explain the following like i am a layperson: ""ntroduction
In the dynamic realm of natural language processing, a revolutionary synergy has emerged between Langchain and Step-Back Prompting. This article delves into the transformative collaboration, exploring how Langchain’s cutting-edge platform incorporates Step-Back Prompting to redefine language processing capabilities. Join us on a journey of innovation and discovery as we unravel the intricacies of this powerful integration. As we explore the uncharted territories of language models, Step-Back Prompting stands as a beacon of progress, promising a journey of nuanced understanding and elevated performance in the world of Large Language Models. Welcome to the future of language processing, where inspiration and innovation converge in a symphony of words and ideas.


Source: Step-Back-Prompting
Definitions
Step-Back Prompting: Step-Back Prompting is a pioneering technique within natural language processing that draws inspiration from human problem-solving strategies. It involves guiding language models to take a step back from the immediate question and instead pose a higher-level, abstract question. This abstraction aims to derive overarching concepts or principles before delving into detailed reasoning. The process emulates the human tendency to abstract information, fostering a more robust understanding of complex tasks. By encouraging models to reason from a broader perspective, Step-Back Prompting addresses challenges associated with intricate, multi-step reasoning tasks.

Langchain: Langchain represents a cutting-edge platform in the landscape of language processing. It goes beyond traditional language models, providing a dynamic environment for language understanding and generation. Langchain’s architecture is designed to facilitate seamless integration with innovative techniques, making it a forefront player in the evolution of language processing technologies.


Source: Benchmark-results
Advantages of Step-Back Prompting over Chain of Thoughts
In the dynamic landscape of language processing, Step-Back Prompting emerges as a beacon of innovation, diverging from the conventional Chain of Thoughts method. This transformative approach is designed to elevate the capabilities of models, introducing a nuanced strategy that goes beyond linear progressions.

2. Nonlinear Progression and Abstraction:

3. Mitigation of Errors:

4. Human-Inspired Approach:

5. Coherence in Complex Reasoning Tasks:


Source: CoT vs Step-Back-Prompting
Integration of Step-Back Prompting with Langchain:
In the dynamic realm of language processing, the integration of Step-Back Prompting with Langchain emerges as a transformative paradigm, redefining the landscape of linguistic capabilities. This collaboration propels language models into a new era of comprehension and reasoning, where innovation takes center stage.

Code Implementation
Lets dive into coding with Langchain and Step-Back Prompting can be an exciting journey. The provided Python code integrates these two powerful tools for advanced language processing. Let’s break down the key components.

Step I: Install Libraries

pip install -U -qq "transformers>=4.35.0" accelerate langchain faiss-cpu chromadb ragatouille "autoawq>=0.1.6" unidic_lite
Step II: Build a vectorDB

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
import torch
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
import os
 
os.environ["OPENAI_API_KEY"] = ""
device = "cuda" if torch.cuda.is_available() else "cpu"
 
# Load blog post
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
data = loader.load()
 
# Split
text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=0)
splits = text_splitter.split_documents(data)
 
# VectorDB
embedding = HuggingFaceEmbeddings(
    model_name="intfloat/multilingual-e5-large",
    model_kwargs={"device": device},
)

vectorstore = Chroma.from_documents(documents=splits, embedding=embedding)

# ragatouille/colbert implmentation
RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")

# Importing the ContextualCompressionRetriever from the Langchain library
from langchain.retrievers import ContextualCompressionRetriever

# Retrieving 10 similar documents from the FAISS vector store
base_retriever = vectorstore.as_retriever(search_kwargs={"k": 10})

# Creating a retriever that calculates similarity using JaColBERT
# based on the results from the above retriever and retrieves the top 3
compression_retriever = ContextualCompressionRetriever(
    base_compressor=RAG.as_langchain_document_compressor(k=3),
    base_retriever=base_retriever,
)

# Testing the retrieval
compressed_docs = compression_retriever.get_relevant_documents(
    "What treatment does this contract give to intellectual property rights?"
)

compressed_docs

#Output
[Document(page_content='capability working in practice. The collection of tool APIs can be provided by other developers (as', metadata={'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'relevance_score': 6.71875}),
 Document(page_content='exploration. Only binary reward is assigned. The source policies are trained with A3C for "dark"', metadata={'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'relevance_score': 6.38671875}),
 Document(page_content='be a combination of task-specific discrete actions and the language space. The former enables LLM', metadata={'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'relevance_score': 5.4453125})]
Step III: Initialize Transformers Using Open-Chat

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers_chat import ChatHuggingFaceModel


model_name_or_path = "TheBloke/openchat-3.5-0106-AWQ"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
generator = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    low_cpu_mem_usage=True,
    device_map="cuda:0"
)
Step IV: Question Gen Chain for Step-back

from langchain.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate
from langchain.schema.output_parser import StrOutputParser
from langchain_core.runnables import RunnablePassthrough, RunnableLambda

# Few Shot Examples
examples = [
     {
         "input": "Could the members of The Police perform lawful arrests?",
         "output": "what can the members of The Police do?",
     },
     {
         "input": "Jan Sindel’s was born in what country?",
         "output": "what is Jan Sindel’s personal history?",
     },
 ]
# We now transform these to example messages
example_prompt = ChatPromptTemplate.from_messages(
    [
        ("human", "{input}"),
        ("ai", "{output}"),
    ]
)
few_shot_prompt = FewShotChatMessagePromptTemplate(
    example_prompt=example_prompt,
    examples=examples,
)

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are an expert at world knowledge. Your task is to step back "
            "and paraphrase a question to a more generic step-back question, which "
            "is easier to answer. Here are a few examples:",
        ),
        # Few shot examples
        few_shot_prompt,
        # New question
        ("user", "{question}"),
        ("ai", ""),
    ]
)

gen_model = ChatHuggingFaceModel(
    generator=generator,
    tokenizer=tokenizer,
    human_message_template="GPT4 Correct User: {}
",
    ai_message_template="GPT4 Correct Assistant: {}",
    repetition_penalty=1.2,
    temperature=0.1,
    max_new_tokens=1024,
)

rerank_retriever = ContextualCompressionRetriever(
    base_retriever=vectorstore.as_retriever(search_kwargs={"k": 15}),
    base_compressor=RAG.as_langchain_document_compressor(k=5),
)

chain2 = (
    prompt
    | gen_model
    | StrOutputParser()
)


chain2.invoke({"question":"In this contract, how are intellectual property rights treated?"})

#Output
\nWhat are the terms and conditions regarding intellectual property rights in this contract?
More abstract and general questions are generated.

Step V: Chain creation

from langchain.prompts import ChatPromptTemplate
from langchain.prompts.chat import (
   AIMessagePromptTemplate,
   HumanMessagePromptTemplate,
)

retriever  = ContextualCompressionRetriever(
    base_retriever=vectorstore.as_retriever(search_kwargs={"k": 15}),
    base_compressor=RAG.as_langchain_document_compressor(k=5),
)

response_prompt_template = """You are an expert of world knowledge. I am going to ask you a question. Your response should be comprehensive and not contradicted with the following context if they are relevant. Otherwise, ignore them if they are not relevant.

{normal_context}
{step_back_context}

Original Question: {question}
Answer:"""

response_prompt = ChatPromptTemplate.from_messages(
   [
       HumanMessagePromptTemplate.from_template(response_prompt_template),
       AIMessagePromptTemplate.from_template(""),
   ]
)

chat_model = ChatHuggingFaceModel(
   generator=generator,
   tokenizer=tokenizer,
   human_message_template="GPT4 Correct User: {}",
   ai_message_template="GPT4 Correct Assistant: {}",
   repetition_penalty=1.2,
   temperature=0.1,
   max_new_tokens=1024,
)

chain = (
   {
       # Extract relevant context from the original question
       "normal_context": RunnableLambda(lambda x: x["question"]) | retriever,
       # Extract relevant context from the step-back question
       "step_back_context": chain2 | retriever,
       # Set the original question
       "question": lambda x: x["question"],
   }
   | response_prompt
   | chat_model
   | StrOutputParser()
)

for s in chain.stream({"question":"What Task Decomposition that work in 2022"}):
            print(s, end="", flush=True)

#Output
Task decomposition is a process of breaking down complex tasks into smaller, manageable subtasks. This approach is particularly useful in the context of large language models (LLMs) like GPT-4, which can be used to solve a wide range of problems by breaking them down into simpler steps.

In 2022, task decomposition was widely used in the development of LLM-powered autonomous agents. These agents use LLMs as their core controllers, with the LLM functioning as the agent's brain, complemented by several key components. Task decomposition is an essential part of the agent system, as it allows the agent to break down large tasks into smaller, manageable subgoals.

For example, in the development of AutoGPT, GPT-Engineer, and BabyAGI, task decomposition was used to break down complex tasks into smaller steps that could be executed by the LLM. This approach allowed the LLM to generate well-written copies, stories, essays, and programs, as well as solve general problems.

In summary, task decomposition is a crucial technique in the development of LLM-powered autonomous agents, and it was widely used in 2022 to break down complex tasks into smaller, manageable subtasks that could be executed by the LLM. This approach has the potential to revolutionize the way we approach problem-solving and automation.
Conclusion
In conclusion, the integration of Step-Back Prompting and Langchain heralds a revolutionary paradigm in language processing. This dynamic duo redefines how models comprehend, reason, and respond, ushering in a new era of nuanced understanding.

Step-Back Prompting, with its departure from traditional linear progressions, encourages models to abstract and derive high-level concepts before delving into detailed reasoning. This departure proves invaluable in tasks requiring intricate understanding, where conventional methods may falter.

Langchain, with its robust architecture and adaptability, seamlessly incorporates Step-Back Prompting, elevating language models to unprecedented heights. This collaboration empowers Langchain to excel in complex tasks involving multi-step reasoning, knowledge-intensive question answering, and domain-specific challenges.

Together, they form a transformative approach that not only enhances performance but also aligns with human-like cognitive skills. The integration of Step-Back Prompting and Langchain stands as a beacon in the evolving landscape of natural language processing, offering a trailblazing solution that transcends conventional boundaries and sets new standards for language understanding and generation.

Resources:""