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A Complete Information to Utilizing Chains in Langchain


Introduction

Step into the forefront of language processing! In a realm the place language is a necessary hyperlink between humanity and expertise, the strides made in Pure Language Processing have unlocked some extraordinary heights. Inside this progress lies the groundbreaking Giant Language Mannequin, a transformative pressure reshaping our interactions with text-based info. On this complete studying journey, you’ll delve into the intricacies of LangChain, a cutting-edge software reshaping how we work together with text-based info. Did you ever marvel what’s chain is the “Langchain”?

LangChain stands alone as a gateway to essentially the most dynamic discipline of Giant Language Fashions, which affords a profound understanding of how these fashions remodel the uncooked inputs into refined and human-like responses. By way of this exploration, you’ll unravel the important constructing blocks of LangChain, from LLMChains and Sequential Chains to the intricate workings of Router Chains.

Studying Goals

  • Perceive the core parts of LangChain, together with LLMChains and Sequential Chains, to see how inputs stream via the system.
  • Study to combine totally different parts coherently, exploring the connection between the immediate templates and language fashions.
  • Achieve sensible expertise in creating useful chains for real-world duties.
  • Develop expertise to boost chain effectivity by fine-tuning the constructions, templates, and parsing strategies.

This text was revealed as part of the Information Science Blogathon.

What’s LLM?

A Giant Language Mannequin (LLM) refers to a sort of synthetic intelligence designed to know and generate human-like textual content. These fashions, like OpenAI’s GPT-3.5, prepare on in depth textual content knowledge to know the patterns and constructions of human language. They’ll carry out numerous language-related duties, together with translation, content material creation, answering questions, and extra.

LLMs are beneficial instruments in pure language processing and have purposes in areas like chatbots, content material technology, and language translation providers.

What’s LangChain?

Earlier than we unravel the intricacies of LangChain Chains, let’s seize the essence of LangChain itself. LangChain is a strong library designed to simplify interactions with numerous massive language mannequin (LLM) suppliers, together with OpenAI, Cohere, Bloom, Huggingface, and others. What units LangChain aside is its distinctive characteristic: the flexibility to create Chains, and logical connections that assist in bridging one or a number of LLMs.

"

Why Use LangChain?

LangChain supplies limitless alternatives, restricted solely by your creativeness.

  • Think about chatbots that not solely present info but additionally have interaction customers with wit and attraction.
  • Image e-commerce platforms suggesting merchandise so precisely that clients are compelled to make a purchase order.
  • Envision healthcare apps providing personalised medical insights, empowering people to make knowledgeable choices about their well-being.

With LangChain, you might have the ability to create extraordinary experiences. The potential to remodel these concepts into actuality is true at your fingertips.

Understanding Chains in LangChain

Central to LangChain is an important element often known as LangChain Chains, forming the core connection amongst one or a number of massive language fashions (LLMs).

"

In sure refined purposes, it turns into essential to chain LLMs collectively, both with one another or with different parts. These Chains empower us to combine quite a few parts, weaving them right into a cohesive utility. Let’s delve deeper into the distinct sorts of Chains.

Furthermore, the structured strategy supplied by Chains in LLM ensures flawless and efficient processing, paving the best way for the event of superior purposes tailor-made to a big selection of person necessities. This represents a big development within the realm of pure language processing, as these intricate connections function the elemental framework of LangChain, facilitating seamless interactions amongst a number of Giant Language Fashions (LLMs).

However first, Why Chains?

Chains are invaluable on account of their capability to effortlessly mix numerous parts, shaping a singular and coherent utility. By way of the creation of chains, a number of parts can seamlessly come collectively. Think about this state of affairs: a sequence is crafted to soak up person enter, polish it utilizing a PromptTemplate, and subsequently cross on this refined response to a big language mannequin (LLM). This streamlined course of not solely simplifies but additionally enriches the general performance of the system. In essence, chains function the linchpin, seamlessly connecting totally different components of the appliance and enhancing its capabilities. Let’s summarize this:

  • Integrating immediate templates with LLMs permits for a robust synergy.
  • By taking the output of 1 LLM and utilizing it as enter for the following, it turns into possible to attach a number of LLMs in a sequential vogue.
  • Mixing LLMs with exterior knowledge allows the system to answer inquiries successfully.
  • Integrating LLMs with long-term reminiscence, corresponding to chat historical past, enhances the general context and depth of interactions.

Moreover, chains present us with the flexibility to construct complicated purposes by linking a number of chains collectively or by incorporating chains with different important parts. This strategy allows a classy and nuanced methodology for creating purposes, permitting for intricate and superior functionalities.

Forms of Chain

There are various totally different Chains in Langchain that we will use. Right here, we’re going via three of the elemental chains – LLM Chain, Sequential Chain and Router Chain.

LLM Chain – The best chain

Probably the most fundamental type of chain inside this technique is the LLMChain, widely known and elementary. Its operation entails a structured association, together with a PromptTemplate, an OpenAI mannequin (both a Giant Language Mannequin or a ChatModel), and an non-compulsory output parser. Inside this setup, the LLMChain accepts numerous enter parameters. It employs the PromptTemplate to remodel these inputs right into a coherent immediate. This polished immediate is then inputted into the mannequin. After receiving the output, the LLMChain makes use of the OutputParser, if supplied, to additional refine and format the end result into its final usable kind. For example the performance of LLM chains, take into account the concrete instance.A concrete instance illustrating the performance of LLM chains is detailed beneath:

LLM Chain - the simplest chain
  • It really works by taking a person’s enter and passing it to the primary ingredient within the chain — a PromptTemplate — to format the enter into a specific immediate.
  • The formatted immediate is then handed to the following (and last) ingredient within the chain — a LLM.

Crafting Chains – LLM Chain

Creating Chains, particularly LLM Chains, is a meticulous endeavor, requiring the harnessing of Giant Language Fashions in LangChain. These chains function intricate channels, facilitating the sleek trade of data and engagement. By way of cautious structuring, builders can design vibrant purposes able to understanding person inputs, using LLMs to generate clever responses, and customizing the output to satisfy particular wants successfully.

Now let’s look deeper into how we will use the LLM Chains within the Langchain.

Import Needed Libraries

import langchain
import openai
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

from getpass import getpass
OPENAI_API_KEY = getpass()

Initialize LLM and Immediate Template

We initialize the OpenAI Giant Language Mannequin with particular parameters, together with a temperature of 0.9, which impacts the range of generated responses. Moreover, customers should outline a ‘PromptTemplate’ to enter a variable (on this case, “product”) and create a standardized immediate construction. At runtime, the placeholder ‘{product}’ could be dynamically populated with totally different product names.

llm = OpenAI(temperature=0.9,
          openai_api_key=OPENAI_API_KEY
       )
immediate = PromptTemplate(
    input_variables=["product"],
    template="What is an effective title for a corporation that makes {product}?",
)

Creating A Chain

We create an occasion of the ‘LLMChain’ class, utilizing a predefined OpenAI Giant Language Mannequin and a specified immediate template. Now, we now have the potential to use the chain to a product corresponding to a “gaming laptop computer” utilizing the chain.run command. This implies the chain can dynamically course of and generate responses tailor-made to this particular product enter.

from langchain.chains import LLMChain
chain = LLMChain(llm=llm, immediate=immediate, verbose=True)

print(chain.run("gaming laptop computer"))

Output:

Creating a chain | Chains in Langchain

Based mostly on this we get the title of an organization known as “GamerTech Laptops”.

Sequential Chain

A sequential chain is a sequence that mixes numerous particular person chains, the place the output of 1 chain serves because the enter for the following in a steady sequence. It operates by operating a sequence of chains consecutively.

There are two sorts of sequential chains:

Easy Sequential Chain, which handles a single enter and output, and

Sequential Chain, handle a number of inputs and outputs concurrently.

"
  • A sequential chain merges numerous chains through the use of the output of 1 chain because the enter for the following.
  • It operates by executing a sequence of chains consecutively.
  • This strategy is efficacious when you’ll want to make the most of the results of one operation as the place to begin for the following one, making a seamless stream of processes.

Easy Sequential Chain

Sequential chains, of their easiest kind, include steps the place every step takes one enter and produces one output. The output from one step turns into the enter for the following.

Simple sequential chains

This easy strategy is efficient when coping with sub-chains designed for singular inputs and outputs. It ensures a easy and steady stream of data, with every step seamlessly passing its output to the next step.

Crafting Chains – Easy Sequential Chains

Easy Sequential Chains enable for a single enter to bear a sequence of coherent transformations, leading to a refined output. This sequential strategy ensures systematic and environment friendly dealing with of knowledge, making it very best for situations the place a linear stream of data processing is important

Importing Needed Libraries

from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.prompts import ChatPromptTemplate
from langchain.chains import SimpleSequentialChain

Initializing and Chaining

We initialize an OpenAI Giant Language Mannequin with a temperature setting of 0.7 and an API key. Then, we create a particular chat immediate template with a placeholder for a product title. Subsequently, we kind an LLMChain, which permits the technology of responses primarily based on the supplied immediate. We repeat this course of for 2 totally different chains.

# That is an LLMChain to jot down first chain.

llm = OpenAI(temperature=0.7, openai_api_key=OPENAI_API_KEY)
first_prompt = ChatPromptTemplate.from_template(
    "What's the greatest title to explain an organization that makes {product}?"
)
chain_one = LLMChain(llm=llm, immediate=first_prompt)

# That is an LLMChain to jot down second chain.

llm = OpenAI(temperature=0.7, openai_api_key=OPENAI_API_KEY)
second_prompt = ChatPromptTemplate.from_template(
    "Write a 20 phrases description for the next firm:{company_name}"
)
chain_two = LLMChain(llm=llm, immediate=second_prompt)

Chaining Two Chains

Create an general Easy Sequential Chain, comprising two totally different particular person chains, chain_one, and chain_two. Execute this with the enter “gaming laptop computer,” it sequentially processes the enter via the outlined chains and supplies an output, that demonstrates the step-by-step sequential execution of the chains.

overall_simple_chain = SimpleSequentialChain(chains=[chain_one, chain_two],
                                             verbose=True
                                            )
overall_simple_chain.run("gaming laptop computer")

Output:

"

Sequential Chain

Not all the sequential chains function with a single string enter and output. In additional intricate setups, these chains deal with a number of inputs and generate a number of last outputs. The cautious naming of enter and output variables holds essential significance in these complicated chains.

Sequential chains | Chains in Langchain

A extra basic type of sequential chains permits for a number of inputs/outputs. Any step within the chain can soak up a number of inputs.

Crafting Chains – Sequential Chains

Importing Needed Libraries

from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.prompts import ChatPromptTemplate
from langchain.chains import SequentialChain

llm = OpenAI(temperature=0.7, openai_api_key=OPENAI_API_KEY)

Initializing and Chaining

We outline a immediate template, instructing the system to carry out a particular job. We then create a corresponding LLMChain, utilizing the designated Giant Language Mannequin (LLM) and the outlined immediate template. The chain is ready as much as take the enter, cross it via the LLM, and generate the output. We repeat this course of to determine 4 distinct chains.

Overview = "Les ordinateurs portables GamersTech impressionne par ses
 performances exceptionnelles et son design élégant. De sa configuration 
 matérielle robuste à un clavier RVB personnalisable et un système de 
 refroidissement efficace, il établit un équilibre parfait entre prouesses 
 de jeu et portabilité."

# immediate template 1: translate to English

first_prompt = ChatPromptTemplate.from_template(
    "Translate the next assessment to english:"
    "nn{Overview}"
)

# chain 1: enter= Overview and output= English_Review

chain_one = LLMChain(llm=llm, immediate=first_prompt,
                     output_key="English_Review"                     
                    )
                   
                    
# immediate template 2: Summarize the English assessment

second_prompt = ChatPromptTemplate.from_template(
    "Are you able to summarize the next assessment in 1 sentence:"
    "nn{English_Review}"
)

# chain 2: enter= English_Review and output= abstract

chain_two = LLMChain(llm=llm, immediate=second_prompt,
                     output_key="abstract"
                    )
# immediate template 3: translate to English

third_prompt = ChatPromptTemplate.from_template(
    "What language is the next assessment:nn{Overview}"
)

# chain 3: enter= Overview and output= language

chain_three = LLMChain(llm=llm, immediate=third_prompt,
                       output_key="language"
                      )
# immediate template 4: observe up message

fourth_prompt = ChatPromptTemplate.from_template(
    "Write a observe up response to the next "
    "abstract within the specified language:"
    "nnSummary: {abstract}nnLanguage: {language}"
)

# chain 4: enter= abstract, language and output= followup_message

chain_four = LLMChain(llm=llm, immediate=fourth_prompt,
                      output_key="followup_message"
                     )

Chaining Two Chains

An general Sequential Chain named ‘overall_chain’ is created, incorporating 4 particular person chains ‘chain_one’, ‘chain_two’, ‘chain_three’, and ‘chain_four’. The enter variable “Overview” is processed via these chains, producing three distinct output variables: “English_Review,” “abstract,” and “followup_message.” The ‘overall_chain’ executes the enter assessment via the desired chains and produces these outputs, facilitating a structured, sequential processing stream with detailed outputs.

overall_chain = SequentialChain(
    chains=[chain_one, chain_two, chain_three, chain_four],
    input_variables=["Review"],
    output_variables=["English_Review", "summary","followup_message"],
    verbose=True
)
overall_chain(Overview)

Output

"

Router Chain

The Router Chain is used for classy duties. If we now have a number of subchains, every of which is specialised for a specific kind of enter, we might have a router chain that decides which subchain to cross the enter to.

It consists of:

  • Router Chain: It’s accountable for choosing the following chain to name.
  • Vacation spot Chains: Chains that the router chain can path to.
  • Default chain: Used when the router can’t determine which subchain to make use of.
Router chain | Chains in Langchain

This entails directing an enter towards a particular chain primarily based on what precisely that enter is. When there are a number of subchains, every tailor-made for distinct enter sorts, a router chain comes into play. This router chain acts as a decision-maker, figuring out which specialised subchain to ship the enter to. Primarily, it allows the seamless routing of inputs to the suitable subchains, making certain environment friendly and exact processing primarily based on the enter’s particular traits.

Crafting Chains – Router Chain

Importing Needed Libraries

from langchain.chains.router import MultiPromptChain
from langchain.chains.router.llm_router import LLMRouterChain,RouterOutputParser
from langchain.prompts import PromptTemplate

llm = OpenAI(temperature=0.7, openai_api_key=OPENAI_API_KEY)

Defining Immediate Templates

Let’s take into account a state of affairs the place we have to direct inputs to specialised chains primarily based on topics corresponding to Maths, Physics, Historical past, or Laptop Science. To perform this, we create distinct prompts for every topic: one for physics questions, one other for math queries, a 3rd for historical past inquiries, and a fourth for laptop science-related issues. We meticulously design these prompts to cater to the distinctive wants of every topic space.

physics_template = """You're a very sensible physics professor. 
You might be nice at answering questions on physics in a concise
and simple to know method. 
When you do not know the reply to a query you admit
that you do not know.

Here's a query:
{enter}"""


math_template = """You're a excellent mathematician. 
You might be nice at answering math questions. 
You might be so good as a result of you'll be able to break down 
arduous issues into their element components,
reply the element components, after which put them collectively
to reply the broader query.

Here's a query:
{enter}"""

history_template = """You're a excellent historian. 
You've gotten a superb information of and understanding of individuals,
occasions and contexts from a spread of historic durations. 
You've gotten the flexibility to suppose, mirror, debate, focus on and 
consider the previous. You've gotten a respect for historic proof
and the flexibility to utilize it to assist your explanations 
and judgements.

Here's a query:
{enter}"""

Moreover, detailed info, together with names and descriptions, could be connected to those immediate templates. This extra context supplies a complete understanding of every template’s goal. This info is then equipped to the router chain. The router chain then determines which subchain to path to primarily based on the precise topic, making certain that the suitable immediate template is utilized for exact and efficient responses.

# Defining the immediate templates
prompt_infos = [
    {
        "name": "physics",
        "description": "Good for answering questions about physics",
        "prompt_template": physics_template
    },
    {
        "name": "math",
        "description": "Good for answering math questions",
        "prompt_template": math_template
    },
    {
        "name": "History",
        "description": "Good for answering history questions",
        "prompt_template": history_template
    }
]

Creating Vacation spot Chains

Subsequent, our focus shifts to crafting vacation spot chains. These chains are activated by the RouterChain, functioning as particular person language mannequin chains, particularly LLM chains. Moreover, a default chain is printed to deal with conditions the place the router encounters ambiguity and can’t decide the appropriate subchain to make the most of. This default chain acts as a fallback choice, making certain a response even in instances of indecision.

destination_chains = {}
for p_info in prompt_infos:
    title = p_info["name"]
    prompt_template = p_info["prompt_template"]
    immediate = ChatPromptTemplate.from_template(template=prompt_template)
    chain = LLMChain(llm=llm, immediate=immediate)
    destination_chains[name] = chain

locations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "n".be part of(locations)

Making a Multi-prompt Router Template

We set up a template guiding the LLM in directing interactions between numerous chains. This template not solely outlines the precise job directions but additionally dictates the exact format that the output ought to adhere to, making certain a standardized and constant response mechanism.

MULTI_PROMPT_ROUTER_TEMPLATE = """Given a uncooked textual content enter to a 
language mannequin choose the mannequin immediate greatest suited to the enter. 
You'll be given the names of the obtainable prompts and a 
description of what the immediate is greatest suited to. 
You might also revise the unique enter should you suppose that revising
it is going to in the end result in a greater response from the language mannequin.

<< FORMATTING >>
Return a markdown code snippet with a JSON object formatted to seem like:
```json
{{{{
    "vacation spot": string  title of the immediate to make use of or "DEFAULT"
    "next_inputs": string  a probably modified model of the unique enter
}}}}
```

REMEMBER: "vacation spot" MUST be one of many candidate immediate 
names specified beneath OR it may be "DEFAULT" if the enter isn't
properly suited to any of the candidate prompts.
REMEMBER: "next_inputs" can simply be the unique enter 
should you do not suppose any modifications are wanted.

<< CANDIDATE PROMPTS >>
{locations}

<< INPUT >>
{{enter}}

<< OUTPUT (bear in mind to incorporate the ```json)>>"""

Making a Default Chain

A preset immediate template is established to accommodate all sorts of enter textual content. An related LLMChain, named ‘default_chain,’ is then crafted utilizing the designated Giant Language Mannequin and the predefined immediate. This setup allows the Giant Language Mannequin to generate responses primarily based on any supplied enter textual content.

default_prompt = ChatPromptTemplate.from_template("{enter}")
default_chain = LLMChain(llm=llm, immediate=default_prompt)

Creating Router Template

Shifting ahead, a versatile router template is developed, encompassing a spread of classes corresponding to Physics, Math, Historical past, and Laptop Science. From this template, a definite immediate template tailor-made for the router is created. Using this personalized template, a router chain is established, using the Giant Language Mannequin and the corresponding router immediate.

To enhance decision-making capabilities, a router output parser is launched. This parser assists the router chain in effectively navigating between subchains. This complete association ensures that inputs are directed exactly to particular subchains, resulting in correct and focused responses throughout numerous vacation spot classes.

router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(
    locations=destinations_str
)
router_prompt = PromptTemplate(
    template=router_template,
    input_variables=["input"],
    output_parser=RouterOutputParser(),
)

router_chain = LLMRouterChain.from_llm(llm, router_prompt)

Chaining All the pieces Collectively

A MultiPromptChain is created, incorporating a router chain to intelligently route inputs to particular vacation spot chains. Moreover, a default chain is included to deal with instances the place the router chain could encounter ambiguity, making certain a structured and efficient processing stream with verbose logging enabled for detailed insights.

chain = MultiPromptChain(router_chain=router_chain,
                         destination_chains=destination_chains,
                         default_chain=default_chain, verbose=True
                        )

Output

Output | Chains in Langchain

Actual-world Use Circumstances of Langchain

Delve into the real-world makes use of and achievements of options pushed by Giant Language Fashions (LLMs), demonstrating their diverse affect throughout sectors. Inside buyer assist, the collaboration between LangChain and LLMs has reworked providers via the implementation of sensible chatbots. These bots present speedy, personalised assist, effectively managing a big inflow of queries. By decreasing wait instances, they considerably elevate buyer satisfaction ranges.

E-commerce

LangChain makes use of the ability of Giant Language Fashions (LLMs) to boost the purchasing journey. Builders can create purposes that perceive product specifics, person likes, and buying patterns. By harnessing the capabilities of LLMs, these platforms provide tailor-made product suggestions, tackle buyer inquiries, and create charming product descriptions. This results in elevated gross sales and better buyer engagement ranges.

Healthcare

LangChain is revolutionizing affected person care and analysis via purposes powered by Giant Language Fashions (LLMs). With LangChain’s assist, develop digital assistants to know medical inquiries. These digital assistants present correct info, assess sufferers primarily based on signs, and expedite entry to healthcare information. This development not solely lightens the workload for medical professionals but additionally allows sufferers to make well-informed choices about their well being.

Content material Era

LangChain empowers builders to create purposes that produce imaginative and contextually related content material, together with weblog articles and product descriptions. These purposes assist content material creators by enhancing creativity, streamlining the writing course of, and sustaining consistency in tone and magnificence.

The sensible implementations highlighted right here reveal the flexibility and influence of options pushed by Giant Language Fashions (LLMs) throughout numerous industries. LangChain’s potential allows builders to create modern options, streamline operations, enhance person engagement, and gasoline enterprise progress. Success tales abound, starting from vital decreases in assist ticket decision instances to increased buyer satisfaction scores for e-commerce chatbots, showcasing the tangible advantages of LLM-powered purposes.

Conclusion

LangChain affords an expansive realm of alternatives for creating purposes that include Giant Language Mannequin capabilities. Whether or not your focus is on duties like textual content completion, language translation, sentiment evaluation, textual content summarization, or named entity recognition, LangChain stands as a flexible answer.

LangChain affords a complete framework for constructing highly effective purposes via clever chaining strategies. By understanding the intricacies of various chains and their configurations, builders can create tailor-made options for complicated duties. Routing inputs via Router Chains provides a layer of clever decision-making, making certain we direct inputs to essentially the most appropriate processing paths. With this information, builders can design modern purposes throughout industries, streamlining processes, enhancing person experiences, and in the end revolutionizing the best way we work together with language within the digital realm.

Key Takeaways

  • LLMChain, the only type of chain in LangChain, transforms person inputs utilizing a PromptTemplate, offering a elementary and extensively employed framework for interacting with Giant Language Fashions (LLMs).
  • Sequential chains in LangChain, whether or not within the type of Easy Sequential Chains or extra complicated setups make sure that the output from one step serves because the enter for the following, simplifying the method and permitting for intricate interactions in numerous purposes.
  • The Router Chain in LangChain serves as an clever decision-maker, directing particular inputs to specialised subchains. This seamless routing enhances the effectivity of duties by matching inputs with essentially the most appropriate processing chains.

Often Requested Questions

Q1: What’s LangChain and the way does it revolutionize language processing?

A1: LangChain is a classy expertise that leverages Giant Language Fashions (LLMs) to streamline language processing duties. It integrates numerous parts corresponding to LLMChains and Router Chains, permitting seamless job routing and environment friendly processing, resulting in the event of clever purposes.

Q2: How does LLMChain work, and what position does it play in LangChain?

A2: LLMChain is a elementary ingredient of LangChain. It operates by using a PromptTemplate to format person inputs, passing them to an LLM for processing. The non-compulsory OutputParser refines the output, making certain it aligns with the specified format, making LLMChain a necessary software for coherent language technology.

Q3: What are Sequential Chains, and the way do they improve the processing of a number of inputs and outputs?

A3: Sequential Chains mix numerous subchains, permitting the output of 1 to function the enter for the following. Easy Sequential Chains deal with single inputs and outputs, whereas extra complicated Sequential Chains handle a number of inputs and outputs concurrently, streamlining the stream of data in LangChain purposes.

This autumn: What’s the goal of Router Chains in LangChain, and the way do they optimize complicated duties?

A4: Router Chains are pivotal for intricate duties with a number of specialised subchains. They decide which subchain to route inputs primarily based on particular traits. The Router Chain, together with Vacation spot Chains and a Default Chain, effectively directs inputs to essentially the most appropriate subchain, making certain exact processing.

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