ДОбился запуска
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6
.gitignore
vendored
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6
.gitignore
vendored
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@@ -0,0 +1,6 @@
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__pycache__
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agent_env
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.lock
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.sqlite
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qdrant_data
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qdrant_storage
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@@ -1,15 +1,13 @@
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from typing import TypedDict, List, Optional, Annotated
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import operator
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from langchain_community.llms import Ollama
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from langchain_community.embeddings import OllamaEmbeddings
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from langchain_community.vectorstores import Qdrant
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_community.document_loaders import DirectoryLoader, PyPDFLoader
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from langgraph.graph import StateGraph, END
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from langgraph.graph.message import add_messages
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from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
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from langchain_core.messages import AIMessage, SystemMessage
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from langchain.tools import tool
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from langchain.agents import AgentExecutor, create_react_agent
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# from langchain.agents import AgentExecutor, create_react_agent
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import numpy as np
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# Определяем состояние агента
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@@ -22,16 +20,11 @@ class AgentState(TypedDict):
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# Инициализация LLM через Ollama
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llm = Ollama(
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model="llama3.2:3b",
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model="qwen2.5-coder-16k:14b",
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temperature=0.1, # Низкая температура для консистентности
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num_predict=1024, # Максимальная длина ответа
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)
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# Инициализация эмбеддингов
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embeddings = OllamaEmbeddings(
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model="nomic-embed-text", # Хорошие локальные эмбеддинги
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)
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# Создаем инструменты для агента
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@tool
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def search_knowledge_base(query: str) -> str:
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@@ -127,6 +120,3 @@ def create_agent_graph():
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workflow.add_edge("generate", END)
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return workflow.compile()
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# Создаем и компилируем граф
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agent_graph = create_agent_graph()
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@@ -1,6 +1,10 @@
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import gradio as gr
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import asyncio
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import os
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from typing import List
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from agent_core import create_agent_graph
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from local_knowledge_base import LocalKnowledgeBase
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from langchain_core.messages import HumanMessage
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class AgenticRAGInterface:
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def __init__(self, agent_graph, knowledge_base):
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@@ -61,8 +65,15 @@ class AgenticRAGInterface:
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self.conversation_history = []
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return "История очищена", ""
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# Инициализируем базу
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kb = LocalKnowledgeBase()
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# Загружаем документы (если есть)
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if os.path.exists("./documents"):
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kb.load_documents("./documents")
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# Создаем интерфейс
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interface = AgenticRAGInterface(agent_graph, kb)
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interface = AgenticRAGInterface(create_agent_graph(), kb)
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# Создаем Gradio интерфейс
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def create_gradio_interface():
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@@ -1,23 +1,32 @@
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import os
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from qdrant_client import QdrantClient
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from qdrant_client.models import Distance, VectorParams, PointStruct
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from langchain_community.document_loaders import TextLoader, PyPDFLoader, DirectoryLoader
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from langchain.text_splitter import Language
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from langchain_community.document_loaders import (
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TextLoader,
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PyPDFLoader,
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DirectoryLoader,
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)
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from langchain_text_splitters import Language, RecursiveCharacterTextSplitter
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from langchain_community.embeddings import OllamaEmbeddings
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class LocalKnowledgeBase:
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def __init__(self, collection_name="documents", persist_dir="./qdrant_data"):
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# Инициализируем клиент Qdrant
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self.client = QdrantClient(
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path=persist_dir, # Локальное хранение
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prefer_grpc=True
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path=persist_dir, prefer_grpc=True # Локальное хранение
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)
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self.collection_name = collection_name
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self.embeddings = embeddings
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# Инициализация эмбеддингов
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self.embeddings = OllamaEmbeddings(
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model="nomic-embed-text", # Хорошие локальные эмбеддинги
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)
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# Создаем коллекцию если её нет
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self._create_collection()
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def _create_collection(self):
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try:
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self.client.get_collection(self.collection_name)
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@@ -28,26 +37,25 @@ class LocalKnowledgeBase:
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collection_name=self.collection_name,
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vectors_config=VectorParams(
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size=768, # Размерность эмбеддингов nomic-embed-text
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distance=Distance.COSINE
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)
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distance=Distance.COSINE,
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),
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)
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print(f"Создана коллекция {self.collection_name}")
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def load_documents(self, directory_path: str):
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"""Загрузка документов из директории"""
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loaders = {
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'.pdf': PyPDFLoader,
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'.txt': lambda path: DirectoryLoader(path, glob="**/*.txt"),
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'.docx': lambda path: DirectoryLoader(path, glob="**/*.docx"),
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'.hpp': lambda path: TextLoader(path, encoding='utf-8'),
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'.cpp': lambda path: TextLoader(path, encoding='utf-8'), # добавить
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'.h': lambda path: TextLoader(path, encoding='utf-8'), # добавить
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'.cc': lambda path: TextLoader(path, encoding='utf-8'), # добавить
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".pdf": PyPDFLoader,
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".txt": lambda path: DirectoryLoader(path, glob="**/*.txt"),
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".docx": lambda path: DirectoryLoader(path, glob="**/*.docx"),
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".hpp": lambda path: TextLoader(path, encoding="utf-8"),
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".cpp": lambda path: TextLoader(path, encoding="utf-8"), # добавить
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".h": lambda path: TextLoader(path, encoding="utf-8"), # добавить
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".cc": lambda path: TextLoader(path, encoding="utf-8"), # добавить
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}
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all_documents = []
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for ext, loader_class in loaders.items():
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for file_path in os.listdir(directory_path):
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if file_path.endswith(ext):
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@@ -59,31 +67,31 @@ class LocalKnowledgeBase:
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print(f"Загружен {file_path}: {len(documents)} страниц")
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except Exception as e:
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print(f"Ошибка загрузки {file_path}: {e}")
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# Разбиваем на чанки
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text_splitter = RecursiveCharacterTextSplitter.from_language(
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language=Language.CPP,
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chunk_size=1000,
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chunk_overlap=200,
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length_function=len
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length_function=len,
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)
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chunks = text_splitter.split_documents(all_documents)
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print(f"Всего чанков: {len(chunks)}")
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# Создаем эмбеддинги и сохраняем в Qdrant
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self._index_documents(chunks)
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return chunks
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def _index_documents(self, documents):
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"""Индексация документов в Qdrant"""
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points = []
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for i, doc in enumerate(documents):
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# Создаем эмбеддинг для каждого чанка
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embedding = self.embeddings.embed_query(doc.page_content)
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point = PointStruct(
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id=i,
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vector=embedding,
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@@ -91,55 +99,45 @@ class LocalKnowledgeBase:
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"text": doc.page_content,
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"source": doc.metadata.get("source", "unknown"),
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"page": doc.metadata.get("page", 0),
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"file_type": "cpp"
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}
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"file_type": "cpp",
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},
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)
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points.append(point)
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# Пакетная загрузка каждые 100 точек
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if len(points) >= 100:
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self.client.upsert(
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collection_name=self.collection_name,
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points=points
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)
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self.client.upsert(collection_name=self.collection_name, points=points)
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points = []
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print(f"Индексировано {i+1} документов")
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# Загружаем оставшиеся
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if points:
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self.client.upsert(
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collection_name=self.collection_name,
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points=points
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)
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self.client.upsert(collection_name=self.collection_name, points=points)
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print(f"Индексация завершена. Всего документов: {len(documents)}")
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def search(self, query: str, top_k: int = 5):
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"""Поиск в базе знаний"""
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# Создаем эмбеддинг запроса
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query_embedding = self.embeddings.embed_query(query)
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# Ищем в Qdrant
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search_result = self.client.search(
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search_result = self.client.query_points(
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collection_name=self.collection_name,
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query_vector=query_embedding,
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limit=top_k
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query=query_embedding,
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limit=top_k,
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)
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# Форматируем результаты
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results = []
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for hit in search_result:
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results.append({
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"text": hit.payload["text"],
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"score": hit.score,
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"source": hit.payload.get("source", "unknown")
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})
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for hit in search_result.points:
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results.append(
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{
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"text": hit.payload["text"],
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"score": hit.score,
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"source": hit.payload.get("source", "unknown"),
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}
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)
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return results
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# Инициализируем базу знаний
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kb = LocalKnowledgeBase()
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# Загружаем документы (если есть)
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if os.path.exists("./documents"):
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kb.load_documents("./documents")
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