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1. LangChain框架

LangChain是一个用于构建基于大语言模型的应用框架，通过模块化设计简化了LLM与外部工具，数据源和复杂逻辑的集成。

连接能力

将多个LLM调用，工具调用或者数据处理步骤串联成工作流

数据感知

外部数据集成

支持连接数据库，API，解决LLM的知识截止问题

from langchain_community.document_loaders import CSVLoader
loader = CSVLoader(你的文件路径)

记忆管理

自动跟踪多轮对话历史，支持短期（内存，调包）或者长期（数据库）存储

from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()

langchain支持本地模型

2. 多模态大模型连接数据库初始化设置

# 插入数据
def insert_data(connection, dataset):connection.execute(users.insert(), dataset)def select_data(connection):result = connection.execute(users.select())for row in result:print(row)
def get_table_schema(engine):inspector = reflection.Inspector.from_engine(engine)table_names = inspector.get_table_names()schema = {}for table_name in table_names:columns = inspector.get_columns(table_name)schema[table_name] = [column['name'] for column in columns]return schemadef execute_query(query):with engine.connect() as conn:result = conn.execute(text(query))return result.fetchall()def query_database(prompt, schema):# 将表结构信息包含在提示中schema_info = "\n".join([f"Table {table}: {', '.join(columns)}" for table, columns in schema.items()])full_prompt = f"""以下是数据库的表结构信息：{schema_info}请根据图片信息生成一个SQL查询请严格按照表结构生成SQL查询在</answer>里面显示"""prompt_final = PromptTemplate(input_variables=[schema_info],  template=full_prompt)print("***************************************************************************")print("full_prompt:", full_prompt.replace('\n', ''))print("***************************************************************************")return full_prompt.replace('\n', '')

3. 输出结果

多模态推理过程

message_search = [# {"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},{"role": "user","content": [{"type": "image", "image": f"file://{image}"},{"type": "text","text": query_database()}]}]messages_prompt.append(message_search)text = [processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in messages_prompt]print("*************************************")print("text:", text)print("*************************************")image_inputs, video_inputs = process_vision_info(messages_prompt)print("*************************************")print("image_inputs:", image_inputs)print("*************************************")inputs = processor(text=text,images=image_inputs,videos=video_inputs,padding=True,return_tensors="pt",)print("*************************************")print("inputs:", inputs)print("*************************************")inputs = inputs.to("cuda:0")# Inference: Generation of the outputgenerated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=300, do_sample=False)generated_ids_trimmed = [out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]batch_output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)all_outputs.extend(batch_output_text)print("==========查询结果")print(all_outputs[0])

4. 编写获取sql语句函数

def extract_sql_answer(content):answer_tag_pattern = r'<Answer>(.*?)</Answer>'sql_pattern = r'```sql(.*?)```'content_answer_match = re.search(answer_tag_pattern, content, re.DOTALL)if content_answer_match:content_answer = content_answer_match.group(1).strip()sql_match = re.search(sql_pattern, content_answer, re.DOTALL)if sql_match:sql_content = sql_match.group(1).strip()posibble_things = execute_query(sql_content)return posibble_thingselif "sql" in content:sql_match = re.search(sql_pattern, content, re.DOTALL)if sql_match:sql_content = sql_match.group(1).strip()posibble_things = execute_query(sql_content)return posibble_thingsreturn ""

5. 将query查询语句与数据库连接并返回查询结果

def execute_query(query):query = query.replace("商品表", "users")with engine.connect() as conn:result = conn.execute(text(query))return result.fetchall()

6. 结果展示