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+# Use the official Python 3.12 image
+FROM python:3.12
+
+# Set the working directory to /app
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Make port 7860 available to the world outside this container
+EXPOSE 7860
+
+# Run main.py when the container launches
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI, File, UploadFile, Depends
+from fastapi.middleware.cors import CORSMiddleware
+from typing import List
+import os
+import aiofiles
+import uuid
+import shutil
+
+# from dotenv import load_dotenv
+
+from langchain_community.document_loaders import TextLoader, Docx2txtLoader, PyPDFLoader
+from langchain.prompts import ChatPromptTemplate, PromptTemplate
+from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate
+from langchain_community.document_loaders.csv_loader import CSVLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.memory import ConversationBufferMemory
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+from langchain_community.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+
+# load_dotenv()
+
+app = FastAPI()
+
+origins = ["https://viboognesh-react-chat.static.hf.space"]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["GET", "POST"],
+    allow_headers=["*"],
+)
+
+
+class ConversationChainManager:
+    def __init__(self):
+        self.conversation_chain = None
+        self.llm_model = ChatOpenAI()
+        self.embeddings = OpenAIEmbeddings()
+
+    def create_conversational_chain(self, file_paths: List[str], session_id: str):
+        docs = self.get_docs(file_paths)
+        memory = ConversationBufferMemory(
+            memory_key="chat_history", return_messages=True
+        )
+        vectordb = Chroma.from_documents(
+            docs,
+            self.embeddings,
+            collection_name=session_id,
+            persist_directory="./chroma_db",
+        )
+        retriever = vectordb.as_retriever()
+        self.conversation_chain = ConversationalRetrievalChain.from_llm(
+            llm=self.llm_model,
+            retriever=retriever,
+            condense_question_prompt=self.get_question_generator_prompt(),
+            combine_docs_chain_kwargs={
+                "document_prompt": self.get_document_prompt(),
+                "prompt": self.get_final_prompt(),
+            },
+            memory=memory,
+        )
+
+    @staticmethod
+    def get_docs(file_paths: List[str]) -> List:
+        docs = []
+        for file_path in file_paths:
+            if file_path.endswith(".txt"):
+                loader = TextLoader(file_path)
+                document = loader.load()
+                splitter = RecursiveCharacterTextSplitter(
+                    chunk_size=1000, chunk_overlap=100
+                )
+                txt_documents = splitter.split_documents(document)
+                docs.extend(txt_documents)
+            elif file_path.endswith(".csv"):
+                loader = CSVLoader(file_path)
+                csv_documents = loader.load()
+                docs.extend(csv_documents)
+            elif file_path.endswith(".docx"):
+                loader = Docx2txtLoader(file_path)
+                document = loader.load()
+                splitter = RecursiveCharacterTextSplitter(
+                    chunk_size=1000, chunk_overlap=100
+                )
+                docx_documents = splitter.split_documents(document)
+                docs.extend(docx_documents)
+            elif file_path.endswith(".pdf"):
+                loader = PyPDFLoader(file_path)
+                pdf_documents = loader.load_and_split()
+                docs.extend(pdf_documents)
+        return docs
+
+    @staticmethod
+    def get_document_prompt() -> PromptTemplate:
+        document_template = """Document Content:{page_content}
+    Document Path: {source}"""
+        return PromptTemplate(
+            input_variables=["page_content", "source"],
+            template=document_template,
+        )
+
+    @staticmethod
+    def get_question_generator_prompt() -> PromptTemplate:
+        question_generator_template = """Combine the chat history and follow up question into
+    a standalone question.\n Chat History: {chat_history}\n
+    Follow up question: {question}
+    """
+        return PromptTemplate.from_template(question_generator_template)
+
+    @staticmethod
+    def get_final_prompt() -> ChatPromptTemplate:
+        final_prompt_template = """Answer question based on the context and chat_history.
+    If you cannot find answers, ask more related questions from the user.
+    Use only the basename of the file path as name of the documents.
+    Mention document name of the documents you used in your answer.
+
+    context:
+    {context}
+
+    chat_history:
+    {chat_history}
+
+    question:
+    {question}
+
+    Answer:
+    """
+
+        messages = [
+            SystemMessagePromptTemplate.from_template(final_prompt_template),
+            HumanMessagePromptTemplate.from_template("{question}"),
+        ]
+
+        return ChatPromptTemplate.from_messages(messages)
+
+
+@app.post("/upload_files/")
+async def upload_files(
+    files: List[UploadFile] = File(...),
+    conversation_chain_manager: ConversationChainManager = Depends(),
+):
+    session_id = str(uuid.uuid4())
+    session_folder = f"uploads/{session_id}"
+    os.makedirs(session_folder, exist_ok=True)
+    file_paths = []
+    for file in files:
+        file_path = f"{session_folder}/{file.filename}"
+        async with aiofiles.open(file_path, "wb") as out_file:
+            content = await file.read()
+            await out_file.write(content)
+        file_paths.append(file_path)
+
+    conversation_chain_manager.create_conversational_chain(file_paths, session_id)
+    shutil.rmtree(session_folder)
+    print("conversational_chain_manager created")
+    return {"message": "ConversationalRetrievalChain is created. Please ask questions."}
+
+
+@app.get("/predict/")
+async def predict(
+    query: str, conversation_chain_manager: ConversationChainManager = Depends()
+):
+    if conversation_chain_manager.conversation_chain is None:
+        system_prompt = "Answer the question and also ask the user to upload files to ask questions from the files.\n"
+        response = conversation_chain_manager.llm_model.invoke(system_prompt + query)
+        answer = response.content
+    else:
+        response = conversation_chain_manager.conversation_chain.invoke(query)
+        answer = response["answer"]
+
+    print("predict called")
+    return {"answer": answer}

requirements.txt

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+fastapi
+uvicorn
+sqlalchemy
+langchain_community
+langchain
+pypdf
+langchain_openai
+python-dotenv
+python-multipart
+chromadb
+aiofiles