id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
3e17c05b1921-21 | try:
from nltk.tokenize import sent_tokenize
self._tokenizer = sent_tokenize
except ImportError:
raise ImportError(
"NLTK is not installed, please install it with `pip install nltk`."
)
self._separator = separator
[docs] def split_text(s... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
3e17c05b1921-22 | [docs] def __init__(self, **kwargs: Any) -> None:
"""Initialize a PythonCodeTextSplitter."""
separators = self.get_separators_for_language(Language.PYTHON)
super().__init__(separators=separators, **kwargs)
[docs]class MarkdownTextSplitter(RecursiveCharacterTextSplitter):
"""Attempts to sp... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
8bda427c070e-0 | Source code for langchain.cache
"""
.. warning::
Beta Feature!
**Cache** provides an optional caching layer for LLMs.
Cache is useful for two reasons:
- It can save you money by reducing the number of API calls you make to the LLM
provider if you're often requesting the same completion multiple times.
- It can spee... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-1 | """Use a deterministic hashing approach."""
return hashlib.md5(_input.encode()).hexdigest()
def _dump_generations_to_json(generations: RETURN_VAL_TYPE) -> str:
"""Dump generations to json.
Args:
generations (RETURN_VAL_TYPE): A list of language model generations.
Returns:
str: Json repre... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-2 | [docs]class InMemoryCache(BaseCache):
"""Cache that stores things in memory."""
[docs] def __init__(self) -> None:
"""Initialize with empty cache."""
self._cache: Dict[Tuple[str, str], RETURN_VAL_TYPE] = {}
[docs] def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-3 | self.cache_schema = cache_schema
self.cache_schema.metadata.create_all(self.engine)
[docs] def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up based on prompt and llm_string."""
stmt = (
select(self.cache_schema.response)
.where(self... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-4 | [docs] def clear(self, **kwargs: Any) -> None:
"""Clear cache."""
with Session(self.engine) as session:
session.query(self.cache_schema).delete()
session.commit()
[docs]class SQLiteCache(SQLAlchemyCache):
"""Cache that uses SQLite as a backend."""
[docs] def __init__(se... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-5 | generations.append(Generation(text=text))
return generations if generations else None
[docs] def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
"""Update cache based on prompt and llm_string."""
for gen in return_val:
if not isinstance(gen, Genera... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-6 | embedding (Embedding): Embedding provider for semantic encoding and search.
score_threshold (float, 0.2):
Example:
.. code-block:: python
import langchain
from langchain.cache import RedisSemanticCache
from langchain.embeddings import OpenAIEmbeddings
... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-7 | return self._cache_dict[index_name]
[docs] def clear(self, **kwargs: Any) -> None:
"""Clear semantic cache for a given llm_string."""
index_name = self._index_name(kwargs["llm_string"])
if index_name in self._cache_dict:
self._cache_dict[index_name].drop_index(
ind... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-8 | " support caching ChatModel outputs."
)
return
llm_cache = self._get_llm_cache(llm_string)
# Write to vectorstore
metadata = {
"llm_string": llm_string,
"prompt": prompt,
"return_val": [generation.text for generation in return_v... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-9 | except ImportError:
raise ImportError(
"Could not import gptcache python package. "
"Please install it with `pip install gptcache`."
)
self.init_gptcache_func: Union[
Callable[[Any, str], None], Callable[[Any], None], None
] = init_func... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-10 | _gptcache = self._new_gptcache(llm_string)
return _gptcache
[docs] def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
"""Look up the cache data.
First, retrieve the corresponding cache object using the `llm_string` parameter,
and then retrieve the data from t... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-11 | """Clear cache."""
from gptcache import Cache
for gptcache_instance in self.gptcache_dict.values():
gptcache_instance = cast(Cache, gptcache_instance)
gptcache_instance.flush()
self.gptcache_dict.clear()
def _ensure_cache_exists(cache_client: momento.CacheClient, cache_na... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-12 | Args:
cache_client (CacheClient): The Momento cache client.
cache_name (str): The name of the cache to use to store the data.
ttl (Optional[timedelta], optional): The time to live for the cache items.
Defaults to None, ie use the client default TTL.
ensure... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-13 | "Please install it with `pip install momento`."
)
if configuration is None:
configuration = Configurations.Laptop.v1()
auth_token = auth_token or get_from_env("auth_token", "MOMENTO_AUTH_TOKEN")
credentials = CredentialProvider.from_string(auth_token)
cache_client... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-14 | elif isinstance(get_response, CacheGet.Miss):
pass
elif isinstance(get_response, CacheGet.Error):
raise get_response.inner_exception
return generations if generations else None
[docs] def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
8bda427c070e-15 | pass
elif isinstance(flush_response, CacheFlush.Error):
raise flush_response.inner_exception | https://api.python.langchain.com/en/latest/_modules/langchain/cache.html |
fc35b1b80207-0 | Source code for langchain.model_laboratory
"""Experiment with different models."""
from __future__ import annotations
from typing import List, Optional, Sequence
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM
from langchain.prompts.prompt import... | https://api.python.langchain.com/en/latest/_modules/langchain/model_laboratory.html |
fc35b1b80207-1 | self.chain_colors = get_color_mapping(chain_range)
self.names = names
[docs] @classmethod
def from_llms(
cls, llms: List[BaseLLM], prompt: Optional[PromptTemplate] = None
) -> ModelLaboratory:
"""Initialize with LLMs to experiment with and optional prompt.
Args:
ll... | https://api.python.langchain.com/en/latest/_modules/langchain/model_laboratory.html |
1d31bc0683c0-0 | 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):
"""OpenLM models."""
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"model": self.model_name}, **s... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openlm.html |
8ba3f0233054-0 | Source code for langchain.llms.edenai
"""Wrapper around EdenAI's Generation API."""
import logging
from typing import Any, Dict, List, Literal, Optional
from aiohttp import ClientSession
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
Ca... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/edenai.html |
8ba3f0233054-1 | Parameters to pass to above subfeature (excluding 'providers' & 'text')
ref text: https://docs.edenai.co/reference/text_generation_create
ref image: https://docs.edenai.co/reference/text_generation_create
"""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""extra parameters"""
stop_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/edenai.html |
8ba3f0233054-2 | """Return type of model."""
return "edenai"
def _format_output(self, output: dict) -> str:
if self.feature == "text":
return output[self.provider]["generated_text"]
else:
return output[self.provider]["items"][0]["image"]
def _call(
self,
prompt: st... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/edenai.html |
8ba3f0233054-3 | elif response.status_code >= 400:
raise ValueError(f"EdenAI received an invalid payload: {response.text}")
elif response.status_code != 200:
raise Exception(
f"EdenAI returned an unexpected response with status "
f"{response.status_code}: {response.text}"
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/edenai.html |
8ba3f0233054-4 | "text": prompt,
**kwargs,
}
async with ClientSession() as session:
print("Requesting")
async with session.post(url, json=payload, headers=headers) as response:
if response.status >= 500:
raise Exception(f"EdenAI Server: Error {respo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/edenai.html |
bc6a4e68db83-0 | Source code for langchain.llms.huggingface_hub
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 enforce_stop_tokens
from langchain.utils i... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
bc6a4e68db83-1 | 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://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
bc6a4e68db83-2 | 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 |
d5ce6d0f793a-0 | Source code for langchain.llms.replicate
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.utils import get_from_dict_or_env
logger = l... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
d5ce6d0f793a-1 | extra = Extra.forbid
@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://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
d5ce6d0f793a-2 | **kwargs: Any,
) -> str:
"""Call to replicate endpoint."""
try:
import replicate as replicate_python
except ImportError:
raise ImportError(
"Could not import replicate python package. "
"Please install it with `pip install replicate`."
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
0771e70d15b1-0 | Source code for langchain.llms.predictionguard
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 import enforce_stop_tokens
from langchain.u... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
0771e70d15b1-1 | 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 exists in environment."""
token = get_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
0771e70d15b1-2 | 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 |
ae4a218ccf7c-0 | Source code for langchain.llms.stochasticai
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 import LLM
from langchain.llms.utils im... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
ae4a218ccf7c-1 | raise ValueError(f"Found {field_name} supplied twice.")
logger.warning(
f"""{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
ae4a218ccf7c-2 | """
params = self.model_kwargs or {}
params = {**params, **kwargs}
response_post = requests.post(
url=self.api_url,
json={"prompt": prompt, "params": params},
headers={
"apiKey": f"{self.stochasticai_api_key}",
"Accept": "applic... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
40ec21de69c3-0 | Source code for langchain.llms.chatglm
import logging
from typing import Any, List, Mapping, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
logger = logging.getLogger(__name__)
[docs]class... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/chatglm.html |
40ec21de69c3-1 | return {
**{"endpoint_url": self.endpoint_url},
**{"model_kwargs": _model_kwargs},
}
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
""... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/chatglm.html |
40ec21de69c3-2 | # Check if response content does exists
if isinstance(parsed_response, dict):
content_keys = "response"
if content_keys in parsed_response:
text = parsed_response[content_keys]
else:
raise ValueError(f"No content in resp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/chatglm.html |
bf0f3ad0423d-0 | Source code for langchain.llms.forefrontai
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 import enforce_stop_tokens
from langc... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
bf0f3ad0423d-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://api.python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
bf0f3ad0423d-2 | response = requests.post(
url=self.endpoint_url,
headers={
"Authorization": f"Bearer {self.forefrontai_api_key}",
"Content-Type": "application/json",
},
json={"text": prompt, **self._default_params, **kwargs},
)
response_jso... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
9d4454505c76-0 | Source code for langchain.llms.base
"""Base interface for large language models to expose."""
from __future__ import annotations
import asyncio
import functools
import inspect
import json
import logging
import warnings
from abc import ABC, abstractmethod
from functools import partial
from pathlib import Path
from typin... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-1 | def _log_error_once(msg: str) -> None:
"""Log an error once."""
logger.error(msg)
[docs]def create_base_retry_decorator(
error_types: List[Type[BaseException]],
max_retries: int = 1,
run_manager: Optional[
Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
] = None,
) -> Call... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-2 | reraise=True,
stop=stop_after_attempt(max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=retry_instance,
before_sleep=_before_sleep,
)
[docs]def get_prompts(
params: Dict[str, Any], prompts: List[str]
) -> Tuple[Dict[int, List], str, List[i... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-3 | llm_output = new_results.llm_output
return llm_output
[docs]class BaseLLM(BaseLanguageModel[str], ABC):
"""Base LLM abstract interface.
It should take in a prompt and return a string."""
cache: Optional[bool] = None
verbose: bool = Field(default_factory=_get_verbosity)
"""Whether to print out re... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-4 | else:
return verbose
# --- Runnable methods ---
def _convert_input(self, input: LanguageModelInput) -> PromptValue:
if isinstance(input, PromptValue):
return input
elif isinstance(input, str):
return StringPromptValue(text=input)
elif isinstance(input,... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-5 | )
return llm_result.generations[0][0].text
[docs] def batch(
self,
inputs: List[LanguageModelInput],
config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
max_concurrency: Optional[int] = None,
**kwargs: Any,
) -> List[str]:
config = self._g... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-6 | None, self.batch, inputs, config, max_concurrency
)
config = self._get_config_list(config, len(inputs))
if max_concurrency is None:
llm_result = await self.agenerate_prompt(
[self._convert_input(input) for input in inputs],
callbacks=[c.get("callba... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-7 | config.get("callbacks"),
self.callbacks,
self.verbose,
config.get("tags"),
self.tags,
config.get("metadata"),
self.metadata,
)
(run_manager,) = callback_manager.on_llm_start(
dumpd(sel... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-8 | config.get("callbacks"),
self.callbacks,
self.verbose,
config.get("tags"),
self.tags,
config.get("metadata"),
self.metadata,
)
(run_manager,) = await callback_manager.on_llm_start(
dum... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-9 | prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
raise NotImplementedError()
def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-10 | **kwargs: Any,
) -> LLMResult:
try:
output = (
self._generate(
prompts,
stop=stop,
# TODO: support multiple run managers
run_manager=run_managers[0] if run_managers else None,
**kw... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-11 | )
# Create callback managers
if isinstance(callbacks, list) and (
isinstance(callbacks[0], (list, BaseCallbackManager))
or callbacks[0] is None
):
# We've received a list of callbacks args to apply to each input
assert len(callbacks) == len(prompts... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-12 | existing_prompts,
llm_string,
missing_prompt_idxs,
missing_prompts,
) = get_prompts(params, prompts)
disregard_cache = self.cache is not None and not self.cache
new_arg_supported = inspect.signature(self._generate).parameters.get(
"run_manager"
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-13 | else None
)
else:
llm_output = {}
run_info = None
generations = [existing_prompts[i] for i in range(len(prompts))]
return LLMResult(generations=generations, llm_output=llm_output, run=run_info)
async def _agenerate_helper(
self,
prompts: Li... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-14 | prompts: List[str],
stop: Optional[List[str]] = None,
callbacks: Optional[Union[Callbacks, List[Callbacks]]] = None,
*,
tags: Optional[Union[List[str], List[List[str]]]] = None,
metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
**kwargs: Any,
) -... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-15 | callback_managers = [
AsyncCallbackManager.configure(
cast(Callbacks, callbacks),
self.callbacks,
self.verbose,
cast(List[str], tags),
self.tags,
cast(Dict[str, Any], metadata),
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-16 | dumpd(self),
[prompts[idx]],
invocation_params=params,
options=options,
)
for idx in missing_prompt_idxs
]
)
run_managers = [r[0] for r in run_managers]
new... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-17 | )
return (
self.generate(
[prompt],
stop=stop,
callbacks=callbacks,
tags=tags,
metadata=metadata,
**kwargs,
)
.generations[0][0]
.text
)
async def _call_asy... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-18 | content = self(text, stop=_stop, **kwargs)
return AIMessage(content=content)
[docs] async def apredict(
self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any
) -> str:
if stop is None:
_stop = None
else:
_stop = list(stop)
return a... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-19 | starter_dict["_type"] = self._llm_type
return starter_dict
[docs] def save(self, file_path: Union[Path, str]) -> None:
"""Save the LLM.
Args:
file_path: Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path="path/llm.yaml")... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-20 | ) -> str:
"""Run the LLM on the given prompt and input."""
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Run the LLM on the given prompt and i... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
9d4454505c76-21 | return await asyncio.get_running_loop().run_in_executor(
None, partial(self._generate, prompts, stop, run_manager, **kwargs)
)
"""Run the LLM on the given prompt and input."""
generations = []
new_arg_supported = inspect.signature(self._acall).parameters.get("run_mana... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/base.html |
eb09e2d28d52-0 | Source code for langchain.llms.xinference
from typing import TYPE_CHECKING, Any, Generator, List, Mapping, Optional, Union
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
if TYPE_CHECKING:
from xinference.client import RESTfulChatModelHandle, RESTfulGenerateModel... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/xinference.html |
eb09e2d28d52-1 | server_url="http://0.0.0.0:9997",
model_uid = {model_uid} # replace model_uid with the model UID return from launching the model
)
llm(
prompt="Q: where can we visit in the capital of France? A:",
generate_config={"max_tokens": 1024, "stream": True... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/xinference.html |
eb09e2d28d52-2 | """Get the identifying parameters."""
return {
**{"server_url": self.server_url},
**{"model_uid": self.model_uid},
}
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/xinference.html |
eb09e2d28d52-3 | ) -> Generator[str, None, None]:
"""
Args:
prompt: The prompt to use for generation.
model: The model used for generation.
stop: Optional list of stop words to use when generating.
generate_config: Optional dictionary for the configuration used for
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/xinference.html |
d9788b77dadb-0 | Source code for langchain.llms.mlflow_ai_gateway
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from pydantic import BaseModel, Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
[docs]class Params(BaseModel, extra=Extra.a... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mlflow_ai_gateway.html |
d9788b77dadb-1 | ) from e
super().__init__(**kwargs)
if self.gateway_uri:
mlflow.gateway.set_gateway_uri(self.gateway_uri)
@property
def _default_params(self) -> Dict[str, Any]:
params: Dict[str, Any] = {
"gateway_uri": self.gateway_uri,
"route": self.route,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mlflow_ai_gateway.html |
f2144de73fa0-0 | Source code for langchain.llms.huggingface_text_gen_inference
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base impo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
f2144de73fa0-1 | - _llm_type: Returns the type of LLM.
- _default_params: Returns the default parameters for calling text generation
inference API.
"""
"""
Example:
.. code-block:: python
# Basic Example (no streaming)
llm = HuggingFaceTextGenInference(
inference_serv... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
f2144de73fa0-2 | stop_sequences: List[str] = Field(default_factory=list)
seed: Optional[int] = None
inference_server_url: str = ""
timeout: int = 120
server_kwargs: Dict[str, Any] = Field(default_factory=dict)
streaming: bool = False
client: Any
async_client: Any
class Config:
"""Configuration fo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
f2144de73fa0-3 | "temperature": self.temperature,
"repetition_penalty": self.repetition_penalty,
"truncate": self.truncate,
"stop_sequences": self.stop_sequences,
"seed": self.seed,
}
def _invocation_params(
self, runtime_stop: Optional[List[str]], **kwargs: Any
) ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
f2144de73fa0-4 | async for chunk in self._astream(prompt, stop, run_manager, **kwargs):
completion += chunk.text
return completion
invocation_params = self._invocation_params(stop, **kwargs)
res = await self.async_client.generate(prompt, **invocation_params)
# remove stop sequences fr... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
f2144de73fa0-5 | # break if stop sequence found
if stop_seq_found:
break
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
58c5601664c8-0 | Source code for langchain.llms.fake
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
[docs]class FakeListLLM(LLM):
"""Fake LLM for testing purposes."""
responses: List
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/fake.html |
b575da701af0-0 | Source code for langchain.llms.pipelineai
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 langchain.llms.utils import enforce_stop_t... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
b575da701af0-1 | 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 transferred to pipeline_kwargs.
Please confirm that {field_name}... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
b575da701af0-2 | )
client = PipelineCloud(token=self.pipeline_api_key)
params = self.pipeline_kwargs or {}
params = {**params, **kwargs}
run = client.run_pipeline(self.pipeline_key, [prompt, params])
try:
text = run.result_preview[0][0]
except AttributeError:
raise... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
7346f532c40e-0 | Source code for langchain.llms.huggingface_endpoint
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 import enforce_stop_tokens
f... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
7346f532c40e-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://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
7346f532c40e-2 | 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_endpoint.html |
7346f532c40e-3 | elif self.task == "summarization":
text = generated_text[0]["summary_text"]
else:
raise ValueError(
f"Got invalid task {self.task}, "
f"currently only {VALID_TASKS} are supported"
)
if stop is not None:
# This is a bit hacky... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
b85d3f6d0b3f-0 | Source code for langchain.llms.rwkv
"""RWKV models.
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, root_validator
fro... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
b85d3f6d0b3f-1 | in the text so far, decreasing the model's likelihood to repeat the same
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 =... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
b85d3f6d0b3f-2 | try:
import tokenizers
except ImportError:
raise ImportError(
"Could not import tokenizers python package. "
"Please install it with `pip install tokenizers`."
)
try:
from rwkv.model import RWKV as RWKVMODEL
from... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
b85d3f6d0b3f-3 | AVOID_REPEAT = "，：？！"
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.... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
b85d3f6d0b3f-4 | occurrence[token] += 1
logits = self.run_rnn([token])
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_ge... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
2d1f1c058695-0 | Source code for langchain.llms.google_palm
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_type,
stop_after_attempt,
wait_exponen... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
2d1f1c058695-1 | """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_retry(**kwargs)
def _strip_erroneous_leading_spaces(text: str) -> str:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
2d1f1c058695-2 | """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 completions if duplicates are generated."""
@root_validator()
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
2d1f1c058695-3 | 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_retry(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
b75b1d3658a9-0 | Source code for langchain.llms.vllm
from typing import Any, Dict, List, Optional
from pydantic import root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import BaseLLM
from langchain.schema.output import Generation, LLMResult
[docs]class VLLM(BaseLLM):
model: st... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vllm.html |
b75b1d3658a9-1 | """List of strings that stop the generation when they are generated."""
ignore_eos: bool = False
"""Whether to ignore the EOS token and continue generating tokens after
the EOS token is generated."""
max_new_tokens: int = 512
"""Maximum number of tokens to generate per output sequence."""
clien... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vllm.html |
b75b1d3658a9-2 | prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
from vllm import SamplingParams
# build sampling parameters
params = {... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vllm.html |
ddb3a5df9545-0 | Source code for langchain.llms.amazon_api_gateway
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
[docs]class Content... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/amazon_api_gateway.html |
ddb3a5df9545-1 | """Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"api_url": self.api_url, "headers": self.headers},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/amazon_api_gateway.html |
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