9iSSKrSSKrSSKJrJr SSKJr SSKJr SSK J r J r SSK J r \ "S5(a SS KJr SS KJr \ "S 5(a SS KJr SS KJr \ "SS 5"SS\\ S95rg)N)ABCabstractmethod)List)logging)class_requires_depsis_dep_available)AutoRegisterABCMetaClass langchain)Document)RecursiveCharacterTextSplitterzlangchain-community) vectorstores)FAISSc ^\rSrSrSrSrSrU4Sjr\S5r \S5r S\ 4S jr S \ S\ 4S jrS \S\ 4S jrS\ S\4SjrS/SQ4S\\ S\S\\ SS4Sjjr SSS\ 4SjrS\ SS4SjrS S\\ SSS\S\S\S\ 4 Sjjr SrU=r$)! BaseRetriever zBase RetrieverTPADDLEX_VECTOR_STOREc>>[TU]5 SUlSUlg)z*Initializes an instance of base retriever.N)super__init__ model_name embedding)self __class__s u/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/paddlex/inference/pipelines/components/retriever/base.pyrBaseRetriever.__init__(s c[S5e)z~ Declaration of an abstract method. Subclasses are expected to provide a concrete implementation of generate_vector_database. zCThe method `generate_vector_database` has not been implemented yet.NotImplementedErrorrs rgenerate_vector_database&BaseRetriever.generate_vector_database.s " Q  rc[S5e)zz Declaration of an abstract method. Subclasses are expected to provide a concrete implementation of similarity_retrieval. z?The method `similarity_retrieval` has not been implemented yet.rr!s rsimilarity_retrieval"BaseRetriever.similarity_retrieval8s " M  rreturncUR$)zT Get the model name used for generating vectors. Returns: str: The model name. )rr!s rget_model_nameBaseRetriever.get_model_nameBsrsc8URUR5$)z Check if the given string starts with the vector store prefix. Args: s (str): The input string to check. Returns: bool: True if the string starts with the vector store prefix, False otherwise. ) startswithVECTOR_STORE_PREFIX)rr+s ris_vector_storeBaseRetriever.is_vector_storeKs||D4455rvector_store_bytescfUR[R"U5RS5-$)z Encode the vector store bytes into a base64 string prefixed with a specific prefix. Args: vector_store_bytes (bytes): The bytes to encode. Returns: str: The encoded string with the prefix. ascii)r.base64 b64encodedecode)rr1s rencode_vector_store!BaseRetriever.encode_vector_storeWs3''&*:*:;M*N*U*U +   rvector_store_strcZ[R"U[UR5S5$)z Decodes the vector store string by removing the prefix and decoding the base64 encoded string. Args: vector_store_str (str): The vector store string with a prefix. Returns: bytes: The decoded vector store data. N)r4 b64decodelenr.)rr9s rdecode_vector_store!BaseRetriever.decode_vector_storees) 0T5M5M1N1P QRRri,)  u。z text_list block_size separatorsrc[USUS9nURSRU55nUVs/sH n[US9PM nn[R "XpR S9nU$s snf![a SnU$f=f)a Generates a vector database from a list of texts. Args: text_list (list[str]): A list of texts to generate the vector database from. block_size (int): The size of each chunk to split the text into. separators (list[str]): A list of separators to use when splitting the text. Returns: FAISS: The generated vector database. Raises: ValueError: If an unsupported API type is configured. ) chunk_size chunk_overlaprDr?) page_content) documentsrN)r split_textjoinr rfrom_documentsr ValueError) rrBrCrD text_splittertextstext all_splits vectorstores rr"r#qs(7!  ((9)=>>CDedhD1e D ..$K E  K sA&A++ A;:A;rScfUcURnU$URUR55nU$)z Encode the vector store serialized to bytes. Args: vectorstore (FAISS): The vector store to be serialized and encoded. Returns: str: The encoded vector store. )r.r7serialize_to_bytes)rrSs rencode_vector_store_to_bytes*BaseRetriever.encode_vector_store_to_bytess>  22K22;3Q3Q3STKrcURU5(d [S5eURU5nUS:Xa[R"S5 g[ R RUURSS9nU$)a: Decode a vector store from bytes according to the specified API type. Args: vectorstore (str): The serialized vector store string. Returns: FAISS: Deserialized vector store object. Raises: ValueError: If the retrieved vector store is not for PaddleX or if an unsupported API type is specified. z-The retrieved vectorstore is not for PaddleX.rz5The retrieved vectorstore is empty,will empty vector.NT) embeddingsallow_dangerous_deserialization) r/rNr=rwarningrrdeserialize_from_bytesr)rrSvectors rdecode_vector_store_from_bytes,BaseRetriever.decode_vector_store_from_bytessx##K00LM M..{; #  OOS T##:: ~~,0;   rquery_text_list sleep_timetopkmin_characterscPSnUcU$UHnUn[R"U5 URXS9n U V V s/sHupU RU 4PM n n n [ U SS9n U SSS2H/upU S:dM [ U5[ U 5-U:a MXm- nM1 M U$s sn n f)aA Retrieve similar contexts based on a list of query texts. Args: query_text_list (list[str]): A list of query texts to search for similar contexts. vectorstore (FAISS): The vector store where to perform the similarity search. sleep_time (float): The time to sleep between each query, in seconds. Default is 0.5. topk (int): The number of results to retrieve per query. Default is 2. min_characters (int): The minimum number of characters required for text processing, defaults to 3500. Returns: str: A concatenated string of all unique contexts found. rAN)kc US$)N)xs r4BaseRetriever.similarity_retrieval..sAaDr)keyg)timesleep'similarity_search_with_relevance_scoresrIsortedr<)rr`rSrarbrcall_C query_textQUESTIONdocsdocumentscorecontextrQs rr%r&s(  L)J!H JJz "FFxFXDMQRT/(--u5TGRW.9G&tt} D=5zCI->ME - * SsB")rr)g?i )__name__ __module__ __qualname____firstlineno____doc___BaseRetriever__is_baser.rrr"r%strr)boolr/bytesr7r=rintrVr^float__static_attributes__ __classcell__)rs@rrr s<I0      6 6 6  e     SC SE S ? !9!!I !  !FC #'D " "c"" "  "  " ""rr) metaclass)r4rnabcrrtypingr paddlex.utilsr utils.depsrr utils.subclass_registerr langchain.docstore.documentr langchain.text_splitterr langchain_communityr langchain_community.vectorstoresrrrhrrrsh #!AAK  4F)**06["78CC#;C9Cr