id stringlengths 14 16 | text stringlengths 31 2.73k | metadata dict |
|---|---|---|
1e9de6ff3e6a-3 | values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY"
)
try:
import aleph_alpha_client
values["client"] = aleph_alpha_client.Client(token=aleph_alpha_api_key)
except ImportError:
raise ValueError(
"Could not import aleph_alpha_client python pack... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html"
} |
1e9de6ff3e6a-4 | "sequence_penalty": self.sequence_penalty,
"sequence_penalty_min_length": self.sequence_penalty_min_length,
"use_multiplicative_sequence_penalty": self.use_multiplicative_sequence_penalty, # noqa: E501
"completion_bias_inclusion": self.completion_bias_inclusion,
"complet... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html"
} |
1e9de6ff3e6a-5 | from aleph_alpha_client import CompletionRequest, Prompt
params = self._default_params
if self.stop_sequences is not None and stop is not None:
raise ValueError(
"stop sequences found in both the input and default params."
)
elif self.stop_sequences is not... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html"
} |
d93016bc1749-0 | Source code for langchain.llms.gooseai
"""Wrapper around GooseAI API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html"
} |
d93016bc1749-1 | """Penalizes repeated tokens."""
n: int = 1
"""How many completions to generate for each prompt."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
logit_bias: Optional[Dict[str, float]] = Field(default_fac... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html"
} |
d93016bc1749-2 | )
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. "
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html"
} |
d93016bc1749-3 | params["stop"] = stop
response = self.client.create(engine=self.model_name, prompt=prompt, **params)
text = response.choices[0].text
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/gooseai.html"
} |
4b8cedc88add-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.llms.base import LLM
from langchain.llms.utils import enforce_stop_t... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html"
} |
4b8cedc88add-1 | logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_coun... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html"
} |
4b8cedc88add-2 | 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.llms import SelfHostedPipeline
import runhouse as rh
gpu = rh.clu... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html"
} |
4b8cedc88add-3 | """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
def __init__(self, **kwargs: Any):
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html"
} |
4b8cedc88add-4 | 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 the cluster and passing the path to the pipeline... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html"
} |
9589c8461e20-0 | Source code for langchain.llms.self_hosted_hugging_face
"""Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware."""
import importlib.util
import logging
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.llms.self_hosted import SelfHostedPipeline... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html"
} |
9589c8461e20-1 | task: str = DEFAULT_TASK,
device: int = 0,
model_kwargs: Optional[dict] = None,
) -> Any:
"""Inference function to send to the remote hardware.
Accepts a huggingface model_id and returns a pipeline for the task.
"""
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokeni... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html"
} |
9589c8461e20-2 | "Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_count,
)
pipeline = hf_pipeline(
task=task,
model=mod... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html"
} |
9589c8461e20-3 | .. code-block:: python
from langchain.llms import SelfHostedHuggingFaceLLM
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def get_pipeline():
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pre... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html"
} |
9589c8461e20-4 | extra = Extra.forbid
def __init__(self, **kwargs: Any):
"""Construct the pipeline remotely using an auxiliary function.
The load function needs to be importable to be imported
and run on the server, i.e. in a module and not a REPL or closure.
Then, initialize the remote inference fun... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html"
} |
a07c77ed0734-0 | Source code for langchain.llms.huggingface_pipeline
"""Wrapper around HuggingFace Pipeline APIs."""
import importlib.util
import logging
from typing import Any, List, Mapping, Optional
from pydantic import Extra
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
DEFAULT_MODEL_ID = ... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html"
} |
a07c77ed0734-1 | """Key word arguments to pass to the model."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @classmethod
def from_model_id(
cls,
model_id: str,
task: str,
device: int = -1,
model_kwargs: Optional[dict] = None,
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html"
} |
a07c77ed0734-2 | import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError(
f"Got device=={device}, "
f"device is required to be within [-1, {cuda_device_count})"
)
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html"
} |
a07c77ed0734-3 | response = self.pipeline(prompt)
if self.pipeline.task == "text-generation":
# Text generation return includes the starter text.
text = response[0]["generated_text"][len(prompt) :]
elif self.pipeline.task == "text2text-generation":
text = response[0]["generated_text"]... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html"
} |
1b9a225a109c-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, SupportsIndex
from pydantic import BaseMo... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html"
} |
1b9a225a109c-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 =... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html"
} |
1b9a225a109c-2 | try:
import tokenizers
except ImportError:
raise ValueError(
"Could not import tokenizers python package. "
"Please install it with `pip install tokenizers`."
)
try:
from rwkv.model import RWKV as RWKVMODEL
from ... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html"
} |
1b9a225a109c-3 | logits = None
state = self.model_state
occurrence = {}
# Feed in the input string
while len(tokens) > 0:
logits, state = self.client.forward(tokens[: self.CHUNK_LEN], state)
tokens = tokens[self.CHUNK_LEN :]
decoded = ""
for i in range(self.max_tok... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html"
} |
1b9a225a109c-4 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html"
} |
fe3fb455bb79-0 | Source code for langchain.llms.gpt4all
"""Wrapper for the GPT4All model."""
from typing import Any, Dict, List, Mapping, Optional, Set
from pydantic import Extra, Field, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
[docs]class GPT4All(LLM):
r"""Wrapper arou... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html"
} |
fe3fb455bb79-1 | vocab_only: bool = Field(False, alias="vocab_only")
"""Only load the vocabulary, no weights."""
use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
embedding: bool = Field(False, alias="embedding")
"""Use embedding mode only."""
n_threads: Optional[int] = F... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html"
} |
fe3fb455bb79-2 | """Get the identifying parameters."""
return {
"seed": self.seed,
"n_predict": self.n_predict,
"n_threads": self.n_threads,
"n_batch": self.n_batch,
"repeat_last_n": self.repeat_last_n,
"repeat_penalty": self.repeat_penalty,
"to... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html"
} |
fe3fb455bb79-3 | return {
"model": self.model,
**self._default_params,
**{
k: v
for k, v in self.__dict__.items()
if k in GPT4All._llama_param_names()
},
}
@property
def _llm_type(self) -> str:
"""Return the type of l... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/gpt4all.html"
} |
9dec2608e770-0 | Source code for langchain.llms.anthropic
"""Wrapper around Anthropic APIs."""
import re
from typing import Any, Dict, Generator, List, Mapping, Optional
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
[docs]class Anthropic(LLM):
r"""Wra... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html"
} |
9dec2608e770-1 | """A non-negative float that tunes the degree of randomness in generation."""
top_k: int = 0
"""Number of most likely tokens to consider at each step."""
top_p: float = 1
"""Total probability mass of tokens to consider at each step."""
streaming: bool = False
"""Whether to stream the results."""... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html"
} |
9dec2608e770-2 | "top_p": self.top_p,
}
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"model": self.model}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "anthropic"
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html"
} |
9dec2608e770-3 | r"""Call out to Anthropic's completion endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html"
} |
9dec2608e770-4 | stream=True,
**self._default_params,
)
current_completion = ""
async for data in stream_resp:
delta = data["completion"][len(current_completion) :]
current_completion = data["completion"]
if self.callback_manager.is_asyn... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html"
} |
9dec2608e770-5 | prompt=self._wrap_prompt(prompt),
stop_sequences=stop,
**self._default_params,
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html"
} |
d14b63ed7d4f-0 | Source code for langchain.embeddings.cohere
"""Wrapper around Cohere embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class CohereEmbeddings(Base... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html"
} |
d14b63ed7d4f-1 | raise ValueError(
"Could not import cohere python package. "
"Please it install it with `pip install cohere`."
)
return values
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to Cohere's embedding endpoint.
Args:... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/cohere.html"
} |
ebbcb153052f-0 | Source code for langchain.embeddings.fake
from typing import List
import numpy as np
from pydantic import BaseModel
from langchain.embeddings.base import Embeddings
[docs]class FakeEmbeddings(Embeddings, BaseModel):
size: int
def _get_embedding(self) -> List[float]:
return list(np.random.normal(size=sel... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/fake.html"
} |
73eaf1cc12d2-0 | Source code for langchain.embeddings.huggingface_hub
"""Wrapper around HuggingFace Hub embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
DEFAULT_REPO_ID... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html"
} |
73eaf1cc12d2-1 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
try:
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html"
} |
73eaf1cc12d2-2 | texts = [text.replace("\n", " ") for text in texts]
_model_kwargs = self.model_kwargs or {}
responses = self.client(inputs=texts, params=_model_kwargs)
return responses
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to HuggingFaceHub's embedding endpoint for embed... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html"
} |
3b65e3f56c7c-0 | Source code for langchain.embeddings.sagemaker_endpoint
"""Wrapper around Sagemaker InvokeEndpoint API."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html"
} |
3b65e3f56c7c-1 | """The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region."""
region_name: str = ""
"""The aws region where the Sagemaker model is deployed, eg. `us-west-2`."""
credentials_profile_name: Optional[str] = None
"""The name of the profile in the ~/.aws/credential... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html"
} |
3b65e3f56c7c-2 | endpoint_kwargs: Optional[Dict] = None
"""Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
"""
class Config:
"""Configuration for this pydantic object."""
extr... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html"
} |
3b65e3f56c7c-3 | _endpoint_kwargs = self.endpoint_kwargs or {}
body = self.content_handler.transform_input(texts, _model_kwargs)
content_type = self.content_handler.content_type
accepts = self.content_handler.accepts
# send request
try:
response = self.client.invoke_endpoint(
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html"
} |
3b65e3f56c7c-4 | """
return self._embedding_func([text])
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html"
} |
2d2425fa4352-0 | Source code for langchain.embeddings.tensorflow_hub
"""Wrapper around TensorflowHub embedding models."""
from typing import Any, List
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
[docs]clas... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html"
} |
2d2425fa4352-1 | Returns:
List of embeddings, one for each text.
"""
texts = list(map(lambda x: x.replace("\n", " "), texts))
embeddings = self.embed(texts).numpy()
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using ... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html"
} |
98e292a3aef9-0 | Source code for langchain.embeddings.openai
"""Wrapper around OpenAI embedding models."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
import numpy as np
from pydantic import BaseModel, Extra, root_validator
from tenacity import (
before_sleep_log,
ret... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html"
} |
98e292a3aef9-1 | retry_decorator = _create_retry_decorator(embeddings)
@retry_decorator
def _completion_with_retry(**kwargs: Any) -> Any:
return embeddings.client.create(**kwargs)
return _completion_with_retry(**kwargs)
[docs]class OpenAIEmbeddings(BaseModel, Embeddings):
"""Wrapper around OpenAI embedding model... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html"
} |
98e292a3aef9-2 | model: str = "text-embedding-ada-002"
# TODO: deprecate these two in favor of model
# https://community.openai.com/t/api-update-engines-models/18597
# https://github.com/openai/openai-python/issues/132
document_model_name: str = "text-embedding-ada-002"
query_model_name: str = "text-embedding-ada-... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html"
} |
98e292a3aef9-3 | )
if "query_model_name" in values:
raise ValueError(
"Both `model_name` and `query_model_name` were provided, "
"but only one should be."
)
model_name = values.pop("model_name")
values["document_model_name"] = f"... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html"
} |
98e292a3aef9-4 | values["client"] = openai.Embedding
except ImportError:
raise ValueError(
"Could not import openai python package. "
"Please it install it with `pip install openai`."
)
return values
# please refer to
# https://github.com/openai/openai-cook... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html"
} |
98e292a3aef9-5 | for i in range(len(indices)):
results[indices[i]].append(batched_embeddings[i])
lens[indices[i]].append(len(batched_embeddings[i]))
for i in range(len(texts)):
average = np.average(results[i], axis=0, weights=lens[i])
embeddings[i] = (average /... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html"
} |
98e292a3aef9-6 | # handle large batches of texts
if self.embedding_ctx_length > 0:
return self._get_len_safe_embeddings(texts, engine=self.document_model_name)
else:
results = []
_chunk_size = chunk_size or self.chunk_size
for i in range(0, len(texts), _chunk_size):
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html"
} |
f8d56027ab0e-0 | Source code for langchain.embeddings.huggingface
"""Wrapper around HuggingFace embedding models."""
from typing import Any, List
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_MODEL = "hkunlp/instruct... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html"
} |
f8d56027ab0e-1 | """Compute doc embeddings using a HuggingFace transformer model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
texts = list(map(lambda x: x.replace("\n", " "), texts))
embeddings = self.client.encode(texts)
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html"
} |
f8d56027ab0e-2 | try:
from InstructorEmbedding import INSTRUCTOR
self.client = INSTRUCTOR(self.model_name)
except ImportError as e:
raise ValueError("Dependencies for InstructorEmbedding not found.") from e
class Config:
"""Configuration for this pydantic object."""
extra ... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html"
} |
a65ba2bdb9ef-0 | Source code for langchain.embeddings.llamacpp
"""Wrapper around llama.cpp embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.embeddings.base import Embeddings
[docs]class LlamaCppEmbeddings(BaseModel, Embeddings):
"""Wrapper ... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html"
} |
a65ba2bdb9ef-1 | use_mlock: bool = Field(False, alias="use_mlock")
"""Force system to keep model in RAM."""
n_threads: Optional[int] = Field(None, alias="n_threads")
"""Number of threads to use. If None, the number
of threads is automatically determined."""
n_batch: Optional[int] = Field(8, alias="n_batch")
"""... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html"
} |
a65ba2bdb9ef-2 | raise ModuleNotFoundError(
"Could not import llama-cpp-python library. "
"Please install the llama-cpp-python library to "
"use this embedding model: pip install llama-cpp-python"
)
except Exception:
raise NameError(f"Could not load Llama m... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html"
} |
185d134c3a3d-0 | Source code for langchain.embeddings.aleph_alpha
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
[docs]class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
"""... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html"
} |
185d134c3a3d-1 | """Attention control parameters only apply to those tokens that have
explicitly been set in the request."""
control_log_additive: Optional[bool] = True
"""Apply controls on prompt items by adding the log(control_factor)
to attention scores."""
@root_validator()
def validate_environment(cls, va... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html"
} |
185d134c3a3d-2 | "representation": SemanticRepresentation.Document,
"compress_to_size": self.compress_to_size,
"normalize": self.normalize,
"contextual_control_threshold": self.contextual_control_threshold,
"control_log_additive": self.control_log_additive,
}
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html"
} |
185d134c3a3d-3 | """The symmetric version of the Aleph Alpha's semantic embeddings.
The main difference is that here, both the documents and
queries are embedded with a SemanticRepresentation.Symmetric
Example:
.. code-block:: python
from aleph_alpha import AlephAlphaSymmetricSemanticEmbedding
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html"
} |
185d134c3a3d-4 | List of embeddings, one for each text.
"""
document_embeddings = []
for text in texts:
document_embeddings.append(self._embed(text))
return document_embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to Aleph Alpha's asymmetric, query em... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/aleph_alpha.html"
} |
dad036d46ad1-0 | Source code for langchain.embeddings.self_hosted
"""Running custom embedding models on self-hosted remote hardware."""
from typing import Any, Callable, List
from pydantic import Extra
from langchain.embeddings.base import Embeddings
from langchain.llms import SelfHostedPipeline
def _embed_documents(pipeline: Any, *arg... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html"
} |
dad036d46ad1-1 | model_load_fn=get_pipeline,
hardware=gpu
model_reqs=["./", "torch", "transformers"],
)
Example passing in a pipeline path:
.. code-block:: python
from langchain.embeddings import SelfHostedHFEmbeddings
import runhouse as rh
from... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html"
} |
dad036d46ad1-2 | [docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
text = text.replace("\n", " ")
embeddings = self.clie... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html"
} |
6afc09438080-0 | Source code for langchain.embeddings.self_hosted_hugging_face
"""Wrapper around HuggingFace embedding models for self-hosted remote hardware."""
import importlib
import logging
from typing import Any, Callable, List, Optional
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
DEFAULT_MODEL_NAME = "senten... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html"
} |
6afc09438080-1 | 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 associated wi... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html"
} |
6afc09438080-2 | model_load_fn: Callable = load_embedding_model
"""Function to load the model remotely on the server."""
load_fn_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model load function."""
inference_fn: Callable = _embed_documents
"""Inference function to extract the embeddings."""
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html"
} |
6afc09438080-3 | model_name=model_name, hardware=gpu)
"""
model_id: str = DEFAULT_INSTRUCT_MODEL
"""Model name to use."""
embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
"""Instruction to use for embedding documents."""
query_instruction: str = DEFAULT_QUERY_INSTRUCTION
"""Instruction to use for embedding... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html"
} |
6afc09438080-4 | text: The text to embed.
Returns:
Embeddings for the text.
"""
instruction_pair = [self.query_instruction, text]
embedding = self.client(self.pipeline_ref, [instruction_pair])[0]
return embedding.tolist()
By Harrison Chase
© Copyright 2023, Harrison Chase.
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html"
} |
367e515ed808-0 | Source code for langchain.agents.tools
"""Interface for tools."""
from inspect import signature
from typing import Any, Awaitable, Callable, Optional, Union
from langchain.tools.base import BaseTool
[docs]class Tool(BaseTool):
"""Tool that takes in function or coroutine directly."""
description: str = ""
fu... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/tools.html"
} |
367e515ed808-1 | return f"{tool_name} is not a valid tool, try another one."
[docs]def tool(*args: Union[str, Callable], return_direct: bool = False) -> Callable:
"""Make tools out of functions, can be used with or without arguments.
Requires:
- Function must be of type (str) -> str
- Function must have a docstr... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/tools.html"
} |
367e515ed808-2 | # if the argument is a function, then we use the function name as the tool name
# Example usage: @tool
return _make_with_name(args[0].__name__)(args[0])
elif len(args) == 0:
# if there are no arguments, then we use the function name as the tool name
# Example usage: @tool(return_dire... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/tools.html"
} |
7fb9e07bc9ed-0 | Source code for langchain.agents.initialize
"""Load agent."""
from typing import Any, Optional, Sequence
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.agents.loading import AGENT_TO_CLASS, load_agent
from langchain.callbacks.base import BaseCallbackMa... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/initialize.html"
} |
7fb9e07bc9ed-1 | "but at most only one should be."
)
if agent is not None:
if agent not in AGENT_TO_CLASS:
raise ValueError(
f"Got unknown agent type: {agent}. "
f"Valid types are: {AGENT_TO_CLASS.keys()}."
)
agent_cls = AGENT_TO_CLASS[agent]
ag... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/initialize.html"
} |
71e9f3b9d989-0 | Source code for langchain.agents.agent_types
from enum import Enum
[docs]class AgentType(str, Enum):
ZERO_SHOT_REACT_DESCRIPTION = "zero-shot-react-description"
REACT_DOCSTORE = "react-docstore"
SELF_ASK_WITH_SEARCH = "self-ask-with-search"
CONVERSATIONAL_REACT_DESCRIPTION = "conversational-react-descri... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/agent_types.html"
} |
a52b9bd0a17e-0 | Source code for langchain.agents.loading
"""Functionality for loading agents."""
import json
from pathlib import Path
from typing import Any, List, Optional, Union
import yaml
from langchain.agents.agent import Agent
from langchain.agents.agent_types import AgentType
from langchain.agents.chat.base import ChatAgent
fro... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/loading.html"
} |
a52b9bd0a17e-1 | raise ValueError(f"Loading {config_type} agent not supported")
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
agent_cls = AGENT_TO_CLASS[config_type]
combined_config = {**config, **kwargs}
return agent_cls.from_llm_and_tools(llm, tools, **com... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/loading.html"
} |
a52b9bd0a17e-2 | elif "llm_chain_path" in config:
config["llm_chain"] = load_chain(config.pop("llm_chain_path"))
else:
raise ValueError("One of `llm_chain` and `llm_chain_path` should be specified.")
combined_config = {**config, **kwargs}
return agent_cls(**combined_config) # type: ignore
[docs]def load_age... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/loading.html"
} |
eb070272db6e-0 | Source code for langchain.agents.load_tools
# flake8: noqa
"""Load tools."""
import warnings
from typing import Any, List, Optional
from langchain.agents.tools import Tool
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.api import news_docs, open_meteo_docs, podcast_docs, tmdb_docs
from l... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html"
} |
eb070272db6e-1 | from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
def _get_python_repl() -> BaseTool:
return PythonREPLTool()
def _get_tools_requests_get() -> BaseTool:
return RequestsGetTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_post() -> BaseTool:
return RequestsPostTool(reque... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html"
} |
eb070272db6e-2 | func=PALChain.from_math_prompt(llm).run,
)
def _get_pal_colored_objects(llm: BaseLLM) -> BaseTool:
return Tool(
name="PAL-COLOR-OBJ",
description="A language model that is really good at reasoning about position and the color attributes of objects. Input should be a fully worded hard reasoning p... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html"
} |
eb070272db6e-3 | "open-meteo-api": _get_open_meteo_api,
}
def _get_news_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
news_api_key = kwargs["news_api_key"]
chain = APIChain.from_llm_and_api_docs(
llm, news_docs.NEWS_DOCS, headers={"X-Api-Key": news_api_key}
)
return Tool(
name="News API",
descrip... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html"
} |
eb070272db6e-4 | )
return Tool(
name="Podcast API",
description="Use the Listen Notes Podcast API to search all podcasts or episodes. The input should be a question in natural language that this API can answer.",
func=chain.run,
)
def _get_wolfram_alpha(**kwargs: Any) -> BaseTool:
return WolframAlpha... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html"
} |
eb070272db6e-5 | def _get_searx_search_results_json(**kwargs: Any) -> BaseTool:
wrapper_kwargs = {k: v for k, v in kwargs.items() if k != "num_results"}
return SearxSearchResults(wrapper=SearxSearchWrapper(**wrapper_kwargs), **kwargs)
def _get_bing_search(**kwargs: Any) -> BaseTool:
return BingSearchRun(api_wrapper=BingSear... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html"
} |
eb070272db6e-6 | "searx-search": (_get_searx_search, ["searx_host", "engines", "aiosession"]),
"wikipedia": (_get_wikipedia, ["top_k_results"]),
"human": (_get_human_tool, ["prompt_func", "input_func"]),
}
[docs]def load_tools(
tool_names: List[str],
llm: Optional[BaseLLM] = None,
callback_manager: Optional[BaseCall... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html"
} |
eb070272db6e-7 | if callback_manager is not None:
tool.callback_manager = callback_manager
tools.append(tool)
elif name in _EXTRA_LLM_TOOLS:
if llm is None:
raise ValueError(f"Tool {name} requires an LLM to be provided")
_get_llm_tool_func, extra_keys = _EXTRA_... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html"
} |
eb070272db6e-8 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 08, 2023. | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html"
} |
48d642a15044-0 | Source code for langchain.agents.agent
"""Chain that takes in an input and produces an action and action input."""
from __future__ import annotations
import asyncio
import json
import logging
import time
from abc import abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tupl... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html"
} |
48d642a15044-1 | **kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
[docs] @abstractmethod
async def aplan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html"
} |
48d642a15044-2 | _dict["_type"] = self._agent_type
return _dict
[docs] def save(self, file_path: Union[Path, str]) -> None:
"""Save the agent.
Args:
file_path: Path to file to save the agent to.
Example:
.. code-block:: python
# If working with agent executor
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html"
} |
48d642a15044-3 | ) -> Union[List[AgentAction], AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
**kwargs: User inputs.
Returns:
Actions specifying what tool to use.
"""
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html"
} |
48d642a15044-4 | raise NotImplementedError
[docs] def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of agent."""
_dict = super().dict()
_dict["_type"] = self._agent_type
return _dict
[docs] def save(self, file_path: Union[Path, str]) -> None:
"""Save the agent.
... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html"
} |
48d642a15044-5 | llm_chain: LLMChain
output_parser: AgentOutputParser
stop: List[str]
@property
def input_keys(self) -> List[str]:
return list(set(self.llm_chain.input_keys) - {"intermediate_steps"})
[docs] def plan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Union... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html"
} |
48d642a15044-6 | }
[docs]class Agent(BaseSingleActionAgent):
"""Class responsible for calling the language model and deciding the action.
This is driven by an LLMChain. The prompt in the LLMChain MUST include
a variable called "agent_scratchpad" where the agent can put its
intermediary work.
"""
llm_chain: LLMCh... | {
"url": "https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html"
} |
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