explain me this code please

@manager.route('/completion_lease', methods=['POST'])  #### paresh
@login_required
def completion_lease():
    print('LOLLLLLLLLL')
    """
    API route to handle the completion of a conversation in LISA - Lease context.
    """
    req = request.json
    # Debug: Log incoming payload for reference
    # print('Lease payload:', req)
    
    # Filter and prepare messages for processing
    msg = []
    for m in req["messages"]:
        if m["role"] == "system":
            continue
        if m["role"] == "assistant" and not msg:
            continue
        msg.append({"role": m["role"], "content": m["content"]})
    
    # Extract the last user message for processing
    test_que = msg[-1]['content']
    
    print("Here is the test question ------",test_que)
    doc_name = extract_from_quotes(test_que)
    print('-----------------------------------',doc_name)

    # Call the separate function to classify the message
    matched_prompt = classify_message(test_que)
    print("##################################################",matched_prompt)
    
    if matched_prompt == 'Quantitative Chat':
        # Debug: Log matched prompt for debugging
        print('Matched prompt, processing document retrieval')

        # Retrieve conversation and dialog details
        e, conv = ConversationService.get_by_id(req["conversation_id"])
        e, dia = DialogService.get_by_id(conv.dialog_id)
        print('Conversation details:', e)
        print('Dialog KB IDs:', dia.kb_ids)

        # Fetch knowledgebase items based on dialog's KB IDs
        kbs = KnowledgebaseService.get_by_ids(dia.kb_ids)
        # print('Fetched knowledgebase:', kbs)

        # Retrieve documents by knowledgebase ID
        docs, tol = DocumentService.get_by_kb_id(dia.kb_ids, 1, 20, "create_time", True, "")
        docs_combined = [{"count": index + 1, "doc_id": doc["id"], "doc_name": doc["name"]} for index, doc in enumerate(docs)]
        print('Documents retrieved:', docs_combined)

        # Append the latest message and assistant response
        conv.message.append(deepcopy(msg[-1]))
        resp_msg = 'Here are the documents requested'
        conv.message.append({"role": "assistant", "content": resp_msg})

        # Add document references to the conversation
        conv.reference.append({"chunks": [], "doc_aggs": docs_combined})
        ConversationService.update_by_id(conv.id, conv.to_dict())

        # Final output to return
        final_output = {
            'answer': resp_msg,
            'reference': {
                'total': tol,
                'chunks': [],
                'doc_aggs': docs_combined
            }
        }

        # Streaming response if requested
        def generate_response():
            yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": final_output}, ensure_ascii=False) + "\n\n"
            yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"

        if req.get("stream", True):
            resp = Response(generate_response(), mimetype="text/event-stream")
            resp.headers.add("Cache-Control", "no-cache")
            resp.headers.add("Connection", "keep-alive")
            resp.headers.add("X-Accel-Buffering", "no")
            resp.headers.add("Content-Type", "text/event-stream; charset=utf-8")
            return resp
        else:
            return get_json_result(data=final_output)

    elif matched_prompt == 'General Chat':
        try:
            # Debug: No prompt match, proceed with alternate logic
            print('No prompt match, proceeding with non-matched flow.')
            # Search for the extracted document name
            def get_doc_id_by_name(doc_name, docs_combined):
                for doc in docs_combined:
                    if doc['doc_name'] == doc_name:
                        return doc['doc_id']
                return None
            # Retrieve the conversation and dialog details
            e, conv = ConversationService.get_by_id(req["conversation_id"])
            if not e:
                return get_data_error_result(retmsg="Conversation not found!")
            
            conv.message.append(deepcopy(msg[-1]))
            e, dia = DialogService.get_by_id(conv.dialog_id)
            print('Dialog KB IDs:', dia.kb_ids)

            # Fetch documents by knowledgebase ID
            docs, tol = DocumentService.get_by_kb_id(dia.kb_ids, 1, 20, "create_time", True, "")
            docs_combined = [{"count": index + 1, "doc_id": doc["id"], "doc_name": doc["name"]} for index, doc in enumerate(docs)]
            print('Documents combined:', docs_combined)

            
            
            doc_name_to_search = doc_name[0]
            print("****************************** doc name to search",doc_name_to_search)
            getteddoc_id = get_doc_id_by_name(doc_name_to_search, docs_combined)
            print('Document ID retrieved:', getteddoc_id)
            print('checkpoint 1\n')
            if not e:
                return get_data_error_result(retmsg="Dialog not found!")
            # Remove unnecessary keys from the request
            del req["conversation_id"]
            del req["messages"]
            print('checkpoint 2\n')
            # Initialize conversation references if not present
            if not conv.reference:
                conv.reference = []
            conv.message.append({"role": "assistant", "content": ""})
            conv.reference.append({"chunks": [], "doc_aggs": []})
            print('checkpoint 3\n')
            def fillin_conv(ans):
                """ Helper function to fill the assistant's response """
                nonlocal conv
                if not conv.reference:
                    conv.reference.append(ans["reference"])
                else:
                    conv.reference[-1] = ans["reference"]
                conv.message[-1] = {"role": "assistant", "content": ans["answer"]}
            print('checkpoint 4\n')
            print("Diaaaaaaaaaaaaaaaaaa=",dia)
            print("Msgggggggggggggggggg=",msg)
            # Streaming responses handling
            def stream():
                try:
                    print('checkpoint 5\n')
                    for ans in chat(dia, msg, stream=True, doc_ids=getteddoc_id, **req):
                        fillin_conv(ans)
                        yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": ans}, ensure_ascii=False) + "\n\n"
                    ConversationService.update_by_id(conv.id, conv.to_dict())
                except Exception as e:
                    print('checkpoint 7\n')
                    yield "data:" + json.dumps({"retcode": 500, "retmsg": str(e),
                                                "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
                                                ensure_ascii=False) + "\n\n"
                yield "data:" + json.dumps({"retcode": 0, "retmsg": "", "data": True}, ensure_ascii=False) + "\n\n"

            if req.get("stream", True):
                print('checkpoint 0111\n')
                resp = Response(stream(), mimetype="text/event-stream")
                resp.headers.add("Cache-Control", "no-cache")
                resp.headers.add("Connection", "keep-alive")
                resp.headers.add("X-Accel-Buffering", "no")
                resp.headers.add("Content-Type", "text/event-stream; charset=utf-8")
                return resp
            else:
                print('checkpoint 0222\n')
                answer = None
                print('checkpoint 8\n')
                for ans in chat(dia, msg, **req):
                    answer = ans
                    fillin_conv(ans)
                    ConversationService.update_by_id(conv.id, conv.to_dict())
                    break
                return get_json_result(data=answer)

        except Exception as e:
            return server_error_response(e)