id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 115 |
|---|---|---|
69119724a4af-0 | Source code for langchain.embeddings.huggingface
"""Wrapper around HuggingFace embedding models."""
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, Field
from langchain.embeddings.base import Embeddings
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
DEFAULT_INSTRUCT_M... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
69119724a4af-1 | """Initialize the sentence_transformer."""
super().__init__(**kwargs)
try:
import sentence_transformers
except ImportError as exc:
raise ValueError(
"Could not import sentence_transformers python package. "
"Please install it with `pip inst... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
69119724a4af-2 | Example:
.. code-block:: python
from langchain.embeddings import HuggingFaceInstructEmbeddings
model_name = "hkunlp/instructor-large"
model_kwargs = {'device': 'cpu'}
hf = HuggingFaceInstructEmbeddings(
model_name=model_name, model_kwargs=model_kwa... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
69119724a4af-3 | Returns:
List of embeddings, one for each text.
"""
instruction_pairs = [[self.embed_instruction, text] for text in texts]
embeddings = self.client.encode(instruction_pairs)
return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]:
"""Compu... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface.html |
60d2ed03455b-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 ... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
60d2ed03455b-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")
"""... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
60d2ed03455b-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/llamacpp.html |
1b16475a599c-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
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
1b16475a599c-1 | credentials_profile_name=credentials_profile_name
)
"""
client: Any #: :meta private:
endpoint_name: str = ""
"""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 ... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
1b16475a599c-2 | """ # noqa: E501
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
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/ap... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
1b16475a599c-3 | # replace newlines, which can negatively affect performance.
texts = list(map(lambda x: x.replace("\n", " "), texts))
_model_kwargs = self.model_kwargs or {}
_endpoint_kwargs = self.endpoint_kwargs or {}
body = self.content_handler.transform_input(texts, _model_kwargs)
content_ty... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
1b16475a599c-4 | """Compute query embeddings using a SageMaker inference endpoint.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self._embedding_func([text])[0]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Ma... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
4261469444ae-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
4261469444ae-1 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
try:
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
4261469444ae-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
1b2a3f08e14d-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/fake.html |
473da1c53ce1-0 | Source code for langchain.embeddings.openai
"""Wrapper around OpenAI embedding models."""
from __future__ import annotations
import logging
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Set,
Tuple,
Union,
)
import numpy as np
from pydantic import BaseModel, Extra... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
473da1c53ce1-1 | """Use tenacity to retry the embedding call."""
retry_decorator = _create_retry_decorator(embeddings)
@retry_decorator
def _embed_with_retry(**kwargs: Any) -> Any:
return embeddings.client.create(**kwargs)
return _embed_with_retry(**kwargs)
[docs]class OpenAIEmbeddings(BaseModel, Embeddings):
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
473da1c53ce1-2 | api_type="azure",
)
text = "This is a test query."
query_result = embeddings.embed_query(text)
"""
client: Any #: :meta private:
model: str = "text-embedding-ada-002"
deployment: str = model # to support Azure OpenAI Service custom deployment names
openai_api_ve... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
473da1c53ce1-3 | "OPENAI_API_BASE",
default="",
)
openai_api_type = get_from_dict_or_env(
values,
"openai_api_type",
"OPENAI_API_TYPE",
default="",
)
openai_api_version = get_from_dict_or_env(
values,
"openai_api_version"... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
473da1c53ce1-4 | for i, text in enumerate(texts):
if self.model.endswith("001"):
# See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
# replace newlines, which can negatively affect performance.
text = text.replace("\n", " ")
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
473da1c53ce1-5 | _result, axis=0, weights=num_tokens_in_batch[i]
)
embeddings[i] = (average / np.linalg.norm(average)).tolist()
return embeddings
except ImportError:
raise ValueError(
"Could not import tiktoken python package. "
"This is... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
473da1c53ce1-6 | # than the maximum context and use length-safe embedding function.
return self._get_len_safe_embeddings(texts, engine=self.deployment)
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint for embedding query text.
Args:
text: The... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/openai.html |
7c64a6156d9b-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
7c64a6156d9b-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
7c64a6156d9b-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."""
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
7c64a6156d9b-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
7c64a6156d9b-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.
... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
ecdea3a3bbe1-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
ecdea3a3bbe1-1 | 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 ... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
ab3fd7e1795d-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
ab3fd7e1795d-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
ab3fd7e1795d-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... | https://python.langchain.com/en/latest/_modules/langchain/embeddings/self_hosted.html |
993f5b4517f8-0 | Source code for langchain.prompts.loading
"""Load prompts from disk."""
import importlib
import json
import logging
from pathlib import Path
from typing import Union
import yaml
from langchain.output_parsers.regex import RegexParser
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.few_shot i... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
993f5b4517f8-1 | if template_path.suffix == ".txt":
with open(template_path) as f:
template = f.read()
else:
raise ValueError
# Set the template variable to the extracted variable.
config[var_name] = template
return config
def _load_examples(config: dict) -> dict:
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
993f5b4517f8-2 | config = _load_template("suffix", config)
config = _load_template("prefix", config)
# Load the example prompt.
if "example_prompt_path" in config:
if "example_prompt" in config:
raise ValueError(
"Only one of example_prompt and example_prompt_path should "
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
993f5b4517f8-3 | # Load from either json or yaml.
if file_path.suffix == ".json":
with open(file_path) as f:
config = json.load(f)
elif file_path.suffix == ".yaml":
with open(file_path, "r") as f:
config = yaml.safe_load(f)
elif file_path.suffix == ".py":
spec = importlib.util... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
a7df052e5c9b-0 | Source code for langchain.prompts.prompt
"""Prompt schema definition."""
from __future__ import annotations
from pathlib import Path
from string import Formatter
from typing import Any, Dict, List, Union
from pydantic import Extra, root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
S... | https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
a7df052e5c9b-1 | """
kwargs = self._merge_partial_and_user_variables(**kwargs)
return DEFAULT_FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
@root_validator()
def template_is_valid(cls, values: Dict) -> Dict:
"""Check that template and input variables are consistent."""
if value... | https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
a7df052e5c9b-2 | [docs] @classmethod
def from_file(
cls, template_file: Union[str, Path], input_variables: List[str], **kwargs: Any
) -> PromptTemplate:
"""Load a prompt from a file.
Args:
template_file: The path to the file containing the prompt template.
input_variables: A li... | https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
fefa10ececee-0 | Source code for langchain.prompts.few_shot
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
StringPromptTemplate,
check_valid_template,
)
from langcha... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
fefa10ececee-1 | """Check that one and only one of examples/example_selector are provided."""
examples = values.get("examples", None)
example_selector = values.get("example_selector", None)
if examples and example_selector:
raise ValueError(
"Only one of 'examples' and 'example_select... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
fefa10ececee-2 | # Get the examples to use.
examples = self._get_examples(**kwargs)
# Format the examples.
example_strings = [
self.example_prompt.format(**example) for example in examples
]
# Create the overall template.
pieces = [self.prefix, *example_strings, self.suffix]
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
45011ad08163-0 | Source code for langchain.prompts.chat
"""Chat prompt template."""
from __future__ import annotations
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, List, Sequence, Tuple, Type, Union
from pydantic import BaseModel, Field
from langchain.memory.buffer import get_buffer_str... | https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
45011ad08163-1 | def input_variables(self) -> List[str]:
"""Input variables for this prompt template."""
return [self.variable_name]
class BaseStringMessagePromptTemplate(BaseMessagePromptTemplate, ABC):
prompt: StringPromptTemplate
additional_kwargs: dict = Field(default_factory=dict)
@classmethod
def f... | https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
45011ad08163-2 | text = self.prompt.format(**kwargs)
return SystemMessage(content=text, additional_kwargs=self.additional_kwargs)
class ChatPromptValue(PromptValue):
messages: List[BaseMessage]
def to_string(self) -> str:
"""Return prompt as string."""
return get_buffer_string(self.messages)
def to_m... | https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
45011ad08163-3 | for role, template in string_messages
]
return cls.from_messages(messages)
@classmethod
def from_messages(
cls, messages: Sequence[Union[BaseMessagePromptTemplate, BaseMessage]]
) -> ChatPromptTemplate:
input_vars = set()
for message in messages:
if isinst... | https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
14bd143874f4-0 | Source code for langchain.prompts.base
"""BasePrompt schema definition."""
from __future__ import annotations
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
import yaml
from pydantic import BaseModel, Extra, Field, roo... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
14bd143874f4-1 | "jinja2 not installed, which is needed to use the jinja2_formatter. "
"Please install it with `pip install jinja2`."
)
env = Environment()
ast = env.parse(template)
variables = meta.find_undeclared_variables(ast)
return variables
DEFAULT_FORMATTER_MAPPING: Dict[str, Callable] = {
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
14bd143874f4-2 | """Base class for all prompt templates, returning a prompt."""
input_variables: List[str]
"""A list of the names of the variables the prompt template expects."""
output_parser: Optional[BaseOutputParser] = None
"""How to parse the output of calling an LLM on this formatted prompt."""
partial_variabl... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
14bd143874f4-3 | prompt_dict["input_variables"] = list(
set(self.input_variables).difference(kwargs)
)
prompt_dict["partial_variables"] = {**self.partial_variables, **kwargs}
return type(self)(**prompt_dict)
def _merge_partial_and_user_variables(self, **kwargs: Any) -> Dict[str, Any]:
# G... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
14bd143874f4-4 | # Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path
directory_path = save_path.parent
directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
prompt_dict = self.... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
0d41c32dc9ed-0 | Source code for langchain.prompts.few_shot_with_templates
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, StringPromptTemplate
from langchain.prompts.example_selec... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
0d41c32dc9ed-1 | examples = values.get("examples", None)
example_selector = values.get("example_selector", None)
if examples and example_selector:
raise ValueError(
"Only one of 'examples' and 'example_selector' should be provided"
)
if examples is None and example_selecto... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
0d41c32dc9ed-2 | kwargs: Any arguments to be passed to the prompt template.
Returns:
A formatted string.
Example:
.. code-block:: python
prompt.format(variable1="foo")
"""
kwargs = self._merge_partial_and_user_variables(**kwargs)
# Get the examples to use.
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
0d41c32dc9ed-3 | if self.example_selector:
raise ValueError("Saving an example selector is not currently supported")
return super().dict(**kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
698954b954d3-0 | Source code for langchain.prompts.example_selector.semantic_similarity
"""Example selector that selects examples based on SemanticSimilarity."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Type
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
fr... | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
698954b954d3-1 | return ids[0]
[docs] def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use based on semantic similarity."""
# Get the docs with the highest similarity.
if self.input_keys:
input_variables = {key: input_variables[key] for key in s... | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
698954b954d3-2 | instead of all variables.
vectorstore_cls_kwargs: optional kwargs containing url for vector store
Returns:
The ExampleSelector instantiated, backed by a vector store.
"""
if input_keys:
string_examples = [
" ".join(sorted_values({k: eg[k] for k... | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
698954b954d3-3 | examples = [dict(e.metadata) for e in example_docs]
# If example keys are provided, filter examples to those keys.
if self.example_keys:
examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
return examples
[docs] @classmethod
def from_examples(
cls,
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
698954b954d3-4 | string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
)
return cls(vectorstore=vectorstore, k=k, fetch_k=fetch_k, input_keys=input_keys)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
ff51f473adf9-0 | Source code for langchain.prompts.example_selector.length_based
"""Select examples based on length."""
import re
from typing import Callable, Dict, List
from pydantic import BaseModel, validator
from langchain.prompts.example_selector.base import BaseExampleSelector
from langchain.prompts.prompt import PromptTemplate
d... | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
ff51f473adf9-1 | get_text_length = values["get_text_length"]
string_examples = [example_prompt.format(**eg) for eg in values["examples"]]
return [get_text_length(eg) for eg in string_examples]
[docs] def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use base... | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
df312bff5f77-0 | 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... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent_types.html |
2899bd35aa90-0 | 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 BaseSingleActionAgent
from langchain.agents.tools import Tool
from langchain.agents.types import AGENT_TO_CLA... | https://python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
2899bd35aa90-1 | "If `load_from_llm_and_tools` is set to True, "
"then LLM must be provided"
)
if tools is None:
raise ValueError(
"If `load_from_llm_and_tools` is set to True, "
"then tools must be provided"
)
return _load_agent_from_to... | https://python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
2899bd35aa90-2 | """Load agent from file."""
# Convert file to Path object.
if isinstance(file, str):
file_path = Path(file)
else:
file_path = file
# Load from either json or yaml.
if file_path.suffix == ".json":
with open(file_path) as f:
config = json.load(f)
elif file_path.... | https://python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
e4631a6ffabd-0 | Source code for langchain.agents.load_tools
# flake8: noqa
"""Load tools."""
import warnings
from typing import Any, Dict, List, Optional, Callable, Tuple
from mypy_extensions import Arg, KwArg
from langchain.agents.tools import Tool
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.api imp... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
e4631a6ffabd-1 | from langchain.utilities.bing_search import BingSearchAPIWrapper
from langchain.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from langchain.utilities.google_search import GoogleSearchAPIWrapper
from langchain.utilities.google_serper import GoogleSerperAPIWrapper
from langchain.utilities.awslambda impor... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
e4631a6ffabd-2 | "requests_delete": _get_tools_requests_delete,
"terminal": _get_terminal,
}
def _get_pal_math(llm: BaseLLM) -> BaseTool:
return Tool(
name="PAL-MATH",
description="A language model that is really good at solving complex word math problems. Input should be a fully worded hard word math problem.",... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
e4631a6ffabd-3 | func=chain.run,
)
_LLM_TOOLS: Dict[str, Callable[[BaseLLM], BaseTool]] = {
"pal-math": _get_pal_math,
"pal-colored-objects": _get_pal_colored_objects,
"llm-math": _get_llm_math,
"open-meteo-api": _get_open_meteo_api,
}
def _get_news_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
news_api_key = kw... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
e4631a6ffabd-4 | listen_api_key = kwargs["listen_api_key"]
chain = APIChain.from_llm_and_api_docs(
llm,
podcast_docs.PODCAST_DOCS,
headers={"X-ListenAPI-Key": listen_api_key},
)
return Tool(
name="Podcast API",
description="Use the Listen Notes Podcast API to search all podcasts or ep... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
e4631a6ffabd-5 | )
def _get_google_search_results_json(**kwargs: Any) -> BaseTool:
return GoogleSearchResults(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
def _get_serpapi(**kwargs: Any) -> BaseTool:
return Tool(
name="Search",
description="A search engine. Useful for when you need to answer questions about cur... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
e4631a6ffabd-6 | ] = {
"news-api": (_get_news_api, ["news_api_key"]),
"tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),
"podcast-api": (_get_podcast_api, ["listen_api_key"]),
}
_EXTRA_OPTIONAL_TOOLS: Dict[str, Tuple[Callable[[KwArg(Any)], BaseTool], List[str]]] = {
"wolfram-alpha": (_get_wolfram_alpha, ["wolfram_alpha... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
e4631a6ffabd-7 | ),
"human": (_get_human_tool, ["prompt_func", "input_func"]),
"awslambda": (
_get_lambda_api,
["awslambda_tool_name", "awslambda_tool_description", "function_name"],
),
"sceneXplain": (_get_scenexplain, []),
}
[docs]def load_tools(
tool_names: List[str],
llm: Optional[BaseLLM] = ... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
e4631a6ffabd-8 | tool = _LLM_TOOLS[name](llm)
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")
... | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
e4631a6ffabd-9 | + list(_LLM_TOOLS)
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https://python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
430e17973d1e-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.base_language import BaseLanguageMod... | https://python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
430e17973d1e-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... | https://python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
0bbf4da26039-0 | 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, Set,... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-1 | callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
callbacks: Callbacks to run.
**kwargs: ... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-2 | f"Got unsupported early_stopping_method `{early_stopping_method}`"
)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
) -> BaseSingleAc... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-3 | with open(file_path, "w") as f:
yaml.dump(agent_dict, f, default_flow_style=False)
else:
raise ValueError(f"{save_path} must be json or yaml")
[docs] def tool_run_logging_kwargs(self) -> Dict:
return {}
[docs]class BaseMultiActionAgent(BaseModel):
"""Base Agent class."... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-4 | Actions specifying what tool to use.
"""
@property
@abstractmethod
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
[docs] def return_stopped_response(
self,
early_stopping_method: str,
intermediate_steps: List[Tuple[A... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-5 | else:
save_path = file_path
directory_path = save_path.parent
directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
agent_dict = self.dict()
if save_path.suffix == ".json":
with open(file_path, "w") as f:
json.dump(ag... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-6 | **kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
output = self.llm_chain.run(
intermediate_steps=intermediate_steps,
stop=self.stop,
callbacks=callbacks,
**kwargs,
)
return self.output_parser.parse... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-7 | output_parser: AgentOutputParser
allowed_tools: Set[str] = set()
[docs] def get_allowed_tools(self) -> Set[str]:
return self.allowed_tools
@property
def return_values(self) -> List[str]:
return ["output"]
def _fix_text(self, text: str) -> str:
"""Fix the text."""
raise... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-8 | """
full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
return self.output_parser.parse(full_output)
[docs] async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
c... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-9 | @root_validator()
def validate_prompt(cls, values: Dict) -> Dict:
"""Validate that prompt matches format."""
prompt = values["llm_chain"].prompt
if "agent_scratchpad" not in prompt.input_variables:
logger.warning(
"`agent_scratchpad` should be a variable in prompt... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-10 | llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
**kwargs: Any,
) -> Agent:
"""Construct an agent from an LLM and tools."""
cls._validate_tools(tools)
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-11 | thoughts += (
"\n\nI now need to return a final answer based on the previous steps:"
)
new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
full_inputs = {**kwargs, **new_inputs}
full_output = self.llm_chain.predict(**full_inputs)
# ... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-12 | agent: Union[BaseSingleActionAgent, BaseMultiActionAgent]
tools: Sequence[BaseTool]
return_intermediate_steps: bool = False
max_iterations: Optional[int] = 15
max_execution_time: Optional[float] = None
early_stopping_method: str = "force"
handle_parsing_errors: bool = False
[docs] @classmetho... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-13 | raise ValueError(
"Tools that have `return_direct=True` are not allowed "
"in multi-action agents"
)
return values
[docs] def save(self, file_path: Union[Path, str]) -> None:
"""Raise error - saving not supported for Agent Executors.... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-14 | self,
output: AgentFinish,
intermediate_steps: list,
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
if run_manager:
run_manager.on_agent_finish(output, color="green", verbose=self.verbose)
final_output = output.return_values
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-15 | **inputs,
)
except Exception as e:
if not self.handle_parsing_errors:
raise e
text = str(e).split("`")[1]
observation = "Invalid or incomplete response"
output = AgentAction("_Exception", observation, text)
tool_run_kwargs =... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-16 | )
else:
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
observation = InvalidTool().run(
agent_action.tool,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manage... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-17 | **tool_run_kwargs,
)
return [(output, observation)]
# If the tool chosen is the finishing tool, then we end and return.
if isinstance(output, AgentFinish):
return output
actions: List[AgentAction]
if isinstance(output, AgentAction):
actions... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-18 | result = await asyncio.gather(
*[_aperform_agent_action(agent_action) for agent_action in actions]
)
return list(result)
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run text... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-19 | if tool_return is not None:
return self._return(tool_return, intermediate_steps)
iterations += 1
time_elapsed = time.time() - start_time
output = self.agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
0bbf4da26039-20 | intermediate_steps.extend(next_step_output)
if len(next_step_output) == 1:
next_step_action = next_step_output[0]
# See if tool should return directly
tool_return = self._get_tool_return(next_step_action)
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
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