id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
ac1774410b1b-41 | return "openai-chat"
[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_token_ids(text)
try:
import tiktoken
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
e1555fe29a1c-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/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-1 | if not response.ok:
raise ValueError(f"HTTP {response.status_code} error: {response.text}")
return response.json()
@abstractmethod
def post(self, request: Any) -> Any:
...
class _DatabricksServingEndpointClient(_DatabricksClientBase):
"""An API client that talks to a Databricks s... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-2 | 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/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-3 | host = values["host"]
cluster_id = values["cluster_id"]
port = values["cluster_driver_port"]
api_url = f"https://{host}/driver-proxy-api/o/0/{cluster_id}/{port}"
values["api_url"] = api_url
return values
def post(self, request: Any) -> Any:
return self... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-4 | 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("context doesn't contain browserHostName.")
except Exc... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-5 | """
if api_token := os.getenv("DATABRICKS_TOKEN"):
return api_token
try:
api_token = get_repl_context().apiToken
if not api_token:
raise ValueError("context doesn't contain apiToken.")
except Exception as e:
raise ValueError(
"api_token was not set and... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-6 | 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::
[{"name": "prompt", "type": "string"},
{"name": "stop", "type":... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-7 | the driver IP address or simply ``0.0.0.0`` instead of localhost only.
To wrap it as an LLM you must have "Can Attach To" permission to the cluster.
Set ``cluster_id`` and ``cluster_driver_port`` and do not set ``endpoint_name``.
The expected server schema (using JSON schema) is:
* inputs::
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-8 | 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`` environment variable if present, or
* the hostname of the current Databricks workspa... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-9 | "no isolation shared" mode.
"""
endpoint_name: Optional[str] = None
"""Name of the model serving endpont.
You must specify the endpoint name to connect to a model serving endpoint.
You must not set both ``endpoint_name`` and ``cluster_id``.
"""
cluster_id: Optional[str] = None
"""ID of t... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-10 | """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: Optional[Dict[str, Any]] = None
"""Extra paramete... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-11 | """
_client: _DatabricksClientBase = PrivateAttr()
class Config:
extra = Extra.forbid
underscore_attrs_are_private = True
@validator("cluster_id", always=True)
def set_cluster_id(cls, v: Any, values: Dict[str, Any]) -> Optional[str]:
if v and values["endpoint_name"]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-12 | 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 ValueError("Cannot set both endpoint_name and cluster_driver_port.")
elif values[... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-13 | if v:
assert "prompt" not in v, "model_kwargs must not contain key 'prompt'"
assert "stop" not in v, "model_kwargs must not contain key 'stop'"
return v
def __init__(self, **data: Any):
super().__init__(**data)
if self.endpoint_name:
self._client = _Databr... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-14 | 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: Optional[Li... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
e1555fe29a1c-15 | response = self._client.post(request)
if self.transform_output_fn:
response = self.transform_output_fn(response)
return response | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
84cb46746fcc-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/latest/_modules/langchain/llms/petals.html |
84cb46746fcc-1 | .. code-block:: python
from langchain.llms import petals
petals = Petals()
"""
client: Any
"""The client to use for the API calls."""
tokenizer: Any
"""The tokenizer to use for the API calls."""
model_name: str = "bigscience/bloom-petals"
"""The model to use."""
t... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
84cb46746fcc-2 | do_sample: bool = True
"""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
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
84cb46746fcc-3 | 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"""WARNING! {field_name} is not default parameter.
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
84cb46746fcc-4 | )
try:
from petals import DistributedBloomForCausalLM
from transformers import BloomTokenizerFast
model_name = values["model_name"]
values["tokenizer"] = BloomTokenizerFast.from_pretrained(model_name)
values["client"] = DistributedBloomForCausalLM.from... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
84cb46746fcc-5 | "top_p": self.top_p,
"top_k": self.top_k,
"do_sample": self.do_sample,
"max_length": self.max_length,
}
return {**normal_params, **self.model_kwargs}
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
84cb46746fcc-6 | """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/latest/_modules/langchain/llms/petals.html |
0ab1005c6d1a-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/latest/_modules/langchain/llms/openllm.html |
0ab1005c6d1a-1 | embedded: bool
llm_kwargs: Dict[str, Any]
logger = logging.getLogger(__name__)
[docs]class OpenLLM(LLM):
"""Wrapper for accessing OpenLLM, supporting both in-process model
instance and remote OpenLLM servers.
To use, you should have the openllm library installed:
.. code-block:: bash
pip ins... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
0ab1005c6d1a-2 | 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/latest/_modules/langchain/llms/openllm.html |
0ab1005c6d1a-3 | server_type: ServerType = "http"
"""Optional server type. Either 'http' or 'grpc'."""
embedded: bool = True
"""Initialize this LLM instance in current process by default. Should
only set to False when using in conjunction with BentoML Service."""
llm_kwargs: Dict[str, Any]
"""Key word arguments... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
0ab1005c6d1a-4 | 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,
) -> None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
0ab1005c6d1a-5 | except ImportError as e:
raise ImportError(
"Could not import openllm. Make sure to install it with "
"'pip install openllm.'"
) from e
llm_kwargs = llm_kwargs or {}
if server_url is not None:
logger.debug("'server_url' is provided, ret... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
0ab1005c6d1a-6 | }
)
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 convert the
# Runner with embedded... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
0ab1005c6d1a-7 | "model_name": model_name,
"model_id": model_id,
"embedded": embedded,
"llm_kwargs": llm_kwargs,
}
)
self._client = None # type: ignore
self._runner = runner
@property
def runner(self) -> openllm.LLMR... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
0ab1005c6d1a-8 | )
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 None:
raise ValueError("OpenLLM must be initialized loca... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
0ab1005c6d1a-9 | model_id = self.model_id
try:
self.llm_kwargs.update(
json.loads(self._runner.identifying_params["configuration"])
)
except (TypeError, json.JSONDecodeError):
pass
return IdentifyingParams(
server_url=self.se... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
0ab1005c6d1a-10 | ) -> 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)
copied.update(... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
0ab1005c6d1a-11 | run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**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'."
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
0ab1005c6d1a-12 | postprocess_kwargs,
) = self._runner.llm.sanitize_parameters(prompt, **kwargs)
generated_result = await self._runner.generate.async_run(
prompt, **generate_kwargs
)
return self._runner.llm.postprocess_generate(
prompt, generated_result, **p... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openllm.html |
7edf09928a1b-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 |
7edf09928a1b-1 | Only supports `text-generation`, `text2text-generation` and `summarization` for now.
Example:
.. code-block:: python
from langchain.llms import HuggingFaceHub
hf = HuggingFaceHub(repo_id="gpt2", huggingfacehub_api_token="my-api-key")
"""
client: Any #: :meta private:
rep... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
7edf09928a1b-2 | 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, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
7edf09928a1b-3 | values["client"] = client
except ImportError:
raise ValueError(
"Could not import huggingface_hub python package. "
"Please install it with `pip install huggingface_hub`."
)
return values
@property
def _identifying_params(self) -> Mapping[s... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
7edf09928a1b-4 | 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 stop words to use when generating.
Returns:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
7edf09928a1b-5 | text = response[0]["generated_text"][len(prompt) :]
elif self.client.task == "text2text-generation":
text = response[0]["generated_text"]
elif self.client.task == "summarization":
text = response[0]["summary_text"]
else:
raise ValueError(
f"Got... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
56a03ee7e000-0 | 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 |
56a03ee7e000-1 | """Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
length: int = 256
"""The maximum number of tokens to generate in the completion."""
top_p: float = 1.0
"""Total probability mass of tokens to consider at each step."""
top_k: int = 40
"""The number ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
56a03ee7e000-2 | @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 |
56a03ee7e000-3 | """Get the identifying parameters."""
return {**{"endpoint_url": self.endpoint_url}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "forefrontai"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
56a03ee7e000-4 | """
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 |
0b70df5a9374-0 | 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 |
0b70df5a9374-1 | .. code-block:: python
from langchain.llms import Banana
banana = Banana(model_key="")
"""
model_key: str = ""
"""model endpoint to use"""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not
explicitly speci... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
0b70df5a9374-2 | 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_name} was transfered to model_kwargs.
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
0b70df5a9374-3 | return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_key": self.model_key},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return typ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
0b70df5a9374-4 | "Please install it with `pip install banana-dev`."
)
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,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
0b70df5a9374-5 | "\n- modify app.py to return the above schema"
"\n- deploy that as a custom repo"
)
if stop is not None:
# I believe this is required since the stop tokens
# are not enforced by the model parameters
text = enforce_stop_tokens(text, stop)
re... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
8a835eb40695-0 | 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 |
8a835eb40695-1 | top_k: Optional[int] = None
"""Number of most likely tokens to consider at each step."""
top_p: Optional[float] = None
"""Total probability mass of tokens to consider at each step."""
streaming: bool = False
"""Whether to stream the results."""
default_request_timeout: Optional[Union[float, Tupl... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
8a835eb40695-2 | 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,
"anthropic_api_url",
"ANTHROPIC_API_URL",
default=... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
8a835eb40695-3 | except ImportError:
raise ImportError(
"Could not import anthropic python package. "
"Please it install it with `pip install anthropic`."
)
return values
@property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameter... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
8a835eb40695-4 | """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 NameError("Please ensure the anthropic package is loaded")
if stop is No... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
8a835eb40695-5 | Example:
.. code-block:: python
import anthropic
from langchain.llms import Anthropic
model = Anthropic(model="<model_name>", anthropic_api_key="my-api-key")
# Simplest invocation, automatically wrapped with HUMAN_PROMPT
# and AI_PROMPT.
re... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
8a835eb40695-6 | """Raise warning that this class is deprecated."""
warnings.warn(
"This Anthropic LLM is deprecated. "
"Please use `from langchain.chat_models import ChatAnthropic` instead"
)
return values
@property
def _llm_type(self) -> str:
"""Return type of llm."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
8a835eb40695-7 | if n_subs == 1:
return corrected_prompt
# As a last resort, wrap the prompt ourselves to emulate instruct-style.
return f"{self.HUMAN_PROMPT} {prompt}{self.AI_PROMPT} Sure, here you go:\n"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
8a835eb40695-8 | prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
response = model(prompt)
"""
stop = self._get_anthropic_stop(stop)
params = {**self._default_params, **kwargs}
if self.streaming:
stream_resp = self.client.completion_stream(
prompt=self._wrap_promp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
8a835eb40695-9 | )
return response["completion"]
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Anthropic's completion endpoint asynchronously."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
8a835eb40695-10 | if run_manager:
await run_manager.on_llm_new_token(delta, **data)
return current_completion
response = await self.client.acompletion(
prompt=self._wrap_prompt(prompt),
stop_sequences=stop,
**params,
)
return response["completion... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
8a835eb40695-11 | .. code-block:: python
prompt = "Write a poem about a stream."
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
generator = anthropic.stream(prompt)
for token in generator:
yield token
"""
stop = self._get_anthropic_stop(st... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
3e56a593f5d5-0 | 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 |
3e56a593f5d5-1 | try:
import google.api_core.exceptions
except ImportError:
raise ImportError(
"Could not import google-api-core python package. "
"Please install it with `pip install google-api-core`."
)
multiplier = 2
min_seconds = 1
max_seconds = 60
max_retries = 10... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
3e56a593f5d5-2 | )
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:
return llm.client.generate_text(**kwargs)
return _generate_with_retr... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
3e56a593f5d5-3 | else:
return text
[docs]class GooglePalm(BaseLLM, BaseModel):
client: Any #: :meta private:
google_api_key: Optional[str]
model_name: str = "models/text-bison-001"
"""Model name to use."""
temperature: float = 0.7
"""Run inference with this temperature. Must by in the closed interval
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
3e56a593f5d5-4 | 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 |
3e56a593f5d5-5 | "Please install it with `pip install google-generativeai`."
)
values["client"] = genai
if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
raise ValueError("temperature must be in the range [0.0, 1.0]")
if values["top_p"] is not None and not 0 <=... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
3e56a593f5d5-6 | 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 |
3e56a593f5d5-7 | prompt_generations.append(Generation(text=stripped_text))
generations.append(prompt_generations)
return LLMResult(generations=generations)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerF... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
e55ef48f0c95-0 | 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 |
e55ef48f0c95-1 | Example:
.. code-block:: python
from langchain.llms import DeepInfra
di = DeepInfra(model_id="google/flan-t5-xl",
deepinfra_api_token="my-api-key")
"""
model_id: str = DEFAULT_MODEL_ID
model_kwargs: Optional[dict] = None
deepinfra_api_token... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
e55ef48f0c95-2 | 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 |
e55ef48f0c95-3 | Returns:
The string generated by the model.
Example:
.. code-block:: python
response = di("Tell me a joke.")
"""
_model_kwargs = self.model_kwargs or {}
_model_kwargs = {**_model_kwargs, **kwargs}
# HTTP headers for authorization
he... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
e55ef48f0c95-4 | 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.JSONDecodeError as e:
raise ValueErr... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
1c780cef8e5c-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 |
1c780cef8e5c-1 | OpenAI API key as well.
Example:
.. code-block:: python
pgllm = PredictionGuard(model="MPT-7B-Instruct",
token="my-access-token",
output={
"type": "boolean"
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
1c780cef8e5c-2 | """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 |
1c780cef8e5c-3 | return {
"max_tokens": self.max_tokens,
"temperature": self.temperature,
}
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"model": self.model}, **self._default_params}
@property
def _llm_type(sel... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
1c780cef8e5c-4 | 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 ValueError("`stop` fo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
1c780cef8e5c-5 | # In order to make this consistent with other endpoints, we strip them.
if stop is not None or self.stop is not None:
text = enforce_stop_tokens(text, params["stop_sequences"])
return text | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
6a88b19e0b54-0 | 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 |
51a16bc2fda2-0 | 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 |
51a16bc2fda2-1 | model_name: str = "finetuned-gpt-neox-20b"
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
min_length: int = 1
"""The minimum number of tokens to generate in the completion."""
max_length: int = 256
"""The maximum number of tokens to generate in the ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
51a16bc2fda2-2 | top_p: int = 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
"""Ex... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
51a16bc2fda2-3 | 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."""
nlpcloud_api_key = get_from_dict_or_env(
value... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
51a16bc2fda2-4 | """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_no_input,
"remove_input": self.remove_input,
"... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
51a16bc2fda2-5 | }
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "nlpcloud"
def _call(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
51a16bc2fda2-6 | 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."
"Pass in a list of length 1."
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
e803b998817f-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 |
e803b998817f-1 | # 4 seconds, then up to 10 seconds, then 10 seconds afterwards
return retry(
reraise=True,
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(retry_if_exception_type(cohere.error.CohereError)),
before_sleep=... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
e803b998817f-2 | [docs]class Cohere(LLM):
"""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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
e803b998817f-3 | k: int = 0
"""Number of most likely tokens to consider at each step."""
p: int = 1
"""Total probability mass of tokens to consider at each step."""
frequency_penalty: float = 0.0
"""Penalizes repeated tokens according to frequency. Between 0 and 1."""
presence_penalty: float = 0.0
"""Penaliz... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
e803b998817f-4 | 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 |
e803b998817f-5 | "temperature": self.temperature,
"k": self.k,
"p": self.p,
"frequency_penalty": self.frequency_penalty,
"presence_penalty": self.presence_penalty,
"truncate": self.truncate,
}
@property
def _identifying_params(self) -> Dict[str, Any]:
"... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
e803b998817f-6 | ) -> str:
"""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
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
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