|
|
|
|
@ -1,114 +0,0 @@
|
|
|
|
|
"""
|
|
|
|
|
conda activate rag
|
|
|
|
|
pip install openai
|
|
|
|
|
"""
|
|
|
|
|
from elasticsearch import Elasticsearch
|
|
|
|
|
from openai import OpenAI
|
|
|
|
|
from Config import Config
|
|
|
|
|
|
|
|
|
|
# 初始化ES连接
|
|
|
|
|
es = Elasticsearch(
|
|
|
|
|
hosts=Config.ES_CONFIG['hosts'],
|
|
|
|
|
basic_auth=Config.ES_CONFIG['basic_auth'],
|
|
|
|
|
verify_certs=Config.ES_CONFIG['verify_certs']
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
# 初始化DeepSeek客户端
|
|
|
|
|
client = OpenAI(
|
|
|
|
|
api_key=Config.DEEPSEEK_API_KEY,
|
|
|
|
|
base_url=Config.DEEPSEEK_URL
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
def generate_report(query, context):
|
|
|
|
|
"""使用DeepSeek生成报告"""
|
|
|
|
|
prompt = f"""根据以下关于'{query}'的相关信息，整理一份结构化的报告：
|
|
|
|
|
要求：
|
|
|
|
|
1. 分章节组织内容
|
|
|
|
|
2. 包含关键数据和事实
|
|
|
|
|
3. 语言简洁专业
|
|
|
|
|
|
|
|
|
|
相关信息：
|
|
|
|
|
{context}"""
|
|
|
|
|
|
|
|
|
|
try:
|
|
|
|
|
response = client.chat.completions.create(
|
|
|
|
|
model="deepseek-chat",
|
|
|
|
|
messages=[
|
|
|
|
|
{"role": "system", "content": "你是一个专业的文档整理助手"},
|
|
|
|
|
{"role": "user", "content": prompt}
|
|
|
|
|
],
|
|
|
|
|
temperature=0.3,
|
|
|
|
|
stream=True
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
# 流式输出处理
|
|
|
|
|
full_response = ""
|
|
|
|
|
for chunk in response:
|
|
|
|
|
if chunk.choices[0].delta.content:
|
|
|
|
|
content = chunk.choices[0].delta.content
|
|
|
|
|
print(content, end="", flush=True)
|
|
|
|
|
full_response += content
|
|
|
|
|
|
|
|
|
|
return full_response
|
|
|
|
|
except Exception as e:
|
|
|
|
|
print(f"生成报告时出错: {str(e)}")
|
|
|
|
|
return ""
|
|
|
|
|
|
|
|
|
|
def process_query(query):
|
|
|
|
|
"""处理用户查询并生成报告"""
|
|
|
|
|
print(f"正在搜索与'{query}'相关的数据...")
|
|
|
|
|
context = search_related_data(query)
|
|
|
|
|
print(f"找到{len(context.split(chr(10)+chr(10)))}条相关数据")
|
|
|
|
|
|
|
|
|
|
print("正在生成报告...")
|
|
|
|
|
report = generate_report(query, context)
|
|
|
|
|
|
|
|
|
|
return report
|
|
|
|
|
|
|
|
|
|
def search_related_data(query):
|
|
|
|
|
"""搜索与查询相关的数据"""
|
|
|
|
|
# 向量搜索
|
|
|
|
|
vector_results = es.search(
|
|
|
|
|
index=Config.ES_CONFIG['index_name'],
|
|
|
|
|
body={
|
|
|
|
|
"query": {
|
|
|
|
|
"match": {
|
|
|
|
|
"content": {
|
|
|
|
|
"query": query,
|
|
|
|
|
"analyzer": "ik_smart" # 指定分词器
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"size": 5
|
|
|
|
|
}
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
# 文本精确搜索
|
|
|
|
|
text_results = es.search(
|
|
|
|
|
index="raw_texts",
|
|
|
|
|
body={
|
|
|
|
|
"query": {
|
|
|
|
|
"match": {
|
|
|
|
|
"text.keyword": query
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"size": 5
|
|
|
|
|
}
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
# 合并结果
|
|
|
|
|
context = ""
|
|
|
|
|
for hit in vector_results['hits']['hits']:
|
|
|
|
|
context += f"向量相似度结果(score={hit['_score']}):\n{hit['_source']['text']}\n\n"
|
|
|
|
|
|
|
|
|
|
for hit in text_results['hits']['hits']:
|
|
|
|
|
context += f"文本精确匹配结果(score={hit['_score']}):\n{hit['_source']['text']}\n\n"
|
|
|
|
|
|
|
|
|
|
return context
|
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
|
#user_query = input("请输入您的查询要求：")
|
|
|
|
|
user_query = "小学数学"
|
|
|
|
|
report = process_query(user_query)
|
|
|
|
|
print("\n=== 生成的报告 ===\n")
|
|
|
|
|
print(report)
|
