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| from langchain_huggingface import HuggingFaceEmbeddings | |
| from langchain_chroma import Chroma | |
| # Importing RunnableLambda for wrapping functions as runnable components. | |
| from langchain_core.runnables import RunnableLambda | |
| from langchain_core.prompts import PromptTemplate # To format prompts | |
| from langchain_core.output_parsers import StrOutputParser # to transform the output of an LLM into a more usable format | |
| from langchain.schema.runnable import RunnableParallel, RunnablePassthrough | |
| # importing HuggingFace model abstraction class from langchain | |
| from langchain_huggingface import HuggingFaceEndpoint | |
| import torch | |
| import gradio | |
| import re | |
| # Initializing a Hugging Face endpoint for text generation using a specific model. | |
| llm = HuggingFaceEndpoint( | |
| repo_id="HuggingFaceH4/zephyr-7b-beta", # Specifies the model to be used (Zephyr-7B Beta), available on Hugging Face. | |
| task="text-generation", # Defines the task type as text generation (commonly used for chatbots, summarization, etc.). | |
| max_new_tokens=512, # Limits the maximum number of new tokens the model can generate in a response. | |
| top_k=30, # Controls the diversity of the generated text by restricting the number of top probable tokens considered at each step. | |
| temperature=0.1, # A low temperature value makes the model’s output more deterministic and focused. | |
| repetition_penalty=1.03, # Slightly penalizes repeated tokens to reduce redundancy in responses. | |
| ) | |
| # Initializing the Hugging Face embedding model. | |
| embedding = HuggingFaceEmbeddings( | |
| model_name="mixedbread-ai/mxbai-embed-large-v1", # Using a pre-trained embedding model for text vectorization. | |
| model_kwargs={'device': "cuda" if torch.cuda.is_available() else "cpu"}, # Configuring the model to use GPU if available. | |
| encode_kwargs={'normalize_embeddings': False} # Disabling normalization to preserve the original embedding scale. | |
| ) | |
| # Creating a Chroma vector database instance. | |
| vectordb = Chroma( | |
| persist_directory= './chroma/', #'RAG_Implementation/chroma/', # Setting the directory where embeddings will be stored. | |
| embedding_function=embedding # Specifying the embedding function to convert text into vector representations. | |
| ) | |
| # Function to retrieve relevant document chunks based on a given question. | |
| def get_context_info(question): | |
| # Creating a retriever using the Chroma vector database with Maximum Marginal Relevance (MMR). | |
| # `search_type="mmr"` ensures diversity in retrieved results. | |
| # `fetch_k=5` first fetches the top 5 most relevant chunks. | |
| # `k=3` selects the 3 most diverse and relevant chunks from the 5 fetched. | |
| retriever = vectordb.as_retriever(search_type="mmr", search_kwargs={"k": 3, "fetch_k": 5}) | |
| # Retrieving the document chunks using the retriever. | |
| docs = retriever.invoke(question) | |
| # Returning the retrieved document chunks. | |
| return docs | |
| # Creating a retrieval pipeline using RunnableParallel. | |
| retrieval = RunnableParallel( | |
| { | |
| # Retrieving relevant document chunks based on the input question. | |
| # RunnableLambda ensures that `get_context_info` runs dynamically when called. | |
| "context": RunnableLambda(lambda x: get_context_info(x["question"])), | |
| # Simply passing the question as is. | |
| "question": RunnableLambda(lambda x: x["question"]) | |
| } | |
| ) | |
| # Defining a prompt template for answering questions based on retrieved context. | |
| template = """Use the following pieces of context to answer the question at the end. | |
| If you don't know the answer, that is if the answer is not in the context, then just say that you don't know, don't try to make up an answer. | |
| Always say "thanks for asking!" at the end of the answer. | |
| {context} | |
| Question: {question} | |
| Helpful Answer:""" | |
| # Creating a PromptTemplate object using the defined template. | |
| # The template takes two input variables: 'context' (retrieved documents) and 'question' (user query). | |
| QA_PROMPT = PromptTemplate(input_variables=["context", "question"], template=template) | |
| # Constructing a Retrieval-Augmented Generation (RAG) chain. | |
| rag_chain = (retrieval # Step 1: Retrieve relevant document chunks based on the user's question. | |
| | QA_PROMPT # Step 2: Format the retrieved context and question into a structured prompt. | |
| | llm # Step 3: Pass the prompt to the language model (LLM) for answer generation. | |
| | StrOutputParser() # Step 4: Convert the LLM's output into a plain string format. | |
| ) | |
| def clean_text(question): | |
| question=re.sub(r'\s+', ' ', question).strip() | |
| question=re.sub(r'\A\d+', '', question).strip() | |
| question = re.sub(r'#\S+', '', question) | |
| question = re.sub(r'@\S+', '', question) | |
| question.replace('\n', ' ') | |
| re.sub(r'\s+(?=(\n+$))', '', question) | |
| pattern = r'[^a-zA-Z0-9\s]' | |
| question=re.sub(pattern, '', question) | |
| return question | |
| def get_rag_chain_response(user_question): | |
| user_question = clean_text(user_question) | |
| return rag_chain.invoke({"question": user_question}) | |
| #Below function is used only for testing. Otherwise it is not actually used | |
| def get_context_string_testing(user_question): | |
| ret_arr = get_context_info(user_question) | |
| context_str = '' | |
| for i in range(len(ret_arr)): | |
| # Printing the content of each document chunk (page content). | |
| context_str += ret_arr[i].page_content + '\n ' | |
| return context_str | |
| # Gradio elements | |
| # Input from user | |
| in_max_length = 200 # YOUR CODE HERE | |
| in_question = gradio.Textbox(type='text', label='Question', max_length=in_max_length) | |
| # Output response | |
| out_response = gradio.Textbox(type='text', label='Response to question') | |
| # Gradio interface to generate UI link | |
| iface = gradio.Interface( # YOUR CODE HERE | |
| fn=get_rag_chain_response, #get_context_string_testing, | |
| inputs=in_question, | |
| outputs=out_response, | |
| title="RAG implementation", | |
| description="via gradio",) | |
| # YOUR CODE HERE to launch the interface | |
| iface.launch() |