feat(mistral-ai/mistral-medium-3-5-0): add new models [bot]#1231
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ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-mistral-ai/mistral-medium-3-5-0"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "How to calculate 3^3^3^3? Think step by step and show all reasoning."}]},
]
system = [{"text": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."}]
response = client.converse(
modelId=_model,
system=system,
messages=messages,
)
_content = response["output"]["message"]["content"]
for _block in _content:
if "reasoningContent" in _block:
print(_block["reasoningContent"]["reasoningText"]["text"])
if "text" in _block:
print(_block["text"])
_content = response["output"]["message"]["content"]
_reasoning_detected = False
for _block in _content:
if "text" in _block:
print(_block["text"])
if "reasoningContent" in _block:
_reasoning_detected = True
_reasoning = _block["reasoningContent"]
if "reasoningText" in _reasoning:
print(f"Reasoning: {_reasoning['reasoningText']['text'][:200]}...")
_usage = response.get("usage", {})
if _usage.get("reasoning_tokens") or _usage.get("reasoningTokens"):
_reasoning_detected = True
if not _reasoning_detected:
print("Response: ", response)
raise Exception("VALIDATION FAILED: reasoning - no reasoning information in Bedrock response")
print("VALIDATION: reasoning SUCCESS")
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-mistral-ai/mistral-medium-3-5-0"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "What is the capital of France?"}]},
]
system = [{"text": "You are a helpful assistant."}]
response = client.converse(
modelId=_model,
system=system,
messages=messages,
inferenceConfig={
"maxTokens": 256,
"temperature": 1,
},
)
_content = response["output"]["message"]["content"]
for _block in _content:
if "text" in _block:
print(_block["text"])
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-mistral-ai/mistral-medium-3-5-0"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
tool_config = {
"tools": [
{
"toolSpec": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"inputSchema": {
"json": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. London",
},
},
"required": ["location"],
}
},
}
}
],
"toolChoice": {"auto": {}},
}
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "Use the get_weather tool to check the weather in London. You must call the tool, do not respond with plain text."}]},
]
system = [{"text": "You are a helpful assistant with access to tools. You MUST strictly use the provided tools to answer. Never respond with plain text when a tool is available."}]
response = client.converse_stream(
modelId=_model,
system=system,
messages=messages,
toolConfig=tool_config,
)
_events = []
for _event in response["stream"]:
_events.append(_event)
if "contentBlockStart" in _event:
_start = _event["contentBlockStart"].get("start", {})
if "toolUse" in _start:
print(f"Tool: {_start['toolUse'].get('name', '')}", flush=True)
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "toolUse" in _delta:
print(_delta["toolUse"].get("input", ""), end="", flush=True)
if "text" in _delta:
print(_delta["text"], end="", flush=True)
_tool_use_detected = False
for _event in _events:
if "contentBlockStart" in _event:
_start = _event["contentBlockStart"].get("start", {})
if "toolUse" in _start:
_tool_use_detected = True
print(f"Tool: {_start['toolUse'].get('name', '')}", flush=True)
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "toolUse" in _delta:
_tool_use_detected = True
print(_delta["toolUse"].get("input", ""), end="", flush=True)
if "text" in _delta:
print(_delta["text"], end="", flush=True)
if not _tool_use_detected:
raise Exception("VALIDATION FAILED: tool-call stream - no tool uses in Bedrock stream")
print("\nVALIDATION: tool-call stream SUCCESS")
ErrorCode snippetimport boto3
import json
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-mistral-ai/mistral-medium-3-5-0"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
response_schema = {
"title": "CalendarEvent",
"type": "object",
"properties": {
"name": {"type": "string"},
"date": {"type": "string"},
"participants": {
"type": "array",
"items": {"type": "string"},
},
},
"required": ["name", "date", "participants"],
}
tool_config = {
"tools": [
{
"toolSpec": {
"name": "CalendarEvent",
"description": "Extract event information as a structured CalendarEvent.",
"inputSchema": {"json": response_schema},
}
}
],
"toolChoice": {"tool": {"name": "CalendarEvent"}},
}
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "Alice and Bob are going to a science fair on Friday."}]},
]
system = [{"text": "Extract the event information using the CalendarEvent tool."}]
response = client.converse_stream(
modelId=_model,
system=system,
messages=messages,
toolConfig=tool_config,
)
_events = []
for _event in response["stream"]:
_events.append(_event)
if "contentBlockStart" in _event:
_start = _event["contentBlockStart"].get("start", {})
if "toolUse" in _start:
print(f"Tool: {_start['toolUse'].get('name', '')}", flush=True)
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "toolUse" in _delta:
print(_delta["toolUse"].get("input", ""), end="", flush=True)
if "text" in _delta:
print(_delta["text"], end="", flush=True)
import json as _json
_accumulated_input = ""
_tool_use_detected = False
_accumulated_text = ""
for _event in _events:
if "contentBlockStart" in _event:
_start = _event["contentBlockStart"].get("start", {})
if "toolUse" in _start:
_tool_use_detected = True
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "toolUse" in _delta:
_accumulated_input += _delta["toolUse"].get("input", "")
if "text" in _delta:
_accumulated_text += _delta["text"]
print(_delta["text"], end="", flush=True)
if _tool_use_detected and _accumulated_input:
_parsed = _json.loads(_accumulated_input)
elif _accumulated_text:
_parsed = _json.loads(_accumulated_text)
else:
raise Exception("VALIDATION FAILED: structured-output stream - no content received from Bedrock stream")
print(_json.dumps(_parsed, indent=2))
if "name" not in _parsed or "date" not in _parsed or "participants" not in _parsed:
raise Exception("VALIDATION FAILED: structured-output stream - missing expected fields (name, date, participants)")
if not isinstance(_parsed.get("participants"), list):
raise Exception("VALIDATION FAILED: structured-output stream - 'participants' is not a list, schema not enforced")
print("\nVALIDATION: structured-output stream SUCCESS")
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-mistral-ai/mistral-medium-3-5-0"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "What is the capital of France?"}]},
]
system = [{"text": "You are a helpful assistant."}]
response = client.converse_stream(
modelId=_model,
system=system,
messages=messages,
inferenceConfig={
"maxTokens": 256,
"temperature": 1,
},
)
_events = []
for _event in response["stream"]:
_events.append(_event)
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "text" in _delta:
print(_delta["text"], end="", flush=True)
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-mistral-ai/mistral-medium-3-5-0"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "How to calculate 3^3^3^3? Think step by step and show all reasoning."}]},
]
system = [{"text": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."}]
response = client.converse_stream(
modelId=_model,
system=system,
messages=messages,
)
_events = []
for _event in response["stream"]:
_events.append(_event)
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "reasoningContent" in _delta:
print(_delta["reasoningContent"].get("text", ""), end="", flush=True)
if "text" in _delta:
print(_delta["text"], end="", flush=True)
_reasoning_detected = False
for _event in _events:
if "contentBlockDelta" in _event:
_delta = _event["contentBlockDelta"].get("delta", {})
if "text" in _delta:
print(_delta["text"], end="", flush=True)
if "reasoningContent" in _delta:
_reasoning_detected = True
_reasoning = _delta["reasoningContent"]
if "text" in _reasoning:
print(_reasoning["text"], end="", flush=True)
if "contentBlockStart" in _event:
_start = _event["contentBlockStart"].get("start", {})
if "reasoningContent" in _start:
_reasoning_detected = True
if "metadata" in _event:
_usage = _event["metadata"].get("usage", {})
if _usage.get("reasoning_tokens") or _usage.get("reasoningTokens"):
_reasoning_detected = True
if not _reasoning_detected:
raise Exception("VALIDATION FAILED: reasoning stream - no reasoning information in Bedrock stream")
print("\nVALIDATION: reasoning stream SUCCESS")
ErrorCode snippetimport boto3
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-mistral-ai/mistral-medium-3-5-0"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
tool_config = {
"tools": [
{
"toolSpec": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"inputSchema": {
"json": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. London",
},
},
"required": ["location"],
}
},
}
}
],
"toolChoice": {"auto": {}},
}
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "Use the get_weather tool to check the weather in London. You must call the tool, do not respond with plain text."}]},
]
system = [{"text": "You are a helpful assistant with access to tools. You MUST strictly use the provided tools to answer. Never respond with plain text when a tool is available."}]
response = client.converse(
modelId=_model,
system=system,
messages=messages,
toolConfig=tool_config,
)
_content = response["output"]["message"]["content"]
_tool_uses = [block for block in _content if "toolUse" in block]
if _tool_uses:
for _tu in _tool_uses:
print(f"Tool: {_tu['toolUse']['name']}")
print(f"Input: {_tu['toolUse']['input']}")
else:
_text_blocks = [block["text"] for block in _content if "text" in block]
print("\n".join(_text_blocks))
_content = response["output"]["message"]["content"]
_tool_uses = [block for block in _content if "toolUse" in block]
if _tool_uses:
for _tu in _tool_uses:
print(f"Tool: {_tu['toolUse']['name']}")
print(f"Input: {_tu['toolUse']['input']}")
else:
_text_blocks = [block["text"] for block in _content if "text" in block]
print("\n".join(_text_blocks))
if not _tool_uses:
raise Exception("VALIDATION FAILED: tool-call - no tool uses in Bedrock response")
print("VALIDATION: tool-call SUCCESS")
ErrorCode snippetimport boto3
import json
from botocore.config import Config
_endpoint = "https://internal.devtest.truefoundry.tech/api/llm"
_api_key = "***"
_model = "test-v2-mistral-ai/mistral-medium-3-5-0"
client = boto3.client(
"bedrock-runtime",
region_name="us-east-1",
endpoint_url=_endpoint,
aws_access_key_id="dummy",
aws_secret_access_key="dummy",
config=Config(inject_host_prefix=False),
)
def _add_auth_header(request, **kwargs):
request.headers["x-tfy-api-key"] = _api_key
client.meta.events.register("before-sign.bedrock-runtime.*", _add_auth_header)
response_schema = {
"title": "CalendarEvent",
"type": "object",
"properties": {
"name": {"type": "string"},
"date": {"type": "string"},
"participants": {
"type": "array",
"items": {"type": "string"},
},
},
"required": ["name", "date", "participants"],
}
tool_config = {
"tools": [
{
"toolSpec": {
"name": "CalendarEvent",
"description": "Extract event information as a structured CalendarEvent.",
"inputSchema": {"json": response_schema},
}
}
],
"toolChoice": {"tool": {"name": "CalendarEvent"}},
}
messages = [
{"role": "user", "content": [{"text": "Hi"}]},
{"role": "assistant", "content": [{"text": "Hi, how can I help you"}]},
{"role": "user", "content": [{"text": "Alice and Bob are going to a science fair on Friday."}]},
]
system = [{"text": "Extract the event information using the CalendarEvent tool."}]
response = client.converse(
modelId=_model,
system=system,
messages=messages,
toolConfig=tool_config,
)
_content = response["output"]["message"]["content"]
for _block in _content:
if "toolUse" in _block:
print(json.dumps(_block["toolUse"]["input"], indent=2))
elif "text" in _block:
print(_block["text"])
import json as _json
_content = response["output"]["message"]["content"]
_tool_uses = [block for block in _content if "toolUse" in block]
if _tool_uses:
_parsed = _tool_uses[0]["toolUse"]["input"]
else:
_text_blocks = [block["text"] for block in _content if "text" in block]
_text = "".join(_text_blocks)
_parsed = _json.loads(_text)
print(_json.dumps(_parsed, indent=2))
if "name" not in _parsed or "date" not in _parsed or "participants" not in _parsed:
raise Exception("VALIDATION FAILED: structured-output - missing expected fields (name, date, participants)")
if not isinstance(_parsed.get("participants"), list):
raise Exception("VALIDATION FAILED: structured-output - 'participants' is not a list, schema not enforced")
print("VALIDATION: structured-output SUCCESS")
ErrorCode snippetfrom openai import OpenAI
import json
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response_schema = json.loads('''{
"title": "CalendarEvent",
"type": "object",
"properties": {
"name": { "type": "string" },
"date": { "type": "string" },
"participants": {
"type": "array",
"items": { "type": "string" }
}
},
"required": ["name", "date", "participants"],
"additionalProperties": false
}''')
response = client.chat.completions.create(
model="test-v2-mistral-ai/mistral-medium-3-5-0",
messages=[
{"role": "system", "content": "Extract the event information as JSON."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "Alice and Bob are going to a science fair on Friday. Extract the event details as JSON."},
],
response_format={"type": "json_schema", "json_schema": {"name": "CalendarEvent", "schema": response_schema}},
stream=True,
)
import json as _json
_accumulated = ""
for chunk in response:
if chunk.choices and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
if delta.content is not None:
_accumulated += delta.content
print(delta.content, end="", flush=True)
if not _accumulated:
raise Exception("VALIDATION FAILED: structured-output stream - no content received")
_parsed = _json.loads(_accumulated)
if "name" not in _parsed or "date" not in _parsed or "participants" not in _parsed:
raise Exception("VALIDATION FAILED: structured-output stream - missing expected fields (name, date, participants)")
if not isinstance(_parsed.get("participants"), list):
raise Exception("VALIDATION FAILED: structured-output stream - 'participants' is not a list, schema not enforced")
if set(_parsed.keys()) != {"name", "date", "participants"}:
raise Exception(
f"VALIDATION FAILED: structured-output stream - unexpected keys present: {set(_parsed.keys())}"
)
print("\nVALIDATION: structured-output stream SUCCESS")
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. London",
},
},
"required": ["location"],
"additionalProperties": False,
},
"strict": True,
},
},
]
response = client.chat.completions.create(
model="test-v2-mistral-ai/mistral-medium-3-5-0",
messages=[
{"role": "system", "content": "You are a helpful assistant with access to tools. You MUST strictly use the provided tools to answer. Never respond with plain text when a tool is available."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "Use the get_weather tool to check the weather in London. You must call the tool, do not respond with plain text."},
],
tools=tools,
tool_choice="auto",
stream=False,
)
_message = response.choices[0].message
if _message.tool_calls:
for _tc in _message.tool_calls:
print(f"Function: {_tc.function.name}")
print(f"Arguments: {_tc.function.arguments}")
else:
print(_message.content)
if not _message.tool_calls or len(_message.tool_calls) == 0:
raise Exception("VALIDATION FAILED: tool-call - no tool calls in response")
print("VALIDATION: tool-call SUCCESS")
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city name, e.g. London",
},
},
"required": ["location"],
"additionalProperties": False,
},
"strict": True,
},
},
]
response = client.chat.completions.create(
model="test-v2-mistral-ai/mistral-medium-3-5-0",
messages=[
{"role": "system", "content": "You are a helpful assistant with access to tools. You MUST strictly use the provided tools to answer. Never respond with plain text when a tool is available."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "Use the get_weather tool to check the weather in London. You must call the tool, do not respond with plain text."},
],
tools=tools,
tool_choice="auto",
stream=True,
)
_tool_calls_made = False
for chunk in response:
if chunk.choices and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
if delta.content is not None:
print(delta.content, end="", flush=True)
if delta.tool_calls:
_tool_calls_made = True
for _tc in delta.tool_calls:
if _tc.function:
print(_tc.function.arguments or "", end="", flush=True)
if not _tool_calls_made:
raise Exception("VALIDATION FAILED: tool-call stream - no tool calls received")
print("\nVALIDATION: tool-call stream SUCCESS")
ErrorCode snippetfrom openai import OpenAI
import json
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response_schema = json.loads('''{
"title": "CalendarEvent",
"type": "object",
"properties": {
"name": { "type": "string" },
"date": { "type": "string" },
"participants": {
"type": "array",
"items": { "type": "string" }
}
},
"required": ["name", "date", "participants"],
"additionalProperties": false
}''')
response = client.chat.completions.create(
model="test-v2-mistral-ai/mistral-medium-3-5-0",
messages=[
{"role": "system", "content": "Extract the event information as JSON."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "Alice and Bob are going to a science fair on Friday. Extract the event details as JSON."},
],
response_format={"type": "json_schema", "json_schema": {"name": "CalendarEvent", "schema": response_schema}},
stream=False,
)
import json as _json
_content = response.choices[0].message.content
print(_content)
if not _content:
raise Exception("VALIDATION FAILED: structured-output - response content is empty")
_parsed = _json.loads(_content)
if "name" not in _parsed or "date" not in _parsed or "participants" not in _parsed:
raise Exception("VALIDATION FAILED: structured-output - missing expected fields (name, date, participants)")
if not isinstance(_parsed.get("participants"), list):
raise Exception("VALIDATION FAILED: structured-output - 'participants' is not a list, schema not enforced")
if set(_parsed.keys()) != {"name", "date", "participants"}:
raise Exception(
f"VALIDATION FAILED: structured-output - unexpected keys present: {set(_parsed.keys())}"
)
print("VALIDATION: structured-output SUCCESS")
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response = client.chat.completions.create(
model="test-v2-mistral-ai/mistral-medium-3-5-0",
messages=[
{"role": "system", "content": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "How to calculate 3^3^3^3? Think step by step and show all reasoning."},
],
reasoning_effort="medium",
stream=False,
)
_usage = getattr(response, "usage", None)
_reasoning_detected = False
_choices = getattr(response, "choices", None)
if _choices and len(_choices) > 0:
_message = getattr(_choices[0], "message", None)
else:
_message = None
if _message and getattr(_message, "content", None) is not None:
print(_message.content)
if _usage is not None:
_output_token_details = getattr(_usage, "completion_tokens_details", None)
if _output_token_details and getattr(_output_token_details, "reasoning_tokens", 0) > 0:
_reasoning_detected = True
elif getattr(_usage, "reasoning", None) is not None:
_reasoning_detected = True
if getattr(_message, "reasoning_content", None) is not None:
_reasoning_detected = True
elif getattr(_message, "reasoning", None) is not None:
_reasoning_detected = True
if not _reasoning_detected:
print("Response: ", response)
raise Exception("VALIDATION FAILED: reasoning - no reasoning information in response")
print("VALIDATION: reasoning SUCCESS")
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response = client.chat.completions.create(
model="test-v2-mistral-ai/mistral-medium-3-5-0",
messages=[
{"role": "system", "content": "You are a helpful assistant. You MUST think step by step and show your reasoning. Never skip reasoning steps."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "How to calculate 3^3^3^3? Think step by step and show all reasoning."},
],
reasoning_effort="medium",
stream=True,
)
_reasoning_detected = False
for chunk in response:
if chunk.choices and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
if delta.content is not None:
print(delta.content, end="", flush=True)
if getattr(delta, "reasoning_content", None) is not None:
_reasoning_detected = True
if getattr(delta, "reasoning", None) is not None:
_reasoning_detected = True
_usage = getattr(chunk, "usage", None)
if _usage is not None:
_details = getattr(_usage, "completion_tokens_details", None)
if _details and getattr(_details, "reasoning_tokens", 0) > 0:
_reasoning_detected = True
if not _reasoning_detected:
raise Exception("VALIDATION FAILED: reasoning stream - no reasoning information in stream")
print("\nVALIDATION: reasoning stream SUCCESS")
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response = client.chat.completions.create(
model="test-v2-mistral-ai/mistral-medium-3-5-0",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "What is the capital of France?"},
],
max_tokens=256,
temperature=1,
stream=False,
)
print(response.choices[0].message.content)
ErrorCode snippetfrom openai import OpenAI
client = OpenAI(api_key="***", base_url="https://internal.devtest.truefoundry.tech/api/llm")
response = client.chat.completions.create(
model="test-v2-mistral-ai/mistral-medium-3-5-0",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hi, how can I help you"},
{"role": "user", "content": "What is the capital of France?"},
],
max_tokens=256,
temperature=1,
stream=True,
)
for chunk in response:
if chunk.choices and len(chunk.choices) > 0:
delta = chunk.choices[0].delta
if delta.content is not None:
print(delta.content, end="", flush=True) |
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Auto-generated by model-addition-agent for
mistral-ai/mistral-medium-3-5-0.Note
Low Risk
Adds a new static model catalog file only; no runtime, auth, or application logic changes.
Overview
Adds a new Mistral AI provider definition for
mistral-medium-3-5-0, registering it as an active chat model with the same token pricing and 262k context window as the existingmistral-medium-3-5entry.The new entry differs in metadata: it advertises
assistant_prefill,docinput (alongside text/image),thinking: true, and areasoning_effortstring param with default temperature 1.0 (vs 0.7 onmistral-medium-3-5). Docs links point at the Mistral Medium 3.5 model card and reasoning capabilities.Reviewed by Cursor Bugbot for commit 22afd0b. Bugbot is set up for automated code reviews on this repo. Configure here.