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[GH-ISSUE #3765] [langchain]: DeepSeek v4 API access #2758

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opened 2026-06-05 17:26:38 -04:00 by yindo · 0 comments
yindo commented 2026-06-05 17:26:38 -04:00
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Originally created by @miajio on GitHub (Apr 28, 2026).
Original GitHub issue: https://github.com/langchain-ai/docs/issues/3765

Type of issue

question

Language

Python

Description

deepseek v4 api access response to 400

Please note that, unlike turns in thinking mode that do not involve tool calls, for turns that do perform tool calls, the reasoning_content must be fully passed back to the API in all subsequent requests.

If your code does not correctly pass back reasoning_content, the API will return a 400 error. Please refer to the sample code below for the correct approach.

Sample Code
Below is a simple sample code for tool calls in thinking mode:

import os
import json
from openai import OpenAI
from datetime import datetime

# The definition of the tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_date",
            "description": "Get the current date",
            "parameters": { "type": "object", "properties": {} },
        }
    },
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather of a location, the user should supply the location and date.",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": { "type": "string", "description": "The city name" },
                    "date": { "type": "string", "description": "The date in format YYYY-mm-dd" },
                },
                "required": ["location", "date"]
            },
        }
    },
]

# The mocked version of the tool calls
def get_date_mock():
    return datetime.now().strftime("%Y-%m-%d")

def get_weather_mock(location, date):
    return "Cloudy 7~13°C"

TOOL_CALL_MAP = {
    "get_date": get_date_mock,
    "get_weather": get_weather_mock
}

def run_turn(turn, messages):
    sub_turn = 1
    while True:
        response = client.chat.completions.create(
            model='deepseek-v4-pro',
            messages=messages,
            tools=tools,
            reasoning_effort="high",
            extra_body={ "thinking": { "type": "enabled" } },
        )
        messages.append(response.choices[0].message)
        reasoning_content = response.choices[0].message.reasoning_content
        content = response.choices[0].message.content
        tool_calls = response.choices[0].message.tool_calls
        print(f"Turn {turn}.{sub_turn}\n{reasoning_content=}\n{content=}\n{tool_calls=}")
        # If there is no tool calls, then the model should get a final answer and we need to stop the loop
        if tool_calls is None:
            break
        for tool in tool_calls:
            tool_function = TOOL_CALL_MAP[tool.function.name]
            tool_result = tool_function(**json.loads(tool.function.arguments))
            print(f"tool result for {tool.function.name}: {tool_result}\n")
            messages.append({
                "role": "tool",
                "tool_call_id": tool.id,
                "content": tool_result,
            })
        sub_turn += 1
    print()

client = OpenAI(
    api_key=os.environ.get('DEEPSEEK_API_KEY'),
    base_url=os.environ.get('DEEPSEEK_BASE_URL'),
)

# The user starts a question
turn = 1
messages = [{
    "role": "user",
    "content": "How's the weather in Hangzhou Tomorrow"
}]
run_turn(turn, messages)

# The user starts a new question
turn = 2
messages.append({
    "role": "user",
    "content": "How's the weather in Guangzhou Tomorrow"
})
run_turn(turn, messages)

In each sub-request of Turn 1, the reasoning_content generated during that turn is sent to the API, allowing the model to continue its previous reasoning. response.choices[0].message contains all necessary fields for the assistant message, including content, reasoning_content, and tool_calls. For simplicity, you can directly append the message to the end of the messages list using the following code:

messages.append(response.choices[0].message)

This line of code is equivalent to:

messages.append({
    'role': 'assistant',
    'content': response.choices[0].message.content,
    'reasoning_content': response.choices[0].message.reasoning_content,
    'tool_calls': response.choices[0].message.tool_calls,
})

Additionally, in the Turn 2 request, we still pass the reasoning_content generated in Turn 1 to the API.

The sample output of this code is as follows:

Turn 1.1
reasoning_content="The user is asking about the weather in Hangzhou tomorrow. I need to get tomorrow's date first, then call the weather function."
content="Let me check tomorrow's weather in Hangzhou for you. First, let me get tomorrow's date."
tool_calls=[ChatCompletionMessageFunctionToolCall(id='call_00_kw66qNnNto11bSfJVIdlV5Oo', function=Function(arguments='{}', name='get_date'), type='function', index=0)]
tool result for get_date: 2026-04-19

Turn 1.2
reasoning_content="Today is 2026-04-19, so tomorrow is 2026-04-20. Now I'll call the weather function for Hangzhou."
content=''
tool_calls=[ChatCompletionMessageFunctionToolCall(id='call_00_H2SCW6136vWJGq9SQlBuhVt4', function=Function(arguments='{"location": "Hangzhou", "date": "2026-04-20"}', name='get_weather'), type='function', index=0)]
tool result for get_weather: Cloudy 7~13°C

Turn 1.3
reasoning_content='The weather result is in. Let me share this with the user.'
content="Here's the weather forecast for **Hangzhou tomorrow (April 20, 2026)**:\n\n- 🌤 **Condition:** Cloudy  \n- 🌡 **Temperature:** 7°C ~ 13°C (45°F ~ 55°F)\n\nIt'll be on the cooler side, so you might want to bring a light jacket if you're heading out! Let me know if you need anything else."
tool_calls=None

Turn 2.1
reasoning_content='The user is asking about the weather in Guangzhou tomorrow. Today is 2026-04-19, so tomorrow is 2026-04-20. I can directly call the weather function.'
content=''
tool_calls=[ChatCompletionMessageFunctionToolCall(id='call_00_8URkLt5NjmNkVKhDmMcNq9Mo', function=Function(arguments='{"location": "Guangzhou", "date": "2026-04-20"}', name='get_weather'), type='function', index=0)]
tool result for get_weather: Cloudy 7~13°C

Turn 2.2
reasoning_content='The weather result for Guangzhou is the same as Hangzhou. Let me share this with the user.'
content="Here's the weather forecast for **Guangzhou tomorrow (April 20, 2026)**:\n\n- 🌤 **Condition:** Cloudy  \n- 🌡 **Temperature:** 7°C ~ 13°C (45°F ~ 55°F)\n\nIt'll be cool and cloudy, so a light jacket would be a good idea if you're going out. Let me know if there's anything else you'd like to know!"
tool_calls=None
Originally created by @miajio on GitHub (Apr 28, 2026). Original GitHub issue: https://github.com/langchain-ai/docs/issues/3765 ### Type of issue question ### Language Python ### Description deepseek v4 api access response to 400 Please note that, unlike turns in thinking mode that do not involve tool calls, for turns that do perform tool calls, the reasoning_content must be fully passed back to the API in all subsequent requests. If your code does not correctly pass back reasoning_content, the API will return a 400 error. Please refer to the sample code below for the correct approach. Sample Code Below is a simple sample code for tool calls in thinking mode: ```python import os import json from openai import OpenAI from datetime import datetime # The definition of the tools tools = [ { "type": "function", "function": { "name": "get_date", "description": "Get the current date", "parameters": { "type": "object", "properties": {} }, } }, { "type": "function", "function": { "name": "get_weather", "description": "Get weather of a location, the user should supply the location and date.", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city name" }, "date": { "type": "string", "description": "The date in format YYYY-mm-dd" }, }, "required": ["location", "date"] }, } }, ] # The mocked version of the tool calls def get_date_mock(): return datetime.now().strftime("%Y-%m-%d") def get_weather_mock(location, date): return "Cloudy 7~13°C" TOOL_CALL_MAP = { "get_date": get_date_mock, "get_weather": get_weather_mock } def run_turn(turn, messages): sub_turn = 1 while True: response = client.chat.completions.create( model='deepseek-v4-pro', messages=messages, tools=tools, reasoning_effort="high", extra_body={ "thinking": { "type": "enabled" } }, ) messages.append(response.choices[0].message) reasoning_content = response.choices[0].message.reasoning_content content = response.choices[0].message.content tool_calls = response.choices[0].message.tool_calls print(f"Turn {turn}.{sub_turn}\n{reasoning_content=}\n{content=}\n{tool_calls=}") # If there is no tool calls, then the model should get a final answer and we need to stop the loop if tool_calls is None: break for tool in tool_calls: tool_function = TOOL_CALL_MAP[tool.function.name] tool_result = tool_function(**json.loads(tool.function.arguments)) print(f"tool result for {tool.function.name}: {tool_result}\n") messages.append({ "role": "tool", "tool_call_id": tool.id, "content": tool_result, }) sub_turn += 1 print() client = OpenAI( api_key=os.environ.get('DEEPSEEK_API_KEY'), base_url=os.environ.get('DEEPSEEK_BASE_URL'), ) # The user starts a question turn = 1 messages = [{ "role": "user", "content": "How's the weather in Hangzhou Tomorrow" }] run_turn(turn, messages) # The user starts a new question turn = 2 messages.append({ "role": "user", "content": "How's the weather in Guangzhou Tomorrow" }) run_turn(turn, messages) ``` In each sub-request of Turn 1, the reasoning_content generated during that turn is sent to the API, allowing the model to continue its previous reasoning. response.choices[0].message contains all necessary fields for the assistant message, including content, reasoning_content, and tool_calls. For simplicity, you can directly append the message to the end of the messages list using the following code: ``` messages.append(response.choices[0].message) ``` This line of code is equivalent to: ``` messages.append({ 'role': 'assistant', 'content': response.choices[0].message.content, 'reasoning_content': response.choices[0].message.reasoning_content, 'tool_calls': response.choices[0].message.tool_calls, }) ``` Additionally, in the Turn 2 request, we still pass the reasoning_content generated in Turn 1 to the API. The sample output of this code is as follows: ``` Turn 1.1 reasoning_content="The user is asking about the weather in Hangzhou tomorrow. I need to get tomorrow's date first, then call the weather function." content="Let me check tomorrow's weather in Hangzhou for you. First, let me get tomorrow's date." tool_calls=[ChatCompletionMessageFunctionToolCall(id='call_00_kw66qNnNto11bSfJVIdlV5Oo', function=Function(arguments='{}', name='get_date'), type='function', index=0)] tool result for get_date: 2026-04-19 Turn 1.2 reasoning_content="Today is 2026-04-19, so tomorrow is 2026-04-20. Now I'll call the weather function for Hangzhou." content='' tool_calls=[ChatCompletionMessageFunctionToolCall(id='call_00_H2SCW6136vWJGq9SQlBuhVt4', function=Function(arguments='{"location": "Hangzhou", "date": "2026-04-20"}', name='get_weather'), type='function', index=0)] tool result for get_weather: Cloudy 7~13°C Turn 1.3 reasoning_content='The weather result is in. Let me share this with the user.' content="Here's the weather forecast for **Hangzhou tomorrow (April 20, 2026)**:\n\n- 🌤 **Condition:** Cloudy \n- 🌡 **Temperature:** 7°C ~ 13°C (45°F ~ 55°F)\n\nIt'll be on the cooler side, so you might want to bring a light jacket if you're heading out! Let me know if you need anything else." tool_calls=None Turn 2.1 reasoning_content='The user is asking about the weather in Guangzhou tomorrow. Today is 2026-04-19, so tomorrow is 2026-04-20. I can directly call the weather function.' content='' tool_calls=[ChatCompletionMessageFunctionToolCall(id='call_00_8URkLt5NjmNkVKhDmMcNq9Mo', function=Function(arguments='{"location": "Guangzhou", "date": "2026-04-20"}', name='get_weather'), type='function', index=0)] tool result for get_weather: Cloudy 7~13°C Turn 2.2 reasoning_content='The weather result for Guangzhou is the same as Hangzhou. Let me share this with the user.' content="Here's the weather forecast for **Guangzhou tomorrow (April 20, 2026)**:\n\n- 🌤 **Condition:** Cloudy \n- 🌡 **Temperature:** 7°C ~ 13°C (45°F ~ 55°F)\n\nIt'll be cool and cloudy, so a light jacket would be a good idea if you're going out. Let me know if there's anything else you'd like to know!" tool_calls=None ```
yindo added the langchainexternal labels 2026-06-05 17:26:38 -04:00
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