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import pandas as pd
import openai
import faiss
import os
# Load your custom CSV data
data = pd.read_csv( os.getcwd() + "\\Learning_Pathway_Index.csv")
# Initialize and populate FAISS index
vector_dimension = 768 # For example, if you use a GPT-3 model with 768-dimensional embeddings
index = faiss.IndexFlatL2(vector_dimension)
vectors = [] # List to store vector representations of data
for text in data['text_column']:
# Vectorize the text using a pre-trained model (e.g., GPT-3)
# Replace 'YOUR_OPENAI_API_KEY' with your actual API key
openai.api_key = os.getenv("OPENAI_API_KEY")
response = openai.Completion.create(
engine="text-davinci-002",
prompt=text,
max_tokens=50 # Adjust the token limit as needed
)
vector = response.choices[0].embedding
vectors.append(vector)
# Convert the list of vectors to a numpy array
vectors = np.array(vectors).astype('float32')
# Add vectors to the FAISS index
index.add(vectors)
# Accept user questions using OpenAI
user_question = input("Ask a question: ")
# Vectorize the user's question
user_vector = vectorize_user_question(user_question) # Implement this function
# Search for similar items in the FAISS index
k = 5 # Number of similar items to retrieve
distances, indices = index.search(user_vector, k)
# Retrieve and display the similar items
similar_items = data.iloc[indices[0]]
print(similar_items)
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