id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
b67389fc406f-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 |
fe330bdf4c7e-0 | Source code for langchain.llms.petals
import logging
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, Field, root_valida... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
fe330bdf4c7e-1 | 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."""
huggingface_api_key: Optional[str] = None
class Config:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
fe330bdf4c7e-2 | from transformers import AutoTokenizer
model_name = values["model_name"]
values["tokenizer"] = AutoTokenizer.from_pretrained(model_name)
values["client"] = AutoDistributedModelForCausalLM.from_pretrained(
model_name
)
values["huggingface_api_ke... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
fe330bdf4c7e-3 | 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:
# I believe this is required si... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
786acbaea8ac-0 | Source code for langchain.llms.gpt4all
from functools import partial
from typing import Any, Dict, List, Mapping, Optional, Set
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extr... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
786acbaea8ac-1 | """Return logits for all tokens, not just the last token."""
vocab_only: bool = Field(False, alias="vocab_only")
"""Only load the vocabulary, no weights."""
use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
embedding: bool = Field(False, alias="embedding")
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
786acbaea8ac-2 | class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@staticmethod
def _model_param_names() -> Set[str]:
return {
"max_tokens",
"n_predict",
"top_k",
"top_p",
"temp",
"n_batch",
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
786acbaea8ac-3 | if values["n_threads"] is not None:
# set n_threads
values["client"].model.set_thread_count(values["n_threads"])
try:
values["backend"] = values["client"].model_type
except AttributeError:
# The below is for compatibility with GPT4All Python bindings <= 0.... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
786acbaea8ac-4 | text = ""
params = {**self._default_params(), **kwargs}
for token in self.client.generate(prompt, **params):
if text_callback:
text_callback(token)
text += token
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text | https://api.python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
6e0e57e64798-0 | Source code for langchain.llms.writer
from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, root_validator
fr... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
6e0e57e64798-1 | logprobs: bool = False
"""Whether to return log probabilities."""
n: Optional[int] = None
"""How many completions to generate."""
writer_api_key: Optional[str] = None
"""Writer API key."""
base_url: Optional[str] = None
"""Base url to use, if None decides based on model name."""
class Co... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
6e0e57e64798-2 | """Get the identifying parameters."""
return {
**{"model_id": self.model_id, "writer_org_id": self.writer_org_id},
**self._default_params,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "writer"
def _call(
self,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
6e0e57e64798-3 | # are not enforced by the model parameters
text = enforce_stop_tokens(text, stop)
return text | https://api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
e63d7435172e-0 | Source code for langchain.llms.replicate
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import Extra, Field, root_valid... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
e63d7435172e-1 | """Optionally pass in the model version object during initialization to avoid
having to make an extra API call to retrieve it during streaming. NOTE: not
serializable, is excluded from serialization.
"""
streaming: bool = False
"""Whether to stream the results."""
stop: List[str] = Fiel... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
e63d7435172e-2 | values["model_kwargs"] = extra
return values
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
replicate_api_token = get_from_dict_or_env(
values, "replicate_api_token", "REPLICATE_API_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
e63d7435172e-3 | stop_conditions = stop or self.stop
for s in stop_conditions:
if s in completion:
completion = completion[: completion.find(s)]
return completion
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[Callba... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
e63d7435172e-4 | model_str, version_str = self.model.split(":")
model = replicate_python.models.get(model_str)
self.version_obj = model.versions.get(version_str)
if self.prompt_key is None:
# sort through the openapi schema to get the name of the first input
input_properties = sor... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
7eef2176b505-0 | Source code for langchain.llms.databricks
import os
from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, List, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import (
BaseModel,
Extra... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
7eef2176b505-1 | values["api_url"] = api_url
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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
7eef2176b505-2 | )
[docs]def get_default_host() -> str:
"""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 hos... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
7eef2176b505-3 | * **Serving endpoint** (recommended for both production and development).
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
``clus... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
7eef2176b505-4 | If the endpoint model signature is different or you want to set extra params,
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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
7eef2176b505-5 | You must not set both ``endpoint_name`` and ``cluster_id``.
"""
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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
7eef2176b505-6 | raise ValueError(
"Neither endpoint_name nor cluster_id was set. "
"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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
7eef2176b505-7 | cluster_driver_port=self.cluster_driver_port,
)
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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e2d4a0f45c50-0 | Source code for langchain.llms.koboldai
import logging
from typing import Any, Dict, List, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
logger = logging.getLogger(__name__)
[docs]def clean_url(url: str) -> str:
"""Remove trailing slash... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/koboldai.html |
e2d4a0f45c50-1 | use_memory: Optional[bool] = False
"""Whether to use the memory from the KoboldAI GUI when generating text."""
max_context_length: Optional[int] = 1600
"""Maximum number of tokens to send to the model.
minimum: 1
"""
max_length: Optional[int] = 80
"""Number of tokens to generate.
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/koboldai.html |
e2d4a0f45c50-2 | """Typical sampling value.
maximum: 1
minimum: 0
"""
@property
def _llm_type(self) -> str:
return "koboldai"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/koboldai.html |
e2d4a0f45c50-3 | "typical": self.typical,
}
if stop is not None:
data["stop_sequence"] = stop
response = requests.post(
f"{clean_url(self.endpoint)}/api/v1/generate", json=data
)
response.raise_for_status()
json_response = response.json()
if (
"... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/koboldai.html |
c296f7a41595-0 | Source code for langchain.llms.openlm
from typing import Any, Dict
from langchain.llms.openai import BaseOpenAI
from langchain.pydantic_v1 import root_validator
[docs]class OpenLM(BaseOpenAI):
"""OpenLM models."""
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"model": self.mod... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openlm.html |
bb52f6e78d64-0 | Source code for langchain.llms.self_hosted
import importlib.util
import logging
import pickle
from typing import Any, Callable, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
bb52f6e78d64-1 | 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 associated wi... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
bb52f6e78d64-2 | model_load_fn=load_pipeline,
hardware=gpu,
model_reqs=model_reqs, inference_fn=inference_fn
)
Example for <2GB model (can be serialized and sent directly to the server):
.. code-block:: python
from langchain.llms import SelfHostedPipeline
i... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
bb52f6e78d64-3 | """Key word arguments to pass to the model load function."""
model_reqs: List[str] = ["./", "torch"]
"""Requirements to install on hardware to inference the model."""
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.html |
bb52f6e78d64-4 | logger.warning(
"Serializing pipeline to send to remote hardware. "
"Note, it can be quite slow"
"to serialize and send large models with each execution. "
"Consider sending the pipeline"
"to the cluster and passing the path to the pipeline... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
543414821bd1-0 | Source code for langchain.llms.javelin_ai_gateway
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.pydantic_v1 import Ba... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/javelin_ai_gateway.html |
543414821bd1-1 | gateway_uri: Optional[str] = None
"""The URI of the Javelin AI Gateway API."""
params: Optional[Params] = None
"""Parameters for the Javelin AI Gateway API."""
javelin_api_key: Optional[str] = None
"""The API key for the Javelin AI Gateway API."""
def __init__(self, **kwargs: Any):
try:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/javelin_ai_gateway.html |
543414821bd1-2 | prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call the Javelin AI Gateway API."""
data: Dict[str, Any] = {
"prompt": prompt,
**(self.params.dict() if self.params else ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/javelin_ai_gateway.html |
543414821bd1-3 | resp_dict = resp.dict()
try:
return resp_dict["llm_response"]["choices"][0]["text"]
except KeyError:
return ""
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "javelin-ai-gateway" | https://api.python.langchain.com/en/latest/_modules/langchain/llms/javelin_ai_gateway.html |
3ceb6c75409b-0 | Source code for langchain.llms.aviary
import dataclasses
import os
from typing import Any, Dict, List, Mapping, Optional, Union, cast
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.p... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
3ceb6c75409b-1 | except requests.JSONDecodeError as e:
raise RuntimeError(
f"Error decoding JSON from {request_url}. Text response: {response.text}"
) from e
result = sorted(
[k.lstrip("/").replace("--", "/") for k in result.keys() if "--" in k]
)
return result
[docs]def get_completions(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
3ceb6c75409b-2 | Attributes:
model: The name of the model to use. Defaults to "amazon/LightGPT".
aviary_url: The URL for the Aviary backend. Defaults to None.
aviary_token: The bearer token for the Aviary backend. Defaults to None.
use_prompt_format: If True, the prompt template for the model will be ign... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
3ceb6c75409b-3 | os.environ["AVIARY_URL"] = aviary_url
os.environ["AVIARY_TOKEN"] = aviary_token
try:
aviary_models = get_models()
except requests.exceptions.RequestException as e:
raise ValueError(e)
model = values.get("model")
if model and model not in aviary_models:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
3ceb6c75409b-4 | )
text = cast(str, output["generated_text"])
if stop:
text = enforce_stop_tokens(text, stop)
return text | https://api.python.langchain.com/en/latest/_modules/langchain/llms/aviary.html |
4801ecf38dab-0 | Source code for langchain.llms.azureml_endpoint
import json
import urllib.request
import warnings
from abc import abstractmethod
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import Ba... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
4801ecf38dab-1 | result = response.read()
return result
[docs]class ContentFormatterBase:
"""Transform request and response of AzureML endpoint to match with
required schema.
"""
"""
Example:
.. code-block:: python
class ContentFormatter(ContentFormatterBase):
con... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
4801ecf38dab-2 | prompt = prompt.replace(escape_sequence, escaped_sequence)
return prompt
[docs] @abstractmethod
def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
"""Formats the request body according to the input schema of
the model. Returns bytes or seekable file like object in... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
4801ecf38dab-3 | """
)
[docs]class HFContentFormatter(ContentFormatterBase):
"""Content handler for LLMs from the HuggingFace catalog."""
[docs] def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
ContentFormatterBase.escape_special_characters(prompt)
request_payload = json.dumps(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
4801ecf38dab-4 | "parameters": model_kwargs,
}
}
)
return str.encode(request_payload)
[docs] def format_response_payload(self, output: bytes) -> str:
"""Formats response"""
return json.loads(output)[0]["0"]
[docs]class AzureMLOnlineEndpoint(LLM, BaseModel):
"""Azure ML ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
4801ecf38dab-5 | @validator("http_client", always=True, allow_reuse=True)
@classmethod
def validate_client(cls, field_value: Any, values: Dict) -> AzureMLEndpointClient:
"""Validate that api key and python package exists in environment."""
endpoint_key = get_from_dict_or_env(
values, "endpoint_api_ke... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
4801ecf38dab-6 | The string generated by the model.
Example:
.. code-block:: python
response = azureml_model("Tell me a joke.")
"""
_model_kwargs = self.model_kwargs or {}
request_payload = self.content_formatter.format_request_payload(
prompt, _model_kwargs
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
4ac8b7a68c29-0 | Source code for langchain.llms.openllm
from __future__ import annotations
import copy
import json
import logging
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
Literal,
Optional,
TypedDict,
Union,
overload,
)
from langchain.callbacks.manager import (
AsyncCallbackManagerFor... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
4ac8b7a68c29-1 | )
llm("What is the difference between a duck and a goose?")
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(se... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
4ac8b7a68c29-2 | @overload
def __init__(
self,
model_name: Optional[str] = ...,
*,
model_id: Optional[str] = ...,
embedded: Literal[True, False] = ...,
**llm_kwargs: Any,
) -> None:
...
@overload
def __init__(
self,
*,
server_url: str = ...,... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
4ac8b7a68c29-3 | super().__init__(
**{
"server_url": server_url,
"server_type": server_type,
"llm_kwargs": llm_kwargs,
}
)
self._runner = None # type: ignore
self._client = client
else:
as... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
4ac8b7a68c29-4 | model_id='google/flan-t5-large',
embedded=False,
)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION
)
svc = bentoml.Service("langchain-openllm... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
4ac8b7a68c29-5 | return "openllm_client" if self._client else "openllm"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> str:
try:
import openllm
except ImportError as e:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
4ac8b7a68c29-6 | 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)
copied.update(kwargs)
config = openllm.Au... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
8635ee565047-0 | Source code for langchain.llms.predibase
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import Field
[docs]class Predibase(LLM):
"""Use your Predibase models with Langchain.
To ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predibase.html |
49348853d12e-0 | Source code for langchain.llms.ai21
from typing import Any, Dict, List, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import BaseModel, Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
49348853d12e-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/latest/_modules/langchain/llms/ai21.html |
49348853d12e-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/latest/_modules/langchain/llms/ai21.html |
49348853d12e-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 |
2de345ca4705-0 | Source code for langchain.llms.bedrock
import json
from abc import ABC
from typing import Any, Dict, Iterator, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 impo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
2de345ca4705-1 | count = 0
# track alternation
for i in range(len(input_text)):
if input_text[i : i + len(HUMAN_PROMPT)] == HUMAN_PROMPT:
if count % 2 == 0:
count += 1
else:
raise ValueError(ALTERNATION_ERROR)
if input_text[i : i + len(ASSISTANT_PROMPT)] ==... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
2de345ca4705-2 | input_body["textGenerationConfig"] = {**model_kwargs}
else:
input_body["inputText"] = prompt
if provider == "anthropic" and "max_tokens_to_sample" not in input_body:
input_body["max_tokens_to_sample"] = 256
return input_body
[docs] @classmethod
def prepare_output(c... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
2de345ca4705-3 | region_name: Optional[str] = None
"""The aws region e.g., `us-west-2`. Fallsback to AWS_DEFAULT_REGION env variable
or region specified in ~/.aws/config in case it is not provided here.
"""
credentials_profile_name: Optional[str] = None
"""The name of the profile in the ~/.aws/credentials or ~/.aws/... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
2de345ca4705-4 | import boto3
if values["credentials_profile_name"] is not None:
session = boto3.Session(profile_name=values["credentials_profile_name"])
else:
# use default credentials
session = boto3.Session()
client_params = {}
if values[... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
2de345ca4705-5 | params = {**_model_kwargs, **kwargs}
input_body = LLMInputOutputAdapter.prepare_input(provider, prompt, params)
body = json.dumps(input_body)
accept = "application/json"
contentType = "application/json"
try:
response = self.client.invoke_model(
body=bo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
2de345ca4705-6 | modelId=self.model_id,
accept="application/json",
contentType="application/json",
)
except Exception as e:
raise ValueError(f"Error raised by bedrock service: {e}")
for chunk in LLMInputOutputAdapter.prepare_output_stream(
provider, res... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
2de345ca4705-7 | **kwargs: Any,
) -> Iterator[GenerationChunk]:
"""Call out to Bedrock service with streaming.
Args:
prompt (str): The prompt to pass into the model
stop (Optional[List[str]], optional): Stop sequences. These will
override any stop sequences in the `model_kwarg... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
cfc499203623-0 | Source code for langchain.llms.clarifai
import logging
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, root_validator
from langc... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
cfc499203623-1 | """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 Clarifai
platform and python package exists in environment."""
values["pat"] = get_from_d... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
cfc499203623-2 | @property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {
**{
"user_id": self.user_id,
"app_id": self.app_id,
"model_id": self.model_id,
}
}
@property
def _llm_type... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
cfc499203623-3 | user_app_id=self.userDataObject,
model_id=self.model_id,
version_id=self.model_version_id,
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(text=resources_pb2.Text(raw=prompt))
)
],
)
post_model_outp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
cfc499203623-4 | raise ImportError(
"Could not import clarifai python package. "
"Please install it with `pip install clarifai`."
)
# TODO: add caching here.
generations = []
batch_size = 32
for i in range(0, len(prompts), batch_size):
batch = promp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
0ed705810520-0 | Source code for langchain.llms.huggingface_endpoint
from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, roo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
0ed705810520-1 | """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_endpoint.html |
0ed705810520-2 | 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_endpoint.html |
0ed705810520-3 | 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 {VALID_TASKS} are supported"
)
if ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
d653fb48aaf8-0 | Source code for langchain.llms.openai
from __future__ import annotations
import logging
import sys
import warnings
from typing import (
AbstractSet,
Any,
AsyncIterator,
Callable,
Collection,
Dict,
Iterator,
List,
Literal,
Mapping,
Optional,
Set,
Tuple,
Union,
)
fr... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-1 | finish_reason=stream_response["choices"][0].get("finish_reason", None),
logprobs=stream_response["choices"][0].get("logprobs", None),
),
)
def _update_response(response: Dict[str, Any], stream_response: Dict[str, Any]) -> None:
"""Update response from the stream response."""
response["ch... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-2 | llm: Union[BaseOpenAI, OpenAIChat],
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@retry_decorator
def _completion_with_retry(**kwargs: Any)... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-3 | """Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
max_tokens: int = 256
"""The maximum number of tokens to generate in the completion.
-1 returns as many tokens as possible given the prompt and
the models maximal context size."""
top_p: float = 1
"... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-4 | """Maximum number of retries to make when generating."""
streaming: bool = False
"""Whether to stream the results or not."""
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
"""Set of special tokens that are allowed。"""
disallowed_special: Union[Literal["all"], Collection[str]] = "al... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-5 | "no longer supported. Instead, please use: "
"`from langchain.chat_models import ChatOpenAI`"
)
return OpenAIChat(**data)
return super().__new__(cls)
class Config:
"""Configuration for this pydantic object."""
allow_population_by_field_name = True
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-6 | except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
if values["streaming"] and values["n"] > 1:
raise ValueError("Cannot stream results when n > 1.")
if valu... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-7 | for stream_resp in completion_with_retry(
self, prompt=prompt, run_manager=run_manager, **params
):
chunk = _stream_response_to_generation_chunk(stream_resp)
yield chunk
if run_manager:
run_manager.on_llm_new_token(
chunk.text,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-8 | """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 |
d653fb48aaf8-9 | self, prompt=_prompts, run_manager=run_manager, **params
)
choices.extend(response["choices"])
update_token_usage(_keys, response, token_usage)
return self.create_llm_result(choices, prompts, token_usage)
async def _agenerate(
self,
prompts: Li... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-10 | "logprobs": generation.generation_info.get("logprobs")
if generation.generation_info
else None,
}
)
else:
response = await acompletion_with_retry(
self, prompt=_prompts, run_manager=run_ma... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-11 | generations = []
for i, _ in enumerate(prompts):
sub_choices = choices[i * self.n : (i + 1) * self.n]
generations.append(
[
Generation(
text=choice["text"],
generation_info=dict(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-12 | [docs] def get_token_ids(self, text: str) -> List[int]:
"""Get the token IDs using the tiktoken package."""
# tiktoken NOT supported for Python < 3.8
if sys.version_info[1] < 8:
return super().get_num_tokens(text)
try:
import tiktoken
except ImportError... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-13 | "gpt-4-0613": 8192,
"gpt-4-32k": 32768,
"gpt-4-32k-0314": 32768,
"gpt-4-32k-0613": 32768,
"gpt-3.5-turbo": 4096,
"gpt-3.5-turbo-0301": 4096,
"gpt-3.5-turbo-0613": 4096,
"gpt-3.5-turbo-16k": 16385,
"gpt-3.5-turbo-16k-0613": 1... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-14 | context_size = model_token_mapping.get(modelname, None)
if context_size is None:
raise ValueError(
f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
"Known models are: " + ", ".join(model_token_mapping.keys())
)
return contex... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-15 | return {**{"model": self.model_name}, **super()._invocation_params}
[docs]class AzureOpenAI(BaseOpenAI):
"""Azure-specific OpenAI large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-16 | "api_version": self.openai_api_version,
}
return {**openai_params, **super()._invocation_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "azure"
[docs]class OpenAIChat(BaseLLM):
"""OpenAI Chat large language models.
To use, you should have t... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
d653fb48aaf8-17 | allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
"""Set of special tokens that are allowed。"""
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
"""Set of special tokens that are not allowed。"""
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
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