13 agentic behavior runner v1 (#14)

* baseline setup of agent abstract

* feat: new implementation of simple AI agent that can follow a goal and return

* refactored import structure and created full tests

* pytest setup a github workflow to run tests + more ignores

* singularity for pushing

* fixing builds of PDFs

* inital structure of docs

* init styles and docs

* basic style implementation

* 13 create outline for research paper draft (#18)

* updated outline for paper from issue

* extra paper sections and some formalization of series data

* algorithms and acknowledgements

* updated outline for paper from issue

* Refactor docker-compose services to use individual Dockerfiles (#20)

* Initial plan

* Refactor services into individual Dockerfiles

Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>

* Add EXPOSE directives to all Dockerfiles with port documentation

Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>

* 2 nextjs scaffold with store mode shop and admin session experiment wiring event emission v1 (#17)

* chore: cleaning gitignore

* formating and env documentation

* feat: context switching of hotel/airline depndent on env var via middleware

* fixed alignment and building

* wrong file

* prods

* fixed applying style

* better session cookie management

* tentative session storage with maybe using airtable

* migrated api of ingestion

* events and products apge

* fixing build

* 13 create outline for research paper draft (#18)

* updated outline for paper from issue

* extra paper sections and some formalization of series data

* algorithms and acknowledgements

* updated outline for paper from issue

* upadted text formating

* event unification

* refactor tracking to ues callbacks instead of refs

* implement a pricing display api with session passing

* moved middleware to proxy according to new changes in Nextjs

* refactoed kafka ingestion to go via backend not web-db

* Refactor docker-compose services to use individual Dockerfiles (#20)

* Initial plan

* Refactor services into individual Dockerfiles

Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>

* Add EXPOSE directives to all Dockerfiles with port documentation

Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: velocitatem <60182044+velocitatem@users.noreply.github.com>

* fixing small bugs and adding exepriments to tracking

* added some doc

* fixing prod

* prod kafka server logging

* topic auto create

* pytest setup a github workflow to run tests + more ignores

* getting data from agents properly

* proper pipeline to handle data and build matrices

* fixing backend dumping

* fixing agents and ignore

* fixing import for tests

---------

Co-authored-by: Copilot <198982749+Copilot@users.noreply.github.com>

experiments/procesing/extract.py

@@ -0,0 +1,84 @@
+import pandas as pd
+import json
+import numpy as np
+import os
+import requests
+from dotenv import load_dotenv
+from sklearn.base import BaseEstimator, TransformerMixin
+load_dotenv()
+
+BACKEND_URL = os.getenv("BACKEND_URL", "http://localhost:5000")
+N_PRICE_BUCKETS = 5
+
+def get_data_from_kafka() -> pd.DataFrame:
+    """fetch all events from backend dump endpoint"""
+    resp = requests.get(f"{BACKEND_URL}/api/kafka/dump")
+    resp.raise_for_status()
+    data = resp.json()
+
+    if not data.get('success') or not data.get('data'):
+        return pd.DataFrame()
+
+    df = pd.DataFrame(data['data'])
+    # explode metadata col json
+    if 'metadata' in df.columns:
+        df = df.join(pd.json_normalize(df.pop("metadata"), sep=".").add_prefix("metadata_"))
+    df = df.dropna(subset=['eventName'])
+    return df
+
+
+def join_with_experiments(df: pd.DataFrame) -> pd.DataFrame:
+    # TODO: Get experiments db from supabase and join on session_id
+    return df
+
+
+def augment_event_titles(df: pd.DataFrame) -> pd.DataFrame:
+    # from taking standard view_item_page in eventName to view_item_page_{metadata_schema}
+    # we want metadata schema to create product specific event names
+
+    # only create price buckets if we have enough unique prices
+    if df["metadata_price"].notnull().sum() > 0:
+        try:
+            price_buckets = pd.qcut(
+                df["metadata_price"],
+                q=N_PRICE_BUCKETS,
+                labels=[f"PB_{i+1}" for i in range(N_PRICE_BUCKETS)],
+                duplicates='drop'  # handle duplicate bin edges
+            )
+        except ValueError:
+            # fallback: if still not enough unique values, use cut with fixed ranges or just use raw price
+            price_buckets = df["metadata_price"].apply(lambda x: f"P_{int(x)}" if pd.notnull(x) else "")
+    else:
+        price_buckets = pd.Series([""] * len(df), index=df.index)
+
+    # metadata_schema: _product_id@price_bucket_{i} only if we have product metadata otherswise keep original event name
+    # TODO: make this adaptive, if we have hover_over_title we append the title, if its view_page we say which page
+    df["metadata_schema"] = np.where(
+        df["productId"].notnull() & df["metadata_price"].notnull(),
+        "_" + df["productId"].astype(str) + "@" + price_buckets.astype(str),
+        ""
+    )
+    df["eventName"] = df["eventName"] + df["metadata_schema"].astype(str)
+    return df
+
+
+def extract() -> pd.DataFrame:
+    df = get_data_from_kafka()
+    df = join_with_experiments(df)
+    df = augment_event_titles(df)
+    return df
+
+
+class DataExtractor(BaseEstimator, TransformerMixin):
+    def fit(self, X=None, y=None):
+        return self
+
+    def transform(self, X=None):
+        return extract()
+
+
+if __name__ == "__main__":
+    df = extract()
+    print(df.head())
+    print(df.tail())
+    print(df.info())