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@ -27,7 +27,10 @@ from langchain.llms.base import BaseLLM, create_base_retry_decorator
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from langchain.pydantic_v1 import Field, root_validator
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from langchain.pydantic_v1 import Field, root_validator
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from langchain.schema import Generation, LLMResult
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from langchain.schema import Generation, LLMResult
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from langchain.schema.output import GenerationChunk
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from langchain.schema.output import GenerationChunk
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from langchain.utils import get_from_dict_or_env, get_pydantic_field_names
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from langchain.utils import (
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get_from_dict_or_env,
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get_pydantic_field_names,
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)
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from langchain.utils.utils import build_extra_kwargs
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from langchain.utils.utils import build_extra_kwargs
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@ -44,7 +47,9 @@ def is_openai_v1() -> bool:
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def update_token_usage(
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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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keys: Set[str],
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response: Dict[str, Any],
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token_usage: Dict[str, Any],
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) -> None:
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) -> None:
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"""Update token usage."""
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"""Update token usage."""
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_keys_to_use = keys.intersection(response["usage"])
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_keys_to_use = keys.intersection(response["usage"])
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@ -65,7 +70,9 @@ def _stream_response_to_generation_chunk(
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finish_reason=stream_response["choices"][0].get(
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finish_reason=stream_response["choices"][0].get(
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"finish_reason", None
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"finish_reason", None
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),
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),
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logprobs=stream_response["choices"][0].get("logprobs", None),
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logprobs=stream_response["choices"][0].get(
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"logprobs", None
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),
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),
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),
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)
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)
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@ -74,13 +81,15 @@ def _update_response(
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response: Dict[str, Any], stream_response: Dict[str, Any]
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response: Dict[str, Any], stream_response: Dict[str, Any]
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) -> None:
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) -> None:
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"""Update response from the stream response."""
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"""Update response from the stream response."""
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response["choices"][0]["text"] += stream_response["choices"][0]["text"]
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response["choices"][0]["text"] += stream_response["choices"][0][
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response["choices"][0]["finish_reason"] = stream_response["choices"][0].get(
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"text"
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"finish_reason", None
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)
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response["choices"][0]["logprobs"] = stream_response["choices"][0][
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"logprobs"
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]
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]
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response["choices"][0]["finish_reason"] = stream_response[
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"choices"
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][0].get("finish_reason", None)
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response["choices"][0]["logprobs"] = stream_response["choices"][
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0
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]["logprobs"]
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def _streaming_response_template() -> Dict[str, Any]:
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def _streaming_response_template() -> Dict[str, Any]:
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@ -111,7 +120,9 @@ def _create_retry_decorator(
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openai.error.ServiceUnavailableError,
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openai.error.ServiceUnavailableError,
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]
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]
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return create_base_retry_decorator(
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return create_base_retry_decorator(
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error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
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error_types=errors,
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max_retries=llm.max_retries,
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run_manager=run_manager,
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)
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)
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@ -121,7 +132,9 @@ def completion_with_retry(
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**kwargs: Any,
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**kwargs: Any,
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) -> Any:
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) -> Any:
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"""Use tenacity to retry the completion call."""
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
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retry_decorator = _create_retry_decorator(
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llm, run_manager=run_manager
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)
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@retry_decorator
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@retry_decorator
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def _completion_with_retry(**kwargs: Any) -> Any:
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def _completion_with_retry(**kwargs: Any) -> Any:
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@ -136,7 +149,9 @@ async def acompletion_with_retry(
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**kwargs: Any,
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**kwargs: Any,
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) -> Any:
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) -> Any:
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"""Use tenacity to retry the async completion call."""
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"""Use tenacity to retry the async completion call."""
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retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
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retry_decorator = _create_retry_decorator(
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llm, run_manager=run_manager
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)
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@retry_decorator
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@retry_decorator
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async def _completion_with_retry(**kwargs: Any) -> Any:
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async def _completion_with_retry(**kwargs: Any) -> Any:
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@ -160,7 +175,9 @@ class BaseOpenAI(BaseLLM):
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attributes["openai_api_base"] = self.openai_api_base
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attributes["openai_api_base"] = self.openai_api_base
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if self.openai_organization != "":
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if self.openai_organization != "":
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attributes["openai_organization"] = self.openai_organization
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attributes["openai_organization"] = (
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self.openai_organization
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)
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if self.openai_proxy != "":
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if self.openai_proxy != "":
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attributes["openai_proxy"] = self.openai_proxy
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attributes["openai_proxy"] = self.openai_proxy
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@ -199,9 +216,13 @@ class BaseOpenAI(BaseLLM):
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openai_proxy: Optional[str] = None
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openai_proxy: Optional[str] = None
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batch_size: int = 20
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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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"""Batch size to use when passing multiple documents to generate."""
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request_timeout: Optional[Union[float, Tuple[float, float]]] = None
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request_timeout: Optional[Union[float, Tuple[float, float]]] = (
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None
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)
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"""Timeout for requests to OpenAI completion API. Default is 600 seconds."""
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"""Timeout for requests to OpenAI completion API. Default is 600 seconds."""
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logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
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logit_bias: Optional[Dict[str, float]] = Field(
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default_factory=dict
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)
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"""Adjust the probability of specific tokens being generated."""
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"""Adjust the probability of specific tokens being generated."""
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max_retries: int = 6
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max_retries: int = 6
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"""Maximum number of retries to make when generating."""
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"""Maximum number of retries to make when generating."""
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@ -278,7 +299,9 @@ class BaseOpenAI(BaseLLM):
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if values["streaming"] and values["n"] > 1:
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if values["streaming"] and values["n"] > 1:
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raise ValueError("Cannot stream results when n > 1.")
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raise ValueError("Cannot stream results when n > 1.")
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if values["streaming"] and values["best_of"] > 1:
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if values["streaming"] and values["best_of"] > 1:
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raise ValueError("Cannot stream results when best_of > 1.")
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raise ValueError(
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"Cannot stream results when best_of > 1."
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)
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return values
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return values
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@property
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@property
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@ -310,7 +333,9 @@ class BaseOpenAI(BaseLLM):
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**kwargs: Any,
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**kwargs: Any,
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) -> Iterator[GenerationChunk]:
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) -> Iterator[GenerationChunk]:
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params = {**self._invocation_params, **kwargs, "stream": True}
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params = {**self._invocation_params, **kwargs, "stream": True}
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self.get_sub_prompts(params, [prompt], stop) # this mutates params
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self.get_sub_prompts(
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params, [prompt], stop
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) # this mutates params
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for stream_resp in completion_with_retry(
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for stream_resp in completion_with_retry(
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self, prompt=prompt, run_manager=run_manager, **params
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self, prompt=prompt, run_manager=run_manager, **params
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):
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):
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@ -336,7 +361,9 @@ class BaseOpenAI(BaseLLM):
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**kwargs: Any,
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**kwargs: Any,
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) -> AsyncIterator[GenerationChunk]:
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) -> AsyncIterator[GenerationChunk]:
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params = {**self._invocation_params, **kwargs, "stream": True}
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params = {**self._invocation_params, **kwargs, "stream": True}
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self.get_sub_prompts(params, [prompt], stop) # this mutate params
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self.get_sub_prompts(
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params, [prompt], stop
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) # this mutate params
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async for stream_resp in await acompletion_with_retry(
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async for stream_resp in await acompletion_with_retry(
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self, prompt=prompt, run_manager=run_manager, **params
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self, prompt=prompt, run_manager=run_manager, **params
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):
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):
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@ -404,7 +431,9 @@ class BaseOpenAI(BaseLLM):
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{
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{
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"text": generation.text,
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"text": generation.text,
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"finish_reason": (
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"finish_reason": (
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generation.generation_info.get("finish_reason")
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generation.generation_info.get(
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"finish_reason"
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)
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if generation.generation_info
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if generation.generation_info
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else None
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else None
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),
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),
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@ -417,7 +446,10 @@ class BaseOpenAI(BaseLLM):
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)
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)
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else:
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else:
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response = completion_with_retry(
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response = completion_with_retry(
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self, prompt=_prompts, run_manager=run_manager, **params
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self,
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prompt=_prompts,
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run_manager=run_manager,
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**params,
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)
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)
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choices.extend(response["choices"])
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choices.extend(response["choices"])
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update_token_usage(_keys, response, token_usage)
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update_token_usage(_keys, response, token_usage)
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@ -459,7 +491,9 @@ class BaseOpenAI(BaseLLM):
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{
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{
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"text": generation.text,
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"text": generation.text,
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"finish_reason": (
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"finish_reason": (
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generation.generation_info.get("finish_reason")
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generation.generation_info.get(
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"finish_reason"
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)
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if generation.generation_info
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if generation.generation_info
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else None
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else None
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),
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),
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@ -472,7 +506,10 @@ class BaseOpenAI(BaseLLM):
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)
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)
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else:
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else:
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response = await acompletion_with_retry(
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response = await acompletion_with_retry(
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self, prompt=_prompts, run_manager=run_manager, **params
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self,
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prompt=_prompts,
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run_manager=run_manager,
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**params,
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)
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)
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choices.extend(response["choices"])
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choices.extend(response["choices"])
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update_token_usage(_keys, response, token_usage)
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update_token_usage(_keys, response, token_usage)
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@ -488,15 +525,19 @@ class BaseOpenAI(BaseLLM):
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if stop is not None:
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if stop is not None:
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if "stop" in params:
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if "stop" in params:
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raise ValueError(
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raise ValueError(
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"`stop` found in both the input and default params."
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"`stop` found in both the input and default"
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" params."
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)
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)
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params["stop"] = stop
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params["stop"] = stop
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if params["max_tokens"] == -1:
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if params["max_tokens"] == -1:
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if len(prompts) != 1:
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if len(prompts) != 1:
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raise ValueError(
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raise ValueError(
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"max_tokens set to -1 not supported for multiple inputs."
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"max_tokens set to -1 not supported for multiple"
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" inputs."
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)
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)
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params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
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params["max_tokens"] = self.max_tokens_for_prompt(
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prompts[0]
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)
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sub_prompts = [
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sub_prompts = [
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prompts[i : i + self.batch_size]
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prompts[i : i + self.batch_size]
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for i in range(0, len(prompts), self.batch_size)
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for i in range(0, len(prompts), self.batch_size)
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@ -504,7 +545,10 @@ class BaseOpenAI(BaseLLM):
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return sub_prompts
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return sub_prompts
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def create_llm_result(
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def create_llm_result(
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self, choices: Any, prompts: List[str], token_usage: Dict[str, int]
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self,
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choices: Any,
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prompts: List[str],
|
|
|
|
|
|
|
|
token_usage: Dict[str, int],
|
|
|
|
) -> LLMResult:
|
|
|
|
) -> LLMResult:
|
|
|
|
"""Create the LLMResult from the choices and prompts."""
|
|
|
|
"""Create the LLMResult from the choices and prompts."""
|
|
|
|
generations = []
|
|
|
|
generations = []
|
|
|
|
@ -522,8 +566,13 @@ class BaseOpenAI(BaseLLM):
|
|
|
|
for choice in sub_choices
|
|
|
|
for choice in sub_choices
|
|
|
|
]
|
|
|
|
]
|
|
|
|
)
|
|
|
|
)
|
|
|
|
llm_output = {"token_usage": token_usage, "model_name": self.model_name}
|
|
|
|
llm_output = {
|
|
|
|
return LLMResult(generations=generations, llm_output=llm_output)
|
|
|
|
"token_usage": token_usage,
|
|
|
|
|
|
|
|
"model_name": self.model_name,
|
|
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
return LLMResult(
|
|
|
|
|
|
|
|
generations=generations, llm_output=llm_output
|
|
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
@property
|
|
|
|
@property
|
|
|
|
def _invocation_params(self) -> Dict[str, Any]:
|
|
|
|
def _invocation_params(self) -> Dict[str, Any]:
|
|
|
|
@ -542,7 +591,10 @@ class BaseOpenAI(BaseLLM):
|
|
|
|
@property
|
|
|
|
@property
|
|
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
|
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
|
|
|
"""Get the identifying parameters."""
|
|
|
|
"""Get the identifying parameters."""
|
|
|
|
return {**{"model_name": self.model_name}, **self._default_params}
|
|
|
|
return {
|
|
|
|
|
|
|
|
**{"model_name": self.model_name},
|
|
|
|
|
|
|
|
**self._default_params,
|
|
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
@property
|
|
|
|
@property
|
|
|
|
def _llm_type(self) -> str:
|
|
|
|
def _llm_type(self) -> str:
|
|
|
|
@ -558,9 +610,9 @@ class BaseOpenAI(BaseLLM):
|
|
|
|
import tiktoken
|
|
|
|
import tiktoken
|
|
|
|
except ImportError:
|
|
|
|
except ImportError:
|
|
|
|
raise ImportError(
|
|
|
|
raise ImportError(
|
|
|
|
"Could not import tiktoken python package. "
|
|
|
|
"Could not import tiktoken python package. This is"
|
|
|
|
"This is needed in order to calculate get_num_tokens. "
|
|
|
|
" needed in order to calculate get_num_tokens. Please"
|
|
|
|
"Please install it with `pip install tiktoken`."
|
|
|
|
" install it with `pip install tiktoken`."
|
|
|
|
)
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
model_name = self.tiktoken_model_name or self.model_name
|
|
|
|
model_name = self.tiktoken_model_name or self.model_name
|
|
|
|
@ -568,7 +620,8 @@ class BaseOpenAI(BaseLLM):
|
|
|
|
enc = tiktoken.encoding_for_model(model_name)
|
|
|
|
enc = tiktoken.encoding_for_model(model_name)
|
|
|
|
except KeyError:
|
|
|
|
except KeyError:
|
|
|
|
logger.warning(
|
|
|
|
logger.warning(
|
|
|
|
"Warning: model not found. Using cl100k_base encoding."
|
|
|
|
"Warning: model not found. Using cl100k_base"
|
|
|
|
|
|
|
|
" encoding."
|
|
|
|
)
|
|
|
|
)
|
|
|
|
model = "cl100k_base"
|
|
|
|
model = "cl100k_base"
|
|
|
|
enc = tiktoken.get_encoding(model)
|
|
|
|
enc = tiktoken.get_encoding(model)
|
|
|
|
@ -630,8 +683,8 @@ class BaseOpenAI(BaseLLM):
|
|
|
|
|
|
|
|
|
|
|
|
if context_size is None:
|
|
|
|
if context_size is None:
|
|
|
|
raise ValueError(
|
|
|
|
raise ValueError(
|
|
|
|
f"Unknown model: {modelname}. Please provide a valid OpenAI"
|
|
|
|
f"Unknown model: {modelname}. Please provide a valid"
|
|
|
|
" model name.Known models are: "
|
|
|
|
" OpenAI model name.Known models are: "
|
|
|
|
+ ", ".join(model_token_mapping.keys())
|
|
|
|
+ ", ".join(model_token_mapping.keys())
|
|
|
|
)
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
@ -678,7 +731,10 @@ class OpenAI(BaseOpenAI):
|
|
|
|
|
|
|
|
|
|
|
|
@property
|
|
|
|
@property
|
|
|
|
def _invocation_params(self) -> Dict[str, Any]:
|
|
|
|
def _invocation_params(self) -> Dict[str, Any]:
|
|
|
|
return {**{"model": self.model_name}, **super()._invocation_params}
|
|
|
|
return {
|
|
|
|
|
|
|
|
**{"model": self.model_name},
|
|
|
|
|
|
|
|
**super()._invocation_params,
|
|
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class AzureOpenAI(BaseOpenAI):
|
|
|
|
class AzureOpenAI(BaseOpenAI):
|
|
|
|
@ -802,7 +858,9 @@ class OpenAIChat(BaseLLM):
|
|
|
|
for field_name in list(values):
|
|
|
|
for field_name in list(values):
|
|
|
|
if field_name not in all_required_field_names:
|
|
|
|
if field_name not in all_required_field_names:
|
|
|
|
if field_name in extra:
|
|
|
|
if field_name in extra:
|
|
|
|
raise ValueError(f"Found {field_name} supplied twice.")
|
|
|
|
raise ValueError(
|
|
|
|
|
|
|
|
f"Found {field_name} supplied twice."
|
|
|
|
|
|
|
|
)
|
|
|
|
extra[field_name] = values.pop(field_name)
|
|
|
|
extra[field_name] = values.pop(field_name)
|
|
|
|
values["model_kwargs"] = extra
|
|
|
|
values["model_kwargs"] = extra
|
|
|
|
return values
|
|
|
|
return values
|
|
|
|
@ -826,7 +884,10 @@ class OpenAIChat(BaseLLM):
|
|
|
|
default="",
|
|
|
|
default="",
|
|
|
|
)
|
|
|
|
)
|
|
|
|
openai_organization = get_from_dict_or_env(
|
|
|
|
openai_organization = get_from_dict_or_env(
|
|
|
|
values, "openai_organization", "OPENAI_ORGANIZATION", default=""
|
|
|
|
values,
|
|
|
|
|
|
|
|
"openai_organization",
|
|
|
|
|
|
|
|
"OPENAI_ORGANIZATION",
|
|
|
|
|
|
|
|
default="",
|
|
|
|
)
|
|
|
|
)
|
|
|
|
try:
|
|
|
|
try:
|
|
|
|
import openai
|
|
|
|
import openai
|
|
|
|
@ -847,9 +908,10 @@ class OpenAIChat(BaseLLM):
|
|
|
|
values["client"] = openai.ChatCompletion
|
|
|
|
values["client"] = openai.ChatCompletion
|
|
|
|
except AttributeError:
|
|
|
|
except AttributeError:
|
|
|
|
raise ValueError(
|
|
|
|
raise ValueError(
|
|
|
|
"`openai` has no `ChatCompletion` attribute, this is likely "
|
|
|
|
"`openai` has no `ChatCompletion` attribute, this is"
|
|
|
|
"due to an old version of the openai package. Try upgrading it "
|
|
|
|
" likely due to an old version of the openai package."
|
|
|
|
"with `pip install --upgrade openai`."
|
|
|
|
" Try upgrading it with `pip install --upgrade"
|
|
|
|
|
|
|
|
" openai`."
|
|
|
|
)
|
|
|
|
)
|
|
|
|
return values
|
|
|
|
return values
|
|
|
|
|
|
|
|
|
|
|
|
@ -863,8 +925,8 @@ class OpenAIChat(BaseLLM):
|
|
|
|
) -> Tuple:
|
|
|
|
) -> Tuple:
|
|
|
|
if len(prompts) > 1:
|
|
|
|
if len(prompts) > 1:
|
|
|
|
raise ValueError(
|
|
|
|
raise ValueError(
|
|
|
|
"OpenAIChat currently only supports single prompt, got"
|
|
|
|
"OpenAIChat currently only supports single prompt,"
|
|
|
|
f" {prompts}"
|
|
|
|
f" got {prompts}"
|
|
|
|
)
|
|
|
|
)
|
|
|
|
messages = self.prefix_messages + [
|
|
|
|
messages = self.prefix_messages + [
|
|
|
|
{"role": "user", "content": prompts[0]}
|
|
|
|
{"role": "user", "content": prompts[0]}
|
|
|
|
@ -876,7 +938,8 @@ class OpenAIChat(BaseLLM):
|
|
|
|
if stop is not None:
|
|
|
|
if stop is not None:
|
|
|
|
if "stop" in params:
|
|
|
|
if "stop" in params:
|
|
|
|
raise ValueError(
|
|
|
|
raise ValueError(
|
|
|
|
"`stop` found in both the input and default params."
|
|
|
|
"`stop` found in both the input and default"
|
|
|
|
|
|
|
|
" params."
|
|
|
|
)
|
|
|
|
)
|
|
|
|
params["stop"] = stop
|
|
|
|
params["stop"] = stop
|
|
|
|
if params.get("max_tokens") == -1:
|
|
|
|
if params.get("max_tokens") == -1:
|
|
|
|
@ -896,7 +959,9 @@ class OpenAIChat(BaseLLM):
|
|
|
|
for stream_resp in completion_with_retry(
|
|
|
|
for stream_resp in completion_with_retry(
|
|
|
|
self, messages=messages, run_manager=run_manager, **params
|
|
|
|
self, messages=messages, run_manager=run_manager, **params
|
|
|
|
):
|
|
|
|
):
|
|
|
|
token = stream_resp["choices"][0]["delta"].get("content", "")
|
|
|
|
token = stream_resp["choices"][0]["delta"].get(
|
|
|
|
|
|
|
|
"content", ""
|
|
|
|
|
|
|
|
)
|
|
|
|
chunk = GenerationChunk(text=token)
|
|
|
|
chunk = GenerationChunk(text=token)
|
|
|
|
yield chunk
|
|
|
|
yield chunk
|
|
|
|
if run_manager:
|
|
|
|
if run_manager:
|
|
|
|
@ -914,7 +979,9 @@ class OpenAIChat(BaseLLM):
|
|
|
|
async for stream_resp in await acompletion_with_retry(
|
|
|
|
async for stream_resp in await acompletion_with_retry(
|
|
|
|
self, messages=messages, run_manager=run_manager, **params
|
|
|
|
self, messages=messages, run_manager=run_manager, **params
|
|
|
|
):
|
|
|
|
):
|
|
|
|
token = stream_resp["choices"][0]["delta"].get("content", "")
|
|
|
|
token = stream_resp["choices"][0]["delta"].get(
|
|
|
|
|
|
|
|
"content", ""
|
|
|
|
|
|
|
|
)
|
|
|
|
chunk = GenerationChunk(text=token)
|
|
|
|
chunk = GenerationChunk(text=token)
|
|
|
|
yield chunk
|
|
|
|
yield chunk
|
|
|
|
if run_manager:
|
|
|
|
if run_manager:
|
|
|
|
@ -929,7 +996,9 @@ class OpenAIChat(BaseLLM):
|
|
|
|
) -> LLMResult:
|
|
|
|
) -> LLMResult:
|
|
|
|
if self.streaming:
|
|
|
|
if self.streaming:
|
|
|
|
generation: Optional[GenerationChunk] = None
|
|
|
|
generation: Optional[GenerationChunk] = None
|
|
|
|
for chunk in self._stream(prompts[0], stop, run_manager, **kwargs):
|
|
|
|
for chunk in self._stream(
|
|
|
|
|
|
|
|
prompts[0], stop, run_manager, **kwargs
|
|
|
|
|
|
|
|
):
|
|
|
|
if generation is None:
|
|
|
|
if generation is None:
|
|
|
|
generation = chunk
|
|
|
|
generation = chunk
|
|
|
|
else:
|
|
|
|
else:
|
|
|
|
@ -950,7 +1019,9 @@ class OpenAIChat(BaseLLM):
|
|
|
|
generations=[
|
|
|
|
generations=[
|
|
|
|
[
|
|
|
|
[
|
|
|
|
Generation(
|
|
|
|
Generation(
|
|
|
|
text=full_response["choices"][0]["message"]["content"]
|
|
|
|
text=full_response["choices"][0]["message"][
|
|
|
|
|
|
|
|
"content"
|
|
|
|
|
|
|
|
]
|
|
|
|
)
|
|
|
|
)
|
|
|
|
]
|
|
|
|
]
|
|
|
|
],
|
|
|
|
],
|
|
|
|
@ -989,7 +1060,9 @@ class OpenAIChat(BaseLLM):
|
|
|
|
generations=[
|
|
|
|
generations=[
|
|
|
|
[
|
|
|
|
[
|
|
|
|
Generation(
|
|
|
|
Generation(
|
|
|
|
text=full_response["choices"][0]["message"]["content"]
|
|
|
|
text=full_response["choices"][0]["message"][
|
|
|
|
|
|
|
|
"content"
|
|
|
|
|
|
|
|
]
|
|
|
|
)
|
|
|
|
)
|
|
|
|
]
|
|
|
|
]
|
|
|
|
],
|
|
|
|
],
|
|
|
|
@ -999,7 +1072,10 @@ class OpenAIChat(BaseLLM):
|
|
|
|
@property
|
|
|
|
@property
|
|
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
|
|
|
def _identifying_params(self) -> Mapping[str, Any]:
|
|
|
|
"""Get the identifying parameters."""
|
|
|
|
"""Get the identifying parameters."""
|
|
|
|
return {**{"model_name": self.model_name}, **self._default_params}
|
|
|
|
return {
|
|
|
|
|
|
|
|
**{"model_name": self.model_name},
|
|
|
|
|
|
|
|
**self._default_params,
|
|
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
@property
|
|
|
|
@property
|
|
|
|
def _llm_type(self) -> str:
|
|
|
|
def _llm_type(self) -> str:
|
|
|
|
@ -1015,9 +1091,9 @@ class OpenAIChat(BaseLLM):
|
|
|
|
import tiktoken
|
|
|
|
import tiktoken
|
|
|
|
except ImportError:
|
|
|
|
except ImportError:
|
|
|
|
raise ImportError(
|
|
|
|
raise ImportError(
|
|
|
|
"Could not import tiktoken python package. "
|
|
|
|
"Could not import tiktoken python package. This is"
|
|
|
|
"This is needed in order to calculate get_num_tokens. "
|
|
|
|
" needed in order to calculate get_num_tokens. Please"
|
|
|
|
"Please install it with `pip install tiktoken`."
|
|
|
|
" install it with `pip install tiktoken`."
|
|
|
|
)
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
enc = tiktoken.encoding_for_model(self.model_name)
|
|
|
|
enc = tiktoken.encoding_for_model(self.model_name)
|
|
|
|
|
Kye
1 year ago