id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
aa045f555b24-0 | 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/stable/_modules/langchain/llms/clarifai.html |
aa045f555b24-1 | api_base: str = "https://api.clarifai.com"
stop: Optional[List[str]] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that we have all required info to access... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/clarifai.html |
aa045f555b24-2 | 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/stable/_modules/langchain/llms/clarifai.html |
aa045f555b24-3 | # 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/stable/_modules/langchain/llms/clarifai.html |
8ae927e30afe-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/stable/_modules/langchain/llms/cerebriumai.html |
8ae927e30afe-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/stable/_modules/langchain/llms/cerebriumai.html |
8ae927e30afe-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/stable/_modules/langchain/llms/cerebriumai.html |
3ec9d98307bb-0 | Source code for langchain.llms.forefrontai
"""Wrapper around ForefrontAI 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.util... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/forefrontai.html |
3ec9d98307bb-1 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
forefrontai_api_key = get_from_dict_or_env(
values, "forefrontai_api_key", "FOREFRONTAI_API_KEY"
)
values["forefrontai_api_key"] = forefrontai_api_key... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/forefrontai.html |
3ec9d98307bb-2 | """
response = requests.post(
url=self.endpoint_url,
headers={
"Authorization": f"Bearer {self.forefrontai_api_key}",
"Content-Type": "application/json",
},
json={"text": prompt, **self._default_params, **kwargs},
)
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/forefrontai.html |
bec63db63b6c-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/stable/_modules/langchain/llms/fake.html |
557f8f25beb7-0 | Source code for langchain.llms.aviary
"""Wrapper around Aviary"""
import dataclasses
import os
from typing import Any, Dict, List, Mapping, Optional, Union, cast
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LL... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/aviary.html |
557f8f25beb7-1 | ) from e
result = sorted(
[k.lstrip("/").replace("--", "/") for k in result.keys() if "--" in k]
)
return result
def get_completions(
model: str,
prompt: str,
use_prompt_format: bool = True,
version: str = "",
) -> Dict[str, Union[str, float, int]]:
"""Get completions from Aviary... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/aviary.html |
557f8f25beb7-2 | os.environ["AVIARY_TOKEN"] = "<TOKEN>"
light = Aviary(model='amazon/LightGPT')
output = light('How do you make fried rice?')
"""
model: str = "amazon/LightGPT"
aviary_url: Optional[str] = None
aviary_token: Optional[str] = None
# If True the prompt template for the model will... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/aviary.html |
557f8f25beb7-3 | "aviary_url": self.aviary_url,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return f"aviary-{self.model.replace('/', '-')}"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLM... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/aviary.html |
feb113ae9e18-0 | Source code for langchain.llms.openllm
"""Wrapper around OpenLLM APIs."""
from __future__ import annotations
import copy
import json
import logging
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Literal,
Optional,
TypedDict,
Union,
overload,
)
from pydantic import PrivateAttr
f... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/openllm.html |
feb113ae9e18-1 | For all available supported models, you can run 'openllm models'.
If you have a OpenLLM server running, you can also use it remotely:
.. code-block:: python
from langchain.llms import OpenLLM
llm = OpenLLM(server_url='http://localhost:3000')
llm("What is the difference be... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/openllm.html |
feb113ae9e18-2 | *,
model_id: Optional[str] = ...,
embedded: Literal[True, False] = ...,
**llm_kwargs: Any,
) -> None:
...
@overload
def __init__(
self,
*,
server_url: str = ...,
server_type: Literal["grpc", "http"] = ...,
**llm_kwargs: Any,
) -> No... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/openllm.html |
feb113ae9e18-3 | "llm_kwargs": llm_kwargs,
}
)
self._runner = None # type: ignore
self._client = client
else:
assert model_name is not None, "Must provide 'model_name' or 'server_url'"
# since the LLM are relatively huge, we don't actually want to conv... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/openllm.html |
feb113ae9e18-4 | tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION
)
svc = bentoml.Service("langchain-openllm", runners=[llm.runner])
@svc.api(input=Text(), output=Text())
def chat(input_text: str):
return agent.run(input_text)
"""
if self._runner is ... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/openllm.html |
feb113ae9e18-5 | **kwargs: Any,
) -> str:
try:
import openllm
except ImportError as e:
raise ImportError(
"Could not import openllm. Make sure to install it with "
"'pip install openllm'."
) from e
copied = copy.deepcopy(self.llm_kwargs)
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/openllm.html |
feb113ae9e18-6 | (
prompt,
generate_kwargs,
postprocess_kwargs,
) = self._runner.llm.sanitize_parameters(prompt, **kwargs)
generated_result = await self._runner.generate.async_run(
prompt, **generate_kwargs
)
return self._run... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/openllm.html |
bab47f46c652-0 | Source code for langchain.llms.huggingface_endpoint
"""Wrapper around HuggingFace 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.... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_endpoint.html |
bab47f46c652-1 | huggingfacehub_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
hugging... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_endpoint.html |
bab47f46c652-2 | return "huggingface_endpoint"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to HuggingFace Hub's inference endpoint.
Args:
prompt: Th... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_endpoint.html |
bab47f46c652-3 | elif self.task == "text2text-generation":
text = generated_text[0]["generated_text"]
elif self.task == "summarization":
text = generated_text[0]["summary_text"]
else:
raise ValueError(
f"Got invalid task {self.task}, "
f"currently only ... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_endpoint.html |
d0177e8a7bf3-0 | 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/stable/_modules/langchain/llms/azureml_endpoint.html |
d0177e8a7bf3-1 | .. code-block:: python
class ContentFormatter(ContentFormatterBase):
content_type = "application/json"
accepts = "application/json"
def format_request_payload(
self,
prompt: str,
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/azureml_endpoint.html |
d0177e8a7bf3-2 | )
return str.encode(input_str)
def format_response_payload(self, output: bytes) -> str:
response_json = json.loads(output)
return response_json[0]["0"]
class HFContentFormatter(ContentFormatterBase):
"""Content handler for LLMs from the HuggingFace catalog."""
def format_request_payl... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/azureml_endpoint.html |
d0177e8a7bf3-3 | )
""" # noqa: E501
endpoint_url: str = ""
"""URL of pre-existing Endpoint. Should be passed to constructor or specified as
env var `AZUREML_ENDPOINT_URL`."""
endpoint_api_key: str = ""
"""Authentication Key for Endpoint. Should be passed to constructor or specified as
env var `AZUR... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/azureml_endpoint.html |
d0177e8a7bf3-4 | """Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"deployment_name": self.deployment_name},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "azureml... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/azureml_endpoint.html |
e60794e89e20-0 | 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/stable/_modules/langchain/llms/promptlayer_openai.html |
e60794e89e20-1 | """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/stable/_modules/langchain/llms/promptlayer_openai.html |
e60794e89e20-2 | 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/stable/_modules/langchain/llms/promptlayer_openai.html |
e60794e89e20-3 | 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/stable/_modules/langchain/llms/promptlayer_openai.html |
e60794e89e20-4 | 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/stable/_modules/langchain/llms/promptlayer_openai.html |
e60794e89e20-5 | 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/stable/_modules/langchain/llms/promptlayer_openai.html |
d70be5320f74-0 | 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/stable/_modules/langchain/llms/anyscale.html |
d70be5320f74-1 | @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"
)
anyscale_service_route = get_... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/anyscale.html |
d70be5320f74-2 | 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 the model.
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/anyscale.html |
38b0c61dc874-0 | Source code for langchain.llms.octoai_endpoint
"""Wrapper around OctoAI 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.llms.utils import enforce_... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/octoai_endpoint.html |
38b0c61dc874-1 | """OCTOAI API Token"""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator(allow_reuse=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
octoai_api_toke... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/octoai_endpoint.html |
38b0c61dc874-2 | _model_kwargs = self.model_kwargs or {}
# Prepare the payload JSON
parameter_payload = {"inputs": prompt, "parameters": _model_kwargs}
try:
# Initialize the OctoAI client
from octoai import client
octoai_client = client.Client(token=self.octoai_api_token)
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/octoai_endpoint.html |
a67451801017-0 | 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/stable/_modules/langchain/llms/ctransformers.html |
a67451801017-1 | "config": self.config,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "ctransformers"
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that ``ctransformers`` package is installed."""
try:
from... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/ctransformers.html |
a67451801017-2 | text.append(chunk)
_run_manager.on_llm_new_token(chunk, verbose=self.verbose)
return "".join(text) | https://api.python.langchain.com/en/stable/_modules/langchain/llms/ctransformers.html |
7a85b46087f0-0 | 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/stable/_modules/langchain/llms/vertexai.html |
7a85b46087f0-1 | 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/stable/_modules/langchain/llms/vertexai.html |
7a85b46087f0-2 | 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/stable/_modules/langchain/llms/vertexai.html |
7a85b46087f0-3 | 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/stable/_modules/langchain/llms/vertexai.html |
64ebcec70aa1-0 | 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/stable/_modules/langchain/llms/ai21.html |
64ebcec70aa1-1 | 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 generate for each prompt."""
logitBia... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/ai21.html |
64ebcec70aa1-2 | "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:
"""Return type of llm."""
return "ai21"
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/ai21.html |
64ebcec70aa1-3 | 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/stable/_modules/langchain/llms/ai21.html |
d5c5dd5c7e7d-0 | Source code for langchain.llms.pipelineai
"""Wrapper around Pipeline Cloud API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from l... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/pipelineai.html |
d5c5dd5c7e7d-1 | extra = values.get("pipeline_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} supplied twice.")
logger.warning(
f"""{field_... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/pipelineai.html |
d5c5dd5c7e7d-2 | "Please install it with `pip install pipeline-ai`."
)
client = PipelineCloud(token=self.pipeline_api_key)
params = self.pipeline_kwargs or {}
params = {**params, **kwargs}
run = client.run_pipeline(self.pipeline_key, [prompt, params])
try:
text = run.resul... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/pipelineai.html |
bbac85703043-0 | Source code for langchain.llms.human
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Field
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
def _display_prompt(prompt: str) -> None:
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/human.html |
bbac85703043-1 | """Returns the type of LLM."""
return "human-input"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""
Displays the prompt to the user and returns the... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/human.html |
d09b1937c265-0 | 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/stable/_modules/langchain/llms/bedrock.html |
d09b1937c265-1 | 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/stable/_modules/langchain/llms/bedrock.html |
d09b1937c265-2 | equivalent to the modelId property in the list-foundation-models api"""
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, ... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/bedrock.html |
d09b1937c265-3 | """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:
"""Call out to Bedrock service model.
Args:
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/bedrock.html |
7e9775428d5d-0 | Source code for langchain.llms.databricks
import os
from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, List, Optional
import requests
from pydantic import BaseModel, Extra, Field, PrivateAttr, root_validator, validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langch... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/databricks.html |
7e9775428d5d-1 | return values
def post(self, request: Any) -> Any:
# See https://docs.databricks.com/machine-learning/model-serving/score-model-serving-endpoints.html
wrapped_request = {"dataframe_records": [request]}
response = self.post_raw(wrapped_request)["predictions"]
# For a single-record que... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/databricks.html |
7e9775428d5d-2 | """Gets the default Databricks workspace hostname.
Raises an error if the hostname cannot be automatically determined.
"""
host = os.getenv("DATABRICKS_HOST")
if not host:
try:
host = get_repl_context().browserHostName
if not host:
raise ValueError("contex... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/databricks.html |
7e9775428d5d-3 | We assume that an LLM was registered and deployed to a serving endpoint.
To wrap it as an LLM you must have "Can Query" permission to the endpoint.
Set ``endpoint_name`` accordingly and do not set ``cluster_id`` and
``cluster_driver_port``.
The expected model signature is:
* inputs::
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/databricks.html |
7e9775428d5d-4 | you can use `transform_input_fn` and `transform_output_fn` to apply necessary
transformations before and after the query.
"""
host: str = Field(default_factory=get_default_host)
"""Databricks workspace hostname.
If not provided, the default value is determined by
* the ``DATABRICKS_HOST`` enviro... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/databricks.html |
7e9775428d5d-5 | """
cluster_driver_port: Optional[str] = None
"""The port number used by the HTTP server running on the cluster driver node.
The server should listen on the driver IP address or simply ``0.0.0.0`` to connect.
We recommend the server using a port number between ``[3000, 8000]``.
"""
model_kwargs:... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/databricks.html |
7e9775428d5d-6 | "And the cluster_id cannot be automatically determined. Received"
f" error: {e}"
)
@validator("cluster_driver_port", always=True)
def set_cluster_driver_port(cls, v: Any, values: Dict[str, Any]) -> Optional[str]:
if v and values["endpoint_name"]:
raise Val... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/databricks.html |
7e9775428d5d-7 | )
else:
raise ValueError(
"Must specify either endpoint_name or cluster_id/cluster_driver_port."
)
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "databricks"
def _call(
self,
prompt: str,
stop: O... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/databricks.html |
6641981c3615-0 | Source code for langchain.llms.anthropic
"""Wrapper around Anthropic APIs."""
import re
import warnings
from typing import Any, Callable, Dict, Generator, List, Mapping, Optional, Tuple, Union
from pydantic import BaseModel, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/anthropic.html |
6641981c3615-1 | """Validate that api key and python package exists in environment."""
anthropic_api_key = get_from_dict_or_env(
values, "anthropic_api_key", "ANTHROPIC_API_KEY"
)
"""Get custom api url from environment."""
anthropic_api_url = get_from_dict_or_env(
values,
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/anthropic.html |
6641981c3615-2 | @property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{}, **self._default_params}
def _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]:
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
raise NameEr... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/anthropic.html |
6641981c3615-3 | response = model(prompt)
"""
@root_validator()
def raise_warning(cls, values: Dict) -> Dict:
"""Raise warning that this class is deprecated."""
warnings.warn(
"This Anthropic LLM is deprecated. "
"Please use `from langchain.chat_models import ChatAnthropic` instead"
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/anthropic.html |
6641981c3615-4 | Returns:
The string generated by the model.
Example:
.. code-block:: python
prompt = "What are the biggest risks facing humanity?"
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
response = model(prompt)
"""
stop = self._get_a... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/anthropic.html |
6641981c3615-5 | )
current_completion = ""
async for data in stream_resp:
delta = data["completion"][len(current_completion) :]
current_completion = data["completion"]
if run_manager:
await run_manager.on_llm_new_token(delta, **data)
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/anthropic.html |
fa2eb6b9f399-0 | Source code for langchain.llms.petals
"""Wrapper around Petals 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.utils imp... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/petals.html |
fa2eb6b9f399-1 | """Whether or not to use sampling; use greedy decoding otherwise."""
max_length: Optional[int] = None
"""The maximum length of the sequence to be generated."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call
not explicitly specified.""... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/petals.html |
fa2eb6b9f399-2 | from petals import DistributedBloomForCausalLM
from transformers import BloomTokenizerFast
model_name = values["model_name"]
values["tokenizer"] = BloomTokenizerFast.from_pretrained(model_name)
values["client"] = DistributedBloomForCausalLM.from_pretrained(model_name)
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/petals.html |
fa2eb6b9f399-3 | """Call the Petals API."""
params = self._default_params
params = {**params, **kwargs}
inputs = self.tokenizer(prompt, return_tensors="pt")["input_ids"]
outputs = self.client.generate(inputs, **params)
text = self.tokenizer.decode(outputs[0])
if stop is not None:
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/petals.html |
29e2571819ee-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/stable/_modules/langchain/llms/huggingface_text_gen_inference.html |
29e2571819ee-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/stable/_modules/langchain/llms/huggingface_text_gen_inference.html |
29e2571819ee-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
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@ro... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_text_gen_inference.html |
29e2571819ee-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/stable/_modules/langchain/llms/huggingface_text_gen_inference.html |
29e2571819ee-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/stable/_modules/langchain/llms/huggingface_text_gen_inference.html |
29e2571819ee-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/stable/_modules/langchain/llms/huggingface_text_gen_inference.html |
ee033db54369-0 | 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/stable/_modules/langchain/llms/amazon_api_gateway.html |
ee033db54369-1 | **{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "amazon_api_gateway"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/amazon_api_gateway.html |
b948fd7bc7b3-0 | Source code for langchain.llms.huggingface_pipeline
"""Wrapper around HuggingFace Pipeline APIs."""
import importlib.util
import logging
from typing import Any, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from la... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_pipeline.html |
b948fd7bc7b3-1 | """
pipeline: Any #: :meta private:
model_id: str = DEFAULT_MODEL_ID
"""Model name to use."""
model_kwargs: Optional[dict] = None
"""Key word arguments passed to the model."""
pipeline_kwargs: Optional[dict] = None
"""Key word arguments passed to the pipeline."""
class Config:
"... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_pipeline.html |
b948fd7bc7b3-2 | else:
raise ValueError(
f"Got invalid task {task}, "
f"currently only {VALID_TASKS} are supported"
)
except ImportError as e:
raise ValueError(
f"Could not load the {task} model due to missing dependencies."
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_pipeline.html |
b948fd7bc7b3-3 | )
return cls(
pipeline=pipeline,
model_id=model_id,
model_kwargs=_model_kwargs,
pipeline_kwargs=_pipeline_kwargs,
**kwargs,
)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/huggingface_pipeline.html |
d1627a75897e-0 | Source code for langchain.llms.rwkv
"""Wrapper for the RWKV model.
Based on https://github.com/saharNooby/rwkv.cpp/blob/master/rwkv/chat_with_bot.py
https://github.com/BlinkDL/ChatRWKV/blob/main/v2/chat.py
"""
from typing import Any, Dict, List, Mapping, Optional, Set
from pydantic import BaseModel, Extra, roo... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/rwkv.html |
d1627a75897e-1 | """Positive values penalize new tokens based on their existing frequency
in the text so far, decreasing the model's likelihood to repeat the same
line verbatim.."""
penalty_alpha_presence: float = 0.4
"""Positive values penalize new tokens based on whether they appear
in the text so far, increasing ... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/rwkv.html |
d1627a75897e-2 | """Validate that the python package exists in the environment."""
try:
import tokenizers
except ImportError:
raise ImportError(
"Could not import tokenizers python package. "
"Please install it with `pip install tokenizers`."
)
... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/rwkv.html |
d1627a75897e-3 | AVOID_REPEAT_TOKENS = []
AVOID_REPEAT = "，：？！"
for i in AVOID_REPEAT:
dd = self.pipeline.encode(i)
assert len(dd) == 1
AVOID_REPEAT_TOKENS += dd
tokens = [int(x) for x in _tokens]
self.model_tokens += tokens
out: Any = None
while len(to... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/rwkv.html |
d1627a75897e-4 | occurrence[token] += 1
logits = self.run_rnn([token])
xxx = self.tokenizer.decode(self.model_tokens[out_last:])
if "\ufffd" not in xxx: # avoid utf-8 display issues
decoded += xxx
out_last = begin + i + 1
if i >= self.max_tokens_per_ge... | https://api.python.langchain.com/en/stable/_modules/langchain/llms/rwkv.html |
3adc13d982a8-0 | 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/stable/_modules/langchain/llms/openai.html |
3adc13d982a8-1 | "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/stable/_modules/langchain/llms/openai.html |
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