id stringlengths 14 16 | text stringlengths 45 2.05k | source stringlengths 53 111 |
|---|---|---|
ada8662685e8-0 | .ipynb
.pdf
Tracing Walkthrough
Tracing Walkthrough#
import os
os.environ["LANGCHAIN_HANDLER"] = "langchain"
## Uncomment this if using hosted setup.
# os.environ["LANGCHAIN_ENDPOINT"] = "https://langchain-api-gateway-57eoxz8z.uc.gateway.dev"
## Uncomment this if you want traces to be recorded to "my_session" instead ... | https://langchain.readthedocs.io/en/latest/tracing/agent_with_tracing.html |
ada8662685e8-1 | # Agent run with tracing using a chat model
agent = initialize_agent(
tools, ChatOpenAI(temperature=0), agent="chat-zero-shot-react-description", verbose=True
)
agent.run("What is 2 raised to .123243 power?")
> Entering new AgentExecutor chain...
Question: What is 2 raised to .123243 power?
Thought: I need a calcul... | https://langchain.readthedocs.io/en/latest/tracing/agent_with_tracing.html |
ada8662685e8-2 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/tracing/agent_with_tracing.html |
755e55f35257-0 | Source code for langchain.python
"""Mock Python REPL."""
import sys
from io import StringIO
from typing import Dict, Optional
from pydantic import BaseModel, Field
[docs]class PythonREPL(BaseModel):
"""Simulates a standalone Python REPL."""
globals: Optional[Dict] = Field(default_factory=dict, alias="_globals")... | https://langchain.readthedocs.io/en/latest/_modules/langchain/python.html |
edbf3b6c8fda-0 | Source code for langchain.text_splitter
"""Functionality for splitting text."""
from __future__ import annotations
import copy
import logging
from abc import ABC, abstractmethod
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Iterable,
List,
Literal,
Optional,
Union,
)
f... | https://langchain.readthedocs.io/en/latest/_modules/langchain/text_splitter.html |
edbf3b6c8fda-1 | page_content=chunk, metadata=copy.deepcopy(_metadatas[i])
)
documents.append(new_doc)
return documents
[docs] def split_documents(self, documents: List[Document]) -> List[Document]:
"""Split documents."""
texts = [doc.page_content for doc in documents]
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/text_splitter.html |
edbf3b6c8fda-2 | # - we have a larger chunk than in the chunk overlap
# - or if we still have any chunks and the length is long
while total > self._chunk_overlap or (
total + _len + (separator_len if len(current_doc) > 0 else 0)
> self._chunk_size
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/text_splitter.html |
edbf3b6c8fda-3 | cls,
encoding_name: str = "gpt2",
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any,
) -> TextSplitter:
"""Text splitter that uses tiktoken encoder to count length."""
tr... | https://langchain.readthedocs.io/en/latest/_modules/langchain/text_splitter.html |
edbf3b6c8fda-4 | splits = text.split(self._separator)
else:
splits = list(text)
return self._merge_splits(splits, self._separator)
[docs]class TokenTextSplitter(TextSplitter):
"""Implementation of splitting text that looks at tokens."""
def __init__(
self,
encoding_name: str = "gpt2",... | https://langchain.readthedocs.io/en/latest/_modules/langchain/text_splitter.html |
edbf3b6c8fda-5 | start_idx += self._chunk_size - self._chunk_overlap
cur_idx = min(start_idx + self._chunk_size, len(input_ids))
chunk_ids = input_ids[start_idx:cur_idx]
return splits
[docs]class RecursiveCharacterTextSplitter(TextSplitter):
"""Implementation of splitting text that looks at character... | https://langchain.readthedocs.io/en/latest/_modules/langchain/text_splitter.html |
edbf3b6c8fda-6 | _good_splits = []
other_info = self.split_text(s)
final_chunks.extend(other_info)
if _good_splits:
merged_text = self._merge_splits(_good_splits, separator)
final_chunks.extend(merged_text)
return final_chunks
[docs]class NLTKTextSplitter(TextSplit... | https://langchain.readthedocs.io/en/latest/_modules/langchain/text_splitter.html |
edbf3b6c8fda-7 | self._tokenizer = spacy.load(pipeline)
self._separator = separator
[docs] def split_text(self, text: str) -> List[str]:
"""Split incoming text and return chunks."""
splits = (str(s) for s in self._tokenizer(text).sents)
return self._merge_splits(splits, self._separator)
[docs]class Ma... | https://langchain.readthedocs.io/en/latest/_modules/langchain/text_splitter.html |
edbf3b6c8fda-8 | """Initialize a LatexTextSplitter."""
separators = [
# First, try to split along Latex sections
"\n\\chapter{",
"\n\\section{",
"\n\\subsection{",
"\n\\subsubsection{",
# Now split by environments
"\n\\begin{enumerate}",
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/text_splitter.html |
d8fdf83a33fd-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://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
d8fdf83a33fd-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://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
d8fdf83a33fd-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://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/huggingface_hub.html |
041e9105d6ae-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 BaseModel, Extra
from langchain.embeddings.base import Embeddings
from langchain.llms import SelfHostedPipeline
def _embed_documents(pipeline... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/self_hosted.html |
041e9105d6ae-1 | embeddings = SelfHostedEmbeddings(
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... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/self_hosted.html |
041e9105d6ae-2 | if not isinstance(embeddings, list):
return embeddings.tolist()
return embeddings
[docs] def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embe... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/self_hosted.html |
e41651ce0cf9-0 | 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://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
e41651ce0cf9-1 | """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... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
e41651ce0cf9-2 | 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... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
e41651ce0cf9-3 | _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(
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
e41651ce0cf9-4 | """
return self._embedding_func([text])
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/sagemaker_endpoint.html |
d51ba4cf81f6-0 | 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://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
d51ba4cf81f6-1 | 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://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/tensorflow_hub.html |
c7cce7b8bfd2-0 | 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 pydantic import BaseModel
from langchain.embeddings.self_hosted import SelfHostedEmbeddin... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
c7cce7b8bfd2-1 | )
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://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
c7cce7b8bfd2-2 | 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://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
c7cce7b8bfd2-3 | 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://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
c7cce7b8bfd2-4 | 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://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/self_hosted_hugging_face.html |
7e4197bc5958-0 | 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... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/cohere.html |
7e4197bc5958-1 | 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:... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/cohere.html |
368d367145ea-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... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/huggingface.html |
368d367145ea-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)
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/huggingface.html |
368d367145ea-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 ... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/huggingface.html |
c47583248c94-0 | 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://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/fake.html |
4093091b8392-0 | 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... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/openai.html |
4093091b8392-1 | 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... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/openai.html |
4093091b8392-2 | 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-... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/openai.html |
4093091b8392-3 | "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"text-search-{model_name}-doc-001"
values["query_model_name"] = f"text-search-{model_name}... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/openai.html |
4093091b8392-4 | def _get_len_safe_embeddings(
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
) -> List[List[float]]:
embeddings: List[List[float]] = [[] for i in range(len(texts))]
try:
import tiktoken
tokens = []
indices = []
encodin... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/openai.html |
4093091b8392-5 | return embeddings
except ImportError:
raise ValueError(
"Could not import tiktoken python package. "
"This is needed in order to for OpenAIEmbeddings. "
"Please it install it with `pip install tiktoken`."
)
def _embedding_func(self, tex... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/openai.html |
4093091b8392-6 | engine=self.document_model_name,
)
results += [r["embedding"] for r in response["data"]]
return results
[docs] def embed_query(self, text: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint for embedding query text.
Args:
text: The ... | https://langchain.readthedocs.io/en/latest/_modules/langchain/embeddings/openai.html |
716d8e1e1e0b-0 | Source code for langchain.agents.initialize
"""Load agent."""
from typing import Any, Optional, Sequence
from langchain.agents.agent import AgentExecutor
from langchain.agents.loading import AGENT_TO_CLASS, load_agent
from langchain.callbacks.base import BaseCallbackManager
from langchain.llms.base import BaseLLM
from ... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/initialize.html |
716d8e1e1e0b-1 | agent = "zero-shot-react-description"
if agent is not None and agent_path is not None:
raise ValueError(
"Both `agent` and `agent_path` are specified, "
"but at most only one should be."
)
if agent is not None:
if agent not in AGENT_TO_CLASS:
raise Val... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/initialize.html |
386869a38995-0 | Source code for langchain.agents.agent
"""Chain that takes in an input and produces an action and action input."""
from __future__ import annotations
import json
import logging
from abc import abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import yaml
from p... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-1 | @property
def _stop(self) -> List[str]:
return [
f"\n{self.observation_prefix.rstrip()}",
f"\n\t{self.observation_prefix.rstrip()}",
]
def _construct_scratchpad(
self, intermediate_steps: List[Tuple[AgentAction, str]]
) -> Union[str, List[BaseMessage]]:
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-2 | output = await self.llm_chain.apredict(**full_inputs)
full_output += output
parsed_output = self._extract_tool_and_input(full_output)
return AgentAction(
tool=parsed_output[0], tool_input=parsed_output[1], log=full_output
)
[docs] def plan(
self, intermedia... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-3 | return action
[docs] def get_full_inputs(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Dict[str, Any]:
"""Create the full inputs for the LLMChain from intermediate steps."""
thoughts = self._construct_scratchpad(intermediate_steps)
new_inputs = {"age... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-4 | prompt.suffix += "\n{agent_scratchpad}"
else:
raise ValueError(f"Got unexpected prompt type {type(prompt)}")
return values
@property
@abstractmethod
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
@property
@abstractmethod... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-5 | """Return response when agent has been stopped due to max iterations."""
if early_stopping_method == "force":
# `force` just returns a constant string
return AgentFinish({"output": "Agent stopped due to max iterations."}, "")
elif early_stopping_method == "generate":
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-6 | "early_stopping_method should be one of `force` or `generate`, "
f"got {early_stopping_method}"
)
@property
@abstractmethod
def _agent_type(self) -> str:
"""Return Identifier of agent type."""
[docs] def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary r... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-7 | agent: Agent
tools: Sequence[BaseTool]
return_intermediate_steps: bool = False
max_iterations: Optional[int] = 15
early_stopping_method: str = "force"
[docs] @classmethod
def from_agent_and_tools(
cls,
agent: Agent,
tools: Sequence[BaseTool],
callback_manager: Opti... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-8 | @property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return self.agent.input_keys
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
:meta private:
"""
if self.return_intermedia... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-9 | color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
) -> Union[AgentFinish, Tuple[AgentAction, str]]:
"""Take a single step in the thought-action-observation loop.
Override this to take control of how the agent makes and acts on c... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-10 | color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
) -> Union[AgentFinish, Tuple[AgentAction, str]]:
"""Take a single step in the thought-action-observation loop.
Override this to take control of how the agent makes and acts on c... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-11 | )
return_direct = False
return output, observation
def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
"""Run text through and get agent response."""
# Do any preparation necessary when receiving a new input.
self.agent.prepare_for_new_call()
# Construct a ... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-12 | """Run text through and get agent response."""
# Do any preparation necessary when receiving a new input.
self.agent.prepare_for_new_call()
# Construct a mapping of tool name to tool for easy lookup
name_to_tool_map = {tool.name: tool for tool in self.tools}
# We construct a mapp... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
386869a38995-13 | agent_action, observation = next_step_output
name_to_tool_map = {tool.name: tool for tool in self.tools}
# Invalid tools won't be in the map, so we return False.
if agent_action.tool in name_to_tool_map:
if name_to_tool_map[agent_action.tool].return_direct:
return Age... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent.html |
e4bddde9abdb-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... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/tools.html |
e4bddde9abdb-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... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/tools.html |
e4bddde9abdb-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... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/tools.html |
7a1b7ed99bb5-0 | 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.chat.base import ChatAgent
from langchain.agents.conversational.base import Conve... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/loading.html |
7a1b7ed99bb5-1 | 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, **combined_config)
def load_agent_from_config(
config: dict,
llm: Optional[BaseLLM] = None,
tools: Optiona... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/loading.html |
7a1b7ed99bb5-2 | 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_agent(path: Union[str, Path], **kwargs: Any) -> Agent:
"""Unified method for loading a agent from LangChain... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/loading.html |
88ac559f881d-0 | Source code for langchain.agents.load_tools
# flake8: noqa
"""Load tools."""
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, tmdb_docs, podcast_docs
from langchain.chains.... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/load_tools.html |
88ac559f881d-1 | def _get_terminal() -> BaseTool:
return Tool(
name="Terminal",
description="Executes commands in a terminal. Input should be valid commands, and the output will be any output from running that command.",
func=BashProcess().run,
)
_BASE_TOOLS = {
"python_repl": _get_python_repl,
"... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/load_tools.html |
88ac559f881d-2 | def _get_open_meteo_api(llm: BaseLLM) -> BaseTool:
chain = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS)
return Tool(
name="Open Meteo API",
description="Useful for when you want to get weather information from the OpenMeteo API. The input should be a question in natural l... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/load_tools.html |
88ac559f881d-3 | )
return Tool(
name="TMDB API",
description="Useful for when you want to get information from The Movie Database. The input should be a question in natural language that this API can answer.",
func=chain.run,
)
def _get_podcast_api(llm: BaseLLM, **kwargs: Any) -> BaseTool:
listen_api... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/load_tools.html |
88ac559f881d-4 | 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 current events. Input should be a search query.",
func=SerpAPIWra... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/load_tools.html |
88ac559f881d-5 | "google-serper": (_get_google_serper, ["serper_api_key"]),
"serpapi": (_get_serpapi, ["serpapi_api_key", "aiosession"]),
"searx-search": (_get_searx_search, ["searx_host"]),
"wikipedia": (_get_wikipedia, ["top_k_results"]),
}
[docs]def load_tools(
tool_names: List[str],
llm: Optional[BaseLLM] = None... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/load_tools.html |
88ac559f881d-6 | missing_keys = set(extra_keys).difference(kwargs)
if missing_keys:
raise ValueError(
f"Tool {name} requires some parameters that were not "
f"provided: {missing_keys}"
)
sub_kwargs = {k: kwargs[k] for k in extra_keys}
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/load_tools.html |
627ef4a50492-0 | Source code for langchain.agents.self_ask_with_search.base
"""Chain that does self ask with search."""
from typing import Any, Optional, Sequence, Tuple, Union
from langchain.agents.agent import Agent, AgentExecutor
from langchain.agents.self_ask_with_search.prompt import PROMPT
from langchain.agents.tools import Tool
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
627ef4a50492-1 | if finish_string not in last_line:
return None
return "Final Answer", last_line[len(finish_string) :]
after_colon = text.split(":")[-1]
if " " == after_colon[0]:
after_colon = after_colon[1:]
return "Intermediate Answer", after_colon
def _fix_text(self... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
627ef4a50492-2 | name="Intermediate Answer", func=search_chain.run, description="Search"
)
agent = SelfAskWithSearchAgent.from_llm_and_tools(llm, [search_tool])
super().__init__(agent=agent, tools=[search_tool], **kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
8e2bc1355ec9-0 | Source code for langchain.agents.mrkl.base
"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
from __future__ import annotations
import re
from typing import Any, Callable, List, NamedTuple, Optional, Sequence, Tuple
from langchain.agents.agent import Agent, AgentExecutor
from langcha... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/mrkl/base.html |
8e2bc1355ec9-1 | regex = r"Action: (.*?)[\n]*Action Input: (.*)"
match = re.search(regex, llm_output, re.DOTALL)
if not match:
raise ValueError(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1).strip()
action_input = match.group(2)
return action, action_input.strip(" ").strip('"')
[docs]... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/mrkl/base.html |
8e2bc1355ec9-2 | Returns:
A PromptTemplate with the template assembled from the pieces here.
"""
tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
tool_names = ", ".join([tool.name for tool in tools])
format_instructions = format_instructions.format(tool_names=t... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/mrkl/base.html |
8e2bc1355ec9-3 | for tool in tools:
if tool.description is None:
raise ValueError(
f"Got a tool {tool.name} without a description. For this agent, "
f"a description must always be provided."
)
def _extract_tool_and_input(self, text: str) -> Optional... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/mrkl/base.html |
8e2bc1355ec9-4 | llm = OpenAI(temperature=0)
search = SerpAPIWrapper()
llm_math_chain = LLMMathChain(llm=llm)
chains = [
ChainConfig(
action_name = "Search",
action=search.search,
action_descriptio... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/mrkl/base.html |
228aa236010f-0 | Source code for langchain.agents.agent_toolkits.openapi.base
"""OpenAPI spec agent."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.openapi.prompt import (
OPENAPI_PREFIX,
OPENAPI_SUFFIX,
)
from langchain.agents.agent_toolkits.opena... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
228aa236010f-1 | return AgentExecutor.from_agent_and_tools(
agent=agent, tools=toolkit.get_tools(), verbose=verbose
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
551ba7dd874b-0 | Source code for langchain.agents.agent_toolkits.json.base
"""Json agent."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
from l... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
551ba7dd874b-1 | )
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/json/base.html |
2b7019c0d7b9-0 | Source code for langchain.agents.agent_toolkits.csv.base
"""Agent for working with csvs."""
from typing import Any, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain.llms.base import BaseLLM
[docs]def create_csv... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/csv/base.html |
325c769a5659-0 | Source code for langchain.agents.agent_toolkits.pandas.base
"""Agent for working with pandas objects."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.pandas.prompt import PREFIX, SUFFIX
from langchain.agents.mrkl.base import ZeroShotAgent
f... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
325c769a5659-1 | )
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=verbose)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 202... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/pandas/base.html |
15ffc20941c6-0 | Source code for langchain.agents.agent_toolkits.vectorstore.base
"""VectorStore agent."""
from typing import Any, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.vectorstore.prompt import PREFIX, ROUTER_PREFIX
from langchain.agents.agent_toolkits.vectorstore.toolkit import... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
15ffc20941c6-1 | prefix: str = ROUTER_PREFIX,
verbose: bool = False,
**kwargs: Any,
) -> AgentExecutor:
"""Construct a vectorstore router agent from an LLM and tools."""
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
llm_chain = LLMChain(
llm=llm,
prompt=pr... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/vectorstore/base.html |
9274591ff1f5-0 | Source code for langchain.agents.agent_toolkits.sql.base
"""SQL agent."""
from typing import Any, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.sql.prompt import SQL_PREFIX, SQL_SUFFIX
from langchain.agents.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
from ... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
9274591ff1f5-1 | return AgentExecutor.from_agent_and_tools(
agent=agent, tools=toolkit.get_tools(), verbose=verbose
)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Mar 22, 2023. | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/agent_toolkits/sql/base.html |
550a3a234792-0 | Source code for langchain.agents.conversational.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
import re
from typing import Any, List, Optional, Sequence, Tuple
from langchain.agents.agent import Agent
from langchain.agents.conversational.prompt import... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/conversational/base.html |
550a3a234792-1 | prompt.
prefix: String to put before the list of tools.
suffix: String to put after the list of tools.
ai_prefix: String to use before AI output.
human_prefix: String to use before human output.
input_variables: List of input variables the final prompt will ex... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/conversational/base.html |
550a3a234792-2 | action = match.group(1)
action_input = match.group(2)
return action.strip(), action_input.strip(" ").strip('"')
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLLM,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/conversational/base.html |
9c96b057d5bd-0 | Source code for langchain.agents.react.base
"""Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
import re
from typing import Any, List, Optional, Sequence, Tuple
from pydantic import BaseModel
from langchain.agents.agent import Agent, AgentExecutor
from langchain.agents.react.textworl... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/react/base.html |
9c96b057d5bd-1 | def _fix_text(self, text: str) -> str:
return text + f"\nAction {self.i}:"
def _extract_tool_and_input(self, text: str) -> Optional[Tuple[str, str]]:
action_prefix = f"Action {self.i}: "
if not text.split("\n")[-1].startswith(action_prefix):
return None
self.i += 1
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/react/base.html |
9c96b057d5bd-2 | self.docstore = docstore
self.document: Optional[Document] = None
def search(self, term: str) -> str:
"""Search for a term in the docstore, and if found save."""
result = self.docstore.search(term)
if isinstance(result, Document):
self.document = result
return... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/react/base.html |
9c96b057d5bd-3 | """Initialize with the LLM and a docstore."""
docstore_explorer = DocstoreExplorer(docstore)
tools = [
Tool(
name="Search",
func=docstore_explorer.search,
description="Search for a term in the docstore.",
),
Tool(
... | https://langchain.readthedocs.io/en/latest/_modules/langchain/agents/react/base.html |
1092c5dca18c-0 | Source code for langchain.utilities.searx_search
"""Utility for using SearxNG meta search API.
SearxNG is a privacy-friendly free metasearch engine that aggregates results from
`multiple search engines
<https://docs.searxng.org/admin/engines/configured_engines.html>`_ and databases and
supports the `OpenSearch
<https:... | https://langchain.readthedocs.io/en/latest/_modules/langchain/utilities/searx_search.html |
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