mirror of
https://github.com/hwchase17/langchain.git
synced 2026-04-08 21:43:40 +00:00
b9f65e5038
Migrate pydantic extra to literals
Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.
This works correctly also in pydantic v1.
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel, extra="forbid"):
x: int
Foo(x=5, y=1)
```
And
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel):
x: int
class Config:
extra = "forbid"
Foo(x=5, y=1)
```
## Enum -> literal using grit pattern:
```
engine marzano(0.1)
language python
or {
`extra=Extra.allow` => `extra="allow"`,
`extra=Extra.forbid` => `extra="forbid"`,
`extra=Extra.ignore` => `extra="ignore"`
}
```
Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)
## Sort attributes in Config:
```
engine marzano(0.1)
language python
function sort($values) js {
return $values.text.split(',').sort().join("\n");
}
class_definition($name, $body) as $C where {
$name <: `Config`,
$body <: block($statements),
$values = [],
$statements <: some bubble($values) assignment() as $A where {
$values += $A
},
$body => sort($values),
}
```
127 lines
4.3 KiB
Python
127 lines
4.3 KiB
Python
"""Chain that interprets a prompt and executes bash operations."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import logging
|
|
import warnings
|
|
from typing import Any, Dict, List, Optional
|
|
|
|
from langchain.chains.base import Chain
|
|
from langchain.chains.llm import LLMChain
|
|
from langchain.schema import BasePromptTemplate, OutputParserException
|
|
from langchain_core.callbacks.manager import CallbackManagerForChainRun
|
|
from langchain_core.language_models import BaseLanguageModel
|
|
|
|
from langchain_experimental.llm_bash.bash import BashProcess
|
|
from langchain_experimental.llm_bash.prompt import PROMPT
|
|
from langchain_experimental.pydantic_v1 import Field, root_validator
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class LLMBashChain(Chain):
|
|
"""Chain that interprets a prompt and executes bash operations.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain.chains import LLMBashChain
|
|
from langchain_community.llms import OpenAI
|
|
llm_bash = LLMBashChain.from_llm(OpenAI())
|
|
"""
|
|
|
|
llm_chain: LLMChain
|
|
llm: Optional[BaseLanguageModel] = None
|
|
"""[Deprecated] LLM wrapper to use."""
|
|
input_key: str = "question" #: :meta private:
|
|
output_key: str = "answer" #: :meta private:
|
|
prompt: BasePromptTemplate = PROMPT
|
|
"""[Deprecated]"""
|
|
bash_process: BashProcess = Field(default_factory=BashProcess) #: :meta private:
|
|
|
|
class Config:
|
|
arbitrary_types_allowed = True
|
|
extra = "forbid"
|
|
|
|
@root_validator(pre=True)
|
|
def raise_deprecation(cls, values: Dict) -> Dict:
|
|
if "llm" in values:
|
|
warnings.warn(
|
|
"Directly instantiating an LLMBashChain with an llm is deprecated. "
|
|
"Please instantiate with llm_chain or using the from_llm class method."
|
|
)
|
|
if "llm_chain" not in values and values["llm"] is not None:
|
|
prompt = values.get("prompt", PROMPT)
|
|
values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
|
|
return values
|
|
|
|
# TODO: move away from `root_validator` since it is deprecated in pydantic v2
|
|
# and causes mypy type-checking failures (hence the `type: ignore`)
|
|
@root_validator # type: ignore[call-overload]
|
|
def validate_prompt(cls, values: Dict) -> Dict:
|
|
if values["llm_chain"].prompt.output_parser is None:
|
|
raise ValueError(
|
|
"The prompt used by llm_chain is expected to have an output_parser."
|
|
)
|
|
return values
|
|
|
|
@property
|
|
def input_keys(self) -> List[str]:
|
|
"""Expect input key.
|
|
|
|
:meta private:
|
|
"""
|
|
return [self.input_key]
|
|
|
|
@property
|
|
def output_keys(self) -> List[str]:
|
|
"""Expect output key.
|
|
|
|
:meta private:
|
|
"""
|
|
return [self.output_key]
|
|
|
|
def _call(
|
|
self,
|
|
inputs: Dict[str, Any],
|
|
run_manager: Optional[CallbackManagerForChainRun] = None,
|
|
) -> Dict[str, str]:
|
|
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
|
|
_run_manager.on_text(inputs[self.input_key], verbose=self.verbose)
|
|
|
|
t = self.llm_chain.predict(
|
|
question=inputs[self.input_key], callbacks=_run_manager.get_child()
|
|
)
|
|
_run_manager.on_text(t, color="green", verbose=self.verbose)
|
|
t = t.strip()
|
|
try:
|
|
parser = self.llm_chain.prompt.output_parser
|
|
command_list = parser.parse(t) # type: ignore[union-attr]
|
|
except OutputParserException as e:
|
|
_run_manager.on_chain_error(e, verbose=self.verbose)
|
|
raise e
|
|
|
|
if self.verbose:
|
|
_run_manager.on_text("\nCode: ", verbose=self.verbose)
|
|
_run_manager.on_text(
|
|
str(command_list), color="yellow", verbose=self.verbose
|
|
)
|
|
output = self.bash_process.run(command_list)
|
|
_run_manager.on_text("\nAnswer: ", verbose=self.verbose)
|
|
_run_manager.on_text(output, color="yellow", verbose=self.verbose)
|
|
return {self.output_key: output}
|
|
|
|
@property
|
|
def _chain_type(self) -> str:
|
|
return "llm_bash_chain"
|
|
|
|
@classmethod
|
|
def from_llm(
|
|
cls,
|
|
llm: BaseLanguageModel,
|
|
prompt: BasePromptTemplate = PROMPT,
|
|
**kwargs: Any,
|
|
) -> LLMBashChain:
|
|
llm_chain = LLMChain(llm=llm, prompt=prompt)
|
|
return cls(llm_chain=llm_chain, **kwargs)
|