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Update example prompt

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Anton Osika
2023-06-04 22:50:35 +02:00
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Instructions:
We are writing a feature computation framework.
It will mainly consist of FeatureBuilder classes.
Each Feature Builder will have the methods:
- get(key, config, context, cache): Call feature builder dependencies and then compute the feature. Returns value and hash of value.
- key: tuple of arguments that are used to compute the feature
- config: the configuration for the feature
- context: dataclass that contains dependencies and general configuration (see below)
- controller: object that can be used to get other features (see below)
- value: object that can be pickled
It will have the class attr:
- deps: list of FeatureBuilder classes
- default_config: function that accepts context and returns a config
The Controller will have the methods:
- get(feature_builder, key, config): Check the cache, and decide to call feature builder and then returns the output and timestamp it was computed
- feature_builder: FeatureBuilder class
- key: tuple of arguments that are used to compute the feature
- configs: dict of configs that are used to compute features
and the attributes:
- context: dataclass that contains dependencies and general configuration (see below)
- cache: cache for the features
Where it is unclear, please make assumptions and add a comment in the code about it
Here is an example of Builders we want:
ProductEmbeddingString: takes product_id, queries the product_db and gets the title as a string
ProductEmbedding: takes string and returns and embedding
ProductEmbeddingDB: takes just `merchant` name, uses all product_ids and returns the blob that is a database of embeddings
ProductEmbeddingSearcher: takes a string, constructs embeddingDB feature (note: all features are cached), embeds the string and searches the db
LLMProductPrompt: queries the ProductEmbeddingString, and formats a template that says "get recommendations for {title}"
LLMSuggestions: Takes product_id, looks up prompts and gets list of suggestions of product descriptions
LLMLogic: Takes the product_id, gets the LLM suggestions, embeds the suggestions, does a search, and returns a list of product_ids
The LLMLogic is the logic_builder in a file such as this one:
```
def main(merchant, market):
cache = get_feature_cache()
interaction_data_db = get_interaction_data_db()
product_db = get_product_db()
merchant_config = get_merchant_config(merchant)
context = Context(
interaction_data_db=interaction_data_db,
product_db=product_db,
merchant_config=merchant_config,
)
product_ids = cache(ProductIds).get(
key=(merchant, market),
context=context,
cache=cache,
)
for logic_builder in merchant_config['logic_builders']:
for product_id in product_ids:
key = (merchant, market, product_id)
p2p_recs = cache(logic_builder).get(key=key, context=context, cache=cache)
redis.set(key, p2p_recs)
```
API to product_db:
```python
async def get_product_attribute_dimensions(
self,
) -> dict[AttributeId, Dimension]:
pass
async def get_products(
self,
attribute_ids: set[AttributeId],
product_ids: set[ProductId] | None = None,
) -> dict[ProductId, dict[AttributeId, dict[IngestionDimensionKey, Any]]]:
pass
```
(note, dimensions are not so important. They related to information that varies by: locale, warehouse, pricelist etc)
---
You will focus on writing the integration test file test_all.py.
This file will Mock a lot of the necessary interfaces, run the logic LLMLogic and print the results from it.
We are writing snake in python. MVC components split in separate files. Keyboard control.