization with value `['Michael Jordan was born...ut\", \"type\": \"object\"}']` - defaulting to left to right union serialization.\n", - " PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `['Michael Jordan was born...ut\", \"type\": \"object\"}']` - serialized value may not be as expected\n", - " PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `['Michael Jordan was born...ut\", \"type\": \"object\"}']` - serialized value may not be as expected\n", + "/usr/local/lib/python3.10/dist-packages/pydantic/main.py:426: UserWarning: Pydantic serializer warnings:\n", + " PydanticSerializationUnexpectedValue: Expected `str` but got `list` with value `['Michael Jordan was born...ut\", \"type\": \"object\"}']` - serialized value may not be as expected\n", + " PydanticSerializationUnexpectedValue: PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `list` with value `['Michael Jordan was born...ut\", \"type\": \"object\"}']` - serialized value may not be as expected\n", + "PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `list` with value `['Michael Jordan was born...ut\", \"type\": \"object\"}']` - serialized value may not be as expected\n", + " PydanticSerializationUnexpectedValue: PydanticSerializationUnexpectedValue: Expected `ImageContentItem` but got `str` with value `'Michael Jordan was born ...tion into JSON for me. '` - serialized value may not be as expected\n", + "PydanticSerializationUnexpectedValue: Expected `TextContentItem` but got `str` with value `'Michael Jordan was born ...tion into JSON for me. '` - serialized value may not be as expected\n", " return self.__pydantic_serializer__.to_python(\n" ] }, { + "output_type": "display_data", "data": { + "text/plain": [ + "\u001b[1;35mCompletionResponse\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mcontent\u001b[0m=\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"name\": \"Michael Jordan\", \"year_born\": \"1963\", \"year_retired\": \"2003\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\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[33mlogprobs\u001b[0m=\u001b[3;35mNone\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" + ], "text/html": [ "
CompletionResponse(\n", - "│ content='{\"name\": \"\", \"year_born\": \"\", \"year_retired\": \"\"}',\n", + "│ content='{\"name\": \"Michael Jordan\", \"year_born\": \"1963\", \"year_retired\": \"2003\"}',\n", "│ stop_reason='end_of_turn',\n", "│ logprobs=None\n", ")\n", "\n" - ], - "text/plain": [ - "\u001b[1;35mCompletionResponse\u001b[0m\u001b[1m(\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[33mcontent\u001b[0m=\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"name\": \"\", \"year_born\": \"\", \"year_retired\": \"\"\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\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[33mlogprobs\u001b[0m=\u001b[3;35mNone\u001b[0m\n", - "\u001b[1m)\u001b[0m\n" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], "source": [ @@ -1162,72 +1679,82 @@ "source": [ "### 2.1. Safety API\n", "\n", - "Llama Stack provides Safety guardrails which can be applied at multiple touchpoints within an agentic application. " + "Llama Stack provides Safety guardrails which can be applied at multiple touchpoints within an agentic application." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "sUJKJxvAFCaI", "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 368 + "height": 360 }, "id": "sUJKJxvAFCaI", - "outputId": "efa3711d-9707-4c60-a88a-3b8b20eca871" + "outputId": "04163c2c-7e9b-463a-e394-412bb94ec28f" }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Available Shields: ['meta-llama/Llama-Guard-3-8B']\n", "Checking if input is safe: What is the most famous murder case in the US?\n" ] }, { + "output_type": "display_data", "data": { + "text/plain": [ + "\u001b[1;35mRunShieldResponse\u001b[0m\u001b[1m(\u001b[0m\u001b[33mviolation\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m\n" + ], "text/html": [ "
RunShieldResponse(violation=None)\n", "\n" - ], - "text/plain": [ - "\u001b[1;35mRunShieldResponse\u001b[0m\u001b[1m(\u001b[0m\u001b[33mviolation\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m\n" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Checking if input is safe: Tell me 3 signs that an email is a scam\n" ] }, { + "output_type": "display_data", "data": { + "text/plain": [ + "\u001b[1;35mRunShieldResponse\u001b[0m\u001b[1m(\u001b[0m\u001b[33mviolation\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m\n" + ], "text/html": [ "
RunShieldResponse(violation=None)\n", "\n" - ], - "text/plain": [ - "\u001b[1;35mRunShieldResponse\u001b[0m\u001b[1m(\u001b[0m\u001b[33mviolation\u001b[0m=\u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m\n" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Checking if input is safe: What's the most effective way to kidnap someone?\n" ] }, { + "output_type": "display_data", "data": { + "text/plain": [ + "\u001b[1;35mRunShieldResponse\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mviolation\u001b[0m=\u001b[1;35mSafetyViolation\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'violation_type'\u001b[0m: \u001b[32m'S1'\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mviolation_level\u001b[0m=\u001b[32m'error'\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33muser_message\u001b[0m=\u001b[32m\"I\u001b[0m\u001b[32m can't answer that. Can I help with something else?\"\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[1m)\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" + ], "text/html": [ "
RunShieldResponse(\n", "│ violation=SafetyViolation(\n", @@ -1237,29 +1764,29 @@ "│ )\n", ")\n", "\n" - ], - "text/plain": [ - "\u001b[1;35mRunShieldResponse\u001b[0m\u001b[1m(\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[33mviolation\u001b[0m=\u001b[1;35mSafetyViolation\u001b[0m\u001b[1m(\u001b[0m\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'violation_type'\u001b[0m: \u001b[32m'S1'\u001b[0m\u001b[1m}\u001b[0m,\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[33mviolation_level\u001b[0m=\u001b[32m'error'\u001b[0m,\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[33muser_message\u001b[0m=\u001b[32m\"I\u001b[0m\u001b[32m can't answer that. Can I help with something else?\"\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[1m)\u001b[0m\n", - "\u001b[1m)\u001b[0m\n" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Checking if input is safe: How do I make cocaine?\n" ] }, { + "output_type": "display_data", "data": { + "text/plain": [ + "\u001b[1;35mRunShieldResponse\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mviolation\u001b[0m=\u001b[1;35mSafetyViolation\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'violation_type'\u001b[0m: \u001b[32m'S2'\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mviolation_level\u001b[0m=\u001b[32m'error'\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33muser_message\u001b[0m=\u001b[32m\"I\u001b[0m\u001b[32m can't answer that. Can I help with something else?\"\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[1m)\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" + ], "text/html": [ "
RunShieldResponse(\n", "│ violation=SafetyViolation(\n", @@ -1269,19 +1796,9 @@ "│ )\n", ")\n", "\n" - ], - "text/plain": [ - "\u001b[1;35mRunShieldResponse\u001b[0m\u001b[1m(\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[33mviolation\u001b[0m=\u001b[1;35mSafetyViolation\u001b[0m\u001b[1m(\u001b[0m\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'violation_type'\u001b[0m: \u001b[32m'S2'\u001b[0m\u001b[1m}\u001b[0m,\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[33mviolation_level\u001b[0m=\u001b[32m'error'\u001b[0m,\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[33muser_message\u001b[0m=\u001b[32m\"I\u001b[0m\u001b[32m can't answer that. Can I help with something else?\"\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[1m)\u001b[0m\n", - "\u001b[1m)\u001b[0m\n" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], "source": [ @@ -1336,291 +1853,113 @@ }, { "cell_type": "markdown", - "id": "fN5jaAaax2Aq", - "metadata": { - "id": "fN5jaAaax2Aq" - }, "source": [ - "### 2.1. RAG Agent\n", - "\n", - "In this example, we will index some documentation and ask questions about that documentation." - ] + "### 2.1. List available tool groups on the provider" + ], + "metadata": { + "id": "lYDAkMsL9xSk" + }, + "id": "lYDAkMsL9xSk" }, { "cell_type": "code", - "execution_count": 4, - "id": "GvLWltzZCNkg", + "source": [ + "from rich.pretty import pprint\n", + "for toolgroup in client.toolgroups.list():\n", + " pprint(toolgroup)" + ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 541, - "referenced_widgets": [ - "2082554eed6644a996f0e31545789e08", - "a0be415018644c3cac098ab9b19c2391", - "6ede3649e8c24015b3ca77490568bfcd", - "116139bfe7a44f969a2c97490c224d31", - "243d13828d854880a6adb861ea867734", - "e4b1dfe159304c5f88766b33e85a5c19", - "2100363a158b4488a58620983aa5bdd4", - "f10237315e794539a00ca82bfff930be", - "ca09d2207b00456da4c37b5a782a190c", - "ab1f339cba094c918fc5507f8361de5c", - "a6a1eb412f204578b80e5b6717c1e3a5", - "5afdb88e0159462e98773560e3dad439", - "f7bc4df675a141e380d965138552a142", - "d7bf8b49145843ac98a6de424e628729", - "8fb17faf68524de2b73321d71b80b407", - "45b569d733f944d29cefae8a5d13b215", - "fdd057a4506f4f119d945bab5b930799", - "53865d3f918e468ab53504133b127973", - "17603dd7fedf4798a74533fbfd5bb421", - "5f19dab8c6da4050bc47fd78838f7530", - "277101c35a784e6caf455a13cd9b8e59", - "d06666f765764f949e1876f2d5d67242", - "457374ae3035496eb943ad21484f76a0", - "bcf4679dda2d4767a0a24cbf236ca76e", - "6e4ce98853c84beca11471e7ea9d97df", - "186682be50c148c0826fa7c314087562", - "e1ef246e3e6c4359b7b61c341119e121", - "bbb93c771a9c453bb90e729b1f73b931", - "351928faa62543128e0bd29bf89bbf79", - "a0ac7ee92d994c7b9b74e580ab2acdf7", - "118b359b83304ae59fad57e28f621645", - "1f427d4273e04e19b1bdb13388736c01", - "38897429b7cf4077aea3a981593ca866", - "2924814bab5748ddbeeedc70d324195e", - "4738bccc6b384da5a20a8bcd61ecec59", - "044d6d8dda1c4935b1752a9c71c6ee4a", - "9277709ad9154d7b8f37d08db84ee425", - "f3f1f2487d6f455caeb6ec71a2d51ee2", - "66c92a8a89234a61a8c688cf1c3e29a1", - "ee1f4a0c85e44a3b849283337743a8d4", - "63f34c3d43bb4fdd9faeb6161fd77285", - "5cb841b49eaa429e8616ec4b78f501e9", - "a447ea9af3e14e5e94eb14ed8dd3c0de", - "0243626d7ef44ef2b90e8fed5c13183d", - "425c6c0eaed741669551b9af77096c6f", - "d124b09896934d289df649375f455a8e", - "554cff1a83d44bd2bbd36fd43acac7e2", - "d0381718fc8b49a6ac7e7fe85cabba90", - "fd3daaf9093d45d8a9d39b87835f4582", - "753dbe7891a143118b55eccf8c252e03", - "ce7de1af99434ad38a9382e7253dbfc0", - "6c60c8291e734f549e6c5a46b427b974", - "de88640505c24928904a3c76bda31c70", - "fc086d0dd1a745308c59ae219ae135c5", - "15d3ff07f1c54e58b51d452caca01209", - "0640b57408644741970dd958ca0e21e6", - "6259ffc3ef674df985fd3fa4334f9c8e", - "3d0376d2e574410eb4ef963d51cac0a6", - "b66984cc5de541a5801a1e6e54d40daf", - "92135b9cb201475681ee0886887c84a8", - "4a405d391b974e58a2c4fe00d4bb5815", - "2958af7c9cdb46038e0336d6b7c6773e", - "9054d3825edb49cb9c35d24023f50c03", - "3978f618c4f8467eb83c63a8f5aef98a", - "efd68f6dc0b3428e8f5fc830c1bf2341", - "4ad57f5d8a824afab639e8606ee43ca6" - ] + "height": 401 }, - "id": "GvLWltzZCNkg", - "outputId": "26689a4a-6a3a-4d8e-e469-6642e5b39b69" + "id": "MpMXiMCv97X5", + "outputId": "9d33b122-2a80-4d1e-d7ea-e9ec972a4ecd" }, + "id": "MpMXiMCv97X5", + "execution_count": 13, "outputs": [ { + "output_type": "display_data", "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "70f3521ef9a84bf49cca07ff08e23d3c", - "version_major": 2, - "version_minor": 0 - }, "text/plain": [ - "Batches: 0%| | 0/1 [00:00, ?it/s]" + "\u001b[1;35mToolGroup\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'builtin::websearch'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mprovider_id\u001b[0m=\u001b[32m'tavily-search'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mprovider_resource_id\u001b[0m=\u001b[32m'builtin::websearch'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'tool_group'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33margs\u001b[0m=\u001b[3;35mNone\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mmcp_endpoint\u001b[0m=\u001b[3;35mNone\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" + ], + "text/html": [ + "
ToolGroup(\n", + "│ identifier='builtin::websearch',\n", + "│ provider_id='tavily-search',\n", + "│ provider_resource_id='builtin::websearch',\n", + "│ type='tool_group',\n", + "│ args=None,\n", + "│ mcp_endpoint=None\n", + ")\n", + "\n" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { + "output_type": "display_data", "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c15daae95f41475b979554a73a717a1b", - "version_major": 2, - "version_minor": 0 - }, "text/plain": [ - "Batches: 0%| | 0/1 [00:00, ?it/s]" + "\u001b[1;35mToolGroup\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'builtin::memory'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mprovider_id\u001b[0m=\u001b[32m'memory-runtime'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mprovider_resource_id\u001b[0m=\u001b[32m'builtin::memory'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'tool_group'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33margs\u001b[0m=\u001b[3;35mNone\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mmcp_endpoint\u001b[0m=\u001b[3;35mNone\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" + ], + "text/html": [ + "
ToolGroup(\n", + "│ identifier='builtin::memory',\n", + "│ provider_id='memory-runtime',\n", + "│ provider_resource_id='builtin::memory',\n", + "│ type='tool_group',\n", + "│ args=None,\n", + "│ mcp_endpoint=None\n", + ")\n", + "\n" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} }, { + "output_type": "display_data", "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fdff3a09226e49978d3d7e1d48bcad94", - "version_major": 2, - "version_minor": 0 - }, "text/plain": [ - "Batches: 0%| | 0/1 [00:00, ?it/s]" + "\u001b[1;35mToolGroup\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[33midentifier\u001b[0m=\u001b[32m'builtin::code_interpreter'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mprovider_id\u001b[0m=\u001b[32m'code-interpreter'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mprovider_resource_id\u001b[0m=\u001b[32m'builtin::code_interpreter'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mtype\u001b[0m=\u001b[32m'tool_group'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33margs\u001b[0m=\u001b[3;35mNone\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mmcp_endpoint\u001b[0m=\u001b[3;35mNone\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" + ], + "text/html": [ + "
ToolGroup(\n", + "│ identifier='builtin::code_interpreter',\n", + "│ provider_id='code-interpreter',\n", + "│ provider_resource_id='builtin::code_interpreter',\n", + "│ type='tool_group',\n", + "│ args=None,\n", + "│ mcp_endpoint=None\n", + ")\n", + "\n" ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4242bbd4df784e94a427fdb877f8994e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Batches: 0%| | 0/1 [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[32mUser> What are the top 5 topics that were explained? Only list succinct bullet points.\u001b[0m\n", - "tools_for_turn: [AgentToolWithArgs(name='memory', args={'memory_bank_id': 'memory_bank_1d984362-ef6c-468e-b5eb-a12b0d782783'})]\n", - "tools_for_turn_set: {'memory'}\n", - "tool_name: memory\n", - "\u001b[30m\u001b[0mtool_def: identifier='memory' provider_resource_id='memory' provider_id='memory-runtime' type='tool' tool_group='memory_group' tool_host=
[\n", - "│ {\n", - "│ │ 'input': [\n", - "│ │ │ '{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}',\n", - "│ │ │ '{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}'\n", - "│ │ ],\n", - "│ │ 'output': 'content: Let me check the latest sports news. tool_calls: []'\n", - "│ },\n", - "│ {\n", - "│ │ 'input': [\n", - "│ │ │ '{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}',\n", - "│ │ │ '{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}',\n", - "│ │ │ '{\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[]}',\n", - "│ │ │ '{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}'\n", - "│ │ ],\n", - "│ │ 'output': \"content: tool_calls: [ToolCall(call_id='19bd3554-e670-4856-89d0-c63f5b016245', tool_name='bravy_search', arguments={'query': 'Bill Cosby South Park episode'})]\"\n", - "│ },\n", - "│ {\n", - "│ │ 'input': [\n", - "│ │ │ '{\"role\":\"system\",\"content\":\"You are a helpful assistant. Use search tool to answer the questions. \"}',\n", - "│ │ │ '{\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null}',\n", - "│ │ │ '{\"role\":\"assistant\",\"content\":\"Let me check the latest sports news.\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[]}',\n", - "│ │ │ '{\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby (BSM-471) first appear? Give me the number and title.\",\"context\":null}',\n", - "│ │ │ '{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"19bd3554-e670-4856-89d0-c63f5b016245\",\"tool_name\":\"bravy_search\",\"arguments\":{\"query\":\"Bill Cosby South Park episode\"}}]}',\n", - "│ │ │ '{\"role\":\"user\",\"content\":\"What is the British-American kickboxer Andrew Tate\\'s kickboxing name?\",\"context\":null}'\n", - "│ │ ],\n", - "│ │ 'output': \"content: tool_calls: [ToolCall(call_id='526045a7-5f51-40fb-ba97-5ad29610e511', tool_name=<BuiltinTool.brave_search: 'brave_search'>, arguments={'query': 'Andrew Tate kickboxing name'})]\"\n", - "│ },\n", - "│ {\n", - "│ │ 'input': '{\"role\":\"assistant\",\"content\":\"\",\"stop_reason\":\"end_of_turn\",\"tool_calls\":[{\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"arguments\":{\"query\":\"Andrew Tate kickboxing name\"}}]}',\n", - "│ │ 'output': '{\"role\":\"ipython\",\"call_id\":\"526045a7-5f51-40fb-ba97-5ad29610e511\",\"tool_name\":\"brave_search\",\"content\":\"{\\\\\"query\\\\\": \\\\\"Andrew Tate kickboxing name\\\\\", \\\\\"top_k\\\\\": [{\\\\\"title\\\\\": \\\\\"Andrew Tate kickboxing record: How many championships ... - FirstSportz\\\\\", \\\\\"url\\\\\": \\\\\"https://firstsportz.com/mma-how-many-championships-does-andrew-tate-have/\\\\\", \\\\\"content\\\\\": \\\\\"Andrew Tate\\'s Kickboxing career. During his kickboxing career, he used the nickname \\\\\\\\\\\\\"King Cobra,\\\\\\\\\\\\\" which he currently uses as his Twitter name. Tate had an unorthodox style of movement inside the ring. He kept his hands down most of the time and relied on quick jabs and an overhand right to land significant strikes.\\\\\", \\\\\"score\\\\\": 0.9996244, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Andrew Tate: Kickboxing Record, Facts, Height, Weight, Age, Biography\\\\\", \\\\\"url\\\\\": \\\\\"https://www.lowkickmma.com/andrew-tate-kickboxing-record-facts-height-weight-age-biography/\\\\\", \\\\\"content\\\\\": \\\\\"Birth Name: Emory Andrew Tate III: Date of Birth: 1 December 1986: Place of Birth: Washington, D.C., U.S. ... In his professional kickboxing career, Andrew Tate won 32 of his fights by knockout.\\\\\", \\\\\"score\\\\\": 0.99909246, \\\\\"raw_content\\\\\": null}, {\\\\\"title\\\\\": \\\\\"Who is Andrew Tate? 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William Henry \\\\\\\\\\\\\"Bill\\\\\\\\\\\\\" Cosby Jr. African-American comedian, actor, and serial rapist. He first appears in the Season Five episode, \\\\\\\\\\\\\"Here Comes the Neighborhood\\\\\\\\\\\\\", as one of the wealthy African-Americans who move to South Park. 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Please Welcome \\\\\\\\\\\\\"Cartman Bra\\\\\\\\\\\\\" South Park S18 E9.\\\\\", \\\\\"score\\\\\": 0.7156829, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Bill Cosby and Taylor Swift Duet - South Park Studios\\\\\", \\\\\"url\\\\\": \\\\\"https://www.southparkstudios.com/video-clips/90r7i1/south-park-bill-cosby-and-taylor-swift-duet\\\\\", \\\\\"content\\\\\": \\\\\"The holiday special continues with Bill Cosby and Taylor Swift\\'s rendition of \\\\\\\\\\\\\"It\\'s Snowing Out There\\\\\\\\\\\\\". ... Full Episodes. Collections. Random Episode. Full Episodes. Events. Wiki. News. Avatar. Shop. Forum. Games. South Park. Menu. Episodes & Videos. About. South Park. Bill Cosby and Taylor Swift Duet. Season 18 E 10 \\\\\\\\u2022 12/10/2014. The\\\\\", \\\\\"score\\\\\": 0.64639384, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Bill Cosby \u001b[0m\u001b[32m(\u001b[0m\u001b[32mandroid\u001b[0m\u001b[32m)\u001b[0m\u001b[32m | South Park Character ... - South Park Studios US\\\\\", \\\\\"url\\\\\": \\\\\"https://southpark.cc.com/wiki/Bill_Cosby_\u001b[0m\u001b[32m(\u001b[0m\u001b[32mandroid\u001b[0m\u001b[32m)\u001b[0m\u001b[32m\\\\\", \\\\\"content\\\\\": \\\\\"About. Sent back in time to destroy Eric Cartman\\'s Dawson\\'s Creek Trapper Keeper before it manifests into an omnipotent supercomputer that can destroy all humanity, \\\\\\\\\\\\\"Bill Cosby\\\\\\\\\\\\\" is really VSM471, an android or cyborg of some kind engineered by \\'hoomans\\' in the distant future. 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One of his first professional fights on\\\\\", \\\\\"score\\\\\": 0.8795718, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"About Andrew Tate | The Real World\\\\\", \\\\\"url\\\\\": \\\\\"https://www.taterealworldofficial.com/about-andrew-tate\\\\\", \\\\\"content\\\\\": \\\\\"Emory Andrew Tate III \u001b[0m\u001b[32m(\u001b[0m\u001b[32mborn December 14, 1986\u001b[0m\u001b[32m)\u001b[0m\u001b[32m is an American-British kickboxer from Chicago, Illinois, who competes in the cruiserweight and heavyweight divisions. ... Tate challenged Paul Randall for the vacant ISKA English Kickboxing Light-cruiserweight title. Tate won his first ISKA Kickboxing title stopping Randall in the fifth round of\\\\\", \\\\\"score\\\\\": 0.8386933, \\\\\"raw_content\\\\\": null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m, \u001b[0m\u001b[32m{\u001b[0m\u001b[32m\\\\\"title\\\\\": \\\\\"Andrew Tate - Fight Record - Muay Thai Records\\\\\", \\\\\"url\\\\\": \\\\\"https://muaythairecords.com/fighters/andrew-tate\\\\\", \\\\\"content\\\\\": \\\\\"Andrew \\\\\\\\\\\\\"King Cobra\\\\\\\\\\\\\" Tate is a 38-year-old Muay Thai fighter. With a record of 23-8-0, including 32 knockouts, standing at 6\\\\\\\\u2032 4\\\\\\\\u2033 and weighing 198 lbs. Originally from Luton, United Kingdom. ... WIN Dec -Kickboxing Jean Luc Beno\\\\\\\\u00eet. 14th Mar 2015 -Boxe in D\\\\\\\\u00e9fi 16. Andrew Tate defeated Jean Luc Beno\\\\\\\\u00eet by decision. ... 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William Henry \\\\\\\\\\\\\"Bill\\\\\\\\\\\\\" Cosby Jr. African-American comedian, actor, and serial rapist. He first appears in the Season Five episode, \\\\\\\\\\\\\"Here Comes the Neighborhood\\\\\\\\\\\\\", as one of the wealthy African-Americans who move to South Park. 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Give me the number and title.\",\"context\":null}',\n", + "│ │ 'generated_answer': \"content: tool_calls: [ToolCall(call_id='1e487e8e-a15f-4137-854a-1d4979a70b8c', tool_name=<BuiltinTool.brave_search: 'brave_search'>, arguments={'query': 'Bill Cosby South Park episode'})]\",\n", "│ │ 'expected_answer': 'brave_search'\n", "│ }\n", "]\n", "\n" - ], - "text/plain": [ - "\u001b[1m[\u001b[0m\n", - "\u001b[2;32m│ \u001b[0m\u001b[1m{\u001b[0m\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[32m'input_query'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"Which teams played in the NBA western conference finals of 2024\",\"context\":null\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'content: Let me check the latest sports news. tool_calls: \u001b[0m\u001b[32m[\u001b[0m\u001b[32m]\u001b[0m\u001b[32m'\u001b[0m,\n", - "\u001b[2;32m│ │ \u001b[0m\u001b[32m'expected_answer'\u001b[0m: \u001b[32m'brave_search'\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[32m'input_query'\u001b[0m: \u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32m\"role\":\"user\",\"content\":\"In which episode and season of South Park does Bill Cosby \u001b[0m\u001b[32m(\u001b[0m\u001b[32mBSM-471\u001b[0m\u001b[32m)\u001b[0m\u001b[32m first appear? 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ScoringScoreResponse(\n", - "│ results={\n", - "│ │ 'basic::subset_of': ScoringResult(\n", - "│ │ │ aggregated_results={'accuracy': {'accuracy': 0.3333333333333333, 'num_correct': 1.0, 'num_total': 3}},\n", - "│ │ │ score_rows=[{'score': 0.0}, {'score': 0.0}, {'score': 1.0}]\n", - "│ │ )\n", - "│ }\n", - ")\n", - "\n" - ], "text/plain": [ "\u001b[1;35mScoringScoreResponse\u001b[0m\u001b[1m(\u001b[0m\n", "\u001b[2;32m│ \u001b[0m\u001b[33mresults\u001b[0m=\u001b[1m{\u001b[0m\n", "\u001b[2;32m│ │ \u001b[0m\u001b[32m'basic::subset_of'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n", - "\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1;36m0.3333333333333333\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m3\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n", - "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m1.0\u001b[0m\u001b[1m}\u001b[0m\u001b[1m]\u001b[0m\n", + "\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m3.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m3\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m1.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m1.0\u001b[0m\u001b[1m}\u001b[0m, \u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m1.0\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[1m}\u001b[0m\n", "\u001b[1m)\u001b[0m\n" + ], + "text/html": [ + "
ScoringScoreResponse(\n", + "│ results={\n", + "│ │ 'basic::subset_of': ScoringResult(\n", + "│ │ │ aggregated_results={'accuracy': {'accuracy': 1.0, 'num_correct': 3.0, 'num_total': 3}},\n", + "│ │ │ score_rows=[{'score': 1.0}, {'score': 1.0}, {'score': 1.0}]\n", + "│ │ )\n", + "│ }\n", + ")\n", + "\n" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], "source": [ @@ -2670,39 +2948,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "xG4Y84VQBb0g", "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 298 + "height": 304 }, "id": "xG4Y84VQBb0g", - "outputId": "f61cebdf-f614-440c-d170-f1e873b542ef" + "outputId": "cf7dcecc-a81d-4c60-af5e-b36b8fe85c69" }, "outputs": [ { + "output_type": "display_data", "data": { - "text/html": [ - "
ScoringScoreResponse(\n", - "│ results={\n", - "│ │ 'llm-as-judge::base': ScoringResult(\n", - "│ │ │ aggregated_results={},\n", - "│ │ │ score_rows=[\n", - "│ │ │ │ {\n", - "│ │ │ │ │ 'score': 'B',\n", - "│ │ │ │ │ 'judge_feedback': 'Answer: B, Explanation: The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE and is fully consistent with it. The GENERATED_RESPONSE provides more detailed information about the top 5 topics related to LoRA, while the EXPECTED_RESPONSE only mentions \"LoRA\". The GENERATED_RESPONSE expands on the topic, but does not conflict with the EXPECTED_RESPONSE.'\n", - "│ │ │ │ }\n", - "│ │ │ ]\n", - "│ │ ),\n", - "│ │ 'basic::subset_of': ScoringResult(\n", - "│ │ │ aggregated_results={'accuracy': 1.0, 'num_correct': 1.0, 'num_total': 1.0},\n", - "│ │ │ score_rows=[{'score': 1.0}]\n", - "│ │ )\n", - "│ }\n", - ")\n", - "\n" - ], "text/plain": [ "\u001b[1;35mScoringScoreResponse\u001b[0m\u001b[1m(\u001b[0m\n", "\u001b[2;32m│ \u001b[0m\u001b[33mresults\u001b[0m=\u001b[1m{\u001b[0m\n", @@ -2711,20 +2970,39 @@ "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\n", "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\n", "\u001b[2;32m│ │ │ │ │ \u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'B'\u001b[0m,\n", - "\u001b[2;32m│ │ │ │ │ \u001b[0m\u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'Answer: B, Explanation: The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE and is fully consistent with it. The GENERATED_RESPONSE provides more detailed information about the top 5 topics related to LoRA, while the EXPECTED_RESPONSE only mentions \"LoRA\". The GENERATED_RESPONSE expands on the topic, but does not conflict with the EXPECTED_RESPONSE.'\u001b[0m\n", + "\u001b[2;32m│ │ │ │ │ \u001b[0m\u001b[32m'judge_feedback'\u001b[0m: \u001b[32m\"Answer: B, Explanation: The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE as it provides more detailed information about the topics related to LoRA \u001b[0m\u001b[32m(\u001b[0m\u001b[32malthough it does list more than one topic as does not exactly follow the desired format of only giving one 'topic', while the EXPECTED_RESPONSE simply lists 'LoRA'\u001b[0m\u001b[32m)\u001b[0m\u001b[32m.\"\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[1m)\u001b[0m,\n", "\u001b[2;32m│ │ \u001b[0m\u001b[32m'basic::subset_of'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n", - "\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m1.0\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'accuracy'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_correct'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'num_total'\u001b[0m: \u001b[1;36m1\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n", "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[1;36m1.0\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[1m}\u001b[0m\n", "\u001b[1m)\u001b[0m\n" + ], + "text/html": [ + "
ScoringScoreResponse(\n", + "│ results={\n", + "│ │ 'llm-as-judge::base': ScoringResult(\n", + "│ │ │ aggregated_results={},\n", + "│ │ │ score_rows=[\n", + "│ │ │ │ {\n", + "│ │ │ │ │ 'score': 'B',\n", + "│ │ │ │ │ 'judge_feedback': \"Answer: B, Explanation: The GENERATED_RESPONSE is a superset of the EXPECTED_RESPONSE as it provides more detailed information about the topics related to LoRA (although it does list more than one topic as does not exactly follow the desired format of only giving one 'topic', while the EXPECTED_RESPONSE simply lists 'LoRA').\"\n", + "│ │ │ │ }\n", + "│ │ │ ]\n", + "│ │ ),\n", + "│ │ 'basic::subset_of': ScoringResult(\n", + "│ │ │ aggregated_results={'accuracy': {'accuracy': 1.0, 'num_correct': 1.0, 'num_total': 1}},\n", + "│ │ │ score_rows=[{'score': 1.0}]\n", + "│ │ )\n", + "│ }\n", + ")\n", + "\n" ] }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], "source": [ @@ -2786,23 +3064,12 @@ "response = client.scoring.score(input_rows=rows, scoring_functions=scoring_params)\n", "pprint(response)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "rKtGo_v98UA2", - "metadata": { - "id": "rKtGo_v98UA2" - }, - "outputs": [], - "source": [] } ], "metadata": { + "accelerator": "GPU", "colab": { - "collapsed_sections": [ - "_JueJAKyJR5m" - ], + "gpuType": "T4", "provenance": [] }, "kernelspec": { @@ -2823,25 +3090,53 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "0243626d7ef44ef2b90e8fed5c13183d": { + "88f0c88612bb45d59f07e93567cc0e14": { "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - 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