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csunny 2023-05-07 17:32:10 +08:00
parent e4899ff7dd
commit 56e9cde86e
6 changed files with 61 additions and 52 deletions
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2
pilot/configs/model_config.py
View File

@ -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

2
pilot/conversation.py
View File

@ -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:

32
pilot/model/vicuna_llm.py
View File

@ -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
View File

@ -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:

25
pilot/server/vectordb_qa.py
View File

@ -3,31 +3,28 @@
from pilot.vector_store.file_loader import KnownLedge2Vector
from langchain.prompts import PromptTemplate
from pilot.conversation import conv_qk_prompt_template
from pilot.conversation import conv_qa_prompt_template
from langchain.chains import RetrievalQA
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

8
pilot/server/webserver.py
View File

@ -170,7 +170,8 @@ 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)
@ -179,7 +180,7 @@ def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Reques
new_state.append_message(new_state.roles[0], query)
new_state.append_message(new_state.roles[1], None)
state = new_state
# try:
# if not db_selector:
# sim_q = get_simlar(query)
@ -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}")