dh;&vSSKJr SSKJrJrJrJrJrJrJ r J r SSK J r SSK Jr SSKJrJr "SS\5rg) ) annotations)AnyDictIterableListOptionalTupleTypeUnion)Document) Embeddings) VectorStoreVectorStoreRetrieverc^\rSrSrSrSSSjjrSSSjjrSSSjjrSSSjjrSSSjjr SSSjjr SSS jjr SSS jjr SSS jjr SSS jjr\SSS jj5rS U4SjjrSrU=r$)! VespaStore a+ `Vespa` vector store. To use, you should have the python client library ``pyvespa`` installed. Example: .. code-block:: python from langchain_community.vectorstores import VespaStore from langchain_community.embeddings.openai import OpenAIEmbeddings from vespa.application import Vespa # Create a vespa client dependent upon your application, # e.g. either connecting to Vespa Cloud or a local deployment # such as Docker. Please refer to the PyVespa documentation on # how to initialize the client. vespa_app = Vespa(url="...", port=..., application_package=...) # You need to instruct LangChain on which fields to use for embeddings vespa_config = dict( page_content_field="text", embedding_field="embedding", input_field="query_embedding", metadata_fields=["date", "rating", "author"] ) embedding_function = OpenAIEmbeddings() vectorstore = VespaStore(vespa_app, embedding_function, **vespa_config) cSSKJn [X5(d[ S[ U535eXlX lX0lX@l XPl X`l g![a [S5ef=f)z# Initialize with a PyVespa client. r)VespazTCould not import Vespa python package. Please install it with `pip install pyvespa`.z:app should be an instance of vespa.application.Vespa, got N) vespa.applicationr ImportError isinstance ValueErrortype _vespa_app_embedding_function_page_content_field_embedding_field _input_field_metadata_fields)selfappembedding_functionpage_content_fieldembedding_field input_fieldmetadata_fieldsrs ^/var/www/html/shao/venv/lib/python3.13/site-packages/langchain_community/vectorstores/vespa.py__init__VespaStore.__init__+s|  / #%%LTRUYKX #5 #5 /' / @  s AA*c SnURb$URR[U55nUc+[U5VVs/sHupg[ US-5PM nnn/n[U5Hupi0n UR bXUR 'UR bUbXVXR 'Ub3URb&URHn XU;dM X&U X'M URX6U S.5 M URRU5n U HTn [ U R5RS5(aM.[SU RSU RS35e U$s snnf)aP Add texts to the vectorstore. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of ids associated with the texts. kwargs: vectorstore specific parameters Returns: List of ids from adding the texts into the vectorstore. N)idfields2z-Could not add document to Vespa. Error code: . Message: message)rembed_documentslist enumeratestrrrrappendr feed_batch status_code startswith RuntimeErrorjson)r texts metadatasidskwargs embeddingsi_batchtextr-metadata_fieldresultsresults r' add_textsVespaStore.add_textsJs(  # # /11AA$u+NJ ;/8/?@/?tq3!a%?/?C@ 'GA9;F''337t//0$$0Z5K0: ,,-$)>)>)J&*&;&;N%151:n1M.'< LL&9 :(//,,U3F**+66s;;"##)#5#5"67 & I 679 /AsE8c UcgUVs/sHnSU0PM nnURRU5n[UVs/sHofRS:XaSOSPM sn5S:H$s snfs snf)NFr,rr+)r delete_batchsumr7)r r=r>r,rBrFrs r'deleteVespaStore.delete|sk ;&)*c$c*--e4fEf#-A14fEF!KK+Es A"A'c UnURnURnSU;aUSOSnSU;aUSOSnSU;aUSOSn U (aSOSn Sn U S US U S 3- n U S US US3- n UbU SU3- n SU SUS3USUSU0n U $)Nrankingdefaultfilter approximateFtruefalsezselect * from sources * where z {targetHits: z, approximate: }znearestNeighbor(z, )z and yqlz input.query(hits)rr) r query_embeddingkr>rZdoc_embedding_fieldinput_embedding_fieldranking_functionrSrTrYquerys r' _create_queryVespaStore._create_querys"33 $ 1 109V0C6),%-%7!T/</Ff]+E +f . v_[MDD !"5!6b9N8OqQQ   U6(# #C 301 3_ ' D    c DSU;aUSnOUR"X40UD6nURRUS9n[ UR5RS 5(d([ S URS URS35eURS nS U;a!SSK n[ URUS 55eUb URc/$/n URHrn U SURn U Sn SU S0n URb)URHnU SRU5X'M [XS9nU R!X45 Mt U $![a<n[ SUR SSSSUR SSS35eSnAff=f)aW Performs similarity search from a embeddings vector. Args: query_embedding: Embeddings vector to search for. k: Number of results to return. custom_query: Use this custom query instead default query (kwargs) kwargs: other vector store specific parameters Returns: List of ids from adding the texts into the vectorstore. custom_query)bodyz$Could not retrieve data from Vespa: rsummaryz . Error: r0Nr.z0Could not retrieve data from Vespa. Error code: r/rooterrorsr- relevancer,) page_contentmetadata)rarr` Exceptionr9argsr4r7r8r:dumpsrZrrgetr r5)r r[r\r>r`responseerhr:docschildrkscorerlfielddocs r'&similarity_search_by_vector_with_score1VespaStore.similarity_search_by_vector_with_scores V #>*E&&DVDE ,,%,8H8''(33C88'3345$MM)457  }}V$ t  tzz$x.9: :  x}}4I]]E ?4+C+CDL+&EeDk*H$$0!22E&+Ho&9&9%&@HO3 HC KK %# A 666!9Q< *+,&&)A,y124  sE F#7FFc ZUR"X40UD6nUVs/sHoUSPM sn$s snfNr)rx)r embeddingr\r>rErMs r'similarity_search_by_vector&VespaStore.similarity_search_by_vectors3==iUfU%&g!g&&& (c |/nURbURRU5nUR"XB40UD6$N)r embed_queryrx)r r`r\r> query_embs r'similarity_search_with_score'VespaStore.similarity_search_with_scoresA  # # /00<rErMs r'similarity_searchVespaStore.similarity_searchs333EGG%&g!g&&&rc [S5e)NzMMR search not implementedNotImplementedError)r r`r\fetch_k lambda_multr>s r'max_marginal_relevance_search(VespaStore.max_marginal_relevance_searchs"">??rcc [S5e)Nz$MMR search by vector not implementedr)r r|r\rrr>s r''max_marginal_relevance_search_by_vector2VespaStore.max_marginal_relevance_search_by_vectors""HIIrcc :U"SSU0UD6nURXUS9 U$)Nr")r;r<r=)rG)clsr;r|r<r=r>vespas r' from_textsVespaStore.from_textss+;y;F; ecB rcc $>[TU]"S0UD6$)Nr)super as_retriever)r r> __class__s r'rVespaStore.as_retriever sw#-f--rc)rrrrrr)NNNNN)r!rr"zOptional[Embeddings]r# Optional[str]r$rr%rr&Optional[List[str]]returnNone)NN) r;z Iterable[str]r<Optional[List[dict]]r=rr>rr List[str]r)r=rr>rrzOptional[bool]))r[ List[float]r\intr>rrr)r[rr\rr>rrList[Tuple[Document, float]])r|rr\rr>rrList[Document])r`r4r\rr>rrr)r`r4r\rr>rrr)rg?) r`r4r\rrrrfloatr>rrr) r|rr\rrrrrr>rrr)rzType[VespaStore]r;rr|r r<rr=rr>rrr)r>rrr)__name__ __module__ __qualname____firstlineno____doc__r(rGrNrarxr}rrrr classmethodrr__static_attributes__ __classcell__)rs@r'rr sF48,0)-%)/30 010* 0 ' 0 # 0-0 0D+/#' 00(0! 0  0  0dL67*/2BE 6676*6/26BE6 %6r01'$'),'rs("JJJ-0IA.A.rc