阺hN ddlmZddlZddlZddlZddlmZmZmZm Z ddl m cm Z ddlmZddlmZddlmZddlmZddlmZdd lmZdMdZdNdZdOdZdPdZdQdZ dRdddddSd%Z dRdddddSd&Zddddddddd'dTd2Zd3Zd3dddddddddd4 dUd8Z ej!e j"d3dddddddddd4 dVd:Z#ej$j%ej$j&fd;krdd?iGd@dAej)Z)dXdFZ+ dRdYdGZ, dRdZdHZ-ddddeddId[dLZ.dS)\) annotationsN)IterableListOptionalUnion)get_default_discuss_client) get_default_discuss_async_client) string_utils) discuss_types) model_types) safety_typescontentdiscuss_types.MessageOptionsreturn glm.Messagect|tjr|St|trtj|Stj|S)z9Creates a `glm.Message` object from the provided content.r) isinstanceglmMessagestrrs c/var/www/html/services/rag-service/venv/lib/python3.11/site-packages/google/generativeai/discuss.py _make_messager!sP'3;'''3${7++++{7###messagesdiscuss_types.MessagesOptionsList[glm.Message]ct|tttjfrt |g}n d|D}t d|dddD}|sd}nz"_make_messages..?s CCCwM'**CCCrc32K|]}|j |jVdSNauthorr!msgs r z!_make_messages..As+IIccjIszIIIIIIrN0z$Authors are not strictly alternatingc32K|]}|j |jVdSr%r&r(s rr*z!_make_messages..Is+IIScjIcjIIIIIIr1c3$K|] }|jV dSr%r&r(s rr*z!_make_messages..Qs$ * *#3: * * * * * *r)rrdictrrrsetlenpopr AuthorErrorall enumerater')r even_authors even_author odd_authors odd_authorauthorsir)s r_make_messagesr>+s~"(S$ 455D!(++,CC(CCCIIXccc]IIIIIL P \  a  "&&(( '(NOOOIIHQTTNIIIIIK P [  Q   __&& '(NOOO * * * * ***J'GH%%$$3QU^ Oritemdiscuss_types.ExampleOptions glm.Examplect|tjr|St|trX|}t |d|d<t |d|d<tj|St|t rBt|\}}tjt |t |Stj|S)z6Creates a `glm.Example` object from the provided item.inputoutputrCrD)rrExampler1copyrrlist)r?rCrDs r _make_examplerI[s$ $$ $!yy{{%d7m44W &tH~66X{4   $!!UT v{u!5!5mF>S>STTTT ;t  rexamples"List[discuss_types.MessageOptions]List[glm.Example]ct|dzdkr2ttjdt|dg}g}t |D]l\}}t |}|||dzdkr3tj|d|d}||g}m|S)a Creates a list of `glm.Example` objects from a list of message options. This function takes a list of `discuss_types.MessageOptions` and pairs them into `glm.Example` objects. The input examples must be in pairs to create valid examples. Args: examples: The list of `discuss_types.MessageOptions`. Returns: A list of `glm.Example objects` created by pairing up the provided messages. Raises: ValueError: If the provided list of examples is not of even length. r+rzt You must pass `Primer` objects, pairs of messages, or an *even* number of messages, got: z messagesr-rE) r3 ValueErrortextwrapdedentr7rappendrrF)rJresultpairnr?r)primers r_make_examples_from_flatrVns$ 8}}qA O*8}}***     F DX&&  4D!! C q5A:: q'7     f Mrdiscuss_types.ExamplesOptionsct|tjr|gSt|trt |gSt |}|s|S|d}t|tr+d|vrt |Sd|vrd|vstdn)t|tj rt |Sg}|D]$}| t |%|S)a Creates a list of `glm.Example` objects from the provided examples. This function takes various types of example content inputs and creates a list of `glm.Example` objects. It handles the conversion of different input types and ensures the appropriate structure for creating valid examples. Args: examples: The examples to convert. Returns: A list of `glm.Example` objects created from the provided examples. rrrCrDzTTo create an `Example` from a dict you must supply both `input` and an `output` keys) rrrFr1rIrHrV TypeErrorr MESSAGE_OPTIONSrQ)rJfirstrRr?s r_make_examplesr\s (CK((z(D!!)h''((H~~H  QKE% 6   +H55 5u$$U):):j*; e]: ; ; 6+H55 5 F++ mD))**** MrcontextrJrprompt"discuss_types.MessagePromptOptionsr^ str | None$discuss_types.ExamplesOptions | None$discuss_types.MessagesOptions | Noneglm.MessagePromptcx|t|||}nS|dup|dup|du}|rtdt|tjr|St|trnd|i}t |}|tj std|tj z | dd}|t||d<| dd}|t||d<d|D}|S)a Creates a `glm.MessagePrompt` object from the provided prompt components. This function constructs a `glm.MessagePrompt` object using the provided `context`, `examples`, or `messages`. It ensures the proper structure and handling of the input components. Either pass a `prompt` or it's component `context`, `examples`, `messages`. Args: prompt: The complete prompt components. context: The context for the prompt. examples: The examples for the prompt. messages: The messages for the prompt. Returns: A `glm.MessagePrompt` object created from the provided prompt components. Nr]zWYou can't set `prompt`, and its fields `(context, examples, messages)` at the same timerz.Found extra entries in the prompt dictionary: rJci|] \}}||| Sr%r )r!kvs r z-_make_message_prompt_dict..s ? ? ?tq!ar)r1rNrr MessagePromptr2keysissubsetr MESSAGE_PROMPT_KEYSKeyErrorgetr\r>items)r_r^rJr flat_promptrks r_make_message_prompt_dictrrsl0~    d*_0D_(Z^J^  $  fc/ 0 0 *M  % % *  &)F v{{}}  D ==: ; ;  gTMDe=e g g   zz*d++H+H55zzz*d++H+H55z ? ?v||~~ ? ? ?F MrcPt||||}tj|S)zICreates a `glm.MessagePrompt` object from the provided prompt components.r_r^rJr)rrrrjrts r_make_message_promptrus4'wHF  V $ $$r)r^rJr temperaturecandidate_counttop_ptop_kr_model&model_types.AnyModelNameOptions | Nonerv float | Nonerw int | Nonerxry)discuss_types.MessagePromptOptions | Noneglm.GenerateMessageRequestc tj|}t||||}tj||||||S)zFCreates a `glm.GenerateMessageRequest` object for generating messages.rt)rzr_rvrxryrw)r make_model_namerurGenerateMessageRequest rzr^rJrrvrwrxryr_s r_make_generate_message_requestrs_  ' . .E !wHF  %'    rzmodels/chat-bison-001) rzr^rJrrvrwrxryr_clientrglm.DiscussServiceClient | Nonediscuss_types.ChatResponsec Tt||||||||| } t| | S)a# Calls the API and returns a `types.ChatResponse` containing the response. Args: model: Which model to call, as a string or a `types.Model`. context: Text that should be provided to the model first, to ground the response. If not empty, this `context` will be given to the model first before the `examples` and `messages`. This field can be a description of your prompt to the model to help provide context and guide the responses. Examples: * "Translate the phrase from English to French." * "Given a statement, classify the sentiment as happy, sad or neutral." Anything included in this field will take precedence over history in `messages` if the total input size exceeds the model's `Model.input_token_limit`. examples: Examples of what the model should generate. This includes both the user input and the response that the model should emulate. These `examples` are treated identically to conversation messages except that they take precedence over the history in `messages`: If the total input size exceeds the model's `input_token_limit` the input will be truncated. Items will be dropped from `messages` before `examples` messages: A snapshot of the conversation history sorted chronologically. Turns alternate between two authors. If the total input size exceeds the model's `input_token_limit` the input will be truncated: The oldest items will be dropped from `messages`. temperature: Controls the randomness of the output. Must be positive. Typical values are in the range: `[0.0,1.0]`. Higher values produce a more random and varied response. A temperature of zero will be deterministic. candidate_count: The **maximum** number of generated response messages to return. This value must be between `[1, 8]`, inclusive. If unset, this will default to `1`. Note: Only unique candidates are returned. Higher temperatures are more likely to produce unique candidates. Setting `temperature=0.0` will always return 1 candidate regardless of the `candidate_count`. top_k: The API uses combined [nucleus](https://arxiv.org/abs/1904.09751) and top-k sampling. `top_k` sets the maximum number of tokens to sample from on each step. top_p: The API uses combined [nucleus](https://arxiv.org/abs/1904.09751) and top-k sampling. `top_p` configures the nucleus sampling. It sets the maximum cumulative probability of tokens to sample from. For example, if the sorted probabilities are `[0.5, 0.2, 0.1, 0.1, 0.05, 0.05]` a `top_p` of `0.8` will sample as `[0.625, 0.25, 0.125, 0, 0, 0]`. Typical values are in the `[0.9, 1.0]` range. prompt: You may pass a `types.MessagePromptOptions` **instead** of a setting `context`/`examples`/`messages`, but not both. client: If you're not relying on the default client, you pass a `glm.DiscussServiceClient` instead. Returns: A `types.ChatResponse` containing the model's reply. rrrequest)r_generate_response rzr^rJrrvrwrxryr_rrs rchatr3sId-'   G VW = = ==r$glm.DiscussServiceAsyncClient | Nonec dKt||||||||| } t| | d{VS)Nrr)r_generate_response_asyncrs r chat_asyncrs^-'   G*III I I I I I IIr) kw_onlyTinitFcneZdZUejddZded<dZee j e j j jdd Z e jdd Z e j e j jjddZe j e j jjddZdS) ChatResponsecdSr%r r rrzChatResponse.sQUrF)defaultreprr_clientc \|D]\}}t|||dSr%)rpsetattr)selfkwargskeyvalues r__init__zChatResponse.__init__s< ,,.. & &JC D#u % % % % & &rrracF|jdr|jddSdS)Nr)r)rs rlastzChatResponse.lasts* =  =$Y/ /4rr"rc|t|}t||}||jd<dS)Nr)rtypeto_dictr)rr"s rrzChatResponse.lasts:((w--''00# brrct|jtjrt d|jt d|j|}| d| ddt|d|d<|d t|tdi|}t||jS)NzBreply can't be called on an async client, use reply_async instead.zThe last response from the model did not return any candidates. Check the `.filters` attribute to see why the responses were filtered: candidatesfiltersrrrr )rrrDiscussServiceAsyncClientrYrrNrrr4rHrQrrrrr"rs rreplyzChatResponse.replys dlC$A B B cabb b 9 "<""  ,,.. L!!! It$$$"7:#677  ""=#9#9:::0;;7;;!'$,GGGGrcKt|jtjrt d|}|d|ddt|d|d<|dt|tdi|}t||jd{VS)NzEreply_async can't be called on a non-async client, use reply instead.rrrrr ) rrrDiscussServiceClientrYrr4rHrQrrrrs r reply_asynczChatResponse.reply_asyncs dlC$< = = X ,,.. L!!! It$$$"7:#677  ""=#9#9:::0;;7;;-gdlSSSSSSSSSSrN)rra)r"r)r"rrr)__name__ __module__ __qualname__ dataclassesfieldr__annotations__rpropertyr set_docr rr__doc__setterrrr rrrrs,0A{/@\a/b/b/bGbbbb&&&\-49ABBCBX  [$$$[$ \-4:BCCHHHDCH$\-4:BCC T T TDC T T Trrrresponseglm.GenerateMessageResponse8glm.DiscussServiceClient | glm.DiscussServiceAsyncClientc:t||}|d}|d|d<|d|d<|d|d<t||}|dtj|d|d<|dr|dd}nd}|d||dd|d dtd d |i||S) Nr_rJr^rrrrrvrwrr )rrr4r convert_filters_to_enumsrQ setdefaultr)rrrr_rs r_build_chat_responsers' 7mm##G,,G [[ " "F ,GJ *GI ,GJH~~%%h//H LL&?@STTHY  %a( Jt$$$ }d+++ ($///  > > >( >g > >>rcn|t}||}t|||Sr%)rgenerate_messagerrrrs rrrs9~+--&&w//H 6 : ::rc~K|t}||d{V}t|||Sr%)r rrrs rrrsO~133,,W55555555H 6 : ::r)r_r^rJrrzrmodel_types.AnyModelNameOptionsdiscuss_types.TokenCountctj|}t||||}|t}|||}t ||S)Nr])rzr_)r rrurcount_message_tokensrr)r_r^rJrrzrrRs rrr&sm  ' . .E !&'HW_ ` ` `F ~+--  ( (uV ( D DF <<   ' ''r)rrrr)rrrr)r?r@rrA)rJrKrrL)rJrWrrLr%) r_r`r^rarJrbrrcrrd)rzr{r^rarJrbrrcrvr|rwr}rxr|ryr|r_r~rr)rzr{r^rarJrbrrcrvr|rwr}rxr|ryr|r_r~rrrr)rzr{r^rarJrbrrcrvr|rwr}rxr|ryr|r_r~rrrrr )rrrrrrrr)rrrrrr)rrrrrr)r_r`r^rarJrbrrcrzrrrrr)/ __future__rrsysrOtypingrrrrgoogle.ai.generativelanguageaigenerativelanguagergoogle.generativeai.clientrr google.generativeair google.generativeai.typesr r r rr>rIrVr\rrrurDEFAULT_DISCUSS_MODELrrrr version_infomajorminorDATACLASS_KWARGS prettyprintr dataclassrrrrr rrrs#""""" 222222222222*********AAAAAAGGGGGG,,,,,,333333111111222222$$$$----`&''''T----b26:5959 ::::::|26 %5959 % % % % % %"5959 $"&8<:0 5L5959 $"&8<.2^>^>^>^>^>^>Bdl##5L5959 $"&8<37JJJJJ$#J8C,23w>>!4(m089966)666666T6T6T6T6T=-6T6T76:96Tr????:/3 ; ; ; ; ;48 ; ; ; ; ;265959-B37((((((((r