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style: .. code-block:: admonition translations (#33400)
biiiiiiiiiiiiiiiigggggggg pass
This commit is contained in:
Mason Daugherty
GitHub
@@ -32,10 +32,10 @@ class ChatDeepSeek(BaseChatOpenAI):
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Setup:
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Install `langchain-deepseek` and set environment variable `DEEPSEEK_API_KEY`.
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.. code-block:: bash
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pip install -U langchain-deepseek
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export DEEPSEEK_API_KEY="your-api-key"
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```bash
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pip install -U langchain-deepseek
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export DEEPSEEK_API_KEY="your-api-key"
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```
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Key init args — completion params:
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model: str
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@@ -56,115 +56,111 @@ class ChatDeepSeek(BaseChatOpenAI):
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See full list of supported init args and their descriptions in the params section.
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Instantiate:
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.. code-block:: python
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```python
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from langchain_deepseek import ChatDeepSeek
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from langchain_deepseek import ChatDeepSeek
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llm = ChatDeepSeek(
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model="...",
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temperature=0,
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max_tokens=None,
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timeout=None,
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max_retries=2,
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# api_key="...",
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# other params...
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)
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llm = ChatDeepSeek(
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model="...",
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temperature=0,
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max_tokens=None,
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timeout=None,
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max_retries=2,
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# api_key="...",
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# other params...
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)
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```
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Invoke:
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.. code-block:: python
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messages = [
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("system", "You are a helpful translator. Translate the user sentence to French."),
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("human", "I love programming."),
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]
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llm.invoke(messages)
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```python
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messages = [
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("system", "You are a helpful translator. Translate the user sentence to French."),
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("human", "I love programming."),
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]
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llm.invoke(messages)
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```
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Stream:
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.. code-block:: python
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for chunk in llm.stream(messages):
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print(chunk.text, end="")
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.. code-block:: python
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stream = llm.stream(messages)
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full = next(stream)
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for chunk in stream:
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full += chunk
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full
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```python
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for chunk in llm.stream(messages):
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print(chunk.text, end="")
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```
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```python
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stream = llm.stream(messages)
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full = next(stream)
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for chunk in stream:
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full += chunk
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full
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```
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Async:
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.. code-block:: python
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```python
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await llm.ainvoke(messages)
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await llm.ainvoke(messages)
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# stream:
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# async for chunk in (await llm.astream(messages))
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# stream:
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# async for chunk in (await llm.astream(messages))
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# batch:
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# await llm.abatch([messages])
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# batch:
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# await llm.abatch([messages])
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```
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Tool calling:
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.. code-block:: python
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from pydantic import BaseModel, Field
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```python
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from pydantic import BaseModel, Field
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class GetWeather(BaseModel):
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'''Get the current weather in a given location'''
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class GetWeather(BaseModel):
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'''Get the current weather in a given location'''
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location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
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location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
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class GetPopulation(BaseModel):
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'''Get the current population in a given location'''
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class GetPopulation(BaseModel):
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'''Get the current population in a given location'''
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location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
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location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
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llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
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ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
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ai_msg.tool_calls
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llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
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ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
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ai_msg.tool_calls
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```
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See `ChatDeepSeek.bind_tools()` method for more.
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Structured output:
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.. code-block:: python
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```python
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from typing import Optional
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from typing import Optional
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from pydantic import BaseModel, Field
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from pydantic import BaseModel, Field
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class Joke(BaseModel):
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'''Joke to tell user.'''
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class Joke(BaseModel):
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'''Joke to tell user.'''
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setup: str = Field(description="The setup of the joke")
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punchline: str = Field(description="The punchline to the joke")
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rating: int | None = Field(description="How funny the joke is, from 1 to 10")
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setup: str = Field(description="The setup of the joke")
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punchline: str = Field(description="The punchline to the joke")
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rating: int | None = Field(description="How funny the joke is, from 1 to 10")
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structured_llm = llm.with_structured_output(Joke)
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structured_llm.invoke("Tell me a joke about cats")
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structured_llm = llm.with_structured_output(Joke)
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structured_llm.invoke("Tell me a joke about cats")
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```
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See `ChatDeepSeek.with_structured_output()` for more.
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Token usage:
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.. code-block:: python
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ai_msg = llm.invoke(messages)
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ai_msg.usage_metadata
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.. code-block:: python
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{"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}
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```python
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ai_msg = llm.invoke(messages)
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ai_msg.usage_metadata
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```
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```python
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{"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}
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```
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Response metadata
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.. code-block:: python
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ai_msg = llm.invoke(messages)
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ai_msg.response_metadata
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```python
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ai_msg = llm.invoke(messages)
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ai_msg.response_metadata
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```
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""" # noqa: E501
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model_name: str = Field(alias="model")
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