| | from ollama import Client
|
| | import ollama
|
| | import chromadb
|
| | import speech_recognition as sr
|
| | import requests
|
| | import pyttsx3
|
| |
|
| |
|
| | client = chromadb.Client()
|
| |
|
| | message_history = [
|
| |
|
| | {
|
| | 'id' : 1,
|
| | 'prompt' : 'What is your name?',
|
| | 'response' : 'My name is TADBot, a bot to help with short term remedial help for mental purposes. '
|
| |
|
| | },
|
| |
|
| | {
|
| | 'id' : 2,
|
| | 'prompt' : 'Bye',
|
| | 'response' : 'Good to see you get better. Hopefully you reach out to me if you have any problems.'
|
| |
|
| | },
|
| |
|
| | {
|
| | 'id' : 3,
|
| | 'prompt' : 'What is the essence of Life?',
|
| | 'response' : 'The essence of life is to create what you want of yourself.'
|
| |
|
| | }
|
| | ]
|
| | convo = []
|
| |
|
| | llm = Client(host='http://localhost:11434')
|
| |
|
| |
|
| | def create_vector_db(conversations):
|
| |
|
| | vector_db_name = 'conversations'
|
| |
|
| | try:
|
| | client.delete_collection(vector_db_name)
|
| | except ValueError as e:
|
| | pass
|
| |
|
| | vector_db = client.create_collection(name=vector_db_name)
|
| | for c in conversations:
|
| |
|
| | serialized_convo = 'prompt: ' + c["prompt"] + ' response: ' + c["response"]
|
| |
|
| | response = ollama.embeddings(model = "nomic-embed-text",prompt = serialized_convo)
|
| | embedding = response["embedding"]
|
| |
|
| | vector_db.add(ids = [str(c['id'])], embeddings = [embedding], documents = [serialized_convo])
|
| |
|
| |
|
| | def stream_response(prompt):
|
| |
|
| | convo.append({'role': "user", 'content': prompt})
|
| | output = llm.chat(model = "TADBot", messages = convo)
|
| | response = output['message']['content']
|
| |
|
| | print("TADBot: ")
|
| | print(response)
|
| | engine = pyttsx3.init('espeak')
|
| | engine.say(response)
|
| | engine.runAndWait()
|
| |
|
| | convo.append({'role': "assistant", 'content': response})
|
| |
|
| | def retrieve_embeddings(prompt):
|
| | response = ollama.embeddings(model = "nomic-embed-text", prompt = prompt)
|
| | propmt_embedding = response['embedding']
|
| |
|
| | vector_db = client.get_collection(name = 'conversations')
|
| | results = vector_db.query(query_embeddings=[propmt_embedding], n_results = 1)
|
| |
|
| | best_embedding = results['documents'][0][0]
|
| | return best_embedding
|
| |
|
| | create_vector_db(message_history)
|
| |
|
| |
|
| | while True:
|
| |
|
| | r = sr.Recognizer()
|
| | m = sr.Microphone()
|
| |
|
| | try:
|
| |
|
| |
|
| | print("Say something!")
|
| | with m as source:
|
| | audio = r.listen(source)
|
| |
|
| |
|
| | try:
|
| |
|
| |
|
| |
|
| | prompt = r.recognize_google(audio)
|
| | print("Tadbot thinks you said: " + prompt)
|
| | except sr.UnknownValueError:
|
| | print("Tadbot could not understand audio")
|
| | except sr.RequestError as e:
|
| | print("Could not request results from Google Speech Recognition service; {0}".format(e))
|
| |
|
| |
|
| | print("Please wait...")
|
| | with m as source:
|
| | r.adjust_for_ambient_noise(source)
|
| |
|
| | if prompt == "bye" or prompt == "Bye":
|
| | print("TADBot: Hopefully I was able to help you out today. Have a Nice Day!")
|
| | break
|
| | """
|
| | context = retrieve_embeddings(prompt)
|
| |
|
| | prompt = prompt + "CONTEXT FROM EMBEDDING: " + context
|
| | """
|
| | stream_response(prompt)
|
| | except KeyboardInterrupt:
|
| | pass
|
| |
|
