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import sklearn
import sqlite3
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
import openai
import os
import gradio as gr
# Set OpenAI API key from environment variable
openai.api_key = os.environ["Secret"]
def find_closest_neighbors(vector1, dictionary_of_vectors):
vector = openai.Embedding.create(
input=vector1,
engine="text-embedding-ada-002"
)['data'][0]['embedding']
vector = np.array(vector)
cosine_similarities = {}
for key, value in dictionary_of_vectors.items():
cosine_similarities[key] = cosine_similarity(vector.reshape(1, -1), value.reshape(1, -1))[0][0]
sorted_cosine_similarities = sorted(cosine_similarities.items(), key=lambda x: x[1], reverse=True)
return sorted_cosine_similarities[0:4]
def predict(message, history):
# Connect to the database
conn = sqlite3.connect('text_chunks_with_embeddings.db') # Update the database name
cursor = conn.cursor()
cursor.execute("SELECT text, embedding FROM chunks")
rows = cursor.fetchall()
dictionary_of_vectors = {}
for row in rows:
text = row[0]
embedding_str = row[1]
embedding = np.fromstring(embedding_str, sep=' ')
dictionary_of_vectors[text] = embedding
conn.close()
match_list = find_closest_neighbors(message, dictionary_of_vectors)
context = ''
for match in match_list:
context += str(match[0])
context = context[:1500] # Limit context to 1500 characters
prep = f"This is an OpenAI model designed to answer questions specific to grant-making applications for an aquarium. Here is some question-specific context: {context}. Q: {message} A: "
history_openai_format = []
for human, assistant in history:
history_openai_format.append({"role": "user", "content": human})
history_openai_format.append({"role": "assistant", "content": assistant})
history_openai_format.append({"role": "user", "content": prep})
response = openai.ChatCompletion.create(
model='gpt-4',
messages=history_openai_format,
temperature=1.0,
stream=True
)
partial_message = ""
for chunk in response:
if len(chunk['choices'][0]['delta']) != 0:
partial_message += chunk['choices'][0]['delta']['content']
yield partial_message
gr.ChatInterface(predict).queue().launch()
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