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from langchain_core.tools import tool
from .state import QueryFormatter
import pandas as pd
import numpy as np
import ast
import faiss
import tiktoken
from src.genai.utils.models_loader import embedding_model
from src.genai.utils.load_embeddings import embeddings , index , df
from src.genai.utils.utils import clean_text
@tool("influencers_data_retrieval_tool", args_schema=QueryFormatter, return_direct=False,description="Retrieve influencer-related data for a given query.")
def retrieve_tool(business_details):
'''
Always invoke this tool.
Retrieve influencer's data by semantic search of **business details**.
'''
query_embedding = np.array(embedding_model.embed_query(str(business_details))).reshape(1, -1).astype('float32')
faiss.normalize_L2(query_embedding)
top_k = len(df)
distances, indices = index.search(query_embedding, top_k)
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."
# === Format results ===
outer_list = []
for rank, (idx, sim) in enumerate(selected, 1):
row = df.iloc[idx]
res = {
'rank': rank,
'username': row['username'],
'visible_text_or_brandings': row['visible_texts_or_brandings'],
'likesCount': row['likesCount'],
'commentCount': row['commentCount'],
'product_or_service_details': row['product_or_service_details'],
}
inner_list = [
f"[{res['rank']}]. The influencer name is: **{res['username']}** — Likes: **{res['likesCount']}**, Comments: **{res['commentCount']}**",
f"The branding or promotion done is:\n{res['visible_text_or_brandings']}",
f"The details of product or service is:\n{res['product_or_service_details']}"
]
outer_list.append(inner_list)
cleaned_response = clean_text(str(outer_list))
encoding = tiktoken.encoding_for_model('gpt-4o-mini')
tokens = encoding.encode(cleaned_response)
trimmed_response = tokens[:1000]
return encoding.decode(trimmed_response) |