U h'@sdZddlZddlmZddlmZmZmZmZm Z m Z ddl Z ddl Z ddlZddlmZddlmZmZddlmZdgZGd ddeeZGd d d ZdS) z-written under MIT Licence, Michael Feil 2023.N)ThreadPoolExecutor)AnyCallableDictListOptionalTuple) Embeddings) BaseModelroot_validator)get_from_dict_or_envInfinityEmbeddingsc@seZdZUdZeed<dZeed<dZeed<GdddZ e d d e e d d d Z e ee e edddZe ee e edddZee edddZee edddZdS)r aBSelf-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_urlNclientc@seZdZdZdS)zInfinityEmbeddings.ConfigZforbidN)__name__ __module__ __qualname__extrarrK/tmp/pip-unpacked-wheel-9gdii04g/langchain_community/embeddings/infinity.pyConfig/srT)pre)valuesreturncCs&t|dd|d<t|dd|d<|S)z?Validate that api key and python package exists in environment.rZINFINITY_API_URL)hostr)r &TinyAsyncOpenAIInfinityEmbeddingClient)clsrrrrvalidate_environment2s z'InfinityEmbeddings.validate_environmenttextsrcCs|jj|j|d}|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 embeddingsrrrembed_documents?s z"InfinityEmbeddings.embed_documentscs|jj|j|dIdH}|S)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#rrraembed_documentsNs  z#InfinityEmbeddings.aembed_documents)textrcCs||gdS)zCall out to Infinity's embedding endpoint. Args: text: The text to embed. Returns: Embeddings for the text. r)r&)r$r)rrr embed_query]s zInfinityEmbeddings.embed_querycs||gIdH}|dS)zAsync call out to Infinity's embedding endpoint. Args: text: The text to embed. Returns: Embeddings for the text. Nr)r()r$r)r%rrr aembed_queryhs zInfinityEmbeddings.aembed_query)rrr__doc__str__annotations__rrrrr rrrfloatr&r(r*r+rrrrr s     c@s,eZdZdZdeeejddddZe e fe ee e e ee fddd Ze ee e ed d d Ze e e ee ed ddZee eeeefdddZee ee e edddZee ee e edddZejeeefe e edddZee ee e edddZdS)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"] ) http://localhost:7797/v1N)r aiosessionrcCs6||_||_|jdks$t|jdkr,tdd|_dS)Nz( param `host` must be set to a valid url)rr1len ValueError _batch_size)r$rr1rrr__init__s z/TinyAsyncOpenAIInfinityEmbeddingClient.__init__)r sorterrcsRtdkrddfStfddDfddD}|fddfS)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) ``` cSs|S)Nr)trrrzATinyAsyncOpenAIInfinityEmbeddingClient._permute..csg|]}| qSrr).0Zsen)r8rr szCTinyAsyncOpenAIInfinityEmbeddingClient._permute..csg|] }|qSrrr=idx)r rrr>scsfddtDS)Ncsg|] }|qSrrr?Zunsorted_embeddingsrrr>szUTinyAsyncOpenAIInfinityEmbeddingClient._permute....)npargsortrA)length_sorted_idxrArr;s )r4rBrC)r r8Z texts_sortedr)rDr8r r_permutes   z/TinyAsyncOpenAIInfinityEmbeddingClient._permutercCsJt|dkr|gSg}tdt||jD]}|||||jq(|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 r9r)r4ranger6append)r$r Zbatches start_indexrrr_batchs z-TinyAsyncOpenAIInfinityEmbeddingClient._batch)batch_of_textsrcCs@t|dkr$t|ddkr$|dSg}|D]}||q,|S)Nr9r)r4extend)rJr Zsublistrrr_unbatchs  z/TinyAsyncOpenAIInfinityEmbeddingClient._unbatch)rr rcCs"t|jdddit||ddS)zBuild the kwargs for the Post request, used by sync Args: model (str): _description_ texts (List[str]): _description_ Returns: Dict[str, Collection[str]]: _description_ z /embeddingsz content-typezapplication/json)inputr)urlheadersjson)dictr)r$rr rrr_kwargs_post_requests  z;TinyAsyncOpenAIInfinityEmbeddingClient._kwargs_post_request)r batch_textsrcCsNtjf|j||d}|jdkr8td|jd|jdd|dDS)Nr!5Infinity returned an unexpected response with status : cSsg|] }|dqS embeddingrr=errrr>szNTinyAsyncOpenAIInfinityEmbeddingClient._sync_request_embed..data)requestspostrR status_code Exceptionr)rP)r$rrSresponserrr_sync_request_embeds  z:TinyAsyncOpenAIInfinityEmbeddingClient._sync_request_embedc Cs||\}}||}|j|gt||f}t|dkrHtt|}n"td}t|j|}W5QRX||} || } | S)zcall the embedding of model Args: model (str): to embedding model texts (List[str]): List of sentences to embed. Returns: List[List[float]]: List of vectors for each sentence r9 )rErIrar4listmaprrL) r$rr perm_textsunpermute_funcperm_texts_batchedZmap_argsembeddings_batch_permpembeddings_permr%rrrr"s      z,TinyAsyncOpenAIInfinityEmbeddingClient.embed)sessionkwargsrc sz|jf|4IdHV}|jdkr8td|jd|j|IdHd}dd|DW5QIdHRSQIdHRXdS)NrTrUrVr[cSsg|] }|dqSrWrrYrrrr>#szITinyAsyncOpenAIInfinityEmbeddingClient._async_request..)r]statusr_r)rP)r$rkrlr`rXrrr_async_requests z5TinyAsyncOpenAIInfinityEmbeddingClient._async_requestc s|\}}|}jdkr:tjdtjddd_j4IdH(tjfdd|DIdH}W5QIdHRX|}||}|S)zcall the embedding of model, async method Args: model (str): to embedding model texts (List[str]): List of sentences to embed. Returns: List[List[float]]: List of vectors for each sentence NTrb)limit) trust_envZ connectorcs$g|]}jj|ddqS)r!)rkrl)rnrR)r=r:rr$rkrrr>9s  zATinyAsyncOpenAIInfinityEmbeddingClient.aembed..) rErIr1aiohttp ClientSessionZ TCPConnectorasyncioZgatherrL) r$rr rerfrgrhrjr%rrqrr'%s     z-TinyAsyncOpenAIInfinityEmbeddingClient.aembed)r0N)rrrr,r-rrrrsr7 staticmethodr4rrrrErIrrLrrRr/rar"rnr'rrrrrus: !    r)r,rtconcurrent.futuresrtypingrrrrrrrrZnumpyrBr\Zlangchain_core.embeddingsr Zlangchain_core.pydantic_v1r r Zlangchain_core.utilsr __all__r rrrrrs    d