id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 115 |
|---|---|---|
11a099a1d2b8-0 | 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://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
11a099a1d2b8-1 | @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"
)
try:
... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
11a099a1d2b8-2 | ) -> 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:
The string generated by the model.
Example:
.. code-block:: p... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
295197187008-0 | Source code for langchain.llms.cerebriumai
"""Wrapper around CerebriumAI API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms... | https://python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
295197187008-1 | all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if field_name in extra:
raise ValueError(f"Found {field_name... | https://python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
295197187008-2 | try:
from cerebrium import model_api_request
except ImportError:
raise ValueError(
"Could not import cerebrium python package. "
"Please install it with `pip install cerebrium`."
)
params = self.model_kwargs or {}
response = mod... | https://python.langchain.com/en/latest/_modules/langchain/llms/cerebriumai.html |
22c3f03df076-0 | Source code for langchain.llms.huggingface_pipeline
"""Wrapper around HuggingFace Pipeline APIs."""
import importlib.util
import logging
from typing import Any, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from la... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
22c3f03df076-1 | """Model name to use."""
model_kwargs: Optional[dict] = None
"""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,
... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
22c3f03df076-2 | if importlib.util.find_spec("torch") is not None:
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... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
22c3f03df076-3 | self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
response = self.pipeline(prompt)
if self.pipeline.task == "text-generation":
# Text generation return includes the starter text.
text... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
4fdfcb070dbd-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://python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
4fdfcb070dbd-1 | return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_id": self.model_id},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type ... | https://python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
4fdfcb070dbd-2 | text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
85171bbf8502-0 | 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 |
85171bbf8502-1 | "finish_reason"
]
response["choices"][0]["logprobs"] = stream_response["choices"][0]["logprobs"]
def _streaming_response_template() -> Dict[str, Any]:
return {
"choices": [
{
"text": "",
"finish_reason": None,
"logprobs": None,
... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-2 | return llm.client.create(**kwargs)
return _completion_with_retry(**kwargs)
async def acompletion_with_retry(
llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any
) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async de... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-3 | model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[str] = None
openai_api_base: Optional[str] = None
openai_organization: Optional[str] = None
batch_size: int = 20
"""Batch size to... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-4 | )
return OpenAIChat(**data)
return super().__new__(cls)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.ignore
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Build extra kwargs from additional... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-5 | try:
import openai
openai.api_key = openai_api_key
if openai_api_base:
openai.api_base = openai_api_base
if openai_organization:
openai.organization = openai_organization
values["client"] = openai.Completion
except Impor... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-6 | run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> LLMResult:
"""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 L... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-7 | choices.extend(response["choices"])
if not self.streaming:
# Can't update token usage if streaming
update_token_usage(_keys, response, token_usage)
return self.create_llm_result(choices, prompts, token_usage)
async def _agenerate(
self,
prompts: Li... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-8 | choices.extend(response["choices"])
if not self.streaming:
# Can't update token usage if streaming
update_token_usage(_keys, response, token_usage)
return self.create_llm_result(choices, prompts, token_usage)
def get_sub_prompts(
self,
params: Dict... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-9 | logprobs=choice.get("logprobs"),
),
)
for choice in sub_choices
]
)
llm_output = {"token_usage": token_usage, "model_name": self.model_name}
return LLMResult(generations=generations, llm_output=llm_output)
de... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-10 | @property
def _invocation_params(self) -> Dict[str, Any]:
"""Get the parameters used to invoke the model."""
return self._default_params
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"model_name": self.model_nam... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-11 | Returns:
The maximum context size
Example:
.. code-block:: python
max_tokens = openai.modelname_to_contextsize("text-davinci-003")
"""
model_token_mapping = {
"gpt-4": 8192,
"gpt-4-0314": 8192,
"gpt-4-32k": 32768,
... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-12 | return context_size
def max_tokens_for_prompt(self, prompt: str) -> int:
"""Calculate the maximum number of tokens possible to generate for a prompt.
Args:
prompt: The prompt to pass into the model.
Returns:
The maximum number of tokens to generate for a prompt.
... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-13 | Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.llms import AzureOpenAI
openai = AzureOpenAI(model_name="text-davinci-003")
"""
deployment_nam... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-14 | model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[str] = None
openai_api_base: Optional[str] = None
max_retries: int = 6
"""Maximum number of retries to make when generating."""
p... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-15 | openai_api_key = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
openai_api_base = get_from_dict_or_env(
values,
"openai_api_base",
"OPENAI_API_BASE",
default="",
)
openai_organization = get_from_dict_or_env(
... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-16 | self, prompts: List[str], stop: Optional[List[str]] = None
) -> Tuple:
if len(prompts) > 1:
raise ValueError(
f"OpenAIChat currently only supports single prompt, got {prompts}"
)
messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}]
... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-17 | llm_output = {
"token_usage": full_response["usage"],
"model_name": self.model_name,
}
return LLMResult(
generations=[
[Generation(text=full_response["choices"][0]["message"]["content"])]
],
llm_o... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
85171bbf8502-18 | """Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "openai-chat"
[docs] def get_num_tokens(self, text: str) -> int:
"""Calculate num tokens with tiktoke... | https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
ac6354339f5f-0 | Source code for langchain.llms.ai21
"""Wrapper around AI21 APIs."""
from typing import Any, Dict, List, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import get_from... | https://python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
ac6354339f5f-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://python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
ac6354339f5f-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://python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
ac6354339f5f-3 | headers={"Authorization": f"Bearer {self.ai21_api_key}"},
json={"prompt": prompt, "stopSequences": stop, **self._default_params},
)
if response.status_code != 200:
optional_detail = response.json().get("error")
raise ValueError(
f"AI21 /complete call f... | https://python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
d6865ec99ad9-0 | 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://python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
d6865ec99ad9-1 | try:
import predictionguard as pg
values["client"] = pg.Client(token=token)
except ImportError:
raise ValueError(
"Could not import predictionguard python package. "
"Please install it with `pip install predictionguard`."
)
... | https://python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
d6865ec99ad9-2 | response = self.client.predict(
name=self.name,
data={
"prompt": prompt,
"max_tokens": params["max_tokens"],
"temperature": params["temperature"],
},
)
text = response["text"]
# If stop tokens are provided, Predi... | https://python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
b31314cd89bf-0 | Source code for langchain.llms.self_hosted_hugging_face
"""Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware."""
import importlib.util
import logging
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerFo... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
b31314cd89bf-1 | model_id: str = DEFAULT_MODEL_ID,
task: str = DEFAULT_TASK,
device: int = 0,
model_kwargs: Optional[dict] = None,
) -> Any:
"""Inference function to send to the remote hardware.
Accepts a huggingface model_id and returns a pipeline for the task.
"""
from transformers import AutoModelForCausa... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
b31314cd89bf-2 | 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 with CUDA device id.",
cuda_device_coun... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
b31314cd89bf-3 | hardware=gpu
)
Example passing fn that generates a pipeline (bc the pipeline is not serializable):
.. code-block:: python
from langchain.llms import SelfHostedHuggingFaceLLM
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
b31314cd89bf-4 | """Inference function to send to the remote hardware."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def __init__(self, **kwargs: Any):
"""Construct the pipeline remotely using an auxiliary function.
The load function needs to be importable to... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
da0bed5ba727-0 | 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, Extra, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMR... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
da0bed5ba727-1 | anthropic_api_key = get_from_dict_or_env(
values, "anthropic_api_key", "ANTHROPIC_API_KEY"
)
try:
import anthropic
values["client"] = anthropic.Client(
api_key=anthropic_api_key,
default_request_timeout=values["default_request_timeout"]... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
da0bed5ba727-2 | if stop is None:
stop = []
# Never want model to invent new turns of Human / Assistant dialog.
stop.extend([self.HUMAN_PROMPT])
return stop
def get_num_tokens(self, text: str) -> int:
"""Calculate number of tokens."""
if not self.count_tokens:
raise Na... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
da0bed5ba727-3 | warnings.warn(
"This Anthropic LLM is deprecated. "
"Please use `from langchain.chat_models import ChatAnthropic` instead"
)
return values
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@property
def _llm_type(self) ->... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
da0bed5ba727-4 | .. code-block:: python
prompt = "What are the biggest risks facing humanity?"
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
response = model(prompt)
"""
stop = self._get_anthropic_stop(stop)
if self.streaming:
stream_resp = self.client.... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
da0bed5ba727-5 | 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,
**self._default_params,
)
return response["completion"]
[docs] def strea... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
dfdf8572e1d8-0 | Source code for langchain.llms.huggingface_endpoint
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
dfdf8572e1d8-1 | """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://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
dfdf8572e1d8-2 | ) -> 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:
The string generated by the model.
Example:
.. code-block:: p... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
dfdf8572e1d8-3 | # This is a bit hacky, but I can't figure out a better way to enforce
# 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 May 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
a683e5e0bd86-0 | Source code for langchain.llms.pipelineai
"""Wrapper around Pipeline Cloud API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from l... | https://python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
a683e5e0bd86-1 | extra = values.get("pipeline_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
logger.warning(
f"""{field_... | https://python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
a683e5e0bd86-2 | "Please install it with `pip install pipeline-ai`."
)
client = PipelineCloud(token=self.pipeline_api_key)
params = self.pipeline_kwargs or {}
run = client.run_pipeline(self.pipeline_key, [prompt, params])
try:
text = run.result_preview[0][0]
except Attribu... | https://python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
878d4408d8b5-0 | 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.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from... | https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
878d4408d8b5-1 | """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://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
878d4408d8b5-2 | @property
def _default_params(self) -> Dict[str, Any]:
"""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,... | https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
878d4408d8b5-3 | @property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
"model": self.model,
**self._default_params,
**{
k: v
for k, v in self.__dict__.items()
if k in GPT4All._ll... | https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html |
67e66e4831b0-0 | 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.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base... | https://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
67e66e4831b0-1 | 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 |
67e66e4831b0-2 | """
params = self.model_kwargs or {}
response_post = requests.post(
url=self.api_url,
json={"prompt": prompt, "params": params},
headers={
"apiKey": f"{self.stochasticai_api_key}",
"Accept": "application/json",
"Content-... | https://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
63dab3a22b26-0 | Source code for langchain.llms.self_hosted
"""Run model inference on self-hosted remote hardware."""
import importlib.util
import logging
import pickle
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llm... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
63dab3a22b26-1 | )
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 ass... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
63dab3a22b26-2 | llm = SelfHostedPipeline(
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.ll... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
63dab3a22b26-3 | load_fn_kwargs: Optional[dict] = None
"""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
... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
63dab3a22b26-4 | if not isinstance(pipeline, str):
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 th... | https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
39c485bb8585-0 | 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://python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
39c485bb8585-1 | 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://python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
39c485bb8585-2 | params = self.model_kwargs or {}
api_key = self.banana_api_key
model_key = self.model_key
model_inputs = {
# a json specific to your model.
"prompt": prompt,
**params,
}
response = banana.run(api_key, model_key, model_inputs)
try:
... | https://python.langchain.com/en/latest/_modules/langchain/llms/bananadev.html |
eafa063cf164-0 | Source code for langchain.retrievers.remote_retriever
from typing import List, Optional
import aiohttp
import requests
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class RemoteLangChainRetriever(BaseRetriever, BaseModel):
url: str
headers: Optional[dict] = None
i... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/remote_retriever.html |
5d6063155eff-0 | Source code for langchain.retrievers.metal
from typing import Any, List, Optional
from langchain.schema import BaseRetriever, Document
[docs]class MetalRetriever(BaseRetriever):
def __init__(self, client: Any, params: Optional[dict] = None):
from metal_sdk.metal import Metal
if not isinstance(client... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/metal.html |
5c87aac05891-0 | Source code for langchain.retrievers.svm
"""SMV Retriever.
Largely based on
https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb"""
from __future__ import annotations
import concurrent.futures
from typing import Any, List, Optional
import numpy as np
from pydantic import BaseModel
from langchain.embedding... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/svm.html |
5c87aac05891-1 | y[0] = 1
clf = svm.LinearSVC(
class_weight="balanced", verbose=False, max_iter=10000, tol=1e-6, C=0.1
)
clf.fit(x, y)
similarities = clf.decision_function(x)
sorted_ix = np.argsort(-similarities)
# svm.LinearSVC in scikit-learn is non-deterministic.
# ... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/svm.html |
2d1a87a5f892-0 | Source code for langchain.retrievers.pinecone_hybrid_search
"""Taken from: https://docs.pinecone.io/docs/hybrid-search"""
import hashlib
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRe... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
2d1a87a5f892-1 | vectors = []
# loop through the data and create dictionaries for upserts
for doc_id, sparse, dense, metadata in zip(
batch_ids, sparse_embeds, dense_embeds, meta
):
vectors.append(
{
"id": doc_id,
"sparse_values": sp... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
2d1a87a5f892-2 | [docs] def get_relevant_documents(self, query: str) -> List[Document]:
from pinecone_text.hybrid import hybrid_convex_scale
sparse_vec = self.sparse_encoder.encode_queries(query)
# convert the question into a dense vector
dense_vec = self.embeddings.embed_query(query)
# scale ... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/pinecone_hybrid_search.html |
1a235c4f2743-0 | Source code for langchain.retrievers.tfidf
"""TF-IDF Retriever.
Largely based on
https://github.com/asvskartheek/Text-Retrieval/blob/master/TF-IDF%20Search%20Engine%20(SKLEARN).ipynb"""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/tfidf.html |
1a235c4f2743-1 | results = cosine_similarity(self.tfidf_array, query_vec).reshape(
(-1,)
) # Op -- (n_docs,1) -- Cosine Sim with each doc
return_docs = []
for i in results.argsort()[-self.k :][::-1]:
return_docs.append(self.docs[i])
return return_docs
[docs] async def aget_rel... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/tfidf.html |
bce16898a563-0 | Source code for langchain.retrievers.time_weighted_retriever
"""Retriever that combines embedding similarity with recency in retrieving values."""
from copy import deepcopy
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
from pydantic import BaseModel, Field
from langchain.schema impor... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
bce16898a563-1 | """
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
def _get_combined_score(
self,
document: Document,
vector_relevance: Optional[float],
current_time: datetime,
) -> float:
"""Return the combined score for a ... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
bce16898a563-2 | for doc in self.memory_stream[-self.k :]
}
# If a doc is considered salient, update the salience score
docs_and_scores.update(self.get_salient_docs(query))
rescored_docs = [
(doc, self._get_combined_score(doc, relevance, current_time))
for doc, relevance in docs_a... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
bce16898a563-3 | self.memory_stream.extend(dup_docs)
return self.vectorstore.add_documents(dup_docs, **kwargs)
[docs] async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Add documents to vectorstore."""
current_time = kwargs.get("current_time", datetime.now(... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/time_weighted_retriever.html |
69ad630066de-0 | Source code for langchain.retrievers.elastic_search_bm25
"""Wrapper around Elasticsearch vector database."""
from __future__ import annotations
import uuid
from typing import Any, Iterable, List
from langchain.docstore.document import Document
from langchain.schema import BaseRetriever
[docs]class ElasticSearchBM25Retr... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
69ad630066de-1 | self.index_name = index_name
[docs] @classmethod
def create(
cls, elasticsearch_url: str, index_name: str, k1: float = 2.0, b: float = 0.75
) -> ElasticSearchBM25Retriever:
from elasticsearch import Elasticsearch
# Create an Elasticsearch client instance
es = Elasticsearch(ela... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
69ad630066de-2 | raise ValueError(
"Could not import elasticsearch python package. "
"Please install it with `pip install elasticsearch`."
)
requests = []
ids = []
for i, text in enumerate(texts):
_id = str(uuid.uuid4())
request = {
... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/elastic_search_bm25.html |
f008216c936b-0 | Source code for langchain.retrievers.chatgpt_plugin_retriever
from __future__ import annotations
from typing import List, Optional
import aiohttp
import requests
from pydantic import BaseModel
from langchain.schema import BaseRetriever, Document
[docs]class ChatGPTPluginRetriever(BaseRetriever, BaseModel):
url: str... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
f008216c936b-1 | docs = []
for d in results:
content = d.pop("text")
docs.append(Document(page_content=content, metadata=d))
return docs
def _create_request(self, query: str) -> tuple[str, dict, dict]:
url = f"{self.url}/query"
json = {
"queries": [
... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/chatgpt_plugin_retriever.html |
d11737faeb71-0 | Source code for langchain.retrievers.vespa_retriever
"""Wrapper for retrieving documents from Vespa."""
from __future__ import annotations
import json
from typing import TYPE_CHECKING, List
from langchain.schema import BaseRetriever, Document
if TYPE_CHECKING:
from vespa.application import Vespa
[docs]class VespaRe... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/vespa_retriever.html |
9de60c5bd9dd-0 | Source code for langchain.retrievers.contextual_compression
"""Retriever that wraps a base retriever and filters the results."""
from typing import List
from pydantic import BaseModel, Extra
from langchain.retrievers.document_compressors.base import (
BaseDocumentCompressor,
)
from langchain.schema import BaseRetri... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/contextual_compression.html |
9de60c5bd9dd-1 | return list(compressed_docs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/retrievers/contextual_compression.html |
6145d8c1895c-0 | Source code for langchain.retrievers.weaviate_hybrid_search
"""Wrapper around weaviate vector database."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from uuid import uuid4
from pydantic import Extra
from langchain.docstore.document import Document
from langchain.schema import BaseR... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/weaviate_hybrid_search.html |
6145d8c1895c-1 | """Upload documents to Weaviate."""
from weaviate.util import get_valid_uuid
with self._client.batch as batch:
ids = []
for i, doc in enumerate(docs):
metadata = doc.metadata or {}
data_properties = {self._text_key: doc.page_content, **metadata}
... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/weaviate_hybrid_search.html |
7d8d124217d5-0 | Source code for langchain.retrievers.databerry
from typing import List, Optional
import aiohttp
import requests
from langchain.schema import BaseRetriever, Document
[docs]class DataberryRetriever(BaseRetriever):
datastore_url: str
top_k: Optional[int]
api_key: Optional[str]
def __init__(
self,
... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/databerry.html |
7d8d124217d5-1 | self.datastore_url,
json={
"query": query,
**({"topK": self.top_k} if self.top_k is not None else {}),
},
headers={
"Content-Type": "application/json",
**(
{"Authorizat... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/databerry.html |
74b2e2cf3d69-0 | Source code for langchain.retrievers.document_compressors.chain_filter
"""Filter that uses an LLM to drop documents that aren't relevant to the query."""
from typing import Any, Callable, Dict, Optional, Sequence
from langchain import BasePromptTemplate, LLMChain, PromptTemplate
from langchain.base_language import Base... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_filter.html |
74b2e2cf3d69-1 | include_doc = self.llm_chain.predict_and_parse(**_input)
if include_doc:
filtered_docs.append(doc)
return filtered_docs
[docs] async def acompress_documents(
self, documents: Sequence[Document], query: str
) -> Sequence[Document]:
"""Filter down documents."""
... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/chain_filter.html |
df52c41c6f0c-0 | Source code for langchain.retrievers.document_compressors.embeddings_filter
"""Document compressor that uses embeddings to drop documents unrelated to the query."""
from typing import Callable, Dict, Optional, Sequence
import numpy as np
from pydantic import root_validator
from langchain.document_transformers import (
... | https://python.langchain.com/en/latest/_modules/langchain/retrievers/document_compressors/embeddings_filter.html |
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