Building a chatbot might seem like a Herculean task, especially one that utilizes the power of advanced AI models and works on your PDF data. However, with LangChain, a Python library designed to facilitate building chatbots and conversation AI, you can create a highly functional and customized chatbot in no time. This article will walk you through how you can create a chatbot using LangChain trained on your own PDF data.
Understanding What is LangChain
LangChain is a highly versatile framework that is primarily used to construct applications that are powered by language models. Essentially, it serves as a bridge between advanced AI language models and other data sources or environments, crafting applications that are data-aware and have the capacity to interact with their surroundings. The power of LangChain resides in its ability to create highly functional and contextually aware language model applications, which in turn can significantly enhance the user experience.
The beauty of LangChain lies in its simplicity and user-friendly interface. Here are some salient features that make LangChain an ideal choice for developing language model applications:
- Models: It provides support for various model types and integrations.
- Prompts: It offers a streamlined way to manage, optimize, and serialize prompts.
- Memory: LangChain enables the persistence of state between different calls of a chain or agent.
- Indexes: It enables combining language models with application-specific data for an enhanced user experience.
- Chains and Agents: Chains and Agents, where chains are structured sequences of calls, while an agent is a chain that allows an LLM to repeatedly decide an action, execute it, and observe the outcome.
- Callbacks: This feature allows logging and streaming of the intermediate steps of any chain, which facilitates debugging and evaluation.
Moreover, LangChain opens up a host of possibilities for its application. From autonomous agents and personal assistants to chatbots and question-answering systems, it can power a wide range of use-cases. The versatility of LangChain is further demonstrated by its capacity to interact with APIs, understand code, extract structured information from text, and even summarize long documents.
It is worth mentioning that LangChain is not only a powerful tool but is also a thriving ecosystem that integrates several different LLMs, systems, and products. Moreover, its adaptability has led to a multitude of systems and products depending on LangChain. For more details, explore the Python-specific documentation of LangChain here.
LangChain and GPT: A Powerful Duo
LangChain is an AI-powered tool that significantly simplifies the process of building a chatbot. What makes it more intriguing is its compatibility with OpenAI’s GPT models. GPT-4, the most recent iteration at the time of writing, is a powerful transformer-based model, making it ideal for tasks such as language translation, text completion, and chatbots.
Transforming Your PDFs Into a Knowledge Base
The first step in building your custom knowledge chatbot involves converting your PDFs into a format suitable for the AI model. You might have used applications like Chat PDF, where you can drag and drop a document and start chatting over it. The method we are discussing today is similar, only that it grants you complete control over your app’s functionality and how your documents are processed.
How Does It Work?
- Document Chunking: LangChain takes your PDF document and splits it into smaller pieces or “chunks”. The goal is to have chunks that are tokens, which makes it easier for the chatbot to recall and query the database and deliver relevant responses to user queries. The chunking process can be customized to match your specific requirements, ensuring an optimal chunk size that can affect the output quality.
- Embedding: Each chunk of your document is then embedded using the adder002 model by OpenAI, an excellent embedding model. These embeddings are then stored in a vector database for later recall.
- Querying the Database: When a user queries your chatbot, the system uses the same embedding model to process the query. The system then searches the database for the chunks most similar to the user’s query.
- Large Language Model Integration: The retrieved document chunks and the user’s query are combined and fed into a large language model. The language model, given the context, generates an answer that’s sent back to the user.
Building Your Customized Chatbot
Now that we understand how the system works, let’s discuss how you can build your own chatbot. For this, we will use a Python notebook that simplifies the process.
- Package Installation: First, install the necessary packages and set up your API key. These packages include all the tools you need to start creating your chatbot.
- Loading PDFs and Chunking Data: Load your PDFs into the notebook and start chunking the data. LangChain offers a straightforward version for this process using the pipe pdfloader. However, for a more advanced method, you can use the text tracker to extract all the information out of the PDF and save it to a dock. Then, it’s saved as a text file and reopened to get around some potential issues. The chunking process can be customized, and a function to count the number of tokens in each chunk can be created.
- Creating the Vector Database: After the chunks are created, they are embedded and stored in a vector database using the faiss package offered by LangChain.
- Querying the Database: With the database set up, you can now query it. For example, if you query “Where is Webuters located?” the system runs a similarity search on the database and returns the our most relevant chunks of data related to your question. You can visualize this by calling LIN_docks after the query. This helps illustrate how much context the vector database is drawing upon to answer each query.
Creating and Utilizing LangChain Chains
With the query functionality in place, you can enhance the system by incorporating a LangChain chain. This chain takes in a user query, retrieves relevant documents from the database, and then provides a response to the query. The chain will conduct a similarity search, retrieve relevant documents, apply a language model (one of OpenAI’s models, for instance) to the query and context, and then generate an answer.
Converting the Functionality into a Chatbot
LangChain includes a component known as the ConversationalRetrievalChain that can be used for this purpose. This tool takes in a language model and utilizes the database as a retriever function. By integrating it, you can create a chatbot loop that allows interaction with the knowledge base.
For instance, when you ask the question “Who established Webuters?” in the chatbot, it generates an informative response based on the information extracted from your PDF data. But it doesn’t stop there – this custom chatbot also includes chat memory. As such, if you follow up your first question with another (“Does Webuters have a team?”, for example), the bot can generate a response based on the cumulative context of the conversation. This chat memory functionality gives the chatbot a greater sense of context and conversational coherence.
So, now you know how to create a customized knowledge chatbot using LangChain and PDF data. This chatbot not only answers your queries but also retains the memory of the conversation, providing a more interactive and engaging user experience.
If you’re interested in exploring the feasibility of AI in your projects or you’re looking to scale this kind of solution for your business or personal use, don’t hesitate to reach out.