id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 115 |
|---|---|---|
4775205f3000-0 | Source code for langchain.tools.file_management.read
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.tools.file_management.utils... | https://python.langchain.com/en/latest/_modules/langchain/tools/file_management/read.html |
4775205f3000-1 | # TODO: Add aiofiles method
raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/tools/file_management/read.html |
3f1eabe5597b-0 | Source code for langchain.tools.file_management.move
import shutil
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.tools.file_ma... | https://python.langchain.com/en/latest/_modules/langchain/tools/file_management/move.html |
3f1eabe5597b-1 | shutil.move(str(source_path_), destination_path_)
return f"File moved successfully from {source_path} to {destination_path}."
except Exception as e:
return "Error: " + str(e)
async def _arun(
self,
source_path: str,
destination_path: str,
run_manager: ... | https://python.langchain.com/en/latest/_modules/langchain/tools/file_management/move.html |
ae502261f43d-0 | Source code for langchain.tools.file_management.delete
import os
from typing import Optional, Type
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.tools.file_mana... | https://python.langchain.com/en/latest/_modules/langchain/tools/file_management/delete.html |
ae502261f43d-1 | raise NotImplementedError
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/tools/file_management/delete.html |
42b9dfe77e20-0 | Source code for langchain.tools.openapi.utils.openapi_utils
"""Utility functions for parsing an OpenAPI spec."""
import copy
import json
import logging
import re
from enum import Enum
from pathlib import Path
from typing import Dict, List, Optional, Union
import requests
import yaml
from openapi_schema_pydantic import ... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
42b9dfe77e20-1 | return path_item
@property
def _components_strict(self) -> Components:
"""Get components or err."""
if self.components is None:
raise ValueError("No components found in spec. ")
return self.components
@property
def _parameters_strict(self) -> Dict[str, Union[Parameter... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
42b9dfe77e20-2 | parameter = self._get_referenced_parameter(ref)
while isinstance(parameter, Reference):
parameter = self._get_referenced_parameter(parameter)
return parameter
[docs] def get_referenced_schema(self, ref: Reference) -> Schema:
"""Get a schema (or nested reference) or err."""
... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
42b9dfe77e20-3 | """Alert if the spec is not supported."""
warning_message = (
" This may result in degraded performance."
+ " Convert your OpenAPI spec to 3.1.* spec"
+ " for better support."
)
swagger_version = obj.get("swagger")
openapi_version = obj.get("openapi")
... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
42b9dfe77e20-4 | def from_spec_dict(cls, spec_dict: dict) -> "OpenAPISpec":
"""Get an OpenAPI spec from a dict."""
return cls.parse_obj(spec_dict)
[docs] @classmethod
def from_text(cls, text: str) -> "OpenAPISpec":
"""Get an OpenAPI spec from a text."""
try:
spec_dict = json.loads(text... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
42b9dfe77e20-5 | if isinstance(operation, Operation):
results.append(method.value)
return results
[docs] def get_operation(self, path: str, method: str) -> Operation:
"""Get the operation object for a given path and HTTP method."""
path_item = self._get_path_strict(path)
operation_obj ... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
42b9dfe77e20-6 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/openapi_utils.html |
fd0815afdd80-0 | Source code for langchain.tools.openapi.utils.api_models
"""Pydantic models for parsing an OpenAPI spec."""
import logging
from enum import Enum
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type, Union
from openapi_schema_pydantic import MediaType, Parameter, Reference, RequestBody, Schema
from pydant... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
fd0815afdd80-1 | + f"Valid values are {[loc.value for loc in SUPPORTED_LOCATIONS]}"
)
SCHEMA_TYPE = Union[str, Type, tuple, None, Enum]
class APIPropertyBase(BaseModel):
"""Base model for an API property."""
# The name of the parameter is required and is case sensitive.
# If "in" is "path", the "name" field must correspond ... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
fd0815afdd80-2 | type_ = schema.type
if not isinstance(type_, list):
return type_
else:
return tuple(type_)
@staticmethod
def _get_schema_type_for_enum(parameter: Parameter, schema: Schema) -> Enum:
"""Get the schema type when the parameter is an enum."""
param_name = f"{p... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
fd0815afdd80-3 | schema_type = APIProperty._get_schema_type_for_enum(parameter, schema)
else:
# Directly use the primitive type
pass
else:
raise NotImplementedError(f"Unsupported type: {schema_type}")
return schema_type
@staticmethod
def _validate_location(... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
fd0815afdd80-4 | location,
parameter.name,
)
cls._validate_content(parameter.content)
schema = cls._get_schema(parameter, spec)
schema_type = cls._get_schema_type(parameter, schema)
default_val = schema.default if schema is not None else None
return cls(
name=param... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
fd0815afdd80-5 | cls.from_schema(
schema=prop_schema,
name=prop_name,
required=prop_name in required_props,
spec=spec,
references_used=references_used,
)
)
return schema.type, properties
@classmeth... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
fd0815afdd80-6 | schema_type, properties = cls._process_object_schema(
schema, spec, references_used
)
elif schema_type == "array":
schema_type = cls._process_array_schema(schema, name, spec, references_used)
elif schema_type in PRIMITIVE_TYPES:
# Use the primitive typ... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
fd0815afdd80-7 | f"Could not resolve schema for media type: {media_type_obj}"
)
api_request_body_properties = []
required_properties = schema.required or []
if schema.type == "object" and schema.properties:
for prop_name, prop_schema in schema.properties.items():
if isinst... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
fd0815afdd80-8 | operation_id: str = Field(alias="operation_id")
"""The unique identifier of the operation."""
description: Optional[str] = Field(alias="description")
"""The description of the operation."""
base_url: str = Field(alias="base_url")
"""The base URL of the operation."""
path: str = Field(alias="path... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
fd0815afdd80-9 | def from_openapi_url(
cls,
spec_url: str,
path: str,
method: str,
) -> "APIOperation":
"""Create an APIOperation from an OpenAPI URL."""
spec = OpenAPISpec.from_url(spec_url)
return cls.from_openapi_spec(spec, path, method)
[docs] @classmethod
def from_... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
fd0815afdd80-10 | # parsing specs that are < v3
return "any"
elif isinstance(type_, str):
return {
"str": "string",
"integer": "number",
"float": "number",
"date-time": "string",
}.get(type_, type_)
elif isinstance(type_, ... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
fd0815afdd80-11 | if self.request_body:
formatted_request_body_props = self._format_nested_properties(
self.request_body.properties
)
params.append(formatted_request_body_props)
for prop in self.properties:
prop_name = prop.name
prop_type = self.ts_type_... | https://python.langchain.com/en/latest/_modules/langchain/tools/openapi/utils/api_models.html |
2fb08eac6e9d-0 | Source code for langchain.tools.ddg_search.tool
"""Tool for the DuckDuckGo search API."""
import warnings
from typing import Any, Optional
from pydantic import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
f... | https://python.langchain.com/en/latest/_modules/langchain/tools/ddg_search/tool.html |
2fb08eac6e9d-1 | description = (
"A wrapper around Duck Duck Go Search. "
"Useful for when you need to answer questions about current events. "
"Input should be a search query. Output is a JSON array of the query results"
)
num_results: int = 4
api_wrapper: DuckDuckGoSearchAPIWrapper = Field(
... | https://python.langchain.com/en/latest/_modules/langchain/tools/ddg_search/tool.html |
2e067a7877c4-0 | Source code for langchain.tools.bing_search.tool
"""Tool for the Bing search API."""
from typing import Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.bing_search import BingSearch... | https://python.langchain.com/en/latest/_modules/langchain/tools/bing_search/tool.html |
2e067a7877c4-1 | api_wrapper: BingSearchAPIWrapper
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
return str(self.api_wrapper.results(query, self.num_results))
async def _arun(
self,
query: str,
... | https://python.langchain.com/en/latest/_modules/langchain/tools/bing_search/tool.html |
fdb230d32e4f-0 | Source code for langchain.tools.human.tool
"""Tool for asking human input."""
from typing import Callable, Optional
from pydantic import Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
def _print_func(text: st... | https://python.langchain.com/en/latest/_modules/langchain/tools/human/tool.html |
e854831b4a08-0 | Source code for langchain.tools.wolfram_alpha.tool
"""Tool for the Wolfram Alpha API."""
from typing import Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.wolfram_alpha import Wolf... | https://python.langchain.com/en/latest/_modules/langchain/tools/wolfram_alpha/tool.html |
ee385ca8cda8-0 | Source code for langchain.tools.google_places.tool
"""Tool for the Google search API."""
from typing import Optional
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langcha... | https://python.langchain.com/en/latest/_modules/langchain/tools/google_places/tool.html |
cdc2f1af4b9c-0 | Source code for langchain.tools.scenexplain.tool
"""Tool for the SceneXplain API."""
from typing import Optional
from pydantic import BaseModel, Field
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.u... | https://python.langchain.com/en/latest/_modules/langchain/tools/scenexplain/tool.html |
10b35fdbacf0-0 | Source code for langchain.tools.shell.tool
import asyncio
import platform
import warnings
from typing import List, Optional, Type
from pydantic import BaseModel, Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base... | https://python.langchain.com/en/latest/_modules/langchain/tools/shell/tool.html |
10b35fdbacf0-1 | name: str = "terminal"
"""Name of tool."""
description: str = f"Run shell commands on this {_get_platform()} machine."
"""Description of tool."""
args_schema: Type[BaseModel] = ShellInput
"""Schema for input arguments."""
def _run(
self,
commands: List[str],
run_manager: ... | https://python.langchain.com/en/latest/_modules/langchain/tools/shell/tool.html |
6919e36c5a23-0 | Source code for langchain.tools.vectorstore.tool
"""Tools for interacting with vectorstores."""
import json
from typing import Any, Dict, Optional
from pydantic import BaseModel, Field
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
... | https://python.langchain.com/en/latest/_modules/langchain/tools/vectorstore/tool.html |
6919e36c5a23-1 | def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
chain = RetrievalQA.from_chain_type(
self.llm, retriever=self.vectorstore.as_retriever()
)
return chain.run(query)
async def _aru... | https://python.langchain.com/en/latest/_modules/langchain/tools/vectorstore/tool.html |
6919e36c5a23-2 | self.llm, retriever=self.vectorstore.as_retriever()
)
return json.dumps(chain({chain.question_key: query}, return_only_outputs=True))
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchr... | https://python.langchain.com/en/latest/_modules/langchain/tools/vectorstore/tool.html |
2c845a928a2f-0 | Source code for langchain.tools.wikipedia.tool
"""Tool for the Wikipedia API."""
from typing import Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool
from langchain.utilities.wikipedia import WikipediaAPIWrap... | https://python.langchain.com/en/latest/_modules/langchain/tools/wikipedia/tool.html |
9a7a6e1f283f-0 | Source code for langchain.docstore.wikipedia
"""Wrapper around wikipedia API."""
from typing import Union
from langchain.docstore.base import Docstore
from langchain.docstore.document import Document
[docs]class Wikipedia(Docstore):
"""Wrapper around wikipedia API."""
def __init__(self) -> None:
"""Chec... | https://python.langchain.com/en/latest/_modules/langchain/docstore/wikipedia.html |
158129ad4e29-0 | Source code for langchain.docstore.in_memory
"""Simple in memory docstore in the form of a dict."""
from typing import Dict, Union
from langchain.docstore.base import AddableMixin, Docstore
from langchain.docstore.document import Document
[docs]class InMemoryDocstore(Docstore, AddableMixin):
"""Simple in memory doc... | https://python.langchain.com/en/latest/_modules/langchain/docstore/in_memory.html |
b9519d08b5ba-0 | Source code for langchain.llms.writer
"""Wrapper around Writer 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 import e... | https://python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
b9519d08b5ba-1 | by fixing the random seed (assuming all other hyperparameters
are also fixed)"""
beam_search_diversity_rate: float = 1.0
"""Only applies to beam search, i.e. when the beam width is >1.
A higher value encourages beam search to return a more diverse
set of candidates"""
beam_width: Optional[int] =... | https://python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
b9519d08b5ba-2 | "temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
"repetition_penalty": self.repetition_penalty,
"random_seed": self.random_seed,
"beam_search_diversity_rate": self.beam_search_diversity_rate,
"beam_width": self.beam_width,... | https://python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
b9519d08b5ba-3 | "Content-Type": "application/json",
"Accept": "application/json",
},
json={"prompt": prompt, **self._default_params},
)
text = response.text
if stop is not None:
# I believe this is required since the stop tokens
# are not enforced ... | https://python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
bd1b60aeb5fd-0 | Source code for langchain.llms.forefrontai
"""Wrapper around ForefrontAI APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.util... | https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
bd1b60aeb5fd-1 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
forefrontai_api_key = get_from_dict_or_env(
values, "forefrontai_api_key", "FOREFRONTAI_API_KEY"
)
values["forefrontai_api_key"] = forefrontai_api_key... | https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
bd1b60aeb5fd-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},
)
response_j... | https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
249bf75e0e8b-0 | Source code for langchain.llms.nlpcloud
"""Wrapper around NLPCloud APIs."""
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_e... | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
249bf75e0e8b-1 | """Total probability mass of tokens to consider at each step."""
top_k: int = 50
"""The number of highest probability tokens to keep for top-k filtering."""
repetition_penalty: float = 1.0
"""Penalizes repeated tokens. 1.0 means no penalty."""
length_penalty: float = 1.0
"""Exponential penalty t... | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
249bf75e0e8b-2 | @property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for calling NLPCloud API."""
return {
"temperature": self.temperature,
"min_length": self.min_length,
"max_length": self.max_length,
"length_no_input": self.length_... | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
249bf75e0e8b-3 | The string generated by the model.
Example:
.. code-block:: python
response = nlpcloud("Tell me a joke.")
"""
if stop and len(stop) > 1:
raise ValueError(
"NLPCloud only supports a single stop sequence per generation."
"Pass... | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
e9837216122f-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.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.... | https://python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
e9837216122f-1 | 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)
values["model_kwargs"] = extra
return values
@property
d... | https://python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
2df1e2de7d5b-0 | Source code for langchain.llms.petals
"""Wrapper around Petals API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils imp... | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
2df1e2de7d5b-1 | """Whether or not to use sampling; use greedy decoding otherwise."""
max_length: Optional[int] = None
"""The maximum length of the sequence to be generated."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call
not explicitly specified.""... | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
2df1e2de7d5b-2 | from petals import DistributedBloomForCausalLM
from transformers import BloomTokenizerFast
model_name = values["model_name"]
values["tokenizer"] = BloomTokenizerFast.from_pretrained(model_name)
values["client"] = DistributedBloomForCausalLM.from_pretrained(model_name)
... | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
2df1e2de7d5b-3 | """Call the Petals API."""
params = self._default_params
inputs = self.tokenizer(prompt, return_tensors="pt")["input_ids"]
outputs = self.client.generate(inputs, **params)
text = self.tokenizer.decode(outputs[0])
if stop is not None:
# I believe this is required since... | https://python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
c2324d4bb296-0 | 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.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_sto... | https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
c2324d4bb296-1 | """Penalizes repeated tokens. Between 0 and 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:
"""Confi... | https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
c2324d4bb296-2 | def _llm_type(self) -> str:
"""Return type of llm."""
return "cohere"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
"""Call out to Cohere's generate endpoint.
Args:... | https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
48112a5c2ab2-0 | Source code for langchain.llms.aleph_alpha
"""Wrapper around Aleph Alpha APIs."""
from typing import Any, Dict, List, Optional, Sequence
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforc... | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
48112a5c2ab2-1 | """Total probability mass of tokens to consider at each step."""
presence_penalty: float = 0.0
"""Penalizes repeated tokens."""
frequency_penalty: float = 0.0
"""Penalizes repeated tokens according to frequency."""
repetition_penalties_include_prompt: Optional[bool] = False
"""Flag deciding whet... | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
48112a5c2ab2-2 | """Echo the prompt in the completion."""
use_multiplicative_frequency_penalty: bool = False
sequence_penalty: float = 0.0
sequence_penalty_min_length: int = 2
use_multiplicative_sequence_penalty: bool = False
completion_bias_inclusion: Optional[Sequence[str]] = None
completion_bias_inclusion_fir... | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
48112a5c2ab2-3 | """Validate that api key and python package exists in environment."""
aleph_alpha_api_key = get_from_dict_or_env(
values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY"
)
try:
import aleph_alpha_client
values["client"] = aleph_alpha_client.Client(token=aleph_alp... | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
48112a5c2ab2-4 | "minimum_tokens": self.minimum_tokens,
"echo": self.echo,
"use_multiplicative_frequency_penalty": self.use_multiplicative_frequency_penalty, # noqa: E501
"sequence_penalty": self.sequence_penalty,
"sequence_penalty_min_length": self.sequence_penalty_min_length,
... | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
48112a5c2ab2-5 | 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 = alpeh_alpha("Tell me a joke.")
"""
... | https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
d0e77afed4ea-0 | Source code for langchain.llms.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms import OpenAI, OpenAIChat
from langchain.schema import LLMResult... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
d0e77afed4ea-1 | """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, run_manager)
request_end_time = ... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
d0e77afed4ea-2 | 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 = await promptlayer_api_request... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
d0e77afed4ea-3 | ``Generation`` object.
Example:
.. code-block:: python
from langchain.llms import PromptLayerOpenAIChat
openaichat = PromptLayerOpenAIChat(model_name="gpt-3.5-turbo")
"""
pl_tags: Optional[List[str]]
return_pl_id: Optional[bool] = False
def _generate(
self,
... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
d0e77afed4ea-4 | generation.generation_info, dict
):
generation.generation_info = {}
generation.generation_info["pl_request_id"] = pl_request_id
return generated_responses
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = N... | https://python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
1b5a66dd6151-0 | Source code for langchain.llms.google_palm
"""Wrapper arround Google's PaLM Text APIs."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMR... | https://python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
1b5a66dd6151-1 | top_k: Optional[int] = None
"""Decode using top-k sampling: consider the set of top_k most probable tokens.
Must be positive."""
max_output_tokens: Optional[int] = None
"""Maximum number of tokens to include in a candidate. Must be greater than zero.
If unset, will default to 64."""
n: int... | https://python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
1b5a66dd6151-2 | raise ValueError("max_output_tokens must be greater than zero")
return values
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> LLMResult:
generations = []
for prompt in ... | https://python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
76fc941c5d09-0 | 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.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import... | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
76fc941c5d09-1 | presence_penalty: float = 0
"""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[... | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
76fc941c5d09-2 | )
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 |
76fc941c5d09-3 | if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
response = self.client.create(engine=self.model_name, prompt=prompt, **params)
text = response.choices[0].text
return tex... | https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html |
ab0e78a22fbc-0 | Source code for langchain.llms.llamacpp
"""Wrapper around llama.cpp."""
import logging
from typing import Any, Dict, Generator, List, Optional
from pydantic import Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
logger = logging.getLogger(__name... | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
ab0e78a22fbc-1 | f16_kv: bool = Field(True, alias="f16_kv")
"""Use half-precision for key/value cache."""
logits_all: bool = Field(False, alias="logits_all")
"""Return logits for all tokens, not just the last token."""
vocab_only: bool = Field(False, alias="vocab_only")
"""Only load the vocabulary, no weights."""
... | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
ab0e78a22fbc-2 | """The penalty to apply to repeated tokens."""
top_k: Optional[int] = 40
"""The top-k value to use for sampling."""
last_n_tokens_size: Optional[int] = 64
"""The number of tokens to look back when applying the repeat_penalty."""
use_mmap: Optional[bool] = True
"""Whether to keep the model loaded... | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
ab0e78a22fbc-3 | vocab_only=vocab_only,
use_mlock=use_mlock,
n_threads=n_threads,
n_batch=n_batch,
use_mmap=use_mmap,
last_n_tokens_size=last_n_tokens_size,
)
except ImportError:
raise ModuleNotFoundError(
"Co... | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
ab0e78a22fbc-4 | Performs sanity check, preparing paramaters in format needed by llama_cpp.
Args:
stop (Optional[List[str]]): List of stop sequences for llama_cpp.
Returns:
Dictionary containing the combined parameters.
"""
# Raise error if stop sequences are in both input and def... | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
ab0e78a22fbc-5 | for token in self.stream(prompt=prompt, stop=stop, run_manager=run_manager):
combined_text_output += token["choices"][0]["text"]
return combined_text_output
else:
params = self._get_parameters(stop)
result = self.client(prompt=prompt, **params)
ret... | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
ab0e78a22fbc-6 | print(result["text"], end='', flush=True)
"""
params = self._get_parameters(stop)
result = self.client(prompt=prompt, stream=True, **params)
for chunk in result:
token = chunk["choices"][0]["text"]
log_probs = chunk["choices"][0].get("logprobs", None)
... | https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
9a62cebf5f7d-0 | Source code for langchain.llms.replicate
"""Wrapper around Replicate 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.utils im... | https://python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
9a62cebf5f7d-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 |
9a62cebf5f7d-2 | except ImportError:
raise ValueError(
"Could not import replicate python package. "
"Please install it with `pip install replicate`."
)
# get the model and version
model_str, version_str = self.model.split(":")
model = replicate_python.mode... | https://python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
00ab93ea8c87-0 | 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 |
00ab93ea8c87-1 | """Positive values penalize new tokens based on their existing frequency
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 ... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
00ab93ea8c87-2 | """Validate that the python package exists in the environment."""
try:
import tokenizers
except ImportError:
raise ValueError(
"Could not import tokenizers python package. "
"Please install it with `pip install tokenizers`."
)
t... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
00ab93ea8c87-3 | AVOID_REPEAT_TOKENS = []
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(to... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
00ab93ea8c87-4 | 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://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
e1d6b73ef2e4-0 | Source code for langchain.llms.sagemaker_endpoint
"""Wrapper around Sagemaker InvokeEndpoint API."""
from abc import abstractmethod
from typing import Any, Dict, Generic, List, Mapping, Optional, TypeVar, Union
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
f... | https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
e1d6b73ef2e4-1 | """The MIME type of the response data returned from endpoint"""
@abstractmethod
def transform_input(self, prompt: INPUT_TYPE, model_kwargs: Dict) -> bytes:
"""Transforms the input to a format that model can accept
as the request Body. Should return bytes or seekable file
like object in t... | https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
e1d6b73ef2e4-2 | )
credentials_profile_name = (
"default"
)
se = SagemakerEndpoint(
endpoint_name=endpoint_name,
region_name=region_name,
credentials_profile_name=credentials_profile_name
)
"""
client: Any #: :meta p... | https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
e1d6b73ef2e4-3 | def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
return response_json[0]["generated_text"]
"""
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
endpoint_kwargs: Optional[D... | https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
e1d6b73ef2e4-4 | @property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"endpoint_name": self.endpoint_name},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(s... | https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
e1d6b73ef2e4-5 | text = self.content_handler.transform_output(response["Body"])
if stop is not None:
# This is a bit hacky, but I can't figure out a better way to enforce
# stop tokens when making calls to the sagemaker endpoint.
text = enforce_stop_tokens(text, stop)
return text
By H... | https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
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