tj}ddlmZddlZddlZddlZddlZddlmZmZm Z m Z m Z m Z m Z mZmZmZddlZddlZddlmZmZddlmZmZddlmZddlmZddlmZdd lm Z  dd lm!Z!n#e"$r dd l#m!Z!YnwxYwdd l$m%Z%dd l&m'Z'dd l(m)Z)ddl*m+Z+dZ,dZ-d"dZ.GddZ/d#dZ0GddZ1Gdde2ej3Z4e4j5Z6Gd d!e+Z7dS)$) annotationsN) AnyCallableDict GeneratorIterableListOptionalTupleTypeUnion)deletefunc)JSONUUID)ProgrammingError)Session) quoted_name)maximal_marginal_relevance)declarative_base)Document) Embeddings)get_from_dict_or_env) VectorStorei langchaindocs_and_scoresrreturnList[Document]cd|DS)z!Return docs from docs and scores.cg|]\}}|Sr!).0doc_s f/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/langchain_community/vectorstores/lantern.py z$_results_to_docs...s . . .FCC . . .r!)rs r%_results_to_docsr(,s . .o . . ..r'ceZdZdZdS)BaseEmbeddingStorez+Base class for the Lantern embedding store.N)__name__ __module__ __qualname____doc__r!r'r%r*r*1s5555r'r*distance_strategyDistanceStrategycollection_namestrcd|tjkr tjn tjt t }Gfdd|t}|S)zGet the embedding store class.N)class_registryceZdZZejeddejZddiZ ejej dZ eje dZ ejej dZejejZdS)+get_embedding_store..EmbeddingStoreT)as_uuid) primary_keydefaultextend_existing)nullableN)r+r,r- __tablename__ sqlalchemyColumnruuiduuid4__table_args__Stringdocumentr cmetadata custom_idARRAY embedding)r1embedding_typesr%EmbeddingStorer6Cs'  z D   D$*   ,T2$:$Z%6FFF%J%dT::: %J%j&7$GGG %J%&6j&6~&F&FGG r'rI)r0HAMMINGr=INTEGERREALrdictr*)r/r1 DynamicBaserIrHs ` @r%get_embedding_storerO5s N,444#+#"$&&999K H H H H H H H H&8 H H H r'c(eZdZUdZded<ded<dS) QueryResultzResult from a query.r*rIfloatdistanceN)r+r,r-r.__annotations__r!r'r%rQrQRs+&&&&OOOOOr'rQceZdZdZdZdZdZdS)r0z&Enumerator of the Distance strategies.l2sqcosinehammingN)r+r,r-r. EUCLIDEANCOSINErJr!r'r%r0r0Ys#00I FGGGr'ceZdZdZeeddddfdsdZdtdZedudZ edvdZ e dwd Z dxd"Z edyd$Zdtd%Zdtd&Zdtd'Zdtd(Zdzd+Zd{d-Zd|d.Zd|d/Zd}d2Zed|d3Zed4d5d5fd~d;Zdtd<Zdtd=Zdtd>Zejdd@Z dddDZ!e ddeedfddKZ"ddMZ# dddOZ$ddRZ% dddXZ& dddYZ' ddd[Z( ddd]Z) ddd^Z*e deeddfddaZ+e deddefdddZ,e edefddeZ-e ddgZ.e eeddfddiZ/ dddoZ0 dddpZ1 dddqZ2 dddrZ3dS)Lanterna`Postgres` with the `lantern` extension as a vector store. lantern uses sequential scan by default. but you can create a HNSW index using the create_hnsw_index method. - `connection_string` is a postgres connection string. - `embedding_function` any embedding function implementing `langchain.embeddings.base.Embeddings` interface. - `collection_name` is the name of the collection to use. (default: langchain) - NOTE: This is the name of the table in which embedding data will be stored The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables. - `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN) - `EUCLIDEAN` is the euclidean distance. - `COSINE` is the cosine distance. - `HAMMING` is the hamming distance. - `pre_delete_collection` if True, will delete the collection if it exists. (default: False) - Useful for testing. NFconnection_stringr2embedding_functionrr/r0r1collection_metadataOptional[dict]pre_delete_collectionboolloggerOptional[logging.Logger]relevance_score_fn"Optional[Callable[[float], float]]rNonec ||_||_||_||_||_||_|pt jt|_ ||_ t|j ||_ |dSN)r]r^r1r__distance_strategyralogging getLoggerr+rcoverride_relevance_score_fnrOr/rI __post_init__) selfr]r^r/r1r_rarcres r%__init__zLantern.__init__ys"3"4.#6 "3%:"; 1( ; ; +=(1  "O   r'c||_||dSri)connect_conncreate_hnsw_extensioncreate_collectionros r%rnzLantern.__post_init__s=\\^^  ""$$$      r'c t|jtr|jS|jtjjkr tjS|jtjjkr tjS|jtjjkr tjStd|jdddtDd)Nz#Got unexpected value for distance: z. 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A.!A%%A.(A%)A.. B8BBcR |dS#t$rYdSwxYwri)rurrvs r%create_tables_if_not_existsz#Lantern.create_tables_if_not_existssA   " " $ $ $ $ $    DD s  &&c| |jj|jjdS#t $rYdSwxYwri)rI __table__droprsrrrvs r% drop_tablezLantern.drop_tablesN    ) . .tz/@ A A A A A    DD s )- ;;c.|dSri)rrvs r% drop_tableszLantern.drop_tabless r'rSrRc|Srir!)rorSs r%_hamming_relevance_score_fnz#Lantern._hamming_relevance_score_fnsr'Callable[[float], float]c|j|jS|jtjkr|jS|jtjkr|jS|jtjkr|jStd|j d)a8 The 'correct' relevance function may differ depending on a few things, including: - the distance / similarity metric used by the VectorStore - the scale of your embeddings (OpenAI's are unit normed. 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Optional Keyword Args for HNSW Index: engine: "nmslib", "faiss", "lucene"; default: "nmslib" ef: Size of the dynamic list used during k-NN searches. Higher values lead to more accurate but slower searches; default: 64 ef_construction: Size of the dynamic list used during k-NN graph creation. Higher values lead to more accurate graph but slower indexing speed; default: 64 m: Number of bidirectional links created for each new element. Large impact on memory consumption. Between 2 and 100; default: 16 dims: Dimensions of the vectors in collection. default: 1536 zCREATE INDEX IF NOT EXISTS {} ON {} USING hnsw (embedding {}) WITH (dim = :dim, m = :m, ef_construction = :ef_construction, ef = :ef);T)dimrrefNz.HNSW extension and index created successfully.) r=rformatrrr1rrrsrrrcinfo)rorrrrrcreate_index_queryrs r%create_hnsw_indexzLantern.create_hnsw_index#s2(_ D,d33D0$77""$$   TZ G OO"'6#     NN                   IJJJJJs60B22B69B6c "t|j5}|tjdt |jd|ddddS#1swxYwYdS)NzDROP INDEX IF EXISTS {}T) rrsrr=rrrrr)rors r% drop_indexzLantern.drop_indexYs TZ G OO-44#D$4d;;    NN                     sA"BB Bch|jr(|||j5 |jj|jjn%#t$r}|j dkrn|Yd}~nd}~wwxYwddddS#1swxYwYdS)Nf405) radelete_collectionrrsbeginrIrcreaterrcode)rors r%ruzLantern.create_collectiones  %   " " $ $ $ OO    Z       #-44TZ5FGGGG#   6V##GDDDD                   s< B' )A54B'5 B?B B'BB''B+.B+cb|jd|dS)NzTrying to delete collection)rcdebugrrvs r%rzLantern.delete_collectionts/ 7888 r'Generator[Session, None, None]c#6Kt|jVdS)z?Create a context manager for the session, bind to _conn string.N)rrsrvs r% _make_sessionzLantern._make_sessionxs$dj!!!!!!!r'idsOptional[List[str]]kwargsc rt|j5}|s|jdt |j|jj|}| || ddddS#1swxYwYdS)z_Delete vectors by ids or uuids. Args: ids: List of ids to delete. NzUTrying to delete vectors by ids (represented by the model using the custom ids field)) rrsrcrrrIwhererEin_rr)rorrrstmts r%rzLantern.delete}sTZ G !!2d12288'155c::%%% NN                     sB B,,B03B0texts List[str]rList[List[float]] metadatasOptional[List[dict]]c  | d|D}|s d|D}|| } || ||||} | jd||||d| | jdi| | S)a Order of elements for lists `ids`, `embeddings`, `texts`, `metadatas` should match, so each row will be associated with correct values. Postgres connection string is required "Either pass it as `connection_string` parameter or set the LANTERN_CONNECTION_STRING environment variable. - `texts` texts to insert into collection. - `embeddings` an Embeddings to insert into collection - `embedding` is :class:`Embeddings` that will be used for embedding the text sent. If none is sent, then the multilingual Tensorflow Universal Sentence Encoder will be used. - `metadatas` row metadata to insert into collection. - `ids` row ids to insert into collection. - `collection_name` is the name of the collection to use. (default: langchain) - NOTE: This is the name of the table in which embedding data will be stored The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables. - `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN) - `EUCLIDEAN` is the euclidean distance. - `COSINE` is the cosine distance. - `HAMMING` is the hamming distance. - `pre_delete_collection` if True, will delete the collection if it exists. (default: False) - Useful for testing. NcNg|]"}ttj#Sr!r2r?r@r"r$s r%r&z7Lantern._initialize_from_embeddings..&4443tz||$$444r'cg|]}iSr!r!rs r%r&z7Lantern._initialize_from_embeddings..++++++r'r]r1r^rar/)rrrrr!)_Lantern__get_connection_stringadd_embeddingsr) rrrrGrrr1r/rarr]stores r%_initialize_from_embeddingsz#Lantern._initialize_from_embeddingssP ;44e444C ,++U+++I77??/+("7/      J)  PV    ))&))) r' List[dict]c t|j5}t||||D]5\}}} } || ||| } || 6|ddddS#1swxYwYdS)NrGrCrDrE)rrsziprIaddr) rorrrrrrrmetadatarGidembedding_stores r%rzLantern.add_embeddingssTZ G14UIzSV1W1W - --h 2"&"5"5'!& #6##  O,,,, NN                     sAA??BB Iterable[str]c | d|D}|jt|}|s d|D}t|j5}t ||||D]5\}}} } || ||| } || 6|dddn #1swxYwY|S)NcNg|]"}ttj#Sr!rrs r%r&z%Lantern.add_texts..rr'cg|]}iSr!r!rs r%r&z%Lantern.add_texts..rr'r) r^embed_documentsrrrsrrIrr) rorrrrrrrrrGrrs r% add_textszLantern.add_textss0 ;44e444C,<.sd     !'!6!?#2<$(#:#FD      r'r!)rordocss` r%_results_to_docs_and_scoresz#Lantern._results_to_docs_and_scoress4     "     r'querykfilterrc h|j|}||||S)N)rrGrr)r^ embed_querysimilarity_search_by_vector)rorrrrrGs r%similarity_searchzLantern.similarity_searchsC+77U7CC //0   r'cj|j|}||||}|SNr)r^r&similarity_search_with_score_by_vector)rorrrrGr s r%similarity_search_with_scorez$Lantern.similarity_search_with_scoresB +77>> ::1V;   r' List[float]c\||||}||Sr)_Lantern__query_collectionr )rorGrrrs r%rz.Lantern.similarity_search_with_score_by_vector*s2 ))I6)RR//888r' List[Any]c t|j5}tjd}tjd}||||d|id}|g}|D]\} } d} t | tr| ttj | vrdd| D} |j j | j | | } || |j j | j t| k} || tj|}||}||j t't(|j|j j|d}|||}||j j|||}dddn #1swxYwY|S)NzSET enable_seqscan = offzSET hnsw.init_k = :krinc>i|]\}}||Sr!)lower)r"rvs r% z.Lantern.__query_collection..Fs3222-1QAGGIIq222r'rS)rrsr=rritemsr|rMrr2r!rIrDastextrappendand_rrgetattrrrrGlabelrorder_byoprlimitall)rorGrrrset_enable_seqscan_stmt set_init_k filter_byfilter_clauseskeyryINvalue_case_insensitivefilter_by_metadatarrs r%__query_collectionzLantern.__query_collection4s TZ , G&0o6P&Q&Q ##)?@@J OO3 4 4 4 OOJa 1 1 1I!!#"(,,..BBJCB!%.. B2SY9N9N3N3N225:[[]]222..2-@-J. %;B%?!@!@+'--.@AAAA-1-@-J. CJJ./*'--.@AAAA&O^< 44Y??IMM#5d455'19% ## E$ Y//JD'144T5G5G5I5IJJ9UUq M, , , , , , , , , , , , , , , \sII11I58I5c P||||}t|Sr)rr()rorGrrrrs r%rz#Lantern.similarity_search_by_vectorjs6EE1VF   000r'r Type[Lantern]c r|t|} |j|| |f|||||d|S)ap Initialize Lantern vectorstore from list of texts. The embeddings will be generated using `embedding` class provided. Order of elements for lists `ids`, `texts`, `metadatas` should match, so each row will be associated with correct values. Postgres connection string is required "Either pass it as `connection_string` parameter or set the LANTERN_CONNECTION_STRING environment variable. - `connection_string` is fully populated connection string for postgres database - `texts` texts to insert into collection. - `embedding` is :class:`Embeddings` that will be used for embedding the text sent. If none is sent, then the multilingual Tensorflow Universal Sentence Encoder will be used. - `metadatas` row metadata to insert into collection. - `collection_name` is the name of the collection to use. (default: langchain) - NOTE: This is the name of the table in which embedding data will be stored The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables. - `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN) - `EUCLIDEAN` is the euclidean distance. - `COSINE` is the cosine distance. - `HAMMING` is the hamming distance. - `ids` row ids to insert into collection. - `pre_delete_collection` if True, will delete the collection if it exists. (default: False) - Useful for testing. rrr1rar/)rrr) rrrGrr1r/rrarrs r% from_textszLantern.from_textsvscT..tE{{;; .s.      +"7/      r'text_embeddingsList[Tuple[str, List[float]]]c ^d|D} d|D} |j| | |f|||||d|S)aConstruct Lantern wrapper from raw documents and pre- generated embeddings. Postgres connection string is required "Either pass it as `connection_string` parameter or set the LANTERN_CONNECTION_STRING environment variable. Order of elements for lists `ids`, `text_embeddings`, `metadatas` should match, so each row will be associated with correct values. - `connection_string` is fully populated connection string for postgres database - `text_embeddings` is array with tuples (text, embedding) to insert into collection. - `embedding` is :class:`Embeddings` that will be used for embedding the text sent. If none is sent, then the multilingual Tensorflow Universal Sentence Encoder will be used. - `metadatas` row metadata to insert into collection. - `collection_name` is the name of the collection to use. (default: langchain) - NOTE: This is the name of the table in which embedding data will be stored The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables. - `ids` row ids to insert into collection. - `pre_delete_collection` if True, will delete the collection if it exists. (default: False) - Useful for testing. - `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN) - `EUCLIDEAN` is the euclidean distance. - `COSINE` is the cosine distance. - `HAMMING` is the hamming distance. cg|] }|d S)rr!r"ts r%r&z+Lantern.from_embeddings..s///!1///r'cg|] }|d S)r!r@s r%r&z+Lantern.from_embeddings..s444qad444r'r:)r) rr<rGrr1rrar/rrrs r%from_embeddingszLantern.from_embeddingsspT0////44O444 .s.      +"7/      r'c P||}||||||}|S)am Get instance of an existing Lantern store.This method will return the instance of the store without inserting any new embeddings Postgres connection string is required "Either pass it as `connection_string` parameter or set the LANTERN_CONNECTION_STRING environment variable. - `connection_string` is a postgres connection string. - `embedding` is :class:`Embeddings` that will be used for embedding the text sent. If none is sent, then the multilingual Tensorflow Universal Sentence Encoder will be used. - `collection_name` is the name of the collection to use. (default: langchain) - NOTE: This is the name of the table in which embedding data will be stored The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables. - `ids` row ids to insert into collection. - `pre_delete_collection` if True, will delete the collection if it exists. (default: False) - Useful for testing. - `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN) - `EUCLIDEAN` is the euclidean distance. - `COSINE` is the cosine distance. - `HAMMING` is the hamming distance. r)r)rrGr1rar/rr]rs r%from_existing_indexzLantern.from_existing_indexsEF 77??/+("7/     r'Dict[str, Any]cLt|dd}|std|S)Nr]LANTERN_CONNECTION_STRING)datar2env_keyzPostgres connection string is requiredEither pass it as `connection_string` parameteror set the LANTERN_CONNECTION_STRING variable.)rr})rrr]s r%__get_connection_stringzLantern.__get_connection_stringsJ!5#/" " "  ! A  ! r' documentsc d|D}d|D} ||} | |d<|jd|||| |||d|S)af Initialize a vector store with a set of documents. Postgres connection string is required "Either pass it as `connection_string` parameter or set the LANTERN_CONNECTION_STRING environment variable. - `connection_string` is a postgres connection string. - `documents` is list of :class:`Document` to initialize the vector store with - `embedding` is :class:`Embeddings` that will be used for embedding the text sent. If none is sent, then the multilingual Tensorflow Universal Sentence Encoder will be used. - `collection_name` is the name of the collection to use. (default: langchain) - NOTE: This is the name of the table in which embedding data will be stored The table will be created when initializing the store (if not exists) So, make sure the user has the right permissions to create tables. - `distance_strategy` is the distance strategy to use. (default: EUCLIDEAN) - `EUCLIDEAN` is the euclidean distance. - `COSINE` is the cosine distance. - `HAMMING` is the hamming distance. - `ids` row ids to insert into collection. - `pre_delete_collection` if True, will delete the collection if it exists. (default: False) - Useful for testing. cg|] }|j Sr!)r r"ds r%r&z*Lantern.from_documents..Ks333A333r'cg|] }|j Sr!)rrPs r%r&z*Lantern.from_documents..Ls333AQZ333r'r])rrarGrrr1r/r!)rr;) rrMrGr1r/rrarrrr]s r%from_documentszLantern.from_documents'sH4333333333 77??&7"#s~  "7+/      r'?fetch_k lambda_multOptional[Dict[str, str]]c  ||||}d|D}ttj|tj||| ||} fdt | DS)aReturn docs selected using the maximal marginal relevance with score to embedding vector. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding: Embedding to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. lambda_mult (float): Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Tuple[Document, float]]: List of Documents selected by maximal marginal relevance to the query and score for each. rc&g|]}|jjSr!)rIrG)r"r s r%r&zNLantern.max_marginal_relevance_search_with_score_by_vector..{sPPPf&/9PPPr')dtype)rrWc"g|] \}}|v | Sr!r!)r"ir mmr_selecteds r%r&zNLantern.max_marginal_relevance_search_with_score_by_vector..s'IIIdaqL7H7H7H7H7Hr')rrnparrayfloat32r  enumerate) rorGrrVrWrrrembedding_list candidatesr_s @r%2max_marginal_relevance_search_with_score_by_vectorz:Lantern.max_marginal_relevance_search_with_score_by_vector\s<))IQW)XXPPPPP1 HYbj 1 1 1 #    55g>> IIIIi 33IIIIr'c \|j|}|j|f||||d|S)avReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query (str): Text to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. lambda_mult (float): Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents selected by maximal marginal relevance. rrVrWr)r^r'max_marginal_relevance_search_by_vector)rorrrVrWrrrGs r%max_marginal_relevance_searchz%Lantern.max_marginal_relevance_searchsT8+77>> ;t;  #       r'c `|j|}|jd|||||d|}|S)aReturn docs selected using the maximal marginal relevance with score. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query (str): Text to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. lambda_mult (float): Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Tuple[Document, float]]: List of Documents selected by maximal marginal relevance to the query and score for each. )rGrrVrWrr!)r^rrf) rorrrVrWrrrGr s r%(max_marginal_relevance_search_with_scorez0Lantern.max_marginal_relevance_search_with_scoresX:+77>> FtF #       r'c F|j|f||||d|}t|S)aReturn docs selected using the maximal marginal relevance to embedding vector. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: embedding (str): Text to look up documents similar to. k (int): Number of Documents to return. Defaults to 4. fetch_k (int): Number of Documents to fetch to pass to MMR algorithm. Defaults to 20. lambda_mult (float): Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. 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