この投稿で説明する履歴認識レトリバーは、LangChain パッケージの create_history_aware_retriever 関数によって返されるものです。この関数は、コンストラクターで次の入力を受け取るように設計されています:

• LLM (クエリを受信して​​回答を返す言語モデル);
• ベクター ストア リトリーバー (クエリを受け取り、関連するドキュメントのリストを返すモデル)。
• チャット履歴 (通常は人間と AI の間で行われるメッセージのやりとりのリスト)。

呼び出されると、履歴認識リトリーバーはユーザーのクエリを入力として受け取り、関連するドキュメントのリストを出力します。関連するドキュメントは、チャット履歴によって提供されるコンテキストと組み合わせたクエリに基づいています。

最後にワークフローをまとめます。

設定する

from langchain.chains import create_history_aware_retriever
from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_chroma import Chroma
from dotenv import load_dotenv
import bs4

load_dotenv() # To get OPENAI_API_KEY

def create_vectorsore_retriever():
    """
    Returns a vector store retriever based on the text of a specific web page.
    """
    URL = r'https://lilianweng.github.io/posts/2023-06-23-agent/'
    loader = WebBaseLoader(
        web_paths=(URL,),
        bs_kwargs=dict(
            parse_only=bs4.SoupStrainer(class_=("post-content", "post-title", "post-header"))
        ))
    docs = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0, add_start_index=True)
    splits = text_splitter.split_documents(docs)
    vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
    return vectorstore.as_retriever()

def create_prompt():
    """
    Returns a prompt instructed to produce a rephrased question based on the user's
    last question, but referencing previous messages (chat history).
    """
    system_instruction = """Given a chat history and the latest user question \
        which might reference context in the chat history, formulate a standalone question \
        which can be understood without the chat history. Do NOT answer the question, \
        just reformulate it if needed and otherwise return it as is."""

    prompt = ChatPromptTemplate.from_messages([
        ("system", system_instruction),
        MessagesPlaceholder("chat_history"),
        ("human", "{input}")])
    return prompt

llm = ChatOpenAI(model='gpt-4o-mini')
vectorstore_retriever = create_vectorsore_retriever()
prompt = create_prompt()

history_aware_retriever = create_history_aware_retriever(
    llm,
    vectorstore_retriever,
    prompt
)

それを使用する

ここでは、チャット履歴なしで質問が行われているため、レトリバーは最後の質問に関連する文書のみを返します。

chat_history = []

docs = history_aware_retriever.invoke({'input': 'what is planning?', 'chat_history': chat_history})
for i, doc in enumerate(docs):
    print(f'Chunk {i 1}:')
    print(doc.page_content)
    print()

Chunk 1:
Planning is essentially in order to optimize believability at the moment vs in time.
Prompt template: {Intro of an agent X}. Here is X's plan today in broad strokes: 1)
Relationships between agents and observations of one agent by another are all taken into consideration for planning and reacting.
Environment information is present in a tree structure.

Chunk 2:
language. Essentially, the planning step is outsourced to an external tool, assuming the availability of domain-specific PDDL and a suitable planner which is common in certain robotic setups but not in many other domains.

Chunk 3:
Another quite distinct approach, LLM P (Liu et al. 2023), involves relying on an external classical planner to do long-horizon planning. This approach utilizes the Planning Domain Definition Language (PDDL) as an intermediate interface to describe the planning problem. In this process, LLM (1) translates the problem into “Problem PDDL”, then (2) requests a classical planner to generate a PDDL plan based on an existing “Domain PDDL”, and finally (3) translates the PDDL plan back into natural

Chunk 4:
Planning

Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.
Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.


Memory

さて、チャット履歴に基づいて、レトリバーは人間が計画だけでなくタスクの分解についても知りたいと思っていることを知ります。したがって、両方のテーマを参照するテキストの塊で応答します。

chat_history = [
    ('human', 'when I ask about planning I want to know about Task Decomposition too.')]

docs = history_aware_retriever.invoke({'input': 'what is planning?', 'chat_history': chat_history})
for i, doc in enumerate(docs):
    print(f'Chunk {i 1}:')
    print(doc.page_content)
    print()

Chunk 1:
Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.

Chunk 2:
Fig. 1. Overview of a LLM-powered autonomous agent system.
Component One: Planning#
A complicated task usually involves many steps. An agent needs to know what they are and plan ahead.
Task Decomposition#

Chunk 3:
Planning

Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.
Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.


Memory

Chunk 4:
Challenges in long-term planning and task decomposition: Planning over a lengthy history and effectively exploring the solution space remain challenging. LLMs struggle to adjust plans when faced with unexpected errors, making them less robust compared to humans who learn from trial and error.

質問全体はチャット履歴に基づいています。そして、正しい概念を参照するテキストの塊で応答していることがわかります。

chat_history = [
    ('human', 'What is ReAct?'),
    ('ai', 'ReAct integrates reasoning and acting within LLM by extending the action space to be a combination of task-specific discrete actions and the language space')]

docs = history_aware_retriever.invoke({'input': 'It is a way of doing what?', 'chat_history': chat_history})
for i, doc in enumerate(docs):
    print(f'Chunk {i 1}:')
    print(doc.page_content)
    print()

Chunk 1:

ReAct (Yao et al. 2023) integrates reasoning and acting within LLM by extending the action space to be a combination of task-specific discrete actions and the language space. The former enables LLM to interact with the environment (e.g. use Wikipedia search API), while the latter prompting LLM to generate reasoning traces in natural language.

The ReAct prompt template incorporates explicit steps for LLM to think, roughly formatted as:

Thought: ...

Action: ...

Observation: ...

Chunk 2:

Fig. 2.  Examples of reasoning trajectories for knowledge-intensive tasks (e.g. HotpotQA, FEVER) and decision-making tasks (e.g. AlfWorld Env, WebShop). (Image source: Yao et al. 2023).

In both experiments on knowledge-intensive tasks and decision-making tasks, ReAct works better than the Act-only baseline where Thought: … step is removed.

Chunk 3:

The LLM is provided with a list of tool names, descriptions of their utility, and details about the expected input/output.

It is then instructed to answer a user-given prompt using the tools provided when necessary. The instruction suggests the model to follow the ReAct format - Thought, Action, Action Input, Observation.

Chunk 4:

Case Studies#

Scientific Discovery Agent#

ChemCrow (Bran et al. 2023) is a domain-specific example in which LLM is augmented with 13 expert-designed tools to accomplish tasks across organic synthesis, drug discovery, and materials design. The workflow, implemented in LangChain, reflects what was previously described in the ReAct and MRKLs and combines CoT reasoning with tools relevant to the tasks:

結論

結論として、 .invoke({'input': '...', 'chat_history': '...'}) が呼び出されたとき、履歴認識レトリバーのワークフローは次のように機能します。

• これは、プロンプト 内の input および chat_history プレースホルダーを指定された値に置き換え、基本的に「このチャット履歴とこの最後の入力を取得し、最後の入力を言い換えてください」というすぐに使用できる新しいプロンプトを作成します。チャット履歴を見なくても誰でも理解できる方法で。」
• 新しいプロンプトを LLM に送信し、言い換えられた入力を受け取ります。
• 次に、言い換えられた入力を ベクトル ストア リトリーバーに送信し、この言い換えられた入力に関連するドキュメントのリストを受け取ります。
• 最後に、この関連ドキュメントのリストが返されます。

Obs.: テキストをベクトルに変換するために使用される埋め込みは、Chroma.from_documents の呼び出し時に指定されるものであることに注意することが重要です。何も指定されていない場合 (現在のケース)、デフォルトのクロマ埋め込みが使用されます。