import os import json import gradio as gr import zipfile import tempfile import requests import urllib.parse import io from huggingface_hub import HfApi, login from PyPDF2 import PdfReader from langchain_huggingface import HuggingFaceEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_groq import ChatGroq from dotenv import load_dotenv from langchain.docstore.document import Document from langchain.schema import Document from chunk_python_code import chunk_python_code_with_metadata from vectorstore import get_chroma_vectorstore # Load environment variables from .env file load_dotenv() # Load configuration from JSON file with open('config.json') as config_file: config = json.load(config_file) with open("config2.json", "r") as file: config2 = json.load(file) PERSIST_DOC_DIRECTORY = config["persist_doc_directory"] PERSIST_CODE_DIRECTORY =config["persist_code_directory"] CHUNK_SIZE = config["chunk_size"] CHUNK_OVERLAP = config["chunk_overlap"] EMBEDDING_MODEL_NAME = config["embedding_model"] LLM_MODEL_NAME = config["llm_model"] LLM_TEMPERATURE = config["llm_temperature"] GITLAB_API_URL = config["gitlab_api_url"] HF_SPACE_NAME = config["hf_space_name"] DATA_DIR = config["data_dir"] GROQ_API_KEY = os.environ["GROQ_API_KEY"] HF_TOKEN = os.environ["HF_Token"] login(HF_TOKEN) api = HfApi() def load_project_id(json_file): with open(json_file, 'r') as f: data = json.load(f) return data['project_id'] def download_gitlab_project_by_version(): try: # Load the configuration from config.json # Extract GitLab project information from the config api_url = config2['gitlab']['api_url'] project_id = urllib.parse.quote(config2['gitlab']['project']['id'], safe="") version = config2['gitlab']['project']['version'] # Construct the URL for the release's zip file url = f"{api_url}/projects/{project_id}/repository/archive.zip?sha={version}" # Send GET request to download the zip file response = requests.get(url, stream=True) archive_bytes = io.BytesIO(response.content) if response.status_code == 200: # Extract filename from content-disposition header content_disposition = response.headers.get("content-disposition") if content_disposition and "filename=" in content_disposition: filename = content_disposition.split("filename=")[-1].strip('"') # test # target_path = f"{DATA_DIR}/{filename}" # Check if the request was successful if response.status_code == 200: api.upload_file( path_or_fileobj= archive_bytes, path_in_repo= f"{DATA_DIR}/{filename}", repo_id=HF_SPACE_NAME, repo_type='space' ) print(f"Release {version} downloaded successfully as {file_path}.") else: print(f"Failed to download the release: {response.status_code} - {response.reason}") print(response.text) except FileNotFoundError: print("The config.json file was not found. Please ensure it exists in the project directory.") except json.JSONDecodeError: print("Failed to parse the config.json file. Please ensure it contains valid JSON.") except Exception as e: print(f"An error occurred: {e}") def download_gitlab_repo(): print("Start the upload_gitRepository function") project_id = load_project_id('repository_ids.json') encoded_project_id = urllib.parse.quote_plus(project_id) # Define the URL to download the repository archive archive_url = f"{GITLAB_API_URL}/projects/{encoded_project_id}/repository/archive.zip" # Download the repository archive response = requests.get(archive_url) archive_bytes = io.BytesIO(response.content) # Retrieve the original file name from the response headers content_disposition = response.headers.get('content-disposition') if content_disposition: filename = content_disposition.split('filename=')[-1].strip('\"') else: filename = 'archive.zip' # Fallback to a default name if not found # Check if the file already exists in the repository existing_files = api.list_repo_files(repo_id=HF_SPACE_NAME, repo_type='space') target_path = f"{DATA_DIR}/{filename}" print(f"Target Path: '{target_path}'") print(f"Existing Files: {[repr(file) for file in existing_files]}") if target_path in existing_files: print(f"File '{target_path}' already exists in the repository. Skipping upload...") else: # Upload the ZIP file to the new folder in the Hugging Face space repository print("Uploading File to directory:") print(f"Archive Bytes: {repr(archive_bytes.getvalue())[:100]}") # Show a preview of bytes print(f"Target Path in Repo: '{target_path}'") api.upload_file( path_or_fileobj=archive_bytes, path_in_repo=target_path, repo_id=HF_SPACE_NAME, repo_type='space' ) print("Upload complete") def get_all_files_in_folder(temp_dir, folder_path): all_files = [] target_dir = os.path.join(temp_dir, folder_path) for root, dirs, files in os.walk(target_dir): print(f"Files in current directory ({root}): {files}") for file in files: print(f"Processing file: {file}") all_files.append(os.path.join(root, file)) return all_files def get_file(temp_dir, file_path): full_path = os.path.join(temp_dir, file_path) return full_path def process_directory(directory, folder_paths, file_paths): all_texts = [] file_references = [] zip_filename = next((file for file in os.listdir(directory) if file.endswith('.zip')), None) zip_file_path = os.path.join(directory, zip_filename) with tempfile.TemporaryDirectory() as tmpdirname: # Unzip the file into the temporary directory with zipfile.ZipFile(zip_file_path, 'r') as zip_ref: zip_ref.extractall(tmpdirname) files = [] print("tmpdirname: " , tmpdirname) unzipped_root = os.listdir(tmpdirname) print("unzipped_root ", unzipped_root) tmpsubdirpath= os.path.join(tmpdirname, unzipped_root[0]) print("tempsubdirpath: ", tmpsubdirpath) if folder_paths: for folder_path in folder_paths: files += get_all_files_in_folder(tmpsubdirpath, folder_path) if file_paths: files += [get_file(tmpsubdirpath, file_path) for file_path in file_paths] print(f"Total number of files: {len(files)}") for file_path in files: # print("111111111:", file_path) file_ext = os.path.splitext(file_path)[1] # print("222222222:", file_ext) if os.path.getsize(file_path) == 0: print(f"Skipping an empty file: {file_path}") continue with open(file_path, 'rb') as f: if file_ext in ['.rst', '.py']: text = f.read().decode('utf-8') all_texts.append(text) print("Filepaths brother:", file_path) relative_path = os.path.relpath(file_path, tmpsubdirpath) print("Relative Filepaths brother:", relative_path) file_references.append(relative_path) return all_texts, file_references def split_python_code_into_chunks(texts, file_paths): chunks = [] for text, file_path in zip(texts, file_paths): document_chunks = chunk_python_code_with_metadata(text, file_path) chunks.extend(document_chunks) return chunks # Split text into chunks def split_into_chunks(texts, references, chunk_size, chunk_overlap): text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) chunks = [] for text, reference in zip(texts, references): chunks.extend([ Document( page_content=chunk, metadata={ "source": reference, "usage": "doc" } ) for chunk in text_splitter.split_text(text) ]) return chunks # Setup Vectorstore def embed_documents_into_vectorstore(chunks, model_name, persist_directory): print("Start setup_vectorstore_function") embedding_model = HuggingFaceEmbeddings(model_name=model_name) vectorstore = get_chroma_vectorstore(embedding_model, persist_directory) vectorstore.add_documents(chunks) return vectorstore # Setup LLM def setup_llm(model_name, temperature, api_key): llm = ChatGroq(model=model_name, temperature=temperature, api_key=api_key) return llm def retrieve_from_vectorstore(vectorstore, query, k): retrieved_docs = vectorstore.similarity_search(query, k=k) return retrieved_docs def retrieve_within_kadiApy_docs(vectorstore, query, k): filter_criteria = {"usage": "doc"} retrieved_docs = vectorstore.similarity_search(query=query, k=k, filter=filter_criteria) return retrieved_docs def retrieve_within_kadiApy_library(vectorstore, query, k): filter_criteria = {"usage": "library"} retrieved_docs = vectorstore.similarity_search(query=query, k=k, filter=filter_criteria) return retrieved_docs def retrieve_within_kadiApy_cli_library(vectorstore, query, k): filter_criteria = {"usage": "cli_library"} retrieved_docs = vectorstore.similarity_search(query=query, k=k, filter=filter_criteria) return retrieved_docs def retrieve_within_kadiApy_cli_library_excluding_cli_commands(vectorstore, query, k): filter_criteria = {"usage": "cli_library"} filtered_docs = [doc for doc in documents if "command" not in doc.metadata] return filtered_docs def retrieve_kadiApy_cli_commands(vectorstore, query, k): filter_criteria = {"type": "command"} results = vectorstore.similarity_search(query=query, k=k, filter=filter_criteria) return results def retrieve_docs_from_vectorstore(vectorstore, query, k): return vectorstore.similarity_search(query, k=k) def format_kadi_apy_library_context(docs): doc_context = [] for doc in docs: # Extract metadata information class_info = doc.metadata.get("class", "Unknown Class") type_info = doc.metadata.get("type", "Unknown Type") source_info = doc.metadata.get("source", "Unknown Type") # Format metadata and document content #print("YYYYYYYEEEEEEEEEEEEEEE222222222222222222222222222222:}\n\n", doc.page_content) formatted_doc = f"# source: {source_info}\n# class: {class_info}\n# type: {type_info}\n{doc.page_content}\n\n\n" doc_context.append(formatted_doc) return doc_context def format_kadi_api_doc_context(docs): doc_context = [] for doc in docs: source_info = doc.metadata.get("source", "Unknown Type") #print("YYYYYYYEEEEEEEEEEEEEEE:}\n\n", doc.page_content) formatted_doc = f"# source: {source_info}\n{doc.page_content}\n\n\n" doc_context.append(formatted_doc) return doc_context def rag_workflow(query): rewritten_query_response = llm.invoke("Hello") rewritten_query = rewritten_query_response.content.strip() #kadi_apy_docs = retrieve_within_kadiApy_docs (vector_store, query, k = 5) #kadi_apy_library_docs = retrieve_within_kadiApy_library (vector_store, query, k = 10) #print(doc_context) #print("AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA:",code_context) prompt = f"The query is: '{query}'. What type of metadata applies to this query? Options: python library, python cli library, command line interface." metadata_prediction = llm.predict(prompt) print("METADATA PREDICTION -------------------------:", metadata_prediction) print(metadata_prediction) kadi_apy_docs = retrieve_within_kadiApy_docs (vector_store, query, k = 5) kadi_apy_library_docs = vector_store.similarity_search(query, filter={"usage": metadata_prediction}) doc_context = format_kadi_api_doc_context(kadi_apy_docs) code_context = format_kadi_apy_library_context(kadi_apy_library_docs) # Assuming kadi_apy_library_docs is a list of Document objects print("HERE WE GHOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO") print(-----------------------------------------------------------------------------) for doc in kadi_apy_library_docs: print(doc.metadata.get("source", "Unknown Type") print(____________________________________________________________________________________) prompt = f"""You are an expert python developer. You are assisting in generating code for users who wants to make use of "kadi-apy", an API library. "Doc-context:" provides you with information how to use this API library by givnig code examples and code documentation. "Code-context:" provides you information of API methods and classes from the "kadi-apy" library. Based on the retrieved contexts and the guidelines answer the query. General Guidelines: - If no related information is found from the contexts to answer the query, reply that you do not know. Guidelines when generating code: - First display the full code and then follow with a well structured explanation of the generated code. Doc-context: {doc_context} Code-context: {code_context} Query: {query} """ response = llm.invoke(prompt) return response.content def initialize(): global vector_store, chunks, llm download_gitlab_project_by_version() #download_gitlab_repo() code_partial_paths = ['kadi_apy/lib/'] code_file_paths = [] doc_partial_paths = [] doc_partial_paths = ['docs/source/setup/'] doc_file_paths = ['docs/source/usage/lib.rst'] kadiAPY_code_texts, kadiAPY_code_references = process_directory(DATA_DIR, code_partial_paths, code_file_paths) print("LEEEEEEEEEEEENGTH of code_texts: ", len(kadiAPY_code_texts)) kadiAPY_doc_texts, kadiAPY_doc_references = process_directory(DATA_DIR, doc_partial_paths, doc_file_paths) print("LEEEEEEEEEEEENGTH of doc_files: ", len(kadiAPY_doc_texts)) kadiAPY_code_chunks = split_python_code_into_chunks(kadiAPY_code_texts, kadiAPY_code_references) kadiAPY_doc_chunks = split_into_chunks(kadiAPY_doc_texts, kadiAPY_doc_references, CHUNK_SIZE, CHUNK_OVERLAP) print(f"Total number of code_chunks: {len(kadiAPY_code_chunks)}") print(f"Total number of doc_chunks: {len(kadiAPY_doc_chunks)}") #docstore = embed_documents_into_vectorstore(kadiAPY_code_chunks, EMBEDDING_MODEL_NAME, PERSIST_DOC_DIRECTORY) #codestore = embed_documents_into_vectorstore(kadiAPY_doc_chunks, EMBEDDING_MODEL_NAME, PERSIST_CODE_DIRECTORY) vector_store = embed_documents_into_vectorstore( chunks= kadiAPY_doc_chunks + kadiAPY_code_chunks, model_name= EMBEDDING_MODEL_NAME, persist_directory= PERSIST_DOC_DIRECTORY ) llm = setup_llm(LLM_MODEL_NAME, LLM_TEMPERATURE, GROQ_API_KEY) initialize() # Gradio utils def check_input_text(text): if not text: gr.Warning("Please input a question.") raise TypeError return True def add_text(history, text): history = history + [(text, None)] yield history, "" import gradio as gr def bot_kadi(history): user_query = history[-1][0] response = rag_workflow(user_query) history[-1] = (user_query, response) yield history def main(): with gr.Blocks() as demo: gr.Markdown("## Kadi4Mat - AI Chat-Bot") gr.Markdown("AI assistant for Kadi4Mat based on RAG architecture powered by LLM") with gr.Tab("Kadi4Mat - AI Assistant"): with gr.Row(): with gr.Column(scale=10): chatbot = gr.Chatbot([], elem_id="chatbot", label="Kadi Bot", bubble_full_width=False, show_copy_button=True, height=600) user_txt = gr.Textbox(label="Question", placeholder="Type in your question and press Enter or click Submit") with gr.Row(): with gr.Column(scale=1): submit_btn = gr.Button("Submit", variant="primary") with gr.Column(scale=1): clear_btn = gr.Button("Clear", variant="stop") gr.Examples( examples=[ "Who is working on Kadi4Mat?", "How do i install the Kadi-Apy library?", "How do i install the Kadi-Apy library for development?", "I need a method to upload a file to a record", ], inputs=user_txt, outputs=chatbot, fn=add_text, label="Try asking...", cache_examples=False, examples_per_page=3, ) user_txt.submit(check_input_text, user_txt, None).success(add_text, [chatbot, user_txt], [chatbot, user_txt]).then(bot_kadi, [chatbot], [chatbot]) submit_btn.click(check_input_text, user_txt, None).success(add_text, [chatbot, user_txt], [chatbot, user_txt]).then(bot_kadi, [chatbot], [chatbot]) #user_txt.submit(check_input_text, user_txt, None).success(add_text, [chatbot, user_txt], [chatbot, user_txt]).then(bot_kadi, [chatbot], [chatbot, doc_citation]) #submit_btn.click(check_input_text, user_txt, None).success(add_text, [chatbot, user_txt], [chatbot, user_txt]).then(bot_kadi, [chatbot], [chatbot, doc_citation]) clear_btn.click(lambda: None, None, chatbot, queue=False) demo.launch() if __name__ == "__main__": main()