id stringlengths 14 15 | text stringlengths 35 2.51k | source stringlengths 61 154 |
|---|---|---|
7e3b2e8745c3-0 | Source code for langchain.llms.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import Any, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms import OpenAI, OpenAIChat
from langchain.schema import LLMR... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
7e3b2e8745c3-1 | """Call OpenAI generate and then call PromptLayer API to log the request."""
from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = super()._generate(prompts, stop, run_manager)
request_end_time = ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
7e3b2e8745c3-2 | generated_responses = await super()._agenerate(prompts, stop, run_manager)
request_end_time = datetime.datetime.now().timestamp()
for i in range(len(prompts)):
prompt = prompts[i]
generation = generated_responses.generations[i][0]
resp = {
"text": gene... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
7e3b2e8745c3-3 | parameters:
``pl_tags``: List of strings to tag the request with.
``return_pl_id``: If True, the PromptLayer request ID will be
returned in the ``generation_info`` field of the
``Generation`` object.
Example:
.. code-block:: python
from langchain.llms impo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
7e3b2e8745c3-4 | resp,
request_start_time,
request_end_time,
get_api_key(),
return_pl_id=self.return_pl_id,
)
if self.return_pl_id:
if generation.generation_info is None or not isinstance(
generation.generation_in... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
7e3b2e8745c3-5 | generation.generation_info, dict
):
generation.generation_info = {}
generation.generation_info["pl_request_id"] = pl_request_id
return generated_responses | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
bc24325c6b08-0 | Source code for langchain.llms.cerebriumai
"""Wrapper around CerebriumAI API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
bc24325c6b08-1 | all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if field_name in extra:
raise ValueError(f"Found {field_name... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
bc24325c6b08-2 | """Call to CerebriumAI endpoint."""
try:
from cerebrium import model_api_request
except ImportError:
raise ValueError(
"Could not import cerebrium python package. "
"Please install it with `pip install cerebrium`."
)
params = se... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
7c2ace038c1e-0 | Source code for langchain.llms.bedrock
import json
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
class LLMInputOutp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
7c2ace038c1e-1 | else:
return response_body.get("results")[0].get("outputText")
[docs]class Bedrock(LLM):
"""LLM provider to invoke Bedrock models.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/crede... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
7c2ace038c1e-2 | equivalent to the modelId property in the list-foundation-models api"""
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
[docs] class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @root_validator()
def validate_envir... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
7c2ace038c1e-3 | }
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "amazon_bedrock"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Ca... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
2a381b90ad3b-0 | Source code for langchain.llms.nlpcloud
"""Wrapper around NLPCloud APIs."""
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_e... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
2a381b90ad3b-1 | """Total probability mass of tokens to consider at each step."""
top_k: int = 50
"""The number of highest probability tokens to keep for top-k filtering."""
repetition_penalty: float = 1.0
"""Penalizes repeated tokens. 1.0 means no penalty."""
length_penalty: float = 1.0
"""Exponential penalty t... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
2a381b90ad3b-2 | @property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for calling NLPCloud API."""
return {
"temperature": self.temperature,
"min_length": self.min_length,
"max_length": self.max_length,
"length_no_input": self.length_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
2a381b90ad3b-3 | Returns:
The string generated by the model.
Example:
.. code-block:: python
response = nlpcloud("Tell me a joke.")
"""
if stop and len(stop) > 1:
raise ValueError(
"NLPCloud only supports a single stop sequence per generation."
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
2435f60deae9-0 | Source code for langchain.llms.self_hosted_hugging_face
"""Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware."""
import importlib.util
import logging
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerFo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
2435f60deae9-1 | text = enforce_stop_tokens(text, stop)
return text
def _load_transformer(
model_id: str = DEFAULT_MODEL_ID,
task: str = DEFAULT_TASK,
device: int = 0,
model_kwargs: Optional[dict] = None,
) -> Any:
"""Inference function to send to the remote hardware.
Accepts a huggingface model_id and retur... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
2435f60deae9-2 | )
if device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer ass... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
2435f60deae9-3 | hf = SelfHostedHuggingFaceLLM(
model_id="google/flan-t5-large", task="text2text-generation",
hardware=gpu
)
Example passing fn that generates a pipeline (bc the pipeline is not serializable):
.. code-block:: python
from langchain.llms import SelfHosted... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
2435f60deae9-4 | """Function to load the model remotely on the server."""
inference_fn: Callable = _generate_text #: :meta private:
"""Inference function to send to the remote hardware."""
[docs] class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def __init__(self, **kwargs:... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
cadb2d79d842-0 | Source code for langchain.llms.baseten
"""Wrapper around Baseten deployed model API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
logger = logging.getLogger(__name__... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baseten.html |
cadb2d79d842-1 | """Return type of model."""
return "baseten"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call to Baseten deployed model endpoint."""
try:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baseten.html |
52f7cdd684a0-0 | Source code for langchain.llms.mosaicml
"""Wrapper around MosaicML APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils impo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
52f7cdd684a0-1 | )
"""
endpoint_url: str = (
"https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict"
)
"""Endpoint URL to use."""
inject_instruction_format: bool = False
"""Whether to inject the instruction format into the prompt."""
model_kwargs: Optional[dict] = None
"""Key word ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
52f7cdd684a0-2 | prompt = PROMPT_FOR_GENERATION_FORMAT.format(
instruction=prompt,
)
return prompt
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
is_retry: bool = False,
**kwar... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
52f7cdd684a0-3 | ):
import time
time.sleep(self.retry_sleep)
return self._call(prompt, stop, run_manager, is_retry=True)
raise ValueError(
f"Error raised by inference API: {parsed_response['error']}"
)
# The infer... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
52f7cdd684a0-4 | if stop is not None:
text = enforce_stop_tokens(text, stop)
return text | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
c164013e0fde-0 | Source code for langchain.llms.huggingface_text_gen_inference
"""Wrapper around Huggingface text generation inference API."""
from functools import partial
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerFor... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
c164013e0fde-1 | - _acall: Async generates text based on a given prompt and stop sequences.
- _llm_type: Returns the type of LLM.
"""
"""
Example:
.. code-block:: python
# Basic Example (no streaming)
llm = HuggingFaceTextGenInference(
inference_server_url = "http://localh... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
c164013e0fde-2 | seed: Optional[int] = None
inference_server_url: str = ""
timeout: int = 120
server_kwargs: Dict[str, Any] = Field(default_factory=dict)
stream: bool = False
client: Any
async_client: Any
[docs] class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
c164013e0fde-3 | res = self.client.generate(
prompt,
stop_sequences=stop,
max_new_tokens=self.max_new_tokens,
top_k=self.top_k,
top_p=self.top_p,
typical_p=self.typical_p,
temperature=self.temperature,
repetit... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
c164013e0fde-4 | prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
if stop is None:
stop = self.stop_sequences
else:
stop += self.stop_sequences
if not self.stream:
r... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
c164013e0fde-5 | token = res.token
is_stop = False
for stop_seq in stop:
if stop_seq in token.text:
is_stop = True
break
if is_stop:
break
if not token.special:
if t... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
b84e9fd40f0c-0 | Source code for langchain.llms.vertexai
"""Wrapper around Google VertexAI models."""
import asyncio
from concurrent.futures import Executor, ThreadPoolExecutor
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.callbacks.manager import (
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
b84e9fd40f0c-1 | project: Optional[str] = None
"The default GCP project to use when making Vertex API calls."
location: str = "us-central1"
"The default location to use when making API calls."
credentials: Any = None
"The default custom credentials (google.auth.credentials.Credentials) to use "
"when making API ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
b84e9fd40f0c-2 | if stop is None and self.stop is not None:
stop = self.stop
if stop:
return enforce_stop_tokens(text, stop)
return text
@property
def _llm_type(self) -> str:
return "vertexai"
@classmethod
def _get_task_executor(cls, request_parallelism: int = 5) -> Execut... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
b84e9fd40f0c-3 | tuned_model_name
)
else:
values["client"] = TextGenerationModel.from_pretrained(model_name)
else:
from vertexai.preview.language_models import CodeGenerationModel
values["client"] = CodeGenerationModel.from_pretrained(mo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
8666b7fa1a4d-0 | Source code for langchain.llms.amazon_api_gateway
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
class ContentHandle... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/amazon_api_gateway.html |
8666b7fa1a4d-1 | _model_kwargs = self.model_kwargs or {}
return {
**{"endpoint_name": self.api_url},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "amazon_api_gateway"
def _call(
self,
prompt... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/amazon_api_gateway.html |
4f724f8ba27c-0 | Source code for langchain.llms.anyscale
"""Wrapper around Anyscale"""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enf... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
4f724f8ba27c-1 | extra = Extra.forbid
[docs] @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
anyscale_service_url = get_from_dict_or_env(
values, "anyscale_service_url", "ANYSCALE_SERVICE_URL"
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
4f724f8ba27c-2 | return "anyscale"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Anyscale Service endpoint.
Args:
prompt: The prompt to pass into t... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
ffc28b81d319-0 | Source code for langchain.llms.azureml_endpoint
"""Wrapper around AzureML Managed Online Endpoint API."""
import json
import urllib.request
from abc import abstractmethod
from typing import Any, Dict, List, Mapping, Optional
from pydantic import BaseModel, validator
from langchain.callbacks.manager import CallbackManag... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ffc28b81d319-1 | """
"""
Example:
.. code-block:: python
class ContentFormatter(ContentFormatterBase):
content_type = "application/json"
accepts = "application/json"
def format_request_payload(
self,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ffc28b81d319-2 | input_str = json.dumps(
{"inputs": {"input_string": [prompt]}, "parameters": model_kwargs}
)
return str.encode(input_str)
[docs] def format_response_payload(self, output: bytes) -> str:
response_json = json.loads(output)
return response_json[0]["0"]
[docs]class HFContentFo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ffc28b81d319-3 | endpoint_api_key="my-api-key",
deployment_name="my-deployment-name",
content_formatter=content_formatter,
)
""" # noqa: E501
endpoint_url: str = ""
"""URL of pre-existing Endpoint. Should be passed to constructor or specified as
env var `AZUREML_ENDPOINT... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ffc28b81d319-4 | http_client = AzureMLEndpointClient(endpoint_url, endpoint_key, deployment_name)
return http_client
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"deployment_name... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
0d7958e73dc2-0 | Source code for langchain.llms.stochasticai
"""Wrapper around StochasticAI APIs."""
import logging
import time
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
0d7958e73dc2-1 | if field_name not in all_required_field_names:
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
logger.warning(
f"""{field_name} was transfered to model_kwargs.
Please confirm that {field_name} is ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
0d7958e73dc2-2 | The string generated by the model.
Example:
.. code-block:: python
response = StochasticAI("Tell me a joke.")
"""
params = self.model_kwargs or {}
params = {**params, **kwargs}
response_post = requests.post(
url=self.api_url,
js... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
31f6e3b8ba31-0 | Source code for langchain.llms.ctransformers
"""Wrapper around the C Transformers library."""
from typing import Any, Dict, Optional, Sequence
from pydantic import root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
[docs]class CTransformers(LLM):
"""W... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
31f6e3b8ba31-1 | "config": self.config,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "ctransformers"
[docs] @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that ``ctransformers`` package is installed."""
try:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
31f6e3b8ba31-2 | text.append(chunk)
_run_manager.on_llm_new_token(chunk, verbose=self.verbose)
return "".join(text) | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
d26c82853b0c-0 | Source code for langchain.llms.base
"""Base interface for large language models to expose."""
import asyncio
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import yaml
from pydantic imp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
d26c82853b0c-1 | else:
missing_prompts.append(prompt)
missing_prompt_idxs.append(i)
return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts
[docs]def update_cache(
existing_prompts: Dict[int, List],
llm_string: str,
missing_prompt_idxs: List[int],
new_results: LLMRes... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
d26c82853b0c-2 | if values.get("callback_manager") is not None:
warnings.warn(
"callback_manager is deprecated. Please use callbacks instead.",
DeprecationWarning,
)
values["callbacks"] = values.pop("callback_manager", None)
return values
[docs] @validator("... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
d26c82853b0c-3 | return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
[docs] async def agenerate_prompt(
self,
prompts: List[PromptValue],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> LLMResult:
prompt_strings = [p.to_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
d26c82853b0c-4 | [docs] def generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
*,
tags: Optional[List[str]] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
if not isinsta... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
d26c82853b0c-5 | run_managers = callback_manager.on_llm_start(
dumpd(self), missing_prompts, invocation_params=params, options=options
)
new_results = self._generate_helper(
missing_prompts, stop, run_managers, bool(new_arg_supported), **kwargs
)
llm_output... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
d26c82853b0c-6 | raise e
flattened_outputs = output.flatten()
await asyncio.gather(
*[
run_manager.on_llm_end(flattened_output)
for run_manager, flattened_output in zip(
run_managers, flattened_outputs
)
]
)
if ru... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
d26c82853b0c-7 | dumpd(self), prompts, invocation_params=params, options=options
)
output = await self._agenerate_helper(
prompts, stop, run_managers, bool(new_arg_supported), **kwargs
)
return output
if len(missing_prompts) > 0:
run_managers = await ca... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
d26c82853b0c-8 | "`generate` instead."
)
return (
self.generate([prompt], stop=stop, callbacks=callbacks, **kwargs)
.generations[0][0]
.text
)
async def _call_async(
self,
prompt: str,
stop: Optional[List[str]] = None,
callbacks: Callbac... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
d26c82853b0c-9 | if stop is None:
_stop = None
else:
_stop = list(stop)
return await self._call_async(text, stop=_stop, **kwargs)
[docs] async def apredict_messages(
self,
messages: List[BaseMessage],
*,
stop: Optional[Sequence[str]] = None,
**kwargs: An... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
d26c82853b0c-10 | Example:
.. code-block:: python
llm.save(file_path="path/llm.yaml")
"""
# Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path
directory_path = save_path.parent
dire... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
d26c82853b0c-11 | **kwargs: Any,
) -> str:
"""Run the LLM on the given prompt and input."""
raise NotImplementedError("Async generation not implemented for this LLM.")
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerFo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
d26c82853b0c-12 | )
generations.append([Generation(text=text)])
return LLMResult(generations=generations) | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
be94b4fea9f7-0 | Source code for langchain.llms.clarifai
"""Wrapper around Clarifai's APIs."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enfor... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
be94b4fea9f7-1 | api_base: str = "https://api.clarifai.com"
stop: Optional[List[str]] = None
[docs] class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that we have all required in... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
be94b4fea9f7-2 | prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any
) -> str:
"""Call out to Clarfai's PostModelOutputs endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of s... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
be94b4fea9f7-3 | # The userDataObject is created in the overview and
# is required when using a PAT
# If version_id None, Defaults to the latest model version
post_model_outputs_request = service_pb2.PostModelOutputsRequest(
user_app_id=self.userDataObject,
model_id=self.model_id,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
b1aabcbe7bbe-0 | Source code for langchain.llms.ai21
"""Wrapper around AI21 APIs."""
from typing import Any, Dict, List, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import get_from... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
b1aabcbe7bbe-1 | """Penalizes repeated tokens."""
countPenalty: AI21PenaltyData = AI21PenaltyData()
"""Penalizes repeated tokens according to count."""
frequencyPenalty: AI21PenaltyData = AI21PenaltyData()
"""Penalizes repeated tokens according to frequency."""
numResults: int = 1
"""How many completions to gene... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
b1aabcbe7bbe-2 | "numResults": self.numResults,
"logitBias": self.logitBias,
}
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"model": self.model}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Ret... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
b1aabcbe7bbe-3 | response = requests.post(
url=f"{base_url}/{self.model}/complete",
headers={"Authorization": f"Bearer {self.ai21_api_key}"},
json={"prompt": prompt, "stopSequences": stop, **params},
)
if response.status_code != 200:
optional_detail = response.json().get("... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
623b51e04ede-0 | Source code for langchain.llms.loading
"""Base interface for loading large language models apis."""
import json
from pathlib import Path
from typing import Union
import yaml
from langchain.llms import type_to_cls_dict
from langchain.llms.base import BaseLLM
[docs]def load_llm_from_config(config: dict) -> BaseLLM:
"... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/loading.html |
8e59b9f13e94-0 | Source code for langchain.llms.fake
"""Fake LLM wrapper for testing purposes."""
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
[docs]class FakeListLLM(LLM):
"""Fake LLM ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/fake.html |
702a273e9d24-0 | Source code for langchain.llms.openai
"""Wrapper around OpenAI APIs."""
from __future__ import annotations
import logging
import sys
import warnings
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Dict,
Generator,
List,
Literal,
Mapping,
Optional,
Set,
Tuple,... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-1 | "finish_reason"
]
response["choices"][0]["logprobs"] = stream_response["choices"][0]["logprobs"]
def _streaming_response_template() -> Dict[str, Any]:
return {
"choices": [
{
"text": "",
"finish_reason": None,
"logprobs": None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-2 | return llm.client.create(**kwargs)
return _completion_with_retry(**kwargs)
async def acompletion_with_retry(
llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any
) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async de... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-3 | """Penalizes repeated tokens."""
n: int = 1
"""How many completions to generate for each prompt."""
best_of: int = 1
"""Generates best_of completions server-side and returns the "best"."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create`... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-4 | Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-5 | extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_na... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-6 | default="",
)
try:
import openai
values["client"] = openai.Completion
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
if valu... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-7 | """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.
Returns:
The full LLM output.
Example:
.. code-block:: python
respo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-8 | if not self.streaming:
# Can't update token usage if streaming
update_token_usage(_keys, response, token_usage)
return self.create_llm_result(choices, prompts, token_usage)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = Non... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-9 | response = await acompletion_with_retry(self, prompt=_prompts, **params)
choices.extend(response["choices"])
if not self.streaming:
# Can't update token usage if streaming
update_token_usage(_keys, response, token_usage)
return self.create_llm_result(c... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-10 | generations.append(
[
Generation(
text=choice["text"],
generation_info=dict(
finish_reason=choice.get("finish_reason"),
logprobs=choice.get("logprobs"),
),
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-11 | if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
params["stream"] = True
return params
@property
def _invocation_params(self) -> Dict[str, Any]:
"""Get the parame... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-12 | "This is needed in order to calculate get_num_tokens. "
"Please install it with `pip install tiktoken`."
)
model_name = self.tiktoken_model_name or self.model_name
try:
enc = tiktoken.encoding_for_model(model_name)
except KeyError:
logger.warni... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-13 | "gpt-3.5-turbo-0613": 4096,
"gpt-3.5-turbo-16k": 16385,
"gpt-3.5-turbo-16k-0613": 16385,
"text-ada-001": 2049,
"ada": 2049,
"text-babbage-001": 2040,
"babbage": 2049,
"text-curie-001": 2049,
"curie": 2049,
"davin... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-14 | [docs] 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:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-15 | Example:
.. code-block:: python
from langchain.llms import AzureOpenAI
openai = AzureOpenAI(model_name="text-davinci-003")
"""
deployment_name: str = ""
"""Deployment name to use."""
openai_api_type: str = "azure"
openai_api_version: str = ""
[docs] @root_validator... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-16 | To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: pyth... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-17 | """Build extra kwargs from additional params that were passed in."""
all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-18 | except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
try:
values["client"] = openai.ChatCompletion
except AttributeError:
raise ValueError(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-19 | # for ChatGPT api, omitting max_tokens is equivalent to having no limit
del params["max_tokens"]
return messages, params
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kw... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-20 | messages, params = self._get_chat_params(prompts, stop)
params = {**params, **kwargs}
if self.streaming:
response = ""
params["stream"] = True
async for stream_resp in await acompletion_with_retry(
self, messages=messages, **params
):
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
702a273e9d24-21 | return super().get_token_ids(text)
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to calculate get_num_tokens. "
"Please install it with `pip install... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
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