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## PR Stack - https://github.com/meta-llama/llama-stack/pull/1573 - https://github.com/meta-llama/llama-stack/pull/1625 - https://github.com/meta-llama/llama-stack/pull/1656 - https://github.com/meta-llama/llama-stack/pull/1657 - https://github.com/meta-llama/llama-stack/pull/1658 - https://github.com/meta-llama/llama-stack/pull/1659 - https://github.com/meta-llama/llama-stack/pull/1660 **Client SDK** - https://github.com/meta-llama/llama-stack-client-python/pull/203 **CI** -1391130488<img width="1042" alt="image" src="https://github.com/user-attachments/assets/69636067-376d-436b-9204-896e2dd490ca" /> -- the test_rag_agent_with_attachments is flaky and not related to this PR ## Doc <img width="789" alt="image" src="https://github.com/user-attachments/assets/b88390f3-73d6-4483-b09a-a192064e32d9" /> ## Client Usage ```python client.datasets.register( source={ "type": "uri", "uri": "lsfs://mydata.jsonl", }, schema="jsonl_messages", # optional dataset_id="my_first_train_data" ) # quick prototype debugging client.datasets.register( data_reference={ "type": "rows", "rows": [ "messages": [...], ], }, schema="jsonl_messages", ) ``` ## Test Plan - CI:1387805545``` LLAMA_STACK_CONFIG=fireworks pytest -v tests/integration/datasets/test_datasets.py ``` ``` LLAMA_STACK_CONFIG=fireworks pytest -v tests/integration/scoring/test_scoring.py ``` ``` pytest -v -s --nbval-lax ./docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb ```
43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
# 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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import base64
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import io
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from urllib.parse import unquote
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import pandas
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from llama_stack.providers.utils.memory.vector_store import parse_data_url
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def get_dataframe_from_uri(uri: str):
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df = None
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if uri.endswith(".csv"):
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df = pandas.read_csv(uri)
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elif uri.endswith(".xlsx"):
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df = pandas.read_excel(uri)
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elif uri.startswith("data:"):
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parts = parse_data_url(uri)
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data = parts["data"]
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if parts["is_base64"]:
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data = base64.b64decode(data)
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else:
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data = unquote(data)
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encoding = parts["encoding"] or "utf-8"
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data = data.encode(encoding)
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mime_type = parts["mimetype"]
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mime_category = mime_type.split("/")[0]
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data_bytes = io.BytesIO(data)
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if mime_category == "text":
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df = pandas.read_csv(data_bytes)
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else:
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df = pandas.read_excel(data_bytes)
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else:
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raise ValueError(f"Unsupported file type: {uri}")
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return df
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