mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-07 15:36:30 +00:00
bf5193bb99
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),
}
```
80 lines
2.3 KiB
Python
80 lines
2.3 KiB
Python
"""Util that calls Lambda."""
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import json
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from typing import Any, Dict, Optional
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from langchain_core.pydantic_v1 import BaseModel, root_validator
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class LambdaWrapper(BaseModel):
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"""Wrapper for AWS Lambda SDK.
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To use, you should have the ``boto3`` package installed
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and a lambda functions built from the AWS Console or
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CLI. Set up your AWS credentials with ``aws configure``
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Example:
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.. code-block:: bash
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pip install boto3
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aws configure
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"""
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lambda_client: Any #: :meta private:
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"""The configured boto3 client"""
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function_name: Optional[str] = None
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"""The name of your lambda function"""
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awslambda_tool_name: Optional[str] = None
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"""If passing to an agent as a tool, the tool name"""
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awslambda_tool_description: Optional[str] = None
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"""If passing to an agent as a tool, the description"""
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class Config:
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extra = "forbid"
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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that python package exists in environment."""
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try:
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import boto3
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except ImportError:
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raise ImportError(
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"boto3 is not installed. Please install it with `pip install boto3`"
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)
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values["lambda_client"] = boto3.client("lambda")
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return values
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def run(self, query: str) -> str:
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"""
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Invokes the lambda function and returns the
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result.
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Args:
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query: an input to passed to the lambda
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function as the ``body`` of a JSON
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object.
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"""
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res = self.lambda_client.invoke(
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FunctionName=self.function_name,
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InvocationType="RequestResponse",
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Payload=json.dumps({"body": query}),
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)
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try:
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payload_stream = res["Payload"]
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payload_string = payload_stream.read().decode("utf-8")
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answer = json.loads(payload_string)["body"]
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except StopIteration:
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return "Failed to parse response from Lambda"
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if answer is None or answer == "":
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# We don't want to return the assumption alone if answer is empty
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return "Request failed."
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else:
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return f"Result: {answer}"
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