3fiddlmZddlmZmZmZmZmZmZm Z ddl Z ddl m Z ddlmZddlmZddlmZddlmZer dd lmZdd lmZdd ZGd d eeZy))ABC) TYPE_CHECKINGAnyIterableListOptionalTupleTypeN)Document) Embeddings) VectorStore)Field)maximal_marginal_relevanceBaseDoc) BaseDocIndexc ddl}|jjd}t|ddk(r+t|ddkrt d|jdyy#t$r t dwxYw)Nr.z_To use the DocArrayHnswSearch VectorStore the docarray version >=0.32.0 is expected, received: z3.To upgrade, please run: `pip install -U docarray`.zXCould not import docarray python package. Please install it with `pip install docarray`.)docarray __version__splitint ImportError)r da_versions l/var/www/auto_recruiter/arenv/lib/python3.12/site-packages/langchain_community/vectorstores/docarray/base.py_check_docarray_importrs ))//4 z!}  "s:a='9R'?;;C;O;O:PQEF (@ "   =   s AAA3c`eZdZdZdddefdZedededfd Z e dedfd Z dd e e d eeededee fdZ dde dededeeeeffdZ dde dededeefdZ dde dededeeeeffdZ ddeedededeefdZ dde dededededeef dZy ) DocArrayIndexz.Base class for `DocArray` based vector stores. doc_indexr embeddingc ||_||_y)z3Initialize a vector store from DocArray's DocIndex.N)r!r")selfr!r"s r__init__zDocArrayIndex.__init__&s #"embeddings_paramsreturnrc >ddlm}ddlmGfdd|}|S)z>Get docarray Document class describing the schema of DocIndex.rr)NdArraycxeZdZUedZeeed<ediWZeWed<edZ ee ed<y)/DocArrayIndex._get_doc_cls..DocArrayDocN)defaulttextr"metadata) __name__ __module__ __qualname__rr.rstr__annotations__r"r/dict)r*r'sr DocArrayDocr,5sA"'"5D(3- 5+0+E3D+EIx( E',T':Hhtn :r&r7)rrdocarray.typingr*)r'rr7r*s` @r _get_doc_clszDocArrayIndex._get_doc_cls/s %+ ;' ; r&cp|jj td|jjS)Nz6doc_index expected to have non-null _schema attribute.)r!_schema ValueError)r$s rdoc_clszDocArrayIndex.doc_cls<s. >> ! ! )UV V~~%%%r&Ntexts metadataskwargsc Lg}|jjt|}tt ||D]e\}\}}|r||ni} |j ||| } |j j| g|jt| jg|S)a%Embed texts and add to the vector store. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. Returns: List of ids from adding the texts into the vectorstore. )r.r"r/) r"embed_documentslist enumeratezipr=r!indexappendr4id) r$r>r?r@ids embeddingsitemdocs r add_textszDocArrayIndex.add_textsBs^^33DK@ "3uj#9: $IAv1 ) ! rA,,AQ,?C NN # ' JJs366{ #  $  r&querykc ,|jj|}|j|}|jj |d|\}}t ||D cgc](\}} t |j|j| f*} }} | Scc} }w)afReturn docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. Returns: List of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity. r"r" search_fieldlimit page_contentr/) r" embed_queryr=r!findrEr r.r/) r$rQrRr@query_embedding query_docdocsscoresrOscoreresults rsimilarity_search_with_scorez*DocArrayIndex.similarity_search_with_score[s..44U;LL?L; ~~**9;VW*X f"$/ U388cll CU K    s-Bc b|j|fd|i|}|Dcgc]\}}| c}}Scc}}w)zReturn docs most similar to query. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. Returns: List of Documents most similar to the query. rR)rb)r$rQrRr@resultsrO_s rsimilarity_searchzDocArrayIndex.similarity_searchss84$33EIQI&I")*Q***s +c t)zzReturn docs and relevance scores, normalized on a scale from 0 to 1. 0 is dissimilar, 1 is most similar. )NotImplementedError)r$rQrRr@s r(_similarity_search_with_relevance_scoresz6DocArrayIndex._similarity_search_with_relevance_scoress "##r&c |j|}|jj|d|j}|Dcgc]#}t |j |j %}}|Scc}w)aReturn docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. Returns: List of Documents most similar to the query vector. rTr"rUrX)r=r!r[ documentsr r.r/)r$r"rRr@r]r^rOras rsimilarity_search_by_vectorz)DocArrayIndex.similarity_search_by_vectorsuLL9L5 ~~"" Kq# ) OS GJH#((S\\ B    s(A+fetch_k lambda_multc x|jj|}|j|}|jj |d|j }t tj||j|} | D cgc])} t|| j|| j+} } | Scc} w)aReturn docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Text to look up documents similar to. k: Number of Documents to return. Defaults to 4. fetch_k: Number of Documents to fetch to pass to MMR algorithm. lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5. Returns: List of Documents selected by maximal marginal relevance. rTr"rU)rRrX) r"rZr=r!r[rkrnparrayr r.r/) r$rQrRrmrnr@r\r]r^ mmr_selectedidxrds rmax_marginal_relevance_searchz+DocArrayIndex.max_marginal_relevance_searchs0..44U;LL?L; ~~"" Kw# ) 2 HH_ %t~~ $  $s)..49;M;M N    s.B7)N))rug?)r1r2r3__doc__r r% staticmethodrr r9propertyr=rr4rrr6rPrr r floatrbrfrirlrtr0r&rr r #s8#!## # $y/  &i&&+/}DJ'  c 4$% 03 eHeO$ %2$% + +  +03 + h +$ $ $  $ $ eHeO$ % $01e),rsALLL-03M 4 $hKhr&