Search is not available for this dataset
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
collection_uuid: string
uuid: string
embedding: list<element: double>
child 0, element: double
document: string
id: string
metadata: string
to
{'uuid': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None), 'metadata': Value(dtype='string', id=None)}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 428, in query
pa_table = pa.concat_tables(
File "pyarrow/table.pxi", line 5245, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: Schema at index 1 was different:
uuid: string
name: string
metadata: string
vs
uuid: string
metadata: string
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 105, in get_rows_content
pa_table = rows_index.query(offset=0, length=rows_max_number)
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 577, in query
return self.parquet_index.query(offset=offset, length=length)
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 435, in query
raise SchemaMismatchError("Parquet files have different schema.", err)
libcommon.parquet_utils.SchemaMismatchError: ('Parquet files have different schema.', ArrowInvalid('Schema at index 1 was different: \nuuid: string\nname: string\nmetadata: string\nvs\nuuid: string\nmetadata: string'))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 328, in compute
compute_first_rows_from_parquet_response(
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 119, in compute_first_rows_from_parquet_response
return create_first_rows_response(
File "/src/libs/libcommon/src/libcommon/viewer_utils/rows.py", line 134, in create_first_rows_response
rows_content = get_rows_content(rows_max_number)
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 114, in get_rows_content
raise SplitParquetSchemaMismatchError(
libcommon.exceptions.SplitParquetSchemaMismatchError: Split parquet files being processed have different schemas. Ensure all files have identical column names.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 126, in get_rows_or_raise
return get_rows(
File "/src/services/worker/src/worker/utils.py", line 64, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 103, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1388, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
for key, pa_table in self.generate_tables_fn(**self.kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 96, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 74, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2194, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
collection_uuid: string
uuid: string
embedding: list<element: double>
child 0, element: double
document: string
id: string
metadata: string
to
{'uuid': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None), 'metadata': Value(dtype='string', id=None)}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for hlm-paraphrase-multilingual-mpnet-base-v2
Dataset Summary
Chromadb vectorstore for 红楼梦, created with
import os
from langchain.document_loaders import TextLoader
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
model_name = 'paraphrase-multilingual-mpnet-base-v2'
embedding = SentenceTransformerEmbeddings(model_name=model_name)
url = 'https://raw.githubusercontent.com/ffreemt/multilingual-dokugpt/master/docs/hlm.txt'
os.system(f'wget -c {url}')
doc = TextLoader('hlm.txt').load()
text_splitter = RecursiveCharacterTextSplitter(
separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""],
chunk_size=620,
chunk_overlap=60,
length_function=len
)
doc_chunks = text_splitter.split_documents(doc)
client_settings = Settings(chroma_db_impl="duckdb+parquet", anonymized_telemetry=False, persist_directory='db')
# takes 8-20 minutes on CPU
vectorstore = Chroma.from_documents(
documents=doc_chunks,
embedding=embedding,
persist_directory='db',
client_settings=client_settings,
)
vectorstore.persist()
How to use
Download the hlm directory to a local directory, e.g., db, for example
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="mikeee/chroma-paraphrase-multilingual-mpnet-base-v2",
repo_type="dataset",
allow_patterns="hlm/*",
local_dir="db",
resume_download=True,
)
Load the vectorestore:
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import Chroma
from chromadb.config import Settings
model_name = 'paraphrase-multilingual-mpnet-base-v2'
embedding = SentenceTransformerEmbeddings(model_name=model_name)
client_settings = Settings(
chroma_db_impl="duckdb+parquet",
anonymized_telemetry=False,
persist_directory='db/hlm'
)
db = Chroma(
# persist_directory='docs',
embedding_function=embedding,
client_settings=client_settings,
)
res = db.search("红楼梦主线", search_type="similarity", k=2)
print(res)
# [Document(page_content='通灵宝玉正面图式\u3000通灵宝玉反面图式\n\n\n\n玉宝灵通\u3000\u3000\u3000\u3000\u3000三二一\n\n仙莫\u3000\u3000\u3000\u3000\u3000\u3000知疗除\n\n寿失\u3000\u3000\u3000\u3000\u3000\u3000祸冤邪\n\n恒莫\u3000\u3000\u3000\u3000\u3000\u3000福疾崇\n\n昌忘\n\n\n\n宝钗看毕，【甲戌双行。。。
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