Update util.py

util.py

@@ -1,101 +1,103 @@
-from pypdf import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
-from langchain_community.embeddings.ollama import OllamaEmbeddings
-from langchain_community.embeddings.bedrock import BedrockEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain.chains import create_retrieval_chain
-from dotenv import load_dotenv
-import streamlit as st
-import os
-
-load_dotenv()
-
-
-# Function to get the API key
-def get_api_key():
-    # Try to get the API key from st.secrets first
-    try:
-        groq_api_key = os.getenv("GROQ_API_KEY", "")
-        
-        return groq_api_key
-    except Exception as e:
-        print(e)
-
-def get_inference_api_key():
-    try:
-        inference_api_key = os.getenv("INFERENCE_API_KEY", "")
-
-        return inference_api_key
-    except Exception as e:
-        print(e)
-
-
-# Function for API configuration at sidebar
-def sidebar_api_key_configuration():
-    groq_api_key = get_api_key()
-    if groq_api_key == '':
-        st.sidebar.warning('Enter the API Key(s) 🗝️')
-        st.session_state.prompt_activation = False
-    elif (groq_api_key.startswith('gsk_') and (len(groq_api_key) == 56)):
-        st.sidebar.success('Lets Proceed!', icon='️👉')
-        st.session_state.prompt_activation = True
-    else:
-        st.sidebar.warning('Please enter the correct API Key 🗝️!', icon='⚠️')
-        st.session_state.prompt_activation = False
-    return groq_api_key
-
-
-def sidebar_groq_model_selection():
-    st.sidebar.subheader("Model Selection")
-    model = st.sidebar.selectbox('Select the Model', ('Llama3-8b-8192', 'Llama3-70b-8192', 'Mixtral-8x7b-32768',
-                                                      'Gemma-7b-it'), label_visibility="collapsed")
-    return model
-
-
-# Read PDF data
-def read_pdf_data(pdf_docs):
-    text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
-
-
-# Split data into chunks
-def split_data(text):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    text_chunks = text_splitter.split_text(text)
-    return text_chunks
-
-
-def get_embedding_function():
-    # embeddings = BedrockEmbeddings(
-    #     credentials_profile_name="default", region_name="us-east-1"
-    # )
-    #embeddings = OllamaEmbeddings(model="nomic-embed-text")
-    inference_api_key = get_inference_api_key()
-
-    embeddings = HuggingFaceInferenceAPIEmbeddings(
-        api_key=inference_api_key, model_name="sentence-transformers/all-MiniLM-l6-v2"
-    )
-    return embeddings
-
-
-# Create vectorstore
-def create_vectorstore(pdf_docs):
-    raw_text = read_pdf_data(pdf_docs)  # Get PDF text
-    text_chunks = split_data(raw_text)  # Get the text chunks
-    embeddings = get_embedding_function()  # Get the embedding function
-    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-    return vectorstore
-
-
-# Get response from llm of user asked question
-def get_llm_response(llm, prompt, question):
-    document_chain = create_stuff_documents_chain(llm, prompt)
-    retrieval_chain = create_retrieval_chain(st.session_state.vector_store.as_retriever(), document_chain)
-    response = retrieval_chain.invoke({'input': question})
-    return response
+from pypdf import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+from langchain_community.embeddings.ollama import OllamaEmbeddings
+from langchain_community.embeddings.bedrock import BedrockEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.chains import create_retrieval_chain
+from dotenv import load_dotenv
+import streamlit as st
+import os
+
+load_dotenv()
+
+
+# Function to get the API key
+def get_api_key():
+    # Try to get the API key from st.secrets first
+    try:
+        groq_api_key = os.getenv("GROQ_API_KEY", "")
+        
+        return groq_api_key
+    except Exception as e:
+        print(e)
+
+def get_inference_api_key():
+    try:
+        inference_api_key = os.getenv("INFERENCE_API_KEY", "")
+
+        return inference_api_key
+    except Exception as e:
+        print(e)
+
+
+# Function for API configuration at sidebar
+def sidebar_api_key_configuration():
+    groq_api_key = get_api_key()
+    if groq_api_key == '':
+        st.sidebar.warning('Enter the API Key(s) 🗝️')
+        st.session_state.prompt_activation = False
+    elif (groq_api_key.startswith('gsk_') and (len(groq_api_key) == 56)):
+        st.sidebar.success('Lets Proceed!', icon='️👉')
+        st.session_state.prompt_activation = True
+    else:
+        st.sidebar.warning('Please enter the correct API Key 🗝️!', icon='⚠️')
+        st.session_state.prompt_activation = False
+    return groq_api_key
+
+
+def sidebar_groq_model_selection():
+    st.sidebar.subheader("Model Selection")
+    model = st.sidebar.selectbox('Select the Model', ('Llama3-8b-8192', 'Llama3-70b-8192', 'Mixtral-8x7b-32768',
+                                                      'Gemma-7b-it'), label_visibility="collapsed")
+    return model
+
+
+# Read PDF data
+def read_pdf_data(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+
+# Split data into chunks
+def split_data(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    text_chunks = text_splitter.split_text(text)
+    return text_chunks
+
+
+def get_embedding_function():
+    # embeddings = BedrockEmbeddings(
+    #     credentials_profile_name="default", region_name="us-east-1"
+    # )
+    #embeddings = OllamaEmbeddings(model="nomic-embed-text")
+    inference_api_key = get_inference_api_key()
+
+    embeddings = HuggingFaceInferenceAPIEmbeddings(
+        api_key=inference_api_key, model_name="sentence-transformers/all-MiniLM-l6-v2"
+    )
+    return embeddings
+
+
+# Create vectorstore
+def create_vectorstore(pdf_docs):
+    raw_text = read_pdf_data(pdf_docs)  # Get PDF text
+    text_chunks = split_data(raw_text)  # Get the text chunks
+    embeddings = get_embedding_function()  # Get the embedding function
+
+    # Pass the callable embedding function (embed_query) to FAISS
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings.embed_query)
+    return vectorstore
+    
+
+# Get response from llm of user asked question
+def get_llm_response(llm, prompt, question):
+    document_chain = create_stuff_documents_chain(llm, prompt)
+    retrieval_chain = create_retrieval_chain(st.session_state.vector_store.as_retriever(), document_chain)
+    response = retrieval_chain.invoke({'input': question})
+    return response