In our ever-evolving world of technology, it’s essential to appreciate the remarkable progress of Natural Language Processing (NLP).
Rewinding back a little, each NLP task necessitated a distinct model, a tedious and time-consuming process. This changed with the introduction of Transformers and the concept of transfer learning in NLP.
Large corporations like Google spearheaded this transformation by investing heavily in training transform models. These models serve as “generalists” with a robust understanding of language, allowing them to perform diverse tasks.
It’s astounding to realize that the technology has reached such a sophisticated stage.
Still, these models are not without their flaws. To illustrate, consider the example of GPT-4, one of the most advanced large language models currently available. When posed with a specific question about Langchain, it diverts to discussing a blockchain-based platform with an imaginary but similar token system. This misinterpretation, or “hallucination“, is a common issue with large language models.
Despite these challenges, there’s no denying the significant impact and popularity of large language models.
Langchain serves as a conduit for creating applications using language models:
It’s composed of modular components and chains, which are pre-set paths to seamlessly integrate these modules.
The modular elements consist of:
- Prompt templates
- Large language models (LLMs)
- Indexing tools
Chains, in this context, refer to predetermined steps, while agents take non-deterministic actions, adjusting their behavior based on observations.
An interesting development from Langchain is their endeavor to add type safety to the language model output, akin to what we might see in programming languages. This novel approach suggests a new paradigm of programming languages.
Langchain presents a well-structured framework and the necessary tools for effectively employing large language models across various applications.
It simplifies the process of using these models in intriguing and innovative ways, driving the future of language model applications.
Understanding the significance of language models in public interfaces is paramount in today’s tech-savvy world.
ChatGPT‘s Chat History
A prime example is ChatGPT, which incorporates the language model and chat history, providing users with a more interactive experience. However, one must remember that this chat history is not inherent to the model, but something developers construct around it within the framework.
When the ChatGPT API was released, many assumed it came with built-in conversation storage.
Contrary to this belief, it’s a Stateless API, and the responsibility of managing memory lies with developers, and this is where Langchain shines.
The power of this model lies in its ability to facilitate follow-up questions and natural conversation rather than simply addressing standalone queries. This opens up a valuable user interface and user experience opportunity.
By incorporating memory and the capacity for interactive dialogue, users can guide the AI’s responses in real-time.
This “conversational memory” unlocks immense value for the user, shaping the way we interact with AI.
At its core, Langchain strives to serve as a comprehensive framework for building with language models.
Langchain offers a diverse set of components, making application development a breeze. Although it’s still early days for these applications, the goal is for Langchain to become the go-to platform for developers.
Looking back at some initial concerns, Langchain encountered issues with the output from GPT-4, the latest model in language technology. When queried about the LM chain, GPT-4 misinterpreted it as a blockchain-based decentralized AI language model. This stems from the fact that the model’s training data only extends until 2021 and hence, has no understanding of recent developments.
However, we implemented a different approach with Langchain that yielded better results.
The same GPT-4 model, when queried about the LM chain, provided a comprehensive walkthrough of the code. This implies that while the model is the same, the method of retrieving information has altered, using a process known as retrievable augmentation.
Retrievable augmentation is a strategy to supplement the knowledge of a large language model, limited and outdated due to its cutoff in late 2021, with up-to-date and relevant information.
This method has been implemented in many recent chatbots, enabling them to access a variety of sources for the most current and valid information. This means that users no longer have to blindly trust the large language model’s output; they can verify its sources.
The retrievable augmentation process involves two steps, Indexing and Retrieval.
The former involves embedding documents with a model and storing them in a vector database. Once indexed, we can feed this information into our large language model.
For retrieval, Langchain introduces a query that gets encoded into the same data space. This query then scans the vector database for similar vectors based on meaning, returning relevant pieces of information from our indexed documents.
Langchain makes this process seamless. It incorporates document loaders that fetch text from files, web pages, and various sources, converting them into a common format that includes text and document metadata.
Semantic Search retrieval
The chains in Langchain, which are pre-defined steps for using these components, come in various forms that utilize this abstraction of semantic search retrieval augmented generation, including Vector DB question answering with and without sources, and Vector DB question answering with memory.
Langchain is revolutionizing the way we interact with large language models, providing a framework and necessary tools for various applications. It simplifies the process of leveraging these models in numerous innovative and compelling ways.
Handling of extensive pieces of text
Langchain offers a variety of options for breaking down, or “chunking“, lengthy documents, making the process intuitive with numerous handy utilities.
Embeddings (Wikipedia Example)
To illustrate this, it is possible to employ a small Wikipedia dataset and Langchain to produce embeddings with an embedding model. The vector database is then initialized and vectors from the dataset are added.
Further highlighting Langchain’s capabilities, developers should demonstrate how they can retrieve pertinent information. A query is posed, leading to the extraction of several Wikipedia documents relevant to the question, thus showcasing the effectiveness of Langchain’s retrieval mechanisms.
Next, it is easy to integrate a large language model, specifically GPT, and initialize the Vector dbqa chain.
The chain accepts the large language model and the Vectorstore and then executes the process, which involves feeding in the initial query and the previously retrieved documents. With the help of the dbqa class, this process becomes seamless, generating a succinct and improved answer based on the retrieved information.
In addition to this, developers should touch upon the Vector DB QA with sources training, an essential component providing the source of the information alongside the query’s answer. Although this article didn’t delve deeply into the chat QA features, it’s another key feature of Langchain that’s worth exploring.
Imagine how developers can address various inquiries about Langchain, such as training embedding models on proprietary data, the optimal low-code approach to utilizing Langchain, and ways to leverage Langchain beyond proprietary data.
It is good idea to start digigng the most effective methods for chunking sentences, the advantages and disadvantages of dense versus sparse vectors, the integration of guardrails within Langchain, and popular embedding tools used in conjunction with Langchain.
In summary, Langchain provides an array of tools and methodologies for processing and retrieving data with large language models. By simplifying these processes, Langchain enhances the usability of these models across numerous applications, paving the way for further advancements in the field.