mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
a0c2281540
```python
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
print(path)
with open(path, "rb") as f:
pyproject = tomllib.load(f)
try:
pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = (
"^1.10"
)
pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = (
"^0.5"
)
except KeyError:
continue
with open(path, "w") as f:
toml.dump(pyproject, f)
cwd = "/".join(path.split("/")[:-1])
completed = subprocess.run(
"poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
logs = completed.stdout.split("\n")
to_ignore = {}
for l in logs:
if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l):
path, line_no, error_type = re.match(
"^(.*)\:(\d+)\: error:.*\[(.*)\]", l
).groups()
if (path, line_no) in to_ignore:
to_ignore[(path, line_no)].append(error_type)
else:
to_ignore[(path, line_no)] = [error_type]
print(len(to_ignore))
for (error_path, line_no), error_types in to_ignore.items():
all_errors = ", ".join(error_types)
full_path = f"{cwd}/{error_path}"
try:
with open(full_path, "r") as f:
file_lines = f.readlines()
except FileNotFoundError:
continue
file_lines[int(line_no) - 1] = (
file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n"
)
with open(full_path, "w") as f:
f.write("".join(file_lines))
subprocess.run(
"poetry run ruff format .; poetry run ruff --select I --fix .",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
if __name__ == "__main__":
main()
```
338 lines
11 KiB
Python
338 lines
11 KiB
Python
from __future__ import annotations
|
|
|
|
import copy
|
|
import json
|
|
import logging
|
|
from typing import (
|
|
TYPE_CHECKING,
|
|
Any,
|
|
Dict,
|
|
List,
|
|
Literal,
|
|
Optional,
|
|
TypedDict,
|
|
Union,
|
|
overload,
|
|
)
|
|
|
|
from langchain_core.callbacks import (
|
|
AsyncCallbackManagerForLLMRun,
|
|
CallbackManagerForLLMRun,
|
|
)
|
|
from langchain_core.language_models.llms import LLM
|
|
from langchain_core.pydantic_v1 import PrivateAttr
|
|
|
|
if TYPE_CHECKING:
|
|
import openllm
|
|
|
|
|
|
ServerType = Literal["http", "grpc"]
|
|
|
|
|
|
class IdentifyingParams(TypedDict):
|
|
"""Parameters for identifying a model as a typed dict."""
|
|
|
|
model_name: str
|
|
model_id: Optional[str]
|
|
server_url: Optional[str]
|
|
server_type: Optional[ServerType]
|
|
embedded: bool
|
|
llm_kwargs: Dict[str, Any]
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class OpenLLM(LLM):
|
|
"""OpenLLM, supporting both in-process model
|
|
instance and remote OpenLLM servers.
|
|
|
|
To use, you should have the openllm library installed:
|
|
|
|
.. code-block:: bash
|
|
|
|
pip install openllm
|
|
|
|
Learn more at: https://github.com/bentoml/openllm
|
|
|
|
Example running an LLM model locally managed by OpenLLM:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.llms import OpenLLM
|
|
llm = OpenLLM(
|
|
model_name='flan-t5',
|
|
model_id='google/flan-t5-large',
|
|
)
|
|
llm.invoke("What is the difference between a duck and a goose?")
|
|
|
|
For all available supported models, you can run 'openllm models'.
|
|
|
|
If you have a OpenLLM server running, you can also use it remotely:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.llms import OpenLLM
|
|
llm = OpenLLM(server_url='http://localhost:3000')
|
|
llm.invoke("What is the difference between a duck and a goose?")
|
|
"""
|
|
|
|
model_name: Optional[str] = None
|
|
"""Model name to use. See 'openllm models' for all available models."""
|
|
model_id: Optional[str] = None
|
|
"""Model Id to use. If not provided, will use the default model for the model name.
|
|
See 'openllm models' for all available model variants."""
|
|
server_url: Optional[str] = None
|
|
"""Optional server URL that currently runs a LLMServer with 'openllm start'."""
|
|
timeout: int = 30
|
|
""""Time out for the openllm client"""
|
|
server_type: ServerType = "http"
|
|
"""Optional server type. Either 'http' or 'grpc'."""
|
|
embedded: bool = True
|
|
"""Initialize this LLM instance in current process by default. Should
|
|
only set to False when using in conjunction with BentoML Service."""
|
|
llm_kwargs: Dict[str, Any]
|
|
"""Keyword arguments to be passed to openllm.LLM"""
|
|
|
|
_runner: Optional[openllm.LLMRunner] = PrivateAttr(default=None)
|
|
_client: Union[openllm.client.HTTPClient, openllm.client.GrpcClient, None] = (
|
|
PrivateAttr(default=None)
|
|
)
|
|
|
|
class Config:
|
|
extra = "forbid"
|
|
|
|
@overload
|
|
def __init__(
|
|
self,
|
|
model_name: Optional[str] = ...,
|
|
*,
|
|
model_id: Optional[str] = ...,
|
|
embedded: Literal[True, False] = ...,
|
|
**llm_kwargs: Any,
|
|
) -> None: ...
|
|
|
|
@overload
|
|
def __init__(
|
|
self,
|
|
*,
|
|
server_url: str = ...,
|
|
server_type: Literal["grpc", "http"] = ...,
|
|
**llm_kwargs: Any,
|
|
) -> None: ...
|
|
|
|
def __init__(
|
|
self,
|
|
model_name: Optional[str] = None,
|
|
*,
|
|
model_id: Optional[str] = None,
|
|
server_url: Optional[str] = None,
|
|
timeout: int = 30,
|
|
server_type: Literal["grpc", "http"] = "http",
|
|
embedded: bool = True,
|
|
**llm_kwargs: Any,
|
|
):
|
|
try:
|
|
import openllm
|
|
except ImportError as e:
|
|
raise ImportError(
|
|
"Could not import openllm. Make sure to install it with "
|
|
"'pip install openllm.'"
|
|
) from e
|
|
|
|
llm_kwargs = llm_kwargs or {}
|
|
|
|
if server_url is not None:
|
|
logger.debug("'server_url' is provided, returning a openllm.Client")
|
|
assert (
|
|
model_id is None and model_name is None
|
|
), "'server_url' and {'model_id', 'model_name'} are mutually exclusive"
|
|
client_cls = (
|
|
openllm.client.HTTPClient
|
|
if server_type == "http"
|
|
else openllm.client.GrpcClient
|
|
)
|
|
client = client_cls(server_url, timeout)
|
|
|
|
super().__init__(
|
|
**{ # type: ignore[arg-type]
|
|
"server_url": server_url,
|
|
"timeout": timeout,
|
|
"server_type": server_type,
|
|
"llm_kwargs": llm_kwargs,
|
|
}
|
|
)
|
|
self._runner = None # type: ignore
|
|
self._client = client
|
|
else:
|
|
assert model_name is not None, "Must provide 'model_name' or 'server_url'"
|
|
# since the LLM are relatively huge, we don't actually want to convert the
|
|
# Runner with embedded when running the server. Instead, we will only set
|
|
# the init_local here so that LangChain users can still use the LLM
|
|
# in-process. Wrt to BentoML users, setting embedded=False is the expected
|
|
# behaviour to invoke the runners remotely.
|
|
# We need to also enable ensure_available to download and setup the model.
|
|
runner = openllm.Runner(
|
|
model_name=model_name,
|
|
model_id=model_id,
|
|
init_local=embedded,
|
|
ensure_available=True,
|
|
**llm_kwargs,
|
|
)
|
|
super().__init__(
|
|
**{ # type: ignore[arg-type]
|
|
"model_name": model_name,
|
|
"model_id": model_id,
|
|
"embedded": embedded,
|
|
"llm_kwargs": llm_kwargs,
|
|
}
|
|
)
|
|
self._client = None # type: ignore
|
|
self._runner = runner
|
|
|
|
@property
|
|
def runner(self) -> openllm.LLMRunner:
|
|
"""
|
|
Get the underlying openllm.LLMRunner instance for integration with BentoML.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
llm = OpenLLM(
|
|
model_name='flan-t5',
|
|
model_id='google/flan-t5-large',
|
|
embedded=False,
|
|
)
|
|
tools = load_tools(["serpapi", "llm-math"], llm=llm)
|
|
agent = initialize_agent(
|
|
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION
|
|
)
|
|
svc = bentoml.Service("langchain-openllm", runners=[llm.runner])
|
|
|
|
@svc.api(input=Text(), output=Text())
|
|
def chat(input_text: str):
|
|
return agent.run(input_text)
|
|
"""
|
|
if self._runner is None:
|
|
raise ValueError("OpenLLM must be initialized locally with 'model_name'")
|
|
return self._runner
|
|
|
|
@property
|
|
def _identifying_params(self) -> IdentifyingParams:
|
|
"""Get the identifying parameters."""
|
|
if self._client is not None:
|
|
self.llm_kwargs.update(self._client._config)
|
|
model_name = self._client._metadata.model_dump()["model_name"]
|
|
model_id = self._client._metadata.model_dump()["model_id"]
|
|
else:
|
|
if self._runner is None:
|
|
raise ValueError("Runner must be initialized.")
|
|
model_name = self.model_name
|
|
model_id = self.model_id
|
|
try:
|
|
self.llm_kwargs.update(
|
|
json.loads(self._runner.identifying_params["configuration"])
|
|
)
|
|
except (TypeError, json.JSONDecodeError):
|
|
pass
|
|
return IdentifyingParams(
|
|
server_url=self.server_url,
|
|
server_type=self.server_type,
|
|
embedded=self.embedded,
|
|
llm_kwargs=self.llm_kwargs,
|
|
model_name=model_name,
|
|
model_id=model_id,
|
|
)
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
return "openllm_client" if self._client else "openllm"
|
|
|
|
def _call(
|
|
self,
|
|
prompt: str,
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> str:
|
|
try:
|
|
import openllm
|
|
except ImportError as e:
|
|
raise ImportError(
|
|
"Could not import openllm. Make sure to install it with "
|
|
"'pip install openllm'."
|
|
) from e
|
|
|
|
copied = copy.deepcopy(self.llm_kwargs)
|
|
copied.update(kwargs)
|
|
config = openllm.AutoConfig.for_model(
|
|
self._identifying_params["model_name"], **copied
|
|
)
|
|
if self._client:
|
|
res = (
|
|
self._client.generate(prompt, **config.model_dump(flatten=True))
|
|
.outputs[0]
|
|
.text
|
|
)
|
|
else:
|
|
assert self._runner is not None
|
|
res = self._runner(prompt, **config.model_dump(flatten=True))
|
|
if isinstance(res, dict) and "text" in res:
|
|
return res["text"]
|
|
elif isinstance(res, str):
|
|
return res
|
|
else:
|
|
raise ValueError(
|
|
"Expected result to be a dict with key 'text' or a string. "
|
|
f"Received {res}"
|
|
)
|
|
|
|
async def _acall(
|
|
self,
|
|
prompt: str,
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> str:
|
|
try:
|
|
import openllm
|
|
except ImportError as e:
|
|
raise ImportError(
|
|
"Could not import openllm. Make sure to install it with "
|
|
"'pip install openllm'."
|
|
) from e
|
|
|
|
copied = copy.deepcopy(self.llm_kwargs)
|
|
copied.update(kwargs)
|
|
config = openllm.AutoConfig.for_model(
|
|
self._identifying_params["model_name"], **copied
|
|
)
|
|
if self._client:
|
|
async_client = openllm.client.AsyncHTTPClient(self.server_url, self.timeout)
|
|
res = (
|
|
(await async_client.generate(prompt, **config.model_dump(flatten=True)))
|
|
.outputs[0]
|
|
.text
|
|
)
|
|
else:
|
|
assert self._runner is not None
|
|
(
|
|
prompt,
|
|
generate_kwargs,
|
|
postprocess_kwargs,
|
|
) = self._runner.llm.sanitize_parameters(prompt, **kwargs)
|
|
generated_result = await self._runner.generate.async_run(
|
|
prompt, **generate_kwargs
|
|
)
|
|
res = self._runner.llm.postprocess_generate(
|
|
prompt, generated_result, **postprocess_kwargs
|
|
)
|
|
|
|
if isinstance(res, dict) and "text" in res:
|
|
return res["text"]
|
|
elif isinstance(res, str):
|
|
return res
|
|
else:
|
|
raise ValueError(
|
|
"Expected result to be a dict with key 'text' or a string. "
|
|
f"Received {res}"
|
|
)
|