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359 lines
12 KiB
Python
359 lines
12 KiB
Python
"""Wrapper around Together AI's Chat Completions API."""
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import os
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from typing import (
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Any,
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Dict,
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List,
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Optional,
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)
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import openai
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from langchain_core.language_models.chat_models import LangSmithParams
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from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
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from langchain_core.utils import (
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convert_to_secret_str,
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get_from_dict_or_env,
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)
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from langchain_openai.chat_models.base import BaseChatOpenAI
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class ChatTogether(BaseChatOpenAI):
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r"""ChatTogether chat model.
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Setup:
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Install ``langchain-together`` and set environment variable ``TOGETHER_API_KEY``.
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.. code-block:: bash
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pip install -U langchain-together
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export TOGETHER_API_KEY="your-api-key"
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Key init args — completion params:
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model: str
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Name of model 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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Key init args — client params:
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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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api_key: Optional[str]
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Together API key. If not passed in will be read from env var OPENAI_API_KEY.
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Instantiate:
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.. code-block:: python
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from langhcain_together import ChatTogether
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llm = ChatTogether(
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model="meta-llama/Llama-3-70b-chat-hf",
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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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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 la programmation.",
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response_metadata={
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'token_usage': {'completion_tokens': 9, 'prompt_tokens': 32, 'total_tokens': 41},
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'model_name': 'meta-llama/Llama-3-70b-chat-hf',
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'system_fingerprint': None,
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'finish_reason': 'stop',
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'logprobs': None
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},
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id='run-168dceca-3b8b-4283-94e3-4c739dbc1525-0',
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usage_metadata={'input_tokens': 32, 'output_tokens': 9, 'total_tokens': 41})
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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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content='J' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
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content="'" id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
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content='ad' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
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content='ore' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
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content=' la' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
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content=' programm' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
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content='ation' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
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content='.' id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
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content='' response_metadata={'finish_reason': 'stop', 'model_name': 'meta-llama/Llama-3-70b-chat-hf'} id='run-1bc996b5-293f-4114-96a1-e0f755c05eb9'
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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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.. code-block:: python
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AIMessage(
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content="J'adore la programmation.",
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response_metadata={
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'token_usage': {'completion_tokens': 9, 'prompt_tokens': 32, 'total_tokens': 41},
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'model_name': 'meta-llama/Llama-3-70b-chat-hf',
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'system_fingerprint': None,
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'finish_reason': 'stop',
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'logprobs': None
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},
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id='run-09371a11-7f72-4c53-8e7c-9de5c238b34c-0',
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usage_metadata={'input_tokens': 32, 'output_tokens': 9, 'total_tokens': 41})
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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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# Only certain models support tool calling, check the together website to confirm compatibility
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llm = ChatTogether(model="mistralai/Mixtral-8x7B-Instruct-v0.1")
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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 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': 'GetPopulation',
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'args': {'location': 'NY'},
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'id': 'call_m5tstyn2004pre9bfuxvom8x',
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'type': 'tool_call'
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},
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{
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'name': 'GetPopulation',
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'args': {'location': 'LA'},
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'id': 'call_0vjgq455gq1av5sp9eb1pw6a',
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'type': 'tool_call'
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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=7
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)
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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"random_ints": [\\n13,\\n54,\\n78,\\n45,\\n67,\\n90,\\n11,\\n29,\\n84,\\n33\\n]\\n}'
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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': 37, 'output_tokens': 6, 'total_tokens': 43}
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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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messages=[("human","Say Hello World! Do not return anything else.")]
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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': None,
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'token_ids': [22557, 3304, 28808, 2],
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'tokens': [' Hello', ' World', '!', '</s>'],
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'token_logprobs': [-4.7683716e-06, -5.9604645e-07, 0, -0.057373047]
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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': 4,
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'prompt_tokens': 19,
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'total_tokens': 23
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},
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'model_name': 'mistralai/Mixtral-8x7B-Instruct-v0.1',
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'system_fingerprint': None,
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'finish_reason': 'eos',
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'logprobs': None
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}
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""" # noqa: E501
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@property
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def lc_secrets(self) -> Dict[str, str]:
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"""A map of constructor argument names to secret ids.
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For example,
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{"together_api_key": "TOGETHER_API_KEY"}
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"""
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return {"together_api_key": "TOGETHER_API_KEY"}
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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return ["langchain", "chat_models", "together"]
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@property
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def lc_attributes(self) -> Dict[str, Any]:
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"""List of attribute names that should be included in the serialized kwargs.
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These attributes must be accepted by the constructor.
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"""
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attributes: Dict[str, Any] = {}
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if self.together_api_base:
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attributes["together_api_base"] = self.together_api_base
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return attributes
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@property
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def _llm_type(self) -> str:
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"""Return type of chat model."""
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return "together-chat"
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def _get_ls_params(
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self, stop: Optional[List[str]] = None, **kwargs: Any
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) -> LangSmithParams:
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"""Get the parameters used to invoke the model."""
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params = super()._get_ls_params(stop=stop, **kwargs)
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params["ls_provider"] = "together"
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return params
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model_name: str = Field(default="meta-llama/Llama-3-8b-chat-hf", alias="model")
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"""Model name to use."""
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together_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
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"""Automatically inferred from env are `TOGETHER_API_KEY` if not provided."""
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together_api_base: Optional[str] = Field(
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default="https://api.together.ai/v1/", alias="base_url"
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)
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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if values["n"] < 1:
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raise ValueError("n must be at least 1.")
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if values["n"] > 1 and values["streaming"]:
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raise ValueError("n must be 1 when streaming.")
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values["together_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "together_api_key", "TOGETHER_API_KEY")
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)
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values["together_api_base"] = values["together_api_base"] or os.getenv(
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"TOGETHER_API_BASE"
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)
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client_params = {
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"api_key": (
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values["together_api_key"].get_secret_value()
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if values["together_api_key"]
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else None
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),
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"base_url": values["together_api_base"],
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"timeout": values["request_timeout"],
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"max_retries": values["max_retries"],
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"default_headers": values["default_headers"],
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"default_query": values["default_query"],
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}
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if not values.get("client"):
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sync_specific = {"http_client": values["http_client"]}
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values["client"] = openai.OpenAI(
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**client_params, **sync_specific
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).chat.completions
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if not values.get("async_client"):
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async_specific = {"http_client": values["http_async_client"]}
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values["async_client"] = openai.AsyncOpenAI(
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**client_params, **async_specific
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).chat.completions
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return values
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