Upload folder using huggingface_hub

.env

@@ -0,0 +1,3 @@
+GROQ_API_KEY="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"
+HUGGINGFACE_API_KEY=""
+PINECONE_API_KEY="pcsk_4x6rrL_JWddywVmVcd16ijWofHRBRkV3epTLGyVcqQHZBzo5263AxXP7d46ruR1TYPwc5x"

README.md

@@ -1,12 +1,6 @@
 ---
-title: PDF Insights QA
-emoji: 👁
-colorFrom: blue
-colorTo: gray
+title: PDF_Insights_QA
+app_file: app.py
 sdk: gradio
 sdk_version: 5.10.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,165 @@
+import os
+import asyncio
+import nest_asyncio
+import pinecone
+import time
+from dotenv import find_dotenv, load_dotenv
+from langchain_groq import ChatGroq
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain_core.runnables import RunnablePassthrough, RunnableParallel
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from langchain_pinecone import PineconeVectorStore
+from pinecone import Pinecone, ServerlessSpec
+import gradio as gr
+from langchain import hub
+
+# Allow nested async calls
+nest_asyncio.apply()
+
+# Load environment variables
+_ = load_dotenv(find_dotenv())
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
+os.environ["HUGGINGFACE_API_KEY"] = os.getenv("HUGGINGFACE_API_KEY")
+os.environ["PINECONE_API_KEY"] = os.getenv("PINECONE_API_KEY")
+
+# Initialize Pinecone
+pc = Pinecone()
+index_name = "intern"
+
+existing_indexes = [index_info["name"] for index_info in pc.list_indexes()]
+
+if index_name not in existing_indexes:
+    print(f"Creating new index: {index_name}")
+    pc.create_index(
+        name=index_name,
+        dimension=384,
+        metric="cosine",
+        spec=ServerlessSpec(cloud="aws", region="us-east-1"),
+    )
+    while not pc.describe_index(index_name).status["ready"]:
+        time.sleep(1)
+
+index = pc.Index(index_name)
+
+#  embeddings model
+print("Initializing embedding model...")
+embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# Load and split documents
+print("Loading documents...")
+loader = PyPDFDirectoryLoader("Data")
+documents = loader.load()
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+docs = text_splitter.split_documents(documents)
+
+def are_documents_indexed(index):
+    try:
+        # Create a simple test embedding
+        test_embedding = embedding_model.embed_query("test")
+        # Query the index
+        results = index.query(vector=test_embedding, top_k=1)
+        return len(results.matches) > 0
+    except Exception as e:
+        print(f"Error checking indexed documents: {e}")
+        return False
+
+# Initialize vector store
+print("Initializing vector store...")
+vector_store = PineconeVectorStore(index=index, embedding=embedding_model)
+
+# Add documents only if not already indexed
+print("Checking if documents are already indexed...")
+if not are_documents_indexed(index):
+    print("Adding documents to index...")
+    vector_store.add_documents(docs)
+    print("Documents added successfully!")
+else:
+    print("Documents are already indexed.")
+
+print("Setting up retriever and LLM...")
+retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
+llm = ChatGroq(model="llama3-8b-8192", temperature=0.7, max_retries=4)
+str_output_parser = StrOutputParser()
+
+prompt = hub.pull("jclemens24/rag-prompt")
+
+relevance_prompt_template = PromptTemplate.from_template(
+    """
+    Given the following question and retrieved context, determine if the context is relevant to the question.
+    Provide a score from 1 to 5, where 1 is not at all relevant and 5 is highly relevant.
+    Return ONLY the numeric score, without any additional text or explanation.
+
+    Question: {question}
+    Retrieved Context: {retrieved_context}
+
+    Relevance Score:"""
+)
+
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+def extract_score(llm_output):
+    try:
+        return float(llm_output.strip())
+    except ValueError:
+        return 0
+
+def conditional_answer(x):
+    relevance_score = extract_score(x["relevance_score"])
+    return "I don't know." if relevance_score < 4 else x["answer"]
+
+# RAG pipeline
+rag_chain_from_docs = (
+    RunnablePassthrough.assign(context=lambda x: format_docs(x["context"]))
+    | RunnableParallel(
+        {
+            "relevance_score": (
+                RunnablePassthrough()
+                | (lambda x: relevance_prompt_template.format(question=x["question"], retrieved_context=x["context"]))
+                | llm
+                | str_output_parser
+            ),
+            "answer": (
+                RunnablePassthrough()
+                | prompt
+                | llm
+                | str_output_parser
+            ),
+        }
+    )
+    | RunnablePassthrough().assign(final_answer=conditional_answer)
+)
+
+rag_chain_with_source = RunnableParallel(
+    {"context": retriever, "question": RunnablePassthrough()}
+).assign(answer=rag_chain_from_docs)
+
+async def process_question(question):
+    try:
+        result = await rag_chain_with_source.ainvoke(question)
+        final_answer = result["answer"]["final_answer"]
+        sources = [doc.metadata.get("source") for doc in result["context"]]
+        source_list = ", ".join(sources)
+        return final_answer, source_list
+    except Exception as e:
+        return f"Error: {str(e)}", "Error retrieving sources"
+
+# Gradio 
+print("Gradio interface...")
+demo = gr.Interface(
+    fn=process_question,
+    inputs=gr.Textbox(label="Enter your question", value=""),
+    outputs=[
+        gr.Textbox(label="Answer"),
+        gr.Textbox(label="Sources"),
+    ],
+    title="RAG Question Answering",
+    description="Enter a question and get an answer from the PDFs.",
+)
+
+if __name__ == "__main__":
+    print("Launching the application...")
+    demo.queue().launch(share=True,debug=True)

requirements.txt

@@ -0,0 +1,10 @@
+
+langchain 
+langchain-text-splitters 
+langchain-huggingface 
+langchain-groq 
+python-dotenv
+langchain_community
+pypdf
+gradio
+langchain-pinecone