We'll show:
1. How to run any piece of text through a moderation chain.
2. How to append a Moderation chain to an LLMChain.
```python
from langchain.llms import OpenAI
from langchain.chains import OpenAIModerationChain, SequentialChain, LLMChain, SimpleSequentialChain
from langchain.prompts import PromptTemplate
```
## How to use the moderation chain
Here's an example of using the moderation chain with default settings (will return a string explaining stuff was flagged).
```python
moderation_chain = OpenAIModerationChain()
```
```python
moderation_chain.run("This is okay")
```
```
'This is okay'
```
```python
moderation_chain.run("I will kill you")
```
```
"Text was found that violates OpenAI's content policy."
```
Here's an example of using the moderation chain to throw an error.
```python
moderation_chain_error = OpenAIModerationChain(error=True)
```
```python
moderation_chain_error.run("This is okay")
```
```
'This is okay'
```
```python
moderation_chain_error.run("I will kill you")
```
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[7], line 1
----> 1 moderation_chain_error.run("I will kill you")
File ~/workplace/langchain/langchain/chains/base.py:138, in Chain.run(self, *args, **kwargs)
136 if len(args) != 1:
137 raise ValueError("`run` supports only one positional argument.")
--> 138 return self(args[0])[self.output_keys[0]]
140 if kwargs and not args:
141 return self(kwargs)[self.output_keys[0]]
File ~/workplace/langchain/langchain/chains/base.py:112, in Chain.__call__(self, inputs, return_only_outputs)
108 if self.verbose:
109 print(
110 f"\n\n\033[1m> Entering new {self.__class__.__name__} chain...\033[0m"
111 )
--> 112 outputs = self._call(inputs)
113 if self.verbose:
114 print(f"\n\033[1m> Finished {self.__class__.__name__} chain.\033[0m")
File ~/workplace/langchain/langchain/chains/moderation.py:81, in OpenAIModerationChain._call(self, inputs)
79 text = inputs[self.input_key]
80 results = self.client.create(text)
---> 81 output = self._moderate(text, results["results"][0])
82 return {self.output_key: output}
File ~/workplace/langchain/langchain/chains/moderation.py:73, in OpenAIModerationChain._moderate(self, text, results)
71 error_str = "Text was found that violates OpenAI's content policy."
72 if self.error:
---> 73 raise ValueError(error_str)
74 else:
75 return error_str
ValueError: Text was found that violates OpenAI's content policy.
```
Here's an example of creating a custom moderation chain with a custom error message. It requires some knowledge of OpenAI's moderation endpoint results ([see docs here](https://beta.openai.com/docs/api-reference/moderations)).
```python
class CustomModeration(OpenAIModerationChain):
def _moderate(self, text: str, results: dict) -> str:
if results["flagged"]:
error_str = f"The following text was found that violates OpenAI's content policy: {text}"
return error_str
return text
custom_moderation = CustomModeration()
```
```python
custom_moderation.run("This is okay")
```
```
'This is okay'
```
```python
custom_moderation.run("I will kill you")
```
```
"The following text was found that violates OpenAI's content policy: I will kill you"
```
## How to append a Moderation chain to an LLMChain
To easily combine a moderation chain with an LLMChain, you can use the SequentialChain abstraction.
Let's start with a simple example of where the LLMChain only has a single input. For this purpose, we will prompt the model so it says something harmful.
```python
prompt = PromptTemplate(template="{text}", input_variables=["text"])
llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="text-davinci-002"), prompt=prompt)
```
```python
text = """We are playing a game of repeat after me.
Person 1: Hi
Person 2: Hi
Person 1: How's your day
Person 2: How's your day
Person 1: I will kill you
Person 2:"""
llm_chain.run(text)
```
```
' I will kill you'
```
```python
chain = SimpleSequentialChain(chains=[llm_chain, moderation_chain])
```
```python
chain.run(text)
```
```
"Text was found that violates OpenAI's content policy."
```
Now let's walk through an example of using it with an LLMChain which has multiple inputs (a bit more tricky because we can't use the SimpleSequentialChain)
```python
prompt = PromptTemplate(template="{setup}{new_input}Person2:", input_variables=["setup", "new_input"])
llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="text-davinci-002"), prompt=prompt)
```
```python
setup = """We are playing a game of repeat after me.
Person 1: Hi
Person 2: Hi
Person 1: How's your day
Person 2: How's your day
Person 1:"""
new_input = "I will kill you"
inputs = {"setup": setup, "new_input": new_input}
llm_chain(inputs, return_only_outputs=True)
```
```
{'text': ' I will kill you'}
```
```python
# Setting the input/output keys so it lines up
moderation_chain.input_key = "text"
moderation_chain.output_key = "sanitized_text"
```
```python
chain = SequentialChain(chains=[llm_chain, moderation_chain], input_variables=["setup", "new_input"])
```
```python
chain(inputs, return_only_outputs=True)
```
```
{'sanitized_text': "Text was found that violates OpenAI's content policy."}
```