From d6498a7938c38a57095b2e7511cf2665382eebdf Mon Sep 17 00:00:00 2001 From: yongwoon Date: Sat, 2 Mar 2024 12:53:50 +0900 Subject: [PATCH 1/5] add docs/summary.md --- docs/summary.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 docs/summary.md diff --git a/docs/summary.md b/docs/summary.md new file mode 100644 index 0000000..ac9323c --- /dev/null +++ b/docs/summary.md @@ -0,0 +1 @@ +# Summary From 60f0663e68247bcb8361b4590e6934485486ff88 Mon Sep 17 00:00:00 2001 From: yongwoon Date: Sat, 9 Mar 2024 21:11:00 +0900 Subject: [PATCH 2/5] chatper 02 --- docs/README.md | 16 ++++ docs/prompt.md | 113 +++++++++++++++++++++++++++++ docs/terms.md | 7 ++ "docs/\353\205\274\353\254\270.md" | 8 ++ 4 files changed, 144 insertions(+) create mode 100644 docs/README.md create mode 100644 docs/prompt.md create mode 100644 docs/terms.md create mode 100644 "docs/\353\205\274\353\254\270.md" diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..6d141ed --- /dev/null +++ b/docs/README.md @@ -0,0 +1,16 @@ +# Getting Start + +## Link + +### Open AI + +- [open AI prompt engineering](https://platform.openai.com/docs/guides/prompt-engineering) +- [Best practices for prompt engineering with the OpenAI API](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-the-openai-api) + +### Guide + +- [prompt engineer guide](https://www.promptingguide.ai/jp/introduction/elements) + +### Blog + +- [画像付きのノベルゲームを遊べるプロンプトを作ったら臨場感が溢れすぎた話](https://note.com/churin_1116/n/n1e3697c9db7f) diff --git a/docs/prompt.md b/docs/prompt.md new file mode 100644 index 0000000..2772119 --- /dev/null +++ b/docs/prompt.md @@ -0,0 +1,113 @@ +# prompt + +LLM に実行して欲しい Task を命令として記述して、User Input data とは独立させます。そして、Input data はわかりやすように「"""」,「###」といった記号で区切ることも多いです。 + +## Chapter 02 + +- receipt + +```txt +以下の料理のレシピを考えてください。 + +料理名: """ +{dish} +""" +``` + +- 文脈(Context)を与える + +前提条件や外部情報などをムン脈(context) として当てると、文脈に従った回答を得ることができます。 + +```text +前提条件を踏まえて、以下の料理のレシピを考えてください。 + +前提条件:""" +分量: 一人分 +味の好み: 乾口 +""" + +料理名: """ +カレー +""" +``` + +- 出力形式を指定する + +````text +前提条件を踏まえて、以下の料理のレシピを考えてください。 +出力は以下のような JSON 形式にしてください。 + +```json +{ + "材料": ["材料1", "材料2"], + "手順": ["手順1", "手順2"] +} +``` + +前提条件:""" +分量: 一人分 +味の好み: 乾口 +""" + +料理名: """ +カレー +""" +``` + +```` + +### zero-shot prompting + +- テキストをポジティブ・ネガティブ・中立のどれかに分類してください。 + +```txt +以下のテキストをポジティブ・ネガティブ・中立のどれかに分類してください。 + +テキスト:ChatGPTはプログラミングの悩みごとをたくさん解決してくれる + +分類: +``` + +### few-shot prompting + +prompt でいくつか demonstration を与えることで、求める回答を得やすくなります。そのような手法を few-show prompting と言います。 + +- zero-shot prompting + +```txt +色を回答してください。 + +Q: バナナ +A: +``` + +- few-shot prompting + +```txt +色を回答してください。 + +Q: りんご +A: 赤 +Q: メロン +A: 緑 +Q: バナナ +A: +``` + +- Zero-shot Chain of Thought(zero-shot Cot) prompting + +「step by step で考えてみましょう」入れたことと入れてないことと答えることを見ると step by step の方が段階的に説明があり、多くの Task に効果的である。 +なお、Zero-shot Cot prompting と呼ぶのは、先に考案された「Chain of Thought(CoT)プロンプティング」では、few-shot プロンプティングを使い、ステップバイステップで考える例をいくつか含めていたためです。 + +```txt +私は市場に行って10個のリンゴを買いました。隣人に2つ、修理工に2つ渡しました。それから5つのリンゴを買って1つ食べました。 +残りは何個ですか? +回答だけ答えてください。 +``` + +```txt +私は市場に行って10個のリンゴを買いました。隣人に2つ、修理工に2つ渡しました。それから5つのリンゴを買って1つ食べました。残りは何個ですか? +ステップバイステップで考えてみましょう。 +``` + +## Chapter 03 diff --git a/docs/terms.md b/docs/terms.md new file mode 100644 index 0000000..d571d6a --- /dev/null +++ b/docs/terms.md @@ -0,0 +1,7 @@ +# Terms + +- Fine tuning + 既存のモデルに追加の学習をさせる + +- In-context Learning (ICL) + few-show prompting のように、prompt ないのいくつかの例によって言語 model に task を学ばせること diff --git "a/docs/\353\205\274\353\254\270.md" "b/docs/\353\205\274\353\254\270.md" new file mode 100644 index 0000000..dde2140 --- /dev/null +++ "b/docs/\353\205\274\353\254\270.md" @@ -0,0 +1,8 @@ +# 논문 + +- LLM に対話にとどまらずアクションを起こさせる「ReAct」 + [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/abs/2210.03629) +- 社内文書などの Q&A の質を高める「HyDE」 + [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://arxiv.org/abs/2212.10496) +- AI エージェントたちを仮想的な街に住まわせる「Generative Agents」 + [Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442) From 3d6c441aeb693010faf64b648d48ed82fca7a8fa Mon Sep 17 00:00:00 2001 From: yongwoon Date: Sun, 17 Mar 2024 16:08:18 +0900 Subject: [PATCH 3/5] Chapter03 finished --- docs/3.6-function-calling.md | 21 +++++++++++++++++ docs/README.md | 22 ++++++++++++++++++ docs/chat-completions-api.md | 5 ++++ ...0\343\203\242\343\203\207\343\203\253.png" | Bin 0 -> 434206 bytes ...I\343\201\256\345\257\276\345\277\234.png" | Bin 0 -> 237040 bytes docs/py-library.md | 7 ++++++ 6 files changed, 55 insertions(+) create mode 100644 docs/3.6-function-calling.md create mode 100644 docs/chat-completions-api.md create mode 100644 "docs/imgs/OpenAI\343\201\256\346\226\207\346\233\270\347\224\237\346\210\220\343\203\242\343\203\207\343\203\253.png" create mode 100644 "docs/imgs/OpenAI\343\201\256\346\226\207\346\233\270\347\224\237\346\210\220\343\203\242\343\203\207\343\203\253\343\201\256\344\270\200\350\246\247\343\201\250API\343\201\256\345\257\276\345\277\234.png" create mode 100644 docs/py-library.md diff --git a/docs/3.6-function-calling.md b/docs/3.6-function-calling.md new file mode 100644 index 0000000..e963803 --- /dev/null +++ b/docs/3.6-function-calling.md @@ -0,0 +1,21 @@ +# Function calling + +Function calling は、2023.06.13 に追加された Chat Completions API の新機能です。簡単に言えば、利用可能な関数を LLM に伝えておいて、 + +LLM に「関数を使いたい」という判断をさせる機能です。 +(LLM が関数を実行する訳ではなく、LLM は「関数を使いたい」という応答を返してくれだけです) + +## Flow + +処理の流れとしては、 + +1. まず、利用可能な関数の一覧とともに、質問などのテキストを送信します。 +2. それに対し LLM が「関数を使いたい」という応答をしてきたら、Python などのプログラムで該当の関数を実行します。 +3. その実行結果を含めたリクエストを再度 LLM に送ると、 +4. 最終的な回答が得られる、 + +という流れです。押さえておきたいのは、LLM はどんな関数をどう使いたいか返してくれるだけで、関数の実行は Python などを使って Chat Completions API の利用者側で実行する必要がある、という点です。 + +## Link + +- [Function callling の sample code](https://platform.openai.com/docs/guides/function-calling/function-calling) diff --git a/docs/README.md b/docs/README.md index 6d141ed..b876637 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,12 +1,34 @@ # Getting Start +## OpenAI の文書生成モデル + +![OpenAIの文書生成モデル](./imgs/OpenAIの文書生成モデル.png) + +## OpenAI の文書生成モデルの一覧と API の対応 + +![OpenAI の文書生成モデルの一覧とAPIの対応](./imgs/OpenAIの文書生成モデルの一覧とAPIの対応.png) + ## Link +### Tools + +- [google colab](https://colab.research.google.com/) + - browser 上で Python などの code を入力して、その場で code を実行できる service + - 行の先頭に「!」をつけると linux shell command を実行することあできる + ### Open AI - [open AI prompt engineering](https://platform.openai.com/docs/guides/prompt-engineering) - [Best practices for prompt engineering with the OpenAI API](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-the-openai-api) +#### Open AI references + +- [OpenAI models](https://platform.openai.com/docs/models) +- [OpenAI Pricing](https://openai.com/pricing) +- [OpenAI deprecations](https://platform.openai.com/docs/deprecations/) +- [Tokenizer](https://platform.openai.com/tokenizer) + - 入力したテキストがトークンとしてどのように分割され、トークン数はいくつなのかを確認できます。 + ### Guide - [prompt engineer guide](https://www.promptingguide.ai/jp/introduction/elements) diff --git a/docs/chat-completions-api.md b/docs/chat-completions-api.md new file mode 100644 index 0000000..78963e4 --- /dev/null +++ b/docs/chat-completions-api.md @@ -0,0 +1,5 @@ +# Chat Completion API + +## paramters + +- https://platform.openai.com/docs/api-reference/chat/create diff --git "a/docs/imgs/OpenAI\343\201\256\346\226\207\346\233\270\347\224\237\346\210\220\343\203\242\343\203\207\343\203\253.png" "b/docs/imgs/OpenAI\343\201\256\346\226\207\346\233\270\347\224\237\346\210\220\343\203\242\343\203\207\343\203\253.png" new file mode 100644 index 0000000000000000000000000000000000000000..4ba05dee37ddccdac09561245d201dcf5c459cba GIT binary patch literal 434206 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z*Y$SM0J5+s$n;Csf4P~eXeIf#&wnvsdmF69)!P;7W)4I{{Q^^q3wh=rHvT*$Ar2CN z+0M1smjn9cUtkKr*ckpY`FD}V2JkAeIb${oj2?f13D8ab>(%K0Pxt>q_c3I(U(tBP S^#Dr&Y-m*-mGY}rA^!oTI`c>X literal 0 HcmV?d00001 diff --git a/docs/py-library.md b/docs/py-library.md new file mode 100644 index 0000000..1ba532c --- /dev/null +++ b/docs/py-library.md @@ -0,0 +1,7 @@ +# Python libraries + +- [tiktoken](https://github.com/openai/tiktoken) + +Python のプログラムでトークン数を確認することができます。 + +- [openai](https://github.com/openai/openai-python) From 2284a01495539516b8be081f2e5ae94b68d42bcf Mon Sep 17 00:00:00 2001 From: yongwoon Date: Tue, 19 Mar 2024 22:05:46 +0900 Subject: [PATCH 4/5] finish chapter 04 --- docs/README.md | 5 + ...13\343\201\237\343\202\201\343\201\253.md" | 6 +- docs/ch04-langchain.md | 254 ++++++++++++++++++ docs/imgs/4.3-prompt-template.png | Bin 0 -> 232626 bytes docs/imgs/4.5-SimpleSequentialChain.png | Bin 0 -> 159771 bytes ...-chain-example-specialized-by-usercase.png | Bin 0 -> 444745 bytes docs/imgs/4.5-langchain-llmchain.png | Bin 0 -> 92089 bytes docs/terms.md | 3 + 8 files changed, 266 insertions(+), 2 deletions(-) rename docs/3.6-function-calling.md => "docs/ch03-chatgpt\343\202\222API\343\201\213\343\202\211\345\210\251\347\224\250\343\201\231\343\202\213\343\201\237\343\202\201\343\201\253.md" (95%) create mode 100644 docs/ch04-langchain.md create mode 100644 docs/imgs/4.3-prompt-template.png create mode 100644 docs/imgs/4.5-SimpleSequentialChain.png create mode 100644 docs/imgs/4.5-chain-example-specialized-by-usercase.png create mode 100644 docs/imgs/4.5-langchain-llmchain.png diff --git a/docs/README.md b/docs/README.md index b876637..adb5aa4 100644 --- a/docs/README.md +++ b/docs/README.md @@ -32,6 +32,11 @@ ### Guide - [prompt engineer guide](https://www.promptingguide.ai/jp/introduction/elements) +- [4.5 simple sequencial chain](https://www.promptingguide.ai/jp/techniques/cot) + +### Langchain + +- [!!! chains !!!](https://python.langchain.com/docs/modules/chains) ### Blog diff --git a/docs/3.6-function-calling.md "b/docs/ch03-chatgpt\343\202\222API\343\201\213\343\202\211\345\210\251\347\224\250\343\201\231\343\202\213\343\201\237\343\202\201\343\201\253.md" similarity index 95% rename from docs/3.6-function-calling.md rename to "docs/ch03-chatgpt\343\202\222API\343\201\213\343\202\211\345\210\251\347\224\250\343\201\231\343\202\213\343\201\237\343\202\201\343\201\253.md" index e963803..0cb74a8 100644 --- a/docs/3.6-function-calling.md +++ "b/docs/ch03-chatgpt\343\202\222API\343\201\213\343\202\211\345\210\251\347\224\250\343\201\231\343\202\213\343\201\237\343\202\201\343\201\253.md" @@ -1,11 +1,13 @@ -# Function calling +# 3.6-function-calling + +## Function calling Function calling は、2023.06.13 に追加された Chat Completions API の新機能です。簡単に言えば、利用可能な関数を LLM に伝えておいて、 LLM に「関数を使いたい」という判断をさせる機能です。 (LLM が関数を実行する訳ではなく、LLM は「関数を使いたい」という応答を返してくれだけです) -## Flow +### Flow 処理の流れとしては、 diff --git a/docs/ch04-langchain.md b/docs/ch04-langchain.md new file mode 100644 index 0000000..49699ca --- /dev/null +++ b/docs/ch04-langchain.md @@ -0,0 +1,254 @@ +# [Langchain](https://python.langchain.com/docs/modules/) + +- [code](https://colab.research.google.com/drive/116W16n0N83r7GkTv3W9qnXprdtMm2r4o#scrollTo=BaEj6h8yF2xl) + +LLM を使った Application 開発 framework です。LLM を使ったさまざまな種類の application で使うことができます。 + +## Langchain の usecase + +langchain には、大きく2つの側面があります。 + +- 1つは、LLM を使った application 開発に必要な部品を、抽象化された module として提供していることです。 +- もう一つは、特定の usecase に特化した機能を提供していることです。 + +LLM を使った application としては、次のような例が挙げられます + +- chatGPT のように会話できる chatbot +- 文書の予約 Tool +- 社内文書や PDF file への Q&A application +- あとほど解説する「AI Agent」 + +LLM を使った application 開発に使える framework, library は langChain 意外にもたくさんあります。いくつか例を挙げると以下の物などが有名です。 + +- [LlamaIndex](https://github.com/jerryjliu/llama_index) + - LangChain の Data connection 機能に特化した framework +- [Semantic Kernel](https://github.com/microsoft/semantic-kernel) +- [Guidance](https://github.com/microsoft/guidance) + +--- + +## LangChain module + +LangChain module は大きく次の 6 つに整理されています。 + +- Model I/O + - Prompts + - Prompt Templates + - Example selector + - Language models + - LLMs + - Chat models +- Data connection + - Document loaders + - Document transformers + - text splitters + - post retrieval + - Text embedding modesl + - Vector stores + - Retrievers +- Chains +- Agents + - Agent Types + - Tools + - ToolKits +- Memory +- Callbacks + +## 4.2 Language models + +Language models は、LangChain での言語モデルの使用方法を提供する module です。LangChain の Language models を使うことで、さまざまな Language model を共通の Interface で使用することができます。簡単にいってしまえば、 LLM を LangChain 流で使えるようにする Wrapper のことです。LangChain では Language model を LLMS, Chat models の大きく2種類に整理しています。 + +### LLMs + +1 つの text input に対して1つの text output を返す、典型的な大規模言語 model を扱う module です。 + +### Callback を使った streaming + +Chat Completions API は streaming で応答を得ることができます。LLM を使った application を実装する場合、UX の工場を目的として、steaming で応答を得たいことが多いはずです。 + +LangChain では、Callback 機能を使うことで chat Completions API の応答を streaming で処理することが可能です。 + +例えば、LangChain が提供している StreamingStdOutCallbackHandler を ChatOpenAI を設定すると、生成されて text が streaming で標準出力に表示されます。 + +```py +from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage + +chat = ChatOpenAI( + model_name="gpt-3.5-turbo", + temperature=0, + streaming=True, + callbacks=[StreamingStdOutCallbackHandler()], +) + +messages = [HumanMessage(content="自己紹介してください")] +result = chat(messages) +``` + +LangChain の callback 機能では、前述の StreamingStdOutCallbackHandler のように公式で提供されているものを使うこともできれば、Custom Callback Hander を日そうして使うこともできます。 + +Custom CallbackHandler を使うと + +- LLM の処理開始 (on_lim_start) +- 新しい Token の生成 (on_lim_new_token) +- LLM 処理の終了 (on_lim_end) + +などの timing で、任意の処理を実行することができます。 + +## 4.3 prompts + +### PromptTemplate + +![4.3-prompt-template.png](./imgs/4.3-prompt-template.png) + +### [Example Selectors](https://python.langchain.com/docs/modules/model_io/prompts/example_selectors) + +Few-show prompting を使うと、LLM から期待する応答を得やすくなります。 +「Example Selectors」は、Few-shot prompting で使う例を選択する機能です。 + +Example selectors を使うと、例を埋め込む際に次のような処理が可能となっています。 + +- prompt の長さの最大値を小エア内容にする目的で、ユーザーの入力が短い場合はたくさんの例を埋め込み、ユーザーの入力が長い場合は少ない例を埋め込む +- ユーザーの入力内容に近い例を自動的に選択して埋め込む + +## 4.4 Output Parsers + +Output Parser は、 + +- JSON などの output 形式を指定する prompt 作成と +- 応答の text の python object への変換機能を提供します。 + +Output Parsers を使うと、LLM の応答から該当箇所を抽出して python object に mapping するという定番の処理を簡単に実装できます。 + +### PydanticOutputParser を使った Python Object の取得 + +PydanticOutputParser を使うことで、LLM の出力から Python object を簡単に取得できます。 + +LLM が出力したレシピを Receipi class の instance に自動で変換する例を見ていきます。 + +```python +from langchain.chat_models import ChatOpenAI +from langchain.output_parsers import PydanticOutputParser +from langchain.prompts import PromptTemplate +from pydantic import BaseModel, Field + +# 1. まず、LLMに出力させたい「材料一覧(ingredients)」と「手順(steps)」を +# フィールドとするRecepiクラスを、Pydantic model として定義します。 +class Recipe(BaseModel): + ingredients: list[str] = Field(description="ingredients of the dish") + steps: list[str] = Field(description="steps to make the dish") + +# ------------- + +# 2. このRecipeクラスを与えて、PydanticOutputParserを作成します。 +output_parser = PydanticOutputParser(pydantic_object=Recipe) + +template = """料理のレシピを考えてください。 + +{format_instructions} + +料理名: {dish} +""" + +# 3. そして、PydanticOutputParserから、プロンプトに含める出力形式の説明文を作成します。 +# ここで作成したformat_instructionsは、Recipeクラスに対応した出力形式の指定の文字列です。 +format_instructions = output_parser.get_format_instructions() + +prompt = PromptTemplate( + template=template, + input_variables=["dish"], + partial_variables={"format_instructions": format_instructions} +) + +chat = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0) +formatted_prompt = prompt.format(dish="カレー") + +# -------- +from langchain.chains import LLMChain + +chain = LLMChain(prompt=prompt, llm=chat, output_parser=output_parser) +recipe = chain.run(dish="カレー") + +print(type(recipe)) +print(recipe) +``` + +Output parser を使う例での Point は以下の 2 つです。 + +- Recipe class の定義をもとに、出力系 s 機を指定する文字列が自動的に作られた +- LLM の出力を簡単に Recepi Class instance に交換できた + +## 4.5 Chains + +### LLMChain - PromptTemplate・Language model・OutputParser を繋ぐ + +![4.5-langchain-llmchain](4.5-langchain-llmchain.png) + +### SimpleSequentialChain - Chain と Chain を繋ぐ + +chain と chain を接続する chain も存在します。SimpleSequentialChain を使うと、Chain と Chain を直列に連結できます。 + +![4.5-SimpleSequentialChain](4.5-SimpleSequentialChain.png) + +SimpleSequentialChain を使うと、さまざまな Chain を直列に接続することができます。Chain 同志を接続する Chain としては、他にも複数の入力に対応した SequentialChain や、LLM の判断で Chain の分岐を実現する LLMRouterChain などがあります。 + +![4.5-chain-example-specialized-by-usercase](4.5-chain-example-specialized-by-usercase.png) + +| chain | summary | +| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| OpenAIModerationChain | OpenAI の Moderation API により、 Text が Open AI の利用 Policy に反していないか Check する | +| LLMRequestsChain | 指定した URL に HTTP request を送信し、 response に踏まえて LLM に質問に高騰させる | +| OpenAPIEndpointChain | 自然言語で入力を与えると、Open API 仕様 (swagger) をもとに LLM が API request を生成し、その内容で API を呼び出す。さらに、API からの response を踏まえて LLM に質問を高騰させることも可能 | +| PALChain (Experimental-実験的) | 自然言語の入力をもとに、LLM が program を生成し、program を実行した結果を返す「PAL: program-aided Lnaguage Models」という論文に基づく実装 | +| SQLDatabaseChain(Experimental-実験的) | 自然言語の入力をもとに、LLM が SQL を生成し、 database に対して実行した上で、最終的な回答を s つつ力する | + +[chains](https://python.langchain.com/docs/modules/chains) + +### COLUMN: Chain の内部の動きを確認するには + +LangChain で code を書いていると、Chain の内部の動作を確認した句なることが多いです。その際、次の設定を使うことができます(例えば、code の先頭に次のような設定を記述します)。 + +```py +import langchain + +# Format された prompt などが表示される +langchain.verbose = True +# LangChain の挙動が最も詳細に出力される +langchain.debug = True +``` + +## Memory + +Chat Completion API は stateless であり、会話履歴を踏まえた応答を得るには、会話履歴を request に含める必要があります。変わり歴の保持などに関する便利な機能を提供するのが LangChain の Memory です。 + +- 種類 + +| class | explain | +| ------------------------------- | ----------------------------------------------------------------------------------------------------------- | +| ConversationBufferWindowMemory | 直近 K この会話だけを prompt に含める | +| ConversationSummaryMemory | LLM を使って会話履歴を要約する | +| ConversationSummaryBufferMemory | 直近の会話はそのまま prompt に含めるが、古い会話内容は要約する | +| ConversationTokenBufferMemory | 指定した token 数までの会話だけを prompt に含める | +| VectorStoreRetrieverMemory | 後述する vector store を応用して、会話履歴のうち入力に関連する k 個の text だけを記憶として prompt に含める | + +### ConversationBufferMemory + +「単純に会話履歴を保持するだけ」する + +```py +from langchain.chains import ConversationChain +from langchain.chat_models import ChatOpenAI +from langchain.memory import ConversationBufferMemory + +chat = ChatOpenAI(model_name="gpt-4", temperature=0) +conversation = ConversationChain( + llm=chat, + memory=ConversationBufferMemory() +) + +while True: + user_message = input("You: ") + ai_message = conversation.run(input=user_message) + print(f"AI: {ai_message}") +``` diff --git a/docs/imgs/4.3-prompt-template.png b/docs/imgs/4.3-prompt-template.png new file mode 100644 index 0000000000000000000000000000000000000000..31c6ebc25ae5d6f07097d6a17cb59b1530436625 GIT binary patch literal 232626 zcmeFX1y@|nmN1M3cXua1aJRRXo*M; zOs%86Z$WkhD09C)V!dM;i<{HK^)Y5>29bzdDc=G_D0lci5ihUgJIqpXdsv4) zOvqK{zUZF$i!zs=Try;gv?r>02t`0FgKd0UIv(562h}WkW>N z8YMf#kHFqY5v1sQ_fiv8!~Zddq@sRs46eDGa|P8j{i%n4TNh9R#VQvnl1IfF5vS7^ zNnin#EW|rsgNX5e4uI9JF4R1EBZ%q!njF1Q(CZ21&qFPe8*$x60k(_L(;;4fDa123FWG{ z$FIOjXQ(#S1yb8{C;#Zgb+>NAaLk~Bo5C5hAzO3rRg{&Rb(j#vJS(~*i)Htq)-f+B zEc$5mn&2m5-|G+V!#72!uA7rT0(ijLL7^V;&qOqs$XbA`-GvjYD#e{@EUzBE>grlQHP~~3x@qn`UcJh#GwD$cM~2$X0%^z>1oc`F!1yYD 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z1(!W3a>F5&vwI8r62eTLK2N7KLErxVes)Grs_snZ>Xy*A+$+i2vAZqf5}G#a+ojXq zhgI9{u70F-7j4oE%!~4$S$X0R{a()S1y~p{QxkotMT?F+T-r&b(f3B_8}HhkoERS^ z3=C%?bb-(08MQQ|a?}E`-6HtKf~~j3!ha(K!Qmo&Y3Gx@1z5Gc5=&0d#iwVE({H>}w!7 zx$Czk5!$>n9Sfc(g?(!lulDW-lF*o$>7(3sAa|(nf10t`idqO;L&0I6rwjCmVXcK{ zfTWiY1M4G9!f2%XC~(}6%fiO*76NP&w||isZVoV_@7+Ki10xfNok!Xr>FA>Wm=9&( zxt;s}<^Z=aR}D(>AAW_&|EtWu3Mbbxout>6JrnuQ$JrwUFiWuaE%_Y(e&JE!qW4K= z9Ra>_W`9-j@46&dE>I;ZRJ{MAc6J^Gr6R1VlVck1f4Aqa!Uq4})c@z|{~cLPWQJfR VpEDL0tT6yTOH*r;Dx+H|{{^Zg2KxX2 literal 0 HcmV?d00001 diff --git a/docs/terms.md b/docs/terms.md index d571d6a..10e266c 100644 --- a/docs/terms.md +++ b/docs/terms.md @@ -5,3 +5,6 @@ - In-context Learning (ICL) few-show prompting のように、prompt ないのいくつかの例によって言語 model に task を学ばせること + +- LangChain + LLM を使った Application 開発 framework です。LLM を使ったさまざまな種類の application で使うことができます。 From 200324169278de8d14ca7d95e8aa5c0fe97f415e Mon Sep 17 00:00:00 2001 From: yongwoon Date: Wed, 20 Mar 2024 18:10:20 +0900 Subject: [PATCH 5/5] Chapter05 finished --- docs/ch05-data-connection.md | 151 ++++++++++++++++++ docs/imgs/5.2-langchain-toolkits-list.png | Bin 0 -> 323327 bytes ...4\343\201\252\343\201\214\343\202\212.png" | Bin 0 -> 261507 bytes docs/imgs/5.5-retrievalQA.png | Bin 0 -> 261414 bytes docs/terms.md | 11 ++ 5 files changed, 162 insertions(+) create mode 100644 docs/ch05-data-connection.md create mode 100644 docs/imgs/5.2-langchain-toolkits-list.png create mode 100644 "docs/imgs/5.4-data-connection-\350\246\201\347\264\240\343\201\256\343\201\244\343\201\252\343\201\214\343\202\212.png" create mode 100644 docs/imgs/5.5-retrievalQA.png diff --git a/docs/ch05-data-connection.md b/docs/ch05-data-connection.md new file mode 100644 index 0000000..423489d --- /dev/null +++ b/docs/ch05-data-connection.md @@ -0,0 +1,151 @@ +# Chapter 05 - Data connection + +data connection は、LLM と外部の data を接続するための機能です。 + +## 5.1 Data Connection + +### RAG(Retrieval Augmented Generation) + +文書を OpenAI の Embeddings API などで vector 化しておいて、入力 vector が違い文書を検索して context に含める手法を RAG と呼ぶ +。文書はあらかじめ用意した database から検索する小 t もあれば、Google などの検索 Engine で web 上から検索することも考えられます。 + +LangChain の Data connection では、とくに Vector store を使い、文書をベクトル化して保存しておいて、入力のテキストとベクトルの近い文書を検索して context に含めて使う方法が提供されています。 + +### Data Connection の概要 + +RAG に使える LangChain module が data connection です。Data connection には次の5シュリの機能があります。 + +- Document Loaders + - data source から document を読み込む +- Document Trasnformers + - document に何らかの変換をかける +- Text embedding models + - document を vector 化する +- Vector stores + - vector 貸した document の保存先 +- Retrievers + - 入力の text と関連する document を検索する + +![5.4-data-connection-要素のつながり](./imgs/5.4-data-connection-要素のつながり.png) + +#### Document loaders + +Github Repository で公開されている document を読み込むことにします。まず、GitPython という package を install する。 + +[DocumentLoader の種類](https://python.langchain.com/docs/integrations/platforms/) + +#### Document Transformers + +読み込んだ document には何らかの変換をかけること。 +ex + +- document をある程度の長さで chunk に分解したい場合 + - document を適切な大きさの chunk に文わりすることで、LLM に入力する token 数を削減したり、より正確な回答を得やすくなります。Langchain の CharacterTextSplitter という class を使った document を chunk に分割する例 + +```py +from langchain.text_splitter import CharacterTextSplitter + +text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) + +docs = text_splitter.split_documents(raw_docs) +print(len(docs)) +``` + +- LangChain から提供する DocumentTransformaer + + - Html2TextTransformer + - HTML を plain text に変換する + - EmbeddingsRedundantFilter + - 類似した document を除外する + - OpenAIMetadataTagger + - metadata を抽出する + - DoctranTextTranslator + - document を翻訳する + - DoctranQATransformer + - user の質問と関連し訳す + +- Link + - [LangChain から提供する DocumentTransformaer](https://python.langchain.com/docs/integrations/document_transformers) + +#### Text embedding models + +#### Vector stores + +#### Retrievers + +text に関連する document を得る interface を Retriever といいます + +#### Retrieval QA (Chain) + +chatbot などの application としては、入力に関連する文書を取得 (Retrieve) するのに加えて、取得した内容を promptTemplate に context として埋め込んで、LLM に質問して回答(QA) してもらいたい場合があります。 + +![5.5-retrievalQA](./imgs/5.5-retrievalQA.png) + +- 他の chain + - ConversationRetrievalChain + +##### (Column) RetrievalQA の chain_type + +- stuff + - 入力と関連する複数の文書を同時に prompt に含めて回答を得る +- map_reduce + - それぞれおの文書に対して回答を得て(map), その後、最終的な回答を得る(reduce)とい j 流れになります。 +- map_rerank + - map_reduce と同様にそれぞれの文書に対して回答を得ると同時に、LLM が回答に score をつけます。その score が最も高かった回答が、最終的な回答として採用されます。 +- refine + - LLM に 1 つずつ文書を与えて回答を徐々に作らせます。つまり、だんだんと回答を brush up していくような流れになります。 + - refine 処理は、Retrieval QA だけでなく、複数の文書の要約といった usecase でも使うことができます。 + +## 5.2 Agents + +### Agents の概要 + +RetrievalQA (Chain) では、Vector store を検索して、検索結果を踏まえて LLM に回答させました。LangChain には他にも、LLM の応答をもとに API を呼び出す `OpenAPIEndpointChain` や SQL を実行する `SQLDatabaseChian` といった Chains もあります。 + +これらの Chains は、固定的な処理の流れを実現するものです。一方で、どんな処理を行うべきか、LLM に選択して動いて欲しい場合があります。 + +例えば、user の入力を踏まえて、必要に応じて、 + +- 社内文書を Vector store から検索して回答したり、 +- Web 上の情報を検索して回答をしてくれたりする + +そんな挙動にできたら、LLM で実現できることは大きく広がります。そのような動作を実現できるのが、LangChain の「Agents」です。 + +LangChain の Agents を使うと、必要に応じて様々なツールを使いながら、LLM に動作してもらうことができます。 +ツールとしては、Vector store を使って特定分野の data を検索して使わせることもできれば、Goolge などの Search Engine API を使わせたりすることもできます。 + +### Agents の仕組み - ReAct という考え方 + +先ほど試した [Zero-shot-react-description という Agent](https://colab.research.google.com/drive/1tPvrOlkDeflGfBL7UMIHMLX-7ES6m8KG#scrollTo=o2B_iB8s56fK&line=1&uniqifier=1) は、「ReAct: Synergizaing Reasoning and Acting in Language Models」という論文にモドづく実装で、「ReAct」という仕組みで動いています。 + +### LangChain の Agents tools + +| tool name | outline | +| ------------- | ------------------------------------ | +| terminal | shell でコマンドを実行する | +| Python_REPL | Python code を実行する | +| google_search | Google で検索する | +| Wikipedia | Wikipedia を検索する | +| human | 人間に入力させる(任官に助けを求める) | + +### Toolkits + +同時に使う tool をいくつか取りまとめたのが Toolkiets です。例えば、Agents を使って、Github に対して色々な操作をさせたいとします。その際、次のようにいくつもの tool を用意することになります。 + +- Issue の一覧の取得 +- Issue の詳細の取得 +- Issue に comment する +- file を作成する +- file を読み込む +- file を更新する +- file 削除する + +LangChain で提供されている Toolkits を使うことで、このような Tool をまとめて用意することができます。 + +![5.2-langchain-toolkits-list](./imgs/5.2-langchain-toolkits-list.png) + +### Function Calling を使う OpenAI Functions Agent + +ReAct や Plan-and-Solve といった Agent を動かすことができますが、この仕組みでは安定的に動作させるのは簡単ではありません + +### 一度に複数 Tool を使う OpenAI Multi Functions Agent diff --git a/docs/imgs/5.2-langchain-toolkits-list.png b/docs/imgs/5.2-langchain-toolkits-list.png new file mode 100644 index 0000000000000000000000000000000000000000..68d7ad9a16e246349541142a4eccade7613136a5 GIT binary patch literal 323327 zcmeEt19xW4vT$s3V%xTD+nP*ldty78Ol&`~Z6_1kwyiJkxjO5-=l+0it=nt$Q?;wB zs(V-WZdCOXt|%`74}%Q@1Ox;xB`K;51O%1<1O%J|1@T3>HkZQ(0)peW5D`(75)mO( zbhP_!VQmHkBpIHp0ja4nf{~@8OjHj9qAYCpGb{;6S@=ip<98BLVBv5kB-OK{yq{K> zuuFf`kilstVwSYfybNe5DNl`i0PRx1dXLGM6OR*_pC3*K0_KyMEsqF5Clt-b4)kTf zf>|YkC^%h|^roFxs%)mxW0*QVa7E zJC0XEvJ!d|8ctmmiBhgBAcBVRnu|-F3gH56MdG<`0X9Oy5ymAQjfT^>ImYTGMCi8< 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