diff --git a/rag/chat.py b/rag/chat.py
new file mode 100644
index 0000000..ccca65e
--- /dev/null
+++ b/rag/chat.py
@@ -0,0 +1,505 @@
+import os
+import requests
+import json
+import time
+import sys
+from qdrant_client import QdrantClient
+from sentence_transformers import SentenceTransformer, CrossEncoder
+
+DEFAULT_CHAT_MODEL = "openchat:7b"
+DEFAULT_EMBED_MODEL = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
+# DEFAULT_RANK_MODEL = "cross-encoder/mmarco-mMiniLMv2-L12-H384-v1"
+DEFAULT_RANK_MODEL = "cross-encoder/ms-marco-MiniLM-L-6-v2"
+# DEFAULT_RANK_MODEL = "cross-encoder/ms-marco-TinyBERT-L-2-v2"
+DEFAULT_MD_FOLDER = "data"
+DEFAULT_OLLAMA_URL = "http://localhost:11434"
+DEFAULT_QDRANT_HOST = "localhost"
+DEFAULT_QDRANT_PORT = 6333
+DEFAULT_QDRANT_COLLECTION = "rag"
+DEFAULT_TOP_K = 30
+DEFAULT_USE_RANK = False
+DEFAULT_TOP_N = 8
+DEFAULT_VERBOSE = False
+DEFAULT_SHOW_STATS = False
+DEFAULT_STREAM = False
+DEFAULT_INTERACTIVE = False
+DEFAULT_SHOW_PROMPT = False
+DEFAULT_MIN_RANK_SCORE = 0
+
+class RagSystem:
+    def __init__(self,
+                 ollama_url: str = DEFAULT_OLLAMA_URL,
+                 qdrant_host: str = DEFAULT_QDRANT_HOST,
+                 qdrant_port: int = DEFAULT_QDRANT_PORT,
+                 embed_model: str = DEFAULT_EMBED_MODEL,
+                 rank_model: str = DEFAULT_RANK_MODEL,
+                 use_rank: bool = DEFAULT_USE_RANK,
+                 chat_model: str = DEFAULT_CHAT_MODEL):
+        self.ollama_url = ollama_url
+        self.qdrant_host = qdrant_host
+        self.qdrant_port = qdrant_port
+        self.chat_model = chat_model
+        self.emb_model = SentenceTransformer(embed_model)
+        self.qdrant = QdrantClient(host=args.qdrant_host, port=args.qdrant_port)
+        self.use_rank = use_rank
+        if self.use_rank:
+            self.rank_model = CrossEncoder(rank_model)
+        self.conversation_history = []
+
+        self.load_chat_model()
+
+    def load_chat_model(self):
+        url = f"{self.ollama_url}/api/generate"
+        body = {"model": self.chat_model}
+        requests.post(url, json=body, timeout=600)
+
+    def search_qdrant(self, query: str, doc_count: int = DEFAULT_TOP_K, collection_name = DEFAULT_QDRANT_COLLECTION):
+        query_vec = self.emb_model.encode(query, show_progress_bar=False).tolist()
+        results = self.qdrant.query_points(
+            collection_name=collection_name,
+            query=query_vec,
+            limit=doc_count,
+            # score_threshold=0.5,
+        )
+        docs = []
+        for point in results.points:
+            docs.append({
+                "payload": point.payload,
+                "score": point.score,
+            })
+        return docs
+
+    def rank_documents(self, query: str, documents: list, top_n: int = DEFAULT_TOP_N, min_score: int = DEFAULT_MIN_RANK_SCORE):
+        if not self.use_rank:
+            return documents
+
+        pairs = [[query, doc["payload"]["text"]] for doc in documents]
+        scores = self.rank_model.predict(pairs)
+
+        ranked_docs = []
+        for i, doc in enumerate(documents):
+            score = float(scores[i])
+            doc["rank_score"] = score
+            if score >= min_score:
+                ranked_docs.append(doc)
+
+        ranked_docs.sort(key=lambda x: x['rank_score'], reverse=True)
+        return ranked_docs[:top_n]
+
+    def generate_answer(self, sys_prompt: str, user_prompt: str):
+        url = f"{self.ollama_url}/api/chat"
+        body = {
+            "model": self.chat_model,
+            # "system": sys_prompt,
+            # "prompt": user_prompt,
+            "messages": self.conversation_history,
+            "stream": False,
+            "options": {
+                "temperature": 0.5,
+                # "top_p": 0.2,
+            },
+        }
+
+        response = requests.post(url, json=body, timeout=900)
+        if response.status_code != 200:
+            return f"Ошибка генерации ответа: {response.status_code} {response.text}"
+        self.response = response.json()
+        return self.response["message"]["content"]
+
+    def generate_answer_stream(self, sys_prompt: str, user_prompt: str):
+        url = f"{self.ollama_url}/api/chat"
+        body = {
+            "model": self.chat_model,
+            # "system": sys_prompt,
+            # "prompt": user_prompt,
+            "messages": self.conversation_history,
+            "stream": True,
+            "options": {
+                "temperature": 0.5,
+                # "top_p": 0.2,
+            },
+        }
+        resp = requests.post(url, json=body, stream=True, timeout=900)
+        if resp.status_code != 200:
+            raise RuntimeError(f"Ошибка генерации ответа: {resp.status_code} {resp.text}")
+
+        answer = ""
+        for chunk in resp.iter_lines():
+            if chunk:
+                try:
+                    decoded_chunk = chunk.decode('utf-8')
+                    data = json.loads(decoded_chunk)
+                    if "response" in data:
+                        yield data["response"]
+                        answer += data["response"]
+                    if "done" in data and data["done"] is True:
+                        self.response = data
+                        break
+                    elif "error" in data:
+                        answer += f" | Ошибка стриминга ответа: {data['error']}"
+                        break
+                except json.JSONDecodeError as e:
+                    answer += f" | Ошибка конвертации чанка: {chunk.decode('utf-8')} - {e}"
+                except Exception as e:
+                    answer += f" | Ошибка обработки чанка: {e}"
+
+    def get_prompt_eval_count(self):
+        if not self.response["prompt_eval_count"]:
+            return 0
+        return self.response["prompt_eval_count"]
+
+    def get_prompt_eval_duration(self):
+        if not self.response["prompt_eval_duration"]:
+            return 0
+        return self.response["prompt_eval_duration"] / (10 ** 9)
+
+    def get_eval_count(self):
+        if not self.response["eval_count"]:
+            return 0
+        return self.response["eval_count"]
+
+    def get_eval_duration(self):
+        if not self.response["eval_duration"]:
+            return 0
+        return self.response["eval_duration"] / (10 ** 9)
+
+    def get_total_duration(self):
+        if not self.response["total_duration"]:
+            return 0
+        return self.response["total_duration"] / (10 ** 9)
+
+    def get_tps(self):
+        eval_count = self.get_eval_count()
+        eval_duration = self.get_eval_duration()
+        if eval_count == 0 or eval_duration == 0:
+            return 0
+        return eval_count / eval_duration
+
+class App:
+    def __init__(
+        self,
+        args: list = []
+    ):
+        if not args.query and not args.interactive:
+            print("Ошибка: укажите запрос (--query) и/или используйте интерактивный режим (--interactive)")
+            sys.exit(1)
+
+        self.args = args
+        self.print_v(text=f"Включить интерактивный режим диалога: {args.interactive}")
+        self.print_v(text=f"Включить потоковый вывод: {args.stream}")
+        if self.is_custom_sys_prompt():
+            self.print_v(text=f"Системный промпт: {args.sys_prompt}")
+        else:
+            self.print_v(text=f"Системный промпт: по умолчанию")
+        self.print_v(text=f"Показать сист. промпт перед запросом: {args.show_prompt}")
+        self.print_v(text=f"Выводить служебные сообщения: {args.verbose}")
+        self.print_v(text=f"Выводить статистику об ответе: {args.show_stats}")
+        self.print_v(text=f"Адрес хоста Qdrant: {args.qdrant_host}")
+        self.print_v(text=f"Номер порта Qdrant: {args.qdrant_port}")
+        self.print_v(text=f"Название коллекции для поиска документов: {args.qdrant_collection}")
+        self.print_v(text=f"Ollama API URL: {args.ollama_url}")
+        self.print_v(text=f"Модель генерации Ollama: {args.chat_model}")
+        self.print_v(text=f"Модель эмбеддинга: {args.emb_model}")
+        self.print_v(text=f"Количество документов для поиска: {args.topk}")
+        self.print_v(text=f"Включить ранжирование: {args.use_rank}")
+        self.print_v(text=f"Модель ранжирования: {args.rank_model}")
+        self.print_v(text=f"Количество документов после ранжирования: {args.topn}")
+        self.init_rag()
+
+    def print_v(self, text: str = "\n"):
+        if self.args.verbose:
+            print(f"{text}")
+
+    def init_rag(self):
+        self.print_v(text="\nИнициализация моделей...")
+        self.rag = RagSystem(
+            ollama_url = self.args.ollama_url,
+            qdrant_host = self.args.qdrant_host,
+            qdrant_port = self.args.qdrant_port,
+            embed_model = self.args.emb_model,
+            rank_model = self.args.rank_model,
+            use_rank = self.args.use_rank,
+            chat_model = self.args.chat_model
+        )
+        self.print_v(text=f"Модели загружены. Если ответ плохой, переформулируйте запрос, укажите --chat-model или улучшите исходные данные RAG")
+
+    def init_query(self):
+        self.query = None
+        if args.interactive:
+            self.print_v(text="\nИНТЕРАКТИВНЫЙ РЕЖИМ")
+            self.print_v(text="Можете вводить запрос (или 'exit' для выхода)\n")
+
+        if self.args.query:
+            self.query = self.args.query.strip()
+            print(f">>> {self.query}")
+        elif args.interactive:
+            self.query = input(">>> ").strip()
+
+    def process_help(self):
+        print("<<< Команды итерактивного режима:")
+        print("save  -- сохранить диалог в файл")
+        print("exit  -- выход\n")
+        self.query = None
+        self.args.query = None
+
+    def process_save(self):
+        import datetime
+        timestamp = int(time.time())
+        dt = datetime.datetime.fromtimestamp(timestamp).strftime('%Y-%m-%dT%H:%M:%SZ')
+        filename = f"chats/chat-{timestamp}-{self.args.chat_model}.md"
+
+        markdown_content = f"# История диалога от {dt}\n\n"
+        markdown_content += f"## Параметры диалога\n"
+        markdown_content += f"```\nargs = {self.args}\n```\n"
+        markdown_content += f"```\nemb_model = {self.rag.emb_model}\n```\n"
+        markdown_content += f"```\nrank_model = {self.rag.rank_model}\n```\n"
+
+        for entry in self.rag.conversation_history:
+            if entry['role'] == 'user':
+                markdown_content += f"## Пользователь\n\n"
+            elif entry['role'] == 'assistant':
+                markdown_content += f"## Модель\n\n"
+                docs = self.rag.prepare_ctx_sources(entry['docs']).replace("```", "")
+                markdown_content += f"```\n{docs}\n```\n\n"
+            markdown_content += f"{entry['content']}\n\n"
+
+        os.makedirs('chats', exist_ok=True)
+        with open(filename, 'w') as fp:
+            fp.write(markdown_content)
+
+        print(f"<<< Диалог сохранён в файл: {filename}\n")
+        self.query = None
+
+    def find_docs(self, query: str, top_k: int, collection_name: str):
+        self.print_v(text="\nПоиск документов...")
+        context_docs = self.rag.search_qdrant(query, top_k, collection_name)
+        self.print_v(text=f"Найдено {len(context_docs)} документов")
+        return context_docs
+
+    def rank_docs(self, docs: list = [], top_n = DEFAULT_TOP_N, min_score: int = DEFAULT_MIN_RANK_SCORE):
+        self.print_v(text="\nРанжирование документов...")
+        ranked_docs = self.rag.rank_documents(self.query, docs, top_n, min_score)
+        self.print_v(text=f"После ранжирования осталось {len(ranked_docs)} документов")
+        return ranked_docs
+
+    def prepare_ctx_sources(self, docs: list):
+        sources = ""
+        for idx, doc in enumerate(docs, start=1):
+            text = doc['payload'].get("text", "").strip()
+            sources = f"{sources}\n<source id=\"{idx}\">\n{text}\n</source>\n"
+        return sources
+
+    def prepare_cli_sources(self, docs: list):
+        sources = "\nИсточники:\n"
+        for idx, doc in enumerate(docs, start=1):
+            title = doc['payload'].get("filename", None)
+            url = doc['payload'].get("url", None)
+            date = doc['payload'].get("date", None)
+            version = doc['payload'].get("version", None)
+            author = doc['payload'].get("author", None)
+
+            if url is None:
+                url = "(нет веб-ссылки)"
+            if date is None:
+                date = "(неизвестно)"
+            if version is None:
+                version = "0"
+            if author is None:
+                author = "(неизвестен)"
+
+            sources += f"{idx}. {title}\n"
+            sources += f"   {url}\n"
+            sources += f"   Версия {version} от {author}, актуальная на {date}\n"
+            if doc['rank_score']:
+                sources += f"   score = {doc['score']} | rank_score = {doc['rank_score']}\n"
+            else:
+                sources += f"   score = {doc['score']}\n"
+        return sources
+
+    def prepare_sys_prompt(self, query: str, docs: list):
+        if self.is_custom_sys_prompt():
+            with open(self.args.sys_prompt, 'r') as fp:
+                prompt = fp.read()
+        else:
+            prompt = """You are a helpful assistant that can answer questions based on the provided context.
+Your user is the person asking the source-related question.
+Your job is to answer the question based on the context alone.
+If the context doesn't provide much information, answer "I don't know."
+Adhere to this in all languages.
+
+Context:
+
+-----------------------------------------
+{{sources}}
+-----------------------------------------
+"""
+
+        sources = self.prepare_ctx_sources(docs)
+        return prompt.replace("{{sources}}", sources).replace("{{query}}", query)
+
+    def show_prompt(self, sys_prompt: str):
+        print("\n================ Системный промпт ==================")
+        print(f"{sys_prompt}\n============ Конец системного промпта ==============\n")
+
+    def process_query(self, sys_prompt: str, user_prompt: str, streaming: bool = DEFAULT_STREAM):
+        answer = ""
+        # try:
+        if streaming:
+            self.print_v(text="\nГенерация потокового ответа (^C для остановки)...\n")
+            print(f"<<< ", end='', flush=True)
+            for token in self.rag.generate_answer_stream(sys_prompt, user_prompt):
+                answer += token
+                print(token, end='', flush=True)
+        else:
+            self.print_v(text="\nГенерация ответа (^C для остановки)...\n")
+            answer = self.rag.generate_answer(sys_prompt, user_prompt)
+            print(f"<<< {answer}\n")
+        # except RuntimeError as e:
+            # answer = str(e)
+
+        print(f"\n===================================================")
+        return answer
+
+    def is_custom_sys_prompt(self):
+        return self.args.sys_prompt and os.path.exists(self.args.sys_prompt)
+
+    def print_stats(self):
+        print(f"* Time: {self.rag.get_total_duration()}s")
+        print(f"* TPS: {self.rag.get_tps()}")
+        print(f"* PEC: {self.rag.get_prompt_eval_count()}")
+        print(f"* PED: {self.rag.get_prompt_eval_duration()}s")
+        print(f"* EC: {self.rag.get_eval_count()}")
+        print(f"* ED: {self.rag.get_eval_duration()}s\n")
+        self.query = None
+        self.args.query = None
+
+    def process(self):
+        while True:
+            try:
+                self.init_query()
+
+                if not self.query or self.query == "":
+                    continue
+
+                if self.query.lower() == "help":
+                    self.process_help()
+                    continue
+
+                if self.query.strip().lower() == "save":
+                    self.process_save()
+                    continue
+
+                if self.query.strip().lower() == "stats":
+                    print("\n<<< Статистика:")
+                    self.print_stats()
+                    continue
+
+                if self.query.strip().lower() == "exit":
+                    self.print_v(text="\n*** Завершение работы")
+                    sys.exit(0)
+
+                context_docs = self.find_docs(self.query, self.args.topk, self.args.qdrant_collection)
+                if not context_docs:
+                    if args.interactive:
+                        print("<<< Релевантные документы не найдены")
+                        self.query = None
+                        self.args.query = None
+                        continue
+                    else:
+                        break
+
+                ranked_docs = self.rank_docs(context_docs, self.args.topn, self.args.min_rank_score)
+                if not ranked_docs:
+                    if args.interactive:
+                        print("<<< Документы были отсеяны полностью")
+                        #TODO сделать ещё 2 попытки перезапроса+реранка других документов без учёта нерелевантных context_docs
+                        self.query = None
+                        self.args.query = None
+                        continue
+                    else:
+                        break
+
+                # ctx = self.prepare_ctx_sources(ranked_docs)
+                sys_prompt = self.prepare_sys_prompt(self.query, ranked_docs)
+                if self.args.show_prompt:
+                    self.show_prompt(sys_prompt)
+
+                # self.rag.conversation_history.append({
+                #     "role": "system",
+                #     "content": sys_prompt,
+                # })
+
+                self.rag.conversation_history.append({
+                    "role": "system",
+                    "content": sys_prompt,
+                })
+
+                self.rag.conversation_history.append({
+                    "role": "user",
+                    "content": self.query,
+                })
+
+                try:
+                    answer = self.process_query(sys_prompt, self.query, self.args.stream)
+                except KeyboardInterrupt:
+                    print("\n*** Генерация ответа прервана")
+                    self.query = None
+                    self.args.query = None
+                    print(self.prepare_cli_sources(ranked_docs))
+                    if self.args.show_stats:
+                        print("\nСтатистика:")
+                        self.print_stats()
+                    continue
+
+                print(self.prepare_cli_sources(ranked_docs))
+
+                if self.args.show_stats:
+                    print("\nСтатистика:")
+                    self.print_stats()
+
+                self.rag.conversation_history.append({
+                    "role": "assistant",
+                    "docs": ranked_docs,
+                    "content": answer,
+                })
+
+                if args.interactive:
+                    self.query = None
+                    self.args.query = None
+                else:
+                    break
+
+            except KeyboardInterrupt:
+                print("\n*** Завершение работы")
+                break
+
+            except Exception as e:
+                print(f"Ошибка: {e}")
+                break
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="RAG-система с использованием Ollama и Qdrant")
+    parser.add_argument("--query", type=str, help="Запрос к RAG")
+    parser.add_argument("--interactive", default=DEFAULT_INTERACTIVE, action=argparse.BooleanOptionalAction, help="Включить интерактивный режим диалога")
+    parser.add_argument("--stream", default=DEFAULT_STREAM, action=argparse.BooleanOptionalAction, help="Включить потоковый вывод")
+    parser.add_argument("--sys-prompt", type=str, help="Путь к файлу шаблона системного промпта")
+    parser.add_argument("--show-prompt", default=DEFAULT_SHOW_PROMPT, action=argparse.BooleanOptionalAction, help="Показать сист. промпт перед запросом")
+    parser.add_argument("--verbose", default=DEFAULT_VERBOSE, action=argparse.BooleanOptionalAction, help="Выводить служебные сообщения")
+    parser.add_argument("--show-stats", default=DEFAULT_SHOW_STATS, action=argparse.BooleanOptionalAction, help="Выводить статистику об ответе (не работает с --stream)")
+    parser.add_argument("--qdrant-host", default=DEFAULT_QDRANT_HOST, help="Адрес хоста Qdrant")
+    parser.add_argument("--qdrant-port", type=int, default=DEFAULT_QDRANT_PORT, help="Номер порта Qdrant")
+    parser.add_argument("--qdrant-collection", type=str, default=DEFAULT_QDRANT_COLLECTION, help="Название коллекции для поиска документов")
+    parser.add_argument("--ollama-url", default=DEFAULT_OLLAMA_URL, help="Ollama API URL")
+    parser.add_argument("--chat-model", default=DEFAULT_CHAT_MODEL, help="Модель генерации Ollama")
+    parser.add_argument("--emb-model", default=DEFAULT_EMBED_MODEL, help="Модель эмбеддинга")
+    parser.add_argument("--topk", type=int, default=DEFAULT_TOP_K, help="Количество документов для поиска")
+    parser.add_argument("--use-rank", default=DEFAULT_USE_RANK, action=argparse.BooleanOptionalAction, help="Включить ранжирование")
+    parser.add_argument("--rank-model", type=str, default=DEFAULT_RANK_MODEL, help="Модель ранжирования")
+    parser.add_argument("--min-rank-score", type=int, default=DEFAULT_MIN_RANK_SCORE, help="Минимальный ранк документа")
+    parser.add_argument("--topn", type=int, default=DEFAULT_TOP_N, help="Количество документов после ранжирования")
+    args = parser.parse_args()
+
+    app = App(args)
+    app.process()