o +ih@sNddlmZddlZddlZddlZddlZddlmZddlm Z m Z m Z m Z m Z mZddlmZddlmZddlmZddlmZdd lmZmZmZe rVdd lmZe d ed e fd ZeeZ dddZ#d?ddZ$d@ddZ%dAd"d#Z&dBd&d'Z'  (dCdDd1d2Z(Gd3d4d4ed(d5Z)edd6d7dEd:d;Z*dS)F) annotationsN)Mapping) TYPE_CHECKINGAnyCallableOptionalTypeVarUnion) TypedDict)client) run_helpers) warn_beta)InputTokenDetailsOutputTokenDetails UsageMetadata)genaiCz genai.Client)boundddictreturncCsdd|DS)z%Remove `None` values from dictionary.cSi|] \}}|dur||qSN.0kvrrW/var/www/html/psymed-ai/venv/lib/python3.10/site-packages/langsmith/wrappers/_gemini.py !z_strip_none..)items)rrrr _strip_nonesr"kwargsNonecCs2d|vrt|dtst|d|d<dSdSdS)zFConvert `GenerateContentConfig` to `dict` for LangSmith compatibility.configN) isinstancervars)r#rrr_convert_config_for_tracing$sr(inputsc Cs|d}|s |St|tr#d|dg|dddd|DSt|trYtdd |DrHd d |D|ddd d|DSg}|D]}t|trG|d d}|dg}g}g}|D]}t|trd|vr|dr||d|d|ddqfd|vr|d} | dd} | dd} t| trt |  d} n| } |dd| d| dddqfd|vr|d} |d| d| d id!d"qfd#|vsd$|vr|d#p|d$}|d%urt|ts| }|d#|d&|d|d'id(d)qft|tr$|||d|dqf|r8td*d |Dr8d+ |}n|r=|nd,}|||dqL||ddd-d|DS|S).uProcess Gemini inputs to normalize them for LangSmith tracing. Example: ```txt {"contents": "Hello", "model": "gemini-pro"} → {"messages": [{"role": "user", "content": "Hello"}], "model": "gemini-pro"} {"contents": [{"role": "user", "parts": [{"text": "What is AI?"}]}], "model": "gemini-pro"} → {"messages": [{"role": "user", "content": "What is AI?"}], "model": "gemini-pro"} ``` contentsuserrolecontentmodel)messagesr/cSi|] \}}|dvr||qS)r*r/rrrrrr?r z*_process_gemini_inputs..css|]}t|tVqdSr)r&strritemrrr Esz)_process_gemini_inputs..cSsg|]}d|dqS)r+r,rr4rrr Hsz*_process_gemini_inputs..cSr1r2rrrrrrJr r-partstexttyper9 inline_data mime_type image/jpegdatautf-8 image_urldata:;base64,highurldetailr;rBfunctionResponsefunction_responsenameresponse)rLrM)r;rK function_call functionCallNidargsrPrL argumentsr;rNcss|] }|ddkVqdS)r;r9Ngetrprrrr6s  cSr1r2rrrrrrr )rVr&r3r!listallrappendbytesbase64 b64encodedecodeto_dictjoin)r)r*r0r.r-r8 text_parts content_partspartr<r=r?data_b64rKrNmessage_contentrrr_process_gemini_inputs*s                   ricCstt|}|di}t|dr|j}t|dd}t|dd}n|d}|d}|d}dd|d|||d S) z*Extract invocation parameters for tracing.r% temperaturemax_output_tokensNstop_sequencesgooglechatr/) ls_provider ls_model_type ls_model_namels_temperature ls_max_tokensls_stop)r"rVhasattrrjgetattr)r#strippedr%rj max_tokensstoprrr_infer_invocation_paramss       rzgemini_usage_metadatarc Cs|dpd}|dp d}|dpd}|dpd}|dp$||}i}|r-||d<i}|r5||d<t|||tdid d |Dtdid d |Dd S)z2Convert Gemini usage metadata to LangSmith format.prompt_token_countrcandidates_token_countcached_content_token_countthoughts_token_counttotal_token_count cache_read reasoningcSrrrrrrrrr z*_create_usage_metadata..cSrrrrrrrrr ) input_tokens output_tokens total_tokensinput_token_detailsoutput_token_detailsNr)rVrrr!r)r{r|r}r~rrrrrrr_create_usage_metadatas2  rrMrc Cszt|dr |}nt|dr|}n dt|dt|i}d}g}d}d|vr|dr|dd}d|vr|d}d |vr|d r|d D]}d|vrc|drc||d7}|d|dd qHd |vr|d dur|d }|d d } |dd} t| trt |  d} n| } |dd| d| dddqHd|vsd|vr|dp|d} | durt| t s| } |d| d| d| diddqHd|vr|dr|d}nd|vr|d}|d|d dd |D} | r |pdd!|d"d t | Dd#}n t|d$ks |r'|dd%dkr'|d!|d&}n|d!|d&}|d'}tdddd(}|ryt|}t}|ryz|jd)id'i}|||Wntyx}ztd*|WYd}~nd}~ww|d+r|dd!||d+|d,WSt|dtr|dd!||d-WS|dd!||d-WSty}ztd.|d/|iWYd}~Sd}~ww)0z$Process Gemini response for tracing.rb model_dumpr9rZN candidatesrr.r8r:r<r=r>r?r@rArBrCrDrErFrIrNrOrPrLrQrRrT finish_reasoncSsg|] }|ddkr|qSrTrUrWrrrr74r z6_process_generate_content_response.. assistantc SsNg|]#\}}|ddpd|d||ddt|ddddqS)rNrPcall_functionrLrS)rLrS)rPr;indexr)rVjsondumps)ritcrrrr7;s  )r.r-r tool_callsr;)r.r-rusage_metadatarrrmetadata!Failed to update usage metadata: r)r.r-rrr)r.r-rrz"Error processing Gemini response: output)rurbrrvr3r]rVr&r^r_r`rar enumeratelenrrr get_current_run_treeextra setdefaultupdatepatch Exceptionloggerwarningdebug)rMrdictcontent_resultrer candidater.rfr<r=r?rgrNrresultr usage_dict current_runmetaerrr"_process_generate_content_responses                     r all_chunksr[c Cs|s dtdddddSd}d}|D]-}zt|dr"|jr"||j7}|}Wqty?}ztd|WYd}~qd}~wwtdddd}|rz~t|dr|jrt|jd r^|j}n-t|jd rj|j}n!t |jd dt |jd dt |jd dt |jddt |jddd}t |}t }|rz|j didi}|||Wnty}ztd|WYd}~nd}~wwWnty}ztd|WYd}~nd}~ww||dS)z/Reduce streaming chunks into a single response.rZrr)r.rNr9zError processing chunk: rrbrr|r}r~rr)r|r}r~rrrrz+Error extracting metadata from last chunk: )rrur9rrrrrbrrvrr rrrrrr) r full_text last_chunkchunkrrrrrrrr_reduce_generate_content_chunkss       rForiginal_generaterrLr3 tracing_extraOptional[TracingExtra] is_streamingboolcsR|pitfdd}tfdd}tr'|S|S)z9Create a wrapper for Gemini's `generate_content` methods.c sHt|tjddr tndtstndtd}||i|SNllm)rLrun_type reduce_fnprocess_inputsprocess_outputs_invocation_params_fnrr(r traceablerrirrzrQr# decoratorrrLrtextrarrgenerates    z_get_wrapper..generatec sPt|tjddrtndtstndtd}||i|IdHSrrrrrr agenerates    z_get_wrapper..agenerate) functoolswrapsr is_async)rrLrrrrrrr _get_wrappers rc@s&eZdZUded<ded<ded<dS) TracingExtrazOptional[Mapping[str, Any]]rzOptional[list[str]]tagszOptional[ls_client.Client]r N)__name__ __module__ __qualname____annotations__rrrrrs  r)totalChatGoogleGenerativeAI)r chat_namer rcCs|pi}t|drt|jdrt|jjdrtdt|dr1t|jdr1t|jj||dd|j_t|drHt|jdrHt|jj||dd|j_t|d rht|jdrht|jjdrht|jjj||dd|jj_t|d rt|jdrt|jjdrt|jjj||dd|jj_|S) a Patch the Google Gen AI client to make it traceable. !!! warning **BETA**: This wrapper is in beta. Supports: - `generate_content` and `generate_content_stream` methods - Sync and async clients - Streaming and non-streaming responses - Tool/function calling with proper UI rendering - Multimodal inputs (text + images) - Image generation with `inline_data` support - Token usage tracking including reasoning tokens Args: client: The Google Gen AI client to patch. tracing_extra: Extra tracing information. chat_name: The run name for the chat endpoint. Returns: The patched client. Example: ```python from google import genai from google.genai import types from langsmith import wrappers # Use Google Gen AI client same as you normally would. client = wrappers.wrap_gemini(genai.Client(api_key="your-api-key")) # Basic text generation: response = client.models.generate_content( model="gemini-2.5-flash", contents="Why is the sky blue?", ) print(response.text) # Streaming: for chunk in client.models.generate_content_stream( model="gemini-2.5-flash", contents="Tell me a story", ): print(chunk.text, end="") # Tool/Function calling: schedule_meeting_function = { "name": "schedule_meeting", "description": "Schedules a meeting with specified attendees.", "parameters": { "type": "object", "properties": { "attendees": {"type": "array", "items": {"type": "string"}}, "date": {"type": "string"}, "time": {"type": "string"}, "topic": {"type": "string"}, }, "required": ["attendees", "date", "time", "topic"], }, } tools = types.Tool(function_declarations=[schedule_meeting_function]) config = types.GenerateContentConfig(tools=[tools]) response = client.models.generate_content( model="gemini-2.5-flash", contents="Schedule a meeting with Bob and Alice tomorrow at 2 PM.", config=config, ) # Image generation: response = client.models.generate_content( model="gemini-2.5-flash-image", contents=["Create a picture of a futuristic city"], ) # Save generated image from io import BytesIO from PIL import Image for part in response.candidates[0].content.parts: if part.inline_data is not None: image = Image.open(BytesIO(part.inline_data.data)) image.save("generated_image.png") ``` !!! version-added "Added in `langsmith` 0.4.33" Initial beta release of Google Gemini wrapper. modelsgenerate_content __wrapped__zfThis Google Gen AI client has already been wrapped. 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