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https://github.com/hwchase17/langchain
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parent
381aedcc61
commit
81064017a9
@ -57,48 +57,417 @@ def _is_pydantic_class(obj: Any) -> bool:
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class AzureChatOpenAI(BaseChatOpenAI):
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"""`Azure OpenAI` Chat Completion API.
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"""Azure OpenAI chat model integration.
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To use this class you
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must have a deployed model on Azure OpenAI. Use `deployment_name` in the
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constructor to refer to the "Model deployment name" in the Azure portal.
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Setup:
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Head to the https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python
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to create your Azure OpenAI deployment.
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In addition, you should have the
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following environment variables set or passed in constructor in lower case:
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- ``AZURE_OPENAI_API_KEY``
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- ``AZURE_OPENAI_ENDPOINT``
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- ``AZURE_OPENAI_AD_TOKEN``
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- ``OPENAI_API_VERSION``
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- ``OPENAI_PROXY``
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Then install ``langchain-openai`` and set environment variables
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``AZURE_OPENAI_API_KEY`` and ``AZURE_OPENAI_ENDPOINT``:
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For example, if you have `gpt-3.5-turbo` deployed, with the deployment name
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`35-turbo-dev`, the constructor should look like:
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.. code-block:: bash
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.. code-block:: python
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pip install -U langchain-openai
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from langchain_openai import AzureChatOpenAI
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export AZURE_OPENAI_API_KEY="your-api-key"
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export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
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AzureChatOpenAI(azure_deployment="35-turbo-dev", openai_api_version="2023-05-15")
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Key init args — completion params:
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azure_deployment: str
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Name of Azure OpenAI deployment to use.
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temperature: float
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Sampling temperature.
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max_tokens: Optional[int]
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Max number of tokens to generate.
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logprobs: Optional[bool]
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Whether to return logprobs.
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Be aware the API version may change.
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Key init args — client params:
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api_version: str
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Azure OpenAI API version to use. See more on the different versions here:
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https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning
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timeout: Union[float, Tuple[float, float], Any, None]
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Timeout for requests.
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max_retries: int
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Max number of retries.
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organization: Optional[str]
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OpenAI organization ID. If not passed in will be read from env
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var OPENAI_ORG_ID.
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You can also specify the version of the model using ``model_version`` constructor
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parameter, as Azure OpenAI doesn't return model version with the response.
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See full list of supported init args and their descriptions in the params section.
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Default is empty. When you specify the version, it will be appended to the
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model name in the response. Setting correct version will help you to calculate the
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cost properly. Model version is not validated, so make sure you set it correctly
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to get the correct cost.
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Instantiate:
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.. code-block:: python
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Any parameters that are valid to be passed to the openai.create call can be passed
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in, even if not explicitly saved on this class.
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from langchain_openai import AzureChatOpenAI
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llm = AzureChatOpenAI(
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azure_deployment="your-deployment",
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api_version="2024-05-01-preview",
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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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# organization="...",
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# other params...
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)
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**NOTE**: Any param which is not explicitly supported will be passed directly to the
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``openai.AzureOpenAI.chat.completions.create(...)`` API every time to the model is
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invoked. For example:
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.. code-block:: python
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from langchain_openai import AzureChatOpenAI
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import openai
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AzureChatOpenAI(..., logprobs=True).invoke(...)
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# results in underlying API call of:
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openai.AzureOpenAI(..).chat.completions.create(..., logprobs=True)
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# which is also equivalent to:
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AzureChatOpenAI(...).invoke(..., logprobs=True)
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Invoke:
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.. code-block:: python
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messages = [
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(
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"system",
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"You are a helpful translator. Translate the user sentence to French.",
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),
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("human", "I love programming."),
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]
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llm.invoke(messages)
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.. code-block:: python
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AIMessage(
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content="J'adore programmer.",
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usage_metadata={"input_tokens": 28, "output_tokens": 6, "total_tokens": 34},
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response_metadata={
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"token_usage": {
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"completion_tokens": 6,
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"prompt_tokens": 28,
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"total_tokens": 34,
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},
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"model_name": "gpt-4",
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"system_fingerprint": "fp_7ec89fabc6",
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"prompt_filter_results": [
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{
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"prompt_index": 0,
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"content_filter_results": {
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"hate": {"filtered": False, "severity": "safe"},
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"self_harm": {"filtered": False, "severity": "safe"},
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"sexual": {"filtered": False, "severity": "safe"},
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"violence": {"filtered": False, "severity": "safe"},
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},
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}
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],
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"finish_reason": "stop",
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"logprobs": None,
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"content_filter_results": {
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"hate": {"filtered": False, "severity": "safe"},
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"self_harm": {"filtered": False, "severity": "safe"},
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"sexual": {"filtered": False, "severity": "safe"},
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"violence": {"filtered": False, "severity": "safe"},
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},
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},
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id="run-6d7a5282-0de0-4f27-9cc0-82a9db9a3ce9-0",
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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)
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.. code-block:: python
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AIMessageChunk(content="", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content="J", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content="'", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content="ad", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content="ore", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content=" la", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content=" programm", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content="ation", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(content=".", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
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AIMessageChunk(
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content="",
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response_metadata={
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"finish_reason": "stop",
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"model_name": "gpt-4",
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"system_fingerprint": "fp_811936bd4f",
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},
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id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f",
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)
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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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.. code-block:: python
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AIMessageChunk(
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content="J'adore la programmation.",
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response_metadata={
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"finish_reason": "stop",
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"model_name": "gpt-4",
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"system_fingerprint": "fp_811936bd4f",
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},
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id="run-ba60e41c-9258-44b8-8f3a-2f10599643b3",
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)
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Async:
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.. code-block:: python
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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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# batch:
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# await llm.abatch([messages])
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Tool calling:
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.. code-block:: python
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from langchain_core.pydantic_v1 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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location: str = Field(
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..., description="The city and state, e.g. San Francisco, CA"
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)
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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(
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..., description="The city and state, e.g. San Francisco, CA"
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)
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llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
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ai_msg = llm_with_tools.invoke(
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"Which city is hotter today and which is bigger: LA or NY?"
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)
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ai_msg.tool_calls
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.. code-block:: python
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[
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{
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"name": "GetWeather",
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"args": {"location": "Los Angeles, CA"},
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"id": "call_6XswGD5Pqk8Tt5atYr7tfenU",
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},
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{
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"name": "GetWeather",
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"args": {"location": "New York, NY"},
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"id": "call_ZVL15vA8Y7kXqOy3dtmQgeCi",
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},
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{
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"name": "GetPopulation",
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"args": {"location": "Los Angeles, CA"},
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"id": "call_49CFW8zqC9W7mh7hbMLSIrXw",
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},
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{
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"name": "GetPopulation",
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"args": {"location": "New York, NY"},
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"id": "call_6ghfKxV264jEfe1mRIkS3PE7",
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},
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]
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Structured output:
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.. code-block:: python
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from typing import Optional
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from langchain_core.pydantic_v1 import BaseModel, Field
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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: Optional[int] = 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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.. code-block:: python
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Joke(
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setup="Why was the cat sitting on the computer?",
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punchline="To keep an eye on the mouse!",
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rating=None,
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)
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See ``AzureChatOpenAI.with_structured_output()`` for more.
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JSON mode:
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.. code-block:: python
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json_llm = llm.bind(response_format={"type": "json_object"})
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ai_msg = json_llm.invoke(
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"Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]"
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)
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ai_msg.content
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.. code-block:: python
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'\\n{\\n "random_ints": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\\n}'
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Image input:
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.. code-block:: python
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import base64
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import httpx
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from langchain_core.messages import HumanMessage
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image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
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image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
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message = HumanMessage(
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content=[
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{"type": "text", "text": "describe the weather in this image"},
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{
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"type": "image_url",
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"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
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},
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]
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)
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ai_msg = llm.invoke([message])
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ai_msg.content
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.. code-block:: python
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"The weather in the image appears to be quite pleasant. The sky is mostly clear"
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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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Logprobs:
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.. code-block:: python
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logprobs_llm = llm.bind(logprobs=True)
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ai_msg = logprobs_llm.invoke(messages)
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ai_msg.response_metadata["logprobs"]
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.. code-block:: python
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{
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"content": [
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{
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"token": "J",
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"bytes": [74],
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"logprob": -4.9617593e-06,
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"top_logprobs": [],
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},
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{
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"token": "'adore",
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"bytes": [39, 97, 100, 111, 114, 101],
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"logprob": -0.25202933,
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"top_logprobs": [],
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},
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{
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"token": " la",
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"bytes": [32, 108, 97],
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"logprob": -0.20141791,
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"top_logprobs": [],
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},
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{
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"token": " programmation",
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"bytes": [
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32,
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112,
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114,
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111,
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103,
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114,
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97,
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109,
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109,
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97,
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116,
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105,
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111,
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110,
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],
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"logprob": -1.9361265e-07,
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"top_logprobs": [],
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},
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{
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"token": ".",
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"bytes": [46],
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"logprob": -1.2233183e-05,
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"top_logprobs": [],
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},
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]
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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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.. code-block:: python
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{
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"token_usage": {
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"completion_tokens": 6,
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"prompt_tokens": 28,
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"total_tokens": 34,
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},
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"model_name": "gpt-35-turbo",
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"system_fingerprint": None,
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"prompt_filter_results": [
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{
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"prompt_index": 0,
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"content_filter_results": {
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"hate": {"filtered": False, "severity": "safe"},
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"self_harm": {"filtered": False, "severity": "safe"},
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"sexual": {"filtered": False, "severity": "safe"},
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"violence": {"filtered": False, "severity": "safe"},
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},
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}
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],
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"finish_reason": "stop",
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"logprobs": None,
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"content_filter_results": {
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"hate": {"filtered": False, "severity": "safe"},
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"self_harm": {"filtered": False, "severity": "safe"},
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"sexual": {"filtered": False, "severity": "safe"},
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"violence": {"filtered": False, "severity": "safe"},
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},
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}
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""" # noqa: E501
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azure_endpoint: Union[str, None] = None
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"""Your Azure endpoint, including the resource.
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Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
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Example: `https://example-resource.azure.openai.com/`
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"""
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deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
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@ -115,7 +484,6 @@ class AzureChatOpenAI(BaseChatOpenAI):
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"""Your Azure Active Directory token.
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Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
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For more:
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https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
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"""
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@ -124,13 +492,25 @@ class AzureChatOpenAI(BaseChatOpenAI):
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Will be invoked on every request.
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"""
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model_version: str = ""
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"""Legacy, for openai<1.0.0 support."""
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"""The version of the model (e.g. "0125" for gpt-3.5-0125).
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Azure OpenAI doesn't return model version with the response by default so it must
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be manually specified if you want to use this information downstream, e.g. when
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calculating costs.
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When you specify the version, it will be appended to the model name in the
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response. Setting correct version will help you to calculate the cost properly.
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Model version is not validated, so make sure you set it correctly to get the
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correct cost.
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"""
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openai_api_type: str = ""
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"""Legacy, for openai<1.0.0 support."""
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validate_base_url: bool = True
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"""For backwards compatibility. If legacy val openai_api_base is passed in, try to
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infer if it is a base_url or azure_endpoint and update accordingly.
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"""If legacy arg openai_api_base is passed in, try to infer if it is a base_url or
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azure_endpoint and update client params accordingly.
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"""
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@classmethod
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Loading…
Reference in New Issue
Block a user