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import sklearn |
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import sqlite3 |
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import numpy as np |
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from sklearn.metrics.pairwise import cosine_similarity |
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import openai |
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import os |
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import gradio as gr |
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openai.api_key = os.environ["Secret"] |
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def find_closest_neighbors(vector1, dictionary_of_vectors): |
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""" |
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Takes a vector and a dictionary of vectors and returns the three closest neighbors |
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""" |
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vector = openai.Embedding.create( |
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input=vector1, |
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engine="text-embedding-ada-002" |
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)['data'][0]['embedding'] |
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vector = np.array(vector) |
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cosine_similarities = {} |
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for key, value in dictionary_of_vectors.items(): |
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cosine_similarities[key] = cosine_similarity(vector.reshape(1, -1), value.reshape(1, -1))[0][0] |
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sorted_cosine_similarities = sorted(cosine_similarities.items(), key=lambda x: x[1], reverse=True) |
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match_list = sorted_cosine_similarities[0:4] |
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return match_list |
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def predict(message, history): |
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conn = sqlite3.connect('QRIdatabase7 (1).db') |
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cursor = conn.cursor() |
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cursor.execute('''SELECT text, embedding FROM chunks''') |
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rows = cursor.fetchall() |
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dictionary_of_vectors = {} |
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for row in rows: |
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text = row[0] |
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embedding_str = row[1] |
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embedding = np.fromstring(embedding_str, sep=' ') |
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dictionary_of_vectors[text] = embedding |
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conn.close() |
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match_list = find_closest_neighbors(message, dictionary_of_vectors) |
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context = '' |
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for match in match_list: |
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context += str(match[0]) |
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context = context[:-1500] |
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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: " |
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history_openai_format = [] |
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for human, assistant in history: |
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history_openai_format.append({"role": "user", "content": human }) |
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history_openai_format.append({"role": "assistant", "content":assistant}) |
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history_openai_format.append({"role": "user", "content": prep}) |
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response = openai.ChatCompletion.create( |
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model='gpt-3.5-turbo', |
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messages= history_openai_format, |
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temperature=1.0, |
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stream=True |
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) |
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partial_message = "" |
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for chunk in response: |
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if len(chunk['choices'][0]['delta']) != 0: |
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partial_message = partial_message + chunk['choices'][0]['delta']['content'] |
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yield partial_message |
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demo = gr.ChatInterface(predict).queue() |
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if __name__ == "__main__": |
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demo.launch() |
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