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Merge pull request #16 from csunny/dev

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magic.chen
2023-05-07 17:59:49 +08:00
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parent 2bb0ddaf04 55b3702aeb
commit 23b9f6e2bb
7 changed files with 82 additions and 67 deletions
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4
README.md
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@@ -1,10 +1,10 @@
# DB-GPT
A Open Database-GPT Experiment, A fully localized project.
一个数据库相关的GPT实验项目, 模型与数据全部本地化部署, 绝对保障数据的隐私安全。 同时此GPT项目可以直接本地部署连接到私有数据库, 进行私有数据处理。
![GitHub Repo stars](https://img.shields.io/github/stars/csunny/db-gpt?style=social)
一个数据库相关的GPT实验项目, 模型与数据全部本地化部署, 绝对保障数据的隐私安全。 同时此GPT项目可以直接本地部署连接到私有数据库, 进行私有数据处理。
[DB-GPT](https://github.com/csunny/DB-GPT) 是一个实验性的开源应用程序，它基于[FastChat](https://github.com/lm-sys/FastChat)，并使用[vicuna-13b](https://huggingface.co/Tribbiani/vicuna-13b)作为基础模型。此外，此程序结合了[langchain](https://github.com/hwchase17/langchain)和[llama-index](https://github.com/jerryjliu/llama_index)基于现有知识库进行[In-Context Learning](https://arxiv.org/abs/2301.00234)来对其进行数据库相关知识的增强。它可以进行SQL生成、SQL诊断、数据库知识问答等一系列的工作。

2
pilot/configs/model_config.py
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@@ -22,7 +22,7 @@ LLM_MODEL_CONFIG = {
}
VECTOR_SEARCH_TOP_K = 5
VECTOR_SEARCH_TOP_K = 3
LLM_MODEL = "vicuna-13b"
LIMIT_MODEL_CONCURRENCY = 5
MAX_POSITION_EMBEDDINGS = 2048

8
pilot/conversation.py
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@@ -147,7 +147,7 @@ conv_vicuna_v1 = Conversation(
)
conv_qk_prompt_template = """ 基于以下已知的信息, 专业、详细的回答用户的问题。
conv_qa_prompt_template = """ 基于以下已知的信息, 专业、详细的回答用户的问题。
如果无法从提供的恶内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题", 但是你可以给出一些与问题相关答案的建议:
已知内容:
@@ -158,6 +158,12 @@ conv_qk_prompt_template = """ 基于以下已知的信息, 专业、详细的回
default_conversation = conv_one_shot
conversation_types = {
"native": "LLM原生对话",
"default_knownledge": "默认知识库对话",
"custome": "新增知识库对话",
}
conv_templates = {
"conv_one_shot": conv_one_shot,
"vicuna_v1": conv_vicuna_v1,

32
pilot/model/vicuna_llm.py
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@@ -10,33 +10,29 @@ from typing import Any, Mapping, Optional, List
from langchain.llms.base import LLM
from pilot.configs.model_config import *
class VicunaRequestLLM(LLM):
class VicunaLLM(LLM):
vicuna_generate_path = "generate_stream"
def _call(self, prompt: str, temperature: float, max_new_tokens: int, stop: Optional[List[str]] = None) -> str:
vicuna_generate_path = "generate"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
if isinstance(stop, list):
stop = stop + ["Observation:"]
skip_echo_len = len(prompt.replace("</s>", " ")) + 1
params = {
"prompt": prompt,
"temperature": 0.7,
"max_new_tokens": 1024,
"temperature": temperature,
"max_new_tokens": max_new_tokens,
"stop": stop
}
response = requests.post(
url=urljoin(VICUNA_MODEL_SERVER, self.vicuna_generate_path),
data=json.dumps(params),
)
response.raise_for_status()
# for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
# if chunk:
# data = json.loads(chunk.decode())
# if data["error_code"] == 0:
# output = data["text"][skip_echo_len:].strip()
# output = self.post_process_code(output)
# yield output
return response.json()["response"]
skip_echo_len = len(params["prompt"]) + 1 - params["prompt"].count("</s>") * 3
for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
if chunk:
data = json.loads(chunk.decode())
if data["error_code"] == 0:
output = data["text"][skip_echo_len:].strip()
yield output
@property
def _llm_type(self) -> str:

44
pilot/server/embdserver.py
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@@ -4,29 +4,44 @@
import requests
import json
import time
import uuid
from urllib.parse import urljoin
import gradio as gr
from pilot.configs.model_config import *
vicuna_base_uri = "http://192.168.31.114:21002/"
vicuna_stream_path = "worker_generate_stream"
vicuna_status_path = "worker_get_status"
from pilot.conversation import conv_qa_prompt_template, conv_templates
from langchain.prompts import PromptTemplate
def generate(prompt):
vicuna_stream_path = "generate_stream"
def generate(query):
template_name = "conv_one_shot"
state = conv_templates[template_name].copy()
pt = PromptTemplate(
template=conv_qa_prompt_template,
input_variables=["context", "question"]
)
result = pt.format(context="This page covers how to use the Chroma ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Chroma wrappers.",
question=query)
print(result)
state.append_message(state.roles[0], result)
state.append_message(state.roles[1], None)
prompt = state.get_prompt()
params = {
"model": "vicuna-13b",
"prompt": prompt,
"temperature": 0.7,
"max_new_tokens": 512,
"max_new_tokens": 1024,
"stop": "###"
}
sts_response = requests.post(
url=urljoin(vicuna_base_uri, vicuna_status_path)
)
print(sts_response.text)
response = requests.post(
url=urljoin(vicuna_base_uri, vicuna_stream_path), data=json.dumps(params)
url=urljoin(VICUNA_MODEL_SERVER, vicuna_stream_path), data=json.dumps(params)
)
skip_echo_len = len(params["prompt"]) + 1 - params["prompt"].count("</s>") * 3
@@ -34,11 +49,10 @@ def generate(prompt):
if chunk:
data = json.loads(chunk.decode())
if data["error_code"] == 0:
output = data["text"]
output = data["text"][skip_echo_len:].strip()
state.messages[-1][-1] = output + "▌"
yield(output)
time.sleep(0.02)
if __name__ == "__main__":
print(LLM_MODEL)
with gr.Blocks() as demo:

26
pilot/server/vectordb_qa.py
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@@ -3,31 +3,27 @@
from pilot.vector_store.file_loader import KnownLedge2Vector
from langchain.prompts import PromptTemplate
from pilot.conversation import conv_qk_prompt_template
from langchain.chains import RetrievalQA
from pilot.conversation import conv_qa_prompt_template
from pilot.configs.model_config import VECTOR_SEARCH_TOP_K
from pilot.model.vicuna_llm import VicunaLLM
class KnownLedgeBaseQA:
llm: object = None
def __init__(self) -> None:
k2v = KnownLedge2Vector()
self.vector_store = k2v.init_vector_store()
self.llm = VicunaLLM()
def get_answer(self, query):
prompt_template = conv_qk_prompt_template
def get_similar_answer(self, query):
prompt = PromptTemplate(
template=prompt_template,
template=conv_qa_prompt_template,
input_variables=["context", "question"]
)
knownledge_chain = RetrievalQA.from_llm(
llm=self.llm,
retriever=self.vector_store.as_retriever(search_kwargs={"k", VECTOR_SEARCH_TOP_K}),
prompt=prompt
)
knownledge_chain.return_source_documents = True
result = knownledge_chain({"query": query})
yield result
retriever = self.vector_store.as_retriever(search_kwargs={"k": VECTOR_SEARCH_TOP_K})
docs = retriever.get_relevant_documents(query=query)
context = [d.page_content for d in docs]
result = prompt.format(context="\n".join(context), question=query)
return result

33
pilot/server/webserver.py
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@@ -11,6 +11,7 @@ import datetime
import requests
from urllib.parse import urljoin
from pilot.configs.model_config import DB_SETTINGS
from pilot.server.vectordb_qa import KnownLedgeBaseQA
from pilot.connections.mysql_conn import MySQLOperator
from pilot.vector_store.extract_tovec import get_vector_storelist, load_knownledge_from_doc, knownledge_tovec_st
@@ -19,6 +20,7 @@ from pilot.configs.model_config import LOGDIR, VICUNA_MODEL_SERVER, LLM_MODEL, D
from pilot.conversation import (
default_conversation,
conv_templates,
conversation_types,
SeparatorStyle
)
@@ -149,7 +151,7 @@ def post_process_code(code):
code = sep.join(blocks)
return code
def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Request):
def http_bot(state, mode, db_selector, temperature, max_new_tokens, request: gr.Request):
start_tstamp = time.time()
model_name = LLM_MODEL
@@ -170,7 +172,8 @@ def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Reques
query = state.messages[-2][1]
# prompt 中添加上下文提示
# prompt 中添加上下文提示, 根据已有知识对话, 上下文提示是否也应该放在第一轮, 还是每一轮都添加上下文?
# 如果用户侧的问题跨度很大, 应该每一轮都加提示。
if db_selector:
new_state.append_message(new_state.roles[0], gen_sqlgen_conversation(dbname) + query)
new_state.append_message(new_state.roles[1], None)
@@ -180,13 +183,11 @@ def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Reques
new_state.append_message(new_state.roles[1], None)
state = new_state
# try:
# if not db_selector:
# sim_q = get_simlar(query)
# print("********vector similar info*************: ", sim_q)
# state.append_message(new_state.roles[0], sim_q + query)
# except Exception as e:
# print(e)
if mode == conversation_types["default_knownledge"] and not db_selector:
query = state.messages[-2][1]
knqa = KnownLedgeBaseQA()
state.messages[-2][1] = knqa.get_similar_answer(query)
prompt = state.get_prompt()
@@ -222,7 +223,7 @@ def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Reques
state.messages[-1][-1] = output
yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
return
time.sleep(0.02)
except requests.exceptions.RequestException as e:
state.messages[-1][-1] = server_error_msg + f" (error_code: 4)"
yield (state, state.to_gradio_chatbot()) + (disable_btn, disable_btn, disable_btn, enable_btn, enable_btn)
@@ -231,6 +232,7 @@ def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Reques
state.messages[-1][-1] = state.messages[-1][-1][:-1]
yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
# 记录运行日志
finish_tstamp = time.time()
logger.info(f"{output}")
@@ -266,7 +268,7 @@ def change_tab(tab):
pass
def change_mode(mode):
if mode == "默认知识库对话":
if mode in ["默认知识库对话", "LLM原生对话"]:
return gr.update(visible=False)
else:
return gr.update(visible=True)
@@ -318,7 +320,8 @@ def build_single_model_ui():
show_label=True).style(container=False)
with gr.TabItem("知识问答", elem_id="QA"):
mode = gr.Radio(["默认知识库对话", "新增知识库"], show_label=False, value="默认知识库对话")
mode = gr.Radio(["LLM原生对话", "默认知识库对话", "新增知识库对话"], show_label=False, value="LLM原生对话")
vs_setting = gr.Accordion("配置知识库", open=False)
mode.change(fn=change_mode, inputs=mode, outputs=vs_setting)
with vs_setting:
@@ -363,7 +366,7 @@ def build_single_model_ui():
btn_list = [regenerate_btn, clear_btn]
regenerate_btn.click(regenerate, state, [state, chatbot, textbox] + btn_list).then(
http_bot,
[state, db_selector, temperature, max_output_tokens],
[state, mode, db_selector, temperature, max_output_tokens],
[state, chatbot] + btn_list,
)
clear_btn.click(clear_history, None, [state, chatbot, textbox] + btn_list)
@@ -372,7 +375,7 @@ def build_single_model_ui():
add_text, [state, textbox], [state, chatbot, textbox] + btn_list
).then(
http_bot,
[state, db_selector, temperature, max_output_tokens],
[state, mode, db_selector, temperature, max_output_tokens],
[state, chatbot] + btn_list,
)
@@ -380,7 +383,7 @@ def build_single_model_ui():
add_text, [state, textbox], [state, chatbot, textbox] + btn_list
).then(
http_bot,
[state, db_selector, temperature, max_output_tokens],
[state, mode, db_selector, temperature, max_output_tokens],
[state, chatbot] + btn_list
)