873 lines
30 KiB
Python
873 lines
30 KiB
Python
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"""Base classes for OpenAI large language models. Chat models are in `chat_models/`."""
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from __future__ import annotations
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import logging
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import sys
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from collections.abc import AsyncIterator, Callable, Collection, Iterator, Mapping
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from typing import Any, Literal
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import openai
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import tiktoken
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.llms import BaseLLM
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from langchain_core.outputs import Generation, GenerationChunk, LLMResult
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from langchain_core.utils import get_pydantic_field_names
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from langchain_core.utils.utils import _build_model_kwargs, from_env, secret_from_env
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from pydantic import ConfigDict, Field, SecretStr, model_validator
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from typing_extensions import Self
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logger = logging.getLogger(__name__)
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def _update_token_usage(
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keys: set[str], response: dict[str, Any], token_usage: dict[str, Any]
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) -> None:
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"""Update token usage."""
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_keys_to_use = keys.intersection(response["usage"])
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for _key in _keys_to_use:
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if _key not in token_usage:
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token_usage[_key] = response["usage"][_key]
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else:
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token_usage[_key] += response["usage"][_key]
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def _stream_response_to_generation_chunk(
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stream_response: dict[str, Any],
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) -> GenerationChunk:
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"""Convert a stream response to a generation chunk."""
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if not stream_response["choices"]:
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return GenerationChunk(text="")
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return GenerationChunk(
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text=stream_response["choices"][0]["text"] or "",
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generation_info={
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"finish_reason": stream_response["choices"][0].get("finish_reason", None),
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"logprobs": stream_response["choices"][0].get("logprobs", None),
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},
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)
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class BaseOpenAI(BaseLLM):
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"""Base OpenAI large language model class.
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Setup:
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Install `langchain-openai` and set environment variable `OPENAI_API_KEY`.
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```bash
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pip install -U langchain-openai
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export OPENAI_API_KEY="your-api-key"
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```
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Key init args — completion params:
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model_name:
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Name of OpenAI model to use.
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temperature:
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Sampling temperature.
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max_tokens:
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Max number of tokens to generate.
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top_p:
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Total probability mass of tokens to consider at each step.
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frequency_penalty:
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Penalizes repeated tokens according to frequency.
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presence_penalty:
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Penalizes repeated tokens.
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n:
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How many completions to generate for each prompt.
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best_of:
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Generates best_of completions server-side and returns the "best".
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logit_bias:
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Adjust the probability of specific tokens being generated.
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seed:
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Seed for generation.
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logprobs:
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Include the log probabilities on the logprobs most likely output tokens.
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streaming:
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Whether to stream the results or not.
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Key init args — client params:
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openai_api_key:
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OpenAI API key. If not passed in will be read from env var
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`OPENAI_API_KEY`.
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openai_api_base:
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Base URL path for API requests, leave blank if not using a proxy or
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service emulator.
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openai_organization:
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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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request_timeout:
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Timeout for requests to OpenAI completion API.
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max_retries:
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Maximum number of retries to make when generating.
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batch_size:
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Batch size to use when passing multiple documents to generate.
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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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```python
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from langchain_openai.llms.base import BaseOpenAI
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model = BaseOpenAI(
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model_name="gpt-3.5-turbo-instruct",
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temperature=0.7,
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max_tokens=256,
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top_p=1,
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frequency_penalty=0,
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presence_penalty=0,
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# openai_api_key="...",
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# openai_api_base="...",
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# openai_organization="...",
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# other params...
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)
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```
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Invoke:
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```python
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input_text = "The meaning of life is "
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response = model.invoke(input_text)
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print(response)
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```
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```txt
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"a philosophical question that has been debated by thinkers and
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scholars for centuries."
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```
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Stream:
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```python
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for chunk in model.stream(input_text):
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print(chunk, end="")
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```
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```txt
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a philosophical question that has been debated by thinkers and
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scholars for centuries.
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```
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Async:
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```python
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response = await model.ainvoke(input_text)
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# stream:
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# async for chunk in model.astream(input_text):
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# print(chunk, end="")
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# batch:
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# await model.abatch([input_text])
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```
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```
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"a philosophical question that has been debated by thinkers and
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scholars for centuries."
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```
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"""
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client: Any = Field(default=None, exclude=True)
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async_client: Any = Field(default=None, exclude=True)
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model_name: str = Field(default="gpt-3.5-turbo-instruct", alias="model")
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"""Model name to use."""
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temperature: float = 0.7
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"""What sampling temperature to use."""
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max_tokens: int = 256
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"""The maximum number of tokens to generate in the completion.
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-1 returns as many tokens as possible given the prompt and
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the models maximal context size."""
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top_p: float = 1
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"""Total probability mass of tokens to consider at each step."""
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frequency_penalty: float = 0
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"""Penalizes repeated tokens according to frequency."""
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presence_penalty: float = 0
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"""Penalizes repeated tokens."""
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n: int = 1
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"""How many completions to generate for each prompt."""
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best_of: int = 1
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"""Generates best_of completions server-side and returns the "best"."""
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model_kwargs: dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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openai_api_key: SecretStr | None | Callable[[], str] = Field(
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alias="api_key", default_factory=secret_from_env("OPENAI_API_KEY", default=None)
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)
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"""Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
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openai_api_base: str | None = Field(
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alias="base_url", default_factory=from_env("OPENAI_API_BASE", default=None)
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)
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"""Base URL path for API requests, leave blank if not using a proxy or service
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emulator."""
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openai_organization: str | None = Field(
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alias="organization",
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default_factory=from_env(
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["OPENAI_ORG_ID", "OPENAI_ORGANIZATION"], default=None
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),
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)
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"""Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
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# to support explicit proxy for OpenAI
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openai_proxy: str | None = Field(
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default_factory=from_env("OPENAI_PROXY", default=None)
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)
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batch_size: int = 20
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"""Batch size to use when passing multiple documents to generate."""
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request_timeout: float | tuple[float, float] | Any | None = Field(
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default=None, alias="timeout"
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)
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"""Timeout for requests to OpenAI completion API. Can be float, `httpx.Timeout` or
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None."""
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logit_bias: dict[str, float] | None = None
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"""Adjust the probability of specific tokens being generated."""
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max_retries: int = 2
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"""Maximum number of retries to make when generating."""
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seed: int | None = None
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"""Seed for generation"""
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logprobs: int | None = None
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"""Include the log probabilities on the logprobs most likely output tokens,
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as well the chosen tokens."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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allowed_special: Literal["all"] | set[str] = set()
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"""Set of special tokens that are allowed。"""
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disallowed_special: Literal["all"] | Collection[str] = "all"
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"""Set of special tokens that are not allowed。"""
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tiktoken_model_name: str | None = None
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"""The model name to pass to tiktoken when using this class.
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Tiktoken is used to count the number of tokens in documents to constrain
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them to be under a certain limit.
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By default, when set to `None`, this will be the same as the embedding model name.
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However, there are some cases where you may want to use this `Embedding` class with
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a model name not supported by tiktoken. This can include when using Azure embeddings
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or when using one of the many model providers that expose an OpenAI-like
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API but with different models. In those cases, in order to avoid erroring
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when tiktoken is called, you can specify a model name to use here.
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"""
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default_headers: Mapping[str, str] | None = None
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default_query: Mapping[str, object] | None = None
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# Configure a custom httpx client. See the
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# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
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http_client: Any | None = None
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"""Optional `httpx.Client`.
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Only used for sync invocations. Must specify `http_async_client` as well if you'd
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like a custom client for async invocations.
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"""
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http_async_client: Any | None = None
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"""Optional `httpx.AsyncClient`.
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Only used for async invocations. Must specify `http_client` as well if you'd like a
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custom client for sync invocations.
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"""
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extra_body: Mapping[str, Any] | None = None
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"""Optional additional JSON properties to include in the request parameters when
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making requests to OpenAI compatible APIs, such as vLLM."""
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model_config = ConfigDict(populate_by_name=True)
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@model_validator(mode="before")
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@classmethod
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def build_extra(cls, values: dict[str, Any]) -> Any:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = get_pydantic_field_names(cls)
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return _build_model_kwargs(values, all_required_field_names)
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@model_validator(mode="after")
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def validate_environment(self) -> Self:
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"""Validate that api key and python package exists in environment."""
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if self.n < 1:
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msg = "n must be at least 1."
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raise ValueError(msg)
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if self.streaming and self.n > 1:
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msg = "Cannot stream results when n > 1."
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raise ValueError(msg)
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if self.streaming and self.best_of > 1:
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msg = "Cannot stream results when best_of > 1."
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raise ValueError(msg)
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# Resolve API key from SecretStr or Callable
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api_key_value: str | Callable[[], str] | None = None
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if self.openai_api_key is not None:
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if isinstance(self.openai_api_key, SecretStr):
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api_key_value = self.openai_api_key.get_secret_value()
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elif callable(self.openai_api_key):
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api_key_value = self.openai_api_key
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client_params: dict = {
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"api_key": api_key_value,
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"organization": self.openai_organization,
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"base_url": self.openai_api_base,
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"timeout": self.request_timeout,
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"max_retries": self.max_retries,
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"default_headers": self.default_headers,
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"default_query": self.default_query,
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}
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if not self.client:
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sync_specific = {"http_client": self.http_client}
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self.client = openai.OpenAI(**client_params, **sync_specific).completions # type: ignore[arg-type]
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if not self.async_client:
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async_specific = {"http_client": self.http_async_client}
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self.async_client = openai.AsyncOpenAI(
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**client_params,
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**async_specific, # type: ignore[arg-type]
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).completions
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return self
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@property
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def _default_params(self) -> dict[str, Any]:
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"""Get the default parameters for calling OpenAI API."""
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normal_params: dict[str, Any] = {
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"temperature": self.temperature,
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"top_p": self.top_p,
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"frequency_penalty": self.frequency_penalty,
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"presence_penalty": self.presence_penalty,
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"n": self.n,
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"seed": self.seed,
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"logprobs": self.logprobs,
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}
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if self.logit_bias is not None:
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normal_params["logit_bias"] = self.logit_bias
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if self.max_tokens is not None:
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normal_params["max_tokens"] = self.max_tokens
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if self.extra_body is not None:
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normal_params["extra_body"] = self.extra_body
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# Azure gpt-35-turbo doesn't support best_of
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# don't specify best_of if it is 1
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if self.best_of > 1:
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normal_params["best_of"] = self.best_of
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return {**normal_params, **self.model_kwargs}
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def _stream(
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self,
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prompt: str,
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stop: list[str] | None = None,
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run_manager: CallbackManagerForLLMRun | None = None,
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**kwargs: Any,
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) -> Iterator[GenerationChunk]:
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|
|
params = {**self._invocation_params, **kwargs, "stream": True}
|
||
|
|
self.get_sub_prompts(params, [prompt], stop) # this mutates params
|
||
|
|
for stream_resp in self.client.create(prompt=prompt, **params):
|
||
|
|
if not isinstance(stream_resp, dict):
|
||
|
|
stream_resp = stream_resp.model_dump()
|
||
|
|
chunk = _stream_response_to_generation_chunk(stream_resp)
|
||
|
|
|
||
|
|
if run_manager:
|
||
|
|
run_manager.on_llm_new_token(
|
||
|
|
chunk.text,
|
||
|
|
chunk=chunk,
|
||
|
|
verbose=self.verbose,
|
||
|
|
logprobs=(
|
||
|
|
chunk.generation_info["logprobs"]
|
||
|
|
if chunk.generation_info
|
||
|
|
else None
|
||
|
|
),
|
||
|
|
)
|
||
|
|
yield chunk
|
||
|
|
|
||
|
|
async def _astream(
|
||
|
|
self,
|
||
|
|
prompt: str,
|
||
|
|
stop: list[str] | None = None,
|
||
|
|
run_manager: AsyncCallbackManagerForLLMRun | None = None,
|
||
|
|
**kwargs: Any,
|
||
|
|
) -> AsyncIterator[GenerationChunk]:
|
||
|
|
params = {**self._invocation_params, **kwargs, "stream": True}
|
||
|
|
self.get_sub_prompts(params, [prompt], stop) # this mutates params
|
||
|
|
async for stream_resp in await self.async_client.create(
|
||
|
|
prompt=prompt, **params
|
||
|
|
):
|
||
|
|
if not isinstance(stream_resp, dict):
|
||
|
|
stream_resp = stream_resp.model_dump()
|
||
|
|
chunk = _stream_response_to_generation_chunk(stream_resp)
|
||
|
|
|
||
|
|
if run_manager:
|
||
|
|
await run_manager.on_llm_new_token(
|
||
|
|
chunk.text,
|
||
|
|
chunk=chunk,
|
||
|
|
verbose=self.verbose,
|
||
|
|
logprobs=(
|
||
|
|
chunk.generation_info["logprobs"]
|
||
|
|
if chunk.generation_info
|
||
|
|
else None
|
||
|
|
),
|
||
|
|
)
|
||
|
|
yield chunk
|
||
|
|
|
||
|
|
def _generate(
|
||
|
|
self,
|
||
|
|
prompts: list[str],
|
||
|
|
stop: list[str] | None = None,
|
||
|
|
run_manager: CallbackManagerForLLMRun | None = None,
|
||
|
|
**kwargs: Any,
|
||
|
|
) -> LLMResult:
|
||
|
|
"""Call out to OpenAI's endpoint with k unique prompts.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
prompts: The prompts to pass into the model.
|
||
|
|
stop: Optional list of stop words to use when generating.
|
||
|
|
run_manager: Optional callback manager to use for the call.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The full LLM output.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
response = openai.generate(["Tell me a joke."])
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
# TODO: write a unit test for this
|
||
|
|
params = self._invocation_params
|
||
|
|
params = {**params, **kwargs}
|
||
|
|
sub_prompts = self.get_sub_prompts(params, prompts, stop)
|
||
|
|
choices = []
|
||
|
|
token_usage: dict[str, int] = {}
|
||
|
|
# Get the token usage from the response.
|
||
|
|
# Includes prompt, completion, and total tokens used.
|
||
|
|
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
|
||
|
|
system_fingerprint: str | None = None
|
||
|
|
for _prompts in sub_prompts:
|
||
|
|
if self.streaming:
|
||
|
|
if len(_prompts) > 1:
|
||
|
|
msg = "Cannot stream results with multiple prompts."
|
||
|
|
raise ValueError(msg)
|
||
|
|
|
||
|
|
generation: GenerationChunk | None = None
|
||
|
|
for chunk in self._stream(_prompts[0], stop, run_manager, **kwargs):
|
||
|
|
if generation is None:
|
||
|
|
generation = chunk
|
||
|
|
else:
|
||
|
|
generation += chunk
|
||
|
|
if generation is None:
|
||
|
|
msg = "Generation is empty after streaming."
|
||
|
|
raise ValueError(msg)
|
||
|
|
choices.append(
|
||
|
|
{
|
||
|
|
"text": generation.text,
|
||
|
|
"finish_reason": (
|
||
|
|
generation.generation_info.get("finish_reason")
|
||
|
|
if generation.generation_info
|
||
|
|
else None
|
||
|
|
),
|
||
|
|
"logprobs": (
|
||
|
|
generation.generation_info.get("logprobs")
|
||
|
|
if generation.generation_info
|
||
|
|
else None
|
||
|
|
),
|
||
|
|
}
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
response = self.client.create(prompt=_prompts, **params)
|
||
|
|
if not isinstance(response, dict):
|
||
|
|
# V1 client returns the response in an PyDantic object instead of
|
||
|
|
# dict. For the transition period, we deep convert it to dict.
|
||
|
|
response = response.model_dump()
|
||
|
|
|
||
|
|
# Sometimes the AI Model calling will get error, we should raise it.
|
||
|
|
# Otherwise, the next code 'choices.extend(response["choices"])'
|
||
|
|
# will throw a "TypeError: 'NoneType' object is not iterable" error
|
||
|
|
# to mask the true error. Because 'response["choices"]' is None.
|
||
|
|
if response.get("error"):
|
||
|
|
raise ValueError(response.get("error"))
|
||
|
|
|
||
|
|
choices.extend(response["choices"])
|
||
|
|
_update_token_usage(_keys, response, token_usage)
|
||
|
|
if not system_fingerprint:
|
||
|
|
system_fingerprint = response.get("system_fingerprint")
|
||
|
|
return self.create_llm_result(
|
||
|
|
choices, prompts, params, token_usage, system_fingerprint=system_fingerprint
|
||
|
|
)
|
||
|
|
|
||
|
|
async def _agenerate(
|
||
|
|
self,
|
||
|
|
prompts: list[str],
|
||
|
|
stop: list[str] | None = None,
|
||
|
|
run_manager: AsyncCallbackManagerForLLMRun | None = None,
|
||
|
|
**kwargs: Any,
|
||
|
|
) -> LLMResult:
|
||
|
|
"""Call out to OpenAI's endpoint async with k unique prompts."""
|
||
|
|
params = self._invocation_params
|
||
|
|
params = {**params, **kwargs}
|
||
|
|
sub_prompts = self.get_sub_prompts(params, prompts, stop)
|
||
|
|
choices = []
|
||
|
|
token_usage: dict[str, int] = {}
|
||
|
|
# Get the token usage from the response.
|
||
|
|
# Includes prompt, completion, and total tokens used.
|
||
|
|
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
|
||
|
|
system_fingerprint: str | None = None
|
||
|
|
for _prompts in sub_prompts:
|
||
|
|
if self.streaming:
|
||
|
|
if len(_prompts) > 1:
|
||
|
|
msg = "Cannot stream results with multiple prompts."
|
||
|
|
raise ValueError(msg)
|
||
|
|
|
||
|
|
generation: GenerationChunk | None = None
|
||
|
|
async for chunk in self._astream(
|
||
|
|
_prompts[0], stop, run_manager, **kwargs
|
||
|
|
):
|
||
|
|
if generation is None:
|
||
|
|
generation = chunk
|
||
|
|
else:
|
||
|
|
generation += chunk
|
||
|
|
if generation is None:
|
||
|
|
msg = "Generation is empty after streaming."
|
||
|
|
raise ValueError(msg)
|
||
|
|
choices.append(
|
||
|
|
{
|
||
|
|
"text": generation.text,
|
||
|
|
"finish_reason": (
|
||
|
|
generation.generation_info.get("finish_reason")
|
||
|
|
if generation.generation_info
|
||
|
|
else None
|
||
|
|
),
|
||
|
|
"logprobs": (
|
||
|
|
generation.generation_info.get("logprobs")
|
||
|
|
if generation.generation_info
|
||
|
|
else None
|
||
|
|
),
|
||
|
|
}
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
response = await self.async_client.create(prompt=_prompts, **params)
|
||
|
|
if not isinstance(response, dict):
|
||
|
|
response = response.model_dump()
|
||
|
|
choices.extend(response["choices"])
|
||
|
|
_update_token_usage(_keys, response, token_usage)
|
||
|
|
return self.create_llm_result(
|
||
|
|
choices, prompts, params, token_usage, system_fingerprint=system_fingerprint
|
||
|
|
)
|
||
|
|
|
||
|
|
def get_sub_prompts(
|
||
|
|
self,
|
||
|
|
params: dict[str, Any],
|
||
|
|
prompts: list[str],
|
||
|
|
stop: list[str] | None = None,
|
||
|
|
) -> list[list[str]]:
|
||
|
|
"""Get the sub prompts for llm call."""
|
||
|
|
if stop is not None:
|
||
|
|
params["stop"] = stop
|
||
|
|
if params["max_tokens"] == -1:
|
||
|
|
if len(prompts) != 1:
|
||
|
|
msg = "max_tokens set to -1 not supported for multiple inputs."
|
||
|
|
raise ValueError(msg)
|
||
|
|
params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
|
||
|
|
return [
|
||
|
|
prompts[i : i + self.batch_size]
|
||
|
|
for i in range(0, len(prompts), self.batch_size)
|
||
|
|
]
|
||
|
|
|
||
|
|
def create_llm_result(
|
||
|
|
self,
|
||
|
|
choices: Any,
|
||
|
|
prompts: list[str],
|
||
|
|
params: dict[str, Any],
|
||
|
|
token_usage: dict[str, int],
|
||
|
|
*,
|
||
|
|
system_fingerprint: str | None = None,
|
||
|
|
) -> LLMResult:
|
||
|
|
"""Create the LLMResult from the choices and prompts."""
|
||
|
|
generations = []
|
||
|
|
n = params.get("n", self.n)
|
||
|
|
for i, _ in enumerate(prompts):
|
||
|
|
sub_choices = choices[i * n : (i + 1) * n]
|
||
|
|
generations.append(
|
||
|
|
[
|
||
|
|
Generation(
|
||
|
|
text=choice["text"],
|
||
|
|
generation_info={
|
||
|
|
"finish_reason": choice.get("finish_reason"),
|
||
|
|
"logprobs": choice.get("logprobs"),
|
||
|
|
},
|
||
|
|
)
|
||
|
|
for choice in sub_choices
|
||
|
|
]
|
||
|
|
)
|
||
|
|
llm_output = {"token_usage": token_usage, "model_name": self.model_name}
|
||
|
|
if system_fingerprint:
|
||
|
|
llm_output["system_fingerprint"] = system_fingerprint
|
||
|
|
return LLMResult(generations=generations, llm_output=llm_output)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _invocation_params(self) -> dict[str, Any]:
|
||
|
|
"""Get the parameters used to invoke the model."""
|
||
|
|
return self._default_params
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
||
|
|
"""Get the identifying parameters."""
|
||
|
|
return {"model_name": self.model_name, **self._default_params}
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _llm_type(self) -> str:
|
||
|
|
"""Return type of llm."""
|
||
|
|
return "openai"
|
||
|
|
|
||
|
|
def get_token_ids(self, text: str) -> list[int]:
|
||
|
|
"""Get the token IDs using the tiktoken package."""
|
||
|
|
if self.custom_get_token_ids is not None:
|
||
|
|
return self.custom_get_token_ids(text)
|
||
|
|
# tiktoken NOT supported for Python < 3.8
|
||
|
|
if sys.version_info[1] < 8:
|
||
|
|
return super().get_num_tokens(text)
|
||
|
|
|
||
|
|
model_name = self.tiktoken_model_name or self.model_name
|
||
|
|
try:
|
||
|
|
enc = tiktoken.encoding_for_model(model_name)
|
||
|
|
except KeyError:
|
||
|
|
enc = tiktoken.get_encoding("cl100k_base")
|
||
|
|
|
||
|
|
return enc.encode(
|
||
|
|
text,
|
||
|
|
allowed_special=self.allowed_special,
|
||
|
|
disallowed_special=self.disallowed_special,
|
||
|
|
)
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def modelname_to_contextsize(modelname: str) -> int:
|
||
|
|
"""Calculate the maximum number of tokens possible to generate for a model.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
modelname: The modelname we want to know the context size for.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The maximum context size
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
max_tokens = openai.modelname_to_contextsize("gpt-3.5-turbo-instruct")
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
model_token_mapping = {
|
||
|
|
"gpt-4o-mini": 128_000,
|
||
|
|
"gpt-4o": 128_000,
|
||
|
|
"gpt-4o-2024-05-13": 128_000,
|
||
|
|
"gpt-4": 8192,
|
||
|
|
"gpt-4-0314": 8192,
|
||
|
|
"gpt-4-0613": 8192,
|
||
|
|
"gpt-4-32k": 32768,
|
||
|
|
"gpt-4-32k-0314": 32768,
|
||
|
|
"gpt-4-32k-0613": 32768,
|
||
|
|
"gpt-3.5-turbo": 4096,
|
||
|
|
"gpt-3.5-turbo-0301": 4096,
|
||
|
|
"gpt-3.5-turbo-0613": 4096,
|
||
|
|
"gpt-3.5-turbo-16k": 16385,
|
||
|
|
"gpt-3.5-turbo-16k-0613": 16385,
|
||
|
|
"gpt-3.5-turbo-instruct": 4096,
|
||
|
|
"text-ada-001": 2049,
|
||
|
|
"ada": 2049,
|
||
|
|
"text-babbage-001": 2040,
|
||
|
|
"babbage": 2049,
|
||
|
|
"text-curie-001": 2049,
|
||
|
|
"curie": 2049,
|
||
|
|
"davinci": 2049,
|
||
|
|
"text-davinci-003": 4097,
|
||
|
|
"text-davinci-002": 4097,
|
||
|
|
"code-davinci-002": 8001,
|
||
|
|
"code-davinci-001": 8001,
|
||
|
|
"code-cushman-002": 2048,
|
||
|
|
"code-cushman-001": 2048,
|
||
|
|
}
|
||
|
|
|
||
|
|
# handling finetuned models
|
||
|
|
if "ft-" in modelname:
|
||
|
|
modelname = modelname.split(":")[0]
|
||
|
|
|
||
|
|
context_size = model_token_mapping.get(modelname)
|
||
|
|
|
||
|
|
if context_size is None:
|
||
|
|
raise ValueError(
|
||
|
|
f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
|
||
|
|
"Known models are: " + ", ".join(model_token_mapping.keys())
|
||
|
|
)
|
||
|
|
|
||
|
|
return context_size
|
||
|
|
|
||
|
|
@property
|
||
|
|
def max_context_size(self) -> int:
|
||
|
|
"""Get max context size for this model."""
|
||
|
|
return self.modelname_to_contextsize(self.model_name)
|
||
|
|
|
||
|
|
def max_tokens_for_prompt(self, prompt: str) -> int:
|
||
|
|
"""Calculate the maximum number of tokens possible to generate for a prompt.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
prompt: The prompt to pass into the model.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
The maximum number of tokens to generate for a prompt.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
max_tokens = openai.max_tokens_for_prompt("Tell me a joke.")
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
num_tokens = self.get_num_tokens(prompt)
|
||
|
|
return self.max_context_size - num_tokens
|
||
|
|
|
||
|
|
|
||
|
|
class OpenAI(BaseOpenAI):
|
||
|
|
"""OpenAI completion model integration.
|
||
|
|
|
||
|
|
Setup:
|
||
|
|
Install `langchain-openai` and set environment variable `OPENAI_API_KEY`.
|
||
|
|
|
||
|
|
```bash
|
||
|
|
pip install -U langchain-openai
|
||
|
|
export OPENAI_API_KEY="your-api-key"
|
||
|
|
```
|
||
|
|
|
||
|
|
Key init args — completion params:
|
||
|
|
model:
|
||
|
|
Name of OpenAI model to use.
|
||
|
|
temperature:
|
||
|
|
Sampling temperature.
|
||
|
|
max_tokens:
|
||
|
|
Max number of tokens to generate.
|
||
|
|
logprobs:
|
||
|
|
Whether to return logprobs.
|
||
|
|
stream_options:
|
||
|
|
Configure streaming outputs, like whether to return token usage when
|
||
|
|
streaming (`{"include_usage": True}`).
|
||
|
|
|
||
|
|
Key init args — client params:
|
||
|
|
timeout:
|
||
|
|
Timeout for requests.
|
||
|
|
max_retries:
|
||
|
|
Max number of retries.
|
||
|
|
api_key:
|
||
|
|
OpenAI API key. If not passed in will be read from env var `OPENAI_API_KEY`.
|
||
|
|
base_url:
|
||
|
|
Base URL for API requests. Only specify if using a proxy or service
|
||
|
|
emulator.
|
||
|
|
organization:
|
||
|
|
OpenAI organization ID. If not passed in will be read from env
|
||
|
|
var `OPENAI_ORG_ID`.
|
||
|
|
|
||
|
|
See full list of supported init args and their descriptions in the params section.
|
||
|
|
|
||
|
|
Instantiate:
|
||
|
|
```python
|
||
|
|
from langchain_openai import OpenAI
|
||
|
|
|
||
|
|
model = OpenAI(
|
||
|
|
model="gpt-3.5-turbo-instruct",
|
||
|
|
temperature=0,
|
||
|
|
max_retries=2,
|
||
|
|
# api_key="...",
|
||
|
|
# base_url="...",
|
||
|
|
# organization="...",
|
||
|
|
# other params...
|
||
|
|
)
|
||
|
|
```
|
||
|
|
|
||
|
|
Invoke:
|
||
|
|
```python
|
||
|
|
input_text = "The meaning of life is "
|
||
|
|
model.invoke(input_text)
|
||
|
|
```
|
||
|
|
```txt
|
||
|
|
"a philosophical question that has been debated by thinkers and scholars for centuries."
|
||
|
|
```
|
||
|
|
|
||
|
|
Stream:
|
||
|
|
```python
|
||
|
|
for chunk in model.stream(input_text):
|
||
|
|
print(chunk, end="|")
|
||
|
|
```
|
||
|
|
```txt
|
||
|
|
a| philosophical| question| that| has| been| debated| by| thinkers| and| scholars| for| centuries|.
|
||
|
|
```
|
||
|
|
|
||
|
|
```python
|
||
|
|
"".join(model.stream(input_text))
|
||
|
|
```
|
||
|
|
```txt
|
||
|
|
"a philosophical question that has been debated by thinkers and scholars for centuries."
|
||
|
|
```
|
||
|
|
|
||
|
|
Async:
|
||
|
|
```python
|
||
|
|
await model.ainvoke(input_text)
|
||
|
|
|
||
|
|
# stream:
|
||
|
|
# async for chunk in (await model.astream(input_text)):
|
||
|
|
# print(chunk)
|
||
|
|
|
||
|
|
# batch:
|
||
|
|
# await model.abatch([input_text])
|
||
|
|
```
|
||
|
|
```txt
|
||
|
|
"a philosophical question that has been debated by thinkers and scholars for centuries."
|
||
|
|
```
|
||
|
|
""" # noqa: E501
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def get_lc_namespace(cls) -> list[str]:
|
||
|
|
"""Get the namespace of the LangChain object.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`["langchain", "llms", "openai"]`
|
||
|
|
"""
|
||
|
|
return ["langchain", "llms", "openai"]
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def is_lc_serializable(cls) -> bool:
|
||
|
|
"""Return whether this model can be serialized by LangChain."""
|
||
|
|
return True
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _invocation_params(self) -> dict[str, Any]:
|
||
|
|
return {"model": self.model_name, **super()._invocation_params}
|
||
|
|
|
||
|
|
@property
|
||
|
|
def lc_secrets(self) -> dict[str, str]:
|
||
|
|
"""Mapping of secret keys to environment variables."""
|
||
|
|
return {"openai_api_key": "OPENAI_API_KEY"}
|
||
|
|
|
||
|
|
@property
|
||
|
|
def lc_attributes(self) -> dict[str, Any]:
|
||
|
|
"""LangChain attributes for this class."""
|
||
|
|
attributes: dict[str, Any] = {}
|
||
|
|
if self.openai_api_base:
|
||
|
|
attributes["openai_api_base"] = self.openai_api_base
|
||
|
|
|
||
|
|
if self.openai_organization:
|
||
|
|
attributes["openai_organization"] = self.openai_organization
|
||
|
|
|
||
|
|
if self.openai_proxy:
|
||
|
|
attributes["openai_proxy"] = self.openai_proxy
|
||
|
|
|
||
|
|
return attributes
|