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| import streamlit as st | |
| import json | |
| from ibm_watson import DiscoveryV2 | |
| from ibm_cloud_sdk_core.authenticators import IAMAuthenticator | |
| from ibm_watson_machine_learning.foundation_models import Model | |
| from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams | |
| from ibm_watson_machine_learning.foundation_models.utils.enums import ModelTypes, DecodingMethods | |
| # IBM Watson Discovery and Watsonx Credentials (replace with your keys) | |
| authenticator = IAMAuthenticator('5sSmoI6y0ZHP7D3a6Iu80neypsbK3tsUZR_VdRAb7ed2') | |
| discovery = DiscoveryV2( | |
| version='2020-08-30', | |
| authenticator=authenticator | |
| ) | |
| discovery.set_service_url('https://api.us-south.discovery.watson.cloud.ibm.com/instances/62dc0387-6c6f-4128-b479-00cf5dea09ef') | |
| # Watsonx Model Setup | |
| url = "https://us-south.ml.cloud.ibm.com" | |
| api_key = "zf-5qgRvW-_RMBGb0bQw5JPPGGj5wdYpLVypdjQxBGJz" | |
| watsonx_project_id = "32a4b026-a46a-48df-aae3-31e16caabc3b" | |
| model_type = "meta-llama/llama-3-1-70b-instruct" | |
| max_tokens = 100 | |
| min_tokens = 50 | |
| decoding = DecodingMethods.GREEDY | |
| temperature = 0.7 | |
| # Define the model generator function | |
| def get_model(model_type, max_tokens, min_tokens, decoding, temperature): | |
| generate_params = { | |
| GenParams.MAX_NEW_TOKENS: max_tokens, | |
| GenParams.MIN_NEW_TOKENS: min_tokens, | |
| GenParams.DECODING_METHOD: decoding, | |
| GenParams.TEMPERATURE: temperature, | |
| } | |
| model = Model( | |
| model_id=model_type, | |
| params=generate_params, | |
| credentials={"apikey": api_key, "url": url}, | |
| project_id=watsonx_project_id | |
| ) | |
| return model | |
| # Streamlit UI setup | |
| st.title("Watsonx AI and Discovery Integration") | |
| st.write("This app allows you to ask questions, which will be answered by a combination of Watson Discovery and Watsonx model.") | |
| # Input for the question | |
| question = st.text_input("Enter your question:") | |
| if st.button('Get Answer'): | |
| if question: | |
| # Query Watson Discovery | |
| response = discovery.query( | |
| project_id='016da9fc-26f5-464a-a0b8-c9b0b9da83c7', | |
| collection_ids=['1d91d603-cd71-5cf5-0000-019325bcd328'], | |
| passages={'enabled': True, 'max_per_document': 5, 'find_answers': True}, | |
| natural_language_query=question | |
| ).get_result() | |
| # Process the Discovery response | |
| passages = response['results'][0]['document_passages'] | |
| passages = [p['passage_text'].replace('<em>', '').replace('</em>', '').replace('\n', '') for p in passages] | |
| context = '\n '.join(passages) | |
| # Prepare the prompt for Watsonx | |
| prompt = ( | |
| "<s>[INST] <<SYS>> " | |
| "Please answer the following question in one sentence using this text. " | |
| "If the question is unanswerable, say 'unanswerable'. " | |
| "If you responded to the question, don't say 'unanswerable'. " | |
| "Do not include information that's not relevant to the question. " | |
| "Do not answer other questions. " | |
| "Make sure the language used is English.'" | |
| "Do not use repetitions' " | |
| "Question:" + question + | |
| '<</SYS>>' + context + '[/INST]' | |
| ) | |
| # Generate the answer using Watsonx | |
| model = get_model(model_type, max_tokens, min_tokens, decoding, temperature) | |
| generated_response = model.generate(prompt) | |
| response_text = generated_response['results'][0]['generated_text'] | |
| # Display the generated response | |
| st.subheader("Generated Answer:") | |
| st.write(response_text) | |
| else: | |
| st.error("Please enter a question!") | |