Techlead 難得的技術相關教學影片，點擊大概是相關教學影片最高的。
https://github.com/techleadhd/chatgpt-retrieval
下面是主要的程式碼片段，可以在本地運行
import os
import sys
import openai
from langchain.chains import ConversationalRetrievalChain, RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import DirectoryLoader, TextLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.indexes import VectorstoreIndexCreator
from langchain.indexes.vectorstore import VectorStoreIndexWrapper
from langchain.llms import OpenAI
from langchain.vectorstores import Chroma
import constants
os.environ["OPENAI_API_KEY"] = constants.APIKEY
# Enable to save to disk & reuse the model (for repeated queries on the same data)
PERSIST = False
query = None
if len(sys.argv) > 1:
query = sys.argv[1]
if PERSIST and os.path.exists("persist"):
print("Reusing index...\n")
vectorstore = Chroma(persist_directory="persist", embedding_function=OpenAIEmbeddings())
index = VectorStoreIndexWrapper(vectorstore=vectorstore)
else:
#loader = TextLoader("data/data.txt") # Use this line if you only need data.txt
loader = DirectoryLoader("data/")
if PERSIST:
index = VectorstoreIndexCreator(vectorstore_kwargs={"persist_directory":"persist"}).from_loaders([loader])
else:
index = VectorstoreIndexCreator().from_loaders([loader])
chain = ConversationalRetrievalChain.from_llm(
llm=ChatOpenAI(model="gpt-3.5-turbo"),
retriever=index.vectorstore.as_retriever(search_kwargs={"k": 1}),
)
chat_history = []
while True:
if not query:
query = input("Prompt: ")
if query in ['quit', 'q', 'exit']:
sys.exit()
result = chain({"question": query, "chat_history": chat_history})
print(result['answer'])
chat_history.append((query, result['answer']))
query = None
2.可以使用 google 的 Agent
https://github.com/JayZeeDesign/researcher-gpt
下面是主要的程式碼片段，這個也可以在本地運行
import os
from dotenv import load_dotenv
from langchain import PromptTemplate
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.prompts import MessagesPlaceholder
from langchain.memory import ConversationSummaryBufferMemory
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
from langchain.tools import BaseTool
from pydantic import BaseModel, Field
from typing import Type
from bs4 import BeautifulSoup
import requests
import json
from langchain.schema import SystemMessage
from fastapi import FastAPI
load_dotenv()
brwoserless_api_key = os.getenv("BROWSERLESS_API_KEY")
serper_api_key = os.getenv("SERP_API_KEY")
# 1. Tool for search
def search(query):
url = "https://google.serper.dev/search"
payload = json.dumps({
"q": query
})
headers = {
'X-API-KEY': serper_api_key,
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
return response.text
# 2. Tool for scraping
def scrape_website(objective: str, url: str):
# scrape website, and also will summarize the content based on objective if the content is too large
# objective is the original objective & task that user give to the agent, url is the url of the website to be scraped
print("Scraping website...")
# Define the headers for the request
headers = {
'Cache-Control': 'no-cache',
'Content-Type': 'application/json',
}
# Define the data to be sent in the request
data = {
"url": url
}
# Convert Python object to JSON string
data_json = json.dumps(data)
# Send the POST request
post_url = f"https://chrome.browserless.io/content?token={brwoserless_api_key}"
response = requests.post(post_url, headers=headers, data=data_json)
# Check the response status code
if response.status_code == 200:
soup = BeautifulSoup(response.content, "html.parser")
text = soup.get_text()
print("CONTENTTTTTT:", text)
if len(text) > 10000:
output = summary(objective, text)
return output
else:
return text
else:
print(f"HTTP request failed with status code {response.status_code}")
def summary(objective, content):
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613")
text_splitter = RecursiveCharacterTextSplitter(
separators=["\n\n", "\n"], chunk_size=10000, chunk_overlap=500)
docs = text_splitter.create_documents([content])
map_prompt = """
Write a summary of the following text for {objective}:
"{text}"
SUMMARY:
"""
map_prompt_template = PromptTemplate(
template=map_prompt, input_variables=["text", "objective"])
summary_chain = load_summarize_chain(
llm=llm,
chain_type='map_reduce',
map_prompt=map_prompt_template,
combine_prompt=map_prompt_template,
verbose=True
)
output = summary_chain.run(input_documents=docs, objective=objective)
return output
class ScrapeWebsiteInput(BaseModel):
"""Inputs for scrape_website"""
objective: str = Field(
description="The objective & task that users give to the agent")
url: str = Field(description="The url of the website to be scraped")
class ScrapeWebsiteTool(BaseTool):
name = "scrape_website"
description = "useful when you need to get data from a website url, passing both url and objective to the function; DO NOT make up any url, the url should only be from the search results"
args_schema: Type[BaseModel] = ScrapeWebsiteInput
def _run(self, objective: str, url: str):
return scrape_website(objective, url)
def _arun(self, url: str):
raise NotImplementedError("error here")
# 3. Create langchain agent with the tools above
tools = [
Tool(
name="Search",
func=search,
description="useful for when you need to answer questions about current events, data. You should ask targeted questions"
),
ScrapeWebsiteTool(),
]
system_message = SystemMessage(
content="""You are a world class researcher, who can do detailed research on any topic and produce facts based results;
you do not make things up, you will try as hard as possible to gather facts & data to back up the research
Please make sure you complete the objective above with the following rules:
1/ You should do enough research to gather as much information as possible about the objective
2/ If there are url of relevant links & articles, you will scrape it to gather more information
3/ After scraping & search, you should think "is there any new things i should search & scraping based on the data I collected to increase research quality?" If answer is yes, continue; But don't do this more than 3 iteratins
4/ You should not make things up, you should only write facts & data that you have gathered
5/ In the final output, You should include all reference data & links to back up your research; You should include all reference data & links to back up your research
6/ In the final output, You should include all reference data & links to back up your research; You should include all reference data & links to back up your research"""
)
agent_kwargs = {
"extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
"system_message": system_message,
}
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613")
memory = ConversationSummaryBufferMemory(
memory_key="memory", return_messages=True, llm=llm, max_token_limit=1000)
agent = initialize_agent(
tools,
llm,
agent=AgentType.OPENAI_FUNCTIONS,
verbose=True,
agent_kwargs=agent_kwargs,
memory=memory,
)
# 4. Use streamlit to create a web app
# def main():
# st.set_page_config(page_title="AI research agent", page_icon=":bird:")
# st.header("AI research agent :bird:")
# query = st.text_input("Research goal")
# if query:
# st.write("Doing research for ", query)
# result = agent({"input": query})
# st.info(result['output'])
# if __name__ == '__main__':
# main()
# 5. Set this as an API endpoint via FastAPI
app = FastAPI()
class Query(BaseModel):
query: str
@app.post("/")
def researchAgent(query: Query):
query = query.query
content = agent({"input": query})
actual_content = content['output']
return actual_content
之後幾天都會是我的修改版，今天主要是尋找參考的專案。
LLM明後天相關的技術也會有colab版的範例，除了分布式訓練的部分。
分布式訓練的部分我大概會去租個多GPU的 Server訓練。
看影片追技術