code: # Import Langchain modules
  from langchain.document_loaders import PyPDFLoader
  from langchain.text_splitter import RecursiveCharacterTextSplitter
  from langchain.embeddings import OpenAIEmbeddings
  from langchain.vectorstores import FAISS 
  from langchain.chains import RetrievalQA
  from langchain.llms import OpenAI
  
  
  # Import Environment Modules
  import os
  from dotenv import load_dotenv
  
  # Import API Modules
  from fastapi import FastAPI
  from fastapi.responses import HTMLResponse, JSONResponse
  import uvicorn
  
  # Import Other Modules
  import json
  import logging
  import warnings
  warnings.filterwarnings("ignore")
  
  # Load configuration
  with open('config.json', 'r') as f:
      config = json.load(f)
  
  # Configure logging
  logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
  
  def environment_setup() -> None:
      """
      Load environment variables and set OpenAI API key.
      """
      load_dotenv()
      os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
  
  def load_documents(document_path: str) -> list:
      """
      Load the pdf file and split it into pages.
      """
      try:
          loader = PyPDFLoader(document_path)
          pages = loader.load_and_split()
          return pages
      except Exception as e:
          logging.error(f"Error loading documents from {document_path}: {e}")
          return []
  
  def split_documents(pages: list) -> list:
      """
      Split the pages into chunks.
      """
      try:
          text_splitter = RecursiveCharacterTextSplitter(
              chunk_size=200,
              chunk_overlap=0,
              length_function=len,
              is_separator_regex=True,
          )
          docs = text_splitter.split_documents(pages)
          return docs
      except Exception as e:
          logging.error(f"Error splitting documents: {e}")
          return []
  
  def process_documents() -> list:
      """
      Process all documents in the specified path.
      """
      document_paths = [os.path.join(config['DOCUMENTS_PATH'], f) for f in os.listdir(config['DOCUMENTS_PATH']) if f.endswith(".pdf")]
  
      all_docs = []
      for document_path in document_paths:
          pages = load_documents(document_path)
          docs = split_documents(pages)
          all_docs.extend(docs)
  
      return all_docs
  
  def embeddings(docs: list) -> FAISS:
      """
      Load the embeddings and store them in a vector store.
      """
      try:
          embeddings = OpenAIEmbeddings()
          db = FAISS.from_documents(docs, embeddings)
          return db
      except Exception as e:
          logging.error(f"Error creating embeddings: {e}")
          return None
  
  def initialize_model() -> OpenAI:
      """
      Initialize the model.
      """
      llm = OpenAI()
      return llm
  
  def LLM_chain(llm: OpenAI, db: FAISS) -> RetrievalQA:
      """
      Create a retrieval chain with the LLM and vector store.
      """
      if db is None:
          logging.error("Error: db is None")
          return None
  
      chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=db.as_retriever(search_kwargs={"k": 5}))
      return chain
  
  def initialize_all() -> tuple:
      """
      Initialize all components.
      """
      environment_setup()
      docs = process_documents()
      db = embeddings(docs)
      llm = initialize_model()
      llm_chain = LLM_chain(llm, db)
      return llm_chain, db
  
  def process_message(chain: RetrievalQA, user_message: str, db: FAISS) -> str:
      """
      Process the user's message and return the bot's response.
      """
      try:
          query = user_message
          docs = db.similarity_search(query)
          result = chain.run(input_documents=docs, query=query)
          return result
      except Exception as e:
          logging.error(f"Error generating response: {e}", exc_info=True)
          return "Sorry, I couldn't understand your message."
  
  
  def setup_fastapi(llm_chain: RetrievalQA, db: FAISS) -> FastAPI:
      """
      Setup FastAPI with routes.
      """
      app = FastAPI()
  
      @app.get("/", response_class=HTMLResponse)
      def read_root() -> HTMLResponse:
          """
          Serve the chatbot HTML page.
          """
          try:
              with open('templates/chatbot.html', 'r') as f:
                  html_content = f.read()
              return HTMLResponse(content=html_content, status_code=200)
          except Exception as e:
              logging.error(f"Error reading HTML file: {e}", exc_info=True)
              return HTMLResponse(content="Sorry, something went wrong.", status_code=500)
  
      @app.get("/chatbot/{user_message}")
      def get_bot_response(user_message: str) -> JSONResponse:
          """
          Process the user's message and return the bot's response.
          """
          try:
              bot_response = process_message(llm_chain, user_message, db)
              return JSONResponse(content={"answer": bot_response})
          except Exception as e:
              logging.error(f"Error processing message: {e}", exc_info=True)
              return JSONResponse(content={"answer": "Sorry, something went wrong."})
  
      return app
  
  if __name__ == "__main__":
      try:
          llm_chain, db = initialize_all()
          fastapi_app = setup_fastapi(llm_chain, db)
          uvicorn.run(fastapi_app, host="0.0.0.0", port=8000)
      except Exception as e:
          logging.error(f"Error during initialization: {e}", exc_info=True)
apis: langchain.text_splitter.RecursiveCharacterTextSplitter langchain.llms.OpenAI langchain.vectorstores.FAISS.from_documents langchain.embeddings.OpenAIEmbeddings langchain.document_loaders.PyPDFLoader
extract_api: [((573, 606), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (596, 606), False, 'import warnings\n'), ((712, 808), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO', 'format': '"""%(asctime)s - %(levelname)s - %(message)s"""'}), "(level=logging.INFO, format=\n    '%(asctime)s - %(levelname)s - %(message)s')\n", (731, 808), False, 'import logging\n'), ((678, 690), 'json.load', 'json.load', (['f'], {}), '(f)\n', (687, 690), False, 'import json\n'), ((913, 926), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (924, 926), False, 'from dotenv import load_dotenv\n'), ((962, 989), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (971, 989), False, 'import os\n'), ((2654, 2662), 'langchain.llms.OpenAI', 'OpenAI', ([], {}), '()\n', (2660, 2662), False, 'from langchain.llms import OpenAI\n'), ((3883, 3892), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (3890, 3892), False, 'from fastapi import FastAPI\n'), ((1128, 1154), 'langchain.document_loaders.PyPDFLoader', 'PyPDFLoader', (['document_path'], {}), '(document_path)\n', (1139, 1154), False, 'from langchain.document_loaders import PyPDFLoader\n'), ((1462, 1575), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(200)', 'chunk_overlap': '(0)', 'length_function': 'len', 'is_separator_regex': '(True)'}), '(chunk_size=200, chunk_overlap=0,\n    length_function=len, is_separator_regex=True)\n', (1492, 1575), False, 'from langchain.text_splitter import RecursiveCharacterTextSplitter\n'), ((1926, 1967), 'os.path.join', 'os.path.join', (["config['DOCUMENTS_PATH']", 'f'], {}), "(config['DOCUMENTS_PATH'], f)\n", (1938, 1967), False, 'import os\n'), ((2374, 2392), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (2390, 2392), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((2406, 2444), 'langchain.vectorstores.FAISS.from_documents', 'FAISS.from_documents', (['docs', 'embeddings'], {}), '(docs, embeddings)\n', (2426, 2444), False, 'from langchain.vectorstores import FAISS\n'), ((2836, 2870), 'logging.error', 'logging.error', (['"""Error: db is None"""'], {}), "('Error: db is None')\n", (2849, 2870), False, 'import logging\n'), ((5102, 5153), 'uvicorn.run', 'uvicorn.run', (['fastapi_app'], {'host': '"""0.0.0.0"""', 'port': '(8000)'}), "(fastapi_app, host='0.0.0.0', port=8000)\n", (5113, 5153), False, 'import uvicorn\n'), ((1251, 1318), 'logging.error', 'logging.error', (['f"""Error loading documents from {document_path}: {e}"""'], {}), "(f'Error loading documents from {document_path}: {e}')\n", (1264, 1318), False, 'import logging\n'), ((1738, 1786), 'logging.error', 'logging.error', (['f"""Error splitting documents: {e}"""'], {}), "(f'Error splitting documents: {e}')\n", (1751, 1786), False, 'import logging\n'), ((1977, 2013), 'os.listdir', 'os.listdir', (["config['DOCUMENTS_PATH']"], {}), "(config['DOCUMENTS_PATH'])\n", (1987, 2013), False, 'import os\n'), ((2498, 2546), 'logging.error', 'logging.error', (['f"""Error creating embeddings: {e}"""'], {}), "(f'Error creating embeddings: {e}')\n", (2511, 2546), False, 'import logging\n'), ((3635, 3698), 'logging.error', 'logging.error', (['f"""Error generating response: {e}"""'], {'exc_info': '(True)'}), "(f'Error generating response: {e}', exc_info=True)\n", (3648, 3698), False, 'import logging\n'), ((4170, 4221), 'fastapi.responses.HTMLResponse', 'HTMLResponse', ([], {'content': 'html_content', 'status_code': '(200)'}), '(content=html_content, status_code=200)\n', (4182, 4221), False, 'from fastapi.responses import HTMLResponse, JSONResponse\n'), ((4712, 4758), 'fastapi.responses.JSONResponse', 'JSONResponse', ([], {'content': "{'answer': bot_response}"}), "(content={'answer': bot_response})\n", (4724, 4758), False, 'from fastapi.responses import HTMLResponse, JSONResponse\n'), ((5189, 5254), 'logging.error', 'logging.error', (['f"""Error during initialization: {e}"""'], {'exc_info': '(True)'}), "(f'Error during initialization: {e}', exc_info=True)\n", (5202, 5254), False, 'import logging\n'), ((4265, 4326), 'logging.error', 'logging.error', (['f"""Error reading HTML file: {e}"""'], {'exc_info': '(True)'}), "(f'Error reading HTML file: {e}', exc_info=True)\n", (4278, 4326), False, 'import logging\n'), ((4346, 4415), 'fastapi.responses.HTMLResponse', 'HTMLResponse', ([], {'content': '"""Sorry, something went wrong."""', 'status_code': '(500)'}), "(content='Sorry, something went wrong.', status_code=500)\n", (4358, 4415), False, 'from fastapi.responses import HTMLResponse, JSONResponse\n'), ((4802, 4864), 'logging.error', 'logging.error', (['f"""Error processing message: {e}"""'], {'exc_info': '(True)'}), "(f'Error processing message: {e}', exc_info=True)\n", (4815, 4864), False, 'import logging\n'), ((4884, 4948), 'fastapi.responses.JSONResponse', 'JSONResponse', ([], {'content': "{'answer': 'Sorry, something went wrong.'}"}), "(content={'answer': 'Sorry, something went wrong.'})\n", (4896, 4948), False, 'from fastapi.responses import HTMLResponse, JSONResponse\n')]