id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
778646bd4fc3-5 | """
cluster_driver_port: Optional[str] = None
"""The port number used by the HTTP server running on the cluster driver node.
The server should listen on the driver IP address or simply ``0.0.0.0`` to connect.
We recommend the server using a port number between ``[3000, 8000]``.
"""
model_kwargs:... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
778646bd4fc3-6 | "And the cluster_id cannot be automatically determined. Received"
f" error: {e}"
)
@validator("cluster_driver_port", always=True)
def set_cluster_driver_port(cls, v: Any, values: Dict[str, Any]) -> Optional[str]:
if v and values["endpoint_name"]:
raise Val... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
778646bd4fc3-7 | )
else:
raise ValueError(
"Must specify either endpoint_name or cluster_id/cluster_driver_port."
)
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "databricks"
def _call(
self,
prompt: str,
stop: O... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
54419b0012e8-0 | Source code for langchain.llms.anthropic
"""Wrapper around Anthropic APIs."""
import re
import warnings
from typing import Any, Callable, Dict, Generator, List, Mapping, Optional, Tuple, Union
from pydantic import BaseModel, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
54419b0012e8-1 | """Validate that api key and python package exists in environment."""
anthropic_api_key = get_from_dict_or_env(
values, "anthropic_api_key", "ANTHROPIC_API_KEY"
)
"""Get custom api url from environment."""
anthropic_api_url = get_from_dict_or_env(
values,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
54419b0012e8-2 | @property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{}, **self._default_params}
def _get_anthropic_stop(self, stop: Optional[List[str]] = None) -> List[str]:
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
raise NameEr... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
54419b0012e8-3 | response = model(prompt)
"""
@root_validator()
def raise_warning(cls, values: Dict) -> Dict:
"""Raise warning that this class is deprecated."""
warnings.warn(
"This Anthropic LLM is deprecated. "
"Please use `from langchain.chat_models import ChatAnthropic` instead"
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
54419b0012e8-4 | Returns:
The string generated by the model.
Example:
.. code-block:: python
prompt = "What are the biggest risks facing humanity?"
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
response = model(prompt)
"""
stop = self._get_a... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
54419b0012e8-5 | )
current_completion = ""
async for data in stream_resp:
delta = data["completion"][len(current_completion) :]
current_completion = data["completion"]
if run_manager:
await run_manager.on_llm_new_token(delta, **data)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
445de265e92d-0 | Source code for langchain.llms.petals
"""Wrapper around Petals API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils imp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
445de265e92d-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://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
445de265e92d-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://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
445de265e92d-3 | """Call the Petals API."""
params = self._default_params
params = {**params, **kwargs}
inputs = self.tokenizer(prompt, return_tensors="pt")["input_ids"]
outputs = self.client.generate(inputs, **params)
text = self.tokenizer.decode(outputs[0])
if stop is not None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
3a1ceebcdcf4-0 | Source code for langchain.llms.huggingface_text_gen_inference
"""Wrapper around Huggingface text generation inference API."""
from functools import partial
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerFor... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
3a1ceebcdcf4-1 | - _acall: Async generates text based on a given prompt and stop sequences.
- _llm_type: Returns the type of LLM.
"""
"""
Example:
.. code-block:: python
# Basic Example (no streaming)
llm = HuggingFaceTextGenInference(
inference_server_url = "http://localh... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
3a1ceebcdcf4-2 | seed: Optional[int] = None
inference_server_url: str = ""
timeout: int = 120
server_kwargs: Dict[str, Any] = Field(default_factory=dict)
stream: bool = False
client: Any
async_client: Any
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@ro... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
3a1ceebcdcf4-3 | res = self.client.generate(
prompt,
stop_sequences=stop,
max_new_tokens=self.max_new_tokens,
top_k=self.top_k,
top_p=self.top_p,
typical_p=self.typical_p,
temperature=self.temperature,
repetit... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
3a1ceebcdcf4-4 | prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
if stop is None:
stop = self.stop_sequences
else:
stop += self.stop_sequences
if not self.stream:
r... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
3a1ceebcdcf4-5 | token = res.token
is_stop = False
for stop_seq in stop:
if stop_seq in token.text:
is_stop = True
break
if is_stop:
break
if not token.special:
if t... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
77183a608bf4-0 | 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
class ContentHandle... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/amazon_api_gateway.html |
77183a608bf4-1 | **{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "amazon_api_gateway"
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/amazon_api_gateway.html |
c077825e8084-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
c077825e8084-1 | """
pipeline: Any #: :meta private:
model_id: str = DEFAULT_MODEL_ID
"""Model name to use."""
model_kwargs: Optional[dict] = None
"""Key word arguments passed to the model."""
pipeline_kwargs: Optional[dict] = None
"""Key word arguments passed to the pipeline."""
class Config:
"... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
c077825e8084-2 | else:
raise ValueError(
f"Got invalid task {task}, "
f"currently only {VALID_TASKS} are supported"
)
except ImportError as e:
raise ValueError(
f"Could not load the {task} model due to missing dependencies."
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
c077825e8084-3 | )
return cls(
pipeline=pipeline,
model_id=model_id,
model_kwargs=_model_kwargs,
pipeline_kwargs=_pipeline_kwargs,
**kwargs,
)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
360e51809216-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
360e51809216-1 | """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://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
360e51809216-2 | """Validate that the python package exists in the environment."""
try:
import tokenizers
except ImportError:
raise ImportError(
"Could not import tokenizers python package. "
"Please install it with `pip install tokenizers`."
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
360e51809216-3 | 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://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
360e51809216-4 | 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 |
aa4e8c33f377-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-1 | "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://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-2 | 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://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-3 | """How many completions to generate for each prompt."""
best_of: int = 1
"""Generates best_of completions server-side and returns the "best"."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_ke... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-4 | be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with differ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-5 | if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-6 | "Please install it with `pip install openai`."
)
if values["streaming"] and values["n"] > 1:
raise ValueError("Cannot stream results when n > 1.")
if values["streaming"] and values["best_of"] > 1:
raise ValueError("Cannot stream results when best_of > 1.")
ret... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-7 | The full LLM output.
Example:
.. code-block:: python
response = openai.generate(["Tell me a joke."])
"""
# TODO: write a unit test for this
params = self._invocation_params
params = {**params, **kwargs}
sub_prompts = self.get_sub_prompts(params... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-8 | prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Call out to OpenAI's endpoint async with k unique prompts."""
params = self._invocation_params
params = {**params, **kw... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-9 | return self.create_llm_result(choices, prompts, token_usage)
def get_sub_prompts(
self,
params: Dict[str, Any],
prompts: List[str],
stop: Optional[List[str]] = None,
) -> List[List[str]]:
"""Get the sub prompts for llm call."""
if stop is not None:
if ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-10 | ),
)
for choice in sub_choices
]
)
llm_output = {"token_usage": token_usage, "model_name": self.model_name}
return LLMResult(generations=generations, llm_output=llm_output)
def stream(self, prompt: str, stop: Optional[List[str]] = N... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-11 | @property
def _invocation_params(self) -> Dict[str, Any]:
"""Get the parameters used to invoke the model."""
openai_creds: Dict[str, Any] = {
"api_key": self.openai_api_key,
"api_base": self.openai_api_base,
"organization": self.openai_organization,
}
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-12 | enc = tiktoken.encoding_for_model(model_name)
except KeyError:
logger.warning("Warning: model not found. Using cl100k_base encoding.")
model = "cl100k_base"
enc = tiktoken.get_encoding(model)
return enc.encode(
text,
allowed_special=self.allowe... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-13 | "text-ada-001": 2049,
"ada": 2049,
"text-babbage-001": 2040,
"babbage": 2049,
"text-curie-001": 2049,
"curie": 2049,
"davinci": 2049,
"text-davinci-003": 4097,
"text-davinci-002": 4097,
"code-davinci-002": 8001,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-14 | max_tokens = openai.max_token_for_prompt("Tell me a joke.")
"""
num_tokens = self.get_num_tokens(prompt)
return self.max_context_size - num_tokens
[docs]class OpenAI(BaseOpenAI):
"""Wrapper around OpenAI large language models.
To use, you should have the ``openai`` python package install... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-15 | openai_api_version: str = ""
@root_validator()
def validate_azure_settings(cls, values: Dict) -> Dict:
values["openai_api_version"] = get_from_dict_or_env(
values,
"openai_api_version",
"OPENAI_API_VERSION",
)
values["openai_api_type"] = get_from_dict_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-16 | .. code-block:: python
from langchain.llms import OpenAIChat
openaichat = OpenAIChat(model_name="gpt-3.5-turbo")
"""
client: Any #: :meta private:
model_name: str = "gpt-3.5-turbo"
"""Model name to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Hol... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-17 | raise ValueError(f"Found {field_name} supplied twice.")
extra[field_name] = values.pop(field_name)
values["model_kwargs"] = extra
return values
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in env... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-18 | "`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
warnings.warn(
"You are trying to use a chat model. This way of initializing it is "
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-19 | run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
messages, params = self._get_chat_params(prompts, stop)
params = {**params, **kwargs}
if self.streaming:
response = ""
params["stream"] = True
for stream_resp in... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-20 | self, messages=messages, **params
):
token = stream_resp["choices"][0]["delta"].get("content", "")
response += token
if run_manager:
await run_manager.on_llm_new_token(
token,
)
return... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
aa4e8c33f377-21 | "Please install it with `pip install tiktoken`."
)
enc = tiktoken.encoding_for_model(self.model_name)
return enc.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
) | https://api.python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
fe2c027dbcef-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://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
fe2c027dbcef-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://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
fe2c027dbcef-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://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
fe2c027dbcef-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://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
fe2c027dbcef-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://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
51eef4596525-0 | Source code for langchain.document_loaders.git
import os
from typing import Callable, List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class GitLoader(BaseLoader):
"""Loads files from a Git repository into a list of documents.
Repositor... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/git.html |
51eef4596525-1 | else:
repo = Repo(self.repo_path)
repo.git.checkout(self.branch)
docs: List[Document] = []
for item in repo.tree().traverse():
if not isinstance(item, Blob):
continue
file_path = os.path.join(self.repo_path, item.path)
ignored_f... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/git.html |
70b474f9adfc-0 | Source code for langchain.document_loaders.recursive_url_loader
from typing import Iterator, List, Optional, Set
from urllib.parse import urlparse
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class RecursiveUrlLoader(BaseLoader):
"""Lo... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/recursive_url_loader.html |
70b474f9adfc-1 | ):
return visited
# Get all links that are relative to the root of the website
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
all_links = [link.get("href") for link in soup.find_all("a")]
# Extract only the links that are children of t... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/recursive_url_loader.html |
a720a9e0a5dc-0 | Source code for langchain.document_loaders.obsidian
"""Loader that loads Obsidian directory dump."""
import re
from pathlib import Path
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class ObsidianLoader(BaseLoader):
"""Loader th... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/obsidian.html |
a720a9e0a5dc-1 | ps = list(Path(self.file_path).glob("**/*.md"))
docs = []
for p in ps:
with open(p, encoding=self.encoding) as f:
text = f.read()
front_matter = self._parse_front_matter(text)
text = self._remove_front_matter(text)
metadata = {
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/obsidian.html |
4ebe47f5fca9-0 | Source code for langchain.document_loaders.json_loader
"""Loader that loads data from JSON."""
import json
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class JSONLoader... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/json_loader.html |
4ebe47f5fca9-1 | """
try:
import jq # noqa:F401
except ImportError:
raise ImportError(
"jq package not found, please install it with `pip install jq`"
)
self.file_path = Path(file_path).resolve()
self._jq_schema = jq.compile(jq_schema)
self._co... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/json_loader.html |
4ebe47f5fca9-2 | else:
content = sample
if self._text_content and not isinstance(content, str):
raise ValueError(
f"Expected page_content is string, got {type(content)} instead. \
Set `text_content=False` if the desired input for \
`page_content` is... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/json_loader.html |
80744b2a35d4-0 | Source code for langchain.document_loaders.whatsapp_chat
import re
from pathlib import Path
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def concatenate_rows(date: str, sender: str, text: str) -> str:
"""Combine message information i... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/whatsapp_chat.html |
80744b2a35d4-1 | )
if result:
date, sender, text = result.groups()
if text not in ignore_lines:
text_content += concatenate_rows(date, sender, text)
metadata = {"source": str(p)}
return [Document(page_content=text_content, metadata=metadata)] | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/whatsapp_chat.html |
982fbf11c9ab-0 | Source code for langchain.document_loaders.youtube
"""Loader that loads YouTube transcript."""
from __future__ import annotations
import logging
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from urllib.parse import parse_qs, urlparse
from pydantic import root_validator
from pyd... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
982fbf11c9ab-1 | """Validate that either folder_id or document_ids is set, but not both."""
if not values.get("credentials_path") and not values.get(
"service_account_path"
):
raise ValueError("Must specify either channel_name or video_ids")
return values
def _load_credentials(self) -... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
982fbf11c9ab-2 | token.write(creds.to_json())
return creds
ALLOWED_SCHEMAS = {"http", "https"}
ALLOWED_NETLOCK = {
"youtu.be",
"m.youtube.com",
"youtube.com",
"www.youtube.com",
"www.youtube-nocookie.com",
"vid.plus",
}
def _parse_video_id(url: str) -> Optional[str]:
"""Parse a youtube url and return... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
982fbf11c9ab-3 | self.add_video_info = add_video_info
self.language = language
if isinstance(language, str):
self.language = [language]
else:
self.language = language
self.translation = translation
self.continue_on_failure = continue_on_failure
[docs] @staticmethod
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
982fbf11c9ab-4 | except TranscriptsDisabled:
return []
try:
transcript = transcript_list.find_transcript(self.language)
except NoTranscriptFound:
en_transcript = transcript_list.find_transcript(["en"])
transcript = en_transcript.translate(self.translation)
transcri... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
982fbf11c9ab-5 | To use, you should have the ``googleapiclient,youtube_transcript_api``
python package installed.
As the service needs a google_api_client, you first have to initialize
the GoogleApiClient.
Additionally you have to either provide a channel name or a list of videoids
"https://developers.google.com/doc... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
982fbf11c9ab-6 | "to use the Google Drive loader"
)
return build("youtube", "v3", credentials=creds)
[docs] @root_validator
def validate_channel_or_videoIds_is_set(
cls, values: Dict[str, Any]
) -> Dict[str, Any]:
"""Validate that either folder_id or document_ids is set, but not both."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
982fbf11c9ab-7 | request = self.youtube_client.search().list(
part="id",
q=channel_name,
type="channel",
maxResults=1, # we only need one result since channel names are unique
)
response = request.execute()
channel_id = response["items"][0]["id"]["channelId"]
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
982fbf11c9ab-8 | metadata=meta_data,
)
)
except (TranscriptsDisabled, NoTranscriptFound) as e:
if self.continue_on_failure:
logger.error(
"Error fetching transscript "
+ f" {ite... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/youtube.html |
9274557a73b6-0 | Source code for langchain.document_loaders.gutenberg
"""Loader that loads .txt web files."""
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class GutenbergLoader(BaseLoader):
"""Loader that uses urllib to load .txt web files."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/gutenberg.html |
24f0900a9812-0 | Source code for langchain.document_loaders.reddit
"""Reddit document loader."""
from __future__ import annotations
from typing import TYPE_CHECKING, Iterable, List, Optional, Sequence
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
if TYPE_CHECKING:
import pra... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/reddit.html |
24f0900a9812-1 | if self.mode == "subreddit":
for search_query in self.search_queries:
for category in self.categories:
docs = self._subreddit_posts_loader(
search_query=search_query, category=category, reddit=reddit
)
result... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/reddit.html |
24f0900a9812-2 | method = getattr(user.submissions, category)
cat_posts = method(limit=self.number_posts)
"""Format reddit posts into a string."""
for post in cat_posts:
metadata = {
"post_subreddit": post.subreddit_name_prefixed,
"post_category": category,
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/reddit.html |
0b479f8f2897-0 | Source code for langchain.document_loaders.figma
"""Loader that loads Figma files json dump."""
import json
import urllib.request
from typing import Any, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.utils import stringify_dict
[docs]class Fi... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/figma.html |
56aa1e743404-0 | Source code for langchain.document_loaders.modern_treasury
"""Loader that fetches data from Modern Treasury"""
import json
import urllib.request
from base64 import b64encode
from typing import List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from lan... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/modern_treasury.html |
56aa1e743404-1 | def __init__(
self,
resource: str,
organization_id: Optional[str] = None,
api_key: Optional[str] = None,
) -> None:
self.resource = resource
organization_id = organization_id or get_from_env(
"organization_id", "MODERN_TREASURY_ORGANIZATION_ID"
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/modern_treasury.html |
f34a116000b7-0 | Source code for langchain.document_loaders.fauna
from typing import Iterator, List, Optional, Sequence
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class FaunaLoader(BaseLoader):
"""FaunaDB Loader.
Attributes:
query (str): The FQL query st... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/fauna.html |
f34a116000b7-1 | document_dict = dict(result.items())
page_content = ""
for key, value in document_dict.items():
if key == self.page_content_field:
page_content = value
document: Document = Document(
page_content=page_content... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/fauna.html |
9cb59252d438-0 | Source code for langchain.document_loaders.excel
"""Loader that loads Microsoft Excel files."""
from typing import Any, List
from langchain.document_loaders.unstructured import (
UnstructuredFileLoader,
validate_unstructured_version,
)
[docs]class UnstructuredExcelLoader(UnstructuredFileLoader):
"""Loader t... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/excel.html |
a632ebd42c74-0 | Source code for langchain.document_loaders.directory
"""Loading logic for loading documents from a directory."""
import concurrent
import logging
from pathlib import Path
from typing import Any, List, Optional, Type, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import Base... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/directory.html |
a632ebd42c74-1 | self.loader_kwargs = loader_kwargs
self.silent_errors = silent_errors
self.recursive = recursive
self.show_progress = show_progress
self.use_multithreading = use_multithreading
self.max_concurrency = max_concurrency
[docs] def load_file(
self, item: Path, path: Path, d... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/directory.html |
a632ebd42c74-2 | logger.warning(e)
else:
raise e
if self.use_multithreading:
with concurrent.futures.ThreadPoolExecutor(
max_workers=self.max_concurrency
) as executor:
executor.map(lambda i: self.load_file(i, p, docs, pbar), items)
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/directory.html |
3d6d456f26b6-0 | Source code for langchain.document_loaders.facebook_chat
"""Loader that loads Facebook chat json dump."""
import datetime
import json
from pathlib import Path
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
def concatenate_rows(row: dict) -... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/facebook_chat.html |
1eac6b87ed55-0 | Source code for langchain.document_loaders.email
"""Loader that loads email files."""
import os
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import (
UnstructuredFileLoader,
satisfies_... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/email.html |
1eac6b87ed55-1 | "`pip install extract_msg`"
)
[docs] def load(self) -> List[Document]:
"""Load data into document objects."""
import extract_msg
msg = extract_msg.Message(self.file_path)
return [
Document(
page_content=msg.body,
metadata={
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/email.html |
74a7ff85f356-0 | Source code for langchain.document_loaders.word_document
"""Loader that loads word documents."""
import os
import tempfile
from abc import ABC
from typing import List
from urllib.parse import urlparse
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/word_document.html |
74a7ff85f356-1 | if hasattr(self, "temp_file"):
self.temp_file.close()
[docs] def load(self) -> List[Document]:
"""Load given path as single page."""
import docx2txt
return [
Document(
page_content=docx2txt.process(self.file_path),
metadata={"source": se... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/word_document.html |
74a7ff85f356-2 | f"You are on unstructured version {__unstructured_version__}. "
"Partitioning .doc files is only supported in unstructured>=0.4.11. "
"Please upgrade the unstructured package and try again."
)
if is_doc:
from unstructured.partition.doc import partition_doc... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/word_document.html |
28684a870a6c-0 | Source code for langchain.document_loaders.html_bs
"""Loader that uses bs4 to load HTML files, enriching metadata with page title."""
import logging
from typing import Dict, List, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__n... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/html_bs.html |
28684a870a6c-1 | title = ""
metadata: Dict[str, Union[str, None]] = {
"source": self.file_path,
"title": title,
}
return [Document(page_content=text, metadata=metadata)] | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/html_bs.html |
153e0849e67e-0 | Source code for langchain.document_loaders.stripe
"""Loader that fetches data from Stripe"""
import json
import urllib.request
from typing import List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.utils import get_from_env, stringify_dic... | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/stripe.html |
153e0849e67e-1 | if endpoint is None:
return []
return self._make_request(endpoint)
[docs] def load(self) -> List[Document]:
return self._get_resource() | https://api.python.langchain.com/en/latest/_modules/langchain/document_loaders/stripe.html |
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