Query the Mistral-Small-4-119B-2603 API#
This page shows how to launch the NIM container and call the Chat Completions API with curl, the OpenAI Python SDK, and LangChain. It covers image inputs, text-only queries, multi-turn conversations, and function calling.
For more information on this model, refer to the model card.
Launch NIM#
Make sure you complete the steps in Get Started with NIM before you launch the NIM.
The following command launches a Docker container for this specific model:
# Choose a container name for bookkeeping
export CONTAINER_NAME=mistralai-mistral-small-4-119b-2603
# The container name from the previous ngc registry image list command
Repository="mistral-small-4-119b-2603"
Latest_Tag="1.7.0-variant"
# Choose a VLM NIM Image from NGC
export IMG_NAME="nvcr.io/nim/mistralai/${Repository}:${Latest_Tag}"
# Choose a path on your system to cache the downloaded models
export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p "$LOCAL_NIM_CACHE"
# Start the VLM NIM
docker run -it --rm --name=$CONTAINER_NAME \
--runtime=nvidia \
--gpus all \
--shm-size=32GB \
-e NGC_API_KEY=$NGC_API_KEY \
-v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \
-p 8000:8000 \
$IMG_NAME
This NIM is built with a specialized base container and is subject to
limitations. It uses the release tag 1.7.0-variant for the
container image. Refer to Notes on NIM Container Variants
for more information.
OpenAI Chat Completions Request#
The Chat Completions endpoint is typically used with chat or instruct-tuned
models designed for a conversational approach. With the endpoint, prompts are
sent as messages with roles and content, providing a natural way to keep track
of a multi-turn conversation.
Note
The snippets below use max_tokens to keep examples short. For reasoning
examples, the max_tokens value is higher because reasoning output is
typically longer.
Provide the URL of an image and query the NIM server.
To stream the result, set "stream": true in the request body.
curl -X 'POST' \
'http://0.0.0.0:8000/v1/chat/completions' \
-H 'Accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "mistralai/mistral-small-4-119b-2603",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What is in this image?"
},
{
"type": "image_url",
"image_url":
{
"url": "https://assets.ngc.nvidia.com/products/api-catalog/phi-3-5-vision/example1b.jpg"
}
}
]
}
],
"max_tokens": 1024
}'
Alternatively, use the OpenAI Python SDK:
pip install -U openai
Run the client and query the Chat Completions API:
from openai import OpenAI
client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key="not-used")
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What is in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "https://assets.ngc.nvidia.com/products/api-catalog/phi-3-5-vision/example1b.jpg"
}
}
]
}
]
chat_response = client.chat.completions.create(
model="mistralai/mistral-small-4-119b-2603",
messages=messages,
max_tokens=1024,
stream=False,
)
assistant_message = chat_response.choices[0].message
print(assistant_message)
To stream responses, pass stream=True and iterate over the response:
# Code preceding `client.chat.completions.create` is the same.
stream = client.chat.completions.create(
model="mistralai/mistral-small-4-119b-2603",
messages=messages,
max_tokens=1024,
# Take note of this param.
stream=True,
)
for chunk in stream:
delta = chunk.choices[0].delta
if delta and delta.content:
text = delta.content
# Print immediately and without a newline to update the output as the response is
# streamed in.
print(text, end="", flush=True)
# Final newline.
print()
Passing Images#
NIM for VLMs follows the OpenAI specification to pass images as part of the HTTP payload in a user message.
Important
The supported image formats are GIF, JPG, JPEG, and PNG.
To adjust the maximum number of images allowed per request, set the
environment variable
NIM_MAX_IMAGES_PER_PROMPT. The default value is 5.
Public direct URL
Passing the direct URL of an image causes the container to download that image at runtime.
{
"type": "image_url",
"image_url": {
"url": "https://www.nvidia.com/content/dam/en-zz/Solutions/data-center/dgx-b200/dgx-b200-hero-bm-v2-l580-d.jpg"
}
}
Base64 data
Another option, useful for images not already on the web, is to Base64-encode the image bytes and send the data in your payload.
{
"type": "image_url",
"image_url": {
"url": "data:image/jpeg;base64,SGVsbG8gZGVh...ciBmZWxsb3chIQ=="
}
}
To convert images to base64, use the base64 command or the following Python code:
with open("image.png", "rb") as f:
image_b64 = base64.b64encode(f.read()).decode()
Function (Tool) Calling#
You can connect NIM to external tools and services using function calling (also known as tool calling). For more information, refer to Call Functions (Tools).
Note
The role function is not supported. Instead, use the role tool.
Note
The chat_template request parameter is not supported. Mistral tokenizers
use a built-in chat template and reject user-provided templates with an error.
Reasoning#
This model supports reasoning (thinking) mode, where the model generates
internal reasoning tokens before producing the final answer. When streaming,
reasoning output is returned in the delta.reasoning field.
The reasoning_effort request parameter controls reasoning behavior:
"high"— Enable reasoning."none"— Disable reasoning (default). The model responds directly without generating reasoning tokens.
curl -X 'POST' \
'http://0.0.0.0:8000/v1/chat/completions' \
-H 'Accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "mistralai/mistral-small-4-119b-2603",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Solve step by step: if a train leaves at 3pm going 60mph and another at 4pm going 80mph, when do they meet?"
}
]
}
],
"reasoning_effort": "high",
"max_tokens": 4096,
"stream": true
}'
To disable reasoning, set "reasoning_effort": "none".
Text-only Queries#
Many VLMs such as mistralai/mistral-small-4-119b-2603 support text-only queries,
where a VLM behaves exactly like a (text-only) LLM.
Important
Text-only capability is not available for all VLMs. Refer to the model cards in Support Matrix for support on text-only queries.
curl -X 'POST' \
'http://0.0.0.0:8000/v1/chat/completions' \
-H 'Accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "mistralai/mistral-small-4-119b-2603",
"messages": [
{
"role": "system",
"content": "You are a helpful assistant"
},
{
"role": "user",
"content": "Create a detailed itinerary for a week-long adventure trip through Southeast Asia."
}
],
"max_tokens": 4096,
"stream": true
}'
Using the OpenAI Python SDK:
from openai import OpenAI
client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key="not-used")
messages = [
{
"role": "system",
"content": "You are a helpful assistant"
},
{
"role": "user",
"content": "Create a detailed itinerary for a week-long adventure trip through Southeast Asia."
}
]
stream = client.chat.completions.create(
model="mistralai/mistral-small-4-119b-2603",
messages=messages,
max_tokens=4096,
stream=True,
)
for chunk in stream:
delta = chunk.choices[0].delta
if delta and delta.content:
text = delta.content
# Print immediately and without a newline to update the output as the response is
# streamed in.
print(text, end="", flush=True)
# Final newline.
print()
Multi-turn Conversation#
This model supports multi-turn conversations: send multiple messages with alternating user and assistant roles.
Important
Multi-turn capability is not available for all VLMs. Refer to the model cards for information on multi-turn conversations.
Important
Messages must follow strict user / assistant role alternation. The
first message must have the role user, and the last message must also be
user. Sending a conversation that ends with an assistant message
returns a 400 BadRequestError.
If you need the last message to be assistant (for example, to continue a
partial response), include the following top-level request parameters:
"add_generation_prompt": false,
"continue_final_message": true
curl -X 'POST' \
'http://0.0.0.0:8000/v1/chat/completions' \
-H 'Accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "mistralai/mistral-small-4-119b-2603",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What is in this image?"
},
{
"type": "image_url",
"image_url":
{
"url": "https://www.nvidia.com/content/dam/en-zz/Solutions/data-center/dgx-b200/dgx-b200-hero-bm-v2-l580-d.jpg"
}
}
]
},
{
"role": "assistant",
"content": "This image shows an **NVIDIA DGX system**, which is NVIDIAs flagship line of AI supercomputers/servers. ..."
},
{
"role": "user",
"content": "When was this system released?"
}
],
"max_tokens": 4096
}'
from openai import OpenAI
client = OpenAI(base_url="http://0.0.0.0:8000/v1", api_key="not-used")
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What is in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "https://www.nvidia.com/content/dam/en-zz/Solutions/data-center/dgx-b200/dgx-b200-hero-bm-v2-l580-d.jpg"
}
}
]
},
{
"role": "assistant",
"content": "This image shows an **NVIDIA DGX system**, which is NVIDIAs flagship line of AI supercomputers/servers. ..."
},
{
"role": "user",
"content": "When was this system released?"
}
]
chat_response = client.chat.completions.create(
model="mistralai/mistral-small-4-119b-2603",
messages=messages,
max_tokens=4096,
stream=False
)
assistant_message = chat_response.choices[0].message
print(assistant_message)
Using LangChain#
You can call NIM from LangChain, a framework for building applications with large language models (LLMs).
Install LangChain using the following command:
pip install -U langchain-openai langchain-core
Query the OpenAI Chat Completions endpoint using LangChain:
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
model = ChatOpenAI(
model="mistralai/mistral-small-4-119b-2603",
openai_api_base="http://0.0.0.0:8000/v1",
openai_api_key="not-needed"
)
message = HumanMessage(
content=[
{"type": "text", "text": "What is in this image?"},
{
"type": "image_url",
"image_url": {"url": "https://assets.ngc.nvidia.com/products/api-catalog/phi-3-5-vision/example1b.jpg"},
},
],
)
print(model.invoke([message]))