Update example prompt

example/main_prompt

@@ -1,86 +1 @@
-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.