kaggle notebook.py

# Import libraries.
# Gradio.
import gradio as gr

# File loading and environment variables.
import os
import sys

# Gradio.
from gradio.themes.base import Base

# HuggingFace LLM.
from huggingface_hub import InferenceClient

# Langchain.
from langchain.document_loaders import TextLoader
from langchain.prompts import PromptTemplate
from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings

# MongoDB.
from pymongo import MongoClient

# Function type hints.
from typing import Dict, Any

#Secrets
from kaggle_secrets import UserSecretsClient

directory_path = "/kaggle/input/rag-dataset/RAG"
sys.path.append(directory_path)
print("sys.path =", sys.path)

my_txts = os.listdir(directory_path)
my_txts

loaders = []
for my_txt in my_txts:
    my_txt_path = os.path.join(directory_path, my_txt)
    text_loader = TextLoader(my_txt_path)
    loaders.append(text_loader)

print("len(loaders) =", len(loaders))

loaders

# Load the TXT.

data = []
for loader in loaders:
    loaded_text = loader.load()
    data.append(loaded_text)

print("len(data) =", len(data), "\n")

# First TXT file.
data[0]

text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)

docs = []
for doc in data:
    chunk = text_splitter.split_documents(doc)
    docs.append(chunk)

merged_documents = []

for doc in docs:
    merged_documents.extend(doc)

# Print the merged list of all the documents.
print("len(merged_documents) =", len(merged_documents))
print(merged_documents)

# Connect to MongoDB Atlas cluster using the connection string.
from kaggle_secrets import UserSecretsClient
user_secrets = UserSecretsClient()
# secret_value_0= user_secrets.get_secret("MONGO_URI")
MONGO_URI = user_secrets.get_secret("MONGO_URI")
cluster = MongoClient(MONGO_URI)

# Define the MongoDB database and collection name.
DB_NAME = "files"
COLLECTION_NAME = "files_collection"

# Connect to the specific collection in the database.
MONGODB_COLLECTION = cluster[DB_NAME][COLLECTION_NAME] 
vector_search_index = "vector_index"

from kaggle_secrets import UserSecretsClient
user_secrets = UserSecretsClient()
HF_TOKEN = user_secrets.get_secret("hugging_face")
embedding_model = HuggingFaceInferenceAPIEmbeddings(
    api_key=HF_TOKEN, model_name="sentence-transformers/all-mpnet-base-v2"
)

# #populated mongo_db
# vector_search = MongoDBAtlasVectorSearch.from_documents(
#     documents=merged_documents, 
#     embedding=embedding_model, 
#     collection=MONGODB_COLLECTION, 
#     index_name=vector_search_index 
# )

vector_search = MongoDBAtlasVectorSearch.from_connection_string(
    connection_string=MONGO_URI,
    namespace=f"{DB_NAME}.{COLLECTION_NAME}",
    embedding=embedding_model,
    index_name=vector_search_index,
)

query = "why EfficientNetB0?"
results = vector_search.similarity_search(query=query, k=25) # 25 most similar documents.

print("\n")
print(results)

# k to search for only the X most relevant documents.
k = 10

# score_threshold to use only documents with a relevance score above 0.80.
score_threshold = 0.80

# Build your retriever
retriever_1 = vector_search.as_retriever(
   search_type = "similarity", # similarity, mmr, similarity_score_threshold. https://api.python.langchain.com/en/latest/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever
   search_kwargs = {"k": k, "score_threshold": score_threshold}
)



# Initialize Hugging Face client
hf_client = InferenceClient(api_key=HF_TOKEN)

# Define the prompt template
prompt = PromptTemplate.from_template(
    """Use the following pieces of context to answer the question at the end.

    START OF CONTEXT:
    {context}
    END OF CONTEXT:

    START OF QUESTION:
    {question}
    END OF QUESTION:

    If you do not know the answer, just say that you do not know.
    NEVER assume things.
    """
)

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


def generate_response(input_dict: Dict[str, Any]) -> str:
    formatted_prompt = prompt.format(**input_dict)
    # print(formatted_prompt)

    ## THIS IS YOUR LLM
    response = hf_client.chat.completions.create(
        model="Qwen/Qwen2.5-1.5B-Instruct",
        messages=[{
            "role": "system",
            "content": formatted_prompt
        },{
            "role": "user",
            "content": input_dict["question"]
        }],
        max_tokens=1000,
        temperature=0.2,
    )

    return response.choices[0].message.content

rag_chain = (
    {
        "context": retriever_1 | RunnableLambda(format_docs),
        "question": RunnablePassthrough()
    }
    | RunnableLambda(generate_response)
)


query = "what is scaling?"
answer = rag_chain.invoke(query)

print("\nQuestion:", query)
print("Answer:", answer)

# Get source documents related to the query.
documents = retriever_1.invoke(query)

# print("\nSource documents:")
# from pprint import pprint
# pprint(results)



query = "How the GUI was implemented?"
answer = rag_chain.invoke(query)

print("\nQuestion:", query)
print("Answer:", answer)

# Get source documents related to the query.
documents = retriever_1.invoke(query)

print("\nSource documents:")
from pprint import pprint
pprint(results)

query = "How the GUI was implemented?"
answer = rag_chain.invoke(query)

print("\nQuestion:", query)
print("Answer:", answer)

# Get source documents related to the query.
documents = retriever_1.invoke(query)
formatted_docs = format_docs(documents)
print("\nSource Documents:\n", formatted_docs)