根据你提供的官方文档中的调用方法，可以使用 Ollama 的 chat 方法来与模型进行交互。我们将调整 FastAPI 应用代码以使用 ollama 模块的 chat 方法。

1. 安装必要的库

确保你已经安装了 FastAPI 和 Ollama Python 客户端库：

pip install fastapi uvicorn ollama

2. 创建 FastAPI 应用

创建一个新的 Python 文件（例如 main.py），并编写 FastAPI 应用代码：

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import ollamaapp = FastAPI()class SQLRequest(BaseModel):question: str@app.post("/generate_sql")
async def generate_sql(request: SQLRequest):prompt = f"""### Instructions:Your task is to convert a question into a SQL query, given a Postgres database schema.Adhere to these rules:- **Deliberately go through the question and database schema word by word** to appropriately answer the question- **Use Table Aliases** to prevent ambiguity. For example, `SELECT table1.col1, table2.col1 FROM table1 JOIN table2 ON table1.id = table2.id`.- When creating a ratio, always cast the numerator as float### Input:Generate a SQL query that answers the question `{request.question}`.This query will run on a database whose schema is represented in this string:CREATE TABLE products (product_id INTEGER PRIMARY KEY, -- Unique ID for each productname VARCHAR(50), -- Name of the productprice DECIMAL(10,2), -- Price of each unit of the productquantity INTEGER  -- Current quantity in stock);CREATE TABLE customers (customer_id INTEGER PRIMARY KEY, -- Unique ID for each customername VARCHAR(50), -- Name of the customeraddress VARCHAR(100) -- Mailing address of the customer);CREATE TABLE salespeople (salesperson_id INTEGER PRIMARY KEY, -- Unique ID for each salespersonname VARCHAR(50), -- Name of the salespersonregion VARCHAR(50) -- Geographic sales region);CREATE TABLE sales (sale_id INTEGER PRIMARY KEY, -- Unique ID for each saleproduct_id INTEGER, -- ID of product soldcustomer_id INTEGER,  -- ID of customer who made purchasesalesperson_id INTEGER, -- ID of salesperson who made the salesale_date DATE, -- Date the sale occurredquantity INTEGER -- Quantity of product sold);CREATE TABLE product_suppliers (supplier_id INTEGER PRIMARY KEY, -- Unique ID for each supplierproduct_id INTEGER, -- Product ID suppliedsupply_price DECIMAL(10,2) -- Unit price charged by supplier);-- sales.product_id can be joined with products.product_id-- sales.customer_id can be joined with customers.customer_id-- sales.salesperson_id can be joined with salespeople.salesperson_id-- product_suppliers.product_id can be joined with products.product_id### Response:Based on your instructions, here is the SQL query I have generated to answer the question `{request.question}`:"""try:response = ollama.chat(model='sqlcoder:7B', messages=[{'role': 'user', 'content': prompt}])return {"sql_query": response['message']['content']}except Exception as e:raise HTTPException(status_code=500, detail=str(e))if __name__ == "__main__":import uvicornuvicorn.run(app, host="0.0.0.0", port=8000)

3. 启动 Ollama 服务

确保 Ollama 服务正在本地运行。你可以使用以下命令启动 Ollama 服务：

ollama serve

4. 启动 FastAPI 应用

在终端中运行以下命令启动 FastAPI 应用：

uvicorn main:app --reload

5. 测试 API

你可以使用 curl 或 Postman 测试 API。例如，使用 curl：

curl -X POST "http://localhost:8000/generate_sql" -H "Content-Type: application/json" -d '{"question": "哪种产品的销售额最高？"}'

这应该会返回一个包含生成的 SQL 查询的 JSON 响应。

详细解释

1. FastAPI：创建一个简单的 Web API 服务。@app.post("/generate_sql") 定义了一个 POST 端点，用于接收用户的问题并调用 Ollama 模型生成 SQL 查询。
2. Pydantic：用于数据验证和解析。SQLRequest 类定义了请求体的数据结构。
3. Ollama：使用 ollama.chat 方法与模型 sqlcoder:7B 进行交互并生成响应。

通过这些步骤，你应该能够正确配置和运行 FastAPI 应用，并通过调用 Ollama 服务来生成 SQL 查询。