init

Author: Lin-jun-xiang

.gitignore

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+.chroma/
+data/
+External_Data_Pipeline/
+PDF/Omren
+config.py
+main.py
+note.md
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
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+# Rope project settings
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+# mkdocs documentation
+/site
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+# mypy
+.mypy_cache/
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+dmypy.json
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+# Pyre type checker
+.pyre/
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+# pytype static type analyzer
+.pytype/
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+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

PDF/uploaded/10-04-580-SPC - Wall-Smart.pdf

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+## docGPT
+
+主要開發工具:
+* `Python`
+* `Langchain`
+* `Streamlit`
+
+使用該工具至少須具備 `openai_api_key`，有關如何取得 key 可以前往[連結](https://platform.openai.com/)
+
+## Introduction
+
+* `docGPT` 是基於 langchain 與 streamlit 開發的
+    * `langchain`: LangChain 是一個用於**開發由語言模型支持的應用程序的框架**。它支持以下應用程序
+        1. 可以將 LLM 模型與外部數據源進行連接
+        2. 允許與 LLM 模型進行交互
+    * `streamlit`: streamlit 使 python 可以快速、免費的部署屬於你的應用程序 (通常拿來部屬AI)
+* 原理
+  * 透過 langchain 結合多種模型
+    * 基於 langchain retrievalQA 開發的 **pdf 問答工具**
+    * 基於 math-llm 開發的 **數學計算工具**
+    * 基於 google-search 開發的 **搜索工具**
+
+## LangChain
+
+* 有關 langchain 的介紹，建議查看官方文件、[Github源專案](https://github.com/hwchase17/langchain)
+
+#### Questions that ChatGPT cannot answer are handed over to LangChain for implementation!
+
+**ChatGPT 無法回答的問題，交給 Langchain 實現!**
+
+在這邊，作者將簡單介紹 langchain 與 chatgpt 之間的差異，相信您理解以下例子，你會對 langchain 這個開源項目感到震驚!
+
+>今天可以想像 chatgpt 無法回答數學問題、超過 2020 年後的事情(例如2023年貴國總統是誰?)
+>
+> * 數學問題: 除了 Openai 模型，還存在專門處理數學問題的 math-llm
+> * 現今問題: 可以使用 google 搜尋
+>
+>因此，我們要設計一個強大通用的 ai 模型，勢必要加入** "chatgpt"、"math-llm"、"google search"** 三個工具
+>
+>如果使用者的提問屬於數學計算類型，我們就使用 math-llm 工具解決、回答
+>
+>非AI時代，我們就會透過 `if...else...` 方式判斷使用者提問屬於哪種類型，此時就必須在使用者介面讓使用者選擇提問類型
+>(UI 介面會有選擇欄位)
+>
+>但在AI時代，我們要讓使用者直接提問，而不需要事先選擇提問類型!
+>在 langchain 中有一個 agent 的概念，它讓我們可以:
+>
+>  * 我們提供工具給他管理，例如 `tools = ['chatgpt', 'math-llm', 'google-search']`
+>  * 工具也可以包含透過 langchain 設計出的 chain，例如使用 `retrievalQA chain` 設計一個可以回答來自文檔內容的提問，並將此 chain append 到 agent 管理的 tools
+>  * **藉由 agent 判斷使用者提問，並自行決策出使用哪個工具處理問題** (完全自動化、ai化)
+
+透過 langchain，我們可以創建屬於自己的 chatgpt 模型，它可以是通用型的模型，也可以是**企業化、商業化**的!
+
+## How to Use?
+
+* 輸入您的 `API_KEY`:
+    * `OpenAI API KEY`: 必須設定
+    * `SERPAPI API KEY`: 根據您需求，如果您要問**PDF文檔沒有出現**的內容，您就需要用此 KEY
+
+* 上傳來自本地的 PDF 檔案
+* 開始進行提問 ! 
+
+![RGB_cleanup](https://github.com/Lin-jun-xiang/docGPT-streamlit/blob/main/img/docGPT.gif?raw=true)
+
+
+## Why Use?
+
+* 本專案開發的 `docGPT` 具有以下功能:
+  * 上傳 PDF
+  * 與GPT進行來回答覆，快速學習PDF內容
+  * 進行文檔總結
+  * 附加 **"math-llm"**，提供您進行**數學計算**相關問答 (chatgpt無法回答的問題)
+  * 附加 **"google-search"**，提供您進行**google搜尋** (chatgpt無法回答的問題)
+
+

README.md

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+from .agent import AgentHelper

agent/__init__.py

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+import os
+from typing import Optional
+
+import openai
+from langchain import LLMMathChain, SerpAPIWrapper
+from langchain.agents import AgentType, Tool, initialize_agent
+from langchain.callbacks import get_openai_callback
+from langchain.llms import OpenAI
+
+
+openai.api_key = os.getenv('OPENAI_API_KEY')
+os.environ['SERPAPI_API_KEY'] = os.getenv('SERPAPI_API_KEY')
+
+
+class AgentHelper:
+    """Add agent to help docGPT can be perfonm better."""
+    def __init__(self) -> None:
+        self.llm = OpenAI(temperature=0)
+        self.agent_ = None
+        self.tools = []
+
+    @property
+    def get_calculate_chain(self) -> Tool:
+        llm_math_chain = LLMMathChain.from_llm(llm=self.llm, verbose=True)
+        tool = Tool(
+            name='Calculator',
+            func=llm_math_chain.run,
+            description='useful for when you need to answer questions about math'
+        )
+        return tool
+
+    @property
+    def get_searp_chain(self) -> Tool:
+
+        search = SerpAPIWrapper()
+        tool = Tool(
+            name='Search',
+            func=search.run,
+            description='useful for when you need to answer questions about current events'
+        )
+        return tool
+
+    def create_doc_chat(self, docGPT) -> Tool:
+        """Add a custom docGPT tool"""
+        tool = Tool(
+            name='DocumentGPT',
+            func=docGPT.run,
+            description="""
+            useful for when you need to answer questions from the context of PDF,
+            especially ask the specification of display.
+            """
+        )
+        return tool
+
+    def initialize(self, tools):
+        for tool in tools:
+            if isinstance(tool, Tool):
+                self.tools.append(tool)
+
+        self.agent_ = initialize_agent(
+            self.tools,
+            self.llm,
+            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+            verbose=True
+        )
+
+    def query(self, query: str) -> Optional[str]:
+        response = None
+        with get_openai_callback() as callback:
+            response = self.agent_.run(query)
+            print(callback)
+        return response