index int64 0 0 | repo_id stringclasses 596 values | file_path stringlengths 31 168 | content stringlengths 1 6.2M |
|---|---|---|---|
0 | lc_public_repos/langchain/libs/langchain/tests/unit_tests/smith | lc_public_repos/langchain/libs/langchain/tests/unit_tests/smith/evaluation/test_string_run_evaluator.py | """Tests for the string run evaluator."""
from unittest.mock import MagicMock
from langchain.evaluation import criteria
from langchain.smith.evaluation.string_run_evaluator import (
ChainStringRunMapper,
StringRunEvaluatorChain,
)
from tests.unit_tests.llms import fake_llm
def test_evaluate_run() -> None:
run_mapper = ChainStringRunMapper()
string_evaluator = criteria.CriteriaEvalChain.from_llm(fake_llm.FakeLLM())
evaluator = StringRunEvaluatorChain(
run_mapper=run_mapper,
example_mapper=None,
name="test_evaluator",
string_evaluator=string_evaluator,
)
run = MagicMock()
example = MagicMock()
res = evaluator.evaluate_run(run, example)
assert str(res.comment).startswith("Error evaluating run ")
assert res.key == string_evaluator.evaluation_name
|
0 | lc_public_repos/langchain/libs/langchain/tests/unit_tests/smith | lc_public_repos/langchain/libs/langchain/tests/unit_tests/smith/evaluation/test_runner_utils.py | """Test the LangSmith evaluation helpers."""
import uuid
from datetime import datetime
from typing import Any, Dict, Iterator, List, Optional, Union
from unittest import mock
import pytest
from freezegun import freeze_time
from langchain_core.language_models import BaseLanguageModel
from langsmith.client import Client
from langsmith.schemas import Dataset, Example
from langchain.chains.base import Chain
from langchain.chains.transform import TransformChain
from langchain.smith.evaluation.runner_utils import (
InputFormatError,
_get_messages,
_get_prompt,
_run_llm,
_run_llm_or_chain,
_validate_example_inputs_for_chain,
_validate_example_inputs_for_language_model,
arun_on_dataset,
)
from tests.unit_tests.llms.fake_chat_model import FakeChatModel
from tests.unit_tests.llms.fake_llm import FakeLLM
_CREATED_AT = datetime(2015, 1, 1, 0, 0, 0)
_TENANT_ID = "7a3d2b56-cd5b-44e5-846f-7eb6e8144ce4"
_EXAMPLE_MESSAGE = {
"data": {"content": "Foo", "example": False, "additional_kwargs": {}},
"type": "human",
}
_VALID_MESSAGES = [
{"messages": [_EXAMPLE_MESSAGE], "other_key": "value"},
{"messages": [], "other_key": "value"},
{
"messages": [[_EXAMPLE_MESSAGE, _EXAMPLE_MESSAGE]],
"other_key": "value",
},
{"any_key": [_EXAMPLE_MESSAGE]},
{"any_key": [[_EXAMPLE_MESSAGE, _EXAMPLE_MESSAGE]]},
]
_VALID_PROMPTS = [
{"prompts": ["foo"], "other_key": "value"},
{"prompt": "foo", "other_key": ["bar", "baz"]},
{"some_key": "foo"},
{"some_key": ["foo"]},
]
_INVALID_PROMPTS = (
[
{"prompts": "foo"},
{"prompt": ["foo"]},
{"some_key": 3},
{"some_key": "foo", "other_key": "bar"},
],
)
@pytest.mark.parametrize(
"inputs",
_VALID_MESSAGES,
)
def test__get_messages_valid(inputs: Dict[str, Any]) -> None:
{"messages": []}
_get_messages(inputs)
@pytest.mark.parametrize(
"inputs",
_VALID_PROMPTS,
)
def test__get_prompts_valid(inputs: Dict[str, Any]) -> None:
_get_prompt(inputs)
@pytest.mark.parametrize(
"inputs",
_VALID_PROMPTS,
)
def test__validate_example_inputs_for_language_model(inputs: Dict[str, Any]) -> None:
mock_ = mock.MagicMock()
mock_.inputs = inputs
_validate_example_inputs_for_language_model(mock_, None)
@pytest.mark.parametrize(
"inputs",
_INVALID_PROMPTS,
)
def test__validate_example_inputs_for_language_model_invalid(
inputs: Dict[str, Any],
) -> None:
mock_ = mock.MagicMock()
mock_.inputs = inputs
with pytest.raises(InputFormatError):
_validate_example_inputs_for_language_model(mock_, None)
def test__validate_example_inputs_for_chain_single_input() -> None:
mock_ = mock.MagicMock()
mock_.inputs = {"foo": "bar"}
chain = mock.MagicMock()
chain.input_keys = ["def not foo"]
_validate_example_inputs_for_chain(mock_, chain, None)
def test__validate_example_inputs_for_chain_input_mapper() -> None:
mock_ = mock.MagicMock()
mock_.inputs = {"foo": "bar", "baz": "qux"}
chain = mock.MagicMock()
chain.input_keys = ["not foo", "not baz", "not qux"]
def wrong_output_format(inputs: dict) -> str:
assert "foo" in inputs
assert "baz" in inputs
return "hehe"
with pytest.raises(InputFormatError, match="must be a dictionary"):
_validate_example_inputs_for_chain(mock_, chain, wrong_output_format)
def wrong_output_keys(inputs: dict) -> dict:
assert "foo" in inputs
assert "baz" in inputs
return {"not foo": "foo", "not baz": "baz"}
with pytest.raises(InputFormatError, match="Missing keys after loading example"):
_validate_example_inputs_for_chain(mock_, chain, wrong_output_keys)
def input_mapper(inputs: dict) -> dict:
assert "foo" in inputs
assert "baz" in inputs
return {"not foo": inputs["foo"], "not baz": inputs["baz"], "not qux": "qux"}
_validate_example_inputs_for_chain(mock_, chain, input_mapper)
def test__validate_example_inputs_for_chain_multi_io() -> None:
mock_ = mock.MagicMock()
mock_.inputs = {"foo": "bar", "baz": "qux"}
chain = mock.MagicMock()
chain.input_keys = ["foo", "baz"]
_validate_example_inputs_for_chain(mock_, chain, None)
def test__validate_example_inputs_for_chain_single_input_multi_expect() -> None:
mock_ = mock.MagicMock()
mock_.inputs = {"foo": "bar"}
chain = mock.MagicMock()
chain.input_keys = ["def not foo", "oh here is another"]
with pytest.raises(InputFormatError, match="Example inputs missing expected"):
_validate_example_inputs_for_chain(mock_, chain, None)
@pytest.mark.parametrize("inputs", _INVALID_PROMPTS)
def test__get_prompts_invalid(inputs: Dict[str, Any]) -> None:
with pytest.raises(InputFormatError):
_get_prompt(inputs)
def test_run_llm_or_chain_with_input_mapper() -> None:
example = Example(
id=uuid.uuid4(),
created_at=_CREATED_AT,
inputs={"the wrong input": "1", "another key": "2"},
outputs={"output": "2"},
dataset_id=str(uuid.uuid4()),
)
def run_val(inputs: dict) -> dict:
assert "the right input" in inputs
return {"output": "2"}
mock_chain = TransformChain( # type: ignore[call-arg]
input_variables=["the right input"],
output_variables=["output"],
transform=run_val,
)
def input_mapper(inputs: dict) -> dict:
assert "the wrong input" in inputs
return {"the right input": inputs["the wrong input"]}
result = _run_llm_or_chain(
example,
{"callbacks": [], "tags": []},
llm_or_chain_factory=lambda: mock_chain,
input_mapper=input_mapper,
)
assert result == {"output": "2", "the right input": "1"}
bad_result = _run_llm_or_chain(
example, {"callbacks": [], "tags": []}, llm_or_chain_factory=lambda: mock_chain
)
assert "Error" in bad_result
# Try with LLM
def llm_input_mapper(inputs: dict) -> str:
assert "the wrong input" in inputs
return "the right input"
mock_llm = FakeLLM(queries={"the right input": "somenumber"})
llm_result = _run_llm_or_chain(
example,
{"callbacks": [], "tags": []},
llm_or_chain_factory=mock_llm,
input_mapper=llm_input_mapper,
)
assert isinstance(llm_result, str)
assert llm_result == "somenumber"
@pytest.mark.parametrize(
"inputs",
[
{"one_key": [_EXAMPLE_MESSAGE], "other_key": "value"},
{
"messages": [[_EXAMPLE_MESSAGE, _EXAMPLE_MESSAGE], _EXAMPLE_MESSAGE],
"other_key": "value",
},
{"prompts": "foo"},
{},
],
)
def test__get_messages_invalid(inputs: Dict[str, Any]) -> None:
with pytest.raises(InputFormatError):
_get_messages(inputs)
@pytest.mark.parametrize("inputs", _VALID_PROMPTS + _VALID_MESSAGES)
def test_run_llm_all_formats(inputs: Dict[str, Any]) -> None:
llm = FakeLLM()
_run_llm(llm, inputs, mock.MagicMock())
@pytest.mark.parametrize("inputs", _VALID_MESSAGES + _VALID_PROMPTS)
def test_run_chat_model_all_formats(inputs: Dict[str, Any]) -> None:
llm = FakeChatModel()
_run_llm(llm, inputs, mock.MagicMock())
@freeze_time("2023-01-01")
async def test_arun_on_dataset(monkeypatch: pytest.MonkeyPatch) -> None:
dataset = Dataset(
id=uuid.uuid4(),
name="test",
description="Test dataset",
owner_id="owner",
created_at=_CREATED_AT,
tenant_id=_TENANT_ID,
_host_url="http://localhost:1984",
)
uuids = [
"0c193153-2309-4704-9a47-17aee4fb25c8",
"0d11b5fd-8e66-4485-b696-4b55155c0c05",
"90d696f0-f10d-4fd0-b88b-bfee6df08b84",
"4ce2c6d8-5124-4c0c-8292-db7bdebcf167",
"7b5a524c-80fa-4960-888e-7d380f9a11ee",
]
examples = [
Example(
id=uuids[0],
created_at=_CREATED_AT,
inputs={"input": "1"},
outputs={"output": "2"},
dataset_id=str(uuid.uuid4()),
),
Example(
id=uuids[1],
created_at=_CREATED_AT,
inputs={"input": "3"},
outputs={"output": "4"},
dataset_id=str(uuid.uuid4()),
),
Example(
id=uuids[2],
created_at=_CREATED_AT,
inputs={"input": "5"},
outputs={"output": "6"},
dataset_id=str(uuid.uuid4()),
),
Example(
id=uuids[3],
created_at=_CREATED_AT,
inputs={"input": "7"},
outputs={"output": "8"},
dataset_id=str(uuid.uuid4()),
),
Example(
id=uuids[4],
created_at=_CREATED_AT,
inputs={"input": "9"},
outputs={"output": "10"},
dataset_id=str(uuid.uuid4()),
),
]
def mock_read_dataset(*args: Any, **kwargs: Any) -> Dataset:
return dataset
def mock_list_examples(*args: Any, **kwargs: Any) -> Iterator[Example]:
return iter(examples)
async def mock_arun_chain(
example: Example,
llm_or_chain: Union[BaseLanguageModel, Chain],
tags: Optional[List[str]] = None,
callbacks: Optional[Any] = None,
**kwargs: Any,
) -> Dict[str, Any]:
return {"result": f"Result for example {example.id}"}
def mock_create_project(*args: Any, **kwargs: Any) -> Any:
proj = mock.MagicMock()
proj.id = "123"
return proj
with mock.patch.object(
Client, "read_dataset", new=mock_read_dataset
), mock.patch.object(Client, "list_examples", new=mock_list_examples), mock.patch(
"langchain.smith.evaluation.runner_utils._arun_llm_or_chain",
new=mock_arun_chain,
), mock.patch.object(Client, "create_project", new=mock_create_project):
client = Client(api_url="http://localhost:1984", api_key="123")
chain = mock.MagicMock()
chain.input_keys = ["foothing"]
results = await arun_on_dataset(
dataset_name="test",
llm_or_chain_factory=lambda: chain,
concurrency_level=2,
project_name="test_project",
client=client,
)
expected = {
str(example.id): {
"output": {
"result": f"Result for example {uuid.UUID(str(example.id))}"
},
"input": {"input": example.inputs["input"]},
"reference": {
"output": example.outputs["output"]
if example.outputs is not None
else None
},
"feedback": [],
# No run since we mock the call to the llm above
"execution_time": None,
"run_id": None,
}
for example in examples
}
assert results["results"] == expected
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/cache.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.cache import (
AstraDBCache,
AstraDBSemanticCache,
AzureCosmosDBSemanticCache,
CassandraCache,
CassandraSemanticCache,
FullLLMCache,
FullMd5LLMCache,
GPTCache,
InMemoryCache,
MomentoCache,
RedisCache,
RedisSemanticCache,
SQLAlchemyCache,
SQLAlchemyMd5Cache,
SQLiteCache,
UpstashRedisCache,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"FullLLMCache": "langchain_community.cache",
"SQLAlchemyCache": "langchain_community.cache",
"SQLiteCache": "langchain_community.cache",
"UpstashRedisCache": "langchain_community.cache",
"RedisCache": "langchain_community.cache",
"RedisSemanticCache": "langchain_community.cache",
"GPTCache": "langchain_community.cache",
"MomentoCache": "langchain_community.cache",
"InMemoryCache": "langchain_community.cache",
"CassandraCache": "langchain_community.cache",
"CassandraSemanticCache": "langchain_community.cache",
"FullMd5LLMCache": "langchain_community.cache",
"SQLAlchemyMd5Cache": "langchain_community.cache",
"AstraDBCache": "langchain_community.cache",
"AstraDBSemanticCache": "langchain_community.cache",
"AzureCosmosDBSemanticCache": "langchain_community.cache",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"FullLLMCache",
"SQLAlchemyCache",
"SQLiteCache",
"UpstashRedisCache",
"RedisCache",
"RedisSemanticCache",
"GPTCache",
"MomentoCache",
"InMemoryCache",
"CassandraCache",
"CassandraSemanticCache",
"FullMd5LLMCache",
"SQLAlchemyMd5Cache",
"AstraDBCache",
"AstraDBSemanticCache",
"AzureCosmosDBSemanticCache",
]
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/requests.py | """DEPRECATED: Kept for backwards compatibility."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.utilities import (
Requests,
RequestsWrapper,
TextRequestsWrapper,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"Requests": "langchain_community.utilities",
"RequestsWrapper": "langchain_community.utilities",
"TextRequestsWrapper": "langchain_community.utilities",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"Requests",
"RequestsWrapper",
"TextRequestsWrapper",
]
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/env.py | import platform
from functools import lru_cache
@lru_cache(maxsize=1)
def get_runtime_environment() -> dict:
"""Get information about the LangChain runtime environment."""
# Lazy import to avoid circular imports
from langchain import __version__
return {
"library_version": __version__,
"library": "langchain",
"platform": platform.platform(),
"runtime": "python",
"runtime_version": platform.python_version(),
}
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/hub.py | """Interface with the LangChain Hub."""
from __future__ import annotations
import json
from typing import Any, Optional, Sequence
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
from langchain_core.prompts import BasePromptTemplate
def _get_client(
api_key: Optional[str] = None,
api_url: Optional[str] = None,
) -> Any:
try:
from langsmith import Client as LangSmithClient
ls_client = LangSmithClient(api_url, api_key=api_key)
if hasattr(ls_client, "push_prompt") and hasattr(ls_client, "pull_prompt"):
return ls_client
else:
from langchainhub import Client as LangChainHubClient
return LangChainHubClient(api_url, api_key=api_key)
except ImportError:
try:
from langchainhub import Client as LangChainHubClient
return LangChainHubClient(api_url, api_key=api_key)
except ImportError as e:
raise ImportError(
"Could not import langsmith or langchainhub (deprecated),"
"please install with `pip install langsmith`."
) from e
def push(
repo_full_name: str,
object: Any,
*,
api_url: Optional[str] = None,
api_key: Optional[str] = None,
parent_commit_hash: Optional[str] = None,
new_repo_is_public: bool = False,
new_repo_description: Optional[str] = None,
readme: Optional[str] = None,
tags: Optional[Sequence[str]] = None,
) -> str:
"""
Push an object to the hub and returns the URL it can be viewed at in a browser.
:param repo_full_name: The full name of the prompt to push to in the format of
`owner/prompt_name` or `prompt_name`.
:param object: The LangChain to serialize and push to the hub.
:param api_url: The URL of the LangChain Hub API. Defaults to the hosted API service
if you have an api key set, or a localhost instance if not.
:param api_key: The API key to use to authenticate with the LangChain Hub API.
:param parent_commit_hash: The commit hash of the parent commit to push to. Defaults
to the latest commit automatically.
:param new_repo_is_public: Whether the prompt should be public. Defaults to
False (Private by default).
:param new_repo_description: The description of the prompt. Defaults to an empty
string.
"""
client = _get_client(api_key=api_key, api_url=api_url)
# Then it's langsmith
if hasattr(client, "push_prompt"):
return client.push_prompt(
repo_full_name,
object=object,
parent_commit_hash=parent_commit_hash,
is_public=new_repo_is_public,
description=new_repo_description,
readme=readme,
tags=tags,
)
# Then it's langchainhub
manifest_json = dumps(object)
message = client.push(
repo_full_name,
manifest_json,
parent_commit_hash=parent_commit_hash,
new_repo_is_public=new_repo_is_public,
new_repo_description=new_repo_description,
)
return message
def pull(
owner_repo_commit: str,
*,
include_model: Optional[bool] = None,
api_url: Optional[str] = None,
api_key: Optional[str] = None,
) -> Any:
"""
Pull an object from the hub and returns it as a LangChain object.
:param owner_repo_commit: The full name of the prompt to pull from in the format of
`owner/prompt_name:commit_hash` or `owner/prompt_name`
or just `prompt_name` if it's your own prompt.
:param api_url: The URL of the LangChain Hub API. Defaults to the hosted API service
if you have an api key set, or a localhost instance if not.
:param api_key: The API key to use to authenticate with the LangChain Hub API.
"""
client = _get_client(api_key=api_key, api_url=api_url)
# Then it's langsmith
if hasattr(client, "pull_prompt"):
response = client.pull_prompt(owner_repo_commit, include_model=include_model)
return response
# Then it's langchainhub
if hasattr(client, "pull_repo"):
# >= 0.1.15
res_dict = client.pull_repo(owner_repo_commit)
obj = loads(json.dumps(res_dict["manifest"]))
if isinstance(obj, BasePromptTemplate):
if obj.metadata is None:
obj.metadata = {}
obj.metadata["lc_hub_owner"] = res_dict["owner"]
obj.metadata["lc_hub_repo"] = res_dict["repo"]
obj.metadata["lc_hub_commit_hash"] = res_dict["commit_hash"]
return obj
# Then it's < 0.1.15 langchainhub
resp: str = client.pull(owner_repo_commit)
return loads(resp)
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/text_splitter.py | """Kept for backwards compatibility."""
from langchain_text_splitters import (
Language,
RecursiveCharacterTextSplitter,
TextSplitter,
Tokenizer,
TokenTextSplitter,
)
from langchain_text_splitters.base import split_text_on_tokens
from langchain_text_splitters.character import CharacterTextSplitter
from langchain_text_splitters.html import ElementType, HTMLHeaderTextSplitter
from langchain_text_splitters.json import RecursiveJsonSplitter
from langchain_text_splitters.konlpy import KonlpyTextSplitter
from langchain_text_splitters.latex import LatexTextSplitter
from langchain_text_splitters.markdown import (
HeaderType,
LineType,
MarkdownHeaderTextSplitter,
MarkdownTextSplitter,
)
from langchain_text_splitters.nltk import NLTKTextSplitter
from langchain_text_splitters.python import PythonCodeTextSplitter
from langchain_text_splitters.sentence_transformers import (
SentenceTransformersTokenTextSplitter,
)
from langchain_text_splitters.spacy import SpacyTextSplitter
__all__ = [
"TokenTextSplitter",
"TextSplitter",
"Tokenizer",
"Language",
"RecursiveCharacterTextSplitter",
"RecursiveJsonSplitter",
"LatexTextSplitter",
"PythonCodeTextSplitter",
"KonlpyTextSplitter",
"SpacyTextSplitter",
"NLTKTextSplitter",
"split_text_on_tokens",
"SentenceTransformersTokenTextSplitter",
"ElementType",
"HeaderType",
"LineType",
"HTMLHeaderTextSplitter",
"MarkdownHeaderTextSplitter",
"MarkdownTextSplitter",
"CharacterTextSplitter",
]
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/model_laboratory.py | """Experiment with different models."""
from __future__ import annotations
from typing import List, Optional, Sequence
from langchain_core.language_models.llms import BaseLLM
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.utils.input import get_color_mapping, print_text
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
class ModelLaboratory:
"""Experiment with different models."""
def __init__(self, chains: Sequence[Chain], names: Optional[List[str]] = None):
"""Initialize with chains to experiment with.
Args:
chains: list of chains to experiment with.
"""
for chain in chains:
if not isinstance(chain, Chain):
raise ValueError(
"ModelLaboratory should now be initialized with Chains. "
"If you want to initialize with LLMs, use the `from_llms` method "
"instead (`ModelLaboratory.from_llms(...)`)"
)
if len(chain.input_keys) != 1:
raise ValueError(
"Currently only support chains with one input variable, "
f"got {chain.input_keys}"
)
if len(chain.output_keys) != 1:
raise ValueError(
"Currently only support chains with one output variable, "
f"got {chain.output_keys}"
)
if names is not None:
if len(names) != len(chains):
raise ValueError("Length of chains does not match length of names.")
self.chains = chains
chain_range = [str(i) for i in range(len(self.chains))]
self.chain_colors = get_color_mapping(chain_range)
self.names = names
@classmethod
def from_llms(
cls, llms: List[BaseLLM], prompt: Optional[PromptTemplate] = None
) -> ModelLaboratory:
"""Initialize with LLMs to experiment with and optional prompt.
Args:
llms: list of LLMs to experiment with
prompt: Optional prompt to use to prompt the LLMs. Defaults to None.
If a prompt was provided, it should only have one input variable.
"""
if prompt is None:
prompt = PromptTemplate(input_variables=["_input"], template="{_input}")
chains = [LLMChain(llm=llm, prompt=prompt) for llm in llms]
names = [str(llm) for llm in llms]
return cls(chains, names=names)
def compare(self, text: str) -> None:
"""Compare model outputs on an input text.
If a prompt was provided with starting the laboratory, then this text will be
fed into the prompt. If no prompt was provided, then the input text is the
entire prompt.
Args:
text: input text to run all models on.
"""
print(f"\033[1mInput:\033[0m\n{text}\n") # noqa: T201
for i, chain in enumerate(self.chains):
if self.names is not None:
name = self.names[i]
else:
name = str(chain)
print_text(name, end="\n")
output = chain.run(text)
print_text(output, color=self.chain_colors[str(i)], end="\n\n")
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/sql_database.py | """Keep here for backwards compatibility."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.utilities import SQLDatabase
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"SQLDatabase": "langchain_community.utilities"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"SQLDatabase",
]
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/python.py | """For backwards compatibility."""
from typing import Any
from langchain._api import create_importer
# Code has been removed from the community package as well.
# We'll proxy to community package, which will raise an appropriate exception,
# but we'll not include this in __all__, so it won't be listed as importable.
_importer = create_importer(
__package__,
deprecated_lookups={"PythonREPL": "langchain_community.utilities.python"},
)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _importer(name)
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/example_generator.py | """Keep here for backwards compatibility."""
from langchain.chains.example_generator import generate_example
__all__ = ["generate_example"]
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/formatting.py | """DEPRECATED: Kept for backwards compatibility."""
from langchain_core.utils.formatting import StrictFormatter, formatter
__all__ = ["StrictFormatter", "formatter"]
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/input.py | """DEPRECATED: Kept for backwards compatibility."""
from langchain_core.utils.input import (
get_bolded_text,
get_color_mapping,
get_colored_text,
print_text,
)
__all__ = [
"get_bolded_text",
"get_color_mapping",
"get_colored_text",
"print_text",
]
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/globals.py | """Global values and configuration that apply to all of LangChain."""
import warnings
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from langchain_core.caches import BaseCache
# DO NOT USE THESE VALUES DIRECTLY!
# Use them only via `get_<X>()` and `set_<X>()` below,
# or else your code may behave unexpectedly with other uses of these global settings:
# https://github.com/langchain-ai/langchain/pull/11311#issuecomment-1743780004
_verbose: bool = False
_debug: bool = False
_llm_cache: Optional["BaseCache"] = None
def set_verbose(value: bool) -> None:
"""Set a new value for the `verbose` global setting."""
import langchain
# We're about to run some deprecated code, don't report warnings from it.
# The user called the correct (non-deprecated) code path and shouldn't get warnings.
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message=(
"Importing verbose from langchain root module is no longer supported"
),
)
# N.B.: This is a workaround for an unfortunate quirk of Python's
# module-level `__getattr__()` implementation:
# https://github.com/langchain-ai/langchain/pull/11311#issuecomment-1743780004
#
# Remove it once `langchain.verbose` is no longer supported, and once all users
# have migrated to using `set_verbose()` here.
langchain.verbose = value
global _verbose
_verbose = value
def get_verbose() -> bool:
"""Get the value of the `verbose` global setting."""
import langchain
# We're about to run some deprecated code, don't report warnings from it.
# The user called the correct (non-deprecated) code path and shouldn't get warnings.
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message=(
"Importing verbose from langchain root module is no longer supported"
),
)
# N.B.: This is a workaround for an unfortunate quirk of Python's
# module-level `__getattr__()` implementation:
# https://github.com/langchain-ai/langchain/pull/11311#issuecomment-1743780004
#
# Remove it once `langchain.verbose` is no longer supported, and once all users
# have migrated to using `set_verbose()` here.
#
# In the meantime, the `verbose` setting is considered True if either the old
# or the new value are True. This accommodates users who haven't migrated
# to using `set_verbose()` yet. Those users are getting deprecation warnings
# directing them to use `set_verbose()` when they import `langhchain.verbose`.
old_verbose = langchain.verbose
global _verbose
return _verbose or old_verbose
def set_debug(value: bool) -> None:
"""Set a new value for the `debug` global setting."""
import langchain
# We're about to run some deprecated code, don't report warnings from it.
# The user called the correct (non-deprecated) code path and shouldn't get warnings.
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="Importing debug from langchain root module is no longer supported",
)
# N.B.: This is a workaround for an unfortunate quirk of Python's
# module-level `__getattr__()` implementation:
# https://github.com/langchain-ai/langchain/pull/11311#issuecomment-1743780004
#
# Remove it once `langchain.debug` is no longer supported, and once all users
# have migrated to using `set_debug()` here.
langchain.debug = value
global _debug
_debug = value
def get_debug() -> bool:
"""Get the value of the `debug` global setting."""
import langchain
# We're about to run some deprecated code, don't report warnings from it.
# The user called the correct (non-deprecated) code path and shouldn't get warnings.
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="Importing debug from langchain root module is no longer supported",
)
# N.B.: This is a workaround for an unfortunate quirk of Python's
# module-level `__getattr__()` implementation:
# https://github.com/langchain-ai/langchain/pull/11311#issuecomment-1743780004
#
# Remove it once `langchain.debug` is no longer supported, and once all users
# have migrated to using `set_debug()` here.
#
# In the meantime, the `debug` setting is considered True if either the old
# or the new value are True. This accommodates users who haven't migrated
# to using `set_debug()` yet. Those users are getting deprecation warnings
# directing them to use `set_debug()` when they import `langhchain.debug`.
old_debug = langchain.debug
global _debug
return _debug or old_debug
def set_llm_cache(value: Optional["BaseCache"]) -> None:
"""Set a new LLM cache, overwriting the previous value, if any."""
import langchain
# We're about to run some deprecated code, don't report warnings from it.
# The user called the correct (non-deprecated) code path and shouldn't get warnings.
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message=(
"Importing llm_cache from langchain root module is no longer supported"
),
)
# N.B.: This is a workaround for an unfortunate quirk of Python's
# module-level `__getattr__()` implementation:
# https://github.com/langchain-ai/langchain/pull/11311#issuecomment-1743780004
#
# Remove it once `langchain.llm_cache` is no longer supported, and
# once all users have migrated to using `set_llm_cache()` here.
langchain.llm_cache = value
global _llm_cache
_llm_cache = value
def get_llm_cache() -> "BaseCache":
"""Get the value of the `llm_cache` global setting."""
import langchain
# We're about to run some deprecated code, don't report warnings from it.
# The user called the correct (non-deprecated) code path and shouldn't get warnings.
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message=(
"Importing llm_cache from langchain root module is no longer supported"
),
)
# N.B.: This is a workaround for an unfortunate quirk of Python's
# module-level `__getattr__()` implementation:
# https://github.com/langchain-ai/langchain/pull/11311#issuecomment-1743780004
#
# Remove it once `langchain.llm_cache` is no longer supported, and
# once all users have migrated to using `set_llm_cache()` here.
#
# In the meantime, the `llm_cache` setting returns whichever of
# its two backing sources is truthy (not `None` and non-empty),
# or the old value if both are falsy. This accommodates users
# who haven't migrated to using `set_llm_cache()` yet.
# Those users are getting deprecation warnings directing them
# to use `set_llm_cache()` when they import `langhchain.llm_cache`.
old_llm_cache = langchain.llm_cache
global _llm_cache
return _llm_cache or old_llm_cache
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/base_language.py | """Deprecated module for BaseLanguageModel class, kept for backwards compatibility."""
from __future__ import annotations
from langchain_core.language_models import BaseLanguageModel
__all__ = ["BaseLanguageModel"]
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/__init__.py | # ruff: noqa: E402
"""Main entrypoint into package."""
import warnings
from importlib import metadata
from typing import Any, Optional
from langchain_core._api.deprecation import surface_langchain_deprecation_warnings
try:
__version__ = metadata.version(__package__)
except metadata.PackageNotFoundError:
# Case where package metadata is not available.
__version__ = ""
del metadata # optional, avoids polluting the results of dir(__package__)
def _warn_on_import(name: str, replacement: Optional[str] = None) -> None:
"""Warn on import of deprecated module."""
from langchain._api.interactive_env import is_interactive_env
if is_interactive_env():
# No warnings for interactive environments.
# This is done to avoid polluting the output of interactive environments
# where users rely on auto-complete and may trigger this warning
# even if they are not using any deprecated modules
return
if replacement:
warnings.warn(
f"Importing {name} from langchain root module is no longer supported. "
f"Please use {replacement} instead."
)
else:
warnings.warn(
f"Importing {name} from langchain root module is no longer supported."
)
# Surfaces Deprecation and Pending Deprecation warnings from langchain.
surface_langchain_deprecation_warnings()
def __getattr__(name: str) -> Any:
if name == "MRKLChain":
from langchain.agents import MRKLChain
_warn_on_import(name, replacement="langchain.agents.MRKLChain")
return MRKLChain
elif name == "ReActChain":
from langchain.agents import ReActChain
_warn_on_import(name, replacement="langchain.agents.ReActChain")
return ReActChain
elif name == "SelfAskWithSearchChain":
from langchain.agents import SelfAskWithSearchChain
_warn_on_import(name, replacement="langchain.agents.SelfAskWithSearchChain")
return SelfAskWithSearchChain
elif name == "ConversationChain":
from langchain.chains import ConversationChain
_warn_on_import(name, replacement="langchain.chains.ConversationChain")
return ConversationChain
elif name == "LLMBashChain":
raise ImportError(
"This module has been moved to langchain-experimental. "
"For more details: "
"https://github.com/langchain-ai/langchain/discussions/11352."
"To access this code, install it with `pip install langchain-experimental`."
"`from langchain_experimental.llm_bash.base "
"import LLMBashChain`"
)
elif name == "LLMChain":
from langchain.chains import LLMChain
_warn_on_import(name, replacement="langchain.chains.LLMChain")
return LLMChain
elif name == "LLMCheckerChain":
from langchain.chains import LLMCheckerChain
_warn_on_import(name, replacement="langchain.chains.LLMCheckerChain")
return LLMCheckerChain
elif name == "LLMMathChain":
from langchain.chains import LLMMathChain
_warn_on_import(name, replacement="langchain.chains.LLMMathChain")
return LLMMathChain
elif name == "QAWithSourcesChain":
from langchain.chains import QAWithSourcesChain
_warn_on_import(name, replacement="langchain.chains.QAWithSourcesChain")
return QAWithSourcesChain
elif name == "VectorDBQA":
from langchain.chains import VectorDBQA
_warn_on_import(name, replacement="langchain.chains.VectorDBQA")
return VectorDBQA
elif name == "VectorDBQAWithSourcesChain":
from langchain.chains import VectorDBQAWithSourcesChain
_warn_on_import(name, replacement="langchain.chains.VectorDBQAWithSourcesChain")
return VectorDBQAWithSourcesChain
elif name == "InMemoryDocstore":
from langchain_community.docstore import InMemoryDocstore
_warn_on_import(name, replacement="langchain.docstore.InMemoryDocstore")
return InMemoryDocstore
elif name == "Wikipedia":
from langchain_community.docstore import Wikipedia
_warn_on_import(name, replacement="langchain.docstore.Wikipedia")
return Wikipedia
elif name == "Anthropic":
from langchain_community.llms import Anthropic
_warn_on_import(name, replacement="langchain_community.llms.Anthropic")
return Anthropic
elif name == "Banana":
from langchain_community.llms import Banana
_warn_on_import(name, replacement="langchain_community.llms.Banana")
return Banana
elif name == "CerebriumAI":
from langchain_community.llms import CerebriumAI
_warn_on_import(name, replacement="langchain_community.llms.CerebriumAI")
return CerebriumAI
elif name == "Cohere":
from langchain_community.llms import Cohere
_warn_on_import(name, replacement="langchain_community.llms.Cohere")
return Cohere
elif name == "ForefrontAI":
from langchain_community.llms import ForefrontAI
_warn_on_import(name, replacement="langchain_community.llms.ForefrontAI")
return ForefrontAI
elif name == "GooseAI":
from langchain_community.llms import GooseAI
_warn_on_import(name, replacement="langchain_community.llms.GooseAI")
return GooseAI
elif name == "HuggingFaceHub":
from langchain_community.llms import HuggingFaceHub
_warn_on_import(name, replacement="langchain_community.llms.HuggingFaceHub")
return HuggingFaceHub
elif name == "HuggingFaceTextGenInference":
from langchain_community.llms import HuggingFaceTextGenInference
_warn_on_import(
name, replacement="langchain_community.llms.HuggingFaceTextGenInference"
)
return HuggingFaceTextGenInference
elif name == "LlamaCpp":
from langchain_community.llms import LlamaCpp
_warn_on_import(name, replacement="langchain_community.llms.LlamaCpp")
return LlamaCpp
elif name == "Modal":
from langchain_community.llms import Modal
_warn_on_import(name, replacement="langchain_community.llms.Modal")
return Modal
elif name == "OpenAI":
from langchain_community.llms import OpenAI
_warn_on_import(name, replacement="langchain_community.llms.OpenAI")
return OpenAI
elif name == "Petals":
from langchain_community.llms import Petals
_warn_on_import(name, replacement="langchain_community.llms.Petals")
return Petals
elif name == "PipelineAI":
from langchain_community.llms import PipelineAI
_warn_on_import(name, replacement="langchain_community.llms.PipelineAI")
return PipelineAI
elif name == "SagemakerEndpoint":
from langchain_community.llms import SagemakerEndpoint
_warn_on_import(name, replacement="langchain_community.llms.SagemakerEndpoint")
return SagemakerEndpoint
elif name == "StochasticAI":
from langchain_community.llms import StochasticAI
_warn_on_import(name, replacement="langchain_community.llms.StochasticAI")
return StochasticAI
elif name == "Writer":
from langchain_community.llms import Writer
_warn_on_import(name, replacement="langchain_community.llms.Writer")
return Writer
elif name == "HuggingFacePipeline":
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
_warn_on_import(
name,
replacement="langchain_community.llms.huggingface_pipeline.HuggingFacePipeline",
)
return HuggingFacePipeline
elif name == "FewShotPromptTemplate":
from langchain_core.prompts import FewShotPromptTemplate
_warn_on_import(
name, replacement="langchain_core.prompts.FewShotPromptTemplate"
)
return FewShotPromptTemplate
elif name == "Prompt":
from langchain_core.prompts import PromptTemplate
_warn_on_import(name, replacement="langchain_core.prompts.PromptTemplate")
# it's renamed as prompt template anyways
# this is just for backwards compat
return PromptTemplate
elif name == "PromptTemplate":
from langchain_core.prompts import PromptTemplate
_warn_on_import(name, replacement="langchain_core.prompts.PromptTemplate")
return PromptTemplate
elif name == "BasePromptTemplate":
from langchain_core.prompts import BasePromptTemplate
_warn_on_import(name, replacement="langchain_core.prompts.BasePromptTemplate")
return BasePromptTemplate
elif name == "ArxivAPIWrapper":
from langchain_community.utilities import ArxivAPIWrapper
_warn_on_import(
name, replacement="langchain_community.utilities.ArxivAPIWrapper"
)
return ArxivAPIWrapper
elif name == "GoldenQueryAPIWrapper":
from langchain_community.utilities import GoldenQueryAPIWrapper
_warn_on_import(
name, replacement="langchain_community.utilities.GoldenQueryAPIWrapper"
)
return GoldenQueryAPIWrapper
elif name == "GoogleSearchAPIWrapper":
from langchain_community.utilities import GoogleSearchAPIWrapper
_warn_on_import(
name, replacement="langchain_community.utilities.GoogleSearchAPIWrapper"
)
return GoogleSearchAPIWrapper
elif name == "GoogleSerperAPIWrapper":
from langchain_community.utilities import GoogleSerperAPIWrapper
_warn_on_import(
name, replacement="langchain_community.utilities.GoogleSerperAPIWrapper"
)
return GoogleSerperAPIWrapper
elif name == "PowerBIDataset":
from langchain_community.utilities import PowerBIDataset
_warn_on_import(
name, replacement="langchain_community.utilities.PowerBIDataset"
)
return PowerBIDataset
elif name == "SearxSearchWrapper":
from langchain_community.utilities import SearxSearchWrapper
_warn_on_import(
name, replacement="langchain_community.utilities.SearxSearchWrapper"
)
return SearxSearchWrapper
elif name == "WikipediaAPIWrapper":
from langchain_community.utilities import WikipediaAPIWrapper
_warn_on_import(
name, replacement="langchain_community.utilities.WikipediaAPIWrapper"
)
return WikipediaAPIWrapper
elif name == "WolframAlphaAPIWrapper":
from langchain_community.utilities import WolframAlphaAPIWrapper
_warn_on_import(
name, replacement="langchain_community.utilities.WolframAlphaAPIWrapper"
)
return WolframAlphaAPIWrapper
elif name == "SQLDatabase":
from langchain_community.utilities import SQLDatabase
_warn_on_import(name, replacement="langchain_community.utilities.SQLDatabase")
return SQLDatabase
elif name == "FAISS":
from langchain_community.vectorstores import FAISS
_warn_on_import(name, replacement="langchain_community.vectorstores.FAISS")
return FAISS
elif name == "ElasticVectorSearch":
from langchain_community.vectorstores import ElasticVectorSearch
_warn_on_import(
name, replacement="langchain_community.vectorstores.ElasticVectorSearch"
)
return ElasticVectorSearch
# For backwards compatibility
elif name == "SerpAPIChain" or name == "SerpAPIWrapper":
from langchain_community.utilities import SerpAPIWrapper
_warn_on_import(
name, replacement="langchain_community.utilities.SerpAPIWrapper"
)
return SerpAPIWrapper
elif name == "verbose":
from langchain.globals import _verbose
_warn_on_import(
name,
replacement=(
"langchain.globals.set_verbose() / langchain.globals.get_verbose()"
),
)
return _verbose
elif name == "debug":
from langchain.globals import _debug
_warn_on_import(
name,
replacement=(
"langchain.globals.set_debug() / langchain.globals.get_debug()"
),
)
return _debug
elif name == "llm_cache":
from langchain.globals import _llm_cache
_warn_on_import(
name,
replacement=(
"langchain.globals.set_llm_cache() / langchain.globals.get_llm_cache()"
),
)
return _llm_cache
else:
raise AttributeError(f"Could not find: {name}")
__all__ = [
"LLMChain",
"LLMCheckerChain",
"LLMMathChain",
"ArxivAPIWrapper",
"GoldenQueryAPIWrapper",
"SelfAskWithSearchChain",
"SerpAPIWrapper",
"SerpAPIChain",
"SearxSearchWrapper",
"GoogleSearchAPIWrapper",
"GoogleSerperAPIWrapper",
"WolframAlphaAPIWrapper",
"WikipediaAPIWrapper",
"Anthropic",
"Banana",
"CerebriumAI",
"Cohere",
"ForefrontAI",
"GooseAI",
"Modal",
"OpenAI",
"Petals",
"PipelineAI",
"StochasticAI",
"Writer",
"BasePromptTemplate",
"Prompt",
"FewShotPromptTemplate",
"PromptTemplate",
"ReActChain",
"Wikipedia",
"HuggingFaceHub",
"SagemakerEndpoint",
"HuggingFacePipeline",
"SQLDatabase",
"PowerBIDataset",
"FAISS",
"MRKLChain",
"VectorDBQA",
"ElasticVectorSearch",
"InMemoryDocstore",
"ConversationChain",
"VectorDBQAWithSourcesChain",
"QAWithSourcesChain",
"LlamaCpp",
"HuggingFaceTextGenInference",
]
|
0 | lc_public_repos/langchain/libs/langchain | lc_public_repos/langchain/libs/langchain/langchain/serpapi.py | """For backwards compatibility."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.utilities import SerpAPIWrapper
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"SerpAPIWrapper": "langchain_community.utilities"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"SerpAPIWrapper",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/rdf_graph.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import RdfGraph
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"RdfGraph": "langchain_community.graphs"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"RdfGraph",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/hugegraph.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import HugeGraph
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"HugeGraph": "langchain_community.graphs"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"HugeGraph",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/nebula_graph.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import NebulaGraph
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"NebulaGraph": "langchain_community.graphs"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"NebulaGraph",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/kuzu_graph.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import KuzuGraph
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"KuzuGraph": "langchain_community.graphs"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"KuzuGraph",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/graph_document.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs.graph_document import (
GraphDocument,
Node,
Relationship,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"Node": "langchain_community.graphs.graph_document",
"Relationship": "langchain_community.graphs.graph_document",
"GraphDocument": "langchain_community.graphs.graph_document",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"Node",
"Relationship",
"GraphDocument",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/networkx_graph.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import NetworkxEntityGraph
from langchain_community.graphs.networkx_graph import (
KnowledgeTriple,
get_entities,
parse_triples,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"KnowledgeTriple": "langchain_community.graphs.networkx_graph",
"parse_triples": "langchain_community.graphs.networkx_graph",
"get_entities": "langchain_community.graphs.networkx_graph",
"NetworkxEntityGraph": "langchain_community.graphs",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"KnowledgeTriple",
"parse_triples",
"get_entities",
"NetworkxEntityGraph",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/falkordb_graph.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import FalkorDBGraph
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"FalkorDBGraph": "langchain_community.graphs"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"FalkorDBGraph",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/arangodb_graph.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import ArangoGraph
from langchain_community.graphs.arangodb_graph import get_arangodb_client
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"ArangoGraph": "langchain_community.graphs",
"get_arangodb_client": "langchain_community.graphs.arangodb_graph",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"ArangoGraph",
"get_arangodb_client",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/memgraph_graph.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import MemgraphGraph
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"MemgraphGraph": "langchain_community.graphs"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"MemgraphGraph",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/__init__.py | """**Graphs** provide a natural language interface to graph databases."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import (
ArangoGraph,
FalkorDBGraph,
HugeGraph,
KuzuGraph,
MemgraphGraph,
NebulaGraph,
Neo4jGraph,
NeptuneGraph,
NetworkxEntityGraph,
RdfGraph,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"MemgraphGraph": "langchain_community.graphs",
"NetworkxEntityGraph": "langchain_community.graphs",
"Neo4jGraph": "langchain_community.graphs",
"NebulaGraph": "langchain_community.graphs",
"NeptuneGraph": "langchain_community.graphs",
"KuzuGraph": "langchain_community.graphs",
"HugeGraph": "langchain_community.graphs",
"RdfGraph": "langchain_community.graphs",
"ArangoGraph": "langchain_community.graphs",
"FalkorDBGraph": "langchain_community.graphs",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"MemgraphGraph",
"NetworkxEntityGraph",
"Neo4jGraph",
"NebulaGraph",
"NeptuneGraph",
"KuzuGraph",
"HugeGraph",
"RdfGraph",
"ArangoGraph",
"FalkorDBGraph",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/neptune_graph.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import NeptuneGraph
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"NeptuneGraph": "langchain_community.graphs"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"NeptuneGraph",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/neo4j_graph.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import Neo4jGraph
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"Neo4jGraph": "langchain_community.graphs"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"Neo4jGraph",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/graphs/graph_store.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs.graph_store import GraphStore
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"GraphStore": "langchain_community.graphs.graph_store"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"GraphStore",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_iterator.py | from __future__ import annotations
import asyncio
import logging
import time
from typing import (
TYPE_CHECKING,
Any,
AsyncIterator,
Dict,
Iterator,
List,
Optional,
Tuple,
Union,
)
from uuid import UUID
from langchain_core.agents import (
AgentAction,
AgentFinish,
AgentStep,
)
from langchain_core.callbacks import (
AsyncCallbackManager,
AsyncCallbackManagerForChainRun,
CallbackManager,
CallbackManagerForChainRun,
Callbacks,
)
from langchain_core.load.dump import dumpd
from langchain_core.outputs import RunInfo
from langchain_core.runnables.utils import AddableDict
from langchain_core.tools import BaseTool
from langchain_core.utils.input import get_color_mapping
from langchain.schema import RUN_KEY
from langchain.utilities.asyncio import asyncio_timeout
if TYPE_CHECKING:
from langchain.agents.agent import AgentExecutor, NextStepOutput
logger = logging.getLogger(__name__)
class AgentExecutorIterator:
"""Iterator for AgentExecutor."""
def __init__(
self,
agent_executor: AgentExecutor,
inputs: Any,
callbacks: Callbacks = None,
*,
tags: Optional[list[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
run_name: Optional[str] = None,
run_id: Optional[UUID] = None,
include_run_info: bool = False,
yield_actions: bool = False,
):
"""
Initialize the AgentExecutorIterator with the given AgentExecutor,
inputs, and optional callbacks.
Args:
agent_executor (AgentExecutor): The AgentExecutor to iterate over.
inputs (Any): The inputs to the AgentExecutor.
callbacks (Callbacks, optional): The callbacks to use during iteration.
Defaults to None.
tags (Optional[list[str]], optional): The tags to use during iteration.
Defaults to None.
metadata (Optional[Dict[str, Any]], optional): The metadata to use
during iteration. Defaults to None.
run_name (Optional[str], optional): The name of the run. Defaults to None.
run_id (Optional[UUID], optional): The ID of the run. Defaults to None.
include_run_info (bool, optional): Whether to include run info
in the output. Defaults to False.
yield_actions (bool, optional): Whether to yield actions as they
are generated. Defaults to False.
"""
self._agent_executor = agent_executor
self.inputs = inputs
self.callbacks = callbacks
self.tags = tags
self.metadata = metadata
self.run_name = run_name
self.run_id = run_id
self.include_run_info = include_run_info
self.yield_actions = yield_actions
self.reset()
_inputs: Dict[str, str]
callbacks: Callbacks
tags: Optional[list[str]]
metadata: Optional[Dict[str, Any]]
run_name: Optional[str]
run_id: Optional[UUID]
include_run_info: bool
yield_actions: bool
@property
def inputs(self) -> Dict[str, str]:
"""The inputs to the AgentExecutor."""
return self._inputs
@inputs.setter
def inputs(self, inputs: Any) -> None:
self._inputs = self.agent_executor.prep_inputs(inputs)
@property
def agent_executor(self) -> AgentExecutor:
"""The AgentExecutor to iterate over."""
return self._agent_executor
@agent_executor.setter
def agent_executor(self, agent_executor: AgentExecutor) -> None:
self._agent_executor = agent_executor
# force re-prep inputs in case agent_executor's prep_inputs fn changed
self.inputs = self.inputs
@property
def name_to_tool_map(self) -> Dict[str, BaseTool]:
"""A mapping of tool names to tools."""
return {tool.name: tool for tool in self.agent_executor.tools}
@property
def color_mapping(self) -> Dict[str, str]:
"""A mapping of tool names to colors."""
return get_color_mapping(
[tool.name for tool in self.agent_executor.tools],
excluded_colors=["green", "red"],
)
def reset(self) -> None:
"""
Reset the iterator to its initial state, clearing intermediate steps,
iterations, and time elapsed.
"""
logger.debug("(Re)setting AgentExecutorIterator to fresh state")
self.intermediate_steps: list[tuple[AgentAction, str]] = []
self.iterations = 0
# maybe better to start these on the first __anext__ call?
self.time_elapsed = 0.0
self.start_time = time.time()
def update_iterations(self) -> None:
"""
Increment the number of iterations and update the time elapsed.
"""
self.iterations += 1
self.time_elapsed = time.time() - self.start_time
logger.debug(
f"Agent Iterations: {self.iterations} ({self.time_elapsed:.2f}s elapsed)"
)
def make_final_outputs(
self,
outputs: Dict[str, Any],
run_manager: Union[CallbackManagerForChainRun, AsyncCallbackManagerForChainRun],
) -> AddableDict:
# have access to intermediate steps by design in iterator,
# so return only outputs may as well always be true.
prepared_outputs = AddableDict(
self.agent_executor.prep_outputs(
self.inputs, outputs, return_only_outputs=True
)
)
if self.include_run_info:
prepared_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id)
return prepared_outputs
def __iter__(self: "AgentExecutorIterator") -> Iterator[AddableDict]:
logger.debug("Initialising AgentExecutorIterator")
self.reset()
callback_manager = CallbackManager.configure(
self.callbacks,
self.agent_executor.callbacks,
self.agent_executor.verbose,
self.tags,
self.agent_executor.tags,
self.metadata,
self.agent_executor.metadata,
)
run_manager = callback_manager.on_chain_start(
dumpd(self.agent_executor),
self.inputs,
self.run_id,
name=self.run_name,
)
try:
while self.agent_executor._should_continue(
self.iterations, self.time_elapsed
):
# take the next step: this plans next action, executes it,
# yielding action and observation as they are generated
next_step_seq: NextStepOutput = []
for chunk in self.agent_executor._iter_next_step(
self.name_to_tool_map,
self.color_mapping,
self.inputs,
self.intermediate_steps,
run_manager,
):
next_step_seq.append(chunk)
# if we're yielding actions, yield them as they come
# do not yield AgentFinish, which will be handled below
if self.yield_actions:
if isinstance(chunk, AgentAction):
yield AddableDict(actions=[chunk], messages=chunk.messages)
elif isinstance(chunk, AgentStep):
yield AddableDict(steps=[chunk], messages=chunk.messages)
# convert iterator output to format handled by _process_next_step_output
next_step = self.agent_executor._consume_next_step(next_step_seq)
# update iterations and time elapsed
self.update_iterations()
# decide if this is the final output
output = self._process_next_step_output(next_step, run_manager)
is_final = "intermediate_step" not in output
# yield the final output always
# for backwards compat, yield int. output if not yielding actions
if not self.yield_actions or is_final:
yield output
# if final output reached, stop iteration
if is_final:
return
except BaseException as e:
run_manager.on_chain_error(e)
raise
# if we got here means we exhausted iterations or time
yield self._stop(run_manager)
async def __aiter__(self) -> AsyncIterator[AddableDict]:
"""
N.B. __aiter__ must be a normal method, so need to initialize async run manager
on first __anext__ call where we can await it
"""
logger.debug("Initialising AgentExecutorIterator (async)")
self.reset()
callback_manager = AsyncCallbackManager.configure(
self.callbacks,
self.agent_executor.callbacks,
self.agent_executor.verbose,
self.tags,
self.agent_executor.tags,
self.metadata,
self.agent_executor.metadata,
)
run_manager = await callback_manager.on_chain_start(
dumpd(self.agent_executor),
self.inputs,
self.run_id,
name=self.run_name,
)
try:
async with asyncio_timeout(self.agent_executor.max_execution_time):
while self.agent_executor._should_continue(
self.iterations, self.time_elapsed
):
# take the next step: this plans next action, executes it,
# yielding action and observation as they are generated
next_step_seq: NextStepOutput = []
async for chunk in self.agent_executor._aiter_next_step(
self.name_to_tool_map,
self.color_mapping,
self.inputs,
self.intermediate_steps,
run_manager,
):
next_step_seq.append(chunk)
# if we're yielding actions, yield them as they come
# do not yield AgentFinish, which will be handled below
if self.yield_actions:
if isinstance(chunk, AgentAction):
yield AddableDict(
actions=[chunk], messages=chunk.messages
)
elif isinstance(chunk, AgentStep):
yield AddableDict(
steps=[chunk], messages=chunk.messages
)
# convert iterator output to format handled by _process_next_step
next_step = self.agent_executor._consume_next_step(next_step_seq)
# update iterations and time elapsed
self.update_iterations()
# decide if this is the final output
output = await self._aprocess_next_step_output(
next_step, run_manager
)
is_final = "intermediate_step" not in output
# yield the final output always
# for backwards compat, yield int. output if not yielding actions
if not self.yield_actions or is_final:
yield output
# if final output reached, stop iteration
if is_final:
return
except (TimeoutError, asyncio.TimeoutError):
yield await self._astop(run_manager)
return
except BaseException as e:
await run_manager.on_chain_error(e)
raise
# if we got here means we exhausted iterations or time
yield await self._astop(run_manager)
def _process_next_step_output(
self,
next_step_output: Union[AgentFinish, List[Tuple[AgentAction, str]]],
run_manager: CallbackManagerForChainRun,
) -> AddableDict:
"""
Process the output of the next step,
handling AgentFinish and tool return cases.
"""
logger.debug("Processing output of Agent loop step")
if isinstance(next_step_output, AgentFinish):
logger.debug(
"Hit AgentFinish: _return -> on_chain_end -> run final output logic"
)
return self._return(next_step_output, run_manager=run_manager)
self.intermediate_steps.extend(next_step_output)
logger.debug("Updated intermediate_steps with step output")
# Check for tool return
if len(next_step_output) == 1:
next_step_action = next_step_output[0]
tool_return = self.agent_executor._get_tool_return(next_step_action)
if tool_return is not None:
return self._return(tool_return, run_manager=run_manager)
return AddableDict(intermediate_step=next_step_output)
async def _aprocess_next_step_output(
self,
next_step_output: Union[AgentFinish, List[Tuple[AgentAction, str]]],
run_manager: AsyncCallbackManagerForChainRun,
) -> AddableDict:
"""
Process the output of the next async step,
handling AgentFinish and tool return cases.
"""
logger.debug("Processing output of async Agent loop step")
if isinstance(next_step_output, AgentFinish):
logger.debug(
"Hit AgentFinish: _areturn -> on_chain_end -> run final output logic"
)
return await self._areturn(next_step_output, run_manager=run_manager)
self.intermediate_steps.extend(next_step_output)
logger.debug("Updated intermediate_steps with step output")
# Check for tool return
if len(next_step_output) == 1:
next_step_action = next_step_output[0]
tool_return = self.agent_executor._get_tool_return(next_step_action)
if tool_return is not None:
return await self._areturn(tool_return, run_manager=run_manager)
return AddableDict(intermediate_step=next_step_output)
def _stop(self, run_manager: CallbackManagerForChainRun) -> AddableDict:
"""
Stop the iterator and raise a StopIteration exception with the stopped response.
"""
logger.warning("Stopping agent prematurely due to triggering stop condition")
# this manually constructs agent finish with output key
output = self.agent_executor._action_agent.return_stopped_response(
self.agent_executor.early_stopping_method,
self.intermediate_steps,
**self.inputs,
)
return self._return(output, run_manager=run_manager)
async def _astop(self, run_manager: AsyncCallbackManagerForChainRun) -> AddableDict:
"""
Stop the async iterator and raise a StopAsyncIteration exception with
the stopped response.
"""
logger.warning("Stopping agent prematurely due to triggering stop condition")
output = self.agent_executor._action_agent.return_stopped_response(
self.agent_executor.early_stopping_method,
self.intermediate_steps,
**self.inputs,
)
return await self._areturn(output, run_manager=run_manager)
def _return(
self, output: AgentFinish, run_manager: CallbackManagerForChainRun
) -> AddableDict:
"""
Return the final output of the iterator.
"""
returned_output = self.agent_executor._return(
output, self.intermediate_steps, run_manager=run_manager
)
returned_output["messages"] = output.messages
run_manager.on_chain_end(returned_output)
return self.make_final_outputs(returned_output, run_manager)
async def _areturn(
self, output: AgentFinish, run_manager: AsyncCallbackManagerForChainRun
) -> AddableDict:
"""
Return the final output of the async iterator.
"""
returned_output = await self.agent_executor._areturn(
output, self.intermediate_steps, run_manager=run_manager
)
returned_output["messages"] = output.messages
await run_manager.on_chain_end(returned_output)
return self.make_final_outputs(returned_output, run_manager)
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/agents/initialize.py | """Load agent."""
from typing import Any, Optional, Sequence
from langchain_core._api import deprecated
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import BaseTool
from langchain._api.deprecation import AGENT_DEPRECATION_WARNING
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.agents.loading import AGENT_TO_CLASS, load_agent
@deprecated(
"0.1.0",
message=AGENT_DEPRECATION_WARNING,
removal="1.0",
)
def initialize_agent(
tools: Sequence[BaseTool],
llm: BaseLanguageModel,
agent: Optional[AgentType] = None,
callback_manager: Optional[BaseCallbackManager] = None,
agent_path: Optional[str] = None,
agent_kwargs: Optional[dict] = None,
*,
tags: Optional[Sequence[str]] = None,
**kwargs: Any,
) -> AgentExecutor:
"""Load an agent executor given tools and LLM.
Args:
tools: List of tools this agent has access to.
llm: Language model to use as the agent.
agent: Agent type to use. If None and agent_path is also None, will default
to AgentType.ZERO_SHOT_REACT_DESCRIPTION. Defaults to None.
callback_manager: CallbackManager to use. Global callback manager is used if
not provided. Defaults to None.
agent_path: Path to serialized agent to use. If None and agent is also None,
will default to AgentType.ZERO_SHOT_REACT_DESCRIPTION. Defaults to None.
agent_kwargs: Additional keyword arguments to pass to the underlying agent.
Defaults to None.
tags: Tags to apply to the traced runs. Defaults to None.
kwargs: Additional keyword arguments passed to the agent executor.
Returns:
An agent executor.
Raises:
ValueError: If both `agent` and `agent_path` are specified.
ValueError: If `agent` is not a valid agent type.
ValueError: If both `agent` and `agent_path` are None.
"""
tags_ = list(tags) if tags else []
if agent is None and agent_path is None:
agent = AgentType.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 ValueError(
f"Got unknown agent type: {agent}. "
f"Valid types are: {AGENT_TO_CLASS.keys()}."
)
tags_.append(agent.value if isinstance(agent, AgentType) else agent)
agent_cls = AGENT_TO_CLASS[agent]
agent_kwargs = agent_kwargs or {}
agent_obj = agent_cls.from_llm_and_tools(
llm, tools, callback_manager=callback_manager, **agent_kwargs
)
elif agent_path is not None:
agent_obj = load_agent(
agent_path, llm=llm, tools=tools, callback_manager=callback_manager
)
try:
# TODO: Add tags from the serialized object directly.
tags_.append(agent_obj._agent_type)
except NotImplementedError:
pass
else:
raise ValueError(
"Somehow both `agent` and `agent_path` are None, "
"this should never happen."
)
return AgentExecutor.from_agent_and_tools(
agent=agent_obj,
tools=tools,
callback_manager=callback_manager,
tags=tags_,
**kwargs,
)
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/agents/types.py | from typing import Dict, Type, Union
from langchain.agents.agent import BaseSingleActionAgent
from langchain.agents.agent_types import AgentType
from langchain.agents.chat.base import ChatAgent
from langchain.agents.conversational.base import ConversationalAgent
from langchain.agents.conversational_chat.base import ConversationalChatAgent
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
from langchain.agents.openai_functions_multi_agent.base import OpenAIMultiFunctionsAgent
from langchain.agents.react.base import ReActDocstoreAgent
from langchain.agents.self_ask_with_search.base import SelfAskWithSearchAgent
from langchain.agents.structured_chat.base import StructuredChatAgent
AGENT_TYPE = Union[Type[BaseSingleActionAgent], Type[OpenAIMultiFunctionsAgent]]
AGENT_TO_CLASS: Dict[AgentType, AGENT_TYPE] = {
AgentType.ZERO_SHOT_REACT_DESCRIPTION: ZeroShotAgent,
AgentType.REACT_DOCSTORE: ReActDocstoreAgent,
AgentType.SELF_ASK_WITH_SEARCH: SelfAskWithSearchAgent,
AgentType.CONVERSATIONAL_REACT_DESCRIPTION: ConversationalAgent,
AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION: ChatAgent,
AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION: ConversationalChatAgent,
AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION: StructuredChatAgent,
AgentType.OPENAI_FUNCTIONS: OpenAIFunctionsAgent,
AgentType.OPENAI_MULTI_FUNCTIONS: OpenAIMultiFunctionsAgent,
}
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/agents/agent.py | """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,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Optional,
Sequence,
Tuple,
Union,
cast,
)
import yaml
from langchain_core._api import deprecated
from langchain_core.agents import AgentAction, AgentFinish, AgentStep
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
AsyncCallbackManagerForToolRun,
BaseCallbackManager,
CallbackManagerForChainRun,
CallbackManagerForToolRun,
Callbacks,
)
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.prompts import BasePromptTemplate
from langchain_core.prompts.few_shot import FewShotPromptTemplate
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.runnables import Runnable, RunnableConfig, ensure_config
from langchain_core.runnables.utils import AddableDict
from langchain_core.tools import BaseTool
from langchain_core.utils.input import get_color_mapping
from pydantic import BaseModel, ConfigDict, model_validator
from typing_extensions import Self
from langchain._api.deprecation import AGENT_DEPRECATION_WARNING
from langchain.agents.agent_iterator import AgentExecutorIterator
from langchain.agents.agent_types import AgentType
from langchain.agents.tools import InvalidTool
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.utilities.asyncio import asyncio_timeout
logger = logging.getLogger(__name__)
class BaseSingleActionAgent(BaseModel):
"""Base Single Action Agent class."""
@property
def return_values(self) -> List[str]:
"""Return values of the agent."""
return ["output"]
def get_allowed_tools(self) -> Optional[List[str]]:
return None
@abstractmethod
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
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: User inputs.
Returns:
Action specifying what tool to use.
"""
@abstractmethod
async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Async given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations.
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
@property
@abstractmethod
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
def return_stopped_response(
self,
early_stopping_method: str,
intermediate_steps: List[Tuple[AgentAction, str]],
**kwargs: Any,
) -> AgentFinish:
"""Return response when agent has been stopped due to max iterations.
Args:
early_stopping_method: Method to use for early stopping.
intermediate_steps: Steps the LLM has taken to date,
along with observations.
**kwargs: User inputs.
Returns:
AgentFinish: Agent finish object.
Raises:
ValueError: If `early_stopping_method` is not supported.
"""
if early_stopping_method == "force":
# `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
else:
raise ValueError(
f"Got unsupported early_stopping_method `{early_stopping_method}`"
)
@classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
**kwargs: Any,
) -> BaseSingleActionAgent:
"""Construct an agent from an LLM and tools.
Args:
llm: Language model to use.
tools: Tools to use.
callback_manager: Callback manager to use.
kwargs: Additional arguments.
Returns:
BaseSingleActionAgent: Agent object.
"""
raise NotImplementedError
@property
def _agent_type(self) -> str:
"""Return Identifier of an agent type."""
raise NotImplementedError
def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of agent.
Returns:
Dict: Dictionary representation of agent.
"""
_dict = super().model_dump()
try:
_type = self._agent_type
except NotImplementedError:
_type = None
if isinstance(_type, AgentType):
_dict["_type"] = str(_type.value)
elif _type is not None:
_dict["_type"] = _type
return _dict
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
agent.agent.save(file_path="path/agent.yaml")
"""
# 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
agent_dict = self.dict()
if "_type" not in agent_dict:
raise NotImplementedError(f"Agent {self} does not support saving")
if save_path.suffix == ".json":
with open(file_path, "w") as f:
json.dump(agent_dict, f, indent=4)
elif save_path.suffix.endswith((".yaml", ".yml")):
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")
def tool_run_logging_kwargs(self) -> Dict:
"""Return logging kwargs for tool run."""
return {}
class BaseMultiActionAgent(BaseModel):
"""Base Multi Action Agent class."""
@property
def return_values(self) -> List[str]:
"""Return values of the agent."""
return ["output"]
def get_allowed_tools(self) -> Optional[List[str]]:
"""Get allowed tools.
Returns:
Optional[List[str]]: Allowed tools.
"""
return None
@abstractmethod
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[List[AgentAction], AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with the observations.
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Actions specifying what tool to use.
"""
@abstractmethod
async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[List[AgentAction], AgentFinish]:
"""Async given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with the observations.
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Actions specifying what tool to use.
"""
@property
@abstractmethod
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
def return_stopped_response(
self,
early_stopping_method: str,
intermediate_steps: List[Tuple[AgentAction, str]],
**kwargs: Any,
) -> AgentFinish:
"""Return response when agent has been stopped due to max iterations.
Args:
early_stopping_method: Method to use for early stopping.
intermediate_steps: Steps the LLM has taken to date,
along with observations.
**kwargs: User inputs.
Returns:
AgentFinish: Agent finish object.
Raises:
ValueError: If `early_stopping_method` is not supported.
"""
if early_stopping_method == "force":
# `force` just returns a constant string
return AgentFinish({"output": "Agent stopped due to max iterations."}, "")
else:
raise ValueError(
f"Got unsupported early_stopping_method `{early_stopping_method}`"
)
@property
def _agent_type(self) -> str:
"""Return Identifier of an agent type."""
raise NotImplementedError
def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of agent."""
_dict = super().model_dump()
try:
_dict["_type"] = str(self._agent_type)
except NotImplementedError:
pass
return _dict
def save(self, file_path: Union[Path, str]) -> None:
"""Save the agent.
Args:
file_path: Path to file to save the agent to.
Raises:
NotImplementedError: If agent does not support saving.
ValueError: If file_path is not json or yaml.
Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path="path/agent.yaml")
"""
# Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path
# Fetch dictionary to save
agent_dict = self.dict()
if "_type" not in agent_dict:
raise NotImplementedError(f"Agent {self} does not support saving.")
directory_path = save_path.parent
directory_path.mkdir(parents=True, exist_ok=True)
if save_path.suffix == ".json":
with open(file_path, "w") as f:
json.dump(agent_dict, f, indent=4)
elif save_path.suffix.endswith((".yaml", ".yml")):
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")
def tool_run_logging_kwargs(self) -> Dict:
"""Return logging kwargs for tool run."""
return {}
class AgentOutputParser(BaseOutputParser[Union[AgentAction, AgentFinish]]):
"""Base class for parsing agent output into agent action/finish."""
@abstractmethod
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
"""Parse text into agent action/finish."""
class MultiActionAgentOutputParser(
BaseOutputParser[Union[List[AgentAction], AgentFinish]]
):
"""Base class for parsing agent output into agent actions/finish.
This is used for agents that can return multiple actions.
"""
@abstractmethod
def parse(self, text: str) -> Union[List[AgentAction], AgentFinish]:
"""Parse text into agent actions/finish.
Args:
text: Text to parse.
Returns:
Union[List[AgentAction], AgentFinish]:
List of agent actions or agent finish.
"""
class RunnableAgent(BaseSingleActionAgent):
"""Agent powered by Runnables."""
runnable: Runnable[dict, Union[AgentAction, AgentFinish]]
"""Runnable to call to get agent action."""
input_keys_arg: List[str] = []
return_keys_arg: List[str] = []
stream_runnable: bool = True
"""Whether to stream from the runnable or not.
If True then underlying LLM is invoked in a streaming fashion to make it possible
to get access to the individual LLM tokens when using stream_log with the Agent
Executor. If False then LLM is invoked in a non-streaming fashion and
individual LLM tokens will not be available in stream_log.
"""
model_config = ConfigDict(
arbitrary_types_allowed=True,
)
@property
def return_values(self) -> List[str]:
"""Return values of the agent."""
return self.return_keys_arg
@property
def input_keys(self) -> List[str]:
"""Return the input keys."""
return self.input_keys_arg
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Based on past history and current inputs, decide what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with the observations.
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}}
final_output: Any = None
if self.stream_runnable:
# Use streaming to make sure that the underlying LLM is invoked in a
# streaming
# fashion to make it possible to get access to the individual LLM tokens
# when using stream_log with the Agent Executor.
# Because the response from the plan is not a generator, we need to
# accumulate the output into final output and return that.
for chunk in self.runnable.stream(inputs, config={"callbacks": callbacks}):
if final_output is None:
final_output = chunk
else:
final_output += chunk
else:
final_output = self.runnable.invoke(inputs, config={"callbacks": callbacks})
return final_output
async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[
AgentAction,
AgentFinish,
]:
"""Async based on past history and current inputs, decide what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations.
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}}
final_output: Any = None
if self.stream_runnable:
# Use streaming to make sure that the underlying LLM is invoked in a
# streaming
# fashion to make it possible to get access to the individual LLM tokens
# when using stream_log with the Agent Executor.
# Because the response from the plan is not a generator, we need to
# accumulate the output into final output and return that.
async for chunk in self.runnable.astream(
inputs, config={"callbacks": callbacks}
):
if final_output is None:
final_output = chunk
else:
final_output += chunk
else:
final_output = await self.runnable.ainvoke(
inputs, config={"callbacks": callbacks}
)
return final_output
class RunnableMultiActionAgent(BaseMultiActionAgent):
"""Agent powered by Runnables."""
runnable: Runnable[dict, Union[List[AgentAction], AgentFinish]]
"""Runnable to call to get agent actions."""
input_keys_arg: List[str] = []
return_keys_arg: List[str] = []
stream_runnable: bool = True
"""Whether to stream from the runnable or not.
If True then underlying LLM is invoked in a streaming fashion to make it possible
to get access to the individual LLM tokens when using stream_log with the Agent
Executor. If False then LLM is invoked in a non-streaming fashion and
individual LLM tokens will not be available in stream_log.
"""
model_config = ConfigDict(
arbitrary_types_allowed=True,
)
@property
def return_values(self) -> List[str]:
"""Return values of the agent."""
return self.return_keys_arg
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
Returns:
List of input keys.
"""
return self.input_keys_arg
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[
List[AgentAction],
AgentFinish,
]:
"""Based on past history and current inputs, decide what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with the observations.
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}}
final_output: Any = None
if self.stream_runnable:
# Use streaming to make sure that the underlying LLM is invoked in a
# streaming
# fashion to make it possible to get access to the individual LLM tokens
# when using stream_log with the Agent Executor.
# Because the response from the plan is not a generator, we need to
# accumulate the output into final output and return that.
for chunk in self.runnable.stream(inputs, config={"callbacks": callbacks}):
if final_output is None:
final_output = chunk
else:
final_output += chunk
else:
final_output = self.runnable.invoke(inputs, config={"callbacks": callbacks})
return final_output
async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[
List[AgentAction],
AgentFinish,
]:
"""Async based on past history and current inputs, decide what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations.
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}}
final_output: Any = None
if self.stream_runnable:
# Use streaming to make sure that the underlying LLM is invoked in a
# streaming
# fashion to make it possible to get access to the individual LLM tokens
# when using stream_log with the Agent Executor.
# Because the response from the plan is not a generator, we need to
# accumulate the output into final output and return that.
async for chunk in self.runnable.astream(
inputs, config={"callbacks": callbacks}
):
if final_output is None:
final_output = chunk
else:
final_output += chunk
else:
final_output = await self.runnable.ainvoke(
inputs, config={"callbacks": callbacks}
)
return final_output
@deprecated(
"0.1.0",
message=AGENT_DEPRECATION_WARNING,
removal="1.0",
)
class LLMSingleActionAgent(BaseSingleActionAgent):
"""Base class for single action agents."""
llm_chain: LLMChain
"""LLMChain to use for agent."""
output_parser: AgentOutputParser
"""Output parser to use for agent."""
stop: List[str]
"""List of strings to stop on."""
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
Returns:
List of input keys.
"""
return list(set(self.llm_chain.input_keys) - {"intermediate_steps"})
def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of agent."""
_dict = super().dict()
del _dict["output_parser"]
return _dict
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
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 the observations.
callbacks: Callbacks to run.
**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(output)
async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Async given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations.
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
output = await self.llm_chain.arun(
intermediate_steps=intermediate_steps,
stop=self.stop,
callbacks=callbacks,
**kwargs,
)
return self.output_parser.parse(output)
def tool_run_logging_kwargs(self) -> Dict:
"""Return logging kwargs for tool run."""
return {
"llm_prefix": "",
"observation_prefix": "" if len(self.stop) == 0 else self.stop[0],
}
@deprecated(
"0.1.0",
message=AGENT_DEPRECATION_WARNING,
removal="1.0",
)
class Agent(BaseSingleActionAgent):
"""Agent that calls the language model and deciding the action.
This is driven by a LLMChain. The prompt in the LLMChain MUST include
a variable called "agent_scratchpad" where the agent can put its
intermediary work.
"""
llm_chain: LLMChain
"""LLMChain to use for agent."""
output_parser: AgentOutputParser
"""Output parser to use for agent."""
allowed_tools: Optional[List[str]] = None
"""Allowed tools for the agent. If None, all tools are allowed."""
def dict(self, **kwargs: Any) -> Dict:
"""Return dictionary representation of agent."""
_dict = super().dict()
del _dict["output_parser"]
return _dict
def get_allowed_tools(self) -> Optional[List[str]]:
"""Get allowed tools."""
return self.allowed_tools
@property
def return_values(self) -> List[str]:
"""Return values of the agent."""
return ["output"]
def _fix_text(self, text: str) -> str:
"""Fix the text.
Args:
text: Text to fix.
Returns:
str: Fixed text.
"""
raise ValueError("fix_text not implemented for this agent.")
@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]]:
"""Construct the scratchpad that lets the agent continue its thought process."""
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
return thoughts
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
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: User inputs.
Returns:
Action specifying what tool to use.
"""
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)
async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Async given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations.
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)
agent_output = await self.output_parser.aparse(full_output)
return agent_output
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.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations.
**kwargs: User inputs.
Returns:
Dict[str, Any]: Full inputs for the LLMChain.
"""
thoughts = self._construct_scratchpad(intermediate_steps)
new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
full_inputs = {**kwargs, **new_inputs}
return full_inputs
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return list(set(self.llm_chain.input_keys) - {"agent_scratchpad"})
@model_validator(mode="after")
def validate_prompt(self) -> Self:
"""Validate that prompt matches format.
Args:
values: Values to validate.
Returns:
Dict: Validated values.
Raises:
ValueError: If `agent_scratchpad` is not in prompt.input_variables
and prompt is not a FewShotPromptTemplate or a PromptTemplate.
"""
prompt = self.llm_chain.prompt
if "agent_scratchpad" not in prompt.input_variables:
logger.warning(
"`agent_scratchpad` should be a variable in prompt.input_variables."
" Did not find it, so adding it at the end."
)
prompt.input_variables.append("agent_scratchpad")
if isinstance(prompt, PromptTemplate):
prompt.template += "\n{agent_scratchpad}"
elif isinstance(prompt, FewShotPromptTemplate):
prompt.suffix += "\n{agent_scratchpad}"
else:
raise ValueError(f"Got unexpected prompt type {type(prompt)}")
return self
@property
@abstractmethod
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
@property
@abstractmethod
def llm_prefix(self) -> str:
"""Prefix to append the LLM call with."""
@classmethod
@abstractmethod
def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
"""Create a prompt for this class.
Args:
tools: Tools to use.
Returns:
BasePromptTemplate: Prompt template.
"""
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
"""Validate that appropriate tools are passed in.
Args:
tools: Tools to use.
"""
pass
@classmethod
@abstractmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
"""Get default output parser for this class."""
@classmethod
def from_llm_and_tools(
cls,
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.
Args:
llm: Language model to use.
tools: Tools to use.
callback_manager: Callback manager to use.
output_parser: Output parser to use.
kwargs: Additional arguments.
Returns:
Agent: Agent object.
"""
cls._validate_tools(tools)
llm_chain = LLMChain(
llm=llm,
prompt=cls.create_prompt(tools),
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
_output_parser = output_parser or cls._get_default_output_parser()
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
)
def return_stopped_response(
self,
early_stopping_method: str,
intermediate_steps: List[Tuple[AgentAction, str]],
**kwargs: Any,
) -> AgentFinish:
"""Return response when agent has been stopped due to max iterations.
Args:
early_stopping_method: Method to use for early stopping.
intermediate_steps: Steps the LLM has taken to date,
along with observations.
**kwargs: User inputs.
Returns:
AgentFinish: Agent finish object.
Raises:
ValueError: If `early_stopping_method` is not in ['force', 'generate'].
"""
if early_stopping_method == "force":
# `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
elif early_stopping_method == "generate":
# Generate does one final forward pass
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += (
f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
)
# Adding to the previous steps, we now tell the LLM to make a final pred
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)
# We try to extract a final answer
parsed_output = self.output_parser.parse(full_output)
if isinstance(parsed_output, AgentFinish):
# If we can extract, we send the correct stuff
return parsed_output
else:
# If we can extract, but the tool is not the final tool,
# we just return the full output
return AgentFinish({"output": full_output}, full_output)
else:
raise ValueError(
"early_stopping_method should be one of `force` or `generate`, "
f"got {early_stopping_method}"
)
def tool_run_logging_kwargs(self) -> Dict:
"""Return logging kwargs for tool run."""
return {
"llm_prefix": self.llm_prefix,
"observation_prefix": self.observation_prefix,
}
class ExceptionTool(BaseTool): # type: ignore[override]
"""Tool that just returns the query."""
name: str = "_Exception"
"""Name of the tool."""
description: str = "Exception tool"
"""Description of the tool."""
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
return query
async def _arun(
self,
query: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
return query
NextStepOutput = List[Union[AgentFinish, AgentAction, AgentStep]]
RunnableAgentType = Union[RunnableAgent, RunnableMultiActionAgent]
class AgentExecutor(Chain):
"""Agent that is using tools."""
agent: Union[BaseSingleActionAgent, BaseMultiActionAgent, Runnable]
"""The agent to run for creating a plan and determining actions
to take at each step of the execution loop."""
tools: Sequence[BaseTool]
"""The valid tools the agent can call."""
return_intermediate_steps: bool = False
"""Whether to return the agent's trajectory of intermediate steps
at the end in addition to the final output."""
max_iterations: Optional[int] = 15
"""The maximum number of steps to take before ending the execution
loop.
Setting to 'None' could lead to an infinite loop."""
max_execution_time: Optional[float] = None
"""The maximum amount of wall clock time to spend in the execution
loop.
"""
early_stopping_method: str = "force"
"""The method to use for early stopping if the agent never
returns `AgentFinish`. Either 'force' or 'generate'.
`"force"` returns a string saying that it stopped because it met a
time or iteration limit.
`"generate"` calls the agent's LLM Chain one final time to generate
a final answer based on the previous steps.
"""
handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = (
False
)
"""How to handle errors raised by the agent's output parser.
Defaults to `False`, which raises the error.
If `true`, the error will be sent back to the LLM as an observation.
If a string, the string itself will be sent to the LLM as an observation.
If a callable function, the function will be called with the exception
as an argument, and the result of that function will be passed to the agent
as an observation.
"""
trim_intermediate_steps: Union[
int, Callable[[List[Tuple[AgentAction, str]]], List[Tuple[AgentAction, str]]]
] = -1
"""How to trim the intermediate steps before returning them.
Defaults to -1, which means no trimming.
"""
@classmethod
def from_agent_and_tools(
cls,
agent: Union[BaseSingleActionAgent, BaseMultiActionAgent, Runnable],
tools: Sequence[BaseTool],
callbacks: Callbacks = None,
**kwargs: Any,
) -> AgentExecutor:
"""Create from agent and tools.
Args:
agent: Agent to use.
tools: Tools to use.
callbacks: Callbacks to use.
kwargs: Additional arguments.
Returns:
AgentExecutor: Agent executor object.
"""
return cls(
agent=agent,
tools=tools,
callbacks=callbacks,
**kwargs,
)
@model_validator(mode="after")
def validate_tools(self) -> Self:
"""Validate that tools are compatible with agent.
Args:
values: Values to validate.
Returns:
Dict: Validated values.
Raises:
ValueError: If allowed tools are different than provided tools.
"""
agent = self.agent
tools = self.tools
allowed_tools = agent.get_allowed_tools() # type: ignore
if allowed_tools is not None:
if set(allowed_tools) != set([tool.name for tool in tools]):
raise ValueError(
f"Allowed tools ({allowed_tools}) different than "
f"provided tools ({[tool.name for tool in tools]})"
)
return self
@model_validator(mode="before")
@classmethod
def validate_runnable_agent(cls, values: Dict) -> Any:
"""Convert runnable to agent if passed in.
Args:
values: Values to validate.
Returns:
Dict: Validated values.
"""
agent = values.get("agent")
if agent and isinstance(agent, Runnable):
try:
output_type = agent.OutputType
except Exception as _:
multi_action = False
else:
multi_action = output_type == Union[List[AgentAction], AgentFinish]
stream_runnable = values.pop("stream_runnable", True)
if multi_action:
values["agent"] = RunnableMultiActionAgent(
runnable=agent, stream_runnable=stream_runnable
)
else:
values["agent"] = RunnableAgent(
runnable=agent, stream_runnable=stream_runnable
)
return values
@property
def _action_agent(self) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
"""Type cast self.agent.
If the `agent` attribute is a Runnable, it will be converted one of
RunnableAgentType in the validate_runnable_agent root_validator.
To support instantiating with a Runnable, here we explicitly cast the type
to reflect the changes made in the root_validator.
"""
if isinstance(self.agent, Runnable):
return cast(RunnableAgentType, self.agent)
else:
return self.agent
def save(self, file_path: Union[Path, str]) -> None:
"""Raise error - saving not supported for Agent Executors.
Args:
file_path: Path to save to.
Raises:
ValueError: Saving not supported for agent executors.
"""
raise ValueError(
"Saving not supported for agent executors. "
"If you are trying to save the agent, please use the "
"`.save_agent(...)`"
)
def save_agent(self, file_path: Union[Path, str]) -> None:
"""Save the underlying agent.
Args:
file_path: Path to save to.
"""
return self._action_agent.save(file_path)
def iter(
self,
inputs: Any,
callbacks: Callbacks = None,
*,
include_run_info: bool = False,
async_: bool = False, # arg kept for backwards compat, but ignored
) -> AgentExecutorIterator:
"""Enables iteration over steps taken to reach final output.
Args:
inputs: Inputs to the agent.
callbacks: Callbacks to run.
include_run_info: Whether to include run info.
async_: Whether to run async. (Ignored)
Returns:
AgentExecutorIterator: Agent executor iterator object.
"""
return AgentExecutorIterator(
self,
inputs,
callbacks,
tags=self.tags,
include_run_info=include_run_info,
)
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
:meta private:
"""
return self._action_agent.input_keys
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
:meta private:
"""
if self.return_intermediate_steps:
return self._action_agent.return_values + ["intermediate_steps"]
else:
return self._action_agent.return_values
def lookup_tool(self, name: str) -> BaseTool:
"""Lookup tool by name.
Args:
name: Name of tool.
Returns:
BaseTool: Tool object.
"""
return {tool.name: tool for tool in self.tools}[name]
def _should_continue(self, iterations: int, time_elapsed: float) -> bool:
if self.max_iterations is not None and iterations >= self.max_iterations:
return False
if (
self.max_execution_time is not None
and time_elapsed >= self.max_execution_time
):
return False
return True
def _return(
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
if self.return_intermediate_steps:
final_output["intermediate_steps"] = intermediate_steps
return final_output
async def _areturn(
self,
output: AgentFinish,
intermediate_steps: list,
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
if run_manager:
await run_manager.on_agent_finish(
output, color="green", verbose=self.verbose
)
final_output = output.return_values
if self.return_intermediate_steps:
final_output["intermediate_steps"] = intermediate_steps
return final_output
def _consume_next_step(
self, values: NextStepOutput
) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
if isinstance(values[-1], AgentFinish):
assert len(values) == 1
return values[-1]
else:
return [
(a.action, a.observation) for a in values if isinstance(a, AgentStep)
]
def _take_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
return self._consume_next_step(
[
a
for a in self._iter_next_step(
name_to_tool_map,
color_mapping,
inputs,
intermediate_steps,
run_manager,
)
]
)
def _iter_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Iterator[Union[AgentFinish, AgentAction, AgentStep]]:
"""Take a single step in the thought-action-observation loop.
Override this to take control of how the agent makes and acts on choices.
"""
try:
intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
# Call the LLM to see what to do.
output = self._action_agent.plan(
intermediate_steps,
callbacks=run_manager.get_child() if run_manager else None,
**inputs,
)
except OutputParserException as e:
if isinstance(self.handle_parsing_errors, bool):
raise_error = not self.handle_parsing_errors
else:
raise_error = False
if raise_error:
raise ValueError(
"An output parsing error occurred. "
"In order to pass this error back to the agent and have it try "
"again, pass `handle_parsing_errors=True` to the AgentExecutor. "
f"This is the error: {str(e)}"
)
text = str(e)
if isinstance(self.handle_parsing_errors, bool):
if e.send_to_llm:
observation = str(e.observation)
text = str(e.llm_output)
else:
observation = "Invalid or incomplete response"
elif isinstance(self.handle_parsing_errors, str):
observation = self.handle_parsing_errors
elif callable(self.handle_parsing_errors):
observation = self.handle_parsing_errors(e)
else:
raise ValueError("Got unexpected type of `handle_parsing_errors`")
output = AgentAction("_Exception", observation, text)
if run_manager:
run_manager.on_agent_action(output, color="green")
tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
observation = ExceptionTool().run(
output.tool_input,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
yield AgentStep(action=output, observation=observation)
return
# If the tool chosen is the finishing tool, then we end and return.
if isinstance(output, AgentFinish):
yield output
return
actions: List[AgentAction]
if isinstance(output, AgentAction):
actions = [output]
else:
actions = output
for agent_action in actions:
yield agent_action
for agent_action in actions:
yield self._perform_agent_action(
name_to_tool_map, color_mapping, agent_action, run_manager
)
def _perform_agent_action(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
agent_action: AgentAction,
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> AgentStep:
if run_manager:
run_manager.on_agent_action(agent_action, color="green")
# Otherwise we lookup the tool
if agent_action.tool in name_to_tool_map:
tool = name_to_tool_map[agent_action.tool]
return_direct = tool.return_direct
color = color_mapping[agent_action.tool]
tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
if return_direct:
tool_run_kwargs["llm_prefix"] = ""
# We then call the tool on the tool input to get an observation
observation = tool.run(
agent_action.tool_input,
verbose=self.verbose,
color=color,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
else:
tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
observation = InvalidTool().run(
{
"requested_tool_name": agent_action.tool,
"available_tool_names": list(name_to_tool_map.keys()),
},
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
return AgentStep(action=agent_action, observation=observation)
async def _atake_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
return self._consume_next_step(
[
a
async for a in self._aiter_next_step(
name_to_tool_map,
color_mapping,
inputs,
intermediate_steps,
run_manager,
)
]
)
async def _aiter_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> AsyncIterator[Union[AgentFinish, AgentAction, AgentStep]]:
"""Take a single step in the thought-action-observation loop.
Override this to take control of how the agent makes and acts on choices.
"""
try:
intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
# Call the LLM to see what to do.
output = await self._action_agent.aplan(
intermediate_steps,
callbacks=run_manager.get_child() if run_manager else None,
**inputs,
)
except OutputParserException as e:
if isinstance(self.handle_parsing_errors, bool):
raise_error = not self.handle_parsing_errors
else:
raise_error = False
if raise_error:
raise ValueError(
"An output parsing error occurred. "
"In order to pass this error back to the agent and have it try "
"again, pass `handle_parsing_errors=True` to the AgentExecutor. "
f"This is the error: {str(e)}"
)
text = str(e)
if isinstance(self.handle_parsing_errors, bool):
if e.send_to_llm:
observation = str(e.observation)
text = str(e.llm_output)
else:
observation = "Invalid or incomplete response"
elif isinstance(self.handle_parsing_errors, str):
observation = self.handle_parsing_errors
elif callable(self.handle_parsing_errors):
observation = self.handle_parsing_errors(e)
else:
raise ValueError("Got unexpected type of `handle_parsing_errors`")
output = AgentAction("_Exception", observation, text)
tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
observation = await ExceptionTool().arun(
output.tool_input,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
yield AgentStep(action=output, observation=observation)
return
# If the tool chosen is the finishing tool, then we end and return.
if isinstance(output, AgentFinish):
yield output
return
actions: List[AgentAction]
if isinstance(output, AgentAction):
actions = [output]
else:
actions = output
for agent_action in actions:
yield agent_action
# Use asyncio.gather to run multiple tool.arun() calls concurrently
result = await asyncio.gather(
*[
self._aperform_agent_action(
name_to_tool_map, color_mapping, agent_action, run_manager
)
for agent_action in actions
],
)
# TODO This could yield each result as it becomes available
for chunk in result:
yield chunk
async def _aperform_agent_action(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
agent_action: AgentAction,
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> AgentStep:
if run_manager:
await run_manager.on_agent_action(
agent_action, verbose=self.verbose, color="green"
)
# Otherwise we lookup the tool
if agent_action.tool in name_to_tool_map:
tool = name_to_tool_map[agent_action.tool]
return_direct = tool.return_direct
color = color_mapping[agent_action.tool]
tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
if return_direct:
tool_run_kwargs["llm_prefix"] = ""
# We then call the tool on the tool input to get an observation
observation = await tool.arun(
agent_action.tool_input,
verbose=self.verbose,
color=color,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
else:
tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
observation = await InvalidTool().arun(
{
"requested_tool_name": agent_action.tool,
"available_tool_names": list(name_to_tool_map.keys()),
},
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
return AgentStep(action=agent_action, observation=observation)
def _call(
self,
inputs: Dict[str, str],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, Any]:
"""Run text through and get agent response."""
# 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 mapping from each tool to a color, used for logging.
color_mapping = get_color_mapping(
[tool.name for tool in self.tools], excluded_colors=["green", "red"]
)
intermediate_steps: List[Tuple[AgentAction, str]] = []
# Let's start tracking the number of iterations and time elapsed
iterations = 0
time_elapsed = 0.0
start_time = time.time()
# We now enter the agent loop (until it returns something).
while self._should_continue(iterations, time_elapsed):
next_step_output = self._take_next_step(
name_to_tool_map,
color_mapping,
inputs,
intermediate_steps,
run_manager=run_manager,
)
if isinstance(next_step_output, AgentFinish):
return self._return(
next_step_output, intermediate_steps, run_manager=run_manager
)
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)
if tool_return is not None:
return self._return(
tool_return, intermediate_steps, run_manager=run_manager
)
iterations += 1
time_elapsed = time.time() - start_time
output = self._action_agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
return self._return(output, intermediate_steps, run_manager=run_manager)
async def _acall(
self,
inputs: Dict[str, str],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
"""Async run text through and get agent response."""
# 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 mapping from each tool to a color, used for logging.
color_mapping = get_color_mapping(
[tool.name for tool in self.tools], excluded_colors=["green"]
)
intermediate_steps: List[Tuple[AgentAction, str]] = []
# Let's start tracking the number of iterations and time elapsed
iterations = 0
time_elapsed = 0.0
start_time = time.time()
# We now enter the agent loop (until it returns something).
try:
async with asyncio_timeout(self.max_execution_time):
while self._should_continue(iterations, time_elapsed):
next_step_output = await self._atake_next_step(
name_to_tool_map,
color_mapping,
inputs,
intermediate_steps,
run_manager=run_manager,
)
if isinstance(next_step_output, AgentFinish):
return await self._areturn(
next_step_output,
intermediate_steps,
run_manager=run_manager,
)
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)
if tool_return is not None:
return await self._areturn(
tool_return, intermediate_steps, run_manager=run_manager
)
iterations += 1
time_elapsed = time.time() - start_time
output = self._action_agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
return await self._areturn(
output, intermediate_steps, run_manager=run_manager
)
except (TimeoutError, asyncio.TimeoutError):
# stop early when interrupted by the async timeout
output = self._action_agent.return_stopped_response(
self.early_stopping_method, intermediate_steps, **inputs
)
return await self._areturn(
output, intermediate_steps, run_manager=run_manager
)
def _get_tool_return(
self, next_step_output: Tuple[AgentAction, str]
) -> Optional[AgentFinish]:
"""Check if the tool is a returning tool."""
agent_action, observation = next_step_output
name_to_tool_map = {tool.name: tool for tool in self.tools}
return_value_key = "output"
if len(self._action_agent.return_values) > 0:
return_value_key = self._action_agent.return_values[0]
# 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 AgentFinish(
{return_value_key: observation},
"",
)
return None
def _prepare_intermediate_steps(
self, intermediate_steps: List[Tuple[AgentAction, str]]
) -> List[Tuple[AgentAction, str]]:
if (
isinstance(self.trim_intermediate_steps, int)
and self.trim_intermediate_steps > 0
):
return intermediate_steps[-self.trim_intermediate_steps :]
elif callable(self.trim_intermediate_steps):
return self.trim_intermediate_steps(intermediate_steps)
else:
return intermediate_steps
def stream(
self,
input: Union[Dict[str, Any], Any],
config: Optional[RunnableConfig] = None,
**kwargs: Any,
) -> Iterator[AddableDict]:
"""Enables streaming over steps taken to reach final output.
Args:
input: Input to the agent.
config: Config to use.
kwargs: Additional arguments.
Yields:
AddableDict: Addable dictionary.
"""
config = ensure_config(config)
iterator = AgentExecutorIterator(
self,
input,
config.get("callbacks"),
tags=config.get("tags"),
metadata=config.get("metadata"),
run_name=config.get("run_name"),
run_id=config.get("run_id"),
yield_actions=True,
**kwargs,
)
for step in iterator:
yield step
async def astream(
self,
input: Union[Dict[str, Any], Any],
config: Optional[RunnableConfig] = None,
**kwargs: Any,
) -> AsyncIterator[AddableDict]:
"""Async enables streaming over steps taken to reach final output.
Args:
input: Input to the agent.
config: Config to use.
kwargs: Additional arguments.
Yields:
AddableDict: Addable dictionary.
"""
config = ensure_config(config)
iterator = AgentExecutorIterator(
self,
input,
config.get("callbacks"),
tags=config.get("tags"),
metadata=config.get("metadata"),
run_name=config.get("run_name"),
run_id=config.get("run_id"),
yield_actions=True,
**kwargs,
)
async for step in iterator:
yield step
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/agents/schema.py | from typing import Any, Dict, List, Tuple
from langchain_core.agents import AgentAction
from langchain_core.prompts.chat import ChatPromptTemplate
class AgentScratchPadChatPromptTemplate(ChatPromptTemplate):
"""Chat prompt template for the agent scratchpad."""
@classmethod
def is_lc_serializable(cls) -> bool:
return False
def _construct_agent_scratchpad(
self, intermediate_steps: List[Tuple[AgentAction, str]]
) -> str:
if len(intermediate_steps) == 0:
return ""
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\nObservation: {observation}\nThought: "
return (
f"This was your previous work "
f"(but I haven't seen any of it! I only see what "
f"you return as final answer):\n{thoughts}"
)
def _merge_partial_and_user_variables(self, **kwargs: Any) -> Dict[str, Any]:
intermediate_steps = kwargs.pop("intermediate_steps")
kwargs["agent_scratchpad"] = self._construct_agent_scratchpad(
intermediate_steps
)
return kwargs
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/agents/tools.py | """Interface for tools."""
from typing import List, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool, tool
class InvalidTool(BaseTool): # type: ignore[override]
"""Tool that is run when invalid tool name is encountered by agent."""
name: str = "invalid_tool"
"""Name of the tool."""
description: str = "Called when tool name is invalid. Suggests valid tool names."
"""Description of the tool."""
def _run(
self,
requested_tool_name: str,
available_tool_names: List[str],
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
"""Use the tool."""
available_tool_names_str = ", ".join([tool for tool in available_tool_names])
return (
f"{requested_tool_name} is not a valid tool, "
f"try one of [{available_tool_names_str}]."
)
async def _arun(
self,
requested_tool_name: str,
available_tool_names: List[str],
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
available_tool_names_str = ", ".join([tool for tool in available_tool_names])
return (
f"{requested_tool_name} is not a valid tool, "
f"try one of [{available_tool_names_str}]."
)
__all__ = ["InvalidTool", "tool"]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_types.py | """Module definitions of agent types together with corresponding agents."""
from enum import Enum
from langchain_core._api import deprecated
from langchain._api.deprecation import AGENT_DEPRECATION_WARNING
@deprecated(
"0.1.0",
message=AGENT_DEPRECATION_WARNING,
removal="1.0",
)
class AgentType(str, Enum):
"""An enum for agent types.
See documentation: https://python.langchain.com/docs/modules/agents/agent_types/
"""
ZERO_SHOT_REACT_DESCRIPTION = "zero-shot-react-description"
"""A zero shot agent that does a reasoning step before acting."""
REACT_DOCSTORE = "react-docstore"
"""A zero shot agent that does a reasoning step before acting.
This agent has access to a document store that allows it to look up
relevant information to answering the question.
"""
SELF_ASK_WITH_SEARCH = "self-ask-with-search"
"""An agent that breaks down a complex question into a series of simpler questions.
This agent uses a search tool to look up answers to the simpler questions
in order to answer the original complex question.
"""
CONVERSATIONAL_REACT_DESCRIPTION = "conversational-react-description"
CHAT_ZERO_SHOT_REACT_DESCRIPTION = "chat-zero-shot-react-description"
"""A zero shot agent that does a reasoning step before acting.
This agent is designed to be used in conjunction
"""
CHAT_CONVERSATIONAL_REACT_DESCRIPTION = "chat-conversational-react-description"
STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION = (
"structured-chat-zero-shot-react-description"
)
"""An zero-shot react agent optimized for chat models.
This agent is capable of invoking tools that have multiple inputs.
"""
OPENAI_FUNCTIONS = "openai-functions"
"""An agent optimized for using open AI functions."""
OPENAI_MULTI_FUNCTIONS = "openai-multi-functions"
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/agents/utils.py | from typing import Sequence
from langchain_core.tools import BaseTool
def validate_tools_single_input(class_name: str, tools: Sequence[BaseTool]) -> None:
"""Validate tools for single input.
Args:
class_name: Name of the class.
tools: List of tools to validate.
Raises:
ValueError: If a multi-input tool is found in tools.
"""
for tool in tools:
if not tool.is_single_input:
raise ValueError(
f"{class_name} does not support multi-input tool {tool.name}."
)
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/agents/__init__.py | """
**Agent** is a class that uses an LLM to choose a sequence of actions to take.
In Chains, a sequence of actions is hardcoded. In Agents,
a language model is used as a reasoning engine to determine which actions
to take and in which order.
Agents select and use **Tools** and **Toolkits** for actions.
**Class hierarchy:**
.. code-block::
BaseSingleActionAgent --> LLMSingleActionAgent
OpenAIFunctionsAgent
XMLAgent
Agent --> <name>Agent # Examples: ZeroShotAgent, ChatAgent
BaseMultiActionAgent --> OpenAIMultiFunctionsAgent
**Main helpers:**
.. code-block::
AgentType, AgentExecutor, AgentOutputParser, AgentExecutorIterator,
AgentAction, AgentFinish
""" # noqa: E501
from pathlib import Path
from typing import TYPE_CHECKING, Any
from langchain_core._api.path import as_import_path
from langchain_core.tools import Tool
from langchain_core.tools.convert import tool
from langchain._api import create_importer
from langchain.agents.agent import (
Agent,
AgentExecutor,
AgentOutputParser,
BaseMultiActionAgent,
BaseSingleActionAgent,
LLMSingleActionAgent,
)
from langchain.agents.agent_iterator import AgentExecutorIterator
from langchain.agents.agent_toolkits.vectorstore.base import (
create_vectorstore_agent,
create_vectorstore_router_agent,
)
from langchain.agents.agent_types import AgentType
from langchain.agents.conversational.base import ConversationalAgent
from langchain.agents.conversational_chat.base import ConversationalChatAgent
from langchain.agents.initialize import initialize_agent
from langchain.agents.json_chat.base import create_json_chat_agent
from langchain.agents.loading import load_agent
from langchain.agents.mrkl.base import MRKLChain, ZeroShotAgent
from langchain.agents.openai_functions_agent.base import (
OpenAIFunctionsAgent,
create_openai_functions_agent,
)
from langchain.agents.openai_functions_multi_agent.base import OpenAIMultiFunctionsAgent
from langchain.agents.openai_tools.base import create_openai_tools_agent
from langchain.agents.react.agent import create_react_agent
from langchain.agents.react.base import ReActChain, ReActTextWorldAgent
from langchain.agents.self_ask_with_search.base import (
SelfAskWithSearchChain,
create_self_ask_with_search_agent,
)
from langchain.agents.structured_chat.base import (
StructuredChatAgent,
create_structured_chat_agent,
)
from langchain.agents.tool_calling_agent.base import create_tool_calling_agent
from langchain.agents.xml.base import XMLAgent, create_xml_agent
if TYPE_CHECKING:
from langchain_community.agent_toolkits.json.base import create_json_agent
from langchain_community.agent_toolkits.load_tools import (
get_all_tool_names,
load_huggingface_tool,
load_tools,
)
from langchain_community.agent_toolkits.openapi.base import create_openapi_agent
from langchain_community.agent_toolkits.powerbi.base import create_pbi_agent
from langchain_community.agent_toolkits.powerbi.chat_base import (
create_pbi_chat_agent,
)
from langchain_community.agent_toolkits.spark_sql.base import create_spark_sql_agent
from langchain_community.agent_toolkits.sql.base import create_sql_agent
DEPRECATED_CODE = [
"create_csv_agent",
"create_pandas_dataframe_agent",
"create_spark_dataframe_agent",
"create_xorbits_agent",
]
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"create_json_agent": "langchain_community.agent_toolkits.json.base",
"create_openapi_agent": "langchain_community.agent_toolkits.openapi.base",
"create_pbi_agent": "langchain_community.agent_toolkits.powerbi.base",
"create_pbi_chat_agent": "langchain_community.agent_toolkits.powerbi.chat_base",
"create_spark_sql_agent": "langchain_community.agent_toolkits.spark_sql.base",
"create_sql_agent": "langchain_community.agent_toolkits.sql.base",
"load_tools": "langchain_community.agent_toolkits.load_tools",
"load_huggingface_tool": "langchain_community.agent_toolkits.load_tools",
"get_all_tool_names": "langchain_community.agent_toolkits.load_tools",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Get attr name."""
if name in DEPRECATED_CODE:
# Get directory of langchain package
HERE = Path(__file__).parents[1]
relative_path = as_import_path(
Path(__file__).parent, suffix=name, relative_to=HERE
)
old_path = "langchain." + relative_path
new_path = "langchain_experimental." + relative_path
raise ImportError(
f"{name} has been moved to langchain experimental. "
"See https://github.com/langchain-ai/langchain/discussions/11680"
"for more information.\n"
f"Please update your import statement from: `{old_path}` to `{new_path}`."
)
return _import_attribute(name)
__all__ = [
"Agent",
"AgentExecutor",
"AgentExecutorIterator",
"AgentOutputParser",
"AgentType",
"BaseMultiActionAgent",
"BaseSingleActionAgent",
"ConversationalAgent",
"ConversationalChatAgent",
"LLMSingleActionAgent",
"MRKLChain",
"OpenAIFunctionsAgent",
"OpenAIMultiFunctionsAgent",
"ReActChain",
"ReActTextWorldAgent",
"SelfAskWithSearchChain",
"StructuredChatAgent",
"ZeroShotAgent",
"create_json_agent",
"create_openapi_agent",
"create_pbi_agent",
"create_pbi_chat_agent",
"create_spark_sql_agent",
"create_sql_agent",
"create_vectorstore_agent",
"create_vectorstore_router_agent",
"get_all_tool_names",
"initialize_agent",
"load_agent",
"load_huggingface_tool",
"load_tools",
"XMLAgent",
"create_openai_functions_agent",
"create_xml_agent",
"create_react_agent",
"create_openai_tools_agent",
"create_self_ask_with_search_agent",
"create_json_chat_agent",
"create_structured_chat_agent",
"create_tool_calling_agent",
"Tool",
"tool",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/agents/load_tools.py | from typing import Any
from langchain._api import create_importer
_importer = create_importer(
__package__, fallback_module="langchain_community.agent_toolkits.load_tools"
)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _importer(name)
|
0 | lc_public_repos/langchain/libs/langchain/langchain | lc_public_repos/langchain/libs/langchain/langchain/agents/loading.py | """Functionality for loading agents."""
import json
import logging
from pathlib import Path
from typing import Any, List, Optional, Union
import yaml
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import Tool
from langchain.agents.agent import BaseMultiActionAgent, BaseSingleActionAgent
from langchain.agents.types import AGENT_TO_CLASS
from langchain.chains.loading import load_chain, load_chain_from_config
logger = logging.getLogger(__file__)
URL_BASE = "https://raw.githubusercontent.com/hwchase17/langchain-hub/master/agents/"
def _load_agent_from_tools(
config: dict, llm: BaseLanguageModel, tools: List[Tool], **kwargs: Any
) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
config_type = config.pop("_type")
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, **combined_config)
@deprecated("0.1.0", removal="1.0")
def load_agent_from_config(
config: dict,
llm: Optional[BaseLanguageModel] = None,
tools: Optional[List[Tool]] = None,
**kwargs: Any,
) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
"""Load agent from Config Dict.
Args:
config: Config dict to load agent from.
llm: Language model to use as the agent.
tools: List of tools this agent has access to.
kwargs: Additional keyword arguments passed to the agent executor.
Returns:
An agent executor.
Raises:
ValueError: If agent type is not specified in the config.
"""
if "_type" not in config:
raise ValueError("Must specify an agent Type in config")
load_from_tools = config.pop("load_from_llm_and_tools", False)
if load_from_tools:
if llm is None:
raise ValueError(
"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_tools(config, llm, tools, **kwargs)
config_type = config.pop("_type")
if config_type not in AGENT_TO_CLASS:
raise ValueError(f"Loading {config_type} agent not supported")
agent_cls = AGENT_TO_CLASS[config_type]
if "llm_chain" in config:
config["llm_chain"] = load_chain_from_config(config.pop("llm_chain"))
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.")
if "output_parser" in config:
logger.warning(
"Currently loading output parsers on agent is not supported, "
"will just use the default one."
)
del config["output_parser"]
combined_config = {**config, **kwargs}
return agent_cls(**combined_config) # type: ignore
@deprecated("0.1.0", removal="1.0")
def load_agent(
path: Union[str, Path], **kwargs: Any
) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
"""Unified method for loading an agent from LangChainHub or local fs.
Args:
path: Path to the agent file.
kwargs: Additional keyword arguments passed to the agent executor.
Returns:
An agent executor.
Raises:
RuntimeError: If loading from the deprecated github-based
Hub is attempted.
"""
if isinstance(path, str) and path.startswith("lc://"):
raise RuntimeError(
"Loading from the deprecated github-based Hub is no longer supported. "
"Please use the new LangChain Hub at https://smith.langchain.com/hub "
"instead."
)
return _load_agent_from_file(path, **kwargs)
def _load_agent_from_file(
file: Union[str, Path], **kwargs: Any
) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
"""Load agent from file."""
valid_suffixes = {"json", "yaml"}
# 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[1:] == "json":
with open(file_path) as f:
config = json.load(f)
elif file_path.suffix[1:] == "yaml":
with open(file_path, "r") as f:
config = yaml.safe_load(f)
else:
raise ValueError(f"Unsupported file type, must be one of {valid_suffixes}.")
# Load the agent from the config now.
return load_agent_from_config(config, **kwargs)
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/conversational_chat/base.py | """An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence, Tuple
from langchain_core._api import deprecated
from langchain_core.agents import AgentAction
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.prompts import BasePromptTemplate
from langchain_core.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
)
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.conversational_chat.output_parser import ConvoOutputParser
from langchain.agents.conversational_chat.prompt import (
PREFIX,
SUFFIX,
TEMPLATE_TOOL_RESPONSE,
)
from langchain.agents.utils import validate_tools_single_input
from langchain.chains import LLMChain
@deprecated("0.1.0", alternative="create_json_chat_agent", removal="1.0")
class ConversationalChatAgent(Agent):
"""An agent designed to hold a conversation in addition to using tools."""
output_parser: AgentOutputParser = Field(default_factory=ConvoOutputParser)
"""Output parser for the agent."""
template_tool_response: str = TEMPLATE_TOOL_RESPONSE
"""Template for the tool response."""
@classmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
return ConvoOutputParser()
@property
def _agent_type(self) -> str:
raise NotImplementedError
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with.
Returns:
"Observation: "
"""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with.
Returns:
"Thought: "
"""
return "Thought:"
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
super()._validate_tools(tools)
validate_tools_single_input(cls.__name__, tools)
@classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
system_message: str = PREFIX,
human_message: str = SUFFIX,
input_variables: Optional[List[str]] = None,
output_parser: Optional[BaseOutputParser] = None,
) -> BasePromptTemplate:
"""Create a prompt for the agent.
Args:
tools: The tools to use.
system_message: The system message to use.
Defaults to the PREFIX.
human_message: The human message to use.
Defaults to the SUFFIX.
input_variables: The input variables to use. Defaults to None.
output_parser: The output parser to use. Defaults to None.
Returns:
A PromptTemplate.
"""
tool_strings = "\n".join(
[f"> {tool.name}: {tool.description}" for tool in tools]
)
tool_names = ", ".join([tool.name for tool in tools])
_output_parser = output_parser or cls._get_default_output_parser()
format_instructions = human_message.format(
format_instructions=_output_parser.get_format_instructions()
)
final_prompt = format_instructions.format(
tool_names=tool_names, tools=tool_strings
)
if input_variables is None:
input_variables = ["input", "chat_history", "agent_scratchpad"]
messages = [
SystemMessagePromptTemplate.from_template(system_message),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template(final_prompt),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
return ChatPromptTemplate(input_variables=input_variables, messages=messages) # type: ignore[arg-type]
def _construct_scratchpad(
self, intermediate_steps: List[Tuple[AgentAction, str]]
) -> List[BaseMessage]:
"""Construct the scratchpad that lets the agent continue its thought process."""
thoughts: List[BaseMessage] = []
for action, observation in intermediate_steps:
thoughts.append(AIMessage(content=action.log))
human_message = HumanMessage(
content=self.template_tool_response.format(observation=observation)
)
thoughts.append(human_message)
return thoughts
@classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
system_message: str = PREFIX,
human_message: str = SUFFIX,
input_variables: Optional[List[str]] = None,
**kwargs: Any,
) -> Agent:
"""Construct an agent from an LLM and tools.
Args:
llm: The language model to use.
tools: A list of tools to use.
callback_manager: The callback manager to use. Default is None.
output_parser: The output parser to use. Default is None.
system_message: The system message to use. Default is PREFIX.
human_message: The human message to use. Default is SUFFIX.
input_variables: The input variables to use. Default is None.
**kwargs: Any additional arguments.
Returns:
An agent.
"""
cls._validate_tools(tools)
_output_parser = output_parser or cls._get_default_output_parser()
prompt = cls.create_prompt(
tools,
system_message=system_message,
human_message=human_message,
input_variables=input_variables,
output_parser=_output_parser,
)
llm_chain = LLMChain( # type: ignore[misc]
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
)
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/conversational_chat/output_parser.py | from __future__ import annotations
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.utils.json import parse_json_markdown
from langchain.agents import AgentOutputParser
from langchain.agents.conversational_chat.prompt import FORMAT_INSTRUCTIONS
# Define a class that parses output for conversational agents
class ConvoOutputParser(AgentOutputParser):
"""Output parser for the conversational agent."""
format_instructions: str = FORMAT_INSTRUCTIONS
"""Default formatting instructions"""
def get_format_instructions(self) -> str:
"""Returns formatting instructions for the given output parser."""
return self.format_instructions
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
"""Attempts to parse the given text into an AgentAction or AgentFinish.
Raises:
OutputParserException if parsing fails.
"""
try:
# Attempt to parse the text into a structured format (assumed to be JSON
# stored as markdown)
response = parse_json_markdown(text)
# If the response contains an 'action' and 'action_input'
if "action" in response and "action_input" in response:
action, action_input = response["action"], response["action_input"]
# If the action indicates a final answer, return an AgentFinish
if action == "Final Answer":
return AgentFinish({"output": action_input}, text)
else:
# Otherwise, return an AgentAction with the specified action and
# input
return AgentAction(action, action_input, text)
else:
# If the necessary keys aren't present in the response, raise an
# exception
raise OutputParserException(
f"Missing 'action' or 'action_input' in LLM output: {text}"
)
except Exception as e:
# If any other exception is raised during parsing, also raise an
# OutputParserException
raise OutputParserException(f"Could not parse LLM output: {text}") from e
@property
def _type(self) -> str:
return "conversational_chat"
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/conversational_chat/__init__.py | """An agent designed to hold a conversation in addition to using tools."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/conversational_chat/prompt.py | # flake8: noqa
PREFIX = """Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
Overall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist."""
FORMAT_INSTRUCTIONS = """RESPONSE FORMAT INSTRUCTIONS
----------------------------
When responding to me, please output a response in one of two formats:
**Option 1:**
Use this if you want the human to use a tool.
Markdown code snippet formatted in the following schema:
```json
{{{{
"action": string, \\\\ The action to take. Must be one of {tool_names}
"action_input": string \\\\ The input to the action
}}}}
```
**Option #2:**
Use this if you want to respond directly to the human. Markdown code snippet formatted in the following schema:
```json
{{{{
"action": "Final Answer",
"action_input": string \\\\ You should put what you want to return to use here
}}}}
```"""
SUFFIX = """TOOLS
------
Assistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:
{{tools}}
{format_instructions}
USER'S INPUT
--------------------
Here is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):
{{{{input}}}}"""
TEMPLATE_TOOL_RESPONSE = """TOOL RESPONSE:
---------------------
{observation}
USER'S INPUT
--------------------
Okay, so what is the response to my last comment? If using information obtained from the tools you must mention it explicitly without mentioning the tool names - I have forgotten all TOOL RESPONSES! Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/openai_assistant/base.py | from __future__ import annotations
import asyncio
import json
from json import JSONDecodeError
from time import sleep
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
)
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import CallbackManager
from langchain_core.load import dumpd
from langchain_core.runnables import RunnableConfig, RunnableSerializable, ensure_config
from langchain_core.tools import BaseTool
from langchain_core.utils.function_calling import convert_to_openai_tool
from pydantic import BaseModel, Field, model_validator
from typing_extensions import Self
if TYPE_CHECKING:
import openai
from openai.types.beta.threads import ThreadMessage
from openai.types.beta.threads.required_action_function_tool_call import (
RequiredActionFunctionToolCall,
)
class OpenAIAssistantFinish(AgentFinish):
"""AgentFinish with run and thread metadata.
Parameters:
run_id: Run id.
thread_id: Thread id.
"""
run_id: str
thread_id: str
@classmethod
def is_lc_serializable(cls) -> bool:
"""Check if the class is serializable by LangChain.
Returns:
False
"""
return False
class OpenAIAssistantAction(AgentAction):
"""AgentAction with info needed to submit custom tool output to existing run.
Parameters:
tool_call_id: Tool call id.
run_id: Run id.
thread_id: Thread id
"""
tool_call_id: str
run_id: str
thread_id: str
@classmethod
def is_lc_serializable(cls) -> bool:
"""Check if the class is serializable by LangChain.
Returns:
False
"""
return False
def _get_openai_client() -> openai.OpenAI:
try:
import openai
return openai.OpenAI()
except ImportError as e:
raise ImportError(
"Unable to import openai, please install with `pip install openai`."
) from e
except AttributeError as e:
raise AttributeError(
"Please make sure you are using a v1.1-compatible version of openai. You "
'can install with `pip install "openai>=1.1"`.'
) from e
def _get_openai_async_client() -> openai.AsyncOpenAI:
try:
import openai
return openai.AsyncOpenAI()
except ImportError as e:
raise ImportError(
"Unable to import openai, please install with `pip install openai`."
) from e
except AttributeError as e:
raise AttributeError(
"Please make sure you are using a v1.1-compatible version of openai. You "
'can install with `pip install "openai>=1.1"`.'
) from e
def _is_assistants_builtin_tool(
tool: Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool],
) -> bool:
"""Determine if tool corresponds to OpenAI Assistants built-in."""
assistants_builtin_tools = ("code_interpreter", "retrieval")
return (
isinstance(tool, dict)
and ("type" in tool)
and (tool["type"] in assistants_builtin_tools)
)
def _get_assistants_tool(
tool: Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool],
) -> Dict[str, Any]:
"""Convert a raw function/class to an OpenAI tool.
Note that OpenAI assistants supports several built-in tools,
such as "code_interpreter" and "retrieval."
"""
if _is_assistants_builtin_tool(tool):
return tool # type: ignore
else:
return convert_to_openai_tool(tool)
OutputType = Union[
List[OpenAIAssistantAction],
OpenAIAssistantFinish,
List["ThreadMessage"],
List["RequiredActionFunctionToolCall"],
]
class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]):
"""Run an OpenAI Assistant.
Example using OpenAI tools:
.. code-block:: python
from langchain_experimental.openai_assistant import OpenAIAssistantRunnable
interpreter_assistant = OpenAIAssistantRunnable.create_assistant(
name="langchain assistant",
instructions="You are a personal math tutor. Write and run code to answer math questions.",
tools=[{"type": "code_interpreter"}],
model="gpt-4-1106-preview"
)
output = interpreter_assistant.invoke({"content": "What's 10 - 4 raised to the 2.7"})
Example using custom tools and AgentExecutor:
.. code-block:: python
from langchain_experimental.openai_assistant import OpenAIAssistantRunnable
from langchain.agents import AgentExecutor
from langchain.tools import E2BDataAnalysisTool
tools = [E2BDataAnalysisTool(api_key="...")]
agent = OpenAIAssistantRunnable.create_assistant(
name="langchain assistant e2b tool",
instructions="You are a personal math tutor. Write and run code to answer math questions.",
tools=tools,
model="gpt-4-1106-preview",
as_agent=True
)
agent_executor = AgentExecutor(agent=agent, tools=tools)
agent_executor.invoke({"content": "What's 10 - 4 raised to the 2.7"})
Example using custom tools and custom execution:
.. code-block:: python
from langchain_experimental.openai_assistant import OpenAIAssistantRunnable
from langchain.agents import AgentExecutor
from langchain_core.agents import AgentFinish
from langchain.tools import E2BDataAnalysisTool
tools = [E2BDataAnalysisTool(api_key="...")]
agent = OpenAIAssistantRunnable.create_assistant(
name="langchain assistant e2b tool",
instructions="You are a personal math tutor. Write and run code to answer math questions.",
tools=tools,
model="gpt-4-1106-preview",
as_agent=True
)
def execute_agent(agent, tools, input):
tool_map = {tool.name: tool for tool in tools}
response = agent.invoke(input)
while not isinstance(response, AgentFinish):
tool_outputs = []
for action in response:
tool_output = tool_map[action.tool].invoke(action.tool_input)
tool_outputs.append({"output": tool_output, "tool_call_id": action.tool_call_id})
response = agent.invoke(
{
"tool_outputs": tool_outputs,
"run_id": action.run_id,
"thread_id": action.thread_id
}
)
return response
response = execute_agent(agent, tools, {"content": "What's 10 - 4 raised to the 2.7"})
next_response = execute_agent(agent, tools, {"content": "now add 17.241", "thread_id": response.thread_id})
""" # noqa: E501
client: Any = Field(default_factory=_get_openai_client)
"""OpenAI or AzureOpenAI client."""
async_client: Any = None
"""OpenAI or AzureOpenAI async client."""
assistant_id: str
"""OpenAI assistant id."""
check_every_ms: float = 1_000.0
"""Frequency with which to check run progress in ms."""
as_agent: bool = False
"""Use as a LangChain agent, compatible with the AgentExecutor."""
@model_validator(mode="after")
def validate_async_client(self) -> Self:
if self.async_client is None:
import openai
api_key = self.client.api_key
self.async_client = openai.AsyncOpenAI(api_key=api_key)
return self
@classmethod
def create_assistant(
cls,
name: str,
instructions: str,
tools: Sequence[Union[BaseTool, dict]],
model: str,
*,
client: Optional[Union[openai.OpenAI, openai.AzureOpenAI]] = None,
**kwargs: Any,
) -> OpenAIAssistantRunnable:
"""Create an OpenAI Assistant and instantiate the Runnable.
Args:
name: Assistant name.
instructions: Assistant instructions.
tools: Assistant tools. Can be passed in OpenAI format or as BaseTools.
model: Assistant model to use.
client: OpenAI or AzureOpenAI client.
Will create a default OpenAI client if not specified.
kwargs: Additional arguments.
Returns:
OpenAIAssistantRunnable configured to run using the created assistant.
"""
client = client or _get_openai_client()
assistant = client.beta.assistants.create(
name=name,
instructions=instructions,
tools=[_get_assistants_tool(tool) for tool in tools], # type: ignore
model=model,
)
return cls(assistant_id=assistant.id, client=client, **kwargs)
def invoke(
self, input: dict, config: Optional[RunnableConfig] = None, **kwargs: Any
) -> OutputType:
"""Invoke assistant.
Args:
input: Runnable input dict that can have:
content: User message when starting a new run.
thread_id: Existing thread to use.
run_id: Existing run to use. Should only be supplied when providing
the tool output for a required action after an initial invocation.
message_metadata: Metadata to associate with new message.
thread_metadata: Metadata to associate with new thread. Only relevant
when new thread being created.
instructions: Additional run instructions.
model: Override Assistant model for this run.
tools: Override Assistant tools for this run.
run_metadata: Metadata to associate with new run.
config: Runnable config. Defaults to None.
Return:
If self.as_agent, will return
Union[List[OpenAIAssistantAction], OpenAIAssistantFinish].
Otherwise, will return OpenAI types
Union[List[ThreadMessage], List[RequiredActionFunctionToolCall]].
"""
config = ensure_config(config)
callback_manager = CallbackManager.configure(
inheritable_callbacks=config.get("callbacks"),
inheritable_tags=config.get("tags"),
inheritable_metadata=config.get("metadata"),
)
run_manager = callback_manager.on_chain_start(
dumpd(self), input, name=config.get("run_name") or self.get_name()
)
try:
# Being run within AgentExecutor and there are tool outputs to submit.
if self.as_agent and input.get("intermediate_steps"):
tool_outputs = self._parse_intermediate_steps(
input["intermediate_steps"]
)
run = self.client.beta.threads.runs.submit_tool_outputs(**tool_outputs)
# Starting a new thread and a new run.
elif "thread_id" not in input:
thread = {
"messages": [
{
"role": "user",
"content": input["content"],
"metadata": input.get("message_metadata"),
}
],
"metadata": input.get("thread_metadata"),
}
run = self._create_thread_and_run(input, thread)
# Starting a new run in an existing thread.
elif "run_id" not in input:
_ = self.client.beta.threads.messages.create(
input["thread_id"],
content=input["content"],
role="user",
metadata=input.get("message_metadata"),
)
run = self._create_run(input)
# Submitting tool outputs to an existing run, outside the AgentExecutor
# framework.
else:
run = self.client.beta.threads.runs.submit_tool_outputs(**input)
run = self._wait_for_run(run.id, run.thread_id)
except BaseException as e:
run_manager.on_chain_error(e)
raise e
try:
response = self._get_response(run)
except BaseException as e:
run_manager.on_chain_error(e, metadata=run.dict())
raise e
else:
run_manager.on_chain_end(response)
return response
@classmethod
async def acreate_assistant(
cls,
name: str,
instructions: str,
tools: Sequence[Union[BaseTool, dict]],
model: str,
*,
async_client: Optional[
Union[openai.AsyncOpenAI, openai.AsyncAzureOpenAI]
] = None,
**kwargs: Any,
) -> OpenAIAssistantRunnable:
"""Async create an AsyncOpenAI Assistant and instantiate the Runnable.
Args:
name: Assistant name.
instructions: Assistant instructions.
tools: Assistant tools. Can be passed in OpenAI format or as BaseTools.
model: Assistant model to use.
async_client: AsyncOpenAI client.
Will create default async_client if not specified.
Returns:
AsyncOpenAIAssistantRunnable configured to run using the created assistant.
"""
async_client = async_client or _get_openai_async_client()
openai_tools = [_get_assistants_tool(tool) for tool in tools]
assistant = await async_client.beta.assistants.create(
name=name,
instructions=instructions,
tools=openai_tools, # type: ignore
model=model,
)
return cls(assistant_id=assistant.id, async_client=async_client, **kwargs)
async def ainvoke(
self, input: dict, config: Optional[RunnableConfig] = None, **kwargs: Any
) -> OutputType:
"""Async invoke assistant.
Args:
input: Runnable input dict that can have:
content: User message when starting a new run.
thread_id: Existing thread to use.
run_id: Existing run to use. Should only be supplied when providing
the tool output for a required action after an initial invocation.
message_metadata: Metadata to associate with a new message.
thread_metadata: Metadata to associate with new thread. Only relevant
when a new thread is created.
instructions: Additional run instructions.
model: Override Assistant model for this run.
tools: Override Assistant tools for this run.
run_metadata: Metadata to associate with new run.
config: Runnable config. Defaults to None.
kwargs: Additional arguments.
Return:
If self.as_agent, will return
Union[List[OpenAIAssistantAction], OpenAIAssistantFinish].
Otherwise, will return OpenAI types
Union[List[ThreadMessage], List[RequiredActionFunctionToolCall]].
"""
config = config or {}
callback_manager = CallbackManager.configure(
inheritable_callbacks=config.get("callbacks"),
inheritable_tags=config.get("tags"),
inheritable_metadata=config.get("metadata"),
)
run_manager = callback_manager.on_chain_start(
dumpd(self), input, name=config.get("run_name") or self.get_name()
)
try:
# Being run within AgentExecutor and there are tool outputs to submit.
if self.as_agent and input.get("intermediate_steps"):
tool_outputs = await self._aparse_intermediate_steps(
input["intermediate_steps"]
)
run = await self.async_client.beta.threads.runs.submit_tool_outputs(
**tool_outputs
)
# Starting a new thread and a new run.
elif "thread_id" not in input:
thread = {
"messages": [
{
"role": "user",
"content": input["content"],
"metadata": input.get("message_metadata"),
}
],
"metadata": input.get("thread_metadata"),
}
run = await self._acreate_thread_and_run(input, thread)
# Starting a new run in an existing thread.
elif "run_id" not in input:
_ = await self.async_client.beta.threads.messages.create(
input["thread_id"],
content=input["content"],
role="user",
metadata=input.get("message_metadata"),
)
run = await self._acreate_run(input)
# Submitting tool outputs to an existing run, outside the AgentExecutor
# framework.
else:
run = await self.async_client.beta.threads.runs.submit_tool_outputs(
**input
)
run = await self._await_for_run(run.id, run.thread_id)
except BaseException as e:
run_manager.on_chain_error(e)
raise e
try:
response = self._get_response(run)
except BaseException as e:
run_manager.on_chain_error(e, metadata=run.dict())
raise e
else:
run_manager.on_chain_end(response)
return response
def _parse_intermediate_steps(
self, intermediate_steps: List[Tuple[OpenAIAssistantAction, str]]
) -> dict:
last_action, last_output = intermediate_steps[-1]
run = self._wait_for_run(last_action.run_id, last_action.thread_id)
required_tool_call_ids = set()
if run.required_action:
required_tool_call_ids = {
tc.id for tc in run.required_action.submit_tool_outputs.tool_calls
}
tool_outputs = [
{"output": str(output), "tool_call_id": action.tool_call_id}
for action, output in intermediate_steps
if action.tool_call_id in required_tool_call_ids
]
submit_tool_outputs = {
"tool_outputs": tool_outputs,
"run_id": last_action.run_id,
"thread_id": last_action.thread_id,
}
return submit_tool_outputs
def _create_run(self, input: dict) -> Any:
params = {
k: v
for k, v in input.items()
if k in ("instructions", "model", "tools", "run_metadata")
}
return self.client.beta.threads.runs.create(
input["thread_id"],
assistant_id=self.assistant_id,
**params,
)
def _create_thread_and_run(self, input: dict, thread: dict) -> Any:
params = {
k: v
for k, v in input.items()
if k in ("instructions", "model", "tools", "run_metadata")
}
run = self.client.beta.threads.create_and_run(
assistant_id=self.assistant_id,
thread=thread,
**params,
)
return run
def _get_response(self, run: Any) -> Any:
# TODO: Pagination
if run.status == "completed":
import openai
major_version = int(openai.version.VERSION.split(".")[0])
minor_version = int(openai.version.VERSION.split(".")[1])
version_gte_1_14 = (major_version > 1) or (
major_version == 1 and minor_version >= 14
)
messages = self.client.beta.threads.messages.list(
run.thread_id, order="asc"
)
new_messages = [msg for msg in messages if msg.run_id == run.id]
if not self.as_agent:
return new_messages
answer: Any = [
msg_content for msg in new_messages for msg_content in msg.content
]
if all(
(
isinstance(content, openai.types.beta.threads.TextContentBlock)
if version_gte_1_14
else isinstance(
content, openai.types.beta.threads.MessageContentText
)
)
for content in answer
):
answer = "\n".join(content.text.value for content in answer)
return OpenAIAssistantFinish(
return_values={
"output": answer,
"thread_id": run.thread_id,
"run_id": run.id,
},
log="",
run_id=run.id,
thread_id=run.thread_id,
)
elif run.status == "requires_action":
if not self.as_agent:
return run.required_action.submit_tool_outputs.tool_calls
actions = []
for tool_call in run.required_action.submit_tool_outputs.tool_calls:
function = tool_call.function
try:
args = json.loads(function.arguments, strict=False)
except JSONDecodeError as e:
raise ValueError(
f"Received invalid JSON function arguments: "
f"{function.arguments} for function {function.name}"
) from e
if len(args) == 1 and "__arg1" in args:
args = args["__arg1"]
actions.append(
OpenAIAssistantAction(
tool=function.name,
tool_input=args,
tool_call_id=tool_call.id,
log="",
run_id=run.id,
thread_id=run.thread_id,
)
)
return actions
else:
run_info = json.dumps(run.dict(), indent=2)
raise ValueError(
f"Unexpected run status: {run.status}. Full run info:\n\n{run_info})"
)
def _wait_for_run(self, run_id: str, thread_id: str) -> Any:
in_progress = True
while in_progress:
run = self.client.beta.threads.runs.retrieve(run_id, thread_id=thread_id)
in_progress = run.status in ("in_progress", "queued")
if in_progress:
sleep(self.check_every_ms / 1000)
return run
async def _aparse_intermediate_steps(
self, intermediate_steps: List[Tuple[OpenAIAssistantAction, str]]
) -> dict:
last_action, last_output = intermediate_steps[-1]
run = await self._wait_for_run(last_action.run_id, last_action.thread_id)
required_tool_call_ids = set()
if run.required_action:
required_tool_call_ids = {
tc.id for tc in run.required_action.submit_tool_outputs.tool_calls
}
tool_outputs = [
{"output": str(output), "tool_call_id": action.tool_call_id}
for action, output in intermediate_steps
if action.tool_call_id in required_tool_call_ids
]
submit_tool_outputs = {
"tool_outputs": tool_outputs,
"run_id": last_action.run_id,
"thread_id": last_action.thread_id,
}
return submit_tool_outputs
async def _acreate_run(self, input: dict) -> Any:
params = {
k: v
for k, v in input.items()
if k in ("instructions", "model", "tools", "run_metadata")
}
return await self.async_client.beta.threads.runs.create(
input["thread_id"],
assistant_id=self.assistant_id,
**params,
)
async def _acreate_thread_and_run(self, input: dict, thread: dict) -> Any:
params = {
k: v
for k, v in input.items()
if k in ("instructions", "model", "tools", "run_metadata")
}
run = await self.async_client.beta.threads.create_and_run(
assistant_id=self.assistant_id,
thread=thread,
**params,
)
return run
async def _aget_response(self, run: Any) -> Any:
# TODO: Pagination
if run.status == "completed":
import openai
major_version = int(openai.version.VERSION.split(".")[0])
minor_version = int(openai.version.VERSION.split(".")[1])
version_gte_1_14 = (major_version > 1) or (
major_version == 1 and minor_version >= 14
)
messages = await self.async_client.beta.threads.messages.list(
run.thread_id, order="asc"
)
new_messages = [msg for msg in messages if msg.run_id == run.id]
if not self.as_agent:
return new_messages
answer: Any = [
msg_content for msg in new_messages for msg_content in msg.content
]
if all(
(
isinstance(content, openai.types.beta.threads.TextContentBlock)
if version_gte_1_14
else isinstance(
content, openai.types.beta.threads.MessageContentText
)
)
for content in answer
):
answer = "\n".join(content.text.value for content in answer)
return OpenAIAssistantFinish(
return_values={
"output": answer,
"thread_id": run.thread_id,
"run_id": run.id,
},
log="",
run_id=run.id,
thread_id=run.thread_id,
)
elif run.status == "requires_action":
if not self.as_agent:
return run.required_action.submit_tool_outputs.tool_calls
actions = []
for tool_call in run.required_action.submit_tool_outputs.tool_calls:
function = tool_call.function
try:
args = json.loads(function.arguments, strict=False)
except JSONDecodeError as e:
raise ValueError(
f"Received invalid JSON function arguments: "
f"{function.arguments} for function {function.name}"
) from e
if len(args) == 1 and "__arg1" in args:
args = args["__arg1"]
actions.append(
OpenAIAssistantAction(
tool=function.name,
tool_input=args,
tool_call_id=tool_call.id,
log="",
run_id=run.id,
thread_id=run.thread_id,
)
)
return actions
else:
run_info = json.dumps(run.dict(), indent=2)
raise ValueError(
f"Unexpected run status: {run.status}. Full run info:\n\n{run_info})"
)
async def _await_for_run(self, run_id: str, thread_id: str) -> Any:
in_progress = True
while in_progress:
run = await self.async_client.beta.threads.runs.retrieve(
run_id, thread_id=thread_id
)
in_progress = run.status in ("in_progress", "queued")
if in_progress:
await asyncio.sleep(self.check_every_ms / 1000)
return run
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/openai_assistant/__init__.py | from langchain.agents.openai_assistant.base import OpenAIAssistantRunnable
__all__ = ["OpenAIAssistantRunnable"]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/chat/base.py | from typing import Any, List, Optional, Sequence, Tuple
from langchain_core._api import deprecated
from langchain_core.agents import AgentAction
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain._api.deprecation import AGENT_DEPRECATION_WARNING
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.chat.output_parser import ChatOutputParser
from langchain.agents.chat.prompt import (
FORMAT_INSTRUCTIONS,
HUMAN_MESSAGE,
SYSTEM_MESSAGE_PREFIX,
SYSTEM_MESSAGE_SUFFIX,
)
from langchain.agents.utils import validate_tools_single_input
from langchain.chains.llm import LLMChain
@deprecated(
"0.1.0",
message=AGENT_DEPRECATION_WARNING,
removal="1.0",
)
class ChatAgent(Agent):
"""Chat Agent."""
output_parser: AgentOutputParser = Field(default_factory=ChatOutputParser)
"""Output parser for the agent."""
@property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
def _construct_scratchpad(
self, intermediate_steps: List[Tuple[AgentAction, str]]
) -> str:
agent_scratchpad = super()._construct_scratchpad(intermediate_steps)
if not isinstance(agent_scratchpad, str):
raise ValueError("agent_scratchpad should be of type string.")
if agent_scratchpad:
return (
f"This was your previous work "
f"(but I haven't seen any of it! I only see what "
f"you return as final answer):\n{agent_scratchpad}"
)
else:
return agent_scratchpad
@classmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
return ChatOutputParser()
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
super()._validate_tools(tools)
validate_tools_single_input(class_name=cls.__name__, tools=tools)
@property
def _stop(self) -> List[str]:
return ["Observation:"]
@classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
system_message_prefix: str = SYSTEM_MESSAGE_PREFIX,
system_message_suffix: str = SYSTEM_MESSAGE_SUFFIX,
human_message: str = HUMAN_MESSAGE,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
) -> BasePromptTemplate:
"""Create a prompt from a list of tools.
Args:
tools: A list of tools.
system_message_prefix: The system message prefix.
Default is SYSTEM_MESSAGE_PREFIX.
system_message_suffix: The system message suffix.
Default is SYSTEM_MESSAGE_SUFFIX.
human_message: The human message. Default is HUMAN_MESSAGE.
format_instructions: The format instructions.
Default is FORMAT_INSTRUCTIONS.
input_variables: The input variables. Default is None.
Returns:
A prompt template.
"""
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=tool_names)
template = "\n\n".join(
[
system_message_prefix,
tool_strings,
format_instructions,
system_message_suffix,
]
)
messages = [
SystemMessagePromptTemplate.from_template(template),
HumanMessagePromptTemplate.from_template(human_message),
]
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
return ChatPromptTemplate(input_variables=input_variables, messages=messages) # type: ignore[arg-type]
@classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
system_message_prefix: str = SYSTEM_MESSAGE_PREFIX,
system_message_suffix: str = SYSTEM_MESSAGE_SUFFIX,
human_message: str = HUMAN_MESSAGE,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variables: Optional[List[str]] = None,
**kwargs: Any,
) -> Agent:
"""Construct an agent from an LLM and tools.
Args:
llm: The language model.
tools: A list of tools.
callback_manager: The callback manager. Default is None.
output_parser: The output parser. Default is None.
system_message_prefix: The system message prefix.
Default is SYSTEM_MESSAGE_PREFIX.
system_message_suffix: The system message suffix.
Default is SYSTEM_MESSAGE_SUFFIX.
human_message: The human message. Default is HUMAN_MESSAGE.
format_instructions: The format instructions.
Default is FORMAT_INSTRUCTIONS.
input_variables: The input variables. Default is None.
kwargs: Additional keyword arguments.
Returns:
An agent.
"""
cls._validate_tools(tools)
prompt = cls.create_prompt(
tools,
system_message_prefix=system_message_prefix,
system_message_suffix=system_message_suffix,
human_message=human_message,
format_instructions=format_instructions,
input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
_output_parser = output_parser or cls._get_default_output_parser()
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
)
@property
def _agent_type(self) -> str:
raise ValueError
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/chat/output_parser.py | import json
import re
from typing import Pattern, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
from langchain.agents.chat.prompt import FORMAT_INSTRUCTIONS
FINAL_ANSWER_ACTION = "Final Answer:"
class ChatOutputParser(AgentOutputParser):
"""Output parser for the chat agent."""
format_instructions: str = FORMAT_INSTRUCTIONS
"""Default formatting instructions"""
pattern: Pattern = re.compile(r"^.*?`{3}(?:json)?\n(.*?)`{3}.*?$", re.DOTALL)
"""Regex pattern to parse the output."""
def get_format_instructions(self) -> str:
"""Returns formatting instructions for the given output parser."""
return self.format_instructions
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
"""Parse the output from the agent into
an AgentAction or AgentFinish object.
Args:
text: The text to parse.
Returns:
An AgentAction or AgentFinish object.
Raises:
OutputParserException: If the output could not be parsed.
ValueError: If the action could not be found.
"""
includes_answer = FINAL_ANSWER_ACTION in text
try:
found = self.pattern.search(text)
if not found:
# Fast fail to parse Final Answer.
raise ValueError("action not found")
action = found.group(1)
response = json.loads(action.strip())
includes_action = "action" in response
if includes_answer and includes_action:
raise OutputParserException(
"Parsing LLM output produced a final answer "
f"and a parse-able action: {text}"
)
return AgentAction(
response["action"], response.get("action_input", {}), text
)
except Exception as exc:
if not includes_answer:
raise OutputParserException(
f"Could not parse LLM output: {text}"
) from exc
output = text.split(FINAL_ANSWER_ACTION)[-1].strip()
return AgentFinish({"output": output}, text)
@property
def _type(self) -> str:
return "chat"
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/chat/prompt.py | # flake8: noqa
SYSTEM_MESSAGE_PREFIX = """Answer the following questions as best you can. You have access to the following tools:"""
FORMAT_INSTRUCTIONS = """The way you use the tools is by specifying a json blob.
Specifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).
The only values that should be in the "action" field are: {tool_names}
The $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:
```
{{{{
"action": $TOOL_NAME,
"action_input": $INPUT
}}}}
```
ALWAYS use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action:
```
$JSON_BLOB
```
Observation: the result of the action
... (this Thought/Action/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question"""
SYSTEM_MESSAGE_SUFFIX = """Begin! Reminder to always use the exact characters `Final Answer` when responding."""
HUMAN_MESSAGE = "{input}\n\n{agent_scratchpad}"
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/format_scratchpad/xml.py | from typing import List, Tuple
from langchain_core.agents import AgentAction
def format_xml(
intermediate_steps: List[Tuple[AgentAction, str]],
) -> str:
"""Format the intermediate steps as XML.
Args:
intermediate_steps: The intermediate steps.
Returns:
The intermediate steps as XML.
"""
log = ""
for action, observation in intermediate_steps:
log += (
f"<tool>{action.tool}</tool><tool_input>{action.tool_input}"
f"</tool_input><observation>{observation}</observation>"
)
return log
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/format_scratchpad/log.py | from typing import List, Tuple
from langchain_core.agents import AgentAction
def format_log_to_str(
intermediate_steps: List[Tuple[AgentAction, str]],
observation_prefix: str = "Observation: ",
llm_prefix: str = "Thought: ",
) -> str:
"""Construct the scratchpad that lets the agent continue its thought process.
Args:
intermediate_steps: List of tuples of AgentAction and observation strings.
observation_prefix: Prefix to append the observation with.
Defaults to "Observation: ".
llm_prefix: Prefix to append the llm call with.
Defaults to "Thought: ".
Returns:
str: The scratchpad.
"""
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\n{observation_prefix}{observation}\n{llm_prefix}"
return thoughts
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/format_scratchpad/openai_functions.py | import json
from typing import List, Sequence, Tuple
from langchain_core.agents import AgentAction, AgentActionMessageLog
from langchain_core.messages import AIMessage, BaseMessage, FunctionMessage
def _convert_agent_action_to_messages(
agent_action: AgentAction, observation: str
) -> List[BaseMessage]:
"""Convert an agent action to a message.
This code is used to reconstruct the original AI message from the agent action.
Args:
agent_action: Agent action to convert.
Returns:
AIMessage or the previous messages plus a FunctionMessage that corresponds to
the original tool invocation
"""
if isinstance(agent_action, AgentActionMessageLog):
return list(agent_action.message_log) + [
_create_function_message(agent_action, observation)
]
else:
return [AIMessage(content=agent_action.log)]
def _create_function_message(
agent_action: AgentAction, observation: str
) -> FunctionMessage:
"""Convert agent action and observation into a function message.
Args:
agent_action: the tool invocation request from the agent.
observation: the result of the tool invocation.
Returns:
FunctionMessage that corresponds to the original tool invocation.
Raises:
ValueError: if the observation cannot be converted to a string.
"""
if not isinstance(observation, str):
try:
content = json.dumps(observation, ensure_ascii=False)
except Exception:
content = str(observation)
else:
content = observation
return FunctionMessage(
name=agent_action.tool,
content=content,
)
def format_to_openai_function_messages(
intermediate_steps: Sequence[Tuple[AgentAction, str]],
) -> List[BaseMessage]:
"""Convert (AgentAction, tool output) tuples into FunctionMessages.
Args:
intermediate_steps: Steps the LLM has taken to date, along with observations
Returns:
list of messages to send to the LLM for the next prediction
Raises:
ValueError: if the observation cannot be converted to a string.
"""
messages = []
for agent_action, observation in intermediate_steps:
messages.extend(_convert_agent_action_to_messages(agent_action, observation))
return messages
# Backwards compatibility
format_to_openai_functions = format_to_openai_function_messages
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/format_scratchpad/openai_tools.py | from langchain.agents.format_scratchpad.tools import (
format_to_tool_messages as format_to_openai_tool_messages,
)
__all__ = ["format_to_openai_tool_messages"]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/format_scratchpad/tools.py | import json
from typing import List, Sequence, Tuple
from langchain_core.agents import AgentAction
from langchain_core.messages import (
AIMessage,
BaseMessage,
ToolMessage,
)
from langchain.agents.output_parsers.tools import ToolAgentAction
def _create_tool_message(
agent_action: ToolAgentAction, observation: str
) -> ToolMessage:
"""Convert agent action and observation into a tool message.
Args:
agent_action: the tool invocation request from the agent.
observation: the result of the tool invocation.
Returns:
ToolMessage that corresponds to the original tool invocation.
Raises:
ValueError: if the observation cannot be converted to a string.
"""
if not isinstance(observation, str):
try:
content = json.dumps(observation, ensure_ascii=False)
except Exception:
content = str(observation)
else:
content = observation
return ToolMessage(
tool_call_id=agent_action.tool_call_id,
content=content,
additional_kwargs={"name": agent_action.tool},
)
def format_to_tool_messages(
intermediate_steps: Sequence[Tuple[AgentAction, str]],
) -> List[BaseMessage]:
"""Convert (AgentAction, tool output) tuples into ToolMessages.
Args:
intermediate_steps: Steps the LLM has taken to date, along with observations.
Returns:
list of messages to send to the LLM for the next prediction.
"""
messages = []
for agent_action, observation in intermediate_steps:
if isinstance(agent_action, ToolAgentAction):
new_messages = list(agent_action.message_log) + [
_create_tool_message(agent_action, observation)
]
messages.extend([new for new in new_messages if new not in messages])
else:
messages.append(AIMessage(content=agent_action.log))
return messages
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/format_scratchpad/log_to_messages.py | from typing import List, Tuple
from langchain_core.agents import AgentAction
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
def format_log_to_messages(
intermediate_steps: List[Tuple[AgentAction, str]],
template_tool_response: str = "{observation}",
) -> List[BaseMessage]:
"""Construct the scratchpad that lets the agent continue its thought process.
Args:
intermediate_steps: List of tuples of AgentAction and observation strings.
template_tool_response: Template to format the observation with.
Defaults to "{observation}".
Returns:
List[BaseMessage]: The scratchpad.
"""
thoughts: List[BaseMessage] = []
for action, observation in intermediate_steps:
thoughts.append(AIMessage(content=action.log))
human_message = HumanMessage(
content=template_tool_response.format(observation=observation)
)
thoughts.append(human_message)
return thoughts
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/format_scratchpad/__init__.py | """Logic for formatting intermediate steps into an agent scratchpad.
Intermediate steps refers to the list of (AgentAction, observation) tuples
that result from previous iterations of the agent.
Depending on the prompting strategy you are using, you may want to format these
differently before passing them into the LLM.
"""
from langchain.agents.format_scratchpad.log import format_log_to_str
from langchain.agents.format_scratchpad.log_to_messages import format_log_to_messages
from langchain.agents.format_scratchpad.openai_functions import (
format_to_openai_function_messages,
format_to_openai_functions,
)
from langchain.agents.format_scratchpad.tools import format_to_tool_messages
from langchain.agents.format_scratchpad.xml import format_xml
__all__ = [
"format_xml",
"format_to_openai_function_messages",
"format_to_openai_functions",
"format_to_tool_messages",
"format_log_to_str",
"format_log_to_messages",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/json_chat/base.py | from typing import List, Sequence, Union
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.tools.render import ToolsRenderer, render_text_description
from langchain.agents.format_scratchpad import format_log_to_messages
from langchain.agents.json_chat.prompt import TEMPLATE_TOOL_RESPONSE
from langchain.agents.output_parsers import JSONAgentOutputParser
def create_json_chat_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: ChatPromptTemplate,
stop_sequence: Union[bool, List[str]] = True,
tools_renderer: ToolsRenderer = render_text_description,
template_tool_response: str = TEMPLATE_TOOL_RESPONSE,
) -> Runnable:
"""Create an agent that uses JSON to format its logic, build for Chat Models.
Args:
llm: LLM to use as the agent.
tools: Tools this agent has access to.
prompt: The prompt to use. See Prompt section below for more.
stop_sequence: bool or list of str.
If True, adds a stop token of "Observation:" to avoid hallucinates.
If False, does not add a stop token.
If a list of str, uses the provided list as the stop tokens.
Default is True. You may to set this to False if the LLM you are using
does not support stop sequences.
tools_renderer: This controls how the tools are converted into a string and
then passed into the LLM. Default is `render_text_description`.
template_tool_response: Template prompt that uses the tool response (observation)
to make the LLM generate the next action to take.
Default is TEMPLATE_TOOL_RESPONSE.
Returns:
A Runnable sequence representing an agent. It takes as input all the same input
variables as the prompt passed in does. It returns as output either an
AgentAction or AgentFinish.
Raises:
ValueError: If the prompt is missing required variables.
ValueError: If the template_tool_response is missing
the required variable 'observation'.
Example:
.. code-block:: python
from langchain import hub
from langchain_community.chat_models import ChatOpenAI
from langchain.agents import AgentExecutor, create_json_chat_agent
prompt = hub.pull("hwchase17/react-chat-json")
model = ChatOpenAI()
tools = ...
agent = create_json_chat_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
agent_executor.invoke({"input": "hi"})
# Using with chat history
from langchain_core.messages import AIMessage, HumanMessage
agent_executor.invoke(
{
"input": "what's my name?",
"chat_history": [
HumanMessage(content="hi! my name is bob"),
AIMessage(content="Hello Bob! How can I assist you today?"),
],
}
)
Prompt:
The prompt must have input keys:
* `tools`: contains descriptions and arguments for each tool.
* `tool_names`: contains all tool names.
* `agent_scratchpad`: must be a MessagesPlaceholder. Contains previous agent actions and tool outputs as messages.
Here's an example:
.. code-block:: python
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
system = '''Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering \
simple questions to providing in-depth explanations and discussions on a wide range of \
topics. As a language model, Assistant is able to generate human-like text based on \
the input it receives, allowing it to engage in natural-sounding conversations and \
provide responses that are coherent and relevant to the topic at hand.
Assistant is constantly learning and improving, and its capabilities are constantly \
evolving. It is able to process and understand large amounts of text, and can use this \
knowledge to provide accurate and informative responses to a wide range of questions. \
Additionally, Assistant is able to generate its own text based on the input it \
receives, allowing it to engage in discussions and provide explanations and \
descriptions on a wide range of topics.
Overall, Assistant is a powerful system that can help with a wide range of tasks \
and provide valuable insights and information on a wide range of topics. Whether \
you need help with a specific question or just want to have a conversation about \
a particular topic, Assistant is here to assist.'''
human = '''TOOLS
------
Assistant can ask the user to use tools to look up information that may be helpful in \
answering the users original question. The tools the human can use are:
{tools}
RESPONSE FORMAT INSTRUCTIONS
----------------------------
When responding to me, please output a response in one of two formats:
**Option 1:**
Use this if you want the human to use a tool.
Markdown code snippet formatted in the following schema:
```json
{{
"action": string, \\ The action to take. Must be one of {tool_names}
"action_input": string \\ The input to the action
}}
```
**Option #2:**
Use this if you want to respond directly to the human. Markdown code snippet formatted \
in the following schema:
```json
{{
"action": "Final Answer",
"action_input": string \\ You should put what you want to return to use here
}}
```
USER'S INPUT
--------------------
Here is the user's input (remember to respond with a markdown code snippet of a json \
blob with a single action, and NOTHING else):
{input}'''
prompt = ChatPromptTemplate.from_messages(
[
("system", system),
MessagesPlaceholder("chat_history", optional=True),
("human", human),
MessagesPlaceholder("agent_scratchpad"),
]
)
""" # noqa: E501
missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
prompt.input_variables + list(prompt.partial_variables)
)
if missing_vars:
raise ValueError(f"Prompt missing required variables: {missing_vars}")
if "{observation}" not in template_tool_response:
raise ValueError(
"Template tool response missing required variable 'observation'"
)
prompt = prompt.partial(
tools=tools_renderer(list(tools)),
tool_names=", ".join([t.name for t in tools]),
)
if stop_sequence:
stop = ["\nObservation"] if stop_sequence is True else stop_sequence
llm_to_use = llm.bind(stop=stop)
else:
llm_to_use = llm
agent = (
RunnablePassthrough.assign(
agent_scratchpad=lambda x: format_log_to_messages(
x["intermediate_steps"], template_tool_response=template_tool_response
)
)
| prompt
| llm_to_use
| JSONAgentOutputParser()
)
return agent
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/json_chat/prompt.py | # flake8: noqa
TEMPLATE_TOOL_RESPONSE = """TOOL RESPONSE:
---------------------
{observation}
USER'S INPUT
--------------------
Okay, so what is the response to my last comment? If using information obtained from the tools you must mention it explicitly without mentioning the tool names - I have forgotten all TOOL RESPONSES! Remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else - even if you just want to respond to the user. Do NOT respond with anything except a JSON snippet no matter what!"""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/tool_calling_agent/base.py | from typing import Callable, List, Sequence, Tuple
from langchain_core.agents import AgentAction
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain.agents.format_scratchpad.tools import (
format_to_tool_messages,
)
from langchain.agents.output_parsers.tools import ToolsAgentOutputParser
MessageFormatter = Callable[[Sequence[Tuple[AgentAction, str]]], List[BaseMessage]]
def create_tool_calling_agent(
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
prompt: ChatPromptTemplate,
*,
message_formatter: MessageFormatter = format_to_tool_messages,
) -> Runnable:
"""Create an agent that uses tools.
Args:
llm: LLM to use as the agent.
tools: Tools this agent has access to.
prompt: The prompt to use. See Prompt section below for more on the expected
input variables.
message_formatter: Formatter function to convert (AgentAction, tool output)
tuples into FunctionMessages.
Returns:
A Runnable sequence representing an agent. It takes as input all the same input
variables as the prompt passed in does. It returns as output either an
AgentAction or AgentFinish.
Example:
.. code-block:: python
from langchain.agents import AgentExecutor, create_tool_calling_agent, tool
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant"),
("placeholder", "{chat_history}"),
("human", "{input}"),
("placeholder", "{agent_scratchpad}"),
]
)
model = ChatAnthropic(model="claude-3-opus-20240229")
@tool
def magic_function(input: int) -> int:
\"\"\"Applies a magic function to an input.\"\"\"
return input + 2
tools = [magic_function]
agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "what is the value of magic_function(3)?"})
# Using with chat history
from langchain_core.messages import AIMessage, HumanMessage
agent_executor.invoke(
{
"input": "what's my name?",
"chat_history": [
HumanMessage(content="hi! my name is bob"),
AIMessage(content="Hello Bob! How can I assist you today?"),
],
}
)
Prompt:
The agent prompt must have an `agent_scratchpad` key that is a
``MessagesPlaceholder``. Intermediate agent actions and tool output
messages will be passed in here.
"""
missing_vars = {"agent_scratchpad"}.difference(
prompt.input_variables + list(prompt.partial_variables)
)
if missing_vars:
raise ValueError(f"Prompt missing required variables: {missing_vars}")
if not hasattr(llm, "bind_tools"):
raise ValueError(
"This function requires a .bind_tools method be implemented on the LLM.",
)
llm_with_tools = llm.bind_tools(tools)
agent = (
RunnablePassthrough.assign(
agent_scratchpad=lambda x: message_formatter(x["intermediate_steps"])
)
| prompt
| llm_with_tools
| ToolsAgentOutputParser()
)
return agent
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/base.py | from langchain_core.tools import BaseToolkit
__all__ = ["BaseToolkit"]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/azure_cognitive_services.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.azure_cognitive_services import (
AzureCognitiveServicesToolkit,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"AzureCognitiveServicesToolkit": (
"langchain_community.agent_toolkits.azure_cognitive_services"
)
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"AzureCognitiveServicesToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/__init__.py | """Agent toolkits contain integrations with various resources and services.
LangChain has a large ecosystem of integrations with various external resources
like local and remote file systems, APIs and databases.
These integrations allow developers to create versatile applications that combine the
power of LLMs with the ability to access, interact with and manipulate external
resources.
When developing an application, developers should inspect the capabilities and
permissions of the tools that underlie the given agent toolkit, and determine
whether permissions of the given toolkit are appropriate for the application.
See [Security](https://python.langchain.com/docs/security) for more information.
"""
from pathlib import Path
from typing import TYPE_CHECKING, Any
from langchain_core._api.path import as_import_path
from langchain_core.tools.retriever import create_retriever_tool
from langchain._api import create_importer
from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import (
create_conversational_retrieval_agent,
)
from langchain.agents.agent_toolkits.vectorstore.base import (
create_vectorstore_agent,
create_vectorstore_router_agent,
)
from langchain.agents.agent_toolkits.vectorstore.toolkit import (
VectorStoreInfo,
VectorStoreRouterToolkit,
VectorStoreToolkit,
)
if TYPE_CHECKING:
from langchain_community.agent_toolkits.ainetwork.toolkit import AINetworkToolkit
from langchain_community.agent_toolkits.amadeus.toolkit import AmadeusToolkit
from langchain_community.agent_toolkits.azure_cognitive_services import (
AzureCognitiveServicesToolkit,
)
from langchain_community.agent_toolkits.file_management.toolkit import (
FileManagementToolkit,
)
from langchain_community.agent_toolkits.gmail.toolkit import GmailToolkit
from langchain_community.agent_toolkits.jira.toolkit import JiraToolkit
from langchain_community.agent_toolkits.json.base import create_json_agent
from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
from langchain_community.agent_toolkits.multion.toolkit import MultionToolkit
from langchain_community.agent_toolkits.nasa.toolkit import NasaToolkit
from langchain_community.agent_toolkits.nla.toolkit import NLAToolkit
from langchain_community.agent_toolkits.office365.toolkit import O365Toolkit
from langchain_community.agent_toolkits.openapi.base import create_openapi_agent
from langchain_community.agent_toolkits.openapi.toolkit import OpenAPIToolkit
from langchain_community.agent_toolkits.playwright.toolkit import (
PlayWrightBrowserToolkit,
)
from langchain_community.agent_toolkits.powerbi.base import create_pbi_agent
from langchain_community.agent_toolkits.powerbi.chat_base import (
create_pbi_chat_agent,
)
from langchain_community.agent_toolkits.powerbi.toolkit import PowerBIToolkit
from langchain_community.agent_toolkits.slack.toolkit import SlackToolkit
from langchain_community.agent_toolkits.spark_sql.base import create_spark_sql_agent
from langchain_community.agent_toolkits.spark_sql.toolkit import SparkSQLToolkit
from langchain_community.agent_toolkits.sql.base import create_sql_agent
from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
from langchain_community.agent_toolkits.steam.toolkit import SteamToolkit
from langchain_community.agent_toolkits.zapier.toolkit import ZapierToolkit
DEPRECATED_AGENTS = [
"create_csv_agent",
"create_pandas_dataframe_agent",
"create_xorbits_agent",
"create_python_agent",
"create_spark_dataframe_agent",
]
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"AINetworkToolkit": "langchain_community.agent_toolkits.ainetwork.toolkit",
"AmadeusToolkit": "langchain_community.agent_toolkits.amadeus.toolkit",
"AzureCognitiveServicesToolkit": (
"langchain_community.agent_toolkits.azure_cognitive_services"
),
"FileManagementToolkit": (
"langchain_community.agent_toolkits.file_management.toolkit"
),
"GmailToolkit": "langchain_community.agent_toolkits.gmail.toolkit",
"JiraToolkit": "langchain_community.agent_toolkits.jira.toolkit",
"JsonToolkit": "langchain_community.agent_toolkits.json.toolkit",
"MultionToolkit": "langchain_community.agent_toolkits.multion.toolkit",
"NasaToolkit": "langchain_community.agent_toolkits.nasa.toolkit",
"NLAToolkit": "langchain_community.agent_toolkits.nla.toolkit",
"O365Toolkit": "langchain_community.agent_toolkits.office365.toolkit",
"OpenAPIToolkit": "langchain_community.agent_toolkits.openapi.toolkit",
"PlayWrightBrowserToolkit": "langchain_community.agent_toolkits.playwright.toolkit",
"PowerBIToolkit": "langchain_community.agent_toolkits.powerbi.toolkit",
"SlackToolkit": "langchain_community.agent_toolkits.slack.toolkit",
"SteamToolkit": "langchain_community.agent_toolkits.steam.toolkit",
"SQLDatabaseToolkit": "langchain_community.agent_toolkits.sql.toolkit",
"SparkSQLToolkit": "langchain_community.agent_toolkits.spark_sql.toolkit",
"ZapierToolkit": "langchain_community.agent_toolkits.zapier.toolkit",
"create_json_agent": "langchain_community.agent_toolkits.json.base",
"create_openapi_agent": "langchain_community.agent_toolkits.openapi.base",
"create_pbi_agent": "langchain_community.agent_toolkits.powerbi.base",
"create_pbi_chat_agent": "langchain_community.agent_toolkits.powerbi.chat_base",
"create_spark_sql_agent": "langchain_community.agent_toolkits.spark_sql.base",
"create_sql_agent": "langchain_community.agent_toolkits.sql.base",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Get attr name."""
if name in DEPRECATED_AGENTS:
relative_path = as_import_path(Path(__file__).parent, suffix=name)
old_path = "langchain." + relative_path
new_path = "langchain_experimental." + relative_path
raise ImportError(
f"{name} has been moved to langchain experimental. "
"See https://github.com/langchain-ai/langchain/discussions/11680"
"for more information.\n"
f"Please update your import statement from: `{old_path}` to `{new_path}`."
)
return _import_attribute(name)
__all__ = [
"AINetworkToolkit",
"AmadeusToolkit",
"AzureCognitiveServicesToolkit",
"FileManagementToolkit",
"GmailToolkit",
"JiraToolkit",
"JsonToolkit",
"MultionToolkit",
"NasaToolkit",
"NLAToolkit",
"O365Toolkit",
"OpenAPIToolkit",
"PlayWrightBrowserToolkit",
"PowerBIToolkit",
"SlackToolkit",
"SteamToolkit",
"SQLDatabaseToolkit",
"SparkSQLToolkit",
"VectorStoreInfo",
"VectorStoreRouterToolkit",
"VectorStoreToolkit",
"ZapierToolkit",
"create_json_agent",
"create_openapi_agent",
"create_pbi_agent",
"create_pbi_chat_agent",
"create_spark_sql_agent",
"create_sql_agent",
"create_vectorstore_agent",
"create_vectorstore_router_agent",
"create_conversational_retrieval_agent",
"create_retriever_tool",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/ainetwork/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.ainetwork.toolkit import AINetworkToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"AINetworkToolkit": "langchain_community.agent_toolkits.ainetwork.toolkit"
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"AINetworkToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/ainetwork/__init__.py | """AINetwork toolkit."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/powerbi/base.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.powerbi.base import create_pbi_agent
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"create_pbi_agent": "langchain_community.agent_toolkits.powerbi.base"
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"create_pbi_agent",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/powerbi/chat_base.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.powerbi.chat_base import (
create_pbi_chat_agent,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"create_pbi_chat_agent": "langchain_community.agent_toolkits.powerbi.chat_base"
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"create_pbi_chat_agent",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/powerbi/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.powerbi.toolkit import PowerBIToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"PowerBIToolkit": "langchain_community.agent_toolkits.powerbi.toolkit"
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"PowerBIToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/powerbi/__init__.py | """Power BI agent."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/powerbi/prompt.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.powerbi.prompt import (
POWERBI_CHAT_PREFIX,
POWERBI_CHAT_SUFFIX,
POWERBI_PREFIX,
POWERBI_SUFFIX,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"POWERBI_CHAT_PREFIX": "langchain_community.agent_toolkits.powerbi.prompt",
"POWERBI_CHAT_SUFFIX": "langchain_community.agent_toolkits.powerbi.prompt",
"POWERBI_PREFIX": "langchain_community.agent_toolkits.powerbi.prompt",
"POWERBI_SUFFIX": "langchain_community.agent_toolkits.powerbi.prompt",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"POWERBI_PREFIX",
"POWERBI_SUFFIX",
"POWERBI_CHAT_PREFIX",
"POWERBI_CHAT_SUFFIX",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/github/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.github.toolkit import (
BranchName,
CommentOnIssue,
CreateFile,
CreatePR,
CreateReviewRequest,
DeleteFile,
DirectoryPath,
GetIssue,
GetPR,
GitHubToolkit,
NoInput,
ReadFile,
SearchCode,
SearchIssuesAndPRs,
UpdateFile,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"NoInput": "langchain_community.agent_toolkits.github.toolkit",
"GetIssue": "langchain_community.agent_toolkits.github.toolkit",
"CommentOnIssue": "langchain_community.agent_toolkits.github.toolkit",
"GetPR": "langchain_community.agent_toolkits.github.toolkit",
"CreatePR": "langchain_community.agent_toolkits.github.toolkit",
"CreateFile": "langchain_community.agent_toolkits.github.toolkit",
"ReadFile": "langchain_community.agent_toolkits.github.toolkit",
"UpdateFile": "langchain_community.agent_toolkits.github.toolkit",
"DeleteFile": "langchain_community.agent_toolkits.github.toolkit",
"DirectoryPath": "langchain_community.agent_toolkits.github.toolkit",
"BranchName": "langchain_community.agent_toolkits.github.toolkit",
"SearchCode": "langchain_community.agent_toolkits.github.toolkit",
"CreateReviewRequest": "langchain_community.agent_toolkits.github.toolkit",
"SearchIssuesAndPRs": "langchain_community.agent_toolkits.github.toolkit",
"GitHubToolkit": "langchain_community.agent_toolkits.github.toolkit",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"NoInput",
"GetIssue",
"CommentOnIssue",
"GetPR",
"CreatePR",
"CreateFile",
"ReadFile",
"UpdateFile",
"DeleteFile",
"DirectoryPath",
"BranchName",
"SearchCode",
"CreateReviewRequest",
"SearchIssuesAndPRs",
"GitHubToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/github/__init__.py | """GitHub Toolkit."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/gitlab/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.gitlab.toolkit import GitLabToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"GitLabToolkit": "langchain_community.agent_toolkits.gitlab.toolkit"
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"GitLabToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/gitlab/__init__.py | """GitLab Toolkit."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/gmail/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.gmail.toolkit import GmailToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"GmailToolkit": "langchain_community.agent_toolkits.gmail.toolkit"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"GmailToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/gmail/__init__.py | """Gmail toolkit."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/multion/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.multion.toolkit import MultionToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"MultionToolkit": "langchain_community.agent_toolkits.multion.toolkit"
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"MultionToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/multion/__init__.py | """MultiOn Toolkit."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/sql/base.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.sql.base import create_sql_agent
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"create_sql_agent": "langchain_community.agent_toolkits.sql.base"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"create_sql_agent",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/sql/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"SQLDatabaseToolkit": "langchain_community.agent_toolkits.sql.toolkit"
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"SQLDatabaseToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/sql/__init__.py | """SQL agent."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/sql/prompt.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.sql.prompt import (
SQL_FUNCTIONS_SUFFIX,
SQL_PREFIX,
SQL_SUFFIX,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"SQL_PREFIX": "langchain_community.agent_toolkits.sql.prompt",
"SQL_SUFFIX": "langchain_community.agent_toolkits.sql.prompt",
"SQL_FUNCTIONS_SUFFIX": "langchain_community.agent_toolkits.sql.prompt",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = ["SQL_PREFIX", "SQL_SUFFIX", "SQL_FUNCTIONS_SUFFIX"]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/slack/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.slack.toolkit import SlackToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"SlackToolkit": "langchain_community.agent_toolkits.slack.toolkit"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"SlackToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/slack/__init__.py | """Slack toolkit."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/vectorstore/base.py | """VectorStore agent."""
from typing import Any, Dict, Optional
from langchain_core._api import deprecated
from langchain_core.callbacks.base import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
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 (
VectorStoreRouterToolkit,
VectorStoreToolkit,
)
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.chains.llm import LLMChain
@deprecated(
since="0.2.13",
removal="1.0",
message=(
"This function will continue to be supported, but it is recommended for new "
"use cases to be built with LangGraph. LangGraph offers a more flexible and "
"full-featured framework for building agents, including support for "
"tool-calling, persistence of state, and human-in-the-loop workflows. "
"See API reference for this function for a replacement implementation: "
"https://api.python.langchain.com/en/latest/agents/langchain.agents.agent_toolkits.vectorstore.base.create_vectorstore_agent.html " # noqa: E501
"Read more here on how to create agents that query vector stores: "
"https://python.langchain.com/docs/how_to/qa_chat_history_how_to/#agents"
),
)
def create_vectorstore_agent(
llm: BaseLanguageModel,
toolkit: VectorStoreToolkit,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = PREFIX,
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> AgentExecutor:
"""Construct a VectorStore agent from an LLM and tools.
Note: this class is deprecated. See below for a replacement that uses tool
calling methods and LangGraph. Install LangGraph with:
.. code-block:: bash
pip install -U langgraph
.. code-block:: python
from langchain_core.tools import create_retriever_tool
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langgraph.prebuilt import create_react_agent
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
vector_store = InMemoryVectorStore.from_texts(
[
"Dogs are great companions, known for their loyalty and friendliness.",
"Cats are independent pets that often enjoy their own space.",
],
OpenAIEmbeddings(),
)
tool = create_retriever_tool(
vector_store.as_retriever(),
"pet_information_retriever",
"Fetches information about pets.",
)
agent = create_react_agent(llm, [tool])
for step in agent.stream(
{"messages": [("human", "What are dogs known for?")]},
stream_mode="values",
):
step["messages"][-1].pretty_print()
Args:
llm (BaseLanguageModel): LLM that will be used by the agent
toolkit (VectorStoreToolkit): Set of tools for the agent
callback_manager (Optional[BaseCallbackManager], optional): Object to handle the callback [ Defaults to None. ]
prefix (str, optional): The prefix prompt for the agent. If not provided uses default PREFIX.
verbose (bool, optional): If you want to see the content of the scratchpad. [ Defaults to False ]
agent_executor_kwargs (Optional[Dict[str, Any]], optional): If there is any other parameter you want to send to the agent. [ Defaults to None ]
kwargs: Additional named parameters to pass to the ZeroShotAgent.
Returns:
AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response
""" # noqa: E501
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
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,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
)
@deprecated(
since="0.2.13",
removal="1.0",
message=(
"This function will continue to be supported, but it is recommended for new "
"use cases to be built with LangGraph. LangGraph offers a more flexible and "
"full-featured framework for building agents, including support for "
"tool-calling, persistence of state, and human-in-the-loop workflows. "
"See API reference for this function for a replacement implementation: "
"https://api.python.langchain.com/en/latest/agents/langchain.agents.agent_toolkits.vectorstore.base.create_vectorstore_router_agent.html " # noqa: E501
"Read more here on how to create agents that query vector stores: "
"https://python.langchain.com/docs/how_to/qa_chat_history_how_to/#agents"
),
)
def create_vectorstore_router_agent(
llm: BaseLanguageModel,
toolkit: VectorStoreRouterToolkit,
callback_manager: Optional[BaseCallbackManager] = None,
prefix: str = ROUTER_PREFIX,
verbose: bool = False,
agent_executor_kwargs: Optional[Dict[str, Any]] = None,
**kwargs: Any,
) -> AgentExecutor:
"""Construct a VectorStore router agent from an LLM and tools.
Note: this class is deprecated. See below for a replacement that uses tool
calling methods and LangGraph. Install LangGraph with:
.. code-block:: bash
pip install -U langgraph
.. code-block:: python
from langchain_core.tools import create_retriever_tool
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langgraph.prebuilt import create_react_agent
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
pet_vector_store = InMemoryVectorStore.from_texts(
[
"Dogs are great companions, known for their loyalty and friendliness.",
"Cats are independent pets that often enjoy their own space.",
],
OpenAIEmbeddings(),
)
food_vector_store = InMemoryVectorStore.from_texts(
[
"Carrots are orange and delicious.",
"Apples are red and delicious.",
],
OpenAIEmbeddings(),
)
tools = [
create_retriever_tool(
pet_vector_store.as_retriever(),
"pet_information_retriever",
"Fetches information about pets.",
),
create_retriever_tool(
food_vector_store.as_retriever(),
"food_information_retriever",
"Fetches information about food.",
)
]
agent = create_react_agent(llm, tools)
for step in agent.stream(
{"messages": [("human", "Tell me about carrots.")]},
stream_mode="values",
):
step["messages"][-1].pretty_print()
Args:
llm (BaseLanguageModel): LLM that will be used by the agent
toolkit (VectorStoreRouterToolkit): Set of tools for the agent which have routing capability with multiple vector stores
callback_manager (Optional[BaseCallbackManager], optional): Object to handle the callback [ Defaults to None. ]
prefix (str, optional): The prefix prompt for the router agent. If not provided uses default ROUTER_PREFIX.
verbose (bool, optional): If you want to see the content of the scratchpad. [ Defaults to False ]
agent_executor_kwargs (Optional[Dict[str, Any]], optional): If there is any other parameter you want to send to the agent. [ Defaults to None ]
kwargs: Additional named parameters to pass to the ZeroShotAgent.
Returns:
AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response.
""" # noqa: E501
tools = toolkit.get_tools()
prompt = ZeroShotAgent.create_prompt(tools, prefix=prefix)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
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,
callback_manager=callback_manager,
verbose=verbose,
**(agent_executor_kwargs or {}),
)
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py | """Toolkit for interacting with a vector store."""
from typing import List
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import BaseTool
from langchain_core.tools.base import BaseToolkit
from langchain_core.vectorstores import VectorStore
from pydantic import BaseModel, ConfigDict, Field
class VectorStoreInfo(BaseModel):
"""Information about a VectorStore."""
vectorstore: VectorStore = Field(exclude=True)
name: str
description: str
model_config = ConfigDict(
arbitrary_types_allowed=True,
)
class VectorStoreToolkit(BaseToolkit):
"""Toolkit for interacting with a Vector Store."""
vectorstore_info: VectorStoreInfo = Field(exclude=True)
llm: BaseLanguageModel
model_config = ConfigDict(
arbitrary_types_allowed=True,
)
def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
try:
from langchain_community.tools.vectorstore.tool import (
VectorStoreQATool,
VectorStoreQAWithSourcesTool,
)
except ImportError:
raise ImportError(
"You need to install langchain-community to use this toolkit."
)
description = VectorStoreQATool.get_description(
self.vectorstore_info.name, self.vectorstore_info.description
)
qa_tool = VectorStoreQATool(
name=self.vectorstore_info.name,
description=description,
vectorstore=self.vectorstore_info.vectorstore,
llm=self.llm,
)
description = VectorStoreQAWithSourcesTool.get_description(
self.vectorstore_info.name, self.vectorstore_info.description
)
qa_with_sources_tool = VectorStoreQAWithSourcesTool(
name=f"{self.vectorstore_info.name}_with_sources",
description=description,
vectorstore=self.vectorstore_info.vectorstore,
llm=self.llm,
)
return [qa_tool, qa_with_sources_tool]
class VectorStoreRouterToolkit(BaseToolkit):
"""Toolkit for routing between Vector Stores."""
vectorstores: List[VectorStoreInfo] = Field(exclude=True)
llm: BaseLanguageModel
model_config = ConfigDict(
arbitrary_types_allowed=True,
)
def get_tools(self) -> List[BaseTool]:
"""Get the tools in the toolkit."""
tools: List[BaseTool] = []
try:
from langchain_community.tools.vectorstore.tool import (
VectorStoreQATool,
)
except ImportError:
raise ImportError(
"You need to install langchain-community to use this toolkit."
)
for vectorstore_info in self.vectorstores:
description = VectorStoreQATool.get_description(
vectorstore_info.name, vectorstore_info.description
)
qa_tool = VectorStoreQATool(
name=vectorstore_info.name,
description=description,
vectorstore=vectorstore_info.vectorstore,
llm=self.llm,
)
tools.append(qa_tool)
return tools
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/vectorstore/__init__.py | """Agent toolkit for interacting with vector stores."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/vectorstore/prompt.py | # flake8: noqa
PREFIX = """You are an agent designed to answer questions about sets of documents.
You have access to tools for interacting with the documents, and the inputs to the tools are questions.
Sometimes, you will be asked to provide sources for your questions, in which case you should use the appropriate tool to do so.
If the question does not seem relevant to any of the tools provided, just return "I don't know" as the answer.
"""
ROUTER_PREFIX = """You are an agent designed to answer questions.
You have access to tools for interacting with different sources, and the inputs to the tools are questions.
Your main task is to decide which of the tools is relevant for answering question at hand.
For complex questions, you can break the question down into sub questions and use tools to answers the sub questions.
"""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/xorbits/__init__.py | from pathlib import Path
from typing import Any
from langchain_core._api.path import as_import_path
def __getattr__(name: str) -> Any:
"""Get attr name."""
if name == "create_xorbits_agent":
# Get directory of langchain package
HERE = Path(__file__).parents[3]
here = as_import_path(Path(__file__).parent, relative_to=HERE)
old_path = "langchain." + here + "." + name
new_path = "langchain_experimental." + here + "." + name
raise ImportError(
"This agent has been moved to langchain experiment. "
"This agent relies on python REPL tool under the hood, so to use it "
"safely please sandbox the python REPL. "
"Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md "
"and https://github.com/langchain-ai/langchain/discussions/11680"
"To keep using this code as is, install langchain experimental and "
f"update your import statement from:\n `{old_path}` to `{new_path}`."
)
raise AttributeError(f"{name} does not exist")
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/jira/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.jira.toolkit import JiraToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {"JiraToolkit": "langchain_community.agent_toolkits.jira.toolkit"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"JiraToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/jira/__init__.py | """Jira Toolkit."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/spark_sql/base.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.spark_sql.base import create_spark_sql_agent
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"create_spark_sql_agent": "langchain_community.agent_toolkits.spark_sql.base"
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"create_spark_sql_agent",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/spark_sql/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.spark_sql.toolkit import SparkSQLToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"SparkSQLToolkit": "langchain_community.agent_toolkits.spark_sql.toolkit"
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"SparkSQLToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/spark_sql/__init__.py | """Spark SQL agent."""
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/spark_sql/prompt.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.spark_sql.prompt import (
SQL_PREFIX,
SQL_SUFFIX,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"SQL_PREFIX": "langchain_community.agent_toolkits.spark_sql.prompt",
"SQL_SUFFIX": "langchain_community.agent_toolkits.spark_sql.prompt",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = ["SQL_PREFIX", "SQL_SUFFIX"]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/file_management/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.file_management.toolkit import (
FileManagementToolkit,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"FileManagementToolkit": (
"langchain_community.agent_toolkits.file_management.toolkit"
)
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"FileManagementToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/file_management/__init__.py | """Local file management toolkit."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.file_management.toolkit import (
FileManagementToolkit,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"FileManagementToolkit": (
"langchain_community.agent_toolkits.file_management.toolkit"
)
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"FileManagementToolkit",
]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/amadeus/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.amadeus.toolkit import AmadeusToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"AmadeusToolkit": "langchain_community.agent_toolkits.amadeus.toolkit"
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = ["AmadeusToolkit"]
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/spark/__init__.py | from pathlib import Path
from typing import Any
from langchain_core._api.path import as_import_path
def __getattr__(name: str) -> Any:
"""Get attr name."""
if name == "create_spark_dataframe_agent":
# Get directory of langchain package
HERE = Path(__file__).parents[3]
here = as_import_path(Path(__file__).parent, relative_to=HERE)
old_path = "langchain." + here + "." + name
new_path = "langchain_experimental." + here + "." + name
raise ImportError(
"This agent has been moved to langchain experiment. "
"This agent relies on python REPL tool under the hood, so to use it "
"safely please sandbox the python REPL. "
"Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md "
"and https://github.com/langchain-ai/langchain/discussions/11680"
"To keep using this code as is, install langchain experimental and "
f"update your import statement from:\n `{old_path}` to `{new_path}`."
)
raise AttributeError(f"{name} does not exist")
|
0 | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits | lc_public_repos/langchain/libs/langchain/langchain/agents/agent_toolkits/office365/toolkit.py | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.office365.toolkit import O365Toolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"O365Toolkit": "langchain_community.agent_toolkits.office365.toolkit"
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute(name)
__all__ = [
"O365Toolkit",
]
|
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