The decision layer library can be used as a super fast decision making layer on top of LLMs. That means that rather than waiting on a slow agent to decide what to do, we can use the magic of semantic vector space to make decisions. Cutting decision making time down from seconds to milliseconds.
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## Getting Started
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``` python
!pipinstall-qU \
decision-layer
```
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We start by defining a dictionary mapping decisions to example phrases that should trigger those decisions.
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``` python
fromdecision_layer.schemaimportDecision
politics=Decision(
name="politics",
utterances=[
"isn't politics the best thing ever",
"why don't you tell me about your political opinions",
"don't you just love the president"
"don't you just hate the president",
"they're going to destroy this country!",
"they will save the country!"
]
)
```
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Let's define another for good measure:
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``` python
chitchat=Decision(
name="chitchat",
utterances=[
"how's the weather today?",
"how are things going?",
"lovely weather today",
"the weather is horrendous",
"let's go to the chippy"
]
)
decisions=[politics,chitchat]
```
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Now we initialize our embedding model (we will add support for Hugging Face):
Now we define the `DecisionLayer`. When called, the decision layer will consume text (a query) and output the category (`Decision`) it belongs to — for now we can only `_query` and get the most similar `Decision``utterances`.
