Ng7dZddlmZddlZddlZddlmZmZmZm Z m Z m Z ddl m Z ddlmZddlmZerddlmZejZGd d eZdS) z'Wrapper around Epsilla vector database.) annotationsN) TYPE_CHECKINGAnyIterableListOptionalTypeDocument) Embeddings) VectorStore)vectordbceZdZUdZdZded<dZded<dZded<eefd8dZe d9dZ d:dZ d;d:dZ dd?d"Z d@dAd+Z dBdCd1Zeddeeed#fdDd5Zedeeed#fdEd7ZdS)FEpsillaar Wrapper around Epsilla vector database. As a prerequisite, you need to install ``pyepsilla`` package and have a running Epsilla vector database (for example, through our docker image) See the following documentation for how to run an Epsilla vector database: https://epsilla-inc.gitbook.io/epsilladb/quick-start Args: client (Any): Epsilla client to connect to. embeddings (Embeddings): Function used to embed the texts. db_path (Optional[str]): The path where the database will be persisted. Defaults to "/tmp/langchain-epsilla". db_name (Optional[str]): Give a name to the loaded database. Defaults to "langchain_store". Example: .. code-block:: python from langchain_community.vectorstores import Epsilla from pyepsilla import vectordb client = vectordb.Client() embeddings = OpenAIEmbeddings() db_path = "/tmp/vectorstore" db_name = "langchain_store" epsilla = Epsilla(client, embeddings, db_path, db_name) langchain_storestr_LANGCHAIN_DEFAULT_DB_NAMEz/tmp/langchain-epsilla_LANGCHAIN_DEFAULT_DB_PATHlangchain_collection_LANGCHAIN_DEFAULT_TABLE_NAMEclientr embeddingsr db_path Optional[str]db_namec ddl}n"#t$r}td|d}~wwxYwt||jj|jjjfstdt|||_ ||_ ||_ tj|_|j |||j |dS)z%Initialize with necessary components.rNziCould not import pyepsilla python package. Please install pyepsilla package with `pip install pyepsilla`.zbclient should be an instance of pyepsilla.vectordb.Client or pyepsilla.cloud.client.Vectordb, got )rrr) pyepsilla ImportError isinstancerClientcloudrVectordb TypeErrortype_client_db_name _embeddingsrr_collection_nameload_dbuse_db)selfrrrrres d/var/www/html/ai-engine/env/lib/python3.11/site-packages/langchain_community/vectorstores/epsilla.py__init__zEpsilla.__init__4s          Q    Y'. 0F0OP   G8>> G,,,,,s &!&returnOptional[Embeddings]c|jSN)r()r,s r.rzEpsilla.embeddingsSs collection_nameNonec||_dS)z~ Set default collection to use. Args: collection_name (str): The name of the collection. N)r)r,r5s r.use_collectionzEpsilla.use_collectionWs!0r4cL|s|j}|j|dS)z Clear data in a collection. Args: collection_name (Optional[str]): The name of the collection. If not provided, the default collection will be used. N)r)r& drop_tabler8s r. clear_datazEpsilla.clear_data`s0 4"3O 00000r4Nresponse_fieldsOptional[List[str]] List[dict]c|s|j}|j||\}}|dkrKtd|dt d|d|dS)aGet the collection. Args: collection_name (Optional[str]): The name of the collection to retrieve data from. If not provided, the default collection will be used. response_fields (Optional[List[str]]): List of field names in the result. If not specified, all available fields will be responded. Returns: A list of the retrieved data. ) table_namer>zFailed to get records: message Error: {}.result)r)r&getloggererror Exceptionformat)r,r5r> status_coderesponses r.rGz Epsilla.getls 4"3O $ 0 0&!1! !  X #   LLH8I3FHH I I IL//0CDDEE E!!r4rBlist metadatasOptional[list[dict]]cF|stdt|d}dddddddd |d g}|d |D}|D]}|D]\}} ||vr t| trd} n[t| t rd} nCt| t rd } n+t| trd } ntd|d||| d|||j ||\} } | dkrt| dkr t d|ddSt d|d| dtd| ddS)NzEmbeddings list is empty.ridINT)namedataTypetextSTRINGr VECTOR_FLOAT)rTrU dimensionscg|] }|d S)rT).0fields r. z.Epsilla._create_collection..s===U5====r4FLOATBOOLzUnsupported data type for .) table_fieldsrCiz#Continuing with the existing table zFailed to create collection : rDrE) ValueErrorlenitemsr rintfloatboolappendr& create_tablerHinforIrJrK) r,rBrrOdimfields field_namesmetadatakeyvalued_typerLrMs r._create_collectionzEpsilla._create_collections  :899 9*Q-  u - - 2 2!~S Q Q   ==f===K% , ,"*.."2"2,,JCk)) !%-- N!)#E3//N!&#E511N!(#E400N!'()Lc)L)L)LMMMMM3F"C"CDDD&&s++++,"!% 9 9 V!:! !  X #  c!! O*OOOPPPPP V:VV)ATVV 3 3HY4G H HIII  r4Ftexts Iterable[str]Optional[List[dict]]drop_oldOptional[bool]kwargs List[str]c d|sj}n|_|rj|t|} j|}n#t $rfd|D}YnwxYwt|dkrt dgS |||d|D}g}t|D]U\} } | || || d} |'||  } | D] \} }|| | < | | Vj|| \}}|d krNtd |d |d t!d|d d|DS)a Embed texts and add them to the database. Args: texts (Iterable[str]): The texts to embed. metadatas (Optional[List[dict]]): Metadata dicts attached to each of the texts. Defaults to None. collection_name (Optional[str]): Which collection to use. Defaults to "langchain_collection". If provided, default collection name will be set as well. drop_old (Optional[bool]): Whether to drop the previous collection and create a new one. Defaults to False. Returns: List of ids of the added texts. rcDg|]}j|Sr[)r( embed_query)r\xr,s r.r^z%Epsilla.add_texts..s*IIIa$*66q99IIIr4rzNothing to insert, skipping.)rBrrOcNg|]"}ttj#Sr[)hashuuiduuid4)r\_s r.r^z%Epsilla.add_texts..s&111atDJLL!!111r4rRrVrN)rBrecordsrCzFailed to add records to rcrDrEc,g|]}t|Sr[)r)r\rRs r.r^z%Epsilla.add_texts..s&&&BB&&&r4)r)r&drop_dbrNr(embed_documentsNotImplementedErrorrerHdebugrt enumeraterfrjinsertrIrJrK)r,rurOr5rxrzridsrindexrRrecordrprqrrrLrMs` r. add_textszEpsilla.add_textss.0 4"3OO$3D !  : L  9 9 9U  J)99%@@JJ" J J JIIII5IIIJJJ J z??a   LL7 8 8 8I &:    215111"3 # #IE2e (/F $$U+1133"*((JC"'F3KK NN6 " " " " $ 3 3&!4! !  X #   LLTOTTx ?RTT   L//0CDDEE E&&#&&&&sAA65A6querykrgList[Document]c |s|j}|j|}|j|d||\}}|dkrLt d|ddtd|dgdttfd |d S) a Return the documents that are semantically most relevant to the query. Args: query (str): String to query the vectorstore with. k (Optional[int]): Number of documents to return. Defaults to 4. collection_name (Optional[str]): Collection to use. Defaults to "langchain_store" or the one provided before. Returns: List of documents that are semantically most relevant to the query r)rB query_field query_vectorlimitrCzSearch failed: rDrarErcNtdfdDS)NrVc*i|]}|v||Sr[r[)r\rq exclude_keysitems r. z?Epsilla.similarity_search....s0+.#\:Q:QT#Y:Q:Q:Qr4) page_contentrpr )rrs`r.z+Epsilla.similarity_search..sIX!%f26r4rF) r)r(r~r&rrHrIrJrKrNmap) r,rrr5rzrrLrMrs @r.similarity_searchzEpsilla.similarity_searchs 4"3O'33E:: $ 2 2&$% !3! !  X #   LLA8I+>AAA B B BL//0CDDEE E333   "     r4cls Type[Epsilla] embeddingc  Pt||||} | j|f|||d| | S)aCreate an Epsilla vectorstore from raw documents. Args: texts (List[str]): List of text data to be inserted. embeddings (Embeddings): Embedding function. client (pyepsilla.vectordb.Client): Epsilla client to connect to. metadatas (Optional[List[dict]]): Metadata for each text. Defaults to None. db_path (Optional[str]): The path where the database will be persisted. Defaults to "/tmp/langchain-epsilla". db_name (Optional[str]): Give a name to the loaded database. Defaults to "langchain_store". collection_name (Optional[str]): Which collection to use. Defaults to "langchain_collection". If provided, default collection name will be set as well. drop_old (Optional[bool]): Whether to drop the previous collection and create a new one. Defaults to False. Returns: Epsilla: Epsilla vector store. )rr)rOr5rx)rr) rrurrOrrrr5rxrzinstances r. from_textszEpsilla.from_textssYD69gwOOO  +       r4 documentsc ^d|D} d|D} |j| |f| |||||d|S)a"Create an Epsilla vectorstore from a list of documents. Args: texts (List[str]): List of text data to be inserted. embeddings (Embeddings): Embedding function. client (pyepsilla.vectordb.Client): Epsilla client to connect to. metadatas (Optional[List[dict]]): Metadata for each text. Defaults to None. db_path (Optional[str]): The path where the database will be persisted. Defaults to "/tmp/langchain-epsilla". db_name (Optional[str]): Give a name to the loaded database. Defaults to "langchain_store". collection_name (Optional[str]): Which collection to use. Defaults to "langchain_collection". If provided, default collection name will be set as well. drop_old (Optional[bool]): Whether to drop the previous collection and create a new one. Defaults to False. Returns: Epsilla: Epsilla vector store. cg|] }|j Sr[)rr\docs r.r^z*Epsilla.from_documents..ms777c!777r4cg|] }|j Sr[)rprs r.r^z*Epsilla.from_documents..ns777cS\777r4)rOrrrr5rx)r) rrrrrrr5rxrzrurOs r.from_documentszEpsilla.from_documentsLsoB87Y77777Y777 s~     +      r4)rrrr rrrr)r0r1)r5rr0r6)r:)r:N)r5rr>r?r0r@r3)rBrrrNrOrPr0r6)Nr:F) rurvrOrwr5rrxryrzrr0r{)rr:) rrrrgr5rrzrr0r)rrrur{rr rOrwrrrrrrr5rrxryrzrr0r)rrrrrr rrrrrrr5rrxryrzrr0r)__name__ __module__ __qualname____doc__r__annotations__rrr/propertyrr9r=rGrtrr classmethodrrr[r4r.rrs8'87777&>>>>>)?!???? "   X 0000 1 1 1 1 1QU"""""4TX*J*J*J*J*J^+/)+#( D'D'D'D'D'N>@& & & & & P +/!;!;)F#(****[*X !;!;)F#(- - - - [- - - r4r)r __future__rloggingrtypingrrrrrr langchain_core.documentsr langchain_core.embeddingsr langchain_core.vectorstoresr rr getLoggerrHrr[r4r.rs--"""""" EEEEEEEEEEEEEEEE------000000333333#""""""    g g g g g kg g g g g r4