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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 | |
| openai.api_key = os.environ["Secret"] | |
| def find_closest_neighbors(vector1, dictionary_of_vectors): | |
| """ | |
| Takes a vector and a dictionary of vectors and returns the three closest neighbors | |
| """ | |
| 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) | |
| match_list = sorted_cosine_similarities[0:4] | |
| return match_list | |
| def predict(message, history): | |
| # Connect to the database | |
| conn = sqlite3.connect('QRIdatabase7 (2).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() | |
| # Find the closest neighbors | |
| match_list = find_closest_neighbors(message, 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).queue().launch() | |