app and requirements files added

app.py

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+import os
+from getpass import getpass
+import csv
+from langchain_core.documents import Document
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+#from langchain.schema import Document
+from langchain_huggingface import HuggingFaceEmbeddings
+import torch
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain_community.cache import InMemoryCache
+from langchain.globals import set_llm_cache
+from langchain_chroma import Chroma
+from langchain.chains import RetrievalQA
+import numpy as np
+import gradio
+import sqlite3
+
+hfapi_key = getpass("Enter you HuggingFace access token:")
+os.environ["HF_TOKEN"] = hfapi_key
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = hfapi_key
+
+set_llm_cache(InMemoryCache())
+
+persist_directory = 'docs/chroma/'
+
+####################################
+def load_file_as_JSON():
+    print("$$$$$ ENTER INTO load_file_as_JSON $$$$$")
+    rows = []
+    with open("mini-llama-articles.csv", mode="r", encoding="utf-8") as file:
+        csv_reader = csv.reader(file)
+        for idx, row in enumerate(csv_reader):
+            if idx == 0:
+                continue
+                # Skip header row
+            rows.append(row)
+
+    print("@@@@@@ EXIT FROM load_file_as_JSON @@@@@")
+    return rows
+####################################
+def get_documents():
+    print("$$$$$ ENTER INTO get_documents $$$$$")
+    documents = [
+        Document(
+            page_content=row[1], metadata={"title": row[0], "url": row[2], "source_name": row[3]}
+        )
+        for row in load_file_as_JSON()
+    ]
+    print("documents lenght is ", len(documents))
+    print("first entry from documents ", documents[0])
+    print("document metadata ", documents[0].metadata)
+    print("@@@@@@ EXIT FROM get_documents @@@@@")
+    return documents
+####################################
+def getDocSplitter():
+    print("$$$$$ ENTER INTO getDocSplitter $$$$$")
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size = 512,
+        chunk_overlap = 128
+    )
+    splits = text_splitter.split_documents(get_documents())
+    print("Split length ", len(splits))
+    print("Page content ", splits[0].page_content)
+    print("@@@@@@ EXIT FROM getDocSplitter @@@@@")
+    return splits
+####################################
+def getEmbeddings():
+    print("$$$$$ ENTER INTO getEmbeddings $$$$$")
+    modelPath="mixedbread-ai/mxbai-embed-large-v1"
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    # Create a dictionary with model configuration options, specifying to use the CPU for computations
+    model_kwargs = {'device': device}      # cuda/cpu
+
+    # Create a dictionary with encoding options, specifically setting 'normalize_embeddings' to False
+    encode_kwargs = {'normalize_embeddings': False}
+
+    embedding =  HuggingFaceEmbeddings(
+        model_name=modelPath,     # Provide the pre-trained model's path
+        model_kwargs=model_kwargs, # Pass the model configuration options
+        encode_kwargs=encode_kwargs # Pass the encoding options
+    )
+    
+    print("Embedding ", embedding)
+    print("@@@@@@ EXIT FROM getEmbeddings @@@@@")
+    return embedding
+####################################
+def getLLM():
+    print("$$$$$ ENTER INTO getLLM $$$$$")
+    llm = HuggingFaceEndpoint(
+        repo_id="HuggingFaceH4/zephyr-7b-beta",
+        #repo_id="chsubhasis/ai-tutor-towardsai",
+        task="text-generation",
+        max_new_tokens = 512,   
+        top_k = 10,
+        temperature = 0.1,
+        repetition_penalty = 1.03,
+    )
+    print("llm ", llm)
+    print("Who is the CEO of Apple? ", llm.invoke("Who is the CEO of Apple?")) #test
+    print("@@@@@@ EXIT FROM getLLM @@@@@")
+    return llm
+####################################
+def is_chroma_db_present(directory: str):
+    """
+    Check if the directory exists and contains any files.
+    """
+    return os.path.exists(directory) and len(os.listdir(directory)) > 0
+####################################
+def getRetiriver():
+    print("$$$$$ ENTER INTO getRetiriver $$$$$")
+    if is_chroma_db_present(persist_directory):
+        print(f"Chroma vector DB found in '{persist_directory}' and will be loaded.")
+        # Load vector store from the local directory
+        #vectordb = Chroma(persist_directory=persist_directory)
+        vectordb = Chroma(
+            persist_directory=persist_directory, 
+            embedding_function=getEmbeddings(),
+            collection_name="ai_tutor")
+    else:
+        vectordb = Chroma.from_documents(
+            collection_name="ai_tutor",
+            documents=getDocSplitter(), # splits we created earlier
+            embedding=getEmbeddings(),
+            persist_directory=persist_directory, # save the directory
+        )
+    print("vectordb collection count ", vectordb._collection.count())
+    
+    docs = vectordb.search("What is Artificial Intelligence", search_type="mmr", k=5)
+    for i in range(len(docs)):
+     print(docs[i].page_content)
+    
+    metadata_filter = {
+        "result": "llama"  # ChromaDB will perform a substring search
+    }
+    
+    retriever = vectordb.as_retriever(search_type="mmr", search_kwargs={"k": 3, "fetch_k":5, "filter": metadata_filter})
+    print("retriever ", retriever)
+    print("@@@@@@ EXIT FROM getRetiriver @@@@@")
+    return retriever
+####################################
+def get_rag_response(query):
+  print("$$$$$ ENTER INTO get_rag_response $$$$$")  
+  qa_chain = RetrievalQA.from_chain_type(
+    llm=getLLM(),
+    chain_type="stuff",
+    retriever=getRetiriver(),
+    return_source_documents=True
+  )
+  
+  #RAG Evaluation
+  # Sample dataset of questions and expected answers
+  dataset = [
+    {"question": "Who is the CEO of Meta?", "expected_answer": "Mark Zuckerberg"},
+    {"question": "Who is the CEO of Apple?", "expected_answer": "Tiiiiiim Coooooook"},
+  ]
+  
+  hit_rate, mrr = evaluate_rag(qa_chain, dataset)
+  print(f"Hit Rate: {hit_rate:.2f}, Mean Reciprocal Rank (MRR): {mrr:.2f}")
+  
+  result = qa_chain({"query": query})
+  print("Result ",result)
+  print("@@@@@@ EXIT FROM get_rag_response @@@@@")
+  return result["result"]
+####################################
+def evaluate_rag(qa, dataset):
+    print("$$$$$ ENTER INTO evaluate_rag $$$$$")
+    hits = 0
+    reciprocal_ranks = []
+
+    for entry in dataset:
+        question = entry["question"]
+        expected_answer = entry["expected_answer"]
+        
+        # Get the answer from the RAG system
+        response = qa({"query": question}) 
+        answer = response["result"] 
+        
+        # Check if the answer matches the expected answer
+        if expected_answer.lower() in answer.lower():
+            hits += 1
+            reciprocal_ranks.append(1)  # Hit at rank 1
+        else:
+            reciprocal_ranks.append(0)
+
+    # Calculate Hit Rate and MRR
+    hit_rate = hits / len(dataset)
+    mrr = np.mean(reciprocal_ranks)
+
+    print("@@@@@@ EXIT FROM evaluate_rag @@@@@")
+    return hit_rate, mrr
+####################################
+def launch_ui():
+    print("$$$$$ ENTER INTO launch_ui $$$$$")
+    # Input from user
+    in_question = gradio.Textbox(lines=10, placeholder=None, value="query", label='Enter your query')
+
+    # Output prediction
+    out_response = gradio.Textbox(type="text", label='RAG Response')
+
+    # Gradio interface to generate UI
+    iface = gradio.Interface(fn = get_rag_response,
+                            inputs = [in_question],
+                            outputs = [out_response],
+                            title = "RAG Response",
+                            description = "Write the query and get the response from the RAG system",
+                            allow_flagging = 'never')
+
+    iface.launch(share = True)
+
+####################################
+if __name__ == "__main__":
+    launch_ui()