Create app.py

app.py

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+import streamlit as st
+from sentence_transformers import SentenceTransformer
+from langchain import hub
+from langchain_chroma import Chroma
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+import bs4
+import torch
+
+# 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")
+
+    def format_docs(docs):
+        return "\n\n".join(doc.page_content for doc in docs)
+
+    rag_chain = (
+        {"context": retriever | format_docs, "question": RunnablePassthrough()}
+        | prompt
+        | lambda x: x  # 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}")