mirror of
https://github.com/deepset-ai/haystack.git
synced 2026-01-08 13:06:29 +00:00
2c4d839b64
* add gpt4generator * add e2e * add tests * reno * fix e2e * Update test/preview/components/generators/openai/test_gpt4_generator.py Co-authored-by: Stefano Fiorucci <44616784+anakin87@users.noreply.github.com> --------- Co-authored-by: Stefano Fiorucci <44616784+anakin87@users.noreply.github.com>
72 lines
4.0 KiB
Python
72 lines
4.0 KiB
Python
from typing import Optional, Callable
|
|
|
|
import logging
|
|
|
|
from haystack.preview import component
|
|
from haystack.preview.components.generators.openai.gpt35 import GPT35Generator, API_BASE_URL
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@component
|
|
class GPT4Generator(GPT35Generator):
|
|
"""
|
|
LLM Generator compatible with GPT4 large language models.
|
|
|
|
Queries the LLM using OpenAI's API. Invocations are made using OpenAI SDK ('openai' package)
|
|
See [OpenAI GPT4 API](https://platform.openai.com/docs/guides/chat) for more details.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
api_key: str,
|
|
model_name: str = "gpt-4",
|
|
system_prompt: Optional[str] = None,
|
|
streaming_callback: Optional[Callable] = None,
|
|
api_base_url: str = API_BASE_URL,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Creates an instance of GPT4Generator for OpenAI's GPT-4 model.
|
|
|
|
:param api_key: The OpenAI API key.
|
|
:param model_name: The name of the model to use.
|
|
:param system_prompt: An additional message to be sent to the LLM at the beginning of each conversation.
|
|
Typically, a conversation is formatted with a system message first, followed by alternating messages from
|
|
the 'user' (the "queries") and the 'assistant' (the "responses"). The system message helps set the behavior
|
|
of the assistant. For example, you can modify the personality of the assistant or provide specific
|
|
instructions about how it should behave throughout the conversation.
|
|
:param streaming_callback: A callback function that is called when a new token is received from the stream.
|
|
The callback function should accept two parameters: the token received from the stream and **kwargs.
|
|
The callback function should return the token to be sent to the stream. If the callback function is not
|
|
provided, the token is printed to stdout.
|
|
:param api_base_url: The OpenAI API Base url, defaults to `https://api.openai.com/v1`.
|
|
:param kwargs: Other parameters to use for the model. These parameters are all sent directly to the OpenAI
|
|
endpoint. See OpenAI [documentation](https://platform.openai.com/docs/api-reference/chat) for more details.
|
|
Some of the supported parameters:
|
|
- `max_tokens`: The maximum number of tokens the output text can have.
|
|
- `temperature`: What sampling temperature to use. Higher values mean the model will take more risks.
|
|
Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
|
|
- `top_p`: An alternative to sampling with temperature, called nucleus sampling, where the model
|
|
considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens
|
|
comprising the top 10% probability mass are considered.
|
|
- `n`: How many completions to generate for each prompt. For example, if the LLM gets 3 prompts and n is 2,
|
|
it will generate two completions for each of the three prompts, ending up with 6 completions in total.
|
|
- `stop`: One or more sequences after which the LLM should stop generating tokens.
|
|
- `presence_penalty`: What penalty to apply if a token is already present at all. Bigger values mean
|
|
the model will be less likely to repeat the same token in the text.
|
|
- `frequency_penalty`: What penalty to apply if a token has already been generated in the text.
|
|
Bigger values mean the model will be less likely to repeat the same token in the text.
|
|
- `logit_bias`: Add a logit bias to specific tokens. The keys of the dictionary are tokens and the
|
|
values are the bias to add to that token.
|
|
"""
|
|
super().__init__(
|
|
api_key=api_key,
|
|
model_name=model_name,
|
|
system_prompt=system_prompt,
|
|
streaming_callback=streaming_callback,
|
|
api_base_url=api_base_url,
|
|
**kwargs,
|
|
)
|