>該教程通過使用Python和Openai構建檢索增強發電（RAG）系統，為您引導您。 RAG通過從您的文檔中檢索相關信息來增強AI的響應，然後再產生答案 - 本質上，讓AI“研究”事先進行。
>您將要學到的內容：
項目結構：
<code>rag-project/ │ ├── src/ │ ├── __init__.py │ ├── document_loader.py │ ├── text_processor.py │ ├── embeddings_manager.py │ ├── retrieval_system.py │ └── rag_system.py │ ├── data/ │ └── documents/ │ ├── requirements.txt ├── test.py ├── README.md └── .env</code>
步驟1：環境設置：
python -m venv venv>
venvScriptsactivate激活它：source venv/bin/activate>安裝軟件包：pip install openai python-dotenv numpy pandas創建requirements.txt
<code>openai==1.12.0 python-dotenv==1.0.0 numpy==1.24.3 pandas==2.1.0</code>
.env
<code>OPENAI_API_KEY=your_api_key_here</code>
）：src/document_loader.py
>
<code class="language-python">import os
from typing import List
class DocumentLoader:
def __init__(self, documents_path: str):
self.documents_path = documents_path
def load_documents(self) -> List[str]:
documents = []
for filename in os.listdir(self.documents_path):
if filename.endswith('.txt'):
with open(os.path.join(self.documents_path, filename), 'r') as file:
documents.append(file.read())
return documents</code>>
src/text_processor.py步驟4：嵌入式創建（
<code class="language-python">from typing import List
class TextProcessor:
def __init__(self, chunk_size: int = 1000):
self.chunk_size = chunk_size
def split_into_chunks(self, text: str) -> List[str]:
words = text.split()
chunks = []
current_chunk = []
current_size = 0
for word in words:
if current_size + len(word) > self.chunk_size:
chunks.append(' '.join(current_chunk))
current_chunk = [word]
current_size = len(word)
else:
current_chunk.append(word)
current_size += len(word) + 1
if current_chunk:
chunks.append(' '.join(current_chunk))
return chunks</code>
src/embeddings_manager.py步驟5：檢索系統（
<code class="language-python">from typing import List
import openai
import numpy as np
class EmbeddingsManager:
def __init__(self, api_key: str):
openai.api_key = api_key
def create_embeddings(self, texts: List[str]) -> List[np.ndarray]:
embeddings = []
for text in texts:
response = openai.embeddings.create(
model="text-embedding-ada-002",
input=text
)
embeddings.append(np.array(response.data[0].embedding))
return embeddings</code>>
src/retrieval_system.py>步驟6：OpenAI Integration（
<code class="language-python">import numpy as np
from typing import List, Tuple
class RetrievalSystem:
def __init__(self, chunks: List[str], embeddings: List[np.ndarray]):
self.chunks = chunks
self.embeddings = embeddings
def find_similar_chunks(self, query_embedding: np.ndarray, top_k: int = 3) -> List[Tuple[str, float]]:
similarities = []
for i, embedding in enumerate(self.embeddings):
similarity = np.dot(query_embedding, embedding) / (
np.linalg.norm(query_embedding) * np.linalg.norm(embedding)
)
similarities.append((self.chunks[i], similarity))
return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_k]</code>
>src/rag_system.py步驟7：系統用法（）：
<code class="language-python">import os
from dotenv import load_dotenv
from typing import List
import openai
from .document_loader import DocumentLoader
from .text_processor import TextProcessor
from .embeddings_manager import EmbeddingsManager
from .retrieval_system import RetrievalSystem
class RAGSystem:
def __init__(self):
load_dotenv()
self.api_key = os.getenv('OPENAI_API_KEY')
self.loader = DocumentLoader('data/documents')
self.processor = TextProcessor()
self.embeddings_manager = EmbeddingsManager(self.api_key)
# Initialize system
self.initialize_system()
def initialize_system(self):
# Load and process documents
documents = self.loader.load_documents()
self.chunks = []
for doc in documents:
self.chunks.extend(self.processor.split_into_chunks(doc))
# Create embeddings
self.embeddings = self.embeddings_manager.create_embeddings(self.chunks)
# Initialize retrieval system
self.retrieval_system = RetrievalSystem(self.chunks, self.embeddings)
def answer_question(self, question: str) -> str:
# Get question embedding
question_embedding = self.embeddings_manager.create_embeddings([question])[0]
# Get relevant chunks
relevant_chunks = self.retrieval_system.find_similar_chunks(question_embedding)
# Prepare context
context = "\n".join([chunk[0] for chunk in relevant_chunks])
# Create prompt
prompt = f"""Context: {context}\n\nQuestion: {question}\n\nAnswer:"""
# Get response from OpenAI
response = openai.chat.completions.create(
model="gpt-4-turbo-preview",
messages=[
{"role": "system", "content": "You are a helpful assistant. Use the provided context to answer the question."},
{"role": "user", "content": prompt}
]
)
return response.choices[0].message.content</code>>將樣本文檔放在test.py>中。 然後，運行：
.txtdata/documents結論：test.py
<code class="language-python"># test.py from src.rag_system import RAGSystem # Initialize the RAG system rag = RAGSystem() # Ask a question question = "What was the answer to the guardian’s riddle, and how did it help Kai?" #Replace with your question based on your documents answer = rag.answer_question(question) print(answer)</code>
以上是使用Python和Openai構建您的第一個抹布系統的詳細內容。更多資訊請關注PHP中文網其他相關文章！
