forked from phoenix-oss/llama-stack-mirror
feat: open benchmark template and doc (#1465)
## What does this PR do? - Provide a distro template to let developer easily run the open benchmarks llama stack supports on llama and non-llama models. - Provide doc on how to run open benchmark eval via CLI and open benchmark contributing guide [//]: # (If resolving an issue, uncomment and update the line below) (Closes #1375 ) ## Test Plan open benchmark eval results on llama, gpt, gemini and clause <img width="771" alt="Screenshot 2025-03-06 at 7 33 05 PM" src="https://github.com/user-attachments/assets/1bd85456-b9b9-4b37-af76-4ce1d2bac00e" /> doc preview <img width="944" alt="Screenshot 2025-03-06 at 7 33 58 PM" src="https://github.com/user-attachments/assets/f4e5866d-b395-4c40-aa8b-080edeb5cdb6" /> <img width="955" alt="Screenshot 2025-03-06 at 7 34 04 PM" src="https://github.com/user-attachments/assets/629defb6-d5e4-473c-aa03-308bce386fb4" /> <img width="965" alt="Screenshot 2025-03-06 at 7 35 29 PM" src="https://github.com/user-attachments/assets/c21ff96c-9e8c-4c54-b6b8-25883125f4cf" /> <img width="957" alt="Screenshot 2025-03-06 at 7 35 37 PM" src="https://github.com/user-attachments/assets/47571c90-1381-4e2c-bbed-c4f3a60578d0" />
This commit is contained in:
Botao Chen
GitHub
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commit
4dccf916d1
7 changed files with 585 additions and 10 deletions
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@ -453,6 +453,42 @@
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"transformers",
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"uvicorn"
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],
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"open_benchmark": [
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"aiosqlite",
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"autoevals",
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"blobfile",
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"chardet",
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"chromadb-client",
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"datasets",
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"fastapi",
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"fire",
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"httpx",
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"litellm",
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"matplotlib",
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"mcp",
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"nltk",
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"numpy",
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"openai",
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"opentelemetry-exporter-otlp-proto-http",
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"opentelemetry-sdk",
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"pandas",
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"pillow",
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"psycopg2-binary",
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"pymongo",
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"pypdf",
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"redis",
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"requests",
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"scikit-learn",
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"scipy",
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"sentencepiece",
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"sqlite-vec",
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"together",
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"tqdm",
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"transformers",
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"uvicorn",
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"sentence-transformers --no-deps",
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"torch torchvision --index-url https://download.pytorch.org/whl/cpu"
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],
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"remote-vllm": [
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"aiosqlite",
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"autoevals",
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@ -24,6 +24,56 @@ The Evaluation APIs are associated with a set of Resources as shown in the follo
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- Associated with `Benchmark` resource.
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## Open-benchmark Eval
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### List of open-benchmarks Llama Stack support
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Llama stack pre-registers several popular open-benchmarks to easily evaluate model perfomance via CLI.
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The list of open-benchmarks we currently support:
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- [MMLU-COT](https://arxiv.org/abs/2009.03300) (Measuring Massive Multitask Language Understanding): Benchmark designed to comprehensively evaluate the breadth and depth of a model's academic and professional understanding
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- [GPQA-COT](https://arxiv.org/abs/2311.12022) (A Graduate-Level Google-Proof Q&A Benchmark): A challenging benchmark of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry.
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- [SimpleQA](https://openai.com/index/introducing-simpleqa/): Benchmark designed to access models to answer short, fact-seeking questions.
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- [MMMU](https://arxiv.org/abs/2311.16502) (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)]: Benchmark designed to evaluate multimodal models.
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You can follow this contributing guidance to add more open-benchmarks to Llama Stack
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### Run evaluation on open-benchmarks via CLI
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We have built-in functionality to run the supported open-benckmarks using llama-stack-client CLI
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#### Spin up Llama Stack server
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Spin up llama stack server with 'open-benchmark' template
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```
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llama stack run llama_stack/templates/open-benchmark/run.yaml
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```
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#### Run eval CLI
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There are 3 necessary inputs to run a benchmark eval
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- `list of benchmark_ids`: The list of benchmark ids to run evaluation on
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- `model-id`: The model id to evaluate on
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- `utput_dir`: Path to store the evaluate results
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```
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llama-stack-client eval run-benchmark <benchmark_id_1> <benchmark_id_2> ... \
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--model_id <model id to evaluate on> \
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--output_dir <directory to store the evaluate results> \
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```
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You can run
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```
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llama-stack-client eval run-benchmark help
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```
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to see the description of all the flags that eval run-benchmark has
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In the output log, you can find the file path that has your evaluation results. Open that file and you can see you aggrgate
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evaluation results over there.
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## What's Next?
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- Check out our Colab notebook on working examples with running benchmark evaluations [here](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb#scrollTo=mxLCsP4MvFqP).
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@ -275,18 +275,25 @@ response = client.scoring.score(
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The following examples give the quick steps to start running evaluations using the llama-stack-client CLI.
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#### Benchmark Evaluation CLI
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Usage: There are 2 inputs necessary for running a benchmark eval
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- `eval-task-id`: the identifier associated with the eval task. Each `Benchmark` is parametrized by
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- `dataset_id`: the identifier associated with the dataset.
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- `List[scoring_function_id]`: list of scoring function identifiers.
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- `eval-task-config`: specifies the configuration of the model / agent to evaluate on.
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There are 3 necessary input for running a benchmark eval
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- `list of benchmark_ids`: The list of benchmark ids to run evaluation on
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- `model-id`: The model id to evaluate on
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- `utput_dir`: Path to store the evaluate results
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```
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llama-stack-client eval run-benchmark <benchmark_id_1> <benchmark_id_2> ... \
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--model_id <model id to evaluate on> \
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--output_dir <directory to store the evaluate results> \
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```
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You can run
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```
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llama-stack-client eval run-benchmark help
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```
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to see the description of all the flags to run benckmark eval
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```
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llama-stack-client eval run_benchmark <eval-task-id> \
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--eval-task-config ~/benchmark_config.json \
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--visualize
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```
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In the output log, you can find the path to the file that has your evaluation results. Open that file and you can see you aggrgate
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evaluation results over there.
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#### Application Evaluation CLI
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@ -338,3 +345,52 @@ The `BenchmarkConfig` are user specified config to define:
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}
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}
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```
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## Open-benchmark Contributing Guide
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### Create the new dataset for your new benchmark
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An eval open-benchmark essentially contains 2 parts:
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- `raw data`: The raw dataset associated with the benchmark. You typically need to search the original paper that introduces the benchmark and find the canonical dataset (usually hosted on huggingface)
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- `prompt template`: How to ask the candidate model to generate the answer (prompt template plays a critical role to the evaluation results). Tyically, you can find the reference prompt template associated with the benchmark in benchmarks author's repo ([exmaple](https://github.com/idavidrein/gpqa/blob/main/prompts/chain_of_thought.txt)) or some other popular open source repos ([example](https://github.com/openai/simple-evals/blob/0a6e8f62e52bc5ae915f752466be3af596caf392/common.py#L14))
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To create new open-benmark in llama stack, you need to combine the prompt template and the raw data into the `chat_completion_input` column in the evaluation dataset.
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Llama stack enforeces the evaluate dataset schema to contain at least 3 columns:
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- `chat_completion_input`: The actual input to the model to run the generation for eval
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- `input_query`: The raw input from the raw dataset without the prompt template
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- `expected_answer`: The ground truth for scoring functions to calcalate the score from.
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You need to write a script [example convert script](https://gist.github.com/yanxi0830/118e9c560227d27132a7fd10e2c92840) to convert the benchmark raw dataset to llama stack format eval dataset and update the dataset to huggingface [example benchmark dataset](https://huggingface.co/datasets/llamastack/mmmu)
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### Find scoring function for your new benchmark
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The purpose of scoring function is to calculate the score for each example based on candidate model generation result and expected_answer. It also aggregates the scores from all the examples and generate the final evaluate results.
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Firstly, you can see if the existing [llama stack scoring functions](https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/inline/scoring) can fulfill your need. If not, you need to write a new scoring function based on what benchmark author / other open source repo describe.
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### Add new benchmark into template
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Firstly, you need to add the evaluation dataset associated with your benchmark under `datasets` resource in templates/open-benchmark/run.yaml
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Secondly, you need to add the new benchmark you just created under the `benchmarks` resource in the same template. To add the new benchmark, you need to have
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- `benchmark_id`: identifier of the benchmark
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- `dataset_id`: identifier of the dataset associated with your benchmark
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- `scoring_functions`: scoring function to calculate the score based on generation results and expected_answer
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### Test the new benchmark
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Spin up llama stack server with 'open-benchmark' templates
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```
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llama stack run llama_stack/templates/open-benchmark/run.yaml
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```
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Run eval benchmark CLI with your new benchmark id
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```
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llama-stack-client eval run-benchmark <new_benchmark_id> \
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--model_id <model id to evaluate on> \
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--output_dir <directory to store the evaluate results> \
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```
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7
llama_stack/templates/open-benchmark/__init__.py
Normal file
7
llama_stack/templates/open-benchmark/__init__.py
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@ -0,0 +1,7 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from .open_benchmark import get_distribution_template # noqa: F401
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36
llama_stack/templates/open-benchmark/build.yaml
Normal file
36
llama_stack/templates/open-benchmark/build.yaml
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@ -0,0 +1,36 @@
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version: '2'
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distribution_spec:
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description: Distribution for running open benchmarks
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providers:
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inference:
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- remote::openai
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- remote::anthropic
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- remote::gemini
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- remote::groq
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- remote::together
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vector_io:
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- inline::sqlite-vec
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- remote::chromadb
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- remote::pgvector
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safety:
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- inline::llama-guard
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agents:
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- inline::meta-reference
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telemetry:
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- inline::meta-reference
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eval:
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- inline::meta-reference
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datasetio:
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- remote::huggingface
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- inline::localfs
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scoring:
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- inline::basic
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- inline::llm-as-judge
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- inline::braintrust
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tool_runtime:
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- remote::brave-search
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- remote::tavily-search
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- inline::code-interpreter
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- inline::rag-runtime
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- remote::model-context-protocol
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image_type: conda
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178
llama_stack/templates/open-benchmark/open_benchmark.py
Normal file
178
llama_stack/templates/open-benchmark/open_benchmark.py
Normal file
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@ -0,0 +1,178 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import List, Tuple
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from llama_stack.distribution.datatypes import (
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ModelInput,
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Provider,
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ShieldInput,
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ToolGroupInput,
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)
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from llama_stack.providers.inline.vector_io.sqlite_vec.config import SQLiteVectorIOConfig
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from llama_stack.providers.remote.inference.anthropic.config import AnthropicConfig
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from llama_stack.providers.remote.inference.anthropic.models import MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES
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from llama_stack.providers.remote.inference.gemini.config import GeminiConfig
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from llama_stack.providers.remote.inference.gemini.models import MODEL_ENTRIES as GEMINI_MODEL_ENTRIES
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from llama_stack.providers.remote.inference.groq.config import GroqConfig
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from llama_stack.providers.remote.inference.groq.models import MODEL_ENTRIES as GROQ_MODEL_ENTRIES
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from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
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from llama_stack.providers.remote.inference.openai.models import MODEL_ENTRIES as OPENAI_MODEL_ENTRIES
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from llama_stack.providers.remote.inference.together.config import TogetherImplConfig
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from llama_stack.providers.remote.inference.together.models import MODEL_ENTRIES as TOGETHER_MODEL_ENTRIES
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from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
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from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
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from llama_stack.templates.template import DistributionTemplate, RunConfigSettings, get_model_registry
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def get_inference_providers() -> Tuple[List[Provider], List[ModelInput]]:
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# in this template, we allow each API key to be optional
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providers = [
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(
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"openai",
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OPENAI_MODEL_ENTRIES,
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OpenAIConfig.sample_run_config(api_key="${env.OPENAI_API_KEY:}"),
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),
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(
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"anthropic",
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ANTHROPIC_MODEL_ENTRIES,
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AnthropicConfig.sample_run_config(api_key="${env.ANTHROPIC_API_KEY:}"),
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),
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(
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"gemini",
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GEMINI_MODEL_ENTRIES,
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GeminiConfig.sample_run_config(api_key="${env.GEMINI_API_KEY:}"),
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),
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(
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"groq",
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GROQ_MODEL_ENTRIES,
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GroqConfig.sample_run_config(api_key="${env.GROQ_API_KEY:}"),
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),
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(
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"together",
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TOGETHER_MODEL_ENTRIES,
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TogetherImplConfig.sample_run_config(api_key="${env.TOGETHER_API_KEY:}"),
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),
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]
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inference_providers = []
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available_models = {}
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for provider_id, model_entries, config in providers:
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inference_providers.append(
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Provider(
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provider_id=provider_id,
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provider_type=f"remote::{provider_id}",
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config=config,
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)
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)
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available_models[provider_id] = model_entries
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return inference_providers, available_models
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def get_distribution_template() -> DistributionTemplate:
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inference_providers, available_models = get_inference_providers()
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providers = {
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"inference": ([p.provider_type for p in inference_providers] + ["inline::sentence-transformers"]),
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"vector_io": ["inline::sqlite-vec", "remote::chromadb", "remote::pgvector"],
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"safety": ["inline::llama-guard"],
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"agents": ["inline::meta-reference"],
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"telemetry": ["inline::meta-reference"],
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"eval": ["inline::meta-reference"],
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"datasetio": ["remote::huggingface", "inline::localfs"],
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"scoring": ["inline::basic", "inline::llm-as-judge", "inline::braintrust"],
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"tool_runtime": [
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"remote::brave-search",
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"remote::tavily-search",
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"inline::code-interpreter",
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"inline::rag-runtime",
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"remote::model-context-protocol",
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],
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}
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name = "open_benchmark"
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vector_io_providers = [
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Provider(
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provider_id="sqlite-vec",
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provider_type="inline::sqlite-vec",
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config=SQLiteVectorIOConfig.sample_run_config(f"~/.llama/distributions/{name}"),
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),
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Provider(
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provider_id="${env.ENABLE_CHROMADB+chromadb}",
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provider_type="remote::chromadb",
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config=ChromaVectorIOConfig.sample_run_config(url="${env.CHROMADB_URL:}"),
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),
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Provider(
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provider_id="${env.ENABLE_PGVECTOR+pgvector}",
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provider_type="remote::pgvector",
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config=PGVectorVectorIOConfig.sample_run_config(
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db="${env.PGVECTOR_DB:}",
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user="${env.PGVECTOR_USER:}",
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password="${env.PGVECTOR_PASSWORD:}",
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),
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),
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]
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default_tool_groups = [
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ToolGroupInput(
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toolgroup_id="builtin::websearch",
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provider_id="tavily-search",
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),
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ToolGroupInput(
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toolgroup_id="builtin::rag",
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provider_id="rag-runtime",
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),
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ToolGroupInput(
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toolgroup_id="builtin::code_interpreter",
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provider_id="code-interpreter",
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),
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]
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default_models = get_model_registry(available_models)
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return DistributionTemplate(
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name=name,
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distro_type="self_hosted",
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description="Distribution for running open benchmarks",
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container_image=None,
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template_path=None,
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providers=providers,
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available_models_by_provider=available_models,
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run_configs={
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"run.yaml": RunConfigSettings(
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provider_overrides={
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"inference": inference_providers,
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"vector_io": vector_io_providers,
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},
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default_models=default_models,
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default_tool_groups=default_tool_groups,
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default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
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),
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},
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run_config_env_vars={
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"LLAMA_STACK_PORT": (
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"5001",
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"Port for the Llama Stack distribution server",
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),
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"OPENAI_API_KEY": (
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"",
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"OpenAI API Key",
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),
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"GEMINI_API_KEY": (
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"",
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"Gemini API Key",
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),
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"GROQ_API_KEY": (
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"",
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"Groq API Key",
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),
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"ANTHROPIC_API_KEY": (
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"",
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"Anthropic API Key",
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||||
),
|
||||
"TOGETHER_API_KEY": (
|
||||
"",
|
||||
"Together API Key",
|
||||
),
|
||||
},
|
||||
)
|
||||
212
llama_stack/templates/open-benchmark/run.yaml
Normal file
212
llama_stack/templates/open-benchmark/run.yaml
Normal file
|
|
@ -0,0 +1,212 @@
|
|||
version: '2'
|
||||
image_name: dev
|
||||
apis:
|
||||
- agents
|
||||
- datasetio
|
||||
- eval
|
||||
- inference
|
||||
- safety
|
||||
- scoring
|
||||
- telemetry
|
||||
- tool_runtime
|
||||
- vector_io
|
||||
providers:
|
||||
inference:
|
||||
- provider_id: openai
|
||||
provider_type: remote::openai
|
||||
config:
|
||||
api_key: ${env.OPENAI_API_KEY:}
|
||||
- provider_id: anthropic
|
||||
provider_type: remote::anthropic
|
||||
config:
|
||||
api_key: ${env.ANTHROPIC_API_KEY:}
|
||||
- provider_id: gemini
|
||||
provider_type: remote::gemini
|
||||
config:
|
||||
api_key: ${env.GEMINI_API_KEY:}
|
||||
- provider_id: groq
|
||||
provider_type: remote::groq
|
||||
config:
|
||||
url: https://api.groq.com
|
||||
api_key: ${env.GROQ_API_KEY:}
|
||||
- provider_id: together
|
||||
provider_type: remote::together
|
||||
config:
|
||||
url: https://api.together.xyz/v1
|
||||
api_key: ${env.TOGETHER_API_KEY}
|
||||
vector_io:
|
||||
- provider_id: sqlite-vec
|
||||
provider_type: inline::sqlite-vec
|
||||
config:
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/sqlite_vec.db
|
||||
- provider_id: ${env.ENABLE_CHROMADB+chromadb}
|
||||
provider_type: remote::chromadb
|
||||
config:
|
||||
url: ${env.CHROMADB_URL:}
|
||||
- provider_id: ${env.ENABLE_PGVECTOR+pgvector}
|
||||
provider_type: remote::pgvector
|
||||
config:
|
||||
host: ${env.PGVECTOR_HOST:localhost}
|
||||
port: ${env.PGVECTOR_PORT:5432}
|
||||
db: ${env.PGVECTOR_DB:}
|
||||
user: ${env.PGVECTOR_USER:}
|
||||
password: ${env.PGVECTOR_PASSWORD:}
|
||||
safety:
|
||||
- provider_id: llama-guard
|
||||
provider_type: inline::llama-guard
|
||||
config: {}
|
||||
agents:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
persistence_store:
|
||||
type: sqlite
|
||||
namespace: null
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/agents_store.db
|
||||
telemetry:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config:
|
||||
service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
|
||||
sinks: ${env.TELEMETRY_SINKS:console,sqlite}
|
||||
sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/dev/trace_store.db}
|
||||
eval:
|
||||
- provider_id: meta-reference
|
||||
provider_type: inline::meta-reference
|
||||
config: {}
|
||||
datasetio:
|
||||
- provider_id: huggingface
|
||||
provider_type: remote::huggingface
|
||||
config: {}
|
||||
- provider_id: localfs
|
||||
provider_type: inline::localfs
|
||||
config: {}
|
||||
scoring:
|
||||
- provider_id: basic
|
||||
provider_type: inline::basic
|
||||
config: {}
|
||||
- provider_id: llm-as-judge
|
||||
provider_type: inline::llm-as-judge
|
||||
config: {}
|
||||
- provider_id: braintrust
|
||||
provider_type: inline::braintrust
|
||||
config:
|
||||
openai_api_key: ${env.OPENAI_API_KEY:}
|
||||
tool_runtime:
|
||||
- provider_id: brave-search
|
||||
provider_type: remote::brave-search
|
||||
config:
|
||||
api_key: ${env.BRAVE_SEARCH_API_KEY:}
|
||||
max_results: 3
|
||||
- provider_id: tavily-search
|
||||
provider_type: remote::tavily-search
|
||||
config:
|
||||
api_key: ${env.TAVILY_SEARCH_API_KEY:}
|
||||
max_results: 3
|
||||
- provider_id: code-interpreter
|
||||
provider_type: inline::code-interpreter
|
||||
config: {}
|
||||
- provider_id: rag-runtime
|
||||
provider_type: inline::rag-runtime
|
||||
config: {}
|
||||
- provider_id: model-context-protocol
|
||||
provider_type: remote::model-context-protocol
|
||||
config: {}
|
||||
metadata_store:
|
||||
type: sqlite
|
||||
db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/dev}/registry.db
|
||||
models:
|
||||
- metadata: {}
|
||||
model_id: openai/gpt-4o
|
||||
provider_id: openai
|
||||
provider_model_id: openai/gpt-4o
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.1-405B-Instruct
|
||||
provider_id: together
|
||||
provider_model_id: meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: anthropic/claude-3-5-sonnet-latest
|
||||
provider_id: anthropic
|
||||
provider_model_id: anthropic/claude-3-5-sonnet-latest
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: gemini/gemini-1.5-flash
|
||||
provider_id: gemini
|
||||
provider_model_id: gemini/gemini-1.5-flash
|
||||
model_type: llm
|
||||
- metadata: {}
|
||||
model_id: meta-llama/Llama-3.3-70B-Instruct
|
||||
provider_id: groq
|
||||
provider_model_id: groq/llama-3.3-70b-versatile
|
||||
model_type: llm
|
||||
shields:
|
||||
- shield_id: meta-llama/Llama-Guard-3-8B
|
||||
vector_dbs: []
|
||||
datasets:
|
||||
- dataset_id: simpleqa
|
||||
provider_id: huggingface
|
||||
url:
|
||||
uri: https://huggingface.co/datasets/llamastack/simpleqa
|
||||
metadata:
|
||||
path: llamastack/simpleqa
|
||||
name:
|
||||
split: train
|
||||
dataset_schema:
|
||||
input_query:
|
||||
type: string
|
||||
expected_answer:
|
||||
type: string
|
||||
chat_completion_input:
|
||||
type: string
|
||||
- dataset_id: mmlu_cot
|
||||
provider_id: huggingface
|
||||
url:
|
||||
uri: https://huggingface.co/datasets/llamastack/mmlu_cot
|
||||
metadata:
|
||||
path: llamastack/mmlu_cot
|
||||
name: all
|
||||
split: test
|
||||
dataset_schema:
|
||||
input_query:
|
||||
type: string
|
||||
expected_answer:
|
||||
type: string
|
||||
chat_completion_input:
|
||||
type: string
|
||||
- dataset_id: gpqa_cot
|
||||
provider_id: huggingface
|
||||
url:
|
||||
uri: https://huggingface.co/datasets/llamastack/gpqa_0shot_cot
|
||||
metadata:
|
||||
path: llamastack/gpqa_0shot_cot
|
||||
name: gpqa_main
|
||||
split: train
|
||||
dataset_schema:
|
||||
input_query:
|
||||
type: string
|
||||
expected_answer:
|
||||
type: string
|
||||
chat_completion_input:
|
||||
type: string
|
||||
scoring_fns: []
|
||||
benchmarks:
|
||||
- benchmark_id: meta-reference-simpleqa
|
||||
dataset_id: simpleqa
|
||||
scoring_functions: ["llm-as-judge::405b-simpleqa"]
|
||||
- benchmark_id: meta-reference-mmlu-cot
|
||||
dataset_id: mmlu_cot
|
||||
scoring_functions: ["basic::regex_parser_multiple_choice_answer"]
|
||||
- benchmark_id: meta-reference-gpqa-cot
|
||||
dataset_id: gpqa_cot
|
||||
scoring_functions: ["basic::regex_parser_multiple_choice_answer"]
|
||||
tool_groups:
|
||||
- toolgroup_id: builtin::websearch
|
||||
provider_id: tavily-search
|
||||
- toolgroup_id: builtin::rag
|
||||
provider_id: rag-runtime
|
||||
- toolgroup_id: builtin::code_interpreter
|
||||
provider_id: code-interpreter
|
||||
server:
|
||||
port: 8321
|
||||
Loading…
Add table
Add a link
Reference in a new issue