j'dZddlZddlmZddlmZmZmZmZm Z m Z ddl Z ddl Z ddlZddlmZddlmZddlmZmZmZdgZGd deeZGd d Zy) z-written under MIT Licence, Michael Feil 2023.N)ThreadPoolExecutor)AnyCallableDictListOptionalTuple) Embeddings)get_from_dict_or_env) BaseModel ConfigDictmodel_validatorInfinityEmbeddingsceZdZUdZeed< dZeed< dZeed< e dZ e d e d e d efd Zdeed eeefdZdeed eeefdZded eefdZded eefdZy)raBSelf-hosted embedding models for `infinity` package. See https://github.com/michaelfeil/infinity This also works for text-embeddings-inference and other self-hosted openai-compatible servers. Infinity is a package to interact with Embedding Models on https://github.com/michaelfeil/infinity Example: .. code-block:: python from langchain_community.embeddings import InfinityEmbeddings InfinityEmbeddings( model="BAAI/bge-small", infinity_api_url="http://localhost:7997", ) modelzhttp://localhost:7997infinity_api_urlNclientforbid)extrabefore)modevaluesreturncJt|dd|d<t|d|d<|S)z?Validate that api key and python package exists in environment.rINFINITY_API_URL)hostr)r &TinyAsyncOpenAIInfinityEmbeddingClient)clsrs q/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain_community/embeddings/infinity.pyvalidate_environmentz'InfinityEmbeddings.validate_environment3s? &: &(:& !"B*+ x textscT|jj|j|}|S)zCall out to Infinity's embedding endpoint. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. rr")rembedrselfr" embeddingss rembed_documentsz"InfinityEmbeddings.embed_documentsAs/[[&&**' r!cpK|jj|j|d{}|S7w)zAsync call out to Infinity's embedding endpoint. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. r$N)raembedrr&s raembed_documentsz#InfinityEmbeddings.aembed_documentsPs= ;;--**.    s +646textc,|j|gdS)zCall out to Infinity's embedding endpoint. Args: text: The text to embed. Returns: Embeddings for the text. r)r))r'r-s r embed_queryzInfinityEmbeddings.embed_query_s##TF+A..r!cLK|j|gd{}|dS7 w)zAsync call out to Infinity's embedding endpoint. Args: text: The text to embed. Returns: Embeddings for the text. Nr)r,)r'r-r(s r aembed_queryzInfinityEmbeddings.aembed_queryjs, 00$88 !}9s $" $)__name__ __module__ __qualname____doc__str__annotations__rrrr model_configr classmethodrr rfloatr)r,r/r1r!rrrs& J#3c3FCL(# $ 3 $  T#Y 4U 3D  DI $tE{:K  / /U / s tE{ r!c eZdZdZ ddedeejddfdZe e fde ede de e ee ffd Zde ede e efd Ze d e e ede efd Zd ede edeeeffdZd ede ede e efdZd ede ede e efdZdejdeeefde e efdZd ede ede e efdZy)ra Helper tool to embed Infinity. It is not a part of Langchain's stable API, direct use discouraged. Example: .. code-block:: python mini_client = TinyAsyncInfinityEmbeddingClient( ) embeds = mini_client.embed( model="BAAI/bge-small", text=["doc1", "doc2"] ) # or embeds = await mini_client.aembed( model="BAAI/bge-small", text=["doc1", "doc2"] ) Nr aiosessionrc||_||_|jt|jdkr tdd|_y)Nz( param `host` must be set to a valid url)rr=len ValueError _batch_size)r'rr=s r__init__z/TinyAsyncOpenAIInfinityEmbeddingClient.__init__s?  $ 99 DII 2GH Hr!r"sorterct|dk(r|dfStj|Dcgc] }||  c}Dcgc]}|| }}|fdfScc}wcc}w)aSort texts in ascending order, and delivers a lambda expr, which can sort a same length list https://github.com/UKPLab/sentence-transformers/blob/ c5f93f70eca933c78695c5bc686ceda59651ae3b/sentence_transformers/SentenceTransformer.py#L156 Args: texts (List[str]): _description_ sorter (Callable, optional): _description_. Defaults to len. Returns: Tuple[List[str], Callable]: _description_ Example: ``` texts = ["one","three","four"] perm_texts, undo = self._permute(texts) texts == undo(perm_texts) ``` c|SNr;)ts rzATinyAsyncOpenAIInfinityEmbeddingClient._permute..sAr!cZtjDcgc]}|| c}Scc}wrI)npargsort)unsorted_embeddingsidxlength_sorted_idxs rrKzATinyAsyncOpenAIInfinityEmbeddingClient._permute..s002 ;L0M: 0M  $0M: : s ()rArMrN)r"rEsenrP texts_sortedrQs @r_permutez/TinyAsyncOpenAIInfinityEmbeddingClient._permutest0 u:?+% %JJ'F 'FG.?@.?sc .? @   (G@s A Act|dk(r|gSg}tdt||jD]#}|j||||jz%|S)aX splits Lists of text parts into batches of size max `self._batch_size` When encoding vector database, Args: texts (List[str]): List of sentences self._batch_size (int, optional): max batch size of one request. Returns: List[List[str]]: Batches of List of sentences rGr)rArangerCappend)r'r"batches start_indexs r_batchz-TinyAsyncOpenAIInfinityEmbeddingClient._batchs\ u:?7N CJ0@0@AK NN5{T=M=M/MN OBr!batch_of_textsct|dk(rt|ddk(r|dSg}|D]}|j||S)NrGr)rAextend)r[r"sublists r_unbatchz/TinyAsyncOpenAIInfinityEmbeddingClient._unbatchsK ~ ! #N1,=(>!(C!!$ $%G LL !& r!rcRt|jdddit||S)zBuild the kwargs for the Post request, used by sync Args: model (str): _description_ texts (List[str]): _description_ Returns: Dict[str, Collection[str]]: _description_ z /embeddingsz content-typezapplication/json)inputr)urlheadersjson)dictr)r'rr"s r_kwargs_post_requestz;TinyAsyncOpenAIInfinityEmbeddingClient._kwargs_post_requests<99+[) 2  r! batch_textsctjdi|j||}|jdk7r%t d|jd|j |j dDcgc]}|d c}Scc}w)Nr$5Infinity returned an unexpected response with status : data embeddingr;)requestspostrf status_code Exceptionr-rd)r'rrgresponsees r_sync_request_embedz:TinyAsyncOpenAIInfinityEmbeddingClient._sync_request_embeds== ''e;'G    3 &G''(8==/; )1 (?@(?1+(?@@@s0 A?cj|j|\}}|j|}|j|gt|z|f}t|dk(rt t |}n,t d5}t |j |}ddd|j} || } | S#1swY$xYw)zcall the embedding of model Args: model (str): to embedding model texts (List[str]): List of sentences to embed. Returns: List[List[float]]: List of vectors for each sentence rG N)rTrZrtrAlistmaprr_) r'rr" perm_textsunpermute_funcperm_texts_batchedmap_argsembeddings_batch_permpembeddings_permr(s rr%z,TinyAsyncOpenAIInfinityEmbeddingClient.embeds&*]]5%9" N![[4  $ $ Gc,- -   ! "a '$(h$8 !#B'1(,UQUUH-=(>%(--(=>#O4  ('s -B))B2sessionkwargscfK|jdi|4d{}|jdk7r%td|jd|j|j d{d}|Dcgc]}|d c}cdddd{S7w7-cc}w7#1d{7swYyxYww)Nrirjrkrlrmr;)rostatusrqr-rd)r'rrrrrmrss r_async_requestz5TinyAsyncOpenAIInfinityEmbeddingClient._async_requests 7<<)&))X#%K'r(--: (}}.7I,56IqAkNI6*)) /6*)))siB1BB1AB%B& B1 B=B? B1 B B1BBB1B."B% #B.*B1c K|j|\}}|j|}tjdtjd4d{}t j |Dcgc]&}|j||j||(c}d{}dddd{|j} || } | S7ycc}w767(#1d{7swY8xYww)zcall the embedding of model, async method Args: model (str): to embedding model texts (List[str]): List of sentences to embed. Returns: List[List[float]]: List of vectors for each sentence Trv)limit) trust_env connectorNr$)rr) rTrZaiohttp ClientSession TCPConnectorasynciogatherrrfr_) r'rr"ryrzr{rrJr}rr(s rr+z-TinyAsyncOpenAIInfinityEmbeddingClient.aembed's&*]]5%9" N![[4((g&:&:&D  *1.. 0  0 '' '#88uA8N(0 +% !  --(=>#O4  %    sfAC1CC1C/+C C!C"C& C11C2 C1CC1C."C% #C.*C1)zhttp://localhost:7797/v1N)r2r3r4r5r6rrrrD staticmethodrArrr rTrZrr_rrfr:rtr%rr+r;r!rrrws2/6:  W223   -0 Cy "* tCy(" #  BDI$tCy/(d3iT#Y # d3i DcN , A A'+Cy A d5k  A3tCyT$u+5F: 7,, 76:38n 7 d5k  7#d3iDersN31==05;;  cJcLMMr!