From ed4c2b6883be85c32486496a593da7251d09af7b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E0=AE=AE=E0=AE=A9=E0=AF=8B=E0=AE=9C=E0=AF=8D=E0=AE=95?= =?UTF-8?q?=E0=AF=81=E0=AE=AE=E0=AE=BE=E0=AE=B0=E0=AF=8D=20=E0=AE=AA?= =?UTF-8?q?=E0=AE=B4=E0=AE=A9=E0=AE=BF=E0=AE=9A=E0=AF=8D=E0=AE=9A=E0=AE=BE?= =?UTF-8?q?=E0=AE=AE=E0=AE=BF?= Date: Mon, 22 Sep 2025 15:28:11 +0530 Subject: [PATCH] Update model references from gemini-pro to gemini-2.5-pro --- docs/my-website/docs/providers/vertex.md | 48 ++++++++++++------------ 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index cb90b7434e..ef407e6c10 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -45,7 +45,7 @@ vertex_credentials_json = json.dumps(vertex_credentials) ## COMPLETION CALL response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{ "content": "Hello, how are you?","role": "user"}], vertex_credentials=vertex_credentials_json ) @@ -69,7 +69,7 @@ vertex_credentials_json = json.dumps(vertex_credentials) response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{"content": "You are a good bot.","role": "system"}, {"content": "Hello, how are you?","role": "user"}], vertex_credentials=vertex_credentials_json ) @@ -189,7 +189,7 @@ print(json.loads(completion.choices[0].message.content)) 1. Add model to config.yaml ```yaml model_list: - - model_name: gemini-pro + - model_name: gemini-2.5-pro litellm_params: model: vertex_ai/gemini-1.5-pro vertex_project: "project-id" @@ -210,7 +210,7 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer sk-1234' \ -D '{ - "model": "gemini-pro", + "model": "gemini-2.5-pro", "messages": [ {"role": "user", "content": "List 5 popular cookie recipes."} ], @@ -262,7 +262,7 @@ except JSONSchemaValidationError as e: 1. Add model to config.yaml ```yaml model_list: - - model_name: gemini-pro + - model_name: gemini-2.5-pro litellm_params: model: vertex_ai/gemini-1.5-pro vertex_project: "project-id" @@ -283,7 +283,7 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer sk-1234' \ -D '{ - "model": "gemini-pro", + "model": "gemini-2.5-pro", "messages": [ {"role": "user", "content": "List 5 popular cookie recipes."} ], @@ -391,7 +391,7 @@ client = OpenAI( ) response = client.chat.completions.create( - model="gemini-pro", + model="gemini-2.5-pro", messages=[{"role": "user", "content": "Who won the world cup?"}], tools=[{"googleSearch": {}}], ) @@ -406,7 +406,7 @@ curl http://localhost:4000/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer sk-1234" \ -d '{ - "model": "gemini-pro", + "model": "gemini-2.5-pro", "messages": [ {"role": "user", "content": "Who won the world cup?"} ], @@ -527,7 +527,7 @@ client = OpenAI( ) response = client.chat.completions.create( - model="gemini-pro", + model="gemini-2.5-pro", messages=[{"role": "user", "content": "Who won the world cup?"}], tools=[{"enterpriseWebSearch": {}}], ) @@ -542,7 +542,7 @@ curl http://localhost:4000/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer sk-1234" \ -d '{ - "model": "gemini-pro", + "model": "gemini-2.5-pro", "messages": [ {"role": "user", "content": "Who won the world cup?"} ], @@ -835,7 +835,7 @@ import litellm litellm.vertex_project = "hardy-device-38811" # Your Project ID litellm.vertex_location = "us-central1" # proj location -response = litellm.completion(model="gemini-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]) +response = litellm.completion(model="gemini-2.5-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]) ``` ## Usage with LiteLLM Proxy Server @@ -876,9 +876,9 @@ Here's how to use Vertex AI with the LiteLLM Proxy Server vertex_location: "us-central1" # proj location model_list: - -model_name: team1-gemini-pro + -model_name: team1-gemini-2.5-pro litellm_params: - model: gemini-pro + model: gemini-2.5-pro ``` @@ -905,7 +905,7 @@ Here's how to use Vertex AI with the LiteLLM Proxy Server ) response = client.chat.completions.create( - model="team1-gemini-pro", + model="team1-gemini-2.5-pro", messages = [ { "role": "user", @@ -925,7 +925,7 @@ Here's how to use Vertex AI with the LiteLLM Proxy Server --header 'Authorization: Bearer sk-1234' \ --header 'Content-Type: application/json' \ --data '{ - "model": "team1-gemini-pro", + "model": "team1-gemini-2.5-pro", "messages": [ { "role": "user", @@ -975,7 +975,7 @@ vertex_credentials_json = json.dumps(vertex_credentials) response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{"content": "You are a good bot.","role": "system"}, {"content": "Hello, how are you?","role": "user"}], vertex_credentials=vertex_credentials_json, vertex_project="my-special-project", @@ -1039,7 +1039,7 @@ In certain use-cases you may need to make calls to the models and pass [safety s ```python response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}] safety_settings=[ { @@ -1153,7 +1153,7 @@ litellm.vertex_ai_safety_settings = [ }, ] response = completion( - model="vertex_ai/gemini-pro", + model="vertex_ai/gemini-2.5-pro", messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}] ) ``` @@ -1212,7 +1212,7 @@ litellm.vertex_location = "us-central1 # Your Location ## Gemini Pro | Model Name | Function Call | |------------------|--------------------------------------| -| gemini-pro | `completion('gemini-pro', messages)`, `completion('vertex_ai/gemini-pro', messages)` | +| gemini-2.5-pro | `completion('gemini-2.5-pro', messages)`, `completion('vertex_ai/gemini-2.5-pro', messages)` | ## Fine-tuned Models @@ -1307,7 +1307,7 @@ curl --location 'https://0.0.0.0:4000/v1/chat/completions' \ ## Gemini Pro Vision | Model Name | Function Call | |------------------|--------------------------------------| -| gemini-pro-vision | `completion('gemini-pro-vision', messages)`, `completion('vertex_ai/gemini-pro-vision', messages)`| +| gemini-2.5-pro-vision | `completion('gemini-2.5-pro-vision', messages)`, `completion('vertex_ai/gemini-2.5-pro-vision', messages)`| ## Gemini 1.5 Pro (and Vision) | Model Name | Function Call | @@ -1321,7 +1321,7 @@ curl --location 'https://0.0.0.0:4000/v1/chat/completions' \ #### Using Gemini Pro Vision -Call `gemini-pro-vision` in the same input/output format as OpenAI [`gpt-4-vision`](https://docs.litellm.ai/docs/providers/openai#openai-vision-models) +Call `gemini-2.5-pro-vision` in the same input/output format as OpenAI [`gpt-4-vision`](https://docs.litellm.ai/docs/providers/openai#openai-vision-models) LiteLLM Supports the following image types passed in `url` - Images with Cloud Storage URIs - gs://cloud-samples-data/generative-ai/image/boats.jpeg @@ -1339,7 +1339,7 @@ LiteLLM Supports the following image types passed in `url` import litellm response = litellm.completion( - model = "vertex_ai/gemini-pro-vision", + model = "vertex_ai/gemini-2.5-pro-vision", messages=[ { "role": "user", @@ -1377,7 +1377,7 @@ image_path = "cached_logo.jpg" # Getting the base64 string base64_image = encode_image(image_path) response = litellm.completion( - model="vertex_ai/gemini-pro-vision", + model="vertex_ai/gemini-2.5-pro-vision", messages=[ { "role": "user", @@ -1433,7 +1433,7 @@ tools = [ messages = [{"role": "user", "content": "What's the weather like in Boston today?"}] response = completion( - model="vertex_ai/gemini-pro-vision", + model="vertex_ai/gemini-2.5-pro-vision", messages=messages, tools=tools, )