id stringlengths 14 16 | text stringlengths 44 2.73k | source stringlengths 49 114 |
|---|---|---|
93469452885e-1 | """Build extra kwargs from additional params that were passed in."""
all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if ... | https://python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
93469452885e-2 | )
# get the model and version
model_str, version_str = self.model.split(":")
model = replicate_python.models.get(model_str)
version = model.versions.get(version_str)
# sort through the openapi schema to get the name of the first input
input_properties = sorted(
... | https://python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
68eccad8232e-0 | Source code for langchain.llms.cohere
"""Wrapper around Cohere APIs."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env
logger ... | https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
68eccad8232e-1 | truncate: Optional[str] = None
"""Specify how the client handles inputs longer than the maximum token
length: Truncate from START, END or NONE"""
cohere_api_key: Optional[str] = None
stop: Optional[List[str]] = None
class Config:
"""Configuration for this pydantic object."""
extra = ... | https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
68eccad8232e-2 | """Return type of llm."""
return "cohere"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> 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.
... | https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
f35bd3557029-0 | Source code for langchain.llms.rwkv
"""Wrapper for the RWKV model.
Based on https://github.com/saharNooby/rwkv.cpp/blob/master/rwkv/chat_with_bot.py
https://github.com/BlinkDL/ChatRWKV/blob/main/v2/chat.py
"""
from typing import Any, Dict, List, Mapping, Optional, Set
from pydantic import BaseModel, Extra, roo... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
f35bd3557029-1 | line verbatim.."""
penalty_alpha_presence: float = 0.4
"""Positive values penalize new tokens based on whether they appear
in the text so far, increasing the model's likelihood to talk about
new topics.."""
CHUNK_LEN: int = 256
"""Batch size for prompt processing."""
max_tokens_per_generatio... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
f35bd3557029-2 | raise ValueError(
"Could not import tokenizers python package. "
"Please install it with `pip install tokenizers`."
)
try:
from rwkv.model import RWKV as RWKVMODEL
from rwkv.utils import PIPELINE
values["tokenizer"] = tokenizers.Tok... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
f35bd3557029-3 | for i in AVOID_REPEAT:
dd = self.pipeline.encode(i)
assert len(dd) == 1
AVOID_REPEAT_TOKENS += dd
tokens = [int(x) for x in _tokens]
self.model_tokens += tokens
out: Any = None
while len(tokens) > 0:
out, self.model_state = self.client.forw... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
f35bd3557029-4 | xxx = self.tokenizer.decode(self.model_tokens[out_last:])
if "\ufffd" not in xxx: # avoid utf-8 display issues
decoded += xxx
out_last = begin + i + 1
if i >= self.max_tokens_per_generation - 100:
break
return decoded
def _call... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
cd3c52feb2aa-0 | Source code for langchain.llms.openai
"""Wrapper around OpenAI APIs."""
from __future__ import annotations
import logging
import sys
import warnings
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Dict,
Generator,
List,
Literal,
Mapping,
Optional,
Set,
Tuple,... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-1 | def _streaming_response_template() -> Dict[str, Any]:
return {
"choices": [
{
"text": "",
"finish_reason": None,
"logprobs": None,
}
]
}
def _create_retry_decorator(llm: Union[BaseOpenAI, OpenAIChat]) -> Callable[[Any], Any]... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-2 | ) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async def _completion_with_retry(**kwargs: Any) -> Any:
# Use OpenAI's async api https://github.com/openai/openai-python#async-api
return await llm.client.acre... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-3 | openai_organization: Optional[str] = None
batch_size: int = 20
"""Batch size to use when passing multiple documents to generate."""
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout for requests to OpenAI completion API. Default is 600 seconds."""
logit_bias: Optional[Di... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-4 | extra = Extra.ignore
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwar... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-5 | if openai_organization:
openai.organization = openai_organization
values["client"] = openai.Completion
except ImportError:
raise ValueError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-6 | response = openai.generate(["Tell me a joke."])
"""
# TODO: write a unit test for this
params = self._invocation_params
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Get the token usage from the response.... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-7 | params = self._invocation_params
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_to... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-8 | def get_sub_prompts(
self,
params: Dict[str, Any],
prompts: List[str],
stop: Optional[List[str]] = None,
) -> List[List[str]]:
"""Get the sub prompts for llm call."""
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found ... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-9 | llm_output = {"token_usage": token_usage, "model_name": self.model_name}
return LLMResult(generations=generations, llm_output=llm_output)
def stream(self, prompt: str, stop: Optional[List[str]] = None) -> Generator:
"""Call OpenAI with streaming flag and return the resulting generator.
BETA:... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-10 | return self._default_params
@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 "opena... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-11 | model_token_mapping = {
"gpt-4": 8192,
"gpt-4-0314": 8192,
"gpt-4-32k": 32768,
"gpt-4-32k-0314": 32768,
"gpt-3.5-turbo": 4096,
"gpt-3.5-turbo-0301": 4096,
"text-ada-001": 2049,
"ada": 2049,
"text-babbage-001": 20... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-12 | Args:
prompt: The prompt to pass into the model.
Returns:
The maximum number of tokens to generate for a prompt.
Example:
.. code-block:: python
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
"""
num_tokens = self.get_num_t... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-13 | .. code-block:: python
from langchain.llms import AzureOpenAI
openai = AzureOpenAI(model_name="text-davinci-003")
"""
deployment_name: str = ""
"""Deployment name to use."""
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {
**{"deploym... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-14 | """Maximum number of retries to make when generating."""
prefix_messages: List = Field(default_factory=list)
"""Series of messages for Chat input."""
streaming: bool = False
"""Whether to stream the results or not."""
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
"""Set of spe... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-15 | default="",
)
openai_organization = get_from_dict_or_env(
values, "openai_organization", "OPENAI_ORGANIZATION", default=""
)
try:
import openai
openai.api_key = openai_api_key
if openai_api_base:
openai.api_base = openai_api... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-16 | params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
if params.get("max_tokens") == -1:
# ... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-17 | ) -> LLMResult:
messages, params = self._get_chat_params(prompts, stop)
if self.streaming:
response = ""
params["stream"] = True
async for stream_resp in await acompletion_with_retry(
self, messages=messages, **params
):
tok... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
cd3c52feb2aa-18 | # tiktoken NOT supported for Python < 3.8
if sys.version_info[1] < 8:
return super().get_num_tokens(text)
try:
import tiktoken
except ImportError:
raise ValueError(
"Could not import tiktoken python package. "
"This is needed in... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
9c5381153529-0 | Source code for langchain.llms.huggingface_endpoint
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
9c5381153529-1 | 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://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
9c5381153529-2 | 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
response = hf("Tell me a joke.")
"""
_mod... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
9c5381153529-3 | # stop tokens when making calls to huggingface_hub.
text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 25, 2023. | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
2daf71d74f5a-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.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dic... | https://python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
2daf71d74f5a-1 | """Get the identifying parameters."""
return {
**{"model_id": self.model_id},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "deepinfra"
def _call(self, prompt: str, stop: Optional[List[s... | https://python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
0c4f487e2724-0 | 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.llms.self_hosted import SelfHostedPipeline... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
0c4f487e2724-1 | 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 returns a pipeline for the task.
"""
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokeni... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
0c4f487e2724-2 | "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 with CUDA device id.",
cuda_device_count,
)
pipeline = hf_pipeline(
task=task,
model=mod... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
0c4f487e2724-3 | .. code-block:: python
from langchain.llms import SelfHostedHuggingFaceLLM
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def get_pipeline():
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pre... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
0c4f487e2724-4 | extra = Extra.forbid
def __init__(self, **kwargs: Any):
"""Construct the pipeline remotely using an auxiliary function.
The load function needs to be importable to be imported
and run on the server, i.e. in a module and not a REPL or closure.
Then, initialize the remote inference fun... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
009fe345866b-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.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from... | https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
009fe345866b-1 | """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
return values
@property
def _default_params(self) -> Mapping[str, ... | https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
009fe345866b-2 | },
json={"text": prompt, **self._default_params},
)
response_json = response.json()
text = response_json["result"][0]["completion"]
if stop is not None:
# I believe this is required since the stop tokens
# are not enforced by the model parameters
... | https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
457a956f94ce-0 | Source code for langchain.llms.huggingface_pipeline
"""Wrapper around HuggingFace Pipeline APIs."""
import importlib.util
import logging
from typing import Any, List, Mapping, Optional
from pydantic import Extra
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
DEFAULT_MODEL_ID = ... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
457a956f94ce-1 | """Key word arguments to pass to the model."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @classmethod
def from_model_id(
cls,
model_id: str,
task: str,
device: int = -1,
model_kwargs: Optional[dict] = None,
... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
457a956f94ce-2 | import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError(
f"Got device=={device}, "
f"device is required to be within [-1, {cuda_device_count})"
)
... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
457a956f94ce-3 | response = self.pipeline(prompt)
if self.pipeline.task == "text-generation":
# Text generation return includes the starter text.
text = response[0]["generated_text"][len(prompt) :]
elif self.pipeline.task == "text2text-generation":
text = response[0]["generated_text"]... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
699bcec3418b-0 | Source code for langchain.llms.stochasticai
"""Wrapper around StochasticAI APIs."""
import logging
import time
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, Field, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
... | https://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
699bcec3418b-1 | raise ValueError(f"Found {field_name} supplied twice.")
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)
... | https://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
699bcec3418b-2 | json={"prompt": prompt, "params": params},
headers={
"apiKey": f"{self.stochasticai_api_key}",
"Accept": "application/json",
"Content-Type": "application/json",
},
)
response_post.raise_for_status()
response_post_json = resp... | https://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
6f099bb2d099-0 | Source code for langchain.llms.cerebriumai
"""Wrapper around CerebriumAI API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get... | https://python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
6f099bb2d099-1 | extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
logger.warning(
f"""{field_nam... | https://python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
6f099bb2d099-2 | "Please install it with `pip install cerebrium`."
)
params = self.model_kwargs or {}
response = model_api_request(
self.endpoint_url, {"prompt": prompt, **params}, self.cerebriumai_api_key
)
text = response["data"]["result"]
if stop is not None:
... | https://python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
afa7a1bb7a08-0 | 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.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_d... | https://python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
afa7a1bb7a08-1 | 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://python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
afa7a1bb7a08-2 | "prompt": prompt,
**params,
}
response = banana.run(api_key, model_key, model_inputs)
try:
text = response["modelOutputs"][0]["output"]
except (KeyError, TypeError):
returned = response["modelOutputs"][0]
raise ValueError(
"... | https://python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
f5c67a620cdb-0 | Source code for langchain.llms.gpt4all
"""Wrapper for the GPT4All model."""
from functools import partial
from typing import Any, Dict, List, Mapping, Optional, Set
from pydantic import Extra, Field, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
[docs]class GPT4... | https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
f5c67a620cdb-1 | 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")
"""Use embedding mode only."""
n_threads: Optional[int] = F... | https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
f5c67a620cdb-2 | """Get the identifying parameters."""
return {
"seed": self.seed,
"n_predict": self.n_predict,
"n_threads": self.n_threads,
"n_batch": self.n_batch,
"repeat_last_n": self.repeat_last_n,
"repeat_penalty": self.repeat_penalty,
"to... | https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
f5c67a620cdb-3 | return {
"model": self.model,
**self._default_params,
**{
k: v
for k, v in self.__dict__.items()
if k in GPT4All._llama_param_names()
},
}
@property
def _llm_type(self) -> str:
"""Return the type of l... | https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
0f2e5341d030-0 | Source code for langchain.llms.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import List, Optional
from langchain.llms import OpenAI, OpenAIChat
from langchain.schema import LLMResult
[docs]class PromptLayerOpenAI(OpenAI):
"""Wrapper around OpenAI large language models.
To use, you s... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
0f2e5341d030-1 | for i in range(len(prompts)):
prompt = prompts[i]
generation = generated_responses.generations[i][0]
resp = {
"text": generation.text,
"llm_output": generated_responses.llm_output,
}
pl_request_id = promptlayer_api_request(
... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
0f2e5341d030-2 | self._identifying_params,
self.pl_tags,
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... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
0f2e5341d030-3 | ) -> LLMResult:
"""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)
request_... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
0f2e5341d030-4 | generation = generated_responses.generations[i][0]
resp = {
"text": generation.text,
"llm_output": generated_responses.llm_output,
}
pl_request_id = await promptlayer_api_request_async(
"langchain.PromptLayerOpenAIChat.async",
... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
d4a2cb10334d-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.llms.base import LLM
from langchain.utils import get_from_dict_or_env
[docs]class NLPCloud(LLM):
"""Wrapper around NLPCloud larg... | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
d4a2cb10334d-1 | 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 to the length."""
do_sample: bool = True
"""Whether to use sam... | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
d4a2cb10334d-2 | 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,
"remove_end_sequence": self.remove_end_sequence,
"bad_wo... | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
d4a2cb10334d-3 | "Pass in a list of length 1."
)
elif stop and len(stop) == 1:
end_sequence = stop[0]
else:
end_sequence = None
response = self.client.generation(
prompt, end_sequence=end_sequence, **self._default_params
)
return response["generated... | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
d4220331fea7-0 | 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.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict... | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
d4220331fea7-1 | 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://python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
d4220331fea7-2 | from transformers import BloomTokenizerFast
model_name = values["model_name"]
values["tokenizer"] = BloomTokenizerFast.from_pretrained(model_name)
values["client"] = DistributedBloomForCausalLM.from_pretrained(model_name)
values["huggingface_api_key"] = huggingface_api_ke... | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
d4220331fea7-3 | text = self.tokenizer.decode(outputs[0])
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)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase... | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
5a6717192d5c-0 | Source code for langchain.llms.anthropic
"""Wrapper around Anthropic APIs."""
import re
from typing import Any, Callable, Dict, Generator, List, Mapping, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
class _AnthropicCo... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
5a6717192d5c-1 | values["AI_PROMPT"] = anthropic.AI_PROMPT
values["count_tokens"] = anthropic.count_tokens
except ImportError:
raise ValueError(
"Could not import anthropic python package. "
"Please it install it with `pip install anthropic`."
)
return ... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
5a6717192d5c-2 | [docs]class Anthropic(LLM, _AnthropicCommon):
r"""Wrapper around Anthropic's large language models.
To use, you should have the ``anthropic`` python package installed, and the
environment variable ``ANTHROPIC_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Exampl... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
5a6717192d5c-3 | # Guard against common errors in specifying wrong number of newlines.
corrected_prompt, n_subs = re.subn(r"^\n*Human:", self.HUMAN_PROMPT, prompt)
if n_subs == 1:
return corrected_prompt
# As a last resort, wrap the prompt ourselves to emulate instruct-style.
return f"{self.H... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
5a6717192d5c-4 | **self._default_params,
)
return response["completion"]
async def _acall(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Call out to Anthropic's completion endpoint asynchronously."""
stop = self._get_anthropic_stop(stop)
if self.streaming:
stream_... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
5a6717192d5c-5 | Example:
.. 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._... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
bdd4023a6c0f-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.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
logger = logging.getLogger(... | https://python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
bdd4023a6c0f-1 | Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
values["model_kwargs"] = extra
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
... | https://python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
1127bcec7a39-0 | Source code for langchain.llms.gooseai
"""Wrapper around GooseAI API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]... | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
1127bcec7a39-1 | """Penalizes repeated tokens."""
n: int = 1
"""How many completions to generate for each prompt."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
logit_bias: Optional[Dict[str, float]] = Field(default_fac... | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
1127bcec7a39-2 | )
try:
import openai
openai.api_key = gooseai_api_key
openai.api_base = "https://api.goose.ai/v1"
values["client"] = openai.Completion
except ImportError:
raise ValueError(
"Could not import openai python package. "
... | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
1127bcec7a39-3 | params["stop"] = stop
response = self.client.create(engine=self.model_name, prompt=prompt, **params)
text = response.choices[0].text
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 25, 2023. | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
d2c352447fe3-0 | Source code for langchain.vectorstores.pinecone
"""Wrapper around Pinecone vector database."""
from __future__ import annotations
import uuid
from typing import Any, Callable, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
d2c352447fe3-1 | self._embedding_function = embedding_function
self._text_key = text_key
self._namespace = namespace
[docs] def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
namespace: Optional[str] = None,
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
d2c352447fe3-2 | filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
d2c352447fe3-3 | namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents most similar to the query and score for each
"""
if namespace is None:
namespace = self._namespace
query_obj = self._embedding_function(query)
docs = []
... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
d2c352447fe3-4 | pinecone.init(api_key="***", environment="...")
embeddings = OpenAIEmbeddings()
pinecone = Pinecone.from_texts(
texts,
embeddings,
index_name="langchain-demo"
)
"""
try:
import pinecon... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
d2c352447fe3-5 | metadata = metadatas[i:i_end]
else:
metadata = [{} for _ in range(i, i_end)]
for j, line in enumerate(lines_batch):
metadata[j][text_key] = line
to_upsert = zip(ids_batch, embeds, metadata)
# upsert to Pinecone
index.upsert(vect... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/pinecone.html |
1a9eb9420660-0 | Source code for langchain.vectorstores.zilliz
from __future__ import annotations
import logging
from typing import Any, List, Optional
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.milvus import Milvus
logger = logging.getLogger(__name__)
[docs]class Zilliz(Milvus):
def _create_index(... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html |
1a9eb9420660-1 | "Failed to create an index on collection: %s", self.collection_name
)
raise e
[docs] @classmethod
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = "LangChainCollecti... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html |
1a9eb9420660-2 | Zilliz: Zilliz Vector Store
"""
vector_db = cls(
embedding_function=embedding,
collection_name=collection_name,
connection_args=connection_args,
consistency_level=consistency_level,
index_params=index_params,
search_params=search_pa... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/zilliz.html |
1ae7f74e8fd6-0 | Source code for langchain.vectorstores.qdrant
"""Wrapper around Qdrant vector database."""
from __future__ import annotations
import uuid
from hashlib import md5
from operator import itemgetter
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union
from langchain.docstore.document import D... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
1ae7f74e8fd6-1 | if not isinstance(client, qdrant_client.QdrantClient):
raise ValueError(
f"client should be an instance of qdrant_client.QdrantClient, "
f"got {type(client)}"
)
self.client: qdrant_client.QdrantClient = client
self.collection_name = collection_name... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
1ae7f74e8fd6-2 | k: int = 4,
filter: Optional[MetadataFilter] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by met... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
1ae7f74e8fd6-3 | self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
amon... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
1ae7f74e8fd6-4 | embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = N... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
1ae7f74e8fd6-5 | grpc_port: Port of the gRPC interface. Default: 6334
prefer_grpc:
If true - use gPRC interface whenever possible in custom methods.
Default: False
https: If true - use HTTPS(SSL) protocol. Default: None
api_key: API key for authentication in Qdrant Clo... | https://python.langchain.com/en/latest/_modules/langchain/vectorstores/qdrant.html |
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