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| import streamlit as st | |
| from langchain_community.document_loaders import PyPDFLoader, PyPDFDirectoryLoader | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain_groq import ChatGroq | |
| loader = PyPDFDirectoryLoader("Dataset") | |
| docx = loader.load() | |
| #st.write("Number of documents loaded:", len(docx)) | |
| text_sp = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) | |
| chunks = text_sp.split_documents(docx) | |
| #st.write("Number of chunks created:", len(chunks)) | |
| if not chunks: | |
| st.error("No chunks were created. Please check the documents or text splitter settings.") | |
| else: | |
| # Step 3: Create metadata | |
| metadatas = [{"source": f"{i}-pl"} for i in range(len(chunks))] | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", | |
| model_kwargs={'device': "cpu"}) | |
| try: | |
| sample_embedding = embeddings.embed_documents([chunks[0].page_content]) | |
| #st.write("Sample embedding length:", len(sample_embedding[0])) | |
| except Exception as e: | |
| st.error(f"Error in generating embeddings: {str(e)}") | |
| try: | |
| vector_store = FAISS.from_documents(chunks, embeddings) | |
| #st.write("Vector store created successfully.") | |
| except IndexError as e: | |
| st.error("IndexError in creating vector store: " + str(e)) | |
| st.write("Check if chunks and embeddings are non-empty and correctly formatted.") | |
| st.write("Chunks:", chunks) | |
| except Exception as e: | |
| st.error(f"An error occurred: {str(e)}") | |
| llm = ChatGroq(model_name="mixtral-8x7b-32768", groq_api_key="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") | |
| def retrieve(query, vector_store, embeddings): | |
| query_embedding = embeddings.embed_query(query) | |
| results = vector_store.similarity_search_by_vector(query_embedding, k=5) | |
| return results | |
| from langchain.schema import HumanMessage, SystemMessage | |
| def generate_response(query, retrieved_docs, llm): | |
| context = " ".join([doc.page_content for doc in retrieved_docs]) | |
| messages = [ | |
| SystemMessage(content="You are an expert in prompt engineering."), | |
| HumanMessage(content=f"Context: {context}\n\nQuestion: {query}\n\nAnswer:") | |
| ] | |
| response = llm(messages) | |
| return response.content.strip() | |
| st.title("PromptGuru 🚀📖") | |
| st.sidebar.markdown("PromptGuru By OpenRAG 🗣️") | |
| st.sidebar.markdown( | |
| """ | |
| PromptGuru is a tool you can use for asking any queries related Prompt Engineering and Get it solved within a couple of minutes. | |
| """ | |
| ) | |
| st.sidebar.markdown( | |
| """ | |
| Note -- This tool is in a beta stage. Kindly have some patience while generating the response and give the model time to think. | |
| """ | |
| ) | |
| st.sidebar.markdown( | |
| """ | |
| 📧 **Get in Touch** | |
| For inquiries or collaboration proposals, please don't hesitate to reach out to us: | |
| 📩 Email: [email protected] | |
| 🔗 LinkedIn: [OpenRAG](https://www.linkedin.com/company/102036854/admin/dashboard/) | |
| 📸 Instagram: [OpenRAG](https://www.instagram.com/open.rag?igsh=MnFwMHd5cjU1OGFj) | |
| Experience the future of Human-Chatbot Interaction with OpenRAG. | |
| """ | |
| ) | |
| query = st.text_input("Ask your question about prompt engineering:") | |
| if query: | |
| with st.spinner("Retrieving documents..."): | |
| retrieved_docs = retrieve(query, vector_store, embeddings) | |
| # st.write("Retrieved Documents:", retrieved_docs) | |
| with st.spinner("Generating response..."): | |
| response = generate_response(query, retrieved_docs, llm) | |
| st.write("Response:", response) | |