kQjFddlmZddlZddlZddlmZmZddlZddlm Z m Z m Z m Z m Z mZmZmZmZmZddlZddlmZddlmZddlmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#ddl$m%Z%dd l&m'Z'dd l(m)Z)m*Z*m+Z+m,Z,dd l-m.Z.dd l/m0Z0m1Z1dd l2m3Z3ddl4m5Z5m6Z6ddl7Z8ddl9m:Z:m;Z;mm?Z?ddl@mAZAddl4mBZBddlCmDZDmEZEmFZFmGZGmHZHddlImJZJmKZKmLZLmMZMmNZNmOZOmPZPmQZQddlRmSZSmTZTddlUmVZVe rddlWZWddlXZYdeZdeZfdZ[GddePZ\de8jde]d e8jfd!Z^y)") timedeltaN)cached_propertywraps) TYPE_CHECKINGAnyCallableDictIterableListOptionalUnionLiteraloverload) __version__)BlobFile) AddColumnsResult AddResultAlterColumnsResultUpdateFieldMetadataResult DeleteResultDropColumnsResultFtsToken IndexConfig RTEWriteSpec MergeResult UpdateResult)EmbeddingFunctionConfig)Expr)FTSBTreeBitmap LabelList)Job)FunctionApplicationRefreshColumnResult)LOOP)IndexConfigType_maybe_add_fts_error_note)DATAVECVECTOR_COLUMN_NAME)validate_blob_mode)LanceMergeInsertBuilder)EmbeddingFunctionRegistry)_normalize_progress)AnalyzePlanDistributedMetricsDocumentGranularity FullTextQueryLanceQueryBuilderLanceTakeQueryBuilder) AsyncTableBlobModeBranchesCompactionOptionsIndexStatisticsQueryTableTags)BaseTokenizerTypeScalarIndexType)infer_vector_column_nameold_namenew_namecfd}|S)zHKeep old remote-only keyword names without changing the Table signature.c4tfd}|S)Nc|vrK|vrtddtjddtd|j |<|g|i|S)NzCannot specify both z and z is deprecated; use z insteadr0 stacklevel) TypeErrorwarningswarnDeprecationWarningpop)selfargskwargsmethodrBrAs Y/home/oem/CyberBrain26-unified/.venv/lib/python3.12/site-packages/lancedb/remote/table.pywrappedz6_deprecated_keyword..decorate..wrappedXsy6!v%#&:8*E($TUU j 4XJhG&  $*::h#7x $000 0)r)rPrRrBrAs` rQdecoratez%_deprecated_keyword..decorateWs v 1  1rS)rArBrTs`` rQ_deprecated_keywordrVTs OrSc0f eZdZddddededeeeegeffdeeefdZ defd Z e defd Z e jdeddfd Z dd Zdefd ZdeddfdZe defdZe deefdZej*deddedeeeffdZdefdZe dej6fdZe defdZe defdZ e de!fdZ"deefdZ# ddedeeddfd Z$e%deee&ffd!Z'd"Z(d#d$d%e)dejTfd&Z+dd%e)fd'Z,dd(Z-ddd)Z.deeeffd*Z/d+Z0ddeeeeffd,Z1de2e3fd-Z4ddd.d/ed0eed1eede2e5fd2Z6e7d3d1d1edee8fd4Z9ej*d5ed6d7d8ddd9d0ed:e:d;e;dZ=ej*d5ed?e7d0d@ddAdBdAddAdCdDdEd7dddd7dFdFdAdGe>j~dddHd@eeeefdIeeeeefd:e:dJeedKe:dLeedMe:dNe@dOedPeedQe:dRee:dSee:dTeeedUe:dVedWedXe:dYedZe>d  - d??Ad S JJ//<<**  #\\}}  rSc|j|j|j|j|j|j dS)N)rZr]rar[checkout_versionry)rqr]rarerfrgrps rQ __getstate__zRemoteTable.__getstate__s? $ A A C||II"22 $ 6 6ll   rSstatecd|_|d|_|d|_|d|_|d|_|d|_|j d|_d|_y)Nrar]rZr[rry) r`rbr]rcrerfgetrgrj)rMrs rQ __setstate__zRemoteTable.__setstate__sb!6] Y' !&'9!:$%56!&'9!:yy*  rSc|jS)zThe name of the table)rbrps rQrazRemoteTable.nameszzrSc|jSr_)rerps rQ namespacezRemoteTable.namespaces###rSz0.33.1z$Use update_field_metadata() instead.) deprecated_incurrent_versiondetails field_name new_metadatacbtj|jj||yr_)r&runrtreplace_field_metadata)rMrrs rQrz"RemoteTable.replace_field_metadatas! 33J MNrSc<d|jd|jdS)Nz RemoteTable(.))r]rarps rQ__repr__zRemoteTable.__repr__sdll^1TYYKq99rSc\tj|jjS)zlThe [Arrow Schema](https://arrow.apache.org/docs/python/api/datatypes.html#) of this Table )r&rrtschemarps rQrzRemoteTable.schemas xx **,--rSc\tj|jjS)z$Get the current version of the table)r&rrtrzrps rQrzzRemoteTable.versions xx ++-..rSc,t|jSr_)r=rtrps rQtagszRemoteTable.tagssDKK  rSct|S)zBranch management for the table. ``create``/``checkout`` return a new table handle scoped to the branch; writes on it do not affect ``main``. )r8rps rQbrancheszRemoteTable.branchess~rSc6|jjS)zDThe branch this table handle is scoped to, or ``None`` for ``main``.)rtcurrent_branchrps rQrzRemoteTable.current_branchs{{))++rS async_tablerzct||j|j|j}|j |_||_|S)NrY)rXr]rcrerrgrf)rMrrzhandles rQ_wrap_branch_handlezRemoteTable._wrap_branch_handlesI   LL!33//   %335#*  rScptjj|jjS)z Get the embedding functions for the table Returns ------- funcs: dict A mapping of the vector column to the embedding function or empty dict if not configured. )r. get_instanceparse_functionsrmetadatarps rQembedding_functionszRemoteTable.embedding_functionss-)557GG KK   rSc\tj|jjS)zList all versions of the table)r&rrt list_versionsrps rQrzRemoteTable.list_versionssxx 11344rSlazy) blob_moderc.t|td)z.to_arrow() is not supported for remote tables.zUto_arrow() is not supported for remote tables. Use table.search().to_arrow() instead.r,NotImplementedError)rMrs rQto_arrowzRemoteTable.to_arrows9%! 5  rSc .t|td)z/to_pandas() is not supported for remote tables.zWto_pandas() is not supported for remote tables. Use table.search().to_pandas() instead.r)rMrrOs rQ to_pandaszRemoteTable.to_pandass9%! 6  rSc `tj|jjdi|S)zDirect Lance dataset access is not supported for remote tables. Use ``table.search().to_arrow()`` to read query results through the server. rU)r&rrtto_lance)rMrOs rQrzRemoteTable.to_lance#s( xx, ,,6v677rSctd)z@Use ``table.search().to_polars()`` to read remote query results.zWto_polars() is not supported for remote tables. Use table.search().to_polars() instead.)r)rM batch_sizes rQ to_polarszRemoteTable.to_polars*s! 6  rSctj|jj|}|j|_|Sr_)r&rrtcheckoutrzrfrMrzresults rQrzRemoteTable.checkout1s0$++..w78!% rScntj|jj}d|_|Sr_)r&rrtcheckout_latestrf)rMrs rQrzRemoteTable.checkout_latest6s*$++5578!% rScptj|jj|}d|_|Sr_)r&rrtrestorerfrs rQrzRemoteTable.restore;s,$++--g67!% rSc\tj|jjS)z!List all the indices on the table)r&rrt list_indicesrps rQrzRemoteTable.list_indices@xx 00233rScolumn index_namequeryrrcdtj|jj|||S)aJTokenize a query using the tokenizer configured on an FTS index. Model-backed tokenizers such as ``jieba/*`` and ``lindera/*`` are rebuilt in the client process from index metadata. For remote tables, this means the same tokenizer model files must also exist locally (under ``LANCE_LANGUAGE_MODEL_HOME``) and must match the files the server uses. A mismatch cannot be detected: the returned tokens may differ from what the server's index produces. If the model files are missing, an error naming the tokenizer is raised. r)r&rrttokenize)rMrrrs rQrzRemoteTable.tokenizeDs."xx KK v* M  rS index_uuidc^tj|jj|S)z'List all the stats of a specified index)r&rrt index_stats)rMrs rQrzRemoteTable.index_statsYs"xx // ;<>> table.create_index( # doctest: +SKIP ... "vector", config=IvfPq(distance_type="l2") ... ) >>> table.create_index("category", config=BTree()) # doctest: +SKIP >>> table.create_index("content", config=FTS()) # doctest: +SKIP Legacy API (deprecated): >>> table.create_index( # doctest: +SKIP ... "l2", vector_column_name="vector" ... ) aFThe create_index() API with metric/num_partitions parameters is deprecated and will be removed in a future version. Please migrate to the new unified API: # Old (deprecated): table.create_index('l2', vector_column_name='my_vector') # New (recommended): table.create_index('my_vector', config=IvfPq(distance_type='l2'))r0rFNzGGPU accelerator is not supported for remote tables and will be ignored.VECTORr) rrrrrr r r rrrrrrrar) _is_legacy_create_index_callrIrJrKloggingwarning'_build_vector_config_from_legacy_paramsupperr&rrtr)rMrrrrrrrrrr r r rrrrarr is_legacyrs rQrzRemoteTable.create_indexsT55          MMV# (F&+ AA##%1$%%'- /!-' /&; BF"Fxx KK $ $) %   rSc |ttj|jj ||||||S)zCreate an index, returning a handle to the indexing job. The job may already be complete when returned; callers must not assume the index exists until :meth:`Job.wait` returns. r)r#r&rrtcreate_index_asyncrs rQrzRemoteTable.create_index_asyncsF HH ..#!!- /   rSdatamodeon_bad_vectors fill_valueprogresswrite_parallelism!allow_external_blob_outside_basesc t|\}} tj|jj ||||||||r|j SS#|r|j wwxYw)aCAdd more data to the [Table][lancedb.table.Table]. It has the same API signature as the OSS version. Parameters ---------- data: DATA The data to insert into the table. Acceptable types are: - dict or list-of-dict - pandas.DataFrame - pyarrow.Table or pyarrow.RecordBatch mode: str The mode to use when writing the data. Valid values are "append" and "overwrite". on_bad_vectors: str, default "error" What to do if any of the vectors are not the same size or contains NaNs. One of "error", "drop", "fill". fill_value: float, default 0. The value to use when filling vectors. Only used if on_bad_vectors="fill". progress: bool, callable, or tqdm-like, optional A callback or tqdm-compatible progress bar. See :meth:`Table.add` for details. write_parallelism: int, optional Number of partitions to write in parallel. Higher values increase throughput but also peak memory use, since each partition buffers data in flight. Defaults to an estimate based on the data size, capped at the number of CPU cores. Lower this if bulk ingestion is using too much memory. allow_external_blob_outside_bases: bool, default False Not supported for remote tables. Setting this raises. Returns ------- AddResult An object containing the new version number of the table after adding data. )r!r"r#r$r%r&)r/r&rrtaddclose) rMr r!r"r#r$r%r&ownss rQr(zRemoteTable.addsub-X6$ !88 #1)%&76W   t s 4AA-ordering_field_name fts_columns fast_searchc t|trd}n#|dk(rt|tr|jsd}t |j |||}t j||||||xsg|S)az Create a search query to find the nearest neighbors of the given query vector. We currently support [vector search](https://lancedb.com/docs/search/vector-search/) All query options are defined in [LanceVectorQueryBuilder][lancedb.query.LanceVectorQueryBuilder]. Examples -------- >>> import lancedb >>> db = lancedb.connect("db://...", api_key="...", # doctest: +SKIP ... region="...") # doctest: +SKIP >>> data = [ ... {"original_width": 100, "caption": "bar", "vector": [0.1, 2.3, 4.5]}, ... {"original_width": 2000, "caption": "foo", "vector": [0.5, 3.4, 1.3]}, ... {"original_width": 3000, "caption": "test", "vector": [0.3, 6.2, 2.6]} ... ] >>> table = db.create_table("my_table", data) # doctest: +SKIP >>> query = [0.4, 1.4, 2.4] >>> (table.search(query) # doctest: +SKIP ... .where("original_width > 1000", prefilter=True) # doctest: +SKIP ... .select(["caption", "original_width"]) # doctest: +SKIP ... .limit(2) # doctest: +SKIP ... .to_pandas()) # doctest: +SKIP caption original_width vector _distance # doctest: +SKIP 0 foo 2000 [0.5, 3.4, 1.3] 5.220000 # doctest: +SKIP 1 test 3000 [0.3, 6.2, 2.6] 23.089996 # doctest: +SKIP Parameters ---------- query: list/np.ndarray/str/PIL.Image.Image, default None The targeted vector to search for. - *default None*. Acceptable types are: list, np.ndarray, PIL.Image.Image vector_column_name: str, optional The name of the vector column to search. - If not specified then the vector column is inferred from the table schema - If the table has multiple vector columns then the *vector_column_name* needs to be specified. Otherwise, an error is raised. query_type: str, default "auto" The type of search: "auto", "vector", "fts", or "hybrid". With "auto", a string query uses the embedding function for the inferred or specified vector column if available, otherwise it uses full-text search. fast_search: bool, optional Skip a flat search of unindexed data. This may improve search performance but search results will not include unindexed data. - *default None*, which leaves fast search disabled. Returns ------- LanceQueryBuilder A query builder object representing the query. Once executed, the query returns - selected columns - the vector - and also the "_distance" column which is the distance between the query vector and the returned vector. ftsauto)r query_typerr)rr+r,r-)rr3rrr@rr4create)rMrrr1r+r,r-s rQsearchzRemoteTable.searchs~^ e] +J & 5#&,,J5;;!1  !''   1 3#)r#  rSrtimeoutrr5ctj|jj|||fd}tj j j|S)Nr4c3zK tjj&#t$rYywxYwwr_)r&r __anext__StopAsyncIteration) async_itersrQ iter_syncz-RemoteTable._execute_query..iter_syncus= ((:#7#7#9::%  s;', 8;8;)r&rrt_execute_querypaRecordBatchReader from_batchesr)rMrrr5r;r:s @rQr<zRemoteTable._execute_queryjsTXX KK & &uW & U  ##001B1BIKPPrSverbosec`tj|jj||Sr_)r&rrt _explain_plan)rMrr@s rQrBzRemoteTable._explain_plan~s"xx 11%ABBrS aggregatedistributed_metricsrEcbtj|jj||S)NrD)r&rrt _analyze_plan)rMrrEs rQrGzRemoteTable._analyze_plans- xx KK % %eAT % U  rSc^tj|jj|Sr_)r&rrt_output_schema)rMrs rQrIzRemoteTable._output_schemas xx 2259::rSonc"t||S)zReturns a [`LanceMergeInsertBuilder`][lancedb.merge.LanceMergeInsertBuilder] that can be used to create a "merge insert" operation. See [`Table.merge_insert`][lancedb.table.Table.merge_insert] for more details. )super merge_insert)rMrJ __class__s rQrMzRemoteTable.merge_inserts w#B''rSmergenew_datacdtj|jj||||Sr_)r&rrt _do_merge)rMrOrPr"r#s rQrRzRemoteTable._do_merges-xx KK ! !%>: N  rS predicatewherec^tj|jj|S)aDelete rows from the table. This can be used to delete a single row, many rows, all rows, or sometimes no rows (if your predicate matches nothing). Parameters ---------- where: Union[str, Expr] The SQL where clause to use when deleting rows. - For example, 'x = 2' or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error. Returns ------- DeleteResult An object containing the new version number of the table after deletion. Examples -------- >>> import lancedb >>> data = [ ... {"x": 1, "vector": [1, 2]}, ... {"x": 2, "vector": [3, 4]}, ... {"x": 3, "vector": [5, 6]} ... ] >>> db = lancedb.connect("db://...", api_key="...", # doctest: +SKIP ... region="...") # doctest: +SKIP >>> table = db.create_table("my_table", data) # doctest: +SKIP >>> table.search([10,10]).to_pandas() # doctest: +SKIP x vector _distance # doctest: +SKIP 0 3 [5.0, 6.0] 41.0 # doctest: +SKIP 1 2 [3.0, 4.0] 85.0 # doctest: +SKIP 2 1 [1.0, 2.0] 145.0 # doctest: +SKIP >>> table.delete("x = 2") # doctest: +SKIP >>> table.search([10,10]).to_pandas() # doctest: +SKIP x vector _distance # doctest: +SKIP 0 3 [5.0, 6.0] 41.0 # doctest: +SKIP 1 1 [1.0, 2.0] 145.0 # doctest: +SKIP If you have a list of values to delete, you can combine them into a stringified list and use the `IN` operator: >>> to_remove = [1, 3] # doctest: +SKIP >>> to_remove = ", ".join([str(v) for v in to_remove]) # doctest: +SKIP >>> table.delete(f"x IN ({to_remove})") # doctest: +SKIP >>> table.search([10,10]).to_pandas() # doctest: +SKIP x vector _distance # doctest: +SKIP 0 2 [3.0, 4.0] 85.0 # doctest: +SKIP )r&rrtdelete)rMrTs rQrVzRemoteTable.deletes#jxx **5122rS) values_sqlvaluesrWcdtj|jj|||S)a This can be used to update zero to all rows depending on how many rows match the where clause. Parameters ---------- where: str or [Expr][lancedb.expr.Expr], optional The filter condition. Can be a SQL string or a type-safe [Expr][lancedb.expr.Expr] built with [col][lancedb.expr.col] and [lit][lancedb.expr.lit]. The filter must not be empty, or it will error. values: dict, optional The values to update. The keys are the column names and the values are the values to set. values_sql: dict, optional The values to update, expressed as SQL expression strings. These can reference existing columns. For example, {"x": "x + 1"} will increment the x column by 1. Returns ------- UpdateResult - rows_updated: The number of rows that were updated - version: The new version number of the table after the update Examples -------- >>> import lancedb >>> data = [ ... {"x": 1, "vector": [1, 2]}, ... {"x": 2, "vector": [3, 4]}, ... {"x": 3, "vector": [5, 6]} ... ] >>> db = lancedb.connect("db://...", api_key="...", # doctest: +SKIP ... region="...") # doctest: +SKIP >>> table = db.create_table("my_table", data) # doctest: +SKIP >>> table.to_pandas() # doctest: +SKIP x vector # doctest: +SKIP 0 1 [1.0, 2.0] # doctest: +SKIP 1 2 [3.0, 4.0] # doctest: +SKIP 2 3 [5.0, 6.0] # doctest: +SKIP >>> table.update(where="x = 2", values={"vector": [10, 10]}) # doctest: +SKIP >>> table.to_pandas() # doctest: +SKIP x vector # doctest: +SKIP 0 1 [1.0, 2.0] # doctest: +SKIP 1 3 [5.0, 6.0] # doctest: +SKIP 2 2 [10.0, 10.0] # doctest: +SKIP )rTupdates updates_sql)r&rrtupdate)rMrTrXrWs rQr\zRemoteTable.updates/pxx KK  UF  S  rS)delete_unverified older_thanr]c2tjddy)zF cleanup_old_versions() is a no-op for remote tables. z4cleanup_old_versions() is a no-op for remote tables.r0rFNrIrJ)rMr^r]s rQcleanup_old_versionsz RemoteTable.cleanup_old_versionss  B  rSc2tjddy)z? compact_files() is a no-op for remote tables. z-compact_files() is a no-op for remote tables.r0rFNr`)rMrNrOs rQ compact_fileszRemoteTable.compact_files#s  ;  rS)cleanup_older_thanr]retraincompaction_optionsrdrerfcdtj|jj|||S)z.optimize() is not supported for remote tables.)rdr]re)r&rrtoptimize)rMrdr]rerfs rQrhzRemoteTable.optimize-s5xx KK #5"3 !   rSfilterc^tj|jj|Sr_)r&rrt count_rows)rMris rQrkzRemoteTable.count_rows>s xx ..v677rScomputedauto_refresh_enabledauto_refresh_interval_seconds transformsrmrnrocftj|jj||||S)Nrl)r&rrt add_columns)rMrprmrnros rQrrzRemoteTable.add_columnsAs8xx KK # #!%9.K $   rSc^tj|jj|Sr_)r&rrtrefresh_columnrMrs rQrtzRemoteTable.refresh_columnWs xx 226:;;rScpttj|jj |Sr_)r#r&rrtrefresh_column_asyncrus rQrwz RemoteTable.refresh_column_asyncZs%488DKK<>wGHHrSrc^tj|jj|S)zInstall an RTEWriteSpec.)r&rrt enable_rte)rMspecs rQrzRemoteTable.enable_rtex xx ..t455rSc\tj|jjS)zRemove the RTEWriteSpec.)r&rrt disable_rterps rQrzRemoteTable.disable_rte|sxx //122rSc\tj|jjS)z-Read the installed RTEWriteSpec, or ``None``.)r&rrt get_rte_specrps rQrzRemoteTable.get_rte_specrrSc6|jjSr_)rt_hybrid_pk_fusion_learnedrps rQrz%RemoteTable._hybrid_pk_fusion_learneds{{4466rSc8|jjyr_)rt_note_hybrid_pk_fusionrps rQrz"RemoteTable._note_hybrid_pk_fusions **,rSc\tj|jjS)z`Synchronous version of [`AsyncTable.checkpoint_rte`][lancedb.AsyncTable.checkpoint_rte].)r&rrtcheckpoint_rterps rQrzRemoteTable.checkpoint_rtes xx 22455rSc\tj|jjS)zVSynchronous version of [`AsyncTable.flush_rte`][lancedb.AsyncTable.flush_rte].)r&rrt flush_rterps rQrzRemoteTable.flush_rtes xx --/00rSc\tj|jjS)zZSynchronous version of [`AsyncTable.compact_rte`][lancedb.AsyncTable.compact_rte].)r&rrt compact_rterps rQrzRemoteTable.compact_rtes xx //122rSinclude_sstable_rowsrc`tj|jj|S)z^Synchronous version of [`AsyncTable.get_rte_stats`][lancedb.AsyncTable.get_rte_stats].r)r&rrt get_rte_stats)rMrs rQrzRemoteTable.get_rte_statss+xx KK % %;O % P  rSc\tj|jjS)z1No-op for remote tables (no local shard writers).)r&rrtclose_rte_writersrps rQrzRemoteTable.close_rte_writerssxx 55788rSc^tj|jj|Sr_)r&rrt drop_indexrMras rQrzRemoteTable.drop_indexrrSc^tj|jj|S)aPrewarm an index in the table. This is a hint to the database that the index will be accessed in the future and should be loaded into memory if possible. This can reduce cold-start latency for subsequent queries. This call initiates prewarming and returns once the request is accepted. It is idempotent and safe to call from multiple clients concurrently. Parameters ---------- name: str The name of the index to prewarm )r&rrt prewarm_indexrs rQrzRemoteTable.prewarm_indexs"xx 11$788rSc^tj|jj|S)aePrewarm data for the table. This is a hint to the database that the given columns will be accessed in the future and the database should prefetch the data if possible. Currently only supported on remote tables. This call initiates prewarming and returns once the request is accepted. It is idempotent and safe to call from multiple clients concurrently. This operation has a large upfront cost but can speed up future queries that need to fetch the given columns. Large columns such as embeddings or binary data may not be practical to prewarm. This feature is intended for workloads that issue many queries against the same columns. Parameters ---------- columns: list of str, optional The columns to prewarm. If None, all columns are prewarmed. )r&rrt prewarm_datars rQrzRemoteTable.prewarm_datas"(xx 009::rS)seconds index_namesc`tj|jj||Sr_)r&rrtwait_for_index)rMrr5s rQrzRemoteTable.wait_for_indexs$xx 22;HIIrSc\tj|jjSr_)r&rrtstatsrps rQrzRemoteTable.statssxx ))+,,rSc\tj|jjS)z~The table URI (storage location). For remote tables, this fetches the location from the server via describe. )r&rrturirps rQrzRemoteTable.uris xx )**rS) with_row_idoffsetsrcrt|jj|}|r|j|Sr_)r5rt take_offsetsr)rMrrbuilders rQrzRemoteTable.take_offsets1( (@(@(IJ     !rSrow_idscrt|jj|}|r|j|Sr_)r5rt take_row_idsr)rMrrrs rQrzRemoteTable.take_row_idsrrSc\tj|jjSr_)r&rrtuses_v2_manifest_pathsrps rQrz"RemoteTable.uses_v2_manifest_pathssxx ::<==rSc\tj|jjSr_)r&rrtmigrate_manifest_paths_v2rps rQmigrate_v2_manifest_pathsz%RemoteTable.migrate_v2_manifest_pathssxx ==?@@rSc\tj|jjSr_)r&rrt blob_columnsrps rQrzRemoteTable.blob_columnssxx 00233rSc`tj|jj||Sr_)r&rrt fetch_blobsrMrrs rQrzRemoteTable.fetch_blobss$ xx //@AArScrtj|jj|t |Sr_)r&rrtfetch_blob_rangesrd)rMrrequestss rQrzRemoteTable.fetch_blob_rangess&xx 55fd8nMNNrSzlist[Optional[BlobFile]]c`tj|jj||Sr_)r&rrtfetch_blob_filesrs rQrzRemoteTable.fetch_blob_filess$ xx 44VWEFFrS descriptorc^tj|jj|Sr_)r&rrtopen_blob_file)rMrs rQrzRemoteTable.open_blob_file s"xx 22:>??rSctj|jjj |j S)z Return the first `n` rows of the table. 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