Integrating LLM Brokers with LangChain into VICA

Find out how we use LLM Brokers to enhance and customise transactions in a chatbot!

Contributors: Nicole Ren (GovTech), Ng Wei Cheng (GovTech)

VICA (Digital Clever Chat Assistant) is GovTech’s Digital Assistant platform that leverages Synthetic Intelligence (AI) to permit customers to create, prepare and deploy chatbots on their web sites. On the time of writing, VICA helps over 100 chatbots and handles over 700,000 consumer queries in a month.

Behind the scenes, VICA’s NLP engine makes use of varied applied sciences and frameworks starting from conventional intent-matching techniques to generative AI frameworks like Retrieval Augmented Technology (RAG). By holding updated with state-of-the-art applied sciences, our engine is consistently evolving, making certain that each citizen’s question will get matched to the very best reply.

Past easy Query-And-Reply (Q&A) capabilities, VICA goals to supercharge chatbots by way of conversational transactions. Our aim is to say goodbye to the robotic and awkward form-like expertise inside a chatbot, and say hi there to personalised conversations with human-like help.

This text is the primary in a two half article sequence to share extra in regards to the generative AI options we have now inbuilt VICA. On this article, we are going to give attention to how LLM brokers might help enhance the transaction course of in chatbots by way of utilizing LangChain’s Agent Framework.

1. Introduction
2. All about LangChain
3. LangChain in manufacturing
4. Challenges of productionizing LangChain
5. Use case of LLM Brokers
6. Conclusion
7. Discover out extra about VICA
8. Acknowledgements
9. References

Transaction-based chatbots are conversational brokers designed to facilitate and execute particular transactions for customers. These chatbots transcend easy Q&A interactions that happen by permitting customers to carry out duties corresponding to reserving, buying, or type submission instantly throughout the chatbot interface.

In an effort to carry out transactions, the chatbots need to be custom-made on the backend to deal with further consumer flows and make API calls.

With the rise of Giant Language Fashions (LLMs), it has opened new avenues for simplifying and enhancing the event of those options for chatbots. LLMs can enormously enhance a chatbot’s skill to grasp and reply to a variety of queries, serving to to handle complicated transactions extra successfully.

Although intent-matching chatbot techniques exist already to information customers by way of predefined flows for transactions, LLMs supply important benefits by sustaining context over multi-turn interactions and dealing with a variety of inputs and language variations. Beforehand, interactions usually felt awkward and stilted, as customers had been required to pick out choices from premade playing cards or sort particular phrases so as to set off a transaction circulate. For instance, a slight variation from “Can I make a fee?” to “Let me pay, please” may stop the transaction circulate from triggering. In distinction, LLMs can adapt to numerous communication types permitting them to interpret consumer enter that doesn’t match neatly into predefined intents.

Recognizing this potential, our crew determined to leverage LLMs for transaction processing, enabling customers to enter transaction flows extra naturally and flexibly by breaking down and understanding their intentions. Provided that LangChain presents a framework for implementing agentic workflows, we selected to make the most of their agent framework to create an clever system to course of transactions.

On this article, we may even share two use circumstances we developed that make the most of LLM Brokers, particularly The Division of Statistics (DOS) Statistic Desk Builder, and the Pure Dialog Facility Reserving chatbot.

Earlier than we cowl how we made use of LLM Brokers to carry out transactions, we are going to first share on what’s LangChain and why we opted to experiment with this framework.

What’s LangChain?

LangChain is an open-source Python framework designed to help builders in constructing AI powered functions leveraging LLMs.

Why use LangChain?

The framework helps to simplify the event course of by offering abstractions and templates that allow speedy software constructing, saving time and decreasing the necessity for our growth crew to code every part from scratch. This enables for us to give attention to higher-level performance and enterprise logic quite than low-level coding particulars. An instance of that is how LangChain helps to streamline third social gathering integration with widespread service suppliers like MongoDB, OpenAI, and AWS, facilitating faster prototyping and decreasing the complexity of integrating varied companies. These abstractions not solely speed up growth but additionally enhance collaboration by offering a constant construction, permitting our crew to effectively construct, check, and deploy AI functions.

What’s LangChain’s Agent Framework?

One of many major options of utilizing Langchain is their agent framework. The framework permits for administration of clever brokers that work together with LLMs and different instruments to carry out complicated duties.

The three major parts of the framework are

Brokers act as a reasoning engine as they resolve the suitable actions to take and the order to take these actions. They make use of an LLM to make the selections for them. An agent has an AgentExecutor that calls the agent and executes the instruments the agent chooses. It additionally takes the output of the motion and passes it to the agent till the ultimate final result is reached.

Instruments are interfaces that the agent could make use of. In an effort to create a instrument, a reputation and outline must be offered. The outline and identify of the instrument are necessary as it is going to be added into the agent immediate. Which means that the agent will resolve the instrument to make use of based mostly on the identify and outline offered.

A series check with sequences of calls. The chain may be coded out steps or only a name to an LLM or a instrument. Chains may be custom-made or be used off-the-shelf based mostly on what LangChain offers. A easy instance of a sequence is LLMChain, a sequence that run queries towards LLMs.

How did we use LangChain in VICA?

In VICA, we arrange a microservice for LangChain invoked by way of REST API. This helps to facilitate integration by permitting totally different parts of VICA to speak with LangChain independently. Consequently, we will effectively construct our LLM agent with out being affected by modifications or growth in different parts of the system.

LangChain as a framework is fairly intensive with regards to the LLM area, overlaying retrieval strategies, brokers and LLM analysis. Listed here are the parts we made use of when growing our LLM Agent.

ReAct Agent

In VICA, we made use of a single agent system. The agent makes use of ReAct logic to find out the sequence of actions to take (Yao et al., 2022). This immediate engineering approach will assist generate the next:

• Thought (Reasoning taken earlier than selecting the motion)
• Motion (Motion to take, usually a instrument)
• Motion Enter (Enter to the motion)
• Commentary (Commentary from the instrument output)
• Last Reply (Generative remaining reply that the agent returns)

> Coming into new AgentExecutor chain…
The consumer desires to know the climate right now
Motion: Climate Device
Motion Enter: "Climate right now"
Commentary: Reply: "31 Levels Celsius, Sunny"
Thought: I now know the ultimate reply.
Last Reply: The climate right now is sunny at 31 levels celsius.
> Completed chain.

Within the above instance, the agent was capable of perceive the consumer’s intention prior to picking the instrument to make use of. There was additionally verbal reasoning being generated that helps the mannequin plan the sequence of motion to take. If the commentary is inadequate to reply the query given, the agent can cycle to a unique motion so as to get nearer to the ultimate reply.

In VICA, we edited the agent immediate to higher swimsuit our use case. The bottom immediate offered by LangChain (hyperlink right here) is usually ample for commonest use circumstances, serving as an efficient start line. Nevertheless, it may be modified to boost efficiency and guarantee larger relevance to particular functions. This may be accomplished through the use of a customized immediate earlier than passing it as a parameter to the create_react_agent (is likely to be totally different based mostly in your model of LangChain).

To find out if our customized immediate was an enchancment, we employed an iterative immediate engineering method: Write, Consider and Refine (extra particulars right here). This course of ensured that the immediate generalized successfully throughout a broad vary of check circumstances. Moreover, we used the bottom immediate offered by LangChain as a benchmark to judge our customized prompts, enabling us to evaluate their efficiency with various further context throughout varied transaction situations.

Customized Instruments & Chains (Immediate Chaining)

For the 2 customized chatbot options on this article, we made use of customized instruments that our Agent could make use of to carry out transactions. Our customized instruments make use of immediate chaining to breakdown and perceive a consumer’s request earlier than deciding what to do within the explicit instrument.

Immediate chaining is a method the place a number of prompts are utilized in sequence to deal with complicated duties or queries. It entails beginning with an preliminary immediate and utilizing its output as enter for subsequent prompts, permitting for iterative refinement and contextual continuity. This technique enhances the dealing with of intricate queries, improves accuracy, and maintains coherence by progressively narrowing down the main target.

For every transaction use case, we broke the method into a number of steps, permitting us to offer clearer directions to the LLM at every stage. This technique improves accuracy by making duties extra particular and manageable. We can also inject localized context into the prompts, which clarifies the goals and enhances the LLM’s understanding. Primarily based on the LLM’s reasoning, our customized chains will make requests to exterior APIs to collect knowledge to carry out the transaction.

At each step of immediate chaining, it’s essential to implement error dealing with, as LLMs can generally produce hallucinations or inaccurate responses. By incorporating error dealing with mechanisms corresponding to validation checks, we recognized and addressed inconsistencies or errors within the outputs. This allowed us to generate fallback responses to our customers that defined what the LLM did not purpose at.

Lastly, in our customized instrument, we kept away from merely utilizing the LLM generated output as the ultimate response because of the danger of hallucination. As a citizen going through chatbot, it’s essential to forestall our chatbots from disseminating any deceptive or inaccurate data. Due to this fact, we be sure that all responses to consumer queries are derived from precise knowledge factors retrieved by way of our customized chains. We then format these knowledge factors into pre-defined responses, making certain that customers don’t see any direct output generated by the LLM.

Challenges of utilizing LLMs

Problem #1: Immediate chaining results in sluggish inference time

A problem with LLMs is their inference instances. LLMs have excessive computational calls for on account of their giant variety of parameters and having to be referred to as repeatedly for actual time processing, resulting in comparatively sluggish inference instances (a number of seconds per immediate). VICA is a chatbot that will get 700,000 queries in a month. To make sure a very good consumer expertise, we purpose to offer our responses as rapidly as attainable whereas making certain accuracy.

Immediate chaining will increase the consistency, controllability and reliability of LLM outputs. Nevertheless, every further chain we incorporate considerably slows down our answer because it necessitates making an additional LLM request. To stability simplicity with effectivity, we set a tough restrict on the variety of chains to forestall extreme wait instances for customers. We additionally opted to not use higher performing LLM fashions corresponding to GPT-4 on account of their slower velocity, however opted for sooner however usually nicely performing LLMs.

Problem #2 :Hallucination

As seen within the current incident with Google’s characteristic, AI Overview, having LLMs producing outputs can result in inaccurate or non-factual particulars. Although grounding the LLM makes it extra constant and fewer prone to hallucinate, it doesn’t get rid of hallucination.

As talked about above, we made use of immediate chaining to carry out reasoning duties for transactions by breaking it down into smaller, simpler to know duties. By chaining LLMs, we’re capable of extract the knowledge wanted to course of complicated queries. Nevertheless, for the ultimate output, we crafted non-generative messages as the ultimate response from the reasoning duties that the LLM performs. Which means that in VICA, our customers don’t see generated responses from our LLM Agent.

Problem #1: An excessive amount of abstraction

The primary difficulty with LangChain is that the framework abstracts away too many particulars, making it very troublesome to customise functions for particular actual world use circumstances.

In an effort to overcome such limitations, we needed to delve into the package deal and customise sure lessons to higher swimsuit our use case. As an illustration, we modified the AgentExecutor class to route the ReAct agent’s motion enter into the instrument that was chosen. This gave our customized instruments further context that helped with extracting data from consumer queries.

Problem #2: Lack of documentation

The second difficulty is the dearth of documentation and the consistently evolving framework. This makes growth troublesome because it takes time to know how the framework works by way of trying on the package deal code. There’s additionally an absence of consistency on how issues work, making it troublesome to choose issues up as you go. Additionally with fixed updates on present lessons, an improve in model can lead to beforehand working code instantly breaking.

If you’re planning to make use of LangChain in manufacturing, an recommendation can be to repair your manufacturing model and check earlier than upgrading.

Use case #1: Division of Statistics (DOS) Desk builder

With regards to statistical knowledge about Singapore, customers can discover it troublesome to seek out and analyze the knowledge that they’re in search of. To handle this difficulty, we got here up with a POC that goals to extract and current statistical knowledge in a desk format as a characteristic in our chatbot.

As DOS’s API is open for public use, we made use of the API documentation that was offered of their web site. Utilizing LLM’s pure language understanding capabilities, we handed the API documentation into the immediate. The LLM was then tasked to choose the right API endpoint based mostly on what the statistical knowledge that the consumer was asking for. This meant that customers may ask for statistical data for annual/half-yearly/quarterly/month-to-month knowledge in share change/absolute values in a given time filter. For instance, we’re capable of question particular data corresponding to “GDP for Building in 2022” or “CPI in quarter 1 for the previous 3 years”.

We then did additional immediate chaining to interrupt the duty down much more, permitting for extra consistency in our remaining output. The queries had been then processed to generate the statistics offered in a desk. As all the knowledge had been obtained from the API, not one of the numbers displayed are generated by LLMs thus avoiding any danger of spreading non-factual data.

Use case #2: Pure Dialog Facility Reserving Chatbot

In right now’s digital age, nearly all of bookings are carried out by way of on-line web sites. Relying on the consumer interface, it might be a course of that entails sifting by way of quite a few dates to safe an accessible slot, making it troublesome as you would possibly have to look by way of a number of dates to seek out an accessible reserving slot.

Reserving by way of pure dialog may simplify this course of. By simply typing one line corresponding to “I need to guide a badminton court docket at Fengshan at 9.30 am”, you’ll be capable of get a reserving or suggestions from a digital assistant.

With regards to reserving a facility, there are three issues we want from a consumer:

• The power sort (e.g. Badminton, Assembly room, Soccer)
• Location (e.g. Ang Mo Kio, Maple Tree Enterprise Centre, Hive)
• Date (this week, 26 Feb, right now)

As soon as we’re capable of detect these data from pure language, we will create a customized reserving chatbot that’s reusable for a number of use circumstances (e.g. the reserving of hotdesk, reserving of sports activities services, and so on).

The above instance illustrates a consumer inquiring in regards to the availability of a soccer discipline at 2.30pm. Nevertheless, the consumer is lacking a required data which is the date. Due to this fact, the chatbot will ask a clarifying query to acquire the lacking date. As soon as the consumer offers the date, the chatbot will course of this multi-turn dialog and try to seek out any accessible reserving slots that matches the consumer’s request. As there was a reserving slot that matches the consumer’s precise description, the chatbot will current this data as a desk.

If there aren’t any accessible reserving slots accessible, our facility reserving chatbot would develop the search, exploring totally different timeslots or rising the search date vary. It might additionally try to advocate customers accessible reserving slots based mostly on their earlier question if there their question ends in no accessible bookings. This goals to boost the consumer expertise by eliminating the necessity to filter out unavailable dates when making a reserving, saving customers the trouble and time.

As a result of we use LLMs as our reasoning engine, an extra profit is their multilingual capabilities, which allow them to purpose and reply to customers writing in several languages.

The instance above illustrates the chatbot’s skill to precisely course of the right facility, dates, and placement from the consumer’s message that was written in Korean to offer the suitable non-generative response though there aren’t any accessible slots for the date vary offered.

What we demonstrated was a short instance of how our LLM Agent handles facility reserving transactions. In actuality, the precise answer is much more complicated, with the ability to give a number of accessible bookings for a number of places, deal with postal codes, deal with places too removed from the acknowledged location, and so on. Though we wanted to make some modifications to the package deal to suit our particular use case, LangChain’s Agent Framework was helpful in serving to us chain a number of prompts collectively and use their outputs within the ReAct Agent.

Moreover, we designed this custom-made answer to be simply extendable to any related reserving system that requires reserving by way of pure language.

On this first a part of our sequence, we explored how GovTech’s Digital Clever Chat Assistant (VICA) leverages LLM Brokers to boost chatbot capabilities, significantly for transaction-based chatbots.

By integrating LangChain’s Agent Framework into VICA’s structure, we demonstrated its potential by way of the Division of Statistics (DOS) Desk Builder and Facility Reserving Chatbot use circumstances. These examples spotlight how LangChain can streamline complicated transaction interactions, enabling chatbots to deal with transaction associated duties like knowledge retrieval and reserving by way of pure dialog.

LangChain presents options to rapidly develop and prototype refined chatbot options, permitting builders to harness the facility of enormous language fashions effectively. Nevertheless, challenges like inadequate documentation and extreme abstraction can result in elevated upkeep efforts as customizing the framework to suit particular wants might require important time and assets. Due to this fact, evaluating an in-house answer would possibly supply larger long run customizability and stability.

Within the subsequent article, we will likely be overlaying how chatbot engines may be improved by way of understanding multi-turn conversations.

Curious in regards to the potential of AI chatbots? If you’re a Singapore public service officer, you may go to our web site at https://www.vica.gov.sg/ to create your individual customized chatbot and discover out extra!

Particular due to Wei Jie Kong for establishing necessities for the Facility Reserving Chatbot. We additionally want to thank Justin Wang and Samantha Yom, our hardworking interns, for his or her preliminary work on the DOS Desk builder.

Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, Ok., & Cao, Y. (2022). React: Synergizing reasoning and appearing in language fashions. arXiv preprint arXiv:2210.03629.