gb.ddlZddlmZddlmZddlmZddlmZddl m Z dd l m Z dd l mZdd lmZdd lmZdd lmZddlmZddlZddlmZddlZddlmZddlmZddlm Z ddl!m"Z"ddl#m$Z$ddl%m&Z&ddl'm(Z(ddl)m*Z*ddl+m,Z,ddl-m.Z.ddl/m0Z0ddl1m2Z2ddl3m4Z4ddl5m6Z6ddl7m8Z8ddl9m:Z:dd l;mZ>dd"l?m@Z@dd#lAmBZBdd$lCmDZDdd%lEmFZFdd&lGmHZHdd'lmIZIejejd(ZLGd)d*ZMGd+d,ZNy)-N)SyncClientWrapper) ChatMessages)ToolV2) V2ChatStreamRequestDocumentsItem)CitationOptions)ResponseFormatV2)V2ChatStreamRequestSafetyMode)V2ChatStreamRequestToolChoice)RequestOptions)StreamedChatResponseV2)'convert_and_respect_annotation_metadata)construct_type)BadRequestError)UnauthorizedError)ForbiddenError) NotFoundError)UnprocessableEntityError)TooManyRequestsError)InvalidTokenError)ClientClosedRequestError)InternalServerError)NotImplementedError)ServiceUnavailableError)GatewayTimeoutError)JSONDecodeError)ApiError)V2ChatRequestDocumentsItem)V2ChatRequestSafetyMode)V2ChatRequestToolChoice) ChatResponse)EmbedInputType) EmbeddingType)V2EmbedRequestTruncate)EmbedByTypeResponse)V2RerankResponse)AsyncClientWrapper.c,eZdZdefdZeeeeeeeeeeeeeeeeedddededejeje deje d ejeje d ejed ejed ejed ejedejejedejedejedejedejedejedejedeje deje dejedejedej*ef*dZeeeeeeeeeeeeeeeeedddededejeje deje d ejejed ejed ejed ejed ejedejejedejedejedejedejedejedejedeje deje dejedejedef*dZeeedddededejedejejed ejejed!ejedejede fd"Z!eeedd#ded$ed ejed%ejed&eje d'ejedejede"fd(Z#y))V2Clientclient_wrapperc||_yN_client_wrapperselfr+s E/var/www/openai/venv/lib/python3.12/site-packages/cohere/v2/client.py__init__zV2Client.__init__0 -Ntools strict_tools documentscitation_optionsresponse_format safety_mode max_tokensstop_sequences temperatureseedfrequency_penaltypresence_penaltykp return_promptlogprobs tool_choicerequest_optionsmodelmessagesr7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHreturnc #ZK|jjjddid|dt|tddt|t j tdd|d t|t j tdd t|tdd t|tdd |d | d| d| d| d| d|d|d|d|||ddddi|t5} d|jcxkrdkrnn}tj|}|jD]K} t j t"t%t"t'j(|j*M dddy|j-|jdk(rvt/t j t j0t j2t%t j0t j2|j'|jd k(rvt5t j t j0t j2t%t j0t j2|j'|jd!k(rvt7t j t j0t j2t%t j0t j2|j'|jd"k(rvt9t j t j0t j2t%t j0t j2|j'|jd#k(rvt;t j t j0t j2t%t j0t j2|j'|jd$k(rvt=t j t j0t j2t%t j0t j2|j'|jd%k(rvt?t j t j0t j2t%t j0t j2|j'|jd&k(rvtAt j t j0t j2t%t j0t j2|j'|jd'k(rvtCt j t j0t j2t%t j0t j2|j'|jd(k(rvtEt j t j0t j2t%t j0t j2|j'|jd)k(rvtGt j t j0t j2t%t j0t j2|j'|jd*k(rvtIt j t j0t j2t%t j0t j2|j'|j'}tM|j|+#YxYw#tJ$r"tM|j|jN+wxYw#1swYyxYww),a Generates a text response to a user message. To learn how to use the Chat API and RAG follow our [Text Generation guides](https://docs.cohere.com/v2/docs/chat-api). Follow the [Migration Guide](https://docs.cohere.com/v2/docs/migrating-v1-to-v2) for instructions on moving from API v1 to API v2. Parameters ---------- model : str The name of a compatible [Cohere model](https://docs.cohere.com/v2/docs/models) (such as command-r or command-r-plus) or the ID of a [fine-tuned](https://docs.cohere.com/v2/docs/chat-fine-tuning) model. messages : ChatMessages tools : typing.Optional[typing.Sequence[ToolV2]] A list of available tools (functions) that the model may suggest invoking before producing a text response. When `tools` is passed (without `tool_results`), the `text` content in the response will be empty and the `tool_calls` field in the response will be populated with a list of tool calls that need to be made. If no calls need to be made, the `tool_calls` array will be empty. strict_tools : typing.Optional[bool] When set to `true`, tool calls in the Assistant message will be forced to follow the tool definition strictly. Learn more in the [Structured Outputs (Tools) guide](https://docs.cohere.com/docs/structured-outputs-json#structured-outputs-tools). **Note**: The first few requests with a new set of tools will take longer to process. documents : typing.Optional[typing.Sequence[V2ChatStreamRequestDocumentsItem]] A list of relevant documents that the model can cite to generate a more accurate reply. Each document is either a string or document object with content and metadata. citation_options : typing.Optional[CitationOptions] response_format : typing.Optional[ResponseFormatV2] safety_mode : typing.Optional[V2ChatStreamRequestSafetyMode] Used to select the [safety instruction](https://docs.cohere.com/v2/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`. When `OFF` is specified, the safety instruction will be omitted. Safety modes are not yet configurable in combination with `tools`, `tool_results` and `documents` parameters. **Note**: This parameter is only compatible with models [Command R 08-2024](https://docs.cohere.com/v2/docs/command-r#august-2024-release), [Command R+ 08-2024](https://docs.cohere.com/v2/docs/command-r-plus#august-2024-release) and newer. **Note**: `command-r7b-12-2024` only supports `"CONTEXTUAL"` and `"STRICT"` modes. max_tokens : typing.Optional[int] The maximum number of tokens the model will generate as part of the response. **Note**: Setting a low value may result in incomplete generations. stop_sequences : typing.Optional[typing.Sequence[str]] A list of up to 5 strings that the model will use to stop generation. If the model generates a string that matches any of the strings in the list, it will stop generating tokens and return the generated text up to that point not including the stop sequence. temperature : typing.Optional[float] Defaults to `0.3`. A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations, and higher temperatures mean more random generations. Randomness can be further maximized by increasing the value of the `p` parameter. seed : typing.Optional[int] If specified, the backend will make a best effort to sample tokens deterministically, such that repeated requests with the same seed and parameters should return the same result. However, determinism cannot be totally guaranteed. frequency_penalty : typing.Optional[float] Defaults to `0.0`, min value of `0.0`, max value of `1.0`. Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation. presence_penalty : typing.Optional[float] Defaults to `0.0`, min value of `0.0`, max value of `1.0`. Used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies. k : typing.Optional[float] Ensures that only the top `k` most likely tokens are considered for generation at each step. When `k` is set to `0`, k-sampling is disabled. Defaults to `0`, min value of `0`, max value of `500`. p : typing.Optional[float] Ensures that only the most likely tokens, with total probability mass of `p`, are considered for generation at each step. If both `k` and `p` are enabled, `p` acts after `k`. Defaults to `0.75`. min value of `0.01`, max value of `0.99`. return_prompt : typing.Optional[bool] Whether to return the prompt in the response. logprobs : typing.Optional[bool] Defaults to `false`. When set to `true`, the log probabilities of the generated tokens will be included in the response. tool_choice : typing.Optional[V2ChatStreamRequestToolChoice] Used to control whether or not the model will be forced to use a tool when answering. When `REQUIRED` is specified, the model will be forced to use at least one of the user-defined tools, and the `tools` parameter must be passed in the request. When `NONE` is specified, the model will be forced **not** to use one of the specified tools, and give a direct response. If tool_choice isn't specified, then the model is free to choose whether to use the specified tools or not. **Note**: This parameter is only compatible with models [Command-r7b](https://docs.cohere.com/v2/docs/command-r7b) and newer. **Note**: The same functionality can be achieved in `/v1/chat` using the `force_single_step` parameter. If `force_single_step=true`, this is equivalent to specifying `REQUIRED`. While if `force_single_step=true` and `tool_results` are passed, this is equivalent to specifying `NONE`. request_options : typing.Optional[RequestOptions] Request-specific configuration. Yields ------ typing.Iterator[StreamedChatResponseV2] Examples -------- from cohere import Client, ToolChatMessageV2 client = Client( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) response = client.v2.chat_stream( model="model", messages=[ ToolChatMessageV2( tool_call_id="messages", content="messages", ) ], ) for chunk in response: yield chunk v2/chatPOSTrIrJwriteobject_ annotation directionr7r8r9r:r;r<r=r>r?r@rArBrCrDrETrFrGstream content-typeapplication/jsonmethodjsonheadersrHomit,type_rQN status_codebody)(r/ httpx_clientrUrrtypingSequencerrr r OMITrn httpx_sse EventSourceiter_ssecastrrrZloadsdatareadrOptionalAnyrrrrrrrrrrrrrtextr1rIrJr7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrH _response _event_source_sse_response_jsons r2 chat_streamzV2Client.chat_stream3sz ! ! . . 5 5 C$ @!foof.EQX  D%&//Bb2cov#$K,T[%"#J+8HT[$${%&j'(!.)*{+,-.$%6/0#$412Q34Q56 78%*=B 2,O6( PJ W)//5#5$-$9$9)$DM - 6 6 8 !"(++ 6 .*@,0JJtyy,A!"#!9o( ( p ((C/) "OOFJJ7*&,oofjj&A(1(8((C/+ "OOFJJ7*&,oofjj&A(1(8((C/( "OOFJJ7*&,oofjj&A(1(8((C/' "OOFJJ7*&,oofjj&A(1(8((C/2 "OOFJJ7*&,oofjj&A(1(8((C/. "OOFJJ7*&,oofjj&A(1(8((C/+ "OOFJJ7*&,oofjj&A(1(8((C/2 "OOFJJ7*&,oofjj&A(1(8((C/- "OOFJJ7*&,oofjj&A(1(8((C/- "OOFJJ7*&,oofjj&A(1(8((C/1 "OOFJJ7*&,oofjj&A(1(8((C/- "OOFJJ7*&,oofjj&A(1(8"+!1y'<'<>R R! x# W9+@+@y~~VV We( ( sWC`+`!A_1"A_)*_1- `+6Y_1`)_.+_11+```($`+c ||jjjddid|dt|tddt|t j tdd|d t|t j tdd t|tdd t|tdd |d | d| d| d| d| d|d|d|d|||ddddi|t} d|jcxkrdkr:nn7t jttt|j!S|jdk(rvt#t jt j$t j&tt j$t j&|j!|jdk(rvt)t jt j$t j&tt j$t j&|j!|jd k(rvt+t jt j$t j&tt j$t j&|j!|jd!k(rvt-t jt j$t j&tt j$t j&|j!|jd"k(rvt/t jt j$t j&tt j$t j&|j!|jd#k(rvt1t jt j$t j&tt j$t j&|j!|jd$k(rvt3t jt j$t j&tt j$t j&|j!|jd%k(rvt5t jt j$t j&tt j$t j&|j!|jd&k(rvt7t jt j$t j&tt j$t j&|j!|jd'k(rvt9t jt j$t j&tt j$t j&|j!|jd(k(rvt;t jt j$t j&tt j$t j&|j!|jd)k(rvt=t jt j$t j&tt j$t j&|j!|j!}tA|j|*#t>$r"tA|j|jB*wxYw)+a Generates a text response to a user message and streams it down, token by token. To learn how to use the Chat API with streaming follow our [Text Generation guides](https://docs.cohere.com/v2/docs/chat-api). Follow the [Migration Guide](https://docs.cohere.com/v2/docs/migrating-v1-to-v2) for instructions on moving from API v1 to API v2. Parameters ---------- model : str The name of a compatible [Cohere model](https://docs.cohere.com/v2/docs/models) (such as command-r or command-r-plus) or the ID of a [fine-tuned](https://docs.cohere.com/v2/docs/chat-fine-tuning) model. messages : ChatMessages tools : typing.Optional[typing.Sequence[ToolV2]] A list of available tools (functions) that the model may suggest invoking before producing a text response. When `tools` is passed (without `tool_results`), the `text` content in the response will be empty and the `tool_calls` field in the response will be populated with a list of tool calls that need to be made. If no calls need to be made, the `tool_calls` array will be empty. strict_tools : typing.Optional[bool] When set to `true`, tool calls in the Assistant message will be forced to follow the tool definition strictly. Learn more in the [Structured Outputs (Tools) guide](https://docs.cohere.com/docs/structured-outputs-json#structured-outputs-tools). **Note**: The first few requests with a new set of tools will take longer to process. documents : typing.Optional[typing.Sequence[V2ChatRequestDocumentsItem]] A list of relevant documents that the model can cite to generate a more accurate reply. Each document is either a string or document object with content and metadata. citation_options : typing.Optional[CitationOptions] response_format : typing.Optional[ResponseFormatV2] safety_mode : typing.Optional[V2ChatRequestSafetyMode] Used to select the [safety instruction](https://docs.cohere.com/v2/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`. When `OFF` is specified, the safety instruction will be omitted. Safety modes are not yet configurable in combination with `tools`, `tool_results` and `documents` parameters. **Note**: This parameter is only compatible with models [Command R 08-2024](https://docs.cohere.com/v2/docs/command-r#august-2024-release), [Command R+ 08-2024](https://docs.cohere.com/v2/docs/command-r-plus#august-2024-release) and newer. **Note**: `command-r7b-12-2024` only supports `"CONTEXTUAL"` and `"STRICT"` modes. max_tokens : typing.Optional[int] The maximum number of tokens the model will generate as part of the response. **Note**: Setting a low value may result in incomplete generations. stop_sequences : typing.Optional[typing.Sequence[str]] A list of up to 5 strings that the model will use to stop generation. If the model generates a string that matches any of the strings in the list, it will stop generating tokens and return the generated text up to that point not including the stop sequence. temperature : typing.Optional[float] Defaults to `0.3`. A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations, and higher temperatures mean more random generations. Randomness can be further maximized by increasing the value of the `p` parameter. seed : typing.Optional[int] If specified, the backend will make a best effort to sample tokens deterministically, such that repeated requests with the same seed and parameters should return the same result. However, determinism cannot be totally guaranteed. frequency_penalty : typing.Optional[float] Defaults to `0.0`, min value of `0.0`, max value of `1.0`. Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation. presence_penalty : typing.Optional[float] Defaults to `0.0`, min value of `0.0`, max value of `1.0`. Used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies. k : typing.Optional[float] Ensures that only the top `k` most likely tokens are considered for generation at each step. When `k` is set to `0`, k-sampling is disabled. Defaults to `0`, min value of `0`, max value of `500`. p : typing.Optional[float] Ensures that only the most likely tokens, with total probability mass of `p`, are considered for generation at each step. If both `k` and `p` are enabled, `p` acts after `k`. Defaults to `0.75`. min value of `0.01`, max value of `0.99`. return_prompt : typing.Optional[bool] Whether to return the prompt in the response. logprobs : typing.Optional[bool] Defaults to `false`. When set to `true`, the log probabilities of the generated tokens will be included in the response. tool_choice : typing.Optional[V2ChatRequestToolChoice] Used to control whether or not the model will be forced to use a tool when answering. When `REQUIRED` is specified, the model will be forced to use at least one of the user-defined tools, and the `tools` parameter must be passed in the request. When `NONE` is specified, the model will be forced **not** to use one of the specified tools, and give a direct response. If tool_choice isn't specified, then the model is free to choose whether to use the specified tools or not. **Note**: This parameter is only compatible with models [Command-r7b](https://docs.cohere.com/v2/docs/command-r7b) and newer. **Note**: The same functionality can be achieved in `/v1/chat` using the `force_single_step` parameter. If `force_single_step=true`, this is equivalent to specifying `REQUIRED`. While if `force_single_step=true` and `tool_results` are passed, this is equivalent to specifying `NONE`. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- ChatResponse Examples -------- from cohere import Client, ToolChatMessageV2 client = Client( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) client.v2.chat( model="model", messages=[ ToolChatMessageV2( tool_call_id="messages", content="messages", ) ], ) rMrNrIrJrOrPr7r8r9r:r;r<r=r>r?r@rArBrCrDrEFrTrVrWrXr]r^r_rarbrcrdrerfrgrhrirjrkrlrm"r/rprequestrrrqrrrrr r rsrnrwr"rrZrr{r|rrrrrrrrrrrrrr}r1rIrJr7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrrs r2chatz V2Client.chatsPv((55== C$ @!foof.EQX  D%&//B\2]ip#$K,T[%"#J+8HT[$${%&j'(!.)*{+,-.$%6/0#$412Q34Q56 78%*=B 2,O>( RC Si++1c1{{ "* ) 0$$+%KK 3&"(//&**"=$-NN$4$$+'KK 3&"(//&**"=$-NN$4$$+$KK 3&"(//&**"=$-NN$4$$+#KK 3&"(//&**"=$-NN$4$$+.KK 3&"(//&**"=$-NN$4$$+*KK 3&"(//&**"=$-NN$4$$+'KK 3&"(//&**"=$-NN$4$$+.KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4$$+-KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4'^^-N9#8#8~NN Sy'<'<9>>R R SsA^-Y ^+^;textsimagestruncaterH input_typeembedding_typesrrrc 4|jjjdd||||||dddi|t} d|jcxkrdkr:nn7t j ttt|j S|jd k(rvtt j t jt jtt jt j|j |jd k(rvtt j t jt jtt jt j|j |jd k(rvtt j t jt jtt jt j|j |jd k(rvtt j t jt jtt jt j|j |jdk(rvt!t j t jt jtt jt j|j |jdk(rvt#t j t jt jtt jt j|j |jdk(rvt%t j t jt jtt jt j|j |jdk(rvt't j t jt jtt jt j|j |jdk(rvt)t j t jt jtt jt j|j |jdk(rvt+t j t jt jtt jt j|j |jdk(rvt-t j t jt jtt jt j|j |jdk(rvt/t j t jt jtt jt j|j |j} t3|j| #t0$r"t3|j|j4wxYw)a This endpoint returns text embeddings. An embedding is a list of floating point numbers that captures semantic information about the text that it represents. Embeddings can be used to create text classifiers as well as empower semantic search. To learn more about embeddings, see the embedding page. If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](https://docs.cohere.com/docs/semantic-search). Parameters ---------- model : str Defaults to embed-english-v2.0 The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID. Available models and corresponding embedding dimensions: * `embed-english-v3.0` 1024 * `embed-multilingual-v3.0` 1024 * `embed-english-light-v3.0` 384 * `embed-multilingual-light-v3.0` 384 * `embed-english-v2.0` 4096 * `embed-english-light-v2.0` 1024 * `embed-multilingual-v2.0` 768 input_type : EmbedInputType embedding_types : typing.Sequence[EmbeddingType] Specifies the types of embeddings you want to get back. Can be one or more of the following types. * `"float"`: Use this when you want to get back the default float embeddings. Valid for all models. * `"int8"`: Use this when you want to get back signed int8 embeddings. Valid for only v3 models. * `"uint8"`: Use this when you want to get back unsigned int8 embeddings. Valid for only v3 models. * `"binary"`: Use this when you want to get back signed binary embeddings. Valid for only v3 models. * `"ubinary"`: Use this when you want to get back unsigned binary embeddings. Valid for only v3 models. texts : typing.Optional[typing.Sequence[str]] An array of strings for the model to embed. Maximum number of texts per call is `96`. We recommend reducing the length of each text to be under `512` tokens for optimal quality. images : typing.Optional[typing.Sequence[str]] An array of image data URIs for the model to embed. Maximum number of images per call is `1`. The image must be a valid [data URI](https://developer.mozilla.org/en-US/docs/Web/URI/Schemes/data). The image must be in either `image/jpeg` or `image/png` format and has a maximum size of 5MB. truncate : typing.Optional[V2EmbedRequestTruncate] One of `NONE|START|END` to specify how the API will handle inputs longer than the maximum token length. Passing `START` will discard the start of the input. `END` will discard the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model. If `NONE` is selected, when the input exceeds the maximum input token length an error will be returned. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- EmbedByTypeResponse OK Examples -------- from cohere import Client client = Client( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) client.v2.embed( model="model", input_type="search_document", embedding_types=["float"], ) v2/embedrNrrrIrrrrVrWrXr]r^r_rarbrcrdrerfrgrhrirjrkrlrmr/rprrsrnrqrwr&rrZrr{r|rrrrrrrrrrrrrr} r1rIrrrrrrHrrs r2embedzV2Client.embeds,h((55==  (#2$  2,> "C Si++1c1{{'"1 ) 0$$+%KK 3&"(//&**"=$-NN$4$$+'KK 3&"(//&**"=$-NN$4$$+$KK 3&"(//&**"=$-NN$4$$+#KK 3&"(//&**"=$-NN$4$$+.KK 3&"(//&**"=$-NN$4$$+*KK 3&"(//&**"=$-NN$4$$+'KK 3&"(//&**"=$-NN$4$$+.KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4$$+-KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4'^^-N9#8#8~NN Sy'<'<9>>R R SA[, Y [,,+\top_nreturn_documentsmax_tokens_per_docrHqueryrrrc 4|jjjdd||||||dddi|t} d|jcxkrdkr:nn7t j ttt|j S|jd k(rvtt j t jt jtt jt j|j |jd k(rvtt j t jt jtt jt j|j |jd k(rvtt j t jt jtt jt j|j |jd k(rvtt j t jt jtt jt j|j |jdk(rvt!t j t jt jtt jt j|j |jdk(rvt#t j t jt jtt jt j|j |jdk(rvt%t j t jt jtt jt j|j |jdk(rvt't j t jt jtt jt j|j |jdk(rvt)t j t jt jtt jt j|j |jdk(rvt+t j t jt jtt jt j|j |jdk(rvt-t j t jt jtt jt j|j |jdk(rvt/t j t jt jtt jt j|j |j} t3|j| #t0$r"t3|j|j4wxYw)a This endpoint takes in a query and a list of texts and produces an ordered array with each text assigned a relevance score. Parameters ---------- model : str The identifier of the model to use, eg `rerank-v3.5`. query : str The search query documents : typing.Sequence[str] A list of texts that will be compared to the `query`. For optimal performance we recommend against sending more than 1,000 documents in a single request. **Note**: long documents will automatically be truncated to the value of `max_tokens_per_doc`. **Note**: structured data should be formatted as YAML strings for best performance. top_n : typing.Optional[int] Limits the number of returned rerank results to the specified value. If not passed, all the rerank results will be returned. return_documents : typing.Optional[bool] - If false, returns results without the doc text - the api will return a list of {index, relevance score} where index is inferred from the list passed into the request. - If true, returns results with the doc text passed in - the api will return an ordered list of {index, text, relevance score} where index + text refers to the list passed into the request. max_tokens_per_doc : typing.Optional[int] Defaults to `4096`. Long documents will be automatically truncated to the specified number of tokens. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- V2RerankResponse OK Examples -------- from cohere import Client client = Client( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) client.v2.rerank( model="model", query="query", documents=["documents"], ) v2/rerankrNrIrr9rrrrVrWrXr]r^r_rarbrcrdrerfrgrhrirjrkrlrmr/rprrsrnrqrwr'rrZrr{r|rrrrrrrrrrrrrr} r1rIrr9rrrrHrrs r2rerankzV2Client.reranks,|((55== &$4&8  2,> "C Si++1c1{{$". ) 0$$+%KK 3&"(//&**"=$-NN$4$$+'KK 3&"(//&**"=$-NN$4$$+$KK 3&"(//&**"=$-NN$4$$+#KK 3&"(//&**"=$-NN$4$$+.KK 3&"(//&**"=$-NN$4$$+*KK 3&"(//&**"=$-NN$4$$+'KK 3&"(//&**"=$-NN$4$$+.KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4$$+-KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4'^^-N9#8#8~NN Sy'<'<9>>R R Sr)$__name__ __module__ __qualname__rr3rsstrrrqr{rrrboolrr r r intfloatr r Iteratorrrrr r!r"rr#r$r%r&rr'rr5r2r*r*/sk.*;.;?.2X\=A=AFJ+/@D.2%)4837$($(/3*.FJ;?-QSQS QS vv67 QS ood+ QS??6??3S#TUQS!///:QS )9:QS__%BCQSOOC(QS(<=QS__U+QSooc"QS"??51QS !//%0!QS" ??5 !#QS$ ??5 !%QS&t,'QS(//$')QS*__%BC+QS, 8-QS. / 0/QSp ;?.2RV=A=A@D+/@D.2%)4837$($(/3*.@D;?-HOHO HO vv67 HO ood+ HO??6??3M#NOHO!///:HO )9:HO__%<=HOOOC(HO(<=HO__U+HOooc"HO"??51HO !//%0!HO" ??5 !#HO$ ??5 !%HO&t,'HO(//$')HO*__%<=+HO, 8-HO. /HO` 8<8<<@;?iOiO# iO  7 iO vs34 iO 45iO//"89iO 8iO iOb'+2637;?SOSO SO ??3' SO s# SO!//$/SO#OOC0SO 8SO SOr5r*c,eZdZdefdZeeeeeeeeeeeeeeeeedddededejeje deje d ejeje d ejed ejed ejed ejedejejedejedejedejedejedejedejedeje deje dejedejedej*ef*dZeeeeeeeeeeeeeeeeedddededejeje deje d ejejed ejed ejed ejed ejedejejedejedejedejedejedejedejedeje deje dejedejedef*dZeeedddededejedejejed ejejed!ejedejede fd"Z!eeedd#ded$ed ejed%ejed&eje d'ejedejede"fd(Z#y)) AsyncV2Clientr+c||_yr-r.r0s r2r3zAsyncV2Client.__init__r4r5Nr6rIrJr7r8r9r:r;r<r=r>r?r@rArBrCrDrErFrGrHrKc K|jjjddid|dt|tddt|t j tdd|d t|t j tdd t|tdd t|tdd |d | d| d| d| d| d|d|d|d|||ddddi|t4d{} d|jcxkrdkr|nnytj|}|j23d{} t j t"t%t"t'j(|j*T|j-d{|jdk(rvt/t j t j0t j2t%t j0t j2|j'|jd k(rvt5t j t j0t j2t%t j0t j2|j'|jd!k(rvt7t j t j0t j2t%t j0t j2|j'|jd"k(rvt9t j t j0t j2t%t j0t j2|j'|jd#k(rvt;t j t j0t j2t%t j0t j2|j'|jd$k(rvt=t j t j0t j2t%t j0t j2|j'|jd%k(rvt?t j t j0t j2t%t j0t j2|j'|jd&k(rvtAt j t j0t j2t%t j0t j2|j'|jd'k(rvtCt j t j0t j2t%t j0t j2|j'|jd(k(rvtEt j t j0t j2t%t j0t j2|j'|jd)k(rvtGt j t j0t j2t%t j0t j2|j'|jd*k(rvtIt j t j0t j2t%t j0t j2|j'|j'}tM|j|+77#YxYw6 dddd{7y7#tJ$r"tM|j|jN+wxYw#1d{7swYyxYww),a Generates a text response to a user message. To learn how to use the Chat API and RAG follow our [Text Generation guides](https://docs.cohere.com/v2/docs/chat-api). Follow the [Migration Guide](https://docs.cohere.com/v2/docs/migrating-v1-to-v2) for instructions on moving from API v1 to API v2. Parameters ---------- model : str The name of a compatible [Cohere model](https://docs.cohere.com/v2/docs/models) (such as command-r or command-r-plus) or the ID of a [fine-tuned](https://docs.cohere.com/v2/docs/chat-fine-tuning) model. messages : ChatMessages tools : typing.Optional[typing.Sequence[ToolV2]] A list of available tools (functions) that the model may suggest invoking before producing a text response. When `tools` is passed (without `tool_results`), the `text` content in the response will be empty and the `tool_calls` field in the response will be populated with a list of tool calls that need to be made. If no calls need to be made, the `tool_calls` array will be empty. strict_tools : typing.Optional[bool] When set to `true`, tool calls in the Assistant message will be forced to follow the tool definition strictly. Learn more in the [Structured Outputs (Tools) guide](https://docs.cohere.com/docs/structured-outputs-json#structured-outputs-tools). **Note**: The first few requests with a new set of tools will take longer to process. documents : typing.Optional[typing.Sequence[V2ChatStreamRequestDocumentsItem]] A list of relevant documents that the model can cite to generate a more accurate reply. Each document is either a string or document object with content and metadata. citation_options : typing.Optional[CitationOptions] response_format : typing.Optional[ResponseFormatV2] safety_mode : typing.Optional[V2ChatStreamRequestSafetyMode] Used to select the [safety instruction](https://docs.cohere.com/v2/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`. When `OFF` is specified, the safety instruction will be omitted. Safety modes are not yet configurable in combination with `tools`, `tool_results` and `documents` parameters. **Note**: This parameter is only compatible with models [Command R 08-2024](https://docs.cohere.com/v2/docs/command-r#august-2024-release), [Command R+ 08-2024](https://docs.cohere.com/v2/docs/command-r-plus#august-2024-release) and newer. **Note**: `command-r7b-12-2024` only supports `"CONTEXTUAL"` and `"STRICT"` modes. max_tokens : typing.Optional[int] The maximum number of tokens the model will generate as part of the response. **Note**: Setting a low value may result in incomplete generations. stop_sequences : typing.Optional[typing.Sequence[str]] A list of up to 5 strings that the model will use to stop generation. If the model generates a string that matches any of the strings in the list, it will stop generating tokens and return the generated text up to that point not including the stop sequence. temperature : typing.Optional[float] Defaults to `0.3`. A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations, and higher temperatures mean more random generations. Randomness can be further maximized by increasing the value of the `p` parameter. seed : typing.Optional[int] If specified, the backend will make a best effort to sample tokens deterministically, such that repeated requests with the same seed and parameters should return the same result. However, determinism cannot be totally guaranteed. frequency_penalty : typing.Optional[float] Defaults to `0.0`, min value of `0.0`, max value of `1.0`. Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation. presence_penalty : typing.Optional[float] Defaults to `0.0`, min value of `0.0`, max value of `1.0`. Used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies. k : typing.Optional[float] Ensures that only the top `k` most likely tokens are considered for generation at each step. When `k` is set to `0`, k-sampling is disabled. Defaults to `0`, min value of `0`, max value of `500`. p : typing.Optional[float] Ensures that only the most likely tokens, with total probability mass of `p`, are considered for generation at each step. If both `k` and `p` are enabled, `p` acts after `k`. Defaults to `0.75`. min value of `0.01`, max value of `0.99`. return_prompt : typing.Optional[bool] Whether to return the prompt in the response. logprobs : typing.Optional[bool] Defaults to `false`. When set to `true`, the log probabilities of the generated tokens will be included in the response. tool_choice : typing.Optional[V2ChatStreamRequestToolChoice] Used to control whether or not the model will be forced to use a tool when answering. When `REQUIRED` is specified, the model will be forced to use at least one of the user-defined tools, and the `tools` parameter must be passed in the request. When `NONE` is specified, the model will be forced **not** to use one of the specified tools, and give a direct response. If tool_choice isn't specified, then the model is free to choose whether to use the specified tools or not. **Note**: This parameter is only compatible with models [Command-r7b](https://docs.cohere.com/v2/docs/command-r7b) and newer. **Note**: The same functionality can be achieved in `/v1/chat` using the `force_single_step` parameter. If `force_single_step=true`, this is equivalent to specifying `REQUIRED`. While if `force_single_step=true` and `tool_results` are passed, this is equivalent to specifying `NONE`. request_options : typing.Optional[RequestOptions] Request-specific configuration. Yields ------ typing.AsyncIterator[StreamedChatResponseV2] Examples -------- import asyncio from cohere import AsyncClient, ToolChatMessageV2 client = AsyncClient( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) async def main() -> None: response = await client.v2.chat_stream( model="model", messages=[ ToolChatMessageV2( tool_call_id="messages", content="messages", ) ], ) async for chunk in response: yield chunk asyncio.run(main()) rMrNrIrJrOrPr7r8r9r:r;r<r=r>r?r@rArBrCrDrETrTrVrWrXNr]r^r_rarbrcrdrerfrgrhrirjrkrlrm)(r/rprUrrrqrrrrr r rsrnrtru aiter_sserwrrrZrxryareadrr{r|rrrrrrrrrrrrrr}r~s r2rzAsyncV2Client.chat_streamsJ''44;; C$ @!foof.EQX  D%&//Bb2cov#$K,T[%"#J+8HT[$${%&j'(!.)*{+,-.$%6/0#$412Q34Q56 78%*=B 2,O<( ( PJ W)//5#5$-$9$9)$DM&3&=&=&? !d !"(++ 6 .*@,0JJtyy,A!"# oo'''((C/) "OOFJJ7*&,oofjj&A(1(8((C/+ "OOFJJ7*&,oofjj&A(1(8((C/( "OOFJJ7*&,oofjj&A(1(8((C/' "OOFJJ7*&,oofjj&A(1(8((C/2 "OOFJJ7*&,oofjj&A(1(8((C/. "OOFJJ7*&,oofjj&A(1(8((C/+ "OOFJJ7*&,oofjj&A(1(8((C/2 "OOFJJ7*&,oofjj&A(1(8((C/- "OOFJJ7*&,oofjj&A(1(8((C/- "OOFJJ7*&,oofjj&A(1(8((C/1 "OOFJJ7*&,oofjj&A(1(8((C/- "OOFJJ7*&,oofjj&A(1(8"+!1y'<'<>R Ri( X !! '@o( ( ( p(t# W9+@+@y~~VV We( ( ( sC!a#_5$a'a)=`&`*_8+`.`0A _;9` `Y`a5a8`;`=` a`a`+aaaa aac K|jjjddid|dt|tddt|t j tdd|d t|t j tdd t|tdd t|tdd |d | d| d| d| d| d|d|d|d|||ddddi|td{} d|jcxkrdkr:nn7t jttt|j!S|jdk(rvt#t jt j$t j&tt j$t j&|j!|jd k(rvt)t jt j$t j&tt j$t j&|j!|jd!k(rvt+t jt j$t j&tt j$t j&|j!|jd"k(rvt-t jt j$t j&tt j$t j&|j!|jd#k(rvt/t jt j$t j&tt j$t j&|j!|jd$k(rvt1t jt j$t j&tt j$t j&|j!|jd%k(rvt3t jt j$t j&tt j$t j&|j!|jd&k(rvt5t jt j$t j&tt j$t j&|j!|jd'k(rvt7t jt j$t j&tt j$t j&|j!|jd(k(rvt9t jt j$t j&tt j$t j&|j!|jd)k(rvt;t jt j$t j&tt j$t j&|j!|jd*k(rvt=t jt j$t j&tt j$t j&|j!|j!}tA|j|+7#t>$r"tA|j|jB+wxYww),a* Generates a text response to a user message and streams it down, token by token. To learn how to use the Chat API with streaming follow our [Text Generation guides](https://docs.cohere.com/v2/docs/chat-api). Follow the [Migration Guide](https://docs.cohere.com/v2/docs/migrating-v1-to-v2) for instructions on moving from API v1 to API v2. Parameters ---------- model : str The name of a compatible [Cohere model](https://docs.cohere.com/v2/docs/models) (such as command-r or command-r-plus) or the ID of a [fine-tuned](https://docs.cohere.com/v2/docs/chat-fine-tuning) model. messages : ChatMessages tools : typing.Optional[typing.Sequence[ToolV2]] A list of available tools (functions) that the model may suggest invoking before producing a text response. When `tools` is passed (without `tool_results`), the `text` content in the response will be empty and the `tool_calls` field in the response will be populated with a list of tool calls that need to be made. If no calls need to be made, the `tool_calls` array will be empty. strict_tools : typing.Optional[bool] When set to `true`, tool calls in the Assistant message will be forced to follow the tool definition strictly. Learn more in the [Structured Outputs (Tools) guide](https://docs.cohere.com/docs/structured-outputs-json#structured-outputs-tools). **Note**: The first few requests with a new set of tools will take longer to process. documents : typing.Optional[typing.Sequence[V2ChatRequestDocumentsItem]] A list of relevant documents that the model can cite to generate a more accurate reply. Each document is either a string or document object with content and metadata. citation_options : typing.Optional[CitationOptions] response_format : typing.Optional[ResponseFormatV2] safety_mode : typing.Optional[V2ChatRequestSafetyMode] Used to select the [safety instruction](https://docs.cohere.com/v2/docs/safety-modes) inserted into the prompt. Defaults to `CONTEXTUAL`. When `OFF` is specified, the safety instruction will be omitted. Safety modes are not yet configurable in combination with `tools`, `tool_results` and `documents` parameters. **Note**: This parameter is only compatible with models [Command R 08-2024](https://docs.cohere.com/v2/docs/command-r#august-2024-release), [Command R+ 08-2024](https://docs.cohere.com/v2/docs/command-r-plus#august-2024-release) and newer. **Note**: `command-r7b-12-2024` only supports `"CONTEXTUAL"` and `"STRICT"` modes. max_tokens : typing.Optional[int] The maximum number of tokens the model will generate as part of the response. **Note**: Setting a low value may result in incomplete generations. stop_sequences : typing.Optional[typing.Sequence[str]] A list of up to 5 strings that the model will use to stop generation. If the model generates a string that matches any of the strings in the list, it will stop generating tokens and return the generated text up to that point not including the stop sequence. temperature : typing.Optional[float] Defaults to `0.3`. A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations, and higher temperatures mean more random generations. Randomness can be further maximized by increasing the value of the `p` parameter. seed : typing.Optional[int] If specified, the backend will make a best effort to sample tokens deterministically, such that repeated requests with the same seed and parameters should return the same result. However, determinism cannot be totally guaranteed. frequency_penalty : typing.Optional[float] Defaults to `0.0`, min value of `0.0`, max value of `1.0`. Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation. presence_penalty : typing.Optional[float] Defaults to `0.0`, min value of `0.0`, max value of `1.0`. Used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies. k : typing.Optional[float] Ensures that only the top `k` most likely tokens are considered for generation at each step. When `k` is set to `0`, k-sampling is disabled. Defaults to `0`, min value of `0`, max value of `500`. p : typing.Optional[float] Ensures that only the most likely tokens, with total probability mass of `p`, are considered for generation at each step. If both `k` and `p` are enabled, `p` acts after `k`. Defaults to `0.75`. min value of `0.01`, max value of `0.99`. return_prompt : typing.Optional[bool] Whether to return the prompt in the response. logprobs : typing.Optional[bool] Defaults to `false`. When set to `true`, the log probabilities of the generated tokens will be included in the response. tool_choice : typing.Optional[V2ChatRequestToolChoice] Used to control whether or not the model will be forced to use a tool when answering. When `REQUIRED` is specified, the model will be forced to use at least one of the user-defined tools, and the `tools` parameter must be passed in the request. When `NONE` is specified, the model will be forced **not** to use one of the specified tools, and give a direct response. If tool_choice isn't specified, then the model is free to choose whether to use the specified tools or not. **Note**: This parameter is only compatible with models [Command-r7b](https://docs.cohere.com/v2/docs/command-r7b) and newer. **Note**: The same functionality can be achieved in `/v1/chat` using the `force_single_step` parameter. If `force_single_step=true`, this is equivalent to specifying `REQUIRED`. While if `force_single_step=true` and `tool_results` are passed, this is equivalent to specifying `NONE`. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- ChatResponse Examples -------- import asyncio from cohere import AsyncClient, ToolChatMessageV2 client = AsyncClient( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) async def main() -> None: await client.v2.chat( model="model", messages=[ ToolChatMessageV2( tool_call_id="messages", content="messages", ) ], ) asyncio.run(main()) rMrNrIrJrOrPr7r8r9r:r;r<r=r>r?r@rArBrCrDrEFrTrVrWrXNr]r^r_rarbrcrdrerfrgrhrirjrkrlrmrrs r2rzAsyncV2Client.chats`F..;;CC C$ @!foof.EQX  D%&//B\2]ip#$K,T[%"#J+8HT[$${%&j'(!.)*{+,-.$%6/0#$412Q34Q56 78%*=B 2,OD( ( RC Si++1c1{{ "* ) 0$$+%KK 3&"(//&**"=$-NN$4$$+'KK 3&"(//&**"=$-NN$4$$+$KK 3&"(//&**"=$-NN$4$$+#KK 3&"(//&**"=$-NN$4$$+.KK 3&"(//&**"=$-NN$4$$+*KK 3&"(//&**"=$-NN$4$$+'KK 3&"(//&**"=$-NN$4$$+.KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4$$+-KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4'^^-N9#8#8~NN[( V Sy'<'<9>>R R Ss8C _ "^#_ (A^6_ 7Y ^_ +__ rrrrrrc RK|jjjdd||||||dddi|td{} d|jcxkrd kr:nn7t j ttt|j S|jd k(rvtt j t jt jtt jt j|j |jd k(rvtt j t jt jtt jt j|j |jd k(rvtt j t jt jtt jt j|j |jdk(rvtt j t jt jtt jt j|j |jdk(rvt!t j t jt jtt jt j|j |jdk(rvt#t j t jt jtt jt j|j |jdk(rvt%t j t jt jtt jt j|j |jdk(rvt't j t jt jtt jt j|j |jdk(rvt)t j t jt jtt jt j|j |jdk(rvt+t j t jt jtt jt j|j |jdk(rvt-t j t jt jtt jt j|j |jdk(rvt/t j t jt jtt jt j|j |j} t3|j| 7#t0$r"t3|j|j4wxYww)a This endpoint returns text embeddings. An embedding is a list of floating point numbers that captures semantic information about the text that it represents. Embeddings can be used to create text classifiers as well as empower semantic search. To learn more about embeddings, see the embedding page. If you want to learn more how to use the embedding model, have a look at the [Semantic Search Guide](https://docs.cohere.com/docs/semantic-search). Parameters ---------- model : str Defaults to embed-english-v2.0 The identifier of the model. Smaller "light" models are faster, while larger models will perform better. [Custom models](https://docs.cohere.com/docs/training-custom-models) can also be supplied with their full ID. Available models and corresponding embedding dimensions: * `embed-english-v3.0` 1024 * `embed-multilingual-v3.0` 1024 * `embed-english-light-v3.0` 384 * `embed-multilingual-light-v3.0` 384 * `embed-english-v2.0` 4096 * `embed-english-light-v2.0` 1024 * `embed-multilingual-v2.0` 768 input_type : EmbedInputType embedding_types : typing.Sequence[EmbeddingType] Specifies the types of embeddings you want to get back. Can be one or more of the following types. * `"float"`: Use this when you want to get back the default float embeddings. Valid for all models. * `"int8"`: Use this when you want to get back signed int8 embeddings. Valid for only v3 models. * `"uint8"`: Use this when you want to get back unsigned int8 embeddings. Valid for only v3 models. * `"binary"`: Use this when you want to get back signed binary embeddings. Valid for only v3 models. * `"ubinary"`: Use this when you want to get back unsigned binary embeddings. Valid for only v3 models. texts : typing.Optional[typing.Sequence[str]] An array of strings for the model to embed. Maximum number of texts per call is `96`. We recommend reducing the length of each text to be under `512` tokens for optimal quality. images : typing.Optional[typing.Sequence[str]] An array of image data URIs for the model to embed. Maximum number of images per call is `1`. The image must be a valid [data URI](https://developer.mozilla.org/en-US/docs/Web/URI/Schemes/data). The image must be in either `image/jpeg` or `image/png` format and has a maximum size of 5MB. truncate : typing.Optional[V2EmbedRequestTruncate] One of `NONE|START|END` to specify how the API will handle inputs longer than the maximum token length. Passing `START` will discard the start of the input. `END` will discard the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model. If `NONE` is selected, when the input exceeds the maximum input token length an error will be returned. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- EmbedByTypeResponse OK Examples -------- import asyncio from cohere import AsyncClient client = AsyncClient( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) async def main() -> None: await client.v2.embed( model="model", input_type="search_document", embedding_types=["float"], ) asyncio.run(main()) rrNrrVrWrXNr]r^r_rarbrcrdrerfrgrhrirjrkrlrmrrs r2rzAsyncV2Client.embedBs<x..;;CC  (#2$  2,D  "C Si++1c1{{'"1 ) 0$$+%KK 3&"(//&**"=$-NN$4$$+'KK 3&"(//&**"=$-NN$4$$+$KK 3&"(//&**"=$-NN$4$$+#KK 3&"(//&**"=$-NN$4$$+.KK 3&"(//&**"=$-NN$4$$+*KK 3&"(//&**"=$-NN$4$$+'KK 3&"(//&**"=$-NN$4$$+.KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4$$+-KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4'^^-N9#8#8~NNk f Sy'<'<9>>R R S5<\'[6\'A[9\'Y [9\'9+\$$\'rrrrrc RK|jjjdd||||||dddi|td{} d|jcxkrd kr:nn7t j ttt|j S|jd k(rvtt j t jt jtt jt j|j |jd k(rvtt j t jt jtt jt j|j |jd k(rvtt j t jt jtt jt j|j |jdk(rvtt j t jt jtt jt j|j |jdk(rvt!t j t jt jtt jt j|j |jdk(rvt#t j t jt jtt jt j|j |jdk(rvt%t j t jt jtt jt j|j |jdk(rvt't j t jt jtt jt j|j |jdk(rvt)t j t jt jtt jt j|j |jdk(rvt+t j t jt jtt jt j|j |jdk(rvt-t j t jt jtt jt j|j |jdk(rvt/t j t jt jtt jt j|j |j} t3|j| 7#t0$r"t3|j|j4wxYww)aL This endpoint takes in a query and a list of texts and produces an ordered array with each text assigned a relevance score. Parameters ---------- model : str The identifier of the model to use, eg `rerank-v3.5`. query : str The search query documents : typing.Sequence[str] A list of texts that will be compared to the `query`. For optimal performance we recommend against sending more than 1,000 documents in a single request. **Note**: long documents will automatically be truncated to the value of `max_tokens_per_doc`. **Note**: structured data should be formatted as YAML strings for best performance. top_n : typing.Optional[int] Limits the number of returned rerank results to the specified value. If not passed, all the rerank results will be returned. return_documents : typing.Optional[bool] - If false, returns results without the doc text - the api will return a list of {index, relevance score} where index is inferred from the list passed into the request. - If true, returns results with the doc text passed in - the api will return an ordered list of {index, text, relevance score} where index + text refers to the list passed into the request. max_tokens_per_doc : typing.Optional[int] Defaults to `4096`. Long documents will be automatically truncated to the specified number of tokens. request_options : typing.Optional[RequestOptions] Request-specific configuration. Returns ------- V2RerankResponse OK Examples -------- import asyncio from cohere import AsyncClient client = AsyncClient( client_name="YOUR_CLIENT_NAME", token="YOUR_TOKEN", ) async def main() -> None: await client.v2.rerank( model="model", query="query", documents=["documents"], ) asyncio.run(main()) rrNrrVrWrXNr]r^r_rarbrcrdrerfrgrhrirjrkrlrmrrs r2rzAsyncV2Client.rerank5s<L..;;CC &$4&8  2,D  "C Si++1c1{{$". ) 0$$+%KK 3&"(//&**"=$-NN$4$$+'KK 3&"(//&**"=$-NN$4$$+$KK 3&"(//&**"=$-NN$4$$+#KK 3&"(//&**"=$-NN$4$$+.KK 3&"(//&**"=$-NN$4$$+*KK 3&"(//&**"=$-NN$4$$+'KK 3&"(//&**"=$-NN$4$$+.KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4$$+-KK 3&"(//&**"=$-NN$4$$+)KK 3&"(//&**"=$-NN$4'^^-N9#8#8~NNk f Sy'<'<9>>R R Sr)$rrrr(r3rsrrrqr{rrrrrr r r rrr r AsyncIteratorrrrr r!r"rr#r$r%r&rr'rrr5r2rrsm.*<.;?.2X\=A=AFJ+/@D.2%)4837$($(/3*.FJ;?-YSYS YS vv67 YS ood+ YS??6??3S#TUYS!///:YS )9:YS__%BCYSOOC(YS(<=YS__U+YSooc"YS"??51YS !//%0!YS" ??5 !#YS$ ??5 !%YS&t,'YS(//$')YS*__%BC+YS, 8-YS.   4 5/YS@ ;?.2RV=A=A@D+/@D.2%)4837$($(/3*.@D;?-POPO PO vv67 PO ood+ PO??6??3M#NOPO!///:PO )9:PO__%<=POOOC(PO(<=PO__U+POooc"PO"??51PO !//%0!PO" ??5 !#PO$ ??5 !%PO&t,'PO(//$')PO*__%<=+PO, 8-PO. /POp 8<8<<@;?qOqO# qO  7 qO vs34 qO 45qO//"89qO 8qO qOr'+2637;?[O[O [O ??3' [O s# [O!//$/[O#OOC0[O 8[O [Or5r)Orqcore.client_wrapperrtypes.chat_messagesr types.tool_v2r*types.v2chat_stream_request_documents_itemrtypes.citation_optionsr types.response_format_v2r 'types.v2chat_stream_request_safety_moder 'types.v2chat_stream_request_tool_choicer core.request_optionsr types.streamed_chat_response_v2rcore.serializationrrtcore.unchecked_base_modelrrZerrors.bad_request_errorrerrors.unauthorized_errorrerrors.forbidden_errorrerrors.not_found_errorr!errors.unprocessable_entity_errorrerrors.too_many_requests_errorrerrors.invalid_token_errorr"errors.client_closed_request_errorrerrors.internal_server_errorrerrors.not_implemented_errorr errors.service_unavailable_errorrerrors.gateway_timeout_errorr json.decoderrcore.api_errorr#types.v2chat_request_documents_itemr types.v2chat_request_safety_moder types.v2chat_request_tool_choicer!types.chat_responser"types.embed_input_typer#types.embedding_typer$types.v2embed_request_truncater%types.embed_by_type_responser&types.v2rerank_responser'r(rwr|rsr*rrr5r2rs3."X47RR1DH6 6932HA:I>>F>(%KEE.30B>54v{{6::s#_O_OD#OOr5