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import re
import numpy as np
import faiss
from src.genai.utils.models_loader import embedding_model , encoding_model
from src.genai.utils.utils import clean_text
from src.genai.utils.data_loader import api_knowledge_df, api_index, caption_df , caption_index
from src.genai.utils.models_loader import embedding_model
import pandas as pd
class APIKnowledgeRetrieveTool:
def __init__(self):
self.df = api_knowledge_df
self.index = api_index
def retrieve(self,query):
query_embedding = np.array(embedding_model.embed_query(query)).reshape(1, -1).astype('float32')
distances, indices = self.index.search(query_embedding, 1)
row=self.df.iloc[indices[0]]
data = {'endpoint':row['endpoint'],
'method':row['method'],
'parameters':row['parameters']}
return data
class RetrieverBackup:
def __init__(self):
self.df = caption_df
self.index = caption_index
def _filter_dataset(self, query):
usernames = self.df["username"].dropna().unique()
matched_users = [u for u in usernames if re.search(rf"\b{re.escape(u)}\b", query)]
if matched_users:
filtered_df = self.df[self.df["username"].isin(matched_users)]
return filtered_df
else:
return self.df
def retrieve_old(self, query):
query_embedding = np.array(embedding_model.embed_query(str(query))).reshape(1, -1).astype('float32')
print('Embeddings Generated')
faiss.normalize_L2(query_embedding)
print('Query embedded')
filtered_df = self._filter_dataset(query)
distances, indices = self.index.search(query_embedding, len(filtered_df))
similarity_threshold = 0.35
selected = [(idx, sim) for idx, sim in zip(indices[0], distances[0]) if sim >= similarity_threshold]
if not selected:
return "No influencers found."
outer_list = []
for rank, (idx, sim) in enumerate(selected, 1):
row = filtered_df.iloc[idx]
inner_list = [
f"[{rank}]. The influencer name is: **{row['username']}** — Likes: **{row['likesCount']}**, Comments: **{row['commentCount']}**",
f"The branding or promotion done is:\n{row['visible_texts_or_brandings']}",
f"The details of product or service is:\n{row['product_or_service_details']}"
]
outer_list.append(inner_list)
cleaned_response = clean_text(str(outer_list))
print('response cleaned')
tokens = encoding_model.encode(cleaned_response)[:500]
print('tokens got')
return encoding_model.decode(tokens)
def retrieve(self, query):
query_embedding = np.array(embedding_model.embed_query(str(query))).reshape(1, -1).astype('float32')
faiss.normalize_L2(query_embedding)
# Search on full dataset (index is built on full df)
distances, indices = self.index.search(query_embedding, len(self.df))
similarity_threshold = 0.35
# Prepare matched usernames
usernames = self.df["username"].dropna().unique()
matched_users = [u for u in usernames if re.search(rf"\b{re.escape(u)}\b", query)]
results = []
rank = 1
for idx, sim in zip(indices[0], distances[0]):
if sim < similarity_threshold:
continue
row = self.df.iloc[idx]
# If query mentions usernames, only keep those rows
if matched_users and row["username"] not in matched_users:
continue
results.append({
'url': row['videoUrl'],
'username': row['username'],
'likesCount': int(row['likesCount']) if pd.notnull(row['likesCount']) else None,
'commentCount': int(row['commentCount']) if pd.notnull(row['commentCount']) else None
})
results = results[:10] if len(results) > 10 else results
return results
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