AnthonyAxenov
a9328b4681
- включение qdrant в контур - использование нормальной эмб-модели - векторизация текста - README и туча мелочей
191 lines
8.3 KiB
Python
191 lines
8.3 KiB
Python
import argparse
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import os
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import hashlib
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import requests
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from sentence_transformers import SentenceTransformer
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class LocalRAGSystem:
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def __init__(self,
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md_folder: str = "input_md",
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ollama_url: str = "http://localhost:11434",
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qdrant_host: str = "localhost",
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qdrant_port: int = 6333,
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embed_model: str = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
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chat_model: str = "qwen2.5:3b"):
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self.md_folder = md_folder
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self.ollama_url = ollama_url
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self.qdrant_host = qdrant_host
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self.qdrant_port = qdrant_port
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self.embed_model = embed_model
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self.chat_model = chat_model
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self.emb_model = SentenceTransformer(embed_model)
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def get_embedding(self, text: str):
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return self.emb_model.encode(text, show_progress_bar=False).tolist()
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def search_qdrant(self, query: str, top_k: int = 6):
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query_vec = self.get_embedding(query)
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url = f"http://{self.qdrant_host}:{self.qdrant_port}/collections/rag_collection/points/search"
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payload = {
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"vector": query_vec,
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"top": top_k,
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"with_payload": True
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}
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resp = requests.post(url, json=payload)
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if resp.status_code != 200:
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raise RuntimeError(f"> Ошибка qdrant: {resp.status_code} {resp.text}")
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results = resp.json().get("result", [])
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return results
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def generate_answer(self, query: str, context_docs: list):
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query = query.strip()
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context = f""
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sources = f"\nИсточники:\n"
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for idx, doc in enumerate(context_docs, start=1):
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text = doc['payload'].get("text", "").strip()
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filename = doc['payload'].get("filename", None)
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url = doc['payload'].get("url", None)
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if filename:
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title = filename
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else:
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snippet = text[:40].replace("\n", " ").strip()
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if len(text) > 40:
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snippet += "..."
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title = snippet if snippet else "Empty text"
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if url is None:
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url = ""
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context = f"{context}\n--- Source [{idx}] ---\n{text}\n"
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sources = f"{sources}\n{idx}. {title}\n {url}"
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if os.path.exists('sys_prompt.txt'):
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with open('sys_prompt.txt', 'r') as fp:
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prompt = fp.read().replace("{{context}}", context).replace("{{query}}", query)
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else:
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prompt = f"""
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Please provide an answer based solely on the provided sources.
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It is prohibited to generate an answer based on your pretrained data.
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If uncertain, ask the user for clarification.
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Respond in the same language as the user's query.
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If there are no sources in context, clearly state that.
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If the context is unreadable or of poor quality, inform the user and provide the best possible answer.
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When referencing information from a source, cite the appropriate source(s) using their corresponding numbers.
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Every answer should include at least one source citation.
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Only cite a source when you are explicitly referencing it.
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If none of the sources are helpful, you should indicate that.
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For example:
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--- Source 1 ---
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The sky is red in the evening and blue in the morning.
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--- Source 2 ---
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Water is wet when the sky is red.
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Query: When is water wet?
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Answer: Water will be wet when the sky is red [2], which occurs in the evening [1].
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Now it's your turn. Below are several numbered sources of information:
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{context}
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User query: {query}
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Your answer:
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"""
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url = f"{self.ollama_url}/api/generate"
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body = {
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"model": self.chat_model,
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"prompt": prompt,
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"stream": False
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}
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resp = requests.post(url, json=body, timeout=600)
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if resp.status_code != 200:
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return f"Ошибка генерации ответа: {resp.status_code} {resp.text}"
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return resp.json().get("response", "").strip() + f"\n{sources}"
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def main():
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import sys
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import argparse
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parser = argparse.ArgumentParser(description="RAG-система с использованием Ollama и Qdrant")
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parser.add_argument("--query", type=str, help="Запрос к RAG")
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parser.add_argument("--interactive", default=False, action=argparse.BooleanOptionalAction, help="Перейти в интерактивный режим диалога")
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parser.add_argument("--show-prompt", default=False, action=argparse.BooleanOptionalAction, help="Показать полный промпт перед обработкой запроса")
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parser.add_argument("--qdrant-host", default="localhost", help="Qdrant host")
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parser.add_argument("--qdrant-port", type=int, default=6333, help="Qdrant port")
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parser.add_argument("--ollama-url", default="http://localhost:11434", help="Ollama API URL")
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parser.add_argument("--emb-model", default="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", help="Модель эмбеддинга")
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parser.add_argument("--chat-model", default="qwen2.5:3b", help="Модель генерации Ollama")
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parser.add_argument("--topk", type=int, default=6, help="Количество документов для поиска")
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args = parser.parse_args()
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if not args.query and not args.interactive:
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print("Ошибка: укажите запрос (--query) и/или используйте интерактивный режим (--interactive)")
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sys.exit(1)
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print(f"Адрес ollama: {args.ollama_url}")
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print(f"Адрес qdrant: {args.qdrant_host}:{args.qdrant_port}")
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print(f"Модель эмбеддинга: {args.emb_model}")
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print(f"Модель чата: {args.chat_model}")
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print(f"Документов для поиска: {args.topk}")
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if os.path.exists('sys_prompt.txt'):
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print("Будет загружен системный промпт из sys_prompt.txt!")
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if args.interactive:
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print("\nИНТЕРАКТИВНЫЙ РЕЖИМ")
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print("Можете вводить запрос (или 'exit' для выхода)\n")
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question = input(">>> ").strip()
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else:
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question = args.query.strip()
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print("\nПервая инициализация моделей...")
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rag = LocalRAGSystem(
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ollama_url=args.ollama_url,
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qdrant_host=args.qdrant_host,
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qdrant_port=args.qdrant_port,
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embed_model=args.emb_model,
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chat_model=args.chat_model
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)
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print(f"Модели загружены. Если ответ плохой, переформулируйте запрос, укажите --chat-model или улучшите исходные данные RAG")
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while True:
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try:
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if not question or question == "":
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question = input(">>> ").strip()
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if not question or question == "":
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continue
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if question.lower() == "exit":
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print("\n*** Завершение работы")
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break
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print("\nПоиск релевантных документов...")
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results = rag.search_qdrant(question, top_k=args.topk)
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if not results:
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print("Релевантные документы не найдены.")
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if args.interactive:
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continue
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else:
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break
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print(f"Найдено {len(results)} релевантных документов")
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if args.show_prompt:
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print("\nПолный системный промпт:\n")
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print(rag.prompt)
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print("Генерация ответа...")
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answer = rag.generate_answer(question, results)
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print(f"\n<<< {answer}\n---------------------------------------------------\n")
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question = None
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except KeyboardInterrupt:
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print("\n*** Завершение работы")
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break
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except Exception as e:
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print(f"Ошибка: {e}")
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if __name__ == "__main__":
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main()
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