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encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.chat.completions.create( + model="${v}", + messages=${JSON.stringify(i,null,4)}${t} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${v}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${N}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${t} +# ) +# print(response_with_file) +`;break}case t.EndpointType.RESPONSES:{let e=Object.keys($).length>0,t="";if(e){let e=JSON.stringify({metadata:$},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`, + extra_body=${e}`}let i=w.length>0?w:[{role:"user",content:j}];a=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${v}", + input=${JSON.stringify(i,null,4)}${t} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${v}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${N}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${t} +# ) +# print(response_with_file.output_text) +`;break}case t.EndpointType.IMAGE:a="azure"===f?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${v}", + prompt="${n}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${N}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${v}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case t.EndpointType.IMAGE_EDITS:a="azure"===f?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${N}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${v}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${N}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${v}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. 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i,{apiKeySource:s,accessToken:n,apiKey:a,inputMessage:r,chatHistory:o,selectedTags:l,selectedVectorStores:d,selectedGuardrails:c,selectedPolicies:u,selectedVoice:p,endpointType:m,selectedModel:g,selectedSdk:h,proxySettings:f}=e,x="session"===s?n:a,b=window.location.origin,v=f?.LITELLM_UI_API_DOC_BASE_URL;v&&v.trim()?b=v:f?.PROXY_BASE_URL&&(b=f.PROXY_BASE_URL);let y=r||"Your prompt here",j=y.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),_=o.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),w={};l.length>0&&(w.tags=l),d.length>0&&(w.vector_stores=d),c.length>0&&(w.guardrails=c),u.length>0&&(w.policies=u);let k=g||"your-model-name",N="azure"===h?`import openai - -client = openai.AzureOpenAI( - api_key="${x||"YOUR_LITELLM_API_KEY"}", - azure_endpoint="${b}", - api_version="2024-02-01" -)`:`import openai - -client = openai.OpenAI( - api_key="${x||"YOUR_LITELLM_API_KEY"}", - base_url="${b}" -)`;switch(m){case t.EndpointType.CHAT:{let e=Object.keys(w).length>0,t="";if(e){let e=JSON.stringify({metadata:w},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`, - extra_body=${e}`}let s=_.length>0?_:[{role:"user",content:y}];i=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.chat.completions.create( - model="${k}", - messages=${JSON.stringify(s,null,4)}${t} -) - -print(response) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.chat.completions.create( -# model="${k}", -# messages=[ -# { -# "role": "user", -# "content": [ -# { -# "type": "text", -# "text": "${j}" -# }, -# { -# "type": "image_url", -# "image_url": { -# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} -# } -# } -# ] -# } -# ]${t} -# ) -# print(response_with_file) -`;break}case t.EndpointType.RESPONSES:{let e=Object.keys(w).length>0,t="";if(e){let e=JSON.stringify({metadata:w},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`, - extra_body=${e}`}let s=_.length>0?_:[{role:"user",content:y}];i=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.responses.create( - model="${k}", - input=${JSON.stringify(s,null,4)}${t} -) - -print(response.output_text) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.responses.create( -# model="${k}", -# input=[ -# { -# "role": "user", -# "content": [ -# {"type": "input_text", "text": "${j}"}, -# { -# "type": "input_image", -# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} -# }, -# ], -# } -# ]${t} -# ) -# print(response_with_file.output_text) -`;break}case t.EndpointType.IMAGE:i="azure"===h?` -# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. -# This snippet uses 'client.images.generate' and will create a new image based on your prompt. -# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. -import os -import requests -import json -import time -from PIL import Image - -result = client.images.generate( - model="${k}", - prompt="${r}", - n=1 -) - -json_response = json.loads(result.model_dump_json()) - -# Set the directory for the stored image -image_dir = os.path.join(os.curdir, 'images') - -# If the directory doesn't exist, create it -if not os.path.isdir(image_dir): - os.mkdir(image_dir) - -# Initialize the image path -image_filename = f"generated_image_{int(time.time())}.png" -image_path = os.path.join(image_dir, image_filename) - -try: - # Retrieve the generated image - if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): - image_url = json_response["data"][0]["url"] - generated_image = requests.get(image_url).content - with open(image_path, "wb") as image_file: - image_file.write(generated_image) - - print(f"Image saved to {image_path}") - # Display the image - image = Image.open(image_path) - image.show() - else: - print("Could not find image URL in response.") - print("Full response:", json_response) -except Exception as e: - print(f"An error occurred: {e}") - print("Full response:", json_response) -`:` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${j}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${k}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": 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Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`;break;case t.EndpointType.IMAGE_EDITS:i="azure"===h?` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# The prompt entered by the user -prompt = "${j}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${k}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`:` -import base64 -import os -import time - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${j}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${k}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. 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api_version="2024-02-01"${$} +)`:`import openai + +client = openai.OpenAI( + api_key="${b||"YOUR_LITELLM_API_KEY"}", + base_url="${_}"${$} +)`;switch(c){case t.EndpointType.CHAT:{let e=Object.keys(N).length>0,t="";if(e){let e=JSON.stringify({metadata:N},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`, + extra_body=${e}`}let i=v.length>0?v:[{role:"user",content:j}];a=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.chat.completions.create( + model="${k}", + messages=${JSON.stringify(i,null,4)}${t} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${k}", +# messages=[ +# { +# "role": "user", +# 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# or .pdf +# response_with_file = client.responses.create( +# model="${k}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${w}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${t} +# ) +# print(response_with_file.output_text) +`;break}case t.EndpointType.IMAGE:a="azure"===f?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${k}", + prompt="${n}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${w}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${k}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case t.EndpointType.IMAGE_EDITS:a="azure"===f?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${w}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${k}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${w}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${k}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case t.EndpointType.EMBEDDINGS:a=` +response = client.embeddings.create( + input="${n||"Your string here"}", + model="${k}", + encoding_format="base64" # or "float" +) + +print(response.data[0].embedding) +`;break;case t.EndpointType.TRANSCRIPTION:a=` +# Open the audio file +audio_file = open("path/to/your/audio/file.mp3", "rb") + +# Make the transcription request +response = client.audio.transcriptions.create( + model="${k}", + file=audio_file${n?`, + prompt="${n.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""} +) + +print(response.text) +`;break;case t.EndpointType.SPEECH:a=` +# Make the text-to-speech request +response = client.audio.speech.create( + model="${k}", + input="${n||"Your text to convert to speech here"}", + voice="${u}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer +) + 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Write a continuation summary that will allow you (or another instance of yourself) to resume work efficiently in a future context window where the conversation history will be replaced with this summary. Your summary should be structured, concise, and actionable. Include: +1. Task Overview +The user's core request and success criteria +Any clarifications or constraints they specified +2. Current State +What has been completed so far +Files created, modified, or analyzed (with paths if relevant) +Key outputs or artifacts produced +3. Important Discoveries +Technical constraints or requirements uncovered +Decisions made and their rationale +Errors encountered and how they were resolved +What approaches were tried that didn't work (and why) +4. Next Steps +Specific actions needed to complete the task +Any blockers or open questions to resolve +Priority order if multiple steps remain +5. Context to Preserve +User preferences or style requirements +Domain-specific details that aren't obvious +Any promises made to the user +Be concise but complete—err on the side of including information that would prevent duplicate work or repeated mistakes. Write in a way that enables immediate resumption of the task. +Wrap your summary in tags.`;function rN(){let e,t;return{promise:new Promise((s,r)=>{e=s,t=r}),resolve:e,reject:t}}class rS{constructor(e,t,s){M.add(this),this.client=e,R.set(this,!1),$.set(this,!1),O.set(this,void 0),L.set(this,void 0),U.set(this,void 0),D.set(this,void 0),z.set(this,void 0),B.set(this,0),t0(this,O,{params:{...t,messages:structuredClone(t.messages)}},"f");const r=["BetaToolRunner",...re(t.tools,t.messages)].join(", ");t0(this,L,{...s,headers:s4([{"x-stainless-helper":r},s?.headers])},"f"),t0(this,z,rN(),"f"),t.compactionControl?.enabled&&console.warn('Anthropic: The `compactionControl` parameter is deprecated and will be removed in a future version. 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this._client.get(s6`/v1/skills/${e}?beta=true`,{...s,headers:s4([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/skills?beta=true",sF,{query:r,...t,headers:s4([{"anthropic-beta":[...s??[],"skills-2025-10-02"].toString()},t?.headers])})}delete(e,t={},s){let{betas:r}=t??{};return this._client.delete(s6`/v1/skills/${e}?beta=true`,{...s,headers:s4([{"anthropic-beta":[...r??[],"skills-2025-10-02"].toString()},s?.headers])})}}r$.Versions=rR;class rO extends s1{create(e,t,s){let{betas:r,...a}=t;return this._client.post(s6`/v1/vaults/${e}/credentials?beta=true`,{body:a,...s,headers:s4([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}retrieve(e,t,s){let{vault_id:r,betas:a}=t;return this._client.get(s6`/v1/vaults/${r}/credentials/${e}?beta=true`,{...s,headers:s4([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}update(e,t,s){let{vault_id:r,betas:a,...n}=t;return this._client.post(s6`/v1/vaults/${r}/credentials/${e}?beta=true`,{body:n,...s,headers:s4([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}list(e,t={},s){let{betas:r,...a}=t??{};return this._client.getAPIList(s6`/v1/vaults/${e}/credentials?beta=true`,sF,{query:a,...s,headers:s4([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}delete(e,t,s){let{vault_id:r,betas:a}=t;return this._client.delete(s6`/v1/vaults/${r}/credentials/${e}?beta=true`,{...s,headers:s4([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}archive(e,t,s){let{vault_id:r,betas:a}=t;return this._client.post(s6`/v1/vaults/${r}/credentials/${e}/archive?beta=true`,{...s,headers:s4([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}}class rL extends s1{constructor(){super(...arguments),this.credentials=new rO(this._client)}create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/vaults?beta=true",{body:r,...t,headers:s4([{"anthropic-beta":[...s??[],"managed-agents-2026-04-01"].toString()},t?.headers])})}retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s6`/v1/vaults/${e}?beta=true`,{...s,headers:s4([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}update(e,t,s){let{betas:r,...a}=t;return this._client.post(s6`/v1/vaults/${e}?beta=true`,{body:a,...s,headers:s4([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return 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rD(e){return e?.output_config?.format}function rz(e,t,s){let r=rD(t);return t&&"parse"in(r??{})?rB(e,t,s):{...e,content:e.content.map(e=>"text"===e.type?Object.defineProperty({...e},"parsed_output",{value:null,enumerable:!1}):e),parsed_output:null}}function rB(e,t,s){let r=null,a=e.content.map(e=>{if("text"===e.type){let s=function(e,t){let s=rD(e);if(s?.type!=="json_schema")return null;try{if("parse"in s)return s.parse(t);return JSON.parse(t)}catch(e){throw new t3(`Failed to parse structured output: ${e}`)}}(t,e.text);return null===r&&(r=s),Object.defineProperty({...e},"parsed_output",{value:s,enumerable:!1})}return e});return{...e,content:a,parsed_output:r}}rU.Models=rr,rU.Messages=rE,rU.Agents=ri,rU.Environments=s8,rU.Sessions=rM,rU.Vaults=rL,rU.MemoryStores=rd,rU.Files=rs,rU.Skills=r$,rU.UserProfiles=ra;let rq="__json_buf";function rF(e){return"tool_use"===e.type||"server_tool_use"===e.type}class rW{constructor(e,t){W.add(this),this.messages=[],this.receivedMessages=[],H.set(this,void 0),V.set(this,null),this.controller=new AbortController,G.set(this,void 0),J.set(this,()=>{}),K.set(this,()=>{}),X.set(this,void 0),Y.set(this,()=>{}),Q.set(this,()=>{}),Z.set(this,{}),ee.set(this,!1),et.set(this,!1),es.set(this,!1),er.set(this,!1),ea.set(this,void 0),en.set(this,void 0),ei.set(this,void 0),ed.set(this,e=>{if(t0(this,et,!0,"f"),t4(e)&&(e=new t8),e instanceof t8)return t0(this,es,!0,"f"),this._emit("abort",e);if(e instanceof t3)return this._emit("error",e);if(e instanceof Error){let t=new t3(e.message);return t.cause=e,this._emit("error",t)}return this._emit("error",new t3(String(e)))}),t0(this,G,new Promise((e,t)=>{t0(this,J,e,"f"),t0(this,K,t,"f")}),"f"),t0(this,X,new Promise((e,t)=>{t0(this,Y,e,"f"),t0(this,Q,t,"f")}),"f"),t1(this,G,"f").catch(()=>{}),t1(this,X,"f").catch(()=>{}),t0(this,V,e,"f"),t0(this,ei,t?.logger??console,"f")}get response(){return t1(this,ea,"f")}get 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r,a=s?.signal;a&&(a.aborted&&this.controller.abort(),r=this.controller.abort.bind(this.controller),a.addEventListener("abort",r));try{t1(this,W,"m",ec).call(this);let{response:r,data:a}=await e.create({...t,stream:!0},{...s,signal:this.controller.signal}).withResponse();for await(let e of(this._connected(r),a))t1(this,W,"m",eu).call(this,e);if(a.controller.signal?.aborted)throw new t8;t1(this,W,"m",em).call(this)}finally{a&&r&&a.removeEventListener("abort",r)}}_connected(e){this.ended||(t0(this,ea,e,"f"),t0(this,en,e?.headers.get("request-id"),"f"),t1(this,J,"f").call(this,e),this._emit("connect"))}get ended(){return t1(this,ee,"f")}get errored(){return t1(this,et,"f")}get aborted(){return t1(this,es,"f")}abort(){this.controller.abort()}on(e,t){return(t1(this,Z,"f")[e]||(t1(this,Z,"f")[e]=[])).push({listener:t}),this}off(e,t){let s=t1(this,Z,"f")[e];if(!s)return this;let r=s.findIndex(e=>e.listener===t);return r>=0&&s.splice(r,1),this}once(e,t){return(t1(this,Z,"f")[e]||(t1(this,Z,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{t0(this,er,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){t0(this,er,!0,"f"),await t1(this,X,"f")}get currentMessage(){return t1(this,H,"f")}async finalMessage(){return await this.done(),t1(this,W,"m",eo).call(this)}async finalText(){return await this.done(),t1(this,W,"m",el).call(this)}_emit(e,...t){if(t1(this,ee,"f"))return;"end"===e&&(t0(this,ee,!0,"f"),t1(this,Y,"f").call(this));let s=t1(this,Z,"f")[e];if(s&&(t1(this,Z,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];t1(this,er,"f")||s?.length||Promise.reject(e),t1(this,K,"f").call(this,e),t1(this,Q,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];t1(this,er,"f")||s?.length||Promise.reject(e),t1(this,K,"f").call(this,e),t1(this,Q,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",t1(this,W,"m",eo).call(this))}async _fromReadableStream(e,t){let s,r=t?.signal;r&&(r.aborted&&this.controller.abort(),s=this.controller.abort.bind(this.controller),r.addEventListener("abort",s));try{t1(this,W,"m",ec).call(this),this._connected(null);let t=sM.fromReadableStream(e,this.controller);for await(let e of t)t1(this,W,"m",eu).call(this,e);if(t.controller.signal?.aborted)throw new t8;t1(this,W,"m",em).call(this)}finally{r&&s&&r.removeEventListener("abort",s)}}[(H=new WeakMap,V=new WeakMap,G=new WeakMap,J=new WeakMap,K=new WeakMap,X=new WeakMap,Y=new WeakMap,Q=new WeakMap,Z=new WeakMap,ee=new WeakMap,et=new WeakMap,es=new WeakMap,er=new WeakMap,ea=new WeakMap,en=new WeakMap,ei=new WeakMap,ed=new WeakMap,W=new WeakSet,eo=function(){if(0===this.receivedMessages.length)throw new t3("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},el=function(){if(0===this.receivedMessages.length)throw new t3("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new t3("stream ended without producing a content block with type=text");return e.join(" ")},ec=function(){this.ended||t0(this,H,void 0,"f")},eu=function(e){if(this.ended)return;let t=t1(this,W,"m",eh).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":rF(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:rH(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(rz(t,t1(this,V,"f"),{logger:t1(this,ei,"f")}),!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":t0(this,H,t,"f")}},em=function(){if(this.ended)throw new t3("stream has ended, this shouldn't happen");let e=t1(this,H,"f");if(!e)throw new t3("request ended without sending any chunks");return t0(this,H,void 0,"f"),rz(e,t1(this,V,"f"),{logger:t1(this,ei,"f")})},eh=function(e){let t=t1(this,H,"f");if("message_start"===e.type){if(t)throw new t3(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new t3(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push({...e.content_block}),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(t.content[e.index]={...s,text:(s.text||"")+e.delta.text});break;case"citations_delta":s?.type==="text"&&(t.content[e.index]={...s,citations:[...s.citations??[],e.delta.citation]});break;case"input_json_delta":if(s&&rF(s)){let r=s[rq]||"";r+=e.delta.partial_json;let a={...s};Object.defineProperty(a,rq,{value:r,enumerable:!1,writable:!0}),r&&(a.input=rx(r)),t.content[e.index]=a}break;case"thinking_delta":s?.type==="thinking"&&(t.content[e.index]={...s,thinking:s.thinking+e.delta.thinking});break;case"signature_delta":s?.type==="thinking"&&(t.content[e.index]={...s,signature:e.delta.signature});break;default:rH(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new sM(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function rH(e){}class rV extends s1{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(s6`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",sq,{query:e,...t})}delete(e,t){return this._client.delete(s6`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(s6`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new t3(`No batch \`results_url\`; Has it finished processing? ${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:s4([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>rc.fromResponse(t.response,t.controller))}}class rG extends s1{constructor(){super(...arguments),this.batches=new rV(this._client)}create(e,t){e.model in rJ&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${rJ[e.model]} +Please migrate to a newer model. Visit https://docs.anthropic.com/en/docs/resources/model-deprecations for more information.`),rK.includes(e.model)&&e.thinking&&"enabled"===e.thinking.type&&console.warn(`Using Claude with ${e.model} and 'thinking.type=enabled' is deprecated. Use 'thinking.type=adaptive' instead which results in better model performance in our testing: https://platform.claude.com/docs/en/build-with-claude/adaptive-thinking`);let s=this._client._options.timeout;if(!e.stream&&null==s){let t=rm[e.model]??void 0;s=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}let r=rt(e.tools,e.messages);return this._client.post("/v1/messages",{body:e,timeout:s??6e5,...t,headers:s4([r,t?.headers]),stream:e.stream??!1})}parse(e,t){return this.create(e,t).then(t=>rB(t,e,{logger:this._client.logger??console}))}stream(e,t){return rW.createMessage(this,e,t,{logger:this._client.logger??console})}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let rJ={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-3-opus-20240229":"January 5th, 2026","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025","claude-3-7-sonnet-latest":"February 19th, 2026","claude-3-7-sonnet-20250219":"February 19th, 2026","claude-3-5-haiku-latest":"February 19th, 2026","claude-3-5-haiku-20241022":"February 19th, 2026","claude-opus-4-0":"June 15th, 2026","claude-opus-4-20250514":"June 15th, 2026","claude-sonnet-4-0":"June 15th, 2026","claude-sonnet-4-20250514":"June 15th, 2026"},rK=["claude-mythos-preview","claude-opus-4-6"];rG.Batches=rV;class rX extends s1{retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s6`/v1/models/${e}`,{...s,headers:s4([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/models",sq,{query:r,...t,headers:s4([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers])})}}class rY extends s1{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:s4([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let rQ=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()||void 0:void 0!==globalThis.Deno&&globalThis.Deno.env?.get?.(e)?.trim()||void 0;class rZ{constructor({baseURL:e=rQ("ANTHROPIC_BASE_URL"),apiKey:t=rQ("ANTHROPIC_API_KEY")??null,authToken:s=rQ("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){ep.add(this),eg.set(this,void 0);const a={apiKey:t,authToken:s,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&"u">typeof window&&void 0!==window.document&&"u">typeof navigator)throw new t3("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??ef.DEFAULT_TIMEOUT,this.logger=a.logger??console;const n="warn";this.logLevel=n,this.logLevel=sk(a.logLevel,"ClientOptions.logLevel",this)??sk(rQ("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??n,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("u">typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),t0(this,eg,sj,"f");const i=rQ("ANTHROPIC_CUSTOM_HEADERS");if(i){const e={};for(const t of i.split("\n")){const s=t.indexOf(":");s>=0&&(e[t.substring(0,s).trim()]=t.substring(s+1).trim())}a.defaultHeaders={...e,...a.defaultHeaders}}this._options=a,this.apiKey="string"==typeof t?t:null,this.authToken=s}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetch:this.fetch,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(e.get("x-api-key")||e.get("authorization")||this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization"))&&!t.has("authorization"))throw Error('Could not resolve authentication method. 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n={};this.idempotencyHeader&&"get"!==s&&(e.idempotencyKey||(e.idempotencyKey=this.defaultIdempotencyKey()),n[this.idempotencyHeader]=e.idempotencyKey);let i=s4([n,{Accept:"application/json","User-Agent":this.getUserAgent(),"X-Stainless-Retry-Count":String(a),...e.timeout?{"X-Stainless-Timeout":String(Math.trunc(e.timeout/1e3))}:{},...t??(t=(()=>{let e="u">typeof Deno&&null!=Deno.build?"deno":"u">typeof EdgeRuntime?"edge":"[object process]"===Object.prototype.toString.call(void 0!==globalThis.process?globalThis.process:0)?"node":"unknown";if("deno"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sp,"X-Stainless-OS":sg(Deno.build.os),"X-Stainless-Arch":sf(Deno.build.arch),"X-Stainless-Runtime":"deno","X-Stainless-Runtime-Version":"string"==typeof Deno.version?Deno.version:Deno.version?.deno??"unknown"};if("u">typeof 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this._client.get(s5`/v1/vaults/${r}/credentials/${e}?beta=true`,{...s,headers:s1([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}update(e,t,s){let{vault_id:r,betas:a,...n}=t;return this._client.post(s5`/v1/vaults/${r}/credentials/${e}?beta=true`,{body:n,...s,headers:s1([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}list(e,t={},s){let{betas:r,...a}=t??{};return this._client.getAPIList(s5`/v1/vaults/${e}/credentials?beta=true`,sB,{query:a,...s,headers:s1([{"anthropic-beta":[...r??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}delete(e,t,s){let{vault_id:r,betas:a}=t;return this._client.delete(s5`/v1/vaults/${r}/credentials/${e}?beta=true`,{...s,headers:s1([{"anthropic-beta":[...a??[],"managed-agents-2026-04-01"].toString()},s?.headers])})}archive(e,t,s){let{vault_id:r,betas:a}=t;return 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${s.processing_status} - ${s.id}`);return this._client.get(s.results_url,{...t,headers:s1([{Accept:"application/binary"},t?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>rl.fromResponse(t.response,t.controller))}}class rV extends sZ{constructor(){super(...arguments),this.batches=new rW(this._client)}create(e,t){e.model in rH&&console.warn(`The model '${e.model}' is deprecated and will reach end-of-life on ${rH[e.model]} -Please migrate to a newer model. 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Use 'thinking.type=adaptive' instead which results in better model performance in our testing: https://platform.claude.com/docs/en/build-with-claude/adaptive-thinking`);let s=this._client._options.timeout;if(!e.stream&&null==s){let t=rc[e.model]??void 0;s=this._client.calculateNonstreamingTimeout(e.max_tokens,t)}let r=s7(e.tools,e.messages);return this._client.post("/v1/messages",{body:e,timeout:s??6e5,...t,headers:s1([r,t?.headers]),stream:e.stream??!1})}parse(e,t){return this.create(e,t).then(t=>rD(t,e,{logger:this._client.logger??console}))}stream(e,t){return rq.createMessage(this,e,t,{logger:this._client.logger??console})}countTokens(e,t){return this._client.post("/v1/messages/count_tokens",{body:e,...t})}}let rH={"claude-1.3":"November 6th, 2024","claude-1.3-100k":"November 6th, 2024","claude-instant-1.1":"November 6th, 2024","claude-instant-1.1-100k":"November 6th, 2024","claude-instant-1.2":"November 6th, 2024","claude-3-sonnet-20240229":"July 21st, 2025","claude-3-opus-20240229":"January 5th, 2026","claude-2.1":"July 21st, 2025","claude-2.0":"July 21st, 2025","claude-3-7-sonnet-latest":"February 19th, 2026","claude-3-7-sonnet-20250219":"February 19th, 2026","claude-3-5-haiku-latest":"February 19th, 2026","claude-3-5-haiku-20241022":"February 19th, 2026","claude-opus-4-0":"June 15th, 2026","claude-opus-4-20250514":"June 15th, 2026","claude-sonnet-4-0":"June 15th, 2026","claude-sonnet-4-20250514":"June 15th, 2026"},rG=["claude-mythos-preview","claude-opus-4-6"];rV.Batches=rW;class rJ extends sZ{retrieve(e,t={},s){let{betas:r}=t??{};return this._client.get(s5`/v1/models/${e}`,{...s,headers:s1([{...r?.toString()!=null?{"anthropic-beta":r?.toString()}:void 0},s?.headers])})}list(e={},t){let{betas:s,...r}=e??{};return this._client.getAPIList("/v1/models",sz,{query:r,...t,headers:s1([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers])})}}class rK extends sZ{create(e,t){let{betas:s,...r}=e;return this._client.post("/v1/complete",{body:r,timeout:this._client._options.timeout??6e5,...t,headers:s1([{...s?.toString()!=null?{"anthropic-beta":s?.toString()}:void 0},t?.headers]),stream:e.stream??!1})}}let rX=e=>void 0!==globalThis.process?globalThis.process.env?.[e]?.trim()||void 0:void 0!==globalThis.Deno&&globalThis.Deno.env?.get?.(e)?.trim()||void 0;class rY{constructor({baseURL:e=rX("ANTHROPIC_BASE_URL"),apiKey:t=rX("ANTHROPIC_API_KEY")??null,authToken:s=rX("ANTHROPIC_AUTH_TOKEN")??null,...r}={}){ep.add(this),eg.set(this,void 0);const a={apiKey:t,authToken:s,...r,baseURL:e||"https://api.anthropic.com"};if(!a.dangerouslyAllowBrowser&&"u">typeof window&&void 0!==window.document&&"u">typeof navigator)throw new t4("It looks like you're running in a browser-like environment.\n\nThis is disabled by default, as it risks exposing your secret API credentials to attackers.\nIf you understand the risks and have appropriate mitigations in place,\nyou can set the `dangerouslyAllowBrowser` option to `true`, e.g.,\n\nnew Anthropic({ apiKey, dangerouslyAllowBrowser: true });\n");this.baseURL=a.baseURL,this.timeout=a.timeout??ef.DEFAULT_TIMEOUT,this.logger=a.logger??console;const n="warn";this.logLevel=n,this.logLevel=sN(a.logLevel,"ClientOptions.logLevel",this)??sN(rX("ANTHROPIC_LOG"),"process.env['ANTHROPIC_LOG']",this)??n,this.fetchOptions=a.fetchOptions,this.maxRetries=a.maxRetries??2,this.fetch=a.fetch??function(){if("u">typeof fetch)return fetch;throw Error("`fetch` is not defined as a global; Either pass `fetch` to the client, `new Anthropic({ fetch })` or polyfill the global, `globalThis.fetch = fetch`")}(),tQ(this,eg,sy,"f");const i=rX("ANTHROPIC_CUSTOM_HEADERS");if(i){const e={};for(const t of i.split("\n")){const s=t.indexOf(":");s>=0&&(e[t.substring(0,s).trim()]=t.substring(s+1).trim())}a.defaultHeaders={...e,...a.defaultHeaders}}this._options=a,this.apiKey="string"==typeof t?t:null,this.authToken=s}withOptions(e){return new this.constructor({...this._options,baseURL:this.baseURL,maxRetries:this.maxRetries,timeout:this.timeout,logger:this.logger,logLevel:this.logLevel,fetch:this.fetch,fetchOptions:this.fetchOptions,apiKey:this.apiKey,authToken:this.authToken,...e})}defaultQuery(){return this._options.defaultQuery}validateHeaders({values:e,nulls:t}){if(!(e.get("x-api-key")||e.get("authorization")||this.apiKey&&e.get("x-api-key")||t.has("x-api-key")||this.authToken&&e.get("authorization"))&&!t.has("authorization"))throw Error('Could not resolve authentication method. Expected either apiKey or authToken to be set. Or for one of the "X-Api-Key" or "Authorization" headers to be explicitly omitted')}async authHeaders(e){return s1([await this.apiKeyAuth(e),await this.bearerAuth(e)])}async apiKeyAuth(e){if(null!=this.apiKey)return s1([{"X-Api-Key":this.apiKey}])}async bearerAuth(e){if(null!=this.authToken)return s1([{Authorization:`Bearer ${this.authToken}`}])}stringifyQuery(e){return Object.entries(e).filter(([e,t])=>void 0!==t).map(([e,t])=>{if("string"==typeof t||"number"==typeof t||"boolean"==typeof t)return`${encodeURIComponent(e)}=${encodeURIComponent(t)}`;if(null===t)return`${encodeURIComponent(e)}=`;throw new t4(`Cannot stringify type ${typeof t}; Expected string, number, boolean, or null. If you need to pass nested query parameters, you can manually encode them, e.g. { query: { 'foo[key1]': value1, 'foo[key2]': value2 } }, and please open a GitHub issue requesting better support for your use case.`)}).join("&")}getUserAgent(){return`${this.constructor.name}/JS ${sm}`}defaultIdempotencyKey(){return`stainless-node-retry-${t0()}`}makeStatusError(e,t,s,r){return t5.generate(e,t,s,r)}buildURL(e,t,s){let r=!tZ(this,ep,"m",ex).call(this)&&s||this.baseURL,a=new URL(si.test(e)?e:r+(r.endsWith("/")&&e.startsWith("/")?e.slice(1):e)),n=this.defaultQuery(),i=Object.fromEntries(a.searchParams);return sc(n)&&sc(i)||(t={...i,...n,...t}),"object"==typeof t&&t&&!Array.isArray(t)&&(a.search=this.stringifyQuery(t)),a.toString()}_calculateNonstreamingTimeout(e){if(3600*e/128e3>600)throw new t4("Streaming is required for operations that may take longer than 10 minutes. See https://github.com/anthropics/anthropic-sdk-typescript#streaming-responses for more details");return 6e5}async prepareOptions(e){}async prepareRequest(e,{url:t,options:s}){}get(e,t){return this.methodRequest("get",e,t)}post(e,t){return this.methodRequest("post",e,t)}patch(e,t){return this.methodRequest("patch",e,t)}put(e,t){return this.methodRequest("put",e,t)}delete(e,t){return this.methodRequest("delete",e,t)}methodRequest(e,t,s){return this.request(Promise.resolve(s).then(s=>({method:e,path:t,...s})))}request(e,t=null){return new sL(this,this.makeRequest(e,t,void 0))}async makeRequest(e,t,s){let r=await e,a=r.maxRetries??this.maxRetries;null==t&&(t=a),await this.prepareOptions(r);let{req:n,url:i,timeout:o}=await this.buildRequest(r,{retryCount:a-t});await this.prepareRequest(n,{url:i,options:r});let l="log_"+(0x1000000*Math.random()|0).toString(16).padStart(6,"0"),d=void 0===s?"":`, retryOf: ${s}`,c=Date.now();if(sE(this).debug(`[${l}] sending request`,sA({retryOfRequestLogID:s,method:r.method,url:i,options:r,headers:n.headers})),r.signal?.aborted)throw new t3;let u=new AbortController,m=await this.fetchWithTimeout(i,n,o,u).catch(t2),h=Date.now();if(m instanceof globalThis.Error){let e=`retrying, ${t} attempts remaining`;if(r.signal?.aborted)throw new t3;let a=t1(m)||/timed? ?out/i.test(String(m)+("cause"in m?String(m.cause):""));if(t)return sE(this).info(`[${l}] connection ${a?"timed out":"failed"} - ${e}`),sE(this).debug(`[${l}] connection ${a?"timed out":"failed"} (${e})`,sA({retryOfRequestLogID:s,url:i,durationMs:h-c,message:m.message})),this.retryRequest(r,t,s??l);if(sE(this).info(`[${l}] connection ${a?"timed out":"failed"} - error; no more retries left`),sE(this).debug(`[${l}] connection ${a?"timed out":"failed"} (error; no more retries left)`,sA({retryOfRequestLogID:s,url:i,durationMs:h-c,message:m.message})),a)throw new t8;throw new t6({cause:m})}let p=[...m.headers.entries()].filter(([e])=>"request-id"===e).map(([e,t])=>", "+e+": "+JSON.stringify(t)).join(""),f=`[${l}${d}${p}] ${n.method} ${i} ${m.ok?"succeeded":"failed"} with status ${m.status} in ${h-c}ms`;if(!m.ok){let e=await this.shouldRetry(m);if(t&&e){let e=`retrying, ${t} attempts remaining`;return await sb(m.body),sE(this).info(`${f} - ${e}`),sE(this).debug(`[${l}] response error (${e})`,sA({retryOfRequestLogID:s,url:m.url,status:m.status,headers:m.headers,durationMs:h-c})),this.retryRequest(r,t,s??l,m.headers)}let a=e?"error; no more retries left":"error; not retryable";sE(this).info(`${f} - ${a}`);let n=await m.text().catch(e=>t2(e).message),i=su(n),o=i?void 0:n;throw sE(this).debug(`[${l}] response error (${a})`,sA({retryOfRequestLogID:s,url:m.url,status:m.status,headers:m.headers,message:o,durationMs:Date.now()-c})),this.makeStatusError(m.status,i,o,m.headers)}return sE(this).info(f),sE(this).debug(`[${l}] response start`,sA({retryOfRequestLogID:s,url:m.url,status:m.status,headers:m.headers,durationMs:h-c})),{response:m,options:r,controller:u,requestLogID:l,retryOfRequestLogID:s,startTime:c}}getAPIList(e,t,s){return this.requestAPIList(t,s&&"then"in s?s.then(t=>({method:"get",path:e,...t})):{method:"get",path:e,...s})}requestAPIList(e,t){return new sD(this,this.makeRequest(t,null,void 0),e)}async fetchWithTimeout(e,t,s,r){let{signal:a,method:n,...i}=t||{},o=this._makeAbort(r);a&&a.addEventListener("abort",o,{once:!0});let l=setTimeout(o,s),d=globalThis.ReadableStream&&i.body instanceof globalThis.ReadableStream||"object"==typeof i.body&&null!==i.body&&Symbol.asyncIterator in i.body,c={signal:r.signal,...d?{duplex:"half"}:{},method:"GET",...i};n&&(c.method=n.toUpperCase());try{return await this.fetch.call(void 0,e,c)}finally{clearTimeout(l)}}async shouldRetry(e){let t=e.headers.get("x-should-retry");return"true"===t||"false"!==t&&(408===e.status||409===e.status||429===e.status||!!(e.status>=500))}async retryRequest(e,t,s,r){let a,n,i=r?.get("retry-after-ms");if(i){let e=parseFloat(i);Number.isNaN(e)||(a=e)}let o=r?.get("retry-after");if(o&&!a){let e=parseFloat(o);a=Number.isNaN(e)?Date.parse(o)-Date.now():1e3*e}if(void 0===a){let s=e.maxRetries??this.maxRetries;a=this.calculateDefaultRetryTimeoutMillis(t,s)}return await (n=a,new Promise(e=>setTimeout(e,n))),this.makeRequest(e,t-1,s)}calculateDefaultRetryTimeoutMillis(e,t){return Math.min(.5*Math.pow(2,t-e),8)*(1-.25*Math.random())*1e3}calculateNonstreamingTimeout(e,t){if(36e5*e/128e3>6e5||null!=t&&e>t)throw new t4("Streaming is required for operations that may take longer than 10 minutes. 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n={};this.idempotencyHeader&&"get"!==s&&(e.idempotencyKey||(e.idempotencyKey=this.defaultIdempotencyKey()),n[this.idempotencyHeader]=e.idempotencyKey);let i=s1([n,{Accept:"application/json","User-Agent":this.getUserAgent(),"X-Stainless-Retry-Count":String(a),...e.timeout?{"X-Stainless-Timeout":String(Math.trunc(e.timeout/1e3))}:{},...t??(t=(()=>{let e="u">typeof Deno&&null!=Deno.build?"deno":"u">typeof EdgeRuntime?"edge":"[object process]"===Object.prototype.toString.call(void 0!==globalThis.process?globalThis.process:0)?"node":"unknown";if("deno"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sm,"X-Stainless-OS":sp(Deno.build.os),"X-Stainless-Arch":sh(Deno.build.arch),"X-Stainless-Runtime":"deno","X-Stainless-Runtime-Version":"string"==typeof Deno.version?Deno.version:Deno.version?.deno??"unknown"};if("u">typeof EdgeRuntime)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sm,"X-Stainless-OS":"Unknown","X-Stainless-Arch":`other:${EdgeRuntime}`,"X-Stainless-Runtime":"edge","X-Stainless-Runtime-Version":globalThis.process.version};if("node"===e)return{"X-Stainless-Lang":"js","X-Stainless-Package-Version":sm,"X-Stainless-OS":sp(globalThis.process.platform??"unknown"),"X-Stainless-Arch":sh(globalThis.process.arch??"unknown"),"X-Stainless-Runtime":"node","X-Stainless-Runtime-Version":globalThis.process.version??"unknown"};let t=function(){if("u"e.abort()}buildBody({options:{body:e,headers:t}}){if(!e)return{bodyHeaders:void 0,body:void 0};let s=s1([t]);return ArrayBuffer.isView(e)||e instanceof ArrayBuffer||e instanceof DataView||"string"==typeof e&&s.values.has("content-type")||globalThis.Blob&&e instanceof globalThis.Blob||e instanceof FormData||e instanceof URLSearchParams||globalThis.ReadableStream&&e instanceof globalThis.ReadableStream?{bodyHeaders:void 0,body:e}:"object"==typeof e&&(Symbol.asyncIterator in e||Symbol.iterator in e&&"next"in e&&"function"==typeof e.next)?{bodyHeaders:void 0,body:sg(e)}:"object"==typeof e&&"application/x-www-form-urlencoded"===s.values.get("content-type")?{bodyHeaders:{"content-type":"application/x-www-form-urlencoded"},body:this.stringifyQuery(e)}:tZ(this,eg,"f").call(this,{body:e,headers:s})}}ef=rY,eg=new WeakMap,ep=new WeakSet,ex=function(){return"https://api.anthropic.com"!==this.baseURL},rY.Anthropic=ef,rY.HUMAN_PROMPT="\\n\\nHuman:",rY.AI_PROMPT="\\n\\nAssistant:",rY.DEFAULT_TIMEOUT=6e5,rY.AnthropicError=t4,rY.APIError=t5,rY.APIConnectionError=t6,rY.APIConnectionTimeoutError=t8,rY.APIUserAbortError=t3,rY.NotFoundError=st,rY.ConflictError=ss,rY.RateLimitError=sa,rY.BadRequestError=t9,rY.AuthenticationError=t7,rY.InternalServerError=sn,rY.PermissionDeniedError=se,rY.UnprocessableEntityError=sr,rY.toFile=sY;class rQ extends rY{constructor(){super(...arguments),this.completions=new rK(this),this.messages=new rV(this),this.models=new rJ(this),this.beta=new rO(this)}}rQ.Completions=rK,rQ.Messages=rV,rQ.Models=rJ,rQ.Beta=rO;let rZ="toolset:",r0=e=>({completionTokens:e.output_tokens,promptTokens:e.input_tokens,totalTokens:e.input_tokens+e.output_tokens,...(0,eH.extractPromptCacheTokens)(e)});async function r1(e,t,s,r,a=[],n,i,o,l,d,c,u,m,h,p,f,g,x,b=!0){if(!r)throw Error("Virtual Key is required");console.log=function(){};let y=p||(0,eU.getProxyBaseUrl)(),v={};a&&a.length>0&&(v["x-litellm-tags"]=a.join(","));let j=new rQ({apiKey:r,baseURL:y,dangerouslyAllowBrowser:!0,defaultHeaders:v});try{let r=Date.now(),a=!1,p={model:s,messages:e.map(e=>({role:e.role,content:e.content})),stream:b,max_tokens:1024,litellm_trace_id:d},y=function({selectedMCPServers:e,mcpServers:t,mcpToolsets:s,mcpServerToolRestrictions:r}){return e&&0!==e.length?e.includes("__all__")?[{type:"mcp",server_label:"litellm",server_url:"litellm_proxy/mcp",require_approval:"never"}]:e.map(e=>{if(e.startsWith(rZ)){let t=e.slice(rZ.length),r=s?.find(e=>e.toolset_id===t),a=r?.toolset_name||t;return{type:"mcp",server_label:a,server_url:`litellm_proxy/mcp/${a}`,require_approval:"never"}}let a=t?.find(t=>t.server_id===e),n=a?.server_name||e,i=r?.[e]||[];return{type:"mcp",server_label:n,server_url:`litellm_proxy/mcp/${n}`,require_approval:"never",...i.length>0?{allowed_tools:i}:{}}}):[]}({selectedMCPServers:h,mcpServers:f,mcpToolsets:x,mcpServerToolRestrictions:g});if(y.length>0&&(p.tools=y),c&&(p.vector_store_ids=c),u&&(p.guardrails=u),m&&(p.policies=m),!b){let e=await j.messages.create({...p,stream:!1},{signal:n});for(let r of e.content)"text"===r.type?t("assistant",r.text,s):"thinking"===r.type&&i&&i(r.thinking);l?.(r0(e.usage));return}for await(let e of j.messages.stream(p,{signal:n})){if("content_block_delta"===e.type){let n=e.delta;if(!a){a=!0;let e=Date.now()-r;o&&o(e)}"text_delta"===n.type?t("assistant",n.text,s):"reasoning_delta"===n.type&&i&&i(n.text)}"message_delta"===e.type&&e.usage&&l&&l(r0(e.usage))}}catch(e){throw n?.aborted||eL.toast.fromError(`Error occurred while generating model response. Please try again. Error: ${e}`),e}}async function r2(e,t,s,r,a,n,i,o,l,d){console.log=function(){};let c=d||(0,eU.getProxyBaseUrl)(),u=new eV.default.OpenAI({apiKey:a,baseURL:c,dangerouslyAllowBrowser:!0,defaultHeaders:n&&n.length>0?{"x-litellm-tags":n.join(",")}:void 0});try{let a=await u.audio.speech.create({model:r,input:e,voice:t,...o?{response_format:o}:{},...l?{speed:l}:{}},{signal:i}),n=await a.blob(),d=URL.createObjectURL(n);s(d,r)}catch(e){throw i?.aborted||eL.toast.fromError(`Error occurred while generating speech. Please try again. Error: ${e}`),e}}async function r4(e,t,s,r,a,n,i,o,l,d,c){console.log=function(){};let u=c||(0,eU.getProxyBaseUrl)(),m=new eV.default.OpenAI({apiKey:r,baseURL:u,dangerouslyAllowBrowser:!0,defaultHeaders:a&&a.length>0?{"x-litellm-tags":a.join(",")}:void 0});try{let r=await m.audio.transcriptions.create({model:s,file:e,...i?{language:i}:{},...o?{prompt:o}:{},...l?{response_format:l}:{},...void 0!==d?{temperature:d}:{}},{signal:n});if(r&&r.text)t(r.text,s),eL.toast.success("Audio transcribed successfully");else throw Error("No transcription text in response")}catch(e){if(console.error("Error making audio transcription request:",e),n?.aborted);else{let t="Failed to transcribe audio";e?.error?.message?t=e.error.message:e?.message&&(t=e.message),eL.toast.fromError(`Audio transcription failed: ${t}`)}throw e}}async function r5(e,t,s,r,a,n){if(!r)throw Error("Virtual Key is required");console.log=function(){};let i=n||(0,eU.getProxyBaseUrl)(),o={};a&&a.length>0&&(o["x-litellm-tags"]=a.join(","));try{let a=i.endsWith("/")?i.slice(0,-1):i,n=`${a}/embeddings`,l=await fetch(n,{method:"POST",headers:{"Content-Type":"application/json",[(0,eU.getGlobalLitellmHeaderName)()]:`Bearer ${r}`,...o},body:JSON.stringify({model:s,input:e})});if(!l.ok){let e=await l.text();throw Error(e||`Request failed with status ${l.status}`)}let d=await l.json(),c=d?.data?.[0]?.embedding;if(!c)throw Error("No embedding returned from server");t(JSON.stringify(c),d?.model??s)}catch(e){throw eL.toast.fromError(`Error occurred while making embeddings request. Please try again. 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This list is a draft and is not final. If you rely on this feature, please share feedback on the `,(0,t.jsx)(o.default,{href:"https://github.com/BerriAI/litellm/discussions/32090",target:"_blank",rel:"noopener noreferrer",className:"underline underline-offset-4",children:"deprecation discussion"}),"."]})]}),(0,t.jsx)("button",{type:"button","aria-label":"Close",onClick:()=>s(!0),className:"shrink-0 rounded-md p-0.5 text-muted-foreground transition-colors hover:text-foreground",children:(0,t.jsx)(a.X,{className:"size-4"})})]})}])},909947,e=>{"use strict";var t=e.i(865361);e.s(["generateCodeSnippet",0,e=>{let r,{apiKeySource:o,accessToken:i,apiKey:a,inputMessage:n,chatHistory:s,selectedTags:l,selectedVectorStores:d,selectedGuardrails:c,selectedPolicies:u,selectedVoice:p,endpointType:h,selectedModel:m,selectedSdk:g,proxySettings:f,customHeaders:b}=e,x="session"===o?i:a,_=window.location.origin,k=f?.LITELLM_UI_API_DOC_BASE_URL;k&&k.trim()?_=k:f?.PROXY_BASE_URL&&(_=f.PROXY_BASE_URL);let y=n||"Your prompt here",v=y.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),w=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),j={};l.length>0&&(j.tags=l),d.length>0&&(j.vector_stores=d),c.length>0&&(j.guardrails=c),u.length>0&&(j.policies=u);let C=m||"your-model-name",S=b&&Object.keys(b).length>0?`, + default_headers=${JSON.stringify(b,null,2).replace(/\n/g,"\n ")}`:"",T="azure"===g?`import openai + +client = openai.AzureOpenAI( + api_key="${x||"YOUR_LITELLM_API_KEY"}", + azure_endpoint="${_}", + api_version="2024-02-01"${S} +)`:`import openai + +client = openai.OpenAI( + api_key="${x||"YOUR_LITELLM_API_KEY"}", + base_url="${_}"${S} +)`;switch(h){case t.EndpointType.CHAT:{let e=Object.keys(j).length>0,t="";if(e){let e=JSON.stringify({metadata:j},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`, + extra_body=${e}`}let o=w.length>0?w:[{role:"user",content:y}];r=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.chat.completions.create( + model="${C}", + messages=${JSON.stringify(o,null,4)}${t} +) + +print(response) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.chat.completions.create( +# model="${C}", +# messages=[ +# { +# "role": "user", +# "content": [ +# { +# "type": "text", +# "text": "${v}" +# }, +# { +# "type": "image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${t} +# ) +# print(response_with_file) +`;break}case t.EndpointType.RESPONSES:{let e=Object.keys(j).length>0,t="";if(e){let e=JSON.stringify({metadata:j},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`, + extra_body=${e}`}let o=w.length>0?w:[{role:"user",content:y}];r=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${C}", + input=${JSON.stringify(o,null,4)}${t} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${C}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${v}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${t} +# ) +# print(response_with_file.output_text) +`;break}case t.EndpointType.IMAGE:r="azure"===g?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${C}", + prompt="${n}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${v}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${C}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case t.EndpointType.IMAGE_EDITS:r="azure"===g?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${v}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${C}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${v}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${C}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case t.EndpointType.EMBEDDINGS:r=` +response = client.embeddings.create( + input="${n||"Your string here"}", + model="${C}", + encoding_format="base64" # or "float" +) + +print(response.data[0].embedding) +`;break;case t.EndpointType.TRANSCRIPTION:r=` +# Open the audio file +audio_file = open("path/to/your/audio/file.mp3", "rb") + +# Make the transcription request +response = client.audio.transcriptions.create( + model="${C}", + file=audio_file${n?`, + prompt="${n.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""} +) + +print(response.text) +`;break;case t.EndpointType.SPEECH:r=` +# Make the text-to-speech request +response = client.audio.speech.create( + model="${C}", + input="${n||"Your text to convert to speech here"}", + voice="${p}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer +) + 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this._addCheck({kind:"min",value:0,inclusive:!1,message:tm.toString(e)})}negative(e){return this._addCheck({kind:"max",value:0,inclusive:!1,message:tm.toString(e)})}nonpositive(e){return this._addCheck({kind:"max",value:0,inclusive:!0,message:tm.toString(e)})}nonnegative(e){return this._addCheck({kind:"min",value:0,inclusive:!0,message:tm.toString(e)})}multipleOf(e,t){return this._addCheck({kind:"multipleOf",value:e,message:tm.toString(t)})}finite(e){return this._addCheck({kind:"finite",message:tm.toString(e)})}safe(e){return this._addCheck({kind:"min",inclusive:!0,value:Number.MIN_SAFE_INTEGER,message:tm.toString(e)})._addCheck({kind:"max",inclusive:!0,value:Number.MAX_SAFE_INTEGER,message:tm.toString(e)})}get minValue(){let e=null;for(let t of this._def.checks)"min"===t.kind&&(null===e||t.value>e)&&(e=t.value);return e}get maxValue(){let e=null;for(let t of this._def.checks)"max"===t.kind&&(null===e||t.value"int"===e.kind||"multipleOf"===e.kind&&tc.isInteger(e.value))}get isFinite(){let e=null,t=null;for(let s of this._def.checks)if("finite"===s.kind||"int"===s.kind||"multipleOf"===s.kind)return!0;else"min"===s.kind?(null===t||s.value>t)&&(t=s.value):"max"===s.kind&&(null===e||s.valuenew V({checks:[],typeName:th.ZodNumber,coerce:e?.coerce||!1,...N(e)});class K extends k{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte}_parse(e){let t;if(this._def.coerce)try{e.data=BigInt(e.data)}catch{return this._getInvalidInput(e)}if(this._getType(e)!==s.bigint)return this._getInvalidInput(e);let a=new p;for(let s of this._def.checks)"min"===s.kind?(s.inclusive?e.datas.value:e.data>=s.value)&&(h(t=this._getOrReturnCtx(e,t),{code:i.too_big,type:"bigint",maximum:s.value,inclusive:s.inclusive,message:s.message}),a.dirty()):"multipleOf"===s.kind?e.data%s.value!==BigInt(0)&&(h(t=this._getOrReturnCtx(e,t),{code:i.not_multiple_of,multipleOf:s.value,message:s.message}),a.dirty()):tc.assertNever(s);return{status:a.value,value:e.data}}_getInvalidInput(e){let t=this._getOrReturnCtx(e);return h(t,{code:i.invalid_type,expected:s.bigint,received:t.parsedType}),f}gte(e,t){return this.setLimit("min",e,!0,tm.toString(t))}gt(e,t){return this.setLimit("min",e,!1,tm.toString(t))}lte(e,t){return this.setLimit("max",e,!0,tm.toString(t))}lt(e,t){return this.setLimit("max",e,!1,tm.toString(t))}setLimit(e,t,s,a){return new K({...this._def,checks:[...this._def.checks,{kind:e,value:t,inclusive:s,message:tm.toString(a)}]})}_addCheck(e){return new K({...this._def,checks:[...this._def.checks,e]})}positive(e){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!1,message:tm.toString(e)})}negative(e){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!1,message:tm.toString(e)})}nonpositive(e){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!0,message:tm.toString(e)})}nonnegative(e){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!0,message:tm.toString(e)})}multipleOf(e,t){return this._addCheck({kind:"multipleOf",value:e,message:tm.toString(t)})}get minValue(){let e=null;for(let t of this._def.checks)"min"===t.kind&&(null===e||t.value>e)&&(e=t.value);return e}get maxValue(){let e=null;for(let t of this._def.checks)"max"===t.kind&&(null===e||t.valuenew K({checks:[],typeName:th.ZodBigInt,coerce:e?.coerce??!1,...N(e)});class H extends k{_parse(e){if(this._def.coerce&&(e.data=!!e.data),this._getType(e)!==s.boolean){let t=this._getOrReturnCtx(e);return h(t,{code:i.invalid_type,expected:s.boolean,received:t.parsedType}),f}return x(e.data)}}H.create=e=>new H({typeName:th.ZodBoolean,coerce:e?.coerce||!1,...N(e)});class W extends k{_parse(e){let t;if(this._def.coerce&&(e.data=new Date(e.data)),this._getType(e)!==s.date){let t=this._getOrReturnCtx(e);return h(t,{code:i.invalid_type,expected:s.date,received:t.parsedType}),f}if(Number.isNaN(e.data.getTime()))return h(this._getOrReturnCtx(e),{code:i.invalid_date}),f;let a=new p;for(let s of this._def.checks)"min"===s.kind?e.data.getTime()s.value&&(h(t=this._getOrReturnCtx(e,t),{code:i.too_big,message:s.message,inclusive:!0,exact:!1,maximum:s.value,type:"date"}),a.dirty()):tc.assertNever(s);return{status:a.value,value:new Date(e.data.getTime())}}_addCheck(e){return new W({...this._def,checks:[...this._def.checks,e]})}min(e,t){return this._addCheck({kind:"min",value:e.getTime(),message:tm.toString(t)})}max(e,t){return this._addCheck({kind:"max",value:e.getTime(),message:tm.toString(t)})}get minDate(){let e=null;for(let t of this._def.checks)"min"===t.kind&&(null===e||t.value>e)&&(e=t.value);return null!=e?new Date(e):null}get maxDate(){let e=null;for(let t of this._def.checks)"max"===t.kind&&(null===e||t.valuenew W({checks:[],coerce:e?.coerce||!1,typeName:th.ZodDate,...N(e)});class G extends k{_parse(e){if(this._getType(e)!==s.symbol){let t=this._getOrReturnCtx(e);return h(t,{code:i.invalid_type,expected:s.symbol,received:t.parsedType}),f}return x(e.data)}}G.create=e=>new G({typeName:th.ZodSymbol,...N(e)});class Y extends k{_parse(e){if(this._getType(e)!==s.undefined){let t=this._getOrReturnCtx(e);return h(t,{code:i.invalid_type,expected:s.undefined,received:t.parsedType}),f}return x(e.data)}}Y.create=e=>new Y({typeName:th.ZodUndefined,...N(e)});class X extends k{_parse(e){if(this._getType(e)!==s.null){let t=this._getOrReturnCtx(e);return h(t,{code:i.invalid_type,expected:s.null,received:t.parsedType}),f}return x(e.data)}}X.create=e=>new X({typeName:th.ZodNull,...N(e)});class J extends k{constructor(){super(...arguments),this._any=!0}_parse(e){return x(e.data)}}J.create=e=>new J({typeName:th.ZodAny,...N(e)});class Q extends k{constructor(){super(...arguments),this._unknown=!0}_parse(e){return x(e.data)}}Q.create=e=>new Q({typeName:th.ZodUnknown,...N(e)});class ee extends k{_parse(e){let t=this._getOrReturnCtx(e);return h(t,{code:i.invalid_type,expected:s.never,received:t.parsedType}),f}}ee.create=e=>new ee({typeName:th.ZodNever,...N(e)});class et extends k{_parse(e){if(this._getType(e)!==s.undefined){let t=this._getOrReturnCtx(e);return h(t,{code:i.invalid_type,expected:s.void,received:t.parsedType}),f}return x(e.data)}}et.create=e=>new et({typeName:th.ZodVoid,...N(e)});class es extends k{_parse(e){let{ctx:t,status:a}=this._processInputParams(e),r=this._def;if(t.parsedType!==s.array)return h(t,{code:i.invalid_type,expected:s.array,received:t.parsedType}),f;if(null!==r.exactLength){let e=t.data.length>r.exactLength.value,s=t.data.lengthr.maxLength.value&&(h(t,{code:i.too_big,maximum:r.maxLength.value,type:"array",inclusive:!0,exact:!1,message:r.maxLength.message}),a.dirty()),t.common.async)return Promise.all([...t.data].map((e,s)=>r.type._parseAsync(new j(t,e,t.path,s)))).then(e=>p.mergeArray(a,e));let n=[...t.data].map((e,s)=>r.type._parseSync(new j(t,e,t.path,s)));return p.mergeArray(a,n)}get element(){return this._def.type}min(e,t){return new es({...this._def,minLength:{value:e,message:tm.toString(t)}})}max(e,t){return new es({...this._def,maxLength:{value:e,message:tm.toString(t)}})}length(e,t){return new es({...this._def,exactLength:{value:e,message:tm.toString(t)}})}nonempty(e){return this.min(1,e)}}es.create=(e,t)=>new es({type:e,minLength:null,maxLength:null,exactLength:null,typeName:th.ZodArray,...N(t)});class ea extends k{constructor(){super(...arguments),this._cached=null,this.nonstrict=this.passthrough,this.augment=this.extend}_getCached(){if(null!==this._cached)return this._cached;let e=this._def.shape(),t=tc.objectKeys(e);return this._cached={shape:e,keys:t},this._cached}_parse(e){if(this._getType(e)!==s.object){let t=this._getOrReturnCtx(e);return h(t,{code:i.invalid_type,expected:s.object,received:t.parsedType}),f}let{status:t,ctx:a}=this._processInputParams(e),{shape:r,keys:n}=this._getCached(),l=[];if(!(this._def.catchall instanceof ee&&"strip"===this._def.unknownKeys))for(let e in a.data)n.includes(e)||l.push(e);let o=[];for(let e of n){let t=r[e],s=a.data[e];o.push({key:{status:"valid",value:e},value:t._parse(new j(a,s,a.path,e)),alwaysSet:e in a.data})}if(this._def.catchall instanceof ee){let e=this._def.unknownKeys;if("passthrough"===e)for(let e of l)o.push({key:{status:"valid",value:e},value:{status:"valid",value:a.data[e]}});else if("strict"===e)l.length>0&&(h(a,{code:i.unrecognized_keys,keys:l}),t.dirty());else if("strip"===e);else throw Error("Internal ZodObject error: invalid unknownKeys value.")}else{let e=this._def.catchall;for(let t of l){let s=a.data[t];o.push({key:{status:"valid",value:t},value:e._parse(new j(a,s,a.path,t)),alwaysSet:t in a.data})}}return a.common.async?Promise.resolve().then(async()=>{let e=[];for(let t of o){let s=await t.key,a=await t.value;e.push({key:s,value:a,alwaysSet:t.alwaysSet})}return e}).then(e=>p.mergeObjectSync(t,e)):p.mergeObjectSync(t,o)}get shape(){return this._def.shape()}strict(e){return tm.errToObj,new ea({...this._def,unknownKeys:"strict",...void 0!==e?{errorMap:(t,s)=>{let a=this._def.errorMap?.(t,s).message??s.defaultError;return"unrecognized_keys"===t.code?{message:tm.errToObj(e).message??a}:{message:a}}}:{}})}strip(){return new ea({...this._def,unknownKeys:"strip"})}passthrough(){return new ea({...this._def,unknownKeys:"passthrough"})}extend(e){return new ea({...this._def,shape:()=>({...this._def.shape(),...e})})}merge(e){return new ea({unknownKeys:e._def.unknownKeys,catchall:e._def.catchall,shape:()=>({...this._def.shape(),...e._def.shape()}),typeName:th.ZodObject})}setKey(e,t){return this.augment({[e]:t})}catchall(e){return new ea({...this._def,catchall:e})}pick(e){let t={};for(let s of tc.objectKeys(e))e[s]&&this.shape[s]&&(t[s]=this.shape[s]);return new ea({...this._def,shape:()=>t})}omit(e){let t={};for(let s of tc.objectKeys(this.shape))e[s]||(t[s]=this.shape[s]);return new ea({...this._def,shape:()=>t})}deepPartial(){return function e(t){if(t instanceof ea){let s={};for(let a in t.shape){let i=t.shape[a];s[a]=ev.create(e(i))}return new ea({...t._def,shape:()=>s})}if(t instanceof es)return new es({...t._def,type:e(t.element)});if(t instanceof ev)return ev.create(e(t.unwrap()));if(t instanceof ey)return ey.create(e(t.unwrap()));if(t instanceof eo)return eo.create(t.items.map(t=>e(t)));else return t}(this)}partial(e){let t={};for(let s of tc.objectKeys(this.shape)){let a=this.shape[s];e&&!e[s]?t[s]=a:t[s]=a.optional()}return new ea({...this._def,shape:()=>t})}required(e){let t={};for(let s of tc.objectKeys(this.shape))if(e&&!e[s])t[s]=this.shape[s];else{let e=this.shape[s];for(;e instanceof ev;)e=e._def.innerType;t[s]=e}return new ea({...this._def,shape:()=>t})}keyof(){return ef(tc.objectKeys(this.shape))}}ea.create=(e,t)=>new ea({shape:()=>e,unknownKeys:"strip",catchall:ee.create(),typeName:th.ZodObject,...N(t)}),ea.strictCreate=(e,t)=>new ea({shape:()=>e,unknownKeys:"strict",catchall:ee.create(),typeName:th.ZodObject,...N(t)}),ea.lazycreate=(e,t)=>new ea({shape:e,unknownKeys:"strip",catchall:ee.create(),typeName:th.ZodObject,...N(t)});class ei extends k{_parse(e){let{ctx:t}=this._processInputParams(e),s=this._def.options;if(t.common.async)return Promise.all(s.map(async e=>{let s={...t,common:{...t.common,issues:[]},parent:null};return{result:await e._parseAsync({data:t.data,path:t.path,parent:s}),ctx:s}})).then(function(e){for(let t of e)if("valid"===t.result.status)return t.result;for(let s of e)if("dirty"===s.result.status)return t.common.issues.push(...s.ctx.common.issues),s.result;let s=e.map(e=>new n(e.ctx.common.issues));return h(t,{code:i.invalid_union,unionErrors:s}),f});{let e,a=[];for(let i of s){let s={...t,common:{...t.common,issues:[]},parent:null},r=i._parseSync({data:t.data,path:t.path,parent:s});if("valid"===r.status)return r;"dirty"!==r.status||e||(e={result:r,ctx:s}),s.common.issues.length&&a.push(s.common.issues)}if(e)return t.common.issues.push(...e.ctx.common.issues),e.result;let r=a.map(e=>new n(e));return h(t,{code:i.invalid_union,unionErrors:r}),f}}get options(){return this._def.options}}ei.create=(e,t)=>new ei({options:e,typeName:th.ZodUnion,...N(t)});let er=e=>{if(e instanceof eh)return er(e.schema);if(e instanceof eb)return er(e.innerType());if(e instanceof ep)return[e.value];if(e instanceof eg)return e.options;if(e instanceof ex)return tc.objectValues(e.enum);else if(e instanceof ej)return er(e._def.innerType);else if(e instanceof Y)return[void 0];else if(e instanceof X)return[null];else if(e instanceof ev)return[void 0,...er(e.unwrap())];else if(e instanceof ey)return[null,...er(e.unwrap())];else if(e instanceof eT)return er(e.unwrap());else if(e instanceof eS)return er(e.unwrap());else if(e instanceof ew)return er(e._def.innerType);else return[]};class en extends k{_parse(e){let{ctx:t}=this._processInputParams(e);if(t.parsedType!==s.object)return h(t,{code:i.invalid_type,expected:s.object,received:t.parsedType}),f;let a=this.discriminator,r=t.data[a],n=this.optionsMap.get(r);return n?t.common.async?n._parseAsync({data:t.data,path:t.path,parent:t}):n._parseSync({data:t.data,path:t.path,parent:t}):(h(t,{code:i.invalid_union_discriminator,options:Array.from(this.optionsMap.keys()),path:[a]}),f)}get discriminator(){return this._def.discriminator}get options(){return this._def.options}get optionsMap(){return this._def.optionsMap}static create(e,t,s){let a=new Map;for(let s of t){let t=er(s.shape[e]);if(!t.length)throw Error(`A discriminator value for key \`${e}\` could not be extracted from all schema options`);for(let i of t){if(a.has(i))throw Error(`Discriminator property ${String(e)} has duplicate value ${String(i)}`);a.set(i,s)}}return new en({typeName:th.ZodDiscriminatedUnion,discriminator:e,options:t,optionsMap:a,...N(s)})}}class el extends k{_parse(e){let{status:t,ctx:r}=this._processInputParams(e),n=(e,n)=>{if(_(e)||_(n))return f;let l=function e(t,i){let r=a(t),n=a(i);if(t===i)return{valid:!0,data:t};if(r===s.object&&n===s.object){let s=tc.objectKeys(i),a=tc.objectKeys(t).filter(e=>-1!==s.indexOf(e)),r={...t,...i};for(let s of a){let a=e(t[s],i[s]);if(!a.valid)return{valid:!1};r[s]=a.data}return{valid:!0,data:r}}if(r===s.array&&n===s.array){if(t.length!==i.length)return{valid:!1};let s=[];for(let a=0;an(e,t)):n(this._def.left._parseSync({data:r.data,path:r.path,parent:r}),this._def.right._parseSync({data:r.data,path:r.path,parent:r}))}}el.create=(e,t,s)=>new el({left:e,right:t,typeName:th.ZodIntersection,...N(s)});class eo extends k{_parse(e){let{status:t,ctx:a}=this._processInputParams(e);if(a.parsedType!==s.array)return h(a,{code:i.invalid_type,expected:s.array,received:a.parsedType}),f;if(a.data.lengththis._def.items.length&&(h(a,{code:i.too_big,maximum:this._def.items.length,inclusive:!0,exact:!1,type:"array"}),t.dirty());let r=[...a.data].map((e,t)=>{let s=this._def.items[t]||this._def.rest;return s?s._parse(new j(a,e,a.path,t)):null}).filter(e=>!!e);return a.common.async?Promise.all(r).then(e=>p.mergeArray(t,e)):p.mergeArray(t,r)}get items(){return this._def.items}rest(e){return new eo({...this._def,rest:e})}}eo.create=(e,t)=>{if(!Array.isArray(e))throw Error("You must pass an array of schemas to z.tuple([ ... ])");return new eo({items:e,typeName:th.ZodTuple,rest:null,...N(t)})};class ed extends k{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(e){let{status:t,ctx:a}=this._processInputParams(e);if(a.parsedType!==s.object)return h(a,{code:i.invalid_type,expected:s.object,received:a.parsedType}),f;let r=[],n=this._def.keyType,l=this._def.valueType;for(let e in a.data)r.push({key:n._parse(new j(a,e,a.path,e)),value:l._parse(new j(a,a.data[e],a.path,e)),alwaysSet:e in a.data});return a.common.async?p.mergeObjectAsync(t,r):p.mergeObjectSync(t,r)}get element(){return this._def.valueType}static create(e,t,s){return new ed(t instanceof k?{keyType:e,valueType:t,typeName:th.ZodRecord,...N(s)}:{keyType:q.create(),valueType:e,typeName:th.ZodRecord,...N(t)})}}class ec extends k{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(e){let{status:t,ctx:a}=this._processInputParams(e);if(a.parsedType!==s.map)return h(a,{code:i.invalid_type,expected:s.map,received:a.parsedType}),f;let r=this._def.keyType,n=this._def.valueType,l=[...a.data.entries()].map(([e,t],s)=>({key:r._parse(new j(a,e,a.path,[s,"key"])),value:n._parse(new j(a,t,a.path,[s,"value"]))}));if(a.common.async){let e=new Map;return Promise.resolve().then(async()=>{for(let s of l){let a=await s.key,i=await s.value;if("aborted"===a.status||"aborted"===i.status)return f;("dirty"===a.status||"dirty"===i.status)&&t.dirty(),e.set(a.value,i.value)}return{status:t.value,value:e}})}{let e=new Map;for(let s of l){let a=s.key,i=s.value;if("aborted"===a.status||"aborted"===i.status)return f;("dirty"===a.status||"dirty"===i.status)&&t.dirty(),e.set(a.value,i.value)}return{status:t.value,value:e}}}}ec.create=(e,t,s)=>new ec({valueType:t,keyType:e,typeName:th.ZodMap,...N(s)});class eu extends k{_parse(e){let{status:t,ctx:a}=this._processInputParams(e);if(a.parsedType!==s.set)return h(a,{code:i.invalid_type,expected:s.set,received:a.parsedType}),f;let r=this._def;null!==r.minSize&&a.data.sizer.maxSize.value&&(h(a,{code:i.too_big,maximum:r.maxSize.value,type:"set",inclusive:!0,exact:!1,message:r.maxSize.message}),t.dirty());let n=this._def.valueType;function l(e){let s=new Set;for(let a of e){if("aborted"===a.status)return f;"dirty"===a.status&&t.dirty(),s.add(a.value)}return{status:t.value,value:s}}let o=[...a.data.values()].map((e,t)=>n._parse(new j(a,e,a.path,t)));return a.common.async?Promise.all(o).then(e=>l(e)):l(o)}min(e,t){return new eu({...this._def,minSize:{value:e,message:tm.toString(t)}})}max(e,t){return new eu({...this._def,maxSize:{value:e,message:tm.toString(t)}})}size(e,t){return this.min(e,t).max(e,t)}nonempty(e){return this.min(1,e)}}eu.create=(e,t)=>new eu({valueType:e,minSize:null,maxSize:null,typeName:th.ZodSet,...N(t)});class em extends 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encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${I}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${C}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`;break;case t.EndpointType.IMAGE_EDITS:i="azure"===g?` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# The prompt entered by the user -prompt = "${I}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${C}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`:` -import base64 -import os -import time - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${I}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${C}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`;break;case t.EndpointType.EMBEDDINGS:i=` -response = client.embeddings.create( - input="${l||"Your string here"}", - model="${C}", - encoding_format="base64" # or "float" -) - -print(response.data[0].embedding) -`;break;case t.EndpointType.TRANSCRIPTION:i=` -# Open the audio file -audio_file = open("path/to/your/audio/file.mp3", "rb") - -# Make the transcription request -response = client.audio.transcriptions.create( - model="${C}", - file=audio_file${l?`, - prompt="${l.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""} -) - -print(response.text) -`;break;case t.EndpointType.SPEECH:i=` -# Make the text-to-speech request -response = client.audio.speech.create( - model="${C}", - input="${l||"Your text to convert to speech here"}", - voice="${m}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer -) - 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"image_url", +# "image_url": { +# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} +# } +# } +# ] +# } +# ]${t} +# ) +# print(response_with_file) +`;break}case t.EndpointType.RESPONSES:{let e=Object.keys(k).length>0,t="";if(e){let e=JSON.stringify({metadata:k},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`, + extra_body=${e}`}let i=w.length>0?w:[{role:"user",content:j}];r=` +import base64 + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Example with text only +response = client.responses.create( + model="${N}", + input=${JSON.stringify(i,null,4)}${t} +) + +print(response.output_text) + +# Example with image or PDF (uncomment and provide file path to use) +# base64_file = encode_image("path/to/your/file.jpg") # or .pdf +# response_with_file = client.responses.create( +# model="${N}", +# input=[ +# { +# "role": "user", +# "content": [ +# {"type": "input_text", "text": "${v}"}, +# { +# "type": "input_image", +# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} +# }, +# ], +# } +# ]${t} +# ) +# print(response_with_file.output_text) +`;break}case t.EndpointType.IMAGE:r="azure"===f?` +# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. +# This snippet uses 'client.images.generate' and will create a new image based on your prompt. +# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. +import os +import requests +import json +import time +from PIL import Image + +result = client.images.generate( + model="${N}", + prompt="${n}", + n=1 +) + +json_response = json.loads(result.model_dump_json()) + +# Set the directory for the stored image +image_dir = os.path.join(os.curdir, 'images') + +# If the directory doesn't exist, create it +if not os.path.isdir(image_dir): + os.mkdir(image_dir) + +# Initialize the image path +image_filename = f"generated_image_{int(time.time())}.png" +image_path = os.path.join(image_dir, image_filename) + +try: + # Retrieve the generated image + if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): + image_url = json_response["data"][0]["url"] + generated_image = requests.get(image_url).content + with open(image_path, "wb") as image_file: + image_file.write(generated_image) + + print(f"Image saved to {image_path}") + # Display the image + image = Image.open(image_path) + image.show() + else: + print("Could not find image URL in response.") + print("Full response:", json_response) +except Exception as e: + print(f"An error occurred: {e}") + print("Full response:", json_response) +`:` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${v}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${N}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case t.EndpointType.IMAGE_EDITS:r="azure"===f?` +import base64 +import os +import time +import json +from PIL import Image +import requests + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# The prompt entered by the user +prompt = "${v}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${N}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`:` +import base64 +import os +import time + +# Helper function to encode images to base64 +def encode_image(image_path): + with open(image_path, "rb") as image_file: + return base64.b64encode(image_file.read()).decode('utf-8') + +# Helper function to create a file (simplified for this example) +def create_file(image_path): + # In a real implementation, this would upload the file to OpenAI + # For this example, we'll just return a placeholder ID + return f"file_{os.path.basename(image_path).replace('.', '_')}" + +# The prompt entered by the user +prompt = "${v}" + +# Encode images to base64 +base64_image1 = encode_image("body-lotion.png") +base64_image2 = encode_image("soap.png") + +# Create file IDs +file_id1 = create_file("body-lotion.png") +file_id2 = create_file("incense-kit.png") + +response = client.responses.create( + model="${N}", + input=[ + { + "role": "user", + "content": [ + {"type": "input_text", "text": prompt}, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image1}", + }, + { + "type": "input_image", + "image_url": f"data:image/jpeg;base64,{base64_image2}", + }, + { + "type": "input_image", + "file_id": file_id1, + }, + { + "type": "input_image", + "file_id": file_id2, + } + ], + } + ], + tools=[{"type": "image_generation"}], +) + +# Process the response +image_generation_calls = [ + output + for output in response.output + if output.type == "image_generation_call" +] + +image_data = [output.result for output in image_generation_calls] + +if image_data: + image_base64 = image_data[0] + image_filename = f"edited_image_{int(time.time())}.png" + with open(image_filename, "wb") as f: + f.write(base64.b64decode(image_base64)) + print(f"Image saved to {image_filename}") +else: + # If no image is generated, there might be a text response with an explanation + text_response = [output.text for output in response.output if hasattr(output, 'text')] + if text_response: + print("No image generated. Model response:") + print("\\n".join(text_response)) + else: + print("No image data found in response.") + print("Full response for debugging:") + print(response) +`;break;case t.EndpointType.EMBEDDINGS:r=` +response = client.embeddings.create( + input="${n||"Your string here"}", + model="${N}", + encoding_format="base64" # or "float" +) + +print(response.data[0].embedding) +`;break;case t.EndpointType.TRANSCRIPTION:r=` +# Open the audio file +audio_file = open("path/to/your/audio/file.mp3", "rb") + +# Make the transcription request +response = client.audio.transcriptions.create( + model="${N}", + file=audio_file${n?`, + prompt="${n.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""} +) + +print(response.text) +`;break;case t.EndpointType.SPEECH:r=` +# Make the text-to-speech request +response = client.audio.speech.create( + model="${N}", + input="${n||"Your text to convert to speech here"}", + voice="${c}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer +) + 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This list is a draft and is not final. If you rely on this feature, please share feedback on the `,(0,t.jsx)(o.default,{href:"https://github.com/BerriAI/litellm/discussions/32090",target:"_blank",rel:"noopener noreferrer",className:"underline underline-offset-4",children:"deprecation discussion"}),"."]})]}),(0,t.jsx)("button",{type:"button","aria-label":"Close",onClick:()=>s(!0),className:"shrink-0 rounded-md p-0.5 text-muted-foreground transition-colors hover:text-foreground",children:(0,t.jsx)(a.X,{className:"size-4"})})]})}])},909947,e=>{"use strict";var t=e.i(865361);e.s(["generateCodeSnippet",0,e=>{let r,{apiKeySource:o,accessToken:i,apiKey:a,inputMessage:n,chatHistory:s,selectedTags:l,selectedVectorStores:d,selectedGuardrails:c,selectedPolicies:u,selectedVoice:p,endpointType:h,selectedModel:m,selectedSdk:g,proxySettings:f}=e,b="session"===o?i:a,x=window.location.origin,_=f?.LITELLM_UI_API_DOC_BASE_URL;_&&_.trim()?x=_:f?.PROXY_BASE_URL&&(x=f.PROXY_BASE_URL);let k=n||"Your prompt here",y=k.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),v=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),w={};l.length>0&&(w.tags=l),d.length>0&&(w.vector_stores=d),c.length>0&&(w.guardrails=c),u.length>0&&(w.policies=u);let j=m||"your-model-name",C="azure"===g?`import openai - -client = openai.AzureOpenAI( - api_key="${b||"YOUR_LITELLM_API_KEY"}", - azure_endpoint="${x}", - api_version="2024-02-01" -)`:`import openai - -client = openai.OpenAI( - api_key="${b||"YOUR_LITELLM_API_KEY"}", - base_url="${x}" -)`;switch(h){case t.EndpointType.CHAT:{let e=Object.keys(w).length>0,t="";if(e){let e=JSON.stringify({metadata:w},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`, - extra_body=${e}`}let o=v.length>0?v:[{role:"user",content:k}];r=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.chat.completions.create( - model="${j}", - messages=${JSON.stringify(o,null,4)}${t} -) - -print(response) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.chat.completions.create( -# model="${j}", -# messages=[ -# { -# "role": "user", -# "content": [ -# { -# "type": "text", -# "text": "${y}" -# }, -# { -# "type": "image_url", -# "image_url": { -# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} -# } -# } -# ] -# } -# ]${t} -# ) -# print(response_with_file) -`;break}case t.EndpointType.RESPONSES:{let e=Object.keys(w).length>0,t="";if(e){let e=JSON.stringify({metadata:w},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`, - extra_body=${e}`}let o=v.length>0?v:[{role:"user",content:k}];r=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.responses.create( - model="${j}", - input=${JSON.stringify(o,null,4)}${t} -) - -print(response.output_text) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.responses.create( -# model="${j}", -# input=[ -# { -# "role": "user", -# "content": [ -# {"type": "input_text", "text": "${y}"}, -# { -# "type": "input_image", -# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} -# }, -# ], -# } -# ]${t} -# ) -# print(response_with_file.output_text) -`;break}case t.EndpointType.IMAGE:r="azure"===g?` -# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. -# This snippet uses 'client.images.generate' and will create a new image based on your prompt. -# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. -import os -import requests -import json -import time -from PIL import Image - -result = client.images.generate( - model="${j}", - prompt="${n}", - n=1 -) - -json_response = json.loads(result.model_dump_json()) - -# Set the directory for the stored image -image_dir = os.path.join(os.curdir, 'images') - -# If the directory doesn't exist, create it -if not os.path.isdir(image_dir): - os.mkdir(image_dir) - -# Initialize the image path -image_filename = f"generated_image_{int(time.time())}.png" -image_path = os.path.join(image_dir, image_filename) - -try: - # Retrieve the generated image - if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): - image_url = json_response["data"][0]["url"] - generated_image = requests.get(image_url).content - with open(image_path, "wb") as image_file: - image_file.write(generated_image) - - print(f"Image saved to {image_path}") - # Display the image - image = Image.open(image_path) - image.show() - else: - print("Could not find image URL in response.") - print("Full response:", json_response) -except Exception as e: - print(f"An error occurred: {e}") - print("Full response:", json_response) -`:` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${y}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${j}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`;break;case t.EndpointType.IMAGE_EDITS:r="azure"===g?` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# The prompt entered by the user -prompt = "${y}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${j}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`:` -import base64 -import os -import time - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${y}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${j}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`;break;case t.EndpointType.EMBEDDINGS:r=` -response = client.embeddings.create( - input="${n||"Your string here"}", - model="${j}", - encoding_format="base64" # or "float" -) - -print(response.data[0].embedding) -`;break;case t.EndpointType.TRANSCRIPTION:r=` -# Open the audio file -audio_file = open("path/to/your/audio/file.mp3", "rb") - -# Make the transcription request -response = client.audio.transcriptions.create( - model="${j}", - file=audio_file${n?`, - prompt="${n.replace(/\\/g,"\\\\").replace(/"/g,'\\"')}"`:""} -) - -print(response.text) -`;break;case t.EndpointType.SPEECH:r=` -# Make the text-to-speech request -response = client.audio.speech.create( - model="${j}", - input="${n||"Your text to convert to speech here"}", - voice="${p}" # Options: alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer -) - 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this._addCheck({kind:"email",...tp.errToObj(e)})}url(e){return this._addCheck({kind:"url",...tp.errToObj(e)})}emoji(e){return this._addCheck({kind:"emoji",...tp.errToObj(e)})}uuid(e){return this._addCheck({kind:"uuid",...tp.errToObj(e)})}nanoid(e){return this._addCheck({kind:"nanoid",...tp.errToObj(e)})}cuid(e){return this._addCheck({kind:"cuid",...tp.errToObj(e)})}cuid2(e){return this._addCheck({kind:"cuid2",...tp.errToObj(e)})}ulid(e){return this._addCheck({kind:"ulid",...tp.errToObj(e)})}base64(e){return this._addCheck({kind:"base64",...tp.errToObj(e)})}base64url(e){return this._addCheck({kind:"base64url",...tp.errToObj(e)})}jwt(e){return this._addCheck({kind:"jwt",...tp.errToObj(e)})}ip(e){return this._addCheck({kind:"ip",...tp.errToObj(e)})}cidr(e){return this._addCheck({kind:"cidr",...tp.errToObj(e)})}datetime(e){return"string"==typeof e?this._addCheck({kind:"datetime",precision:null,offset:!1,local:!1,message:e}):this._addCheck({kind:"datetime",precision:void 0===e?.precision?null:e?.precision,offset:e?.offset??!1,local:e?.local??!1,...tp.errToObj(e?.message)})}date(e){return this._addCheck({kind:"date",message:e})}time(e){return"string"==typeof e?this._addCheck({kind:"time",precision:null,message:e}):this._addCheck({kind:"time",precision:void 0===e?.precision?null:e?.precision,...tp.errToObj(e?.message)})}duration(e){return this._addCheck({kind:"duration",...tp.errToObj(e)})}regex(e,t){return this._addCheck({kind:"regex",regex:e,...tp.errToObj(t)})}includes(e,t){return this._addCheck({kind:"includes",value:e,position:t?.position,...tp.errToObj(t?.message)})}startsWith(e,t){return this._addCheck({kind:"startsWith",value:e,...tp.errToObj(t)})}endsWith(e,t){return this._addCheck({kind:"endsWith",value:e,...tp.errToObj(t)})}min(e,t){return this._addCheck({kind:"min",value:e,...tp.errToObj(t)})}max(e,t){return this._addCheck({kind:"max",value:e,...tp.errToObj(t)})}length(e,t){return this._addCheck({kind:"length",value:e,...tp.errToObj(t)})}nonempty(e){return this.min(1,tp.errToObj(e))}trim(){return new K({...this._def,checks:[...this._def.checks,{kind:"trim"}]})}toLowerCase(){return new K({...this._def,checks:[...this._def.checks,{kind:"toLowerCase"}]})}toUpperCase(){return new K({...this._def,checks:[...this._def.checks,{kind:"toUpperCase"}]})}get isDatetime(){return!!this._def.checks.find(e=>"datetime"===e.kind)}get isDate(){return!!this._def.checks.find(e=>"date"===e.kind)}get isTime(){return!!this._def.checks.find(e=>"time"===e.kind)}get isDuration(){return!!this._def.checks.find(e=>"duration"===e.kind)}get isEmail(){return!!this._def.checks.find(e=>"email"===e.kind)}get isURL(){return!!this._def.checks.find(e=>"url"===e.kind)}get isEmoji(){return!!this._def.checks.find(e=>"emoji"===e.kind)}get isUUID(){return!!this._def.checks.find(e=>"uuid"===e.kind)}get isNANOID(){return!!this._def.checks.find(e=>"nanoid"===e.kind)}get isCUID(){return!!this._def.checks.find(e=>"cuid"===e.kind)}get isCUID2(){return!!this._def.checks.find(e=>"cuid2"===e.kind)}get isULID(){return!!this._def.checks.find(e=>"ulid"===e.kind)}get isIP(){return!!this._def.checks.find(e=>"ip"===e.kind)}get isCIDR(){return!!this._def.checks.find(e=>"cidr"===e.kind)}get isBase64(){return!!this._def.checks.find(e=>"base64"===e.kind)}get isBase64url(){return!!this._def.checks.find(e=>"base64url"===e.kind)}get minLength(){let e=null;for(let t of this._def.checks)"min"===t.kind&&(null===e||t.value>e)&&(e=t.value);return e}get maxLength(){let e=null;for(let t of this._def.checks)"max"===t.kind&&(null===e||t.valuenew K({checks:[],typeName:tf.ZodString,coerce:e?.coerce??!1,...T(e)});class H extends C{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte,this.step=this.multipleOf}_parse(e){let t;if(this._def.coerce&&(e.data=Number(e.data)),this._getType(e)!==r.number){let t=this._getOrReturnCtx(e);return f(t,{code:n.invalid_type,expected:r.number,received:t.parsedType}),x}let s=new g;for(let a of this._def.checks)"int"===a.kind?tm.isInteger(e.data)||(f(t=this._getOrReturnCtx(e,t),{code:n.invalid_type,expected:"integer",received:"float",message:a.message}),s.dirty()):"min"===a.kind?(a.inclusive?e.dataa.value:e.data>=a.value)&&(f(t=this._getOrReturnCtx(e,t),{code:n.too_big,maximum:a.value,type:"number",inclusive:a.inclusive,exact:!1,message:a.message}),s.dirty()):"multipleOf"===a.kind?0!==function(e,t){let s=(e.toString().split(".")[1]||"").length,a=(t.toString().split(".")[1]||"").length,r=s>a?s:a;return Number.parseInt(e.toFixed(r).replace(".",""))%Number.parseInt(t.toFixed(r).replace(".",""))/10**r}(e.data,a.value)&&(f(t=this._getOrReturnCtx(e,t),{code:n.not_multiple_of,multipleOf:a.value,message:a.message}),s.dirty()):"finite"===a.kind?Number.isFinite(e.data)||(f(t=this._getOrReturnCtx(e,t),{code:n.not_finite,message:a.message}),s.dirty()):tm.assertNever(a);return{status:s.value,value:e.data}}gte(e,t){return this.setLimit("min",e,!0,tp.toString(t))}gt(e,t){return this.setLimit("min",e,!1,tp.toString(t))}lte(e,t){return this.setLimit("max",e,!0,tp.toString(t))}lt(e,t){return this.setLimit("max",e,!1,tp.toString(t))}setLimit(e,t,s,a){return new H({...this._def,checks:[...this._def.checks,{kind:e,value:t,inclusive:s,message:tp.toString(a)}]})}_addCheck(e){return new H({...this._def,checks:[...this._def.checks,e]})}int(e){return this._addCheck({kind:"int",message:tp.toString(e)})}positive(e){return this._addCheck({kind:"min",value:0,inclusive:!1,message:tp.toString(e)})}negative(e){return this._addCheck({kind:"max",value:0,inclusive:!1,message:tp.toString(e)})}nonpositive(e){return this._addCheck({kind:"max",value:0,inclusive:!0,message:tp.toString(e)})}nonnegative(e){return this._addCheck({kind:"min",value:0,inclusive:!0,message:tp.toString(e)})}multipleOf(e,t){return this._addCheck({kind:"multipleOf",value:e,message:tp.toString(t)})}finite(e){return this._addCheck({kind:"finite",message:tp.toString(e)})}safe(e){return this._addCheck({kind:"min",inclusive:!0,value:Number.MIN_SAFE_INTEGER,message:tp.toString(e)})._addCheck({kind:"max",inclusive:!0,value:Number.MAX_SAFE_INTEGER,message:tp.toString(e)})}get minValue(){let e=null;for(let t of this._def.checks)"min"===t.kind&&(null===e||t.value>e)&&(e=t.value);return e}get maxValue(){let e=null;for(let t of this._def.checks)"max"===t.kind&&(null===e||t.value"int"===e.kind||"multipleOf"===e.kind&&tm.isInteger(e.value))}get isFinite(){let e=null,t=null;for(let s of this._def.checks)if("finite"===s.kind||"int"===s.kind||"multipleOf"===s.kind)return!0;else"min"===s.kind?(null===t||s.value>t)&&(t=s.value):"max"===s.kind&&(null===e||s.valuenew H({checks:[],typeName:tf.ZodNumber,coerce:e?.coerce||!1,...T(e)});class W extends C{constructor(){super(...arguments),this.min=this.gte,this.max=this.lte}_parse(e){let t;if(this._def.coerce)try{e.data=BigInt(e.data)}catch{return this._getInvalidInput(e)}if(this._getType(e)!==r.bigint)return this._getInvalidInput(e);let s=new g;for(let a of this._def.checks)"min"===a.kind?(a.inclusive?e.dataa.value:e.data>=a.value)&&(f(t=this._getOrReturnCtx(e,t),{code:n.too_big,type:"bigint",maximum:a.value,inclusive:a.inclusive,message:a.message}),s.dirty()):"multipleOf"===a.kind?e.data%a.value!==BigInt(0)&&(f(t=this._getOrReturnCtx(e,t),{code:n.not_multiple_of,multipleOf:a.value,message:a.message}),s.dirty()):tm.assertNever(a);return{status:s.value,value:e.data}}_getInvalidInput(e){let t=this._getOrReturnCtx(e);return f(t,{code:n.invalid_type,expected:r.bigint,received:t.parsedType}),x}gte(e,t){return this.setLimit("min",e,!0,tp.toString(t))}gt(e,t){return this.setLimit("min",e,!1,tp.toString(t))}lte(e,t){return this.setLimit("max",e,!0,tp.toString(t))}lt(e,t){return this.setLimit("max",e,!1,tp.toString(t))}setLimit(e,t,s,a){return new W({...this._def,checks:[...this._def.checks,{kind:e,value:t,inclusive:s,message:tp.toString(a)}]})}_addCheck(e){return new W({...this._def,checks:[...this._def.checks,e]})}positive(e){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!1,message:tp.toString(e)})}negative(e){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!1,message:tp.toString(e)})}nonpositive(e){return this._addCheck({kind:"max",value:BigInt(0),inclusive:!0,message:tp.toString(e)})}nonnegative(e){return this._addCheck({kind:"min",value:BigInt(0),inclusive:!0,message:tp.toString(e)})}multipleOf(e,t){return this._addCheck({kind:"multipleOf",value:e,message:tp.toString(t)})}get minValue(){let e=null;for(let t of this._def.checks)"min"===t.kind&&(null===e||t.value>e)&&(e=t.value);return e}get maxValue(){let e=null;for(let t of this._def.checks)"max"===t.kind&&(null===e||t.valuenew W({checks:[],typeName:tf.ZodBigInt,coerce:e?.coerce??!1,...T(e)});class G extends C{_parse(e){if(this._def.coerce&&(e.data=!!e.data),this._getType(e)!==r.boolean){let t=this._getOrReturnCtx(e);return f(t,{code:n.invalid_type,expected:r.boolean,received:t.parsedType}),x}return b(e.data)}}G.create=e=>new G({typeName:tf.ZodBoolean,coerce:e?.coerce||!1,...T(e)});class Y extends C{_parse(e){let t;if(this._def.coerce&&(e.data=new Date(e.data)),this._getType(e)!==r.date){let t=this._getOrReturnCtx(e);return f(t,{code:n.invalid_type,expected:r.date,received:t.parsedType}),x}if(Number.isNaN(e.data.getTime()))return f(this._getOrReturnCtx(e),{code:n.invalid_date}),x;let s=new g;for(let a of this._def.checks)"min"===a.kind?e.data.getTime()a.value&&(f(t=this._getOrReturnCtx(e,t),{code:n.too_big,message:a.message,inclusive:!0,exact:!1,maximum:a.value,type:"date"}),s.dirty()):tm.assertNever(a);return{status:s.value,value:new Date(e.data.getTime())}}_addCheck(e){return new Y({...this._def,checks:[...this._def.checks,e]})}min(e,t){return this._addCheck({kind:"min",value:e.getTime(),message:tp.toString(t)})}max(e,t){return this._addCheck({kind:"max",value:e.getTime(),message:tp.toString(t)})}get minDate(){let e=null;for(let t of this._def.checks)"min"===t.kind&&(null===e||t.value>e)&&(e=t.value);return null!=e?new Date(e):null}get maxDate(){let e=null;for(let t of this._def.checks)"max"===t.kind&&(null===e||t.valuenew Y({checks:[],coerce:e?.coerce||!1,typeName:tf.ZodDate,...T(e)});class X extends C{_parse(e){if(this._getType(e)!==r.symbol){let t=this._getOrReturnCtx(e);return f(t,{code:n.invalid_type,expected:r.symbol,received:t.parsedType}),x}return b(e.data)}}X.create=e=>new X({typeName:tf.ZodSymbol,...T(e)});class Q extends C{_parse(e){if(this._getType(e)!==r.undefined){let t=this._getOrReturnCtx(e);return f(t,{code:n.invalid_type,expected:r.undefined,received:t.parsedType}),x}return b(e.data)}}Q.create=e=>new Q({typeName:tf.ZodUndefined,...T(e)});class J extends C{_parse(e){if(this._getType(e)!==r.null){let t=this._getOrReturnCtx(e);return f(t,{code:n.invalid_type,expected:r.null,received:t.parsedType}),x}return b(e.data)}}J.create=e=>new J({typeName:tf.ZodNull,...T(e)});class ee extends C{constructor(){super(...arguments),this._any=!0}_parse(e){return b(e.data)}}ee.create=e=>new ee({typeName:tf.ZodAny,...T(e)});class et extends C{constructor(){super(...arguments),this._unknown=!0}_parse(e){return b(e.data)}}et.create=e=>new et({typeName:tf.ZodUnknown,...T(e)});class es extends C{_parse(e){let t=this._getOrReturnCtx(e);return f(t,{code:n.invalid_type,expected:r.never,received:t.parsedType}),x}}es.create=e=>new es({typeName:tf.ZodNever,...T(e)});class ea extends C{_parse(e){if(this._getType(e)!==r.undefined){let t=this._getOrReturnCtx(e);return f(t,{code:n.invalid_type,expected:r.void,received:t.parsedType}),x}return b(e.data)}}ea.create=e=>new ea({typeName:tf.ZodVoid,...T(e)});class er extends C{_parse(e){let{ctx:t,status:s}=this._processInputParams(e),a=this._def;if(t.parsedType!==r.array)return f(t,{code:n.invalid_type,expected:r.array,received:t.parsedType}),x;if(null!==a.exactLength){let e=t.data.length>a.exactLength.value,r=t.data.lengtha.maxLength.value&&(f(t,{code:n.too_big,maximum:a.maxLength.value,type:"array",inclusive:!0,exact:!1,message:a.maxLength.message}),s.dirty()),t.common.async)return Promise.all([...t.data].map((e,s)=>a.type._parseAsync(new N(t,e,t.path,s)))).then(e=>g.mergeArray(s,e));let i=[...t.data].map((e,s)=>a.type._parseSync(new N(t,e,t.path,s)));return g.mergeArray(s,i)}get element(){return this._def.type}min(e,t){return new er({...this._def,minLength:{value:e,message:tp.toString(t)}})}max(e,t){return new er({...this._def,maxLength:{value:e,message:tp.toString(t)}})}length(e,t){return new er({...this._def,exactLength:{value:e,message:tp.toString(t)}})}nonempty(e){return this.min(1,e)}}er.create=(e,t)=>new er({type:e,minLength:null,maxLength:null,exactLength:null,typeName:tf.ZodArray,...T(t)});class ei extends C{constructor(){super(...arguments),this._cached=null,this.nonstrict=this.passthrough,this.augment=this.extend}_getCached(){if(null!==this._cached)return this._cached;let e=this._def.shape(),t=tm.objectKeys(e);return this._cached={shape:e,keys:t},this._cached}_parse(e){if(this._getType(e)!==r.object){let t=this._getOrReturnCtx(e);return f(t,{code:n.invalid_type,expected:r.object,received:t.parsedType}),x}let{status:t,ctx:s}=this._processInputParams(e),{shape:a,keys:i}=this._getCached(),l=[];if(!(this._def.catchall instanceof es&&"strip"===this._def.unknownKeys))for(let e in s.data)i.includes(e)||l.push(e);let o=[];for(let e of i){let t=a[e],r=s.data[e];o.push({key:{status:"valid",value:e},value:t._parse(new N(s,r,s.path,e)),alwaysSet:e in s.data})}if(this._def.catchall instanceof es){let e=this._def.unknownKeys;if("passthrough"===e)for(let e of l)o.push({key:{status:"valid",value:e},value:{status:"valid",value:s.data[e]}});else if("strict"===e)l.length>0&&(f(s,{code:n.unrecognized_keys,keys:l}),t.dirty());else if("strip"===e);else throw Error("Internal ZodObject error: invalid unknownKeys value.")}else{let e=this._def.catchall;for(let t of l){let a=s.data[t];o.push({key:{status:"valid",value:t},value:e._parse(new N(s,a,s.path,t)),alwaysSet:t in s.data})}}return s.common.async?Promise.resolve().then(async()=>{let e=[];for(let t of o){let s=await t.key,a=await t.value;e.push({key:s,value:a,alwaysSet:t.alwaysSet})}return e}).then(e=>g.mergeObjectSync(t,e)):g.mergeObjectSync(t,o)}get shape(){return this._def.shape()}strict(e){return tp.errToObj,new ei({...this._def,unknownKeys:"strict",...void 0!==e?{errorMap:(t,s)=>{let a=this._def.errorMap?.(t,s).message??s.defaultError;return"unrecognized_keys"===t.code?{message:tp.errToObj(e).message??a}:{message:a}}}:{}})}strip(){return new ei({...this._def,unknownKeys:"strip"})}passthrough(){return new ei({...this._def,unknownKeys:"passthrough"})}extend(e){return new ei({...this._def,shape:()=>({...this._def.shape(),...e})})}merge(e){return new ei({unknownKeys:e._def.unknownKeys,catchall:e._def.catchall,shape:()=>({...this._def.shape(),...e._def.shape()}),typeName:tf.ZodObject})}setKey(e,t){return this.augment({[e]:t})}catchall(e){return new ei({...this._def,catchall:e})}pick(e){let t={};for(let s of tm.objectKeys(e))e[s]&&this.shape[s]&&(t[s]=this.shape[s]);return new ei({...this._def,shape:()=>t})}omit(e){let t={};for(let s of tm.objectKeys(this.shape))e[s]||(t[s]=this.shape[s]);return new ei({...this._def,shape:()=>t})}deepPartial(){return function e(t){if(t instanceof ei){let s={};for(let a in t.shape){let r=t.shape[a];s[a]=ej.create(e(r))}return new ei({...t._def,shape:()=>s})}if(t instanceof er)return new er({...t._def,type:e(t.element)});if(t instanceof ej)return ej.create(e(t.unwrap()));if(t instanceof ew)return ew.create(e(t.unwrap()));if(t instanceof ec)return ec.create(t.items.map(t=>e(t)));else return t}(this)}partial(e){let t={};for(let s of tm.objectKeys(this.shape)){let a=this.shape[s];e&&!e[s]?t[s]=a:t[s]=a.optional()}return new ei({...this._def,shape:()=>t})}required(e){let t={};for(let s of tm.objectKeys(this.shape))if(e&&!e[s])t[s]=this.shape[s];else{let e=this.shape[s];for(;e instanceof ej;)e=e._def.innerType;t[s]=e}return new ei({...this._def,shape:()=>t})}keyof(){return ex(tm.objectKeys(this.shape))}}ei.create=(e,t)=>new ei({shape:()=>e,unknownKeys:"strip",catchall:es.create(),typeName:tf.ZodObject,...T(t)}),ei.strictCreate=(e,t)=>new ei({shape:()=>e,unknownKeys:"strict",catchall:es.create(),typeName:tf.ZodObject,...T(t)}),ei.lazycreate=(e,t)=>new ei({shape:e,unknownKeys:"strip",catchall:es.create(),typeName:tf.ZodObject,...T(t)});class en extends C{_parse(e){let{ctx:t}=this._processInputParams(e),s=this._def.options;if(t.common.async)return Promise.all(s.map(async e=>{let s={...t,common:{...t.common,issues:[]},parent:null};return{result:await e._parseAsync({data:t.data,path:t.path,parent:s}),ctx:s}})).then(function(e){for(let t of e)if("valid"===t.result.status)return t.result;for(let s of e)if("dirty"===s.result.status)return t.common.issues.push(...s.ctx.common.issues),s.result;let s=e.map(e=>new o(e.ctx.common.issues));return f(t,{code:n.invalid_union,unionErrors:s}),x});{let e,a=[];for(let r of s){let s={...t,common:{...t.common,issues:[]},parent:null},i=r._parseSync({data:t.data,path:t.path,parent:s});if("valid"===i.status)return i;"dirty"!==i.status||e||(e={result:i,ctx:s}),s.common.issues.length&&a.push(s.common.issues)}if(e)return t.common.issues.push(...e.ctx.common.issues),e.result;let r=a.map(e=>new o(e));return f(t,{code:n.invalid_union,unionErrors:r}),x}}get options(){return this._def.options}}en.create=(e,t)=>new en({options:e,typeName:tf.ZodUnion,...T(t)});let el=e=>{if(e instanceof ef)return el(e.schema);if(e instanceof ey)return el(e.innerType());if(e instanceof eg)return[e.value];if(e instanceof e_)return e.options;if(e instanceof eb)return tm.objectValues(e.enum);else if(e instanceof eN)return el(e._def.innerType);else if(e instanceof Q)return[void 0];else if(e instanceof J)return[null];else if(e instanceof ej)return[void 0,...el(e.unwrap())];else if(e instanceof ew)return[null,...el(e.unwrap())];else if(e instanceof eS)return el(e.unwrap());else if(e instanceof eE)return el(e.unwrap());else if(e instanceof ek)return el(e._def.innerType);else return[]};class eo extends C{_parse(e){let{ctx:t}=this._processInputParams(e);if(t.parsedType!==r.object)return f(t,{code:n.invalid_type,expected:r.object,received:t.parsedType}),x;let s=this.discriminator,a=t.data[s],i=this.optionsMap.get(a);return i?t.common.async?i._parseAsync({data:t.data,path:t.path,parent:t}):i._parseSync({data:t.data,path:t.path,parent:t}):(f(t,{code:n.invalid_union_discriminator,options:Array.from(this.optionsMap.keys()),path:[s]}),x)}get discriminator(){return this._def.discriminator}get options(){return this._def.options}get optionsMap(){return this._def.optionsMap}static create(e,t,s){let a=new Map;for(let s of t){let t=el(s.shape[e]);if(!t.length)throw Error(`A discriminator value for key \`${e}\` could not be extracted from all schema options`);for(let r of t){if(a.has(r))throw Error(`Discriminator property ${String(e)} has duplicate value ${String(r)}`);a.set(r,s)}}return new eo({typeName:tf.ZodDiscriminatedUnion,discriminator:e,options:t,optionsMap:a,...T(s)})}}class ed extends C{_parse(e){let{status:t,ctx:s}=this._processInputParams(e),a=(e,a)=>{if(v(e)||v(a))return x;let l=function e(t,s){let a=i(t),n=i(s);if(t===s)return{valid:!0,data:t};if(a===r.object&&n===r.object){let a=tm.objectKeys(s),r=tm.objectKeys(t).filter(e=>-1!==a.indexOf(e)),i={...t,...s};for(let a of r){let r=e(t[a],s[a]);if(!r.valid)return{valid:!1};i[a]=r.data}return{valid:!0,data:i}}if(a===r.array&&n===r.array){if(t.length!==s.length)return{valid:!1};let a=[];for(let r=0;ra(e,t)):a(this._def.left._parseSync({data:s.data,path:s.path,parent:s}),this._def.right._parseSync({data:s.data,path:s.path,parent:s}))}}ed.create=(e,t,s)=>new ed({left:e,right:t,typeName:tf.ZodIntersection,...T(s)});class ec extends C{_parse(e){let{status:t,ctx:s}=this._processInputParams(e);if(s.parsedType!==r.array)return f(s,{code:n.invalid_type,expected:r.array,received:s.parsedType}),x;if(s.data.lengththis._def.items.length&&(f(s,{code:n.too_big,maximum:this._def.items.length,inclusive:!0,exact:!1,type:"array"}),t.dirty());let a=[...s.data].map((e,t)=>{let a=this._def.items[t]||this._def.rest;return a?a._parse(new N(s,e,s.path,t)):null}).filter(e=>!!e);return s.common.async?Promise.all(a).then(e=>g.mergeArray(t,e)):g.mergeArray(t,a)}get items(){return this._def.items}rest(e){return new ec({...this._def,rest:e})}}ec.create=(e,t)=>{if(!Array.isArray(e))throw Error("You must pass an array of schemas to z.tuple([ ... ])");return new ec({items:e,typeName:tf.ZodTuple,rest:null,...T(t)})};class eu extends C{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(e){let{status:t,ctx:s}=this._processInputParams(e);if(s.parsedType!==r.object)return f(s,{code:n.invalid_type,expected:r.object,received:s.parsedType}),x;let a=[],i=this._def.keyType,l=this._def.valueType;for(let e in s.data)a.push({key:i._parse(new N(s,e,s.path,e)),value:l._parse(new N(s,s.data[e],s.path,e)),alwaysSet:e in s.data});return s.common.async?g.mergeObjectAsync(t,a):g.mergeObjectSync(t,a)}get element(){return this._def.valueType}static create(e,t,s){return new eu(t instanceof C?{keyType:e,valueType:t,typeName:tf.ZodRecord,...T(s)}:{keyType:K.create(),valueType:e,typeName:tf.ZodRecord,...T(t)})}}class em extends C{get keySchema(){return this._def.keyType}get valueSchema(){return this._def.valueType}_parse(e){let{status:t,ctx:s}=this._processInputParams(e);if(s.parsedType!==r.map)return f(s,{code:n.invalid_type,expected:r.map,received:s.parsedType}),x;let a=this._def.keyType,i=this._def.valueType,l=[...s.data.entries()].map(([e,t],r)=>({key:a._parse(new N(s,e,s.path,[r,"key"])),value:i._parse(new N(s,t,s.path,[r,"value"]))}));if(s.common.async){let e=new Map;return Promise.resolve().then(async()=>{for(let s of l){let a=await s.key,r=await s.value;if("aborted"===a.status||"aborted"===r.status)return x;("dirty"===a.status||"dirty"===r.status)&&t.dirty(),e.set(a.value,r.value)}return{status:t.value,value:e}})}{let e=new Map;for(let s of l){let a=s.key,r=s.value;if("aborted"===a.status||"aborted"===r.status)return x;("dirty"===a.status||"dirty"===r.status)&&t.dirty(),e.set(a.value,r.value)}return{status:t.value,value:e}}}}em.create=(e,t,s)=>new em({valueType:t,keyType:e,typeName:tf.ZodMap,...T(s)});class eh extends C{_parse(e){let{status:t,ctx:s}=this._processInputParams(e);if(s.parsedType!==r.set)return f(s,{code:n.invalid_type,expected:r.set,received:s.parsedType}),x;let a=this._def;null!==a.minSize&&s.data.sizea.maxSize.value&&(f(s,{code:n.too_big,maximum:a.maxSize.value,type:"set",inclusive:!0,exact:!1,message:a.maxSize.message}),t.dirty());let i=this._def.valueType;function l(e){let s=new Set;for(let a of e){if("aborted"===a.status)return x;"dirty"===a.status&&t.dirty(),s.add(a.value)}return{status:t.value,value:s}}let o=[...s.data.values()].map((e,t)=>i._parse(new N(s,e,s.path,t)));return s.common.async?Promise.all(o).then(e=>l(e)):l(o)}min(e,t){return new eh({...this._def,minSize:{value:e,message:tp.toString(t)}})}max(e,t){return new eh({...this._def,maxSize:{value:e,message:tp.toString(t)}})}size(e,t){return this.min(e,t).max(e,t)}nonempty(e){return this.min(1,e)}}eh.create=(e,t)=>new eh({valueType:e,minSize:null,maxSize:null,typeName:tf.ZodSet,...T(t)});class ep extends C{constructor(){super(...arguments),this.validate=this.implement}_parse(e){let{ctx:t}=this._processInputParams(e);if(t.parsedType!==r.function)return f(t,{code:n.invalid_type,expected:r.function,received:t.parsedType}),x;function s(e,s){return h({data:e,path:t.path,errorMaps:[t.common.contextualErrorMap,t.schemaErrorMap,m(),d].filter(e=>!!e),issueData:{code:n.invalid_arguments,argumentsError:s}})}function a(e,s){return h({data:e,path:t.path,errorMaps:[t.common.contextualErrorMap,t.schemaErrorMap,m(),d].filter(e=>!!e),issueData:{code:n.invalid_return_type,returnTypeError:s}})}let i={errorMap:t.common.contextualErrorMap},l=t.data;if(this._def.returns instanceof ev){let e=this;return b(async function(...t){let r=new o([]),n=await e._def.args.parseAsync(t,i).catch(e=>{throw r.addIssue(s(t,e)),r}),d=await Reflect.apply(l,this,n);return await e._def.returns._def.type.parseAsync(d,i).catch(e=>{throw r.addIssue(a(d,e)),r})})}{let e=this;return b(function(...t){let r=e._def.args.safeParse(t,i);if(!r.success)throw new o([s(t,r.error)]);let n=Reflect.apply(l,this,r.data),d=e._def.returns.safeParse(n,i);if(!d.success)throw new o([a(n,d.error)]);return d.data})}}parameters(){return this._def.args}returnType(){return this._def.returns}args(...e){return new ep({...this._def,args:ec.create(e).rest(et.create())})}returns(e){return new ep({...this._def,returns:e})}implement(e){return this.parse(e)}strictImplement(e){return this.parse(e)}static create(e,t,s){return new ep({args:e||ec.create([]).rest(et.create()),returns:t||et.create(),typeName:tf.ZodFunction,...T(s)})}}class ef extends C{get schema(){return this._def.getter()}_parse(e){let{ctx:t}=this._processInputParams(e);return this._def.getter()._parse({data:t.data,path:t.path,parent:t})}}ef.create=(e,t)=>new ef({getter:e,typeName:tf.ZodLazy,...T(t)});class eg extends C{_parse(e){if(e.data!==this._def.value){let t=this._getOrReturnCtx(e);return f(t,{received:t.data,code:n.invalid_literal,expected:this._def.value}),x}return{status:"valid",value:e.data}}get value(){return this._def.value}}function ex(e,t){return new e_({values:e,typeName:tf.ZodEnum,...T(t)})}eg.create=(e,t)=>new eg({value:e,typeName:tf.ZodLiteral,...T(t)});class e_ extends C{_parse(e){if("string"!=typeof e.data){let t=this._getOrReturnCtx(e),s=this._def.values;return f(t,{expected:tm.joinValues(s),received:t.parsedType,code:n.invalid_type}),x}if(this._cache||(this._cache=new Set(this._def.values)),!this._cache.has(e.data)){let t=this._getOrReturnCtx(e),s=this._def.values;return f(t,{received:t.data,code:n.invalid_enum_value,options:s}),x}return b(e.data)}get options(){return this._def.values}get enum(){let e={};for(let t of this._def.values)e[t]=t;return e}get Values(){let e={};for(let t of this._def.values)e[t]=t;return e}get Enum(){let e={};for(let t of this._def.values)e[t]=t;return e}extract(e,t=this._def){return e_.create(e,{...this._def,...t})}exclude(e,t=this._def){return e_.create(this.options.filter(t=>!e.includes(t)),{...this._def,...t})}}e_.create=ex;class eb extends C{_parse(e){let t=tm.getValidEnumValues(this._def.values),s=this._getOrReturnCtx(e);if(s.parsedType!==r.string&&s.parsedType!==r.number){let e=tm.objectValues(t);return f(s,{expected:tm.joinValues(e),received:s.parsedType,code:n.invalid_type}),x}if(this._cache||(this._cache=new Set(tm.getValidEnumValues(this._def.values))),!this._cache.has(e.data)){let e=tm.objectValues(t);return f(s,{received:s.data,code:n.invalid_enum_value,options:e}),x}return b(e.data)}get enum(){return this._def.values}}eb.create=(e,t)=>new eb({values:e,typeName:tf.ZodNativeEnum,...T(t)});class ev extends C{unwrap(){return this._def.type}_parse(e){let{ctx:t}=this._processInputParams(e);return t.parsedType!==r.promise&&!1===t.common.async?(f(t,{code:n.invalid_type,expected:r.promise,received:t.parsedType}),x):b((t.parsedType===r.promise?t.data:Promise.resolve(t.data)).then(e=>this._def.type.parseAsync(e,{path:t.path,errorMap:t.common.contextualErrorMap})))}}ev.create=(e,t)=>new ev({type:e,typeName:tf.ZodPromise,...T(t)});class ey extends C{innerType(){return this._def.schema}sourceType(){return this._def.schema._def.typeName===tf.ZodEffects?this._def.schema.sourceType():this._def.schema}_parse(e){let{status:t,ctx:s}=this._processInputParams(e),a=this._def.effect||null,r={addIssue:e=>{f(s,e),e.fatal?t.abort():t.dirty()},get path(){return s.path}};if(r.addIssue=r.addIssue.bind(r),"preprocess"===a.type){let e=a.transform(s.data,r);if(s.common.async)return Promise.resolve(e).then(async e=>{if("aborted"===t.value)return x;let a=await this._def.schema._parseAsync({data:e,path:s.path,parent:s});return"aborted"===a.status?x:"dirty"===a.status||"dirty"===t.value?_(a.value):a});{if("aborted"===t.value)return x;let a=this._def.schema._parseSync({data:e,path:s.path,parent:s});return"aborted"===a.status?x:"dirty"===a.status||"dirty"===t.value?_(a.value):a}}if("refinement"===a.type){let e=e=>{let t=a.refinement(e,r);if(s.common.async)return Promise.resolve(t);if(t instanceof Promise)throw Error("Async refinement encountered during synchronous parse operation. Use .parseAsync instead.");return e};if(!1!==s.common.async)return this._def.schema._parseAsync({data:s.data,path:s.path,parent:s}).then(s=>"aborted"===s.status?x:("dirty"===s.status&&t.dirty(),e(s.value).then(()=>({status:t.value,value:s.value}))));{let a=this._def.schema._parseSync({data:s.data,path:s.path,parent:s});return"aborted"===a.status?x:("dirty"===a.status&&t.dirty(),e(a.value),{status:t.value,value:a.value})}}if("transform"===a.type)if(!1!==s.common.async)return this._def.schema._parseAsync({data:s.data,path:s.path,parent:s}).then(e=>j(e)?Promise.resolve(a.transform(e.value,r)).then(e=>({status:t.value,value:e})):x);else{let e=this._def.schema._parseSync({data:s.data,path:s.path,parent:s});if(!j(e))return x;let i=a.transform(e.value,r);if(i instanceof Promise)throw Error("Asynchronous transform encountered during synchronous parse operation. Use .parseAsync instead.");return{status:t.value,value:i}}tm.assertNever(a)}}ey.create=(e,t,s)=>new ey({schema:e,typeName:tf.ZodEffects,effect:t,...T(s)}),ey.createWithPreprocess=(e,t,s)=>new ey({schema:t,effect:{type:"preprocess",transform:e},typeName:tf.ZodEffects,...T(s)});class ej extends C{_parse(e){return this._getType(e)===r.undefined?b(void 0):this._def.innerType._parse(e)}unwrap(){return this._def.innerType}}ej.create=(e,t)=>new ej({innerType:e,typeName:tf.ZodOptional,...T(t)});class ew extends C{_parse(e){return this._getType(e)===r.null?b(null):this._def.innerType._parse(e)}unwrap(){return this._def.innerType}}ew.create=(e,t)=>new ew({innerType:e,typeName:tf.ZodNullable,...T(t)});class eN extends C{_parse(e){let{ctx:t}=this._processInputParams(e),s=t.data;return t.parsedType===r.undefined&&(s=this._def.defaultValue()),this._def.innerType._parse({data:s,path:t.path,parent:t})}removeDefault(){return this._def.innerType}}eN.create=(e,t)=>new 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here",j=y.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),v=o.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),w={};l.length>0&&(w.tags=l),d.length>0&&(w.vector_stores=d),p.length>0&&(w.guardrails=p),m.length>0&&(w.policies=m);let N=g||"your-model-name",k="azure"===f?`import openai - -client = openai.AzureOpenAI( - api_key="${x||"YOUR_LITELLM_API_KEY"}", - azure_endpoint="${b}", - api_version="2024-02-01" -)`:`import openai - -client = openai.OpenAI( - api_key="${x||"YOUR_LITELLM_API_KEY"}", - base_url="${b}" -)`;switch(c){case t.EndpointType.CHAT:{let e=Object.keys(w).length>0,t="";if(e){let e=JSON.stringify({metadata:w},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`, - extra_body=${e}`}let i=v.length>0?v:[{role:"user",content:y}];a=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.chat.completions.create( - model="${N}", - messages=${JSON.stringify(i,null,4)}${t} -) - -print(response) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.chat.completions.create( -# model="${N}", -# messages=[ -# { -# "role": "user", -# "content": [ -# { -# "type": "text", -# "text": "${j}" -# }, -# { -# "type": "image_url", -# "image_url": { -# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file} -# } -# } -# ] -# } -# ]${t} -# ) -# print(response_with_file) -`;break}case t.EndpointType.RESPONSES:{let e=Object.keys(w).length>0,t="";if(e){let e=JSON.stringify({metadata:w},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();t=`, - extra_body=${e}`}let i=v.length>0?v:[{role:"user",content:y}];a=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.responses.create( - model="${N}", - input=${JSON.stringify(i,null,4)}${t} -) - -print(response.output_text) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.responses.create( -# model="${N}", -# input=[ -# { -# "role": "user", -# "content": [ -# {"type": "input_text", "text": "${j}"}, -# { -# "type": "input_image", -# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} -# }, -# ], -# } -# ]${t} -# ) -# print(response_with_file.output_text) -`;break}case t.EndpointType.IMAGE:a="azure"===f?` -# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. -# This snippet uses 'client.images.generate' and will create a new image based on your prompt. -# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. -import os -import requests -import json -import time -from PIL import Image - -result = client.images.generate( - model="${N}", - prompt="${n}", - n=1 -) - -json_response = json.loads(result.model_dump_json()) - -# Set the directory for the stored image -image_dir = os.path.join(os.curdir, 'images') - -# If the directory doesn't exist, create it -if not os.path.isdir(image_dir): - os.mkdir(image_dir) - -# Initialize the image path -image_filename = f"generated_image_{int(time.time())}.png" -image_path = os.path.join(image_dir, image_filename) - -try: - # Retrieve the generated image - if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): - image_url = json_response["data"][0]["url"] - generated_image = requests.get(image_url).content - with open(image_path, "wb") as image_file: - image_file.write(generated_image) - - print(f"Image saved to {image_path}") - # Display the image - image = Image.open(image_path) - image.show() - else: - print("Could not find image URL in response.") - print("Full response:", json_response) -except Exception as e: - print(f"An error occurred: {e}") - print("Full response:", json_response) -`:` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${j}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${N}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`;break;case t.EndpointType.IMAGE_EDITS:a="azure"===f?` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# The prompt entered by the user -prompt = "${j}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${N}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. Model response:") - print("\\n".join(text_response)) - else: - print("No image data found in response.") - print("Full response for debugging:") - print(response) -`:` -import base64 -import os -import time - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${j}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${N}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, - }, - { - "type": "input_image", - "file_id": file_id2, - } - ], - } - ], - tools=[{"type": "image_generation"}], -) - -# Process the response -image_generation_calls = [ - output - for output in response.output - if output.type == "image_generation_call" -] - -image_data = [output.result for output in image_generation_calls] - -if image_data: - image_base64 = image_data[0] - image_filename = f"edited_image_{int(time.time())}.png" - with open(image_filename, "wb") as f: - f.write(base64.b64decode(image_base64)) - print(f"Image saved to {image_filename}") -else: - # If no image is generated, there might be a text response with an explanation - text_response = [output.text for output in response.output if hasattr(output, 'text')] - if text_response: - print("No image generated. 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