id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 54 121 |
|---|---|---|
aa28b4b5edff-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/latest/_modules/langchain/llms/openllm.html |
aa28b4b5edff-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/latest/_modules/langchain/llms/openllm.html |
aa28b4b5edff-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/latest/_modules/langchain/llms/openllm.html |
aa28b4b5edff-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/latest/_modules/langchain/llms/openllm.html |
9ad2e0a02605-0 | Source code for langchain.llms.huggingface_hub
"""Wrapper around HuggingFace 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 enf... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
9ad2e0a02605-1 | """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."""
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfac... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
9ad2e0a02605-2 | 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: The prompt to pass into the model.
stop: Optional list of... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
aeaeafa6f0b8-0 | 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/latest/_modules/langchain/llms/forefrontai.html |
aeaeafa6f0b8-1 | @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/latest/_modules/langchain/llms/forefrontai.html |
aeaeafa6f0b8-2 | """
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/latest/_modules/langchain/llms/forefrontai.html |
b05c516b7cf6-0 | Source code for langchain.llms.bananadev
"""Wrapper around Banana 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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
b05c516b7cf6-1 | 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/bananadev.html |
b05c516b7cf6-2 | )
params = self.model_kwargs or {}
params = {**params, **kwargs}
api_key = self.banana_api_key
model_key = self.model_key
model_inputs = {
# a json specific to your model.
"prompt": prompt,
**params,
}
response = banana.run(api_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
91a9c2c0e066-0 | 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/latest/_modules/langchain/llms/anthropic.html |
91a9c2c0e066-1 | """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/latest/_modules/langchain/llms/anthropic.html |
91a9c2c0e066-2 | @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/latest/_modules/langchain/llms/anthropic.html |
91a9c2c0e066-3 | 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/latest/_modules/langchain/llms/anthropic.html |
91a9c2c0e066-4 | 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/latest/_modules/langchain/llms/anthropic.html |
91a9c2c0e066-5 | )
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/latest/_modules/langchain/llms/anthropic.html |
7393076f3330-0 | Source code for langchain.llms.google_palm
"""Wrapper arround Google's PaLM Text APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from pydantic import BaseModel, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
7393076f3330-1 | ),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def generate_with_retry(llm: GooglePalm, **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator()
@retry_decorator
def _generate_with_retry(**kwargs: Any) -> Any:
r... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
7393076f3330-2 | Must be positive."""
max_output_tokens: Optional[int] = None
"""Maximum number of tokens to include in a candidate. Must be greater than zero.
If unset, will default to 64."""
n: int = 1
"""Number of chat completions to generate for each prompt. Note that the API may
not return the full n ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
7393076f3330-3 | return values
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
generations = []
for prompt in prompts:
completion = generate_with_r... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
7bc0b3b6d851-0 | Source code for langchain.llms.deepinfra
"""Wrapper around DeepInfra 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 im... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
7bc0b3b6d851-1 | return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_id": self.model_id},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
7bc0b3b6d851-2 | if res.status_code != 200:
raise ValueError(
"Error raised by inference API HTTP code: %s, %s"
% (res.status_code, res.text)
)
try:
t = res.json()
text = t["results"][0]["generated_text"]
except requests.exceptions.JSONDecod... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
466233e04674-0 | Source code for langchain.llms.predictionguard
"""Wrapper around Prediction Guard 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
466233e04674-1 | """Your Prediction Guard access token."""
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 the access token and python package ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
466233e04674-2 | Returns:
The string generated by the model.
Example:
.. code-block:: python
response = pgllm("Tell me a joke.")
"""
import predictionguard as pg
params = self._default_params
if self.stop is not None and stop is not None:
raise ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
7f185d3d5063-0 | Source code for langchain.llms.openlm
from typing import Any, Dict
from pydantic import root_validator
from langchain.llms.openai import BaseOpenAI
[docs]class OpenLM(BaseOpenAI):
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"model": self.model_name}, **super()._invocation_params... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openlm.html |
c5eee539fba7-0 | 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 |
c5eee539fba7-1 | """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 |
c5eee539fba7-2 | @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 |
c5eee539fba7-3 | 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 |
b24ad666a9e0-0 | Source code for langchain.llms.cohere
"""Wrapper around Cohere APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from pydantic import Extra, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
b24ad666a9e0-1 | """Wrapper around Cohere large language models.
To use, you should have the ``cohere`` python package installed, and the
environment variable ``COHERE_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.l... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
b24ad666a9e0-2 | extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
cohere_api_key = get_from_dict_or_env(
values, "cohere_api_key", "COHERE_API_KEY"
)
try:
impor... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
b24ad666a9e0-3 | """Call out to Cohere's generate endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
respon... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
bc94350dcf5b-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 |
bc94350dcf5b-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 |
bc94350dcf5b-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 |
bc94350dcf5b-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 |
bc94350dcf5b-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 |
bc94350dcf5b-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 |
68ae42b97792-0 | Source code for langchain.llms.vertexai
"""Wrapper around Google VertexAI models."""
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils i... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
68ae42b97792-1 | 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 calls. If not provided, credentials will be ascertained from "
"the environment."
@property
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
68ae42b97792-2 | def _try_init_vertexai(cls, values: Dict) -> None:
allowed_params = ["project", "location", "credentials"]
params = {k: v for k, v in values.items() if k in allowed_params}
init_vertexai(**params)
return None
[docs]class VertexAI(_VertexAICommon, LLM):
"""Wrapper around Google Vertex... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
68ae42b97792-3 | **kwargs: Any,
) -> str:
"""Call Vertex model to get predictions based on the prompt.
Args:
prompt: The prompt to pass into the model.
stop: A list of stop words (optional).
run_manager: A Callbackmanager for LLM run, optional.
Returns:
The str... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
52495bd4bb90-0 | 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 |
52495bd4bb90-1 | )
"""
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 |
52495bd4bb90-2 | instruction=prompt,
)
return prompt
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
is_retry: bool = False,
**kwargs: Any,
) -> str:
"""Call out to a MosaicML LLM i... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
52495bd4bb90-3 | raise ValueError(
f"Error raised by inference API: {parsed_response['error']}"
)
# The inference API has changed a couple of times, so we add some handling
# to be robust to multiple response formats.
if isinstance(parsed_response, dict):
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
3185287c4654-0 | Source code for langchain.llms.modal
"""Wrapper around Modal API."""
import logging
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 import LLM
from langchain.... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
3185287c4654-1 | logger.warning(
f"""{field_name} was transfered to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
values["model_kwargs"] = extra
return values
@property
d... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
b2f20384c6b5-0 | 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 |
b2f20384c6b5-1 | 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 |
b2f20384c6b5-2 | )
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 |
b2f20384c6b5-3 | 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 |
b2f20384c6b5-4 | """Function to load the model remotely on the server."""
inference_fn: Callable = _generate_text #: :meta private:
"""Inference function to send to the remote hardware."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def __init__(self, **kwargs: Any):... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
3fb41742b28b-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 |
3fb41742b28b-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 |
3fb41742b28b-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/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
3fb41742b28b-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 |
3fb41742b28b-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 |
3fb41742b28b-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 |
f8cd799a2197-0 | Source code for langchain.llms.manifest
"""Wrapper around HazyResearch's Manifest library."""
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
[docs]class ManifestWrapper(... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/manifest.html |
f8cd799a2197-1 | if stop is not None and len(stop) != 1:
raise NotImplementedError(
f"Manifest currently only supports a single stop token, got {stop}"
)
params = self.llm_kwargs or {}
params = {**params, **kwargs}
if stop is not None:
params["stop_token"] = st... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/manifest.html |
ae490ba62939-0 | 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/latest/_modules/langchain/llms/pipelineai.html |
ae490ba62939-1 | 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/latest/_modules/langchain/llms/pipelineai.html |
ae490ba62939-2 | "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/latest/_modules/langchain/llms/pipelineai.html |
f38ca04ac557-0 | Source code for langchain.llms.sagemaker_endpoint
"""Wrapper around Sagemaker InvokeEndpoint API."""
from abc import abstractmethod
from typing import Any, Dict, Generic, List, Mapping, Optional, TypeVar, Union
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
f... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
f38ca04ac557-1 | """The MIME type of the response data returned from endpoint"""
@abstractmethod
def transform_input(self, prompt: INPUT_TYPE, model_kwargs: Dict) -> bytes:
"""Transforms the input to a format that model can accept
as the request Body. Should return bytes or seekable file
like object in t... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
f38ca04ac557-2 | )
credentials_profile_name = (
"default"
)
se = SagemakerEndpoint(
endpoint_name=endpoint_name,
region_name=region_name,
credentials_profile_name=credentials_profile_name
)
"""
client: Any #: :meta p... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
f38ca04ac557-3 | def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]["generated_text"]
"""
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
endpoint_kwargs: Optional[D... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
f38ca04ac557-4 | @property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"endpoint_name": self.endpoint_name},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(s... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
f38ca04ac557-5 | text = self.content_handler.transform_output(response["Body"])
if stop is not None:
# This is a bit hacky, but I can't figure out a better way to enforce
# stop tokens when making calls to the sagemaker endpoint.
text = enforce_stop_tokens(text, stop)
return text | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
e2037d2aa88a-0 | Source code for langchain.llms.llamacpp
"""Wrapper around llama.cpp."""
import logging
from typing import Any, Dict, Generator, List, Optional
from pydantic import Field, root_validator
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/llamacpp.html |
e2037d2aa88a-1 | f16_kv: bool = Field(True, alias="f16_kv")
"""Use half-precision for key/value cache."""
logits_all: bool = Field(False, alias="logits_all")
"""Return logits for all tokens, not just the last token."""
vocab_only: bool = Field(False, alias="vocab_only")
"""Only load the vocabulary, no weights."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
e2037d2aa88a-2 | """Whether to echo the prompt."""
stop: Optional[List[str]] = []
"""A list of strings to stop generation when encountered."""
repeat_penalty: Optional[float] = 1.1
"""The penalty to apply to repeated tokens."""
top_k: Optional[int] = 40
"""The top-k value to use for sampling."""
last_n_token... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
e2037d2aa88a-3 | except ImportError:
raise ModuleNotFoundError(
"Could not import llama-cpp-python library. "
"Please install the llama-cpp-python library to "
"use this embedding model: pip install llama-cpp-python"
)
except Exception as e:
rai... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
e2037d2aa88a-4 | Returns:
Dictionary containing the combined parameters.
"""
# Raise error if stop sequences are in both input and default params
if self.stop and stop is not None:
raise ValueError("`stop` found in both the input and default params.")
params = self._default_params... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
e2037d2aa88a-5 | return combined_text_output
else:
params = self._get_parameters(stop)
params = {**params, **kwargs}
result = self.client(prompt=prompt, **params)
return result["choices"][0]["text"]
[docs] def stream(
self,
prompt: str,
stop: Optional[Li... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
e2037d2aa88a-6 | """
params = self._get_parameters(stop)
result = self.client(prompt=prompt, stream=True, **params)
for chunk in result:
token = chunk["choices"][0]["text"]
log_probs = chunk["choices"][0].get("logprobs", None)
if run_manager:
run_manager.on_llm... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
d1f654c4abbb-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 |
d1f654c4abbb-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/latest/_modules/langchain/llms/azureml_endpoint.html |
d1f654c4abbb-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/latest/_modules/langchain/llms/azureml_endpoint.html |
d1f654c4abbb-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/latest/_modules/langchain/llms/azureml_endpoint.html |
d1f654c4abbb-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/latest/_modules/langchain/llms/azureml_endpoint.html |
28d1eb6acef6-0 | 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 |
28d1eb6acef6-1 | **{"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/latest/_modules/langchain/llms/amazon_api_gateway.html |
a7214af167b7-0 | Source code for langchain.llms.beam
"""Wrapper around Beam API."""
import base64
import json
import logging
import subprocess
import textwrap
import time
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import Callba... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
a7214af167b7-1 | max_length=50)
llm._deploy()
call_result = llm._call(input)
"""
model_name: str = ""
name: str = ""
cpu: str = ""
memory: str = ""
gpu: str = ""
python_version: str = ""
python_packages: List[str] = []
max_length: str = ""
url: str = ""
"""model endpoi... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
a7214af167b7-2 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
beam_client_id = get_from_dict_or_env(
values, "beam_client_id", "BEAM_CLIENT_ID"
)
beam_client_secret = get_from_dict_or_env(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
a7214af167b7-3 | python_packages={python_packages},
)
app.Trigger.RestAPI(
inputs={{"prompt": beam.Types.String(), "max_length": beam.Types.String()}},
outputs={{"text": beam.Types.String()}},
handler="run.py:beam_langchain",
)
"""
)
script_name = "app.... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
a7214af167b7-4 | file.write(script.format(model_name=self.model_name))
def _deploy(self) -> str:
"""Call to Beam."""
try:
import beam # type: ignore
if beam.__path__ == "":
raise ImportError
except ImportError:
raise ImportError(
"Could not... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
a7214af167b7-5 | self,
prompt: str,
stop: Optional[list] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call to Beam."""
url = "https://apps.beam.cloud/" + self.app_id if self.app_id else self.url
payload = {"prompt": prompt, "max_l... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
5fe6e6d8f65c-0 | Source code for langchain.callbacks.clearml_callback
import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.utils import (
BaseMetadataCallbackHandler,
flat... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
5fe6e6d8f65c-1 | and adds the response to the list of records for both the {method}_records and
action. It then logs the response to the ClearML console.
"""
def __init__(
self,
task_type: Optional[str] = "inference",
project_name: Optional[str] = "langchain_callback_demo",
tags: Optional[Seq... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
5fe6e6d8f65c-2 | )
self.logger.report_text(warning, level=30, print_console=True)
self.callback_columns: list = []
self.action_records: list = []
self.complexity_metrics = complexity_metrics
self.visualize = visualize
self.nlp = spacy.load("en_core_web_sm")
def _init_resp(self) -> Dic... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
5fe6e6d8f65c-3 | if self.stream_logs:
self.logger.report_text(resp)
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM ends running."""
self.step += 1
self.llm_ends += 1
self.ends += 1
resp = self._init_resp()
resp.update({"action": "on... | https://api.python.langchain.com/en/latest/_modules/langchain/callbacks/clearml_callback.html |
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