github/DB-GPT
1
0
Fork 0
mirror of https://github.com/csunny/DB-GPT.git synced 2025-09-07 12:00:46 +00:00
Code Issues Packages Projects Releases Wiki Activity

ci: make ci happy lint the code, delete unused imports

Browse Source 
Signed-off-by: yihong0618 <zouzou0208@gmail.com>
This commit is contained in:
yihong0618
2023-05-24 18:42:55 +08:00
parent 562d5a98cc
commit b098a48898
75 changed files with 1110 additions and 824 deletions
Download Patch File Download Diff File Expand all files Collapse all files

48
examples/app.py
View File

@@ -2,24 +2,28 @@
# -*- coding:utf-8 -*-
import gradio as gr
from langchain.agents import (
load_tools,
initialize_agent,
AgentType
)
from pilot.model.vicuna_llm import VicunaRequestLLM, VicunaEmbeddingLLM
from llama_index import LLMPredictor, LangchainEmbedding, ServiceContext
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import Document, GPTSimpleVectorIndex
from llama_index import (
Document,
GPTSimpleVectorIndex,
LangchainEmbedding,
LLMPredictor,
ServiceContext,
)
from pilot.model.vicuna_llm import VicunaEmbeddingLLM, VicunaRequestLLM
def agent_demo():
llm = VicunaRequestLLM()
tools = load_tools(['python_repl'], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent.run(
"Write a SQL script that Query 'select count(1)!'"
tools = load_tools(["python_repl"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run("Write a SQL script that Query 'select count(1)!'")
def knowledged_qa_demo(text_list):
llm_predictor = LLMPredictor(llm=VicunaRequestLLM())
@@ -27,27 +31,34 @@ def knowledged_qa_demo(text_list):
embed_model = LangchainEmbedding(hfemb)
documents = [Document(t) for t in text_list]
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)
index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
service_context = ServiceContext.from_defaults(
llm_predictor=llm_predictor, embed_model=embed_model
)
index = GPTSimpleVectorIndex.from_documents(
documents, service_context=service_context
)
return index
def get_answer(q):
base_knowledge = """ """
base_knowledge = """ """
text_list = [base_knowledge]
index = knowledged_qa_demo(text_list)
response = index.query(q)
return response.response
def get_similar(q):
from pilot.vector_store.extract_tovec import knownledge_tovec, knownledge_tovec_st
docsearch = knownledge_tovec_st("./datasets/plan.md")
docs = docsearch.similarity_search_with_score(q, k=1)
for doc in docs:
dc, s = doc
dc, s = doc
print(s)
yield dc.page_content
yield dc.page_content
if __name__ == "__main__":
# agent_demo()
@@ -58,8 +69,7 @@ if __name__ == "__main__":
text_input = gr.TextArea()
text_output = gr.TextArea()
text_button = gr.Button()
text_button.click(get_similar, inputs=text_input, outputs=text_output)
demo.queue(concurrency_count=3).launch(server_name="0.0.0.0")