3fiBddlmZddlZddlmZddlmZddlmZm Z m Z m Z m Z m Z ddlmZddlmZddlmZmZd d lmZe d ZGd d e ZeGddZGddZy)) annotationsN) dataclass)Thread)Any AwaitableOptional TypedDictTypeVarUnion)text)URL) AsyncEnginecreate_async_engine)HybridSearchConfigTc,eZdZUded<ded<ded<y) ColumnDictstrname data_typeboolnullableN)__name__ __module__ __qualname____annotations__Z/var/www/auto_recruiter/arenv/lib/python3.12/site-packages/langchain_postgres/v2/engine.pyrrs INNrrc8eZdZUded<ded<dZded<d dZy) ColumnrrrTrrct|jts tdt|jts tdy)zCheck if initialization parameters are valid. Raises: ValueError: If Column name is not string. 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Args: table_name (str): The database table name. vector_size (int): Vector size for the embedding model to be used. schema_name (str): The schema name. Default: "public". content_column (str): Name of the column to store document content. Default: "page_content". embedding_column (str) : Name of the column to store vector embeddings. Default: "embedding". metadata_columns (Optional[list[Union[Column, ColumnDict]]]): A list of Columns to create for custom metadata. Default: None. Optional. metadata_json_column (str): The column to store extra metadata in JSON format. Default: "langchain_metadata". Optional. id_column (Union[str, Column, ColumnDict]) : Column to store ids. Default: "langchain_id" column name with data type UUID. Optional. overwrite_existing (bool): Whether to drop existing table. Default: False. store_metadata (bool): Whether to store metadata in the table. Default: True. hybrid_search_config (HybridSearchConfig): Hybrid search configuration. Default: None. Raises: :class:`DuplicateTableError `: if table already exists. :class:`UndefinedObjectError `: if the data type of the id column is not a postgreSQL data type. _tsvNrz%CREATE EXTENSION IF NOT EXISTS vectorDROP TABLE IF EXISTS ""."rXUUIDrz,"z" TSVECTOR NOT NULLzCREATE TABLE "z"( "z" z PRIMARY KEY, "z" TEXT NOT NULL, "z " vector(z) NOT NULL zNOT NULLz, " rz" JSONz );)rZr$r"rdictr_rr5connectexecuter commitr tsv_columnr)r' table_name vector_sizerfrgrhrirjrkrlrmrn!hybrid_search_default_column_namer^conn id_data_typeid_column_namehybrid_search_columnhybrid_search_column_namequerycolumnrs r _ainit_vectorstore_tablez!PGEngine._ainit_vectorstore_tablesDX66{C 55jA ,:V,C)99.I;;ON?O P%O&$P O P%O'3O4O' O! O' PO$C?PA P)O=*P-P O? P#P$P( P3P4P<O?OPO O O PO'!O'$P'O:-O0 .O:5 P?PPPP P PPc vK|j|j||||||||| | |  d{y7wa Create a table for saving of vectors to be used with PGVectorStore. Args: table_name (str): The database table name. vector_size (int): Vector size for the embedding model to be used. schema_name (str): The schema name. Default: "public". content_column (str): Name of the column to store document content. Default: "page_content". embedding_column (str) : Name of the column to store vector embeddings. Default: "embedding". metadata_columns (Optional[list[Union[Column, ColumnDict]]]): A list of Columns to create for custom metadata. Default: None. Optional. metadata_json_column (str): The column to store extra metadata in JSON format. Default: "langchain_metadata". Optional. id_column (Union[str, Column, ColumnDict]) : Column to store ids. Default: "langchain_id" column name with data type UUID. Optional. overwrite_existing (bool): Whether to drop existing table. Default: False. store_metadata (bool): Whether to store metadata in the table. Default: True. hybrid_search_config (HybridSearchConfig): Hybrid search configuration. Note that queries might be slow if the hybrid search column does not exist. For best hybrid search performance, consider creating a TSV column and adding GIN index. Default: None. reN)rPr r'r{r|rfrgrhrirjrkrlrmrns r ainit_vectorstore_tablez PGEngine.ainit_vectorstore_table sUR   ) )'-!1!1%9##5-%9 *    s /979c Z|j|j||||||||| | |  yr)rSrrs r init_vectorstore_tablezPGEngine.init_vectorstore_tableCsGR   ) )'-!1!1%9##5-%9 *  r)rfc2Kd|d|d}|jj4d{}|jt|d{|j d{dddd{y7O7/77 #1d{7swYyxYww)zDrop the vector store tablerqrrz";N)r5rwrxr ry)r'r{rfrr~s r _adrop_tablezPGEngine._adrop_table|s) S BG::%%'  4,,tE{+ + +++-     +     sh(BA:BB A< B$A>%B) B4B5B<B>BBBB BBcdK|j|j||d{y7wN)r{rf)rPrr'r{rfs r adrop_tablezPGEngine.adrop_tables3       M   s &0.0cH|j|j||yr)rSrrs r drop_tablezPGEngine.drop_tables%      M r) r8objectr9rr:r.r;r0r*r+)N)r>ztype[PGEngine]r?rr:r.r*r-)rHz str | URLrIrr*r-)rOz Awaitable[T]r*rr))rrr*r)r^rr*r+)r{rr|intrfrrgrrhrriz)Optional[list[Union[Column, ColumnDict]]]rjrrkzUnion[str, Column, ColumnDict]rlrrmrrnzOptional[HybridSearchConfig]r*r+)r{rrfrr*r+)rrr__doc__r/rr1rr3r< classmethodr@rJrPrSrVrZr_rrrrrrrrr r-r-+s1B9=M6=(,O%,8L 2  !   6599 9929  99U UU  UU8 K7'G$' +FJ$84B#(#=At t t  t  t t Dt "t 2t !t t ;t  t v$' +FJ$84B#(#=A7 7 7  7  7 7 D7 "7 27 !7 7 ;7  7 |$' +FJ$84B#(#=A7 7 7  7  7 7 D7 "7 27 !7 7 ;7  7 z$        $       $       rr-) __future__rrD dataclassesr threadingrtypingrrrr r r sqlalchemyr sqlalchemy.enginer sqlalchemy.ext.asynciorrrnrrrr"r-rrr rs`"!FF!C4 CL  EE E&o o r