o Zhc@sdZddlmZddlZddlZddlZddlZddlmZm Z m Z m Z m Z m Z mZmZddlmZddlmZddlmZddlmZeeZGd d d eZd ZGd d d ZdS)z,Module providing Infinispan as a VectorStore) annotationsN)AnyIterableListOptionalTupleTypeUnioncast)Response)Document) Embeddings) VectorStorec@seZdZdZ  dWdXd d ZdYddZdZddZd[ddZd\ddZd]ddZ d^d_d!d"Z d]d#d$Z d]d%d&Z d`d(d)Z d]d*d+Zd]d,d-Z  dWdad5d6Z 7dbdcd;d<Z 7dbddd>d?Z 7dbdedAdBZ 7dbdfdCdDZdgdFdGZdhdJdKZdidLdMZe   N NdjdkdUdVZdS)l InfinispanVSa`Infinispan` VectorStore interface. This class exposes the method to present Infinispan as a VectorStore. It relies on the Infinispan class (below) which takes care of the REST interface with the server. Example: ... code-block:: python from langchain_community.vectorstores import InfinispanVS from mymodels import RGBEmbeddings ... vectorDb = InfinispanVS.from_documents(docs, embedding=RGBEmbeddings(), output_fields=["texture", "color"], lambda_key=lambda text,meta: str(meta["_key"]), lambda_content=lambda item: item["color"]) or an empty InfinispanVS instance can be created if preliminary setup is required before populating the store ... code-block:: python from langchain_community.vectorstores import InfinispanVS from mymodels import RGBEmbeddings ... ispnVS = InfinispanVS() # configure Infinispan here # i.e. create cache and schema # then populate the store vectorDb = InfinispanVS.from_documents(docs, embedding=RGBEmbeddings(), output_fields: ["texture", "color"], lambda_key: lambda text,meta: str(meta["_key"]), lambda_content: lambda item: item["color"]) N embeddingOptional[Embeddings]idsOptional[List[str]]kwargsrc stdi|_|_tjdd_tjdd_|_jdd_jdkr8jdd_nt dt jd d_ j dkrTjd d_ nt d t jd fd d_ jdfdd_jd_|_dS)at Parameters ---------- cache_name: str Embeddings cache name. Default "vector" entity_name: str Protobuf entity name for the embeddings. Default "vector" text_field: str Protobuf field name for text. Default "text" vector_field: str Protobuf field name for vector. Default "vector" lambda_content: lambda Lambda returning the content part of an item. Default returns text_field lambda_metadata: lambda Lambda returning the metadata part of an item. Default returns items fields excepts text_field, vector_field, _type output_fields: List[str] List of fields to be returned from item, if None return all fields. Default None kwargs: Any Rest of arguments passed to Infinispan. See docs cache_namevector entity_nameZ textfieldZ text_fieldtextz9`textfield` is deprecated. Please use `text_field` param.Z vectorfieldZ vector_fieldz=`vectorfield` is deprecated. Please use `vector_field` param.Zlambda_contentc |SN)_default_contentitemselfd/var/www/html/lang_env/lib/python3.10/site-packages/langchain_community/vectorstores/infinispanvs.pyi z'InfinispanVS.__init__..Zlambda_metadatacrr)_default_metadatarrr!r"r#lr$Z output_fieldsNr!) 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Unhandled type for field: z} ) r,r2items isinstancer)r@floatbytesbool Exception)r r>r?Zmetadata_proto_tplZmetadata_protoidxfvr!r!r"schema_builder{s4       zInfinispanVS.schema_builderprotor cCs|j|jd|S)zDeploy the schema for the vector db Args: proto(str): protobuf schema Returns: An http Response containing the result of the operation .proto)r' schema_postr,)r rMr!r!r" schema_createszInfinispanVS.schema_createcCs|j|jdS)zDelete the schema for the vector db Returns: An http Response containing the result of the operation rN)r' schema_deleter,rr!r!r"rQszInfinispanVS.schema_deleterconfigcCs&|dkr d|jd}|j|j|S)zCreate the cache for the vector db Args: config(str): configuration of the cache. Returns: An http Response containing the result of the operation raZ { "distributed-cache": { "owners": "2", "mode": "SYNC", "statistics": true, "encoding": { "media-type": "application/x-protostream" }, "indexing": { "enabled": true, "storage": "filesystem", "startup-mode": "AUTO", "indexing-mode": "AUTO", "indexed-entities": [ "z" ] } } } )r,r' cache_postr+)r rRr!r!r" cache_createszInfinispanVS.cache_createcC|j|jS)zDelete the cache for the vector db Returns: An http Response containing the result of the operation )r' cache_deleter+rr!r!r"rVzInfinispanVS.cache_deletecCrU)zClear the cache for the vector db Returns: An http Response containing the result of the operation )r' cache_clearr+rr!r!r"rXrWzInfinispanVS.cache_clearrGcCrU)zOChecks if the cache exists Returns: true if exists )r' cache_existsr+rr!r!r"rYrWzInfinispanVS.cache_existscCrU)zClear the index for the vector db Returns: An http Response containing the result of the operation )r' index_clearr+rr!r!r"cache_index_clearrWzInfinispanVS.cache_index_clearcCrU)z{Rebuild the for the vector db Returns: An http Response containing the result of the operation )r' index_reindexr+rr!r!r"cache_index_reindexrWz InfinispanVS.cache_index_reindextexts Iterable[str] metadatasOptional[List[dict]] last_vectorOptional[List[float]] List[str]cKsg}t|}|r ||j|}|r|||s"dd|D}|jp+dd|D}tt|||} | D]%\} } } d|j|j| i} | | t | }|j | ||j|| q6|S)NcSsg|]}iqSr!r!.0_r!r!r" sz*InfinispanVS.add_texts..cSsg|]}ttqSr!)r)uuiduuid4rer!r!r"rhr:)listr;r-Zembed_documentsappendr6zipr,r2updatejsondumpsr'putr+)r r^r`rbrresultZtexts_lZembedsrZ data_inputmetadataembedkeydataZdata_strr!r!r" add_textss$     zInfinispanVS.add_textsquerykList[Document]cKs|j||d}dd|DS)z"Return docs most similar to query.)rzr{cSg|]\}}|qSr!r!rfdocrgr!r!r"rhz2InfinispanVS.similarity_search..)similarity_search_with_score)r rzr{r documentsr!r!r"similarity_searchszInfinispanVS.similarity_searchList[Tuple[Document, float]]cKs|j|}|j||d}|S)aPerform a search on a query string and return results with score. Args: query (str): The text being searched. k (int, optional): The amount of results to return. Defaults to 4. Returns: List[Tuple[Document, float]] )rr{)r- embed_query&similarity_search_with_score_by_vector)r rzr{rrurr!r!r"rs z)InfinispanVS.similarity_search_with_score List[float]cKs|||}dd|DS)NcSr}r!r!r~r!r!r"rh+rz.)r)r rr{rresr!r!r"similarity_search_by_vector's z(InfinispanVS.similarity_search_by_vectorcCs|jdurd|jd|jdt|dt|}n6d}|jddD] }|d|d }q&|d|jd}|d |jd|jdt|dt|}|j||j}t |j }| |S) a$Return docs most similar to embedding vector. Args: embedding: Embedding to look up documents similar to. k: Number of Documents to return. Defaults to 4. Returns: List of pair (Documents, score) most similar to the query vector. Nzselect v, score(v) from z v where v.z <-> ~zselect zv.,z, score(v) from ) r5r,r2rprqr)r' req_queryr+loadsr_query_result_to_docs)r rr{ query_strZ query_projfieldZ query_resrsr!r!r"r-sT    z3InfinispanVS.similarity_search_with_score_by_vectorrscstg}|dD]1}|dp i|jdurd}n fdd|jD}t||||d}||dfq|S)Nhitshit*csi|]}||qSr!)r*)rfrvrr!r" arkz6InfinispanVS._query_result_to_docs..)Z page_contentrtzscore())r5r r3r4rm)r rsrrowentityrr!rr"rXs    z"InfinispanVS._query_result_to_docsrtNonecCs|||}||}|j|jjjksJd t|jddus$J| s?| }|j|jjjks8Jd | dSdS)Nz)Unable to create schema. Already exists? errorz(Unable to create cache. Already exists? ) rLrP status_coder'CodesOKrprrrYrTr[)r rtr?schemaoutputr!r!r" configureis   zInfinispanVS.configurecCs||dSr)rQrVrr!r!r" config_clearzs zInfinispanVS.config_clearTclsType[InfinispanVS]r clear_oldOptional[bool] auto_configc Ks|d||d|}|r7t|pgdkr7|r|||t|d} ttt|}||dt| n|r=|||t|d} |rR|j||| d|S)aReturn VectorStore initialized from texts and embeddings. In addition to parameters described by the super method, this implementation provides other configuration params if different configuration from default is needed. Parameters ---------- ids : List[str] Additional list of keys associated to the embedding. If not provided UUIDs will be generated clear_old : bool Whether old data must be deleted. Default True auto_config: bool Whether to do a complete server setup (caches, protobuf definition...). Default True kwargs: Any Rest of arguments passed to InfinispanVS. See docs)rrrrB)rNr!) lenrrr rr8rrXrx) rr^rr`rrrrZ infinispanvsZvecr!r!r" from_texts~szInfinispanVS.from_texts)NN)rrrrrr)rr8r9r8)rr=r9r)r>r8r?r@r9r))rMr)r9r )r9r )r)rRr)r9r )r9rG) r^r_r`rarbrcrrr9rd)ry)rzr)r{r@rrr9r|)rzr)r{r@rrr9r)rrr{r@rrr9r|)rrr{r@r9r)rsr=r9r)rtr8r?r@r9r)r9r)NNTT)rrr^rdrr r`rarrrrrrrrr9r)__name__ __module__ __qualname____doc__r7r%rrLrPrQrTrVrXrYr[r]rxrrrrrrr classmethodrr!r!r!r"rsH& 9   %  #          +  r c @seZdZdZddddgdddddf dDddZdEdFddZ dEdGd!d"Z dEdGd#d$ZdHd'd(ZdHd)d*Z dId+d,Z dJd/d0Z dKd2d3Z dLd4d5Z dLd6d7ZdMd8d9ZdNd:d;ZdOd=d>ZdMd?d@ZdMdAdBZdCS)Pr&a>Helper class for `Infinispan` REST interface. This class exposes the Infinispan operations needed to create and set up a vector db. You need a running Infinispan (15+) server without authentication. You can easily start one, see: https://github.com/rigazilla/infinispan-vector#run-infinispan httprz127.0.0.1:11222z/rest/v2/cachesz/rest/v2/schemasTrr)userpasswordhostsrd cache_url schema_urluse_post_for_queryrGhttp2verifyrrc Kszddl} Wn tytdw| j|_| |_||_||_||_|d|_|jd|j|_ ||_ ||_ ||_ ||_ |jrf|jrf|jdkrQ| j|j|jd} n|j|jf} | j|j |j | | d|_dS| j|j |j | d|_dS) aK Parameters ---------- schema: str Schema for HTTP request: "http" or "https". Default "http" user, password: str User and password if auth is required. Default None hosts: List[str] List of server addresses. Default ["127.0.0.1:11222"] cache_url: str URL endpoint for cache API. Default "/rest/v2/caches" schema_url: str URL endpoint for schema API. Default "/rest/v2/schemas" use_post_for_query: bool Whether POST method should be used for query. Default True http2: bool Whether HTTP/2 protocol should be used. `pip install "httpx[http2]"` is needed for HTTP/2. Default True verify: bool Whether TLS certificate must be verified. 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Returns: An http Response containing the result of the operation r ?action=clearrr)r rrrr!r!r"rXszInfinispan.cache_clearcCs"|j|jd|d}||S)zCheck if a cache exists Args: cache_name(str): name of the cache. Returns: True if cache exists rr)rrresource_existsr rrr!r!r"rYs zInfinispan.cache_existsrcCs|jj|td}|j|jjkS)zCheck if a resource exists Args: api_url(str): url of the resource. Returns: true if resource exists r)rheadrrrr)r rrr!r!r"rszInfinispan.resource_existscC(|j|jd|d}|jj|tdS)zClear an index on a cache Args: cache_name(str): name of the cache. Returns: An http Response containing the result of the operation rz/search/indexes?action=clearrrrr!r!r"rZzInfinispan.index_clearcCr)zRebuild index on a cache Args: cache_name(str): name of the cache. Returns: An http Response containing the result of the operation rz/search/indexes?action=reindexrrrr!r!r"r\rzInfinispan.index_reindexN)rr)rr)rr)rrdrr)rr)rrGrrGrrGrr)F)rzr)rr)rrGr9r )rr)rr)rrGr9r )rvr)rwr)rr)r9r )rvr)rr)r9r )rr)rMr)r9r )rr)rRr)r9r )rr)r9r )rr)r9r )rr)r9rG)rr)r9rG)rrrrr7rrrrrrr*rOrSrQrVrXrYrrZr\r!r!r!r"r&s:  L       r&)r __future__rrploggingrir/typingrrrrrrr r rr Zlangchain_core.documentsr Zlangchain_core.embeddingsr Zlangchain_core.vectorstoresr getLoggerrloggerrrr&r!r!r!r"s$ (