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import openai | |
import gradio as gr | |
import sqlite3 | |
import numpy as np | |
from sklearn.metrics.pairwise import cosine_similarity | |
openai.api_key = "sk-..." # Replace with your key | |
def find_closest_neighbors(vector, dictionary_of_vectors): | |
""" | |
Takes a vector and a dictionary of vectors and returns the three closest neighbors | |
""" | |
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) | |
match_list = sorted_cosine_similarities[0:4] | |
return match_list | |
def handle_input(user_input): | |
""" | |
Checks if the user input is a text file or a string. | |
If it's a text file, it reads the file, splits it into 250-character chunks, and returns the chunks. | |
If it's a string, it just returns the string. | |
""" | |
if isinstance(user_input, gr.inputs.File): | |
with open(user_input.name, 'r') as file: | |
text = file.read() | |
chunks = [text[i:i+250] for i in range(0, len(text), 250)] | |
return chunks | |
else: | |
return [user_input] | |
def predict(user_input, history): | |
# Connect to the database | |
conn = sqlite3.connect('QRIdatabase7 (1).db') | |
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() | |
input_chunks = handle_input(user_input) | |
for message in input_chunks: | |
# Create embedding for the message | |
message_vector = openai.Embedding.create( | |
input=message, | |
engine="text-embedding-ada-002" | |
)['data'][0]['embedding'] | |
message_vector = np.array(message_vector) | |
# Find the closest neighbors | |
match_list = find_closest_neighbors(message_vector, dictionary_of_vectors) | |
context = '' | |
for match in match_list: | |
context += str(match[0]) | |
context = context[:-1500] | |
prep = f"This is an OpenAI model tuned to answer questions specific to the Qualia Research institute, a research institute that focuses on consciousness. Here is some question-specific context, and then the Question to answer, related to consciousness, the human experience, and phenomenology: {context}. Here is a question specific to QRI and consciousness in general 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 = partial_message + chunk['choices'][0]['delta']['content'] | |
yield partial_message | |
gr.ChatInterface(predict, inputs=gr.inputs.Mixed([gr.inputs.Textbox(lines=3), gr.inputs.File()]), allow_flagging=False).queue().launch() | |