我正在开发一个 Django 应用程序，我希望使用 GPT-3 等 OpenAI 语言模型 (LLM) 来启用自然语言查询功能。为了构建和简化数据库访问，我计划使用 DBT（数据构建工具）语义层。
我的目标是允许用户用自然语言提出问题，然后利用 DBT 提供的语义定义，通过 LLM 将其转换为 SQL 查询。理想情况下，此设置应支持跨多个表的复杂查询，利用语义层中定义的关系和维度。
以下是设置的简要概述：
1. Django Application: Serves the frontend and backend, managing user requests.
2. DBT Semantic Layer: Defines the data models, metrics, and relationships.
3. OpenAI LLM (e.g., GPT-3): Used for interpreting natural language inputs and generating SQL queries.
4. PostgreSQL Database: The source of data queried and managed via DBT.
具体问题：
1. How should I integrate the DBT semantic layer within the Django app? Should the semantic layer be exposed via an API, or is there a more integrated approach?
2. What are the best practices for using an LLM to generate SQL queries from natural language, especially using the constructs defined in the DBT models? How can I ensure that the queries generated are efficient and secure?
3. Are there any existing libraries or frameworks that facilitate the integration of LLMs with DBT or similar semantic layers? If not, what should be the focus while building this integration?
任何指导、示例或资源将不胜感激！我特别有兴趣了解此类集成中类似的经历或面临的挑战。
谢谢！
我尝试创建语义模型
协议.sql
select
Agreement_Type_Code,
Agreement_Name,
Agreement_Original_Inception_Date,
Product_Identifier
from
dbt_cdw_benchmark__seed.agreement
协议.yaml
semantic_models:
- name: agreement
model: ref('agreement')
entities:
- name: agreement_type_code
type: primary
- name: product_identifier
type: foreign
dimensions:
- name: agreement_name
type: categorical
- name: agreement_original_inception_date
type: time
type_params:
time_granularity: day
理想情况下，您应该隔离 DBT（转换层）、GPT3 定制和 Django 应用程序，并将它们分开。
在这里我们可以管理读取源数据、执行转换和计算指标。
它将单独安排运行给定的时间表。或者可以在必要时触发。
要使用特定数据集自定义 GPT-3，您需要微调模型。微调涉及在根据您的用例定制的数据集上训练模型，这有助于模型学习与您的需求相关的特定模式和响应。
步骤：
pip install openai获取OPENAI API密钥
准备数据集：您的数据集应采用 JSONL 格式，其中每一行都是一个带有“提示”和“完成”字段的 JSON 对象。
{"prompt": "Customer: I recently purchased a product from your website, but it arrived damaged. What should I do?\nAgent:", "completion": " I'm sorry to hear that. Please send us a picture of the damaged product, and we'll arrange for a replacement or refund."}
{"prompt": "Customer: Can I return a product I bought last month?\nAgent:", "completion": " Yes, you can return it within 30 days of purchase. Please visit our return portal to start the process."}
使用 OpenAI 的 API 上传您的数据集。
import openai
openai.api_key = 'your-api-key'
# Upload the dataset
response = openai.File.create(
file=open("dataset.jsonl"),
purpose='fine-tune'
)
file_id = response['id']
print(f"Uploaded file ID: {file_id}")
数据集上传后，您就可以开始微调过程。
response = openai.FineTune.create(
training_file=file_id,
model="davinci" # or another model you want to fine-tune
)
fine_tune_id = response['id']
print(f"Fine-tune ID: {fine_tune_id}")
微调完成后，您可以使用定制的模型来生成响应。
def get_custom_response(prompt):
response = openai.Completion.create(
model="davinci:ft-your-custom-model-id",
prompt=prompt,
max_tokens=150,
temperature=0.7
)
return response.choices[0].text.strip()
# Example usage
prompt = "Customer: I recently purchased a product from your website, but it arrived damaged. What should I do?\nAgent:"
response = get_custom_response(prompt)
print(response)
这应该只向微调 gpt3 模型发出请求，以根据用户提示生成响应。