Update app.py

app.py

@@ -10,102 +10,82 @@ import bs4
 import torch
 import getpass
 
-
 # Prompt the user to enter their Langchain API key
 api_key_langchain = st.text_input("Enter your LANGCHAIN_API_KEY", type="password")
 
-# Check if the API key has been provided
-if api_key_langchain:
-    # Use the API key in your app
-    st.write("LangChain API Key is set.")
-else:
-    st.write("Please enter your LangChain API key.")
-
-# Initialize LangChain client (hypothetical example)
-#lc_client = Client(api_key=LANGCHAIN_API_KEY)
-
-#from langchain import SomeLangChainClass  # Replace with the correct class
-#client = SomeLangChainClass(api_key=api_key_langchain)
-#lc = LangChain(api_key=api_key_langchain)
-
-
-
 # Prompt the user to enter their Groq API key
 api_key_Groq = st.text_input("Enter your Groq_API_KEY", type="password")
 
-# Check if the Groq API key has been provided
-if api_key_Groq:
-    # Use the Groq API key in your app
-    st.write("Groq API Key is set.")
+# Check if both API keys have been provided
+if not api_key_langchain or not api_key_Groq:
+    st.write("Please enter both API keys.")
 else:
-    st.write("Please enter your Groq API key.")
-
-
-
-from langchain_groq import ChatGroq
-
-llm = ChatGroq(model="llama3-8b-8192", groq_api_key = api_key_Groq)
-
-# Define the embedding class
-class SentenceTransformerEmbedding:
-    def __init__(self, model_name):
-        self.model = SentenceTransformer(model_name)
-    
-    def embed_documents(self, texts):
-        embeddings = self.model.encode(texts, convert_to_tensor=True)
-        if isinstance(embeddings, torch.Tensor):
-            return embeddings.cpu().detach().numpy().tolist()  # Convert tensor to list
-        return embeddings
-    
-    def embed_query(self, query):
-        embedding = self.model.encode([query], convert_to_tensor=True)
-        if isinstance(embedding, torch.Tensor):
-            return embedding.cpu().detach().numpy().tolist()[0]  # Convert tensor to list
-        return embedding[0]
-
-# Initialize the embedding class
-embedding_model = SentenceTransformerEmbedding('all-MiniLM-L6-v2')
-
-# Load, chunk, and index the contents of the blog
-def load_data():
-    loader = WebBaseLoader(
-        web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
-        bs_kwargs=dict(
-            parse_only=bs4.SoupStrainer(
-                class_=("post-content", "post-title", "post-header")
-            )
-        ),
-    )
-    docs = loader.load()
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-    splits = text_splitter.split_documents(docs)
-    vectorstore = Chroma.from_documents(documents=splits, embedding=embedding_model)
-    return vectorstore
-
-vectorstore = load_data()
-
-# Streamlit UI
-st.title("Blog Retrieval and Question Answering")
-
-question = st.text_input("Enter your question:")
-
-if question:
-    retriever = vectorstore.as_retriever()
-    prompt = hub.pull("rlm/rag-prompt", api_key=api_key_langchain)
-
-    def format_docs(docs):
-        return "\n\n".join(doc.page_content for doc in docs)
-
-    rag_chain = (
-        {"context": retriever | format_docs, "question": RunnablePassthrough()}
-        | prompt
-        | llm  # Replace with your LLM or appropriate function if needed
-        | StrOutputParser()
-    )
-
-    # Example invocation
-    try:
-        result = rag_chain.invoke(question)
-        st.write("Answer:", result)
-    except Exception as e:
-        st.error(f"An error occurred: {e}")
+    st.write("Both API keys are set.")
+
+    # Initialize the LLM with the provided Groq API key
+    from langchain_groq import ChatGroq
+    llm = ChatGroq(model="llama3-8b-8192", groq_api_key=api_key_Groq)
+
+    # Define the embedding class
+    class SentenceTransformerEmbedding:
+        def __init__(self, model_name):
+            self.model = SentenceTransformer(model_name)
+        
+        def embed_documents(self, texts):
+            embeddings = self.model.encode(texts, convert_to_tensor=True)
+            if isinstance(embeddings, torch.Tensor):
+                return embeddings.cpu().detach().numpy().tolist()  # Convert tensor to list
+            return embeddings
+        
+        def embed_query(self, query):
+            embedding = self.model.encode([query], convert_to_tensor=True)
+            if isinstance(embedding, torch.Tensor):
+                return embedding.cpu().detach().numpy().tolist()[0]  # Convert tensor to list
+            return embedding[0]
+
+    # Initialize the embedding class
+    embedding_model = SentenceTransformerEmbedding('all-MiniLM-L6-v2')
+
+    # Load, chunk, and index the contents of the blog
+    def load_data():
+        loader = WebBaseLoader(
+            web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
+            bs_kwargs=dict(
+                parse_only=bs4.SoupStrainer(
+                    class_=("post-content", "post-title", "post-header")
+                )
+            ),
+        )
+        docs = loader.load()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+        splits = text_splitter.split_documents(docs)
+        vectorstore = Chroma.from_documents(documents=splits, embedding=embedding_model)
+        return vectorstore
+
+    vectorstore = load_data()
+
+    # Streamlit UI
+    st.title("Blog Retrieval and Question Answering")
+
+    question = st.text_input("Enter your question:")
+
+    if question:
+        retriever = vectorstore.as_retriever()
+        prompt = hub.pull("rlm/rag-prompt", api_key=api_key_langchain)
+
+        def format_docs(docs):
+            return "\n\n".join(doc.page_content for doc in docs)
+
+        rag_chain = (
+            {"context": retriever | format_docs, "question": RunnablePassthrough()}
+            | prompt
+            | llm  # Replace with your LLM or appropriate function if needed
+            | StrOutputParser()
+        )
+
+        # Example invocation
+        try:
+            result = rag_chain.invoke(question)
+            st.write("Answer:", result)
+        except Exception as e:
+            st.error(f"An error occurred: {e}")