Update utils.py

utils.py

@@ -1,72 +1,72 @@
-from sentence_transformers import SentenceTransformer
-from langchain_community.vectorstores import Chroma
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.llms import HuggingFaceHub
-import openai
-import streamlit as st
-import re
-
-#openai_api_key = "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"
-model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
-# Define the embedding function using HuggingFaceEmbeddings
-embeddings = HuggingFaceEmbeddings(model_name = 'sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
-
-vectordb = Chroma(persist_directory= r"C:\Users\Lakshita\PycharmProjects\trial_bot\vector db", #enter chroma directory
-                  embedding_function=embeddings)
-#index = pinecone.Index('langchain-chatbot')
-
-# Create a retriever from the Chroma object
-retriever = vectordb.as_retriever()
-
-def find_match(input_text):
-    # Retrieve relevant documents based on the input query
-    docs = retriever.get_relevant_documents(input_text)
-    
-    match_texts = [doc.page_content for doc in docs]
-    
-    # Return the concatenated texts of the relevant documents
-    return "\n".join(match_texts)
-
-
-from transformers import pipeline
-
-# Load the text generation pipeline from Hugging Face
-text_generator = pipeline("text-generation", model="gpt2")
-
-def query_refiner(conversation, query):
-    # Formulate the prompt for the model
-    prompt = f"Given the following user query and conversation log, formulate a question that would be the most relevant to provide the user with an answer from a knowledge base.\n\nCONVERSATION LOG: \n{conversation}\n\nQuery: {query}\n\nRefined Query:"
-    
-    # Generate the response using the Hugging Face model
-    response = text_generator(prompt, max_length=256, temperature=0.7, top_p=1.0, pad_token_id=text_generator.tokenizer.eos_token_id)
-    
-    # Extract the refined query from the response
-    refined_query = response[0]['generated_text'].split('Refined Query:')[-1].strip()
-    
-    return refined_query
-
-
-def get_conversation_string():
-    conversation_string = ""
-    for i in range(len(st.session_state['responses'])-1):
-        
-        conversation_string += "Human: "+st.session_state['requests'][i] + "\n"
-        conversation_string += "Bot: "+ st.session_state['responses'][i+1] + "\n"
-    return conversation_string
-
-
-"""  
-from openai import OpenAI
-from audio_recorder_streamlit import audio_recorder
-
-client=OpenAI(api_key="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")
-
-def speech_to_text(audio_data):
-     with open(audio_data, "rb") as audio_file:
-         transcript = client.audio.transcriptions.create(
-            model="whisper-1",
-            response_format="text",
-            file=audio_file
-         )
-    return transcript
-"""
+from sentence_transformers import SentenceTransformer
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.llms import HuggingFaceHub
+import openai
+import streamlit as st
+import re
+
+#openai_api_key = "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"
+model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
+# Define the embedding function using HuggingFaceEmbeddings
+embeddings = HuggingFaceEmbeddings(model_name = 'sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
+
+vectordb = Chroma(persist_directory= r"\vector db", #enter chroma directory
+                  embedding_function=embeddings)
+#index = pinecone.Index('langchain-chatbot')
+
+# Create a retriever from the Chroma object
+retriever = vectordb.as_retriever()
+
+def find_match(input_text):
+    # Retrieve relevant documents based on the input query
+    docs = retriever.get_relevant_documents(input_text)
+    
+    match_texts = [doc.page_content for doc in docs]
+    
+    # Return the concatenated texts of the relevant documents
+    return "\n".join(match_texts)
+
+
+from transformers import pipeline
+
+# Load the text generation pipeline from Hugging Face
+text_generator = pipeline("text-generation", model="gpt2")
+
+def query_refiner(conversation, query):
+    # Formulate the prompt for the model
+    prompt = f"Given the following user query and conversation log, formulate a question that would be the most relevant to provide the user with an answer from a knowledge base.\n\nCONVERSATION LOG: \n{conversation}\n\nQuery: {query}\n\nRefined Query:"
+    
+    # Generate the response using the Hugging Face model
+    response = text_generator(prompt, max_length=256, temperature=0.7, top_p=1.0, pad_token_id=text_generator.tokenizer.eos_token_id)
+    
+    # Extract the refined query from the response
+    refined_query = response[0]['generated_text'].split('Refined Query:')[-1].strip()
+    
+    return refined_query
+
+
+def get_conversation_string():
+    conversation_string = ""
+    for i in range(len(st.session_state['responses'])-1):
+        
+        conversation_string += "Human: "+st.session_state['requests'][i] + "\n"
+        conversation_string += "Bot: "+ st.session_state['responses'][i+1] + "\n"
+    return conversation_string
+
+
+"""  
+from openai import OpenAI
+from audio_recorder_streamlit import audio_recorder
+
+client=OpenAI(api_key="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")
+
+def speech_to_text(audio_data):
+     with open(audio_data, "rb") as audio_file:
+         transcript = client.audio.transcriptions.create(
+            model="whisper-1",
+            response_format="text",
+            file=audio_file
+         )
+    return transcript
+"""