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How to Natively Chat with PDFs

There are many new tools that allow you to have a conversation or ask questions of your PDFs, think ChatGPT for your data, one is better than the others


PDFs can contain a treasure trove of information. In an era where information only seems to be growing, having the ability to access proprietary knowledge contained in hundreds & thousands of giant files and quickly find specific bits and pieces of information is becoming increasingly important. Along with the rapid adoption of technologies such as ChatGPT, tools that allow you to “Chat” with your PDFs have surfaced too. Challenges associated with using such tools include the lack of true information retrieval and conversational experience with the information you’re seeking, as well as the complexity behind building such a tool from the ground up. This piece will discuss the workflow, requirements, and limitations on using ChatGPT for information retrieval, what alternatives exist, and how Vectara provides an out-of-the-box solution with ChatGPT-like functionality but for PDF information retrieval/conversational AI.

Can ChatGPT Work with PDFs?

Natively, ChatGPT doesn’t have the ability to read PDFs. Summarizing, conversing within, answering questions with, and retrieving information from PDFs are not natively supported by ChatGPT, as it lacks specialized knowledge of PDF structures and the capacity to analyze PDF material. Instead, it is primarily intended for producing human-like text responses. Although it can offer broad information and provide answers, specialized tools or libraries are more appropriate for jobs involving PDFs.

What You’d Need to Make ChatGPT do Information Retrieval with PDFs

To make ChatGPT capable of doing information retrieval using PDFs necessitates a very complex process. It entails including PDF extraction and parsing, setting up an indexing system, and developing a communication interface between ChatGPT and the information retrieval system. The system also has to be able to handle a variety of query types and provide precise answers depending on the information that has been retrieved. Coordinating these components and technologies is crucial for bridging the gap between PDF documents and the capabilities of ChatGPT, making the workflow intricate and complex.

PDF Extraction

The first step involves extracting text and metadata from PDF documents and making them machine-readable. This requires using tools or libraries like Tabula, PyPDF2, and Apache Tika. By leveraging the functionalities provided by these libraries, the workflow gains the ability to parse and extract relevant information from PDFs.

Preprocessing and Cleaning

The next step involves “cleaning” up the data. This entails eliminating any formatting errors, special characters, line breaks, and other features obstructing the text’s ability to be processed and analyzed accurately. Cleaning the collected text ensures that it is in a format that may be used for further analysis, integration with ChatGPT, or other information retrieval systems.

Information Retrieval

Once the text has been preprocessed and cleaned, ChatGPT can be utilized for the conversational component of the information retrieval system. You’d need to integrate ChatGPT into an application to allow users to ask questions and perform searches in a conversational manner.


To make the information retrieval process accurate and precise, leveraging NLP (Natural Language Processing) techniques becomes crucial. NLP techniques, such as entity recognition, query expansion, or semantic analysis help improve the precision and relevance of search results.

User Interaction

Next, a user interface or API integration is essential for user interaction and query input. It can be in the form of a chatbot interface or search bar, enabling users to ask questions or perform searches. This interface acts as a bridge between users and the information retrieval system, facilitating seamless interaction, and enabling users to input queries and receive relevant responses easily.

Query Processing

Leveraging query processing techniques like query parsing, intent classification, and question analysis techniques enhance the information retrieval system by breaking down user queries, identifying their intent, and analyzing the question type. This enables a better understanding of user queries, targeted information retrieval, and customization of the retrieval process, meaning more accurate and pertinent results.

Interaction with ChatGPT

The next step involves passing the processed queries to ChatGPT, allowing it to generate responses related to the PDF content. By integrating the processed queries with ChatGPT, the system can leverage the language model’s capabilities to provide relevant and informative responses based on the PDF information. This enables a seamless flow between information retrieval, query processing, and generating responses using ChatGPT.

User Interface and Presentation

The last piece of the puzzle would be having a system that presents the responses in an organized and visually appealing way. This would include having the correct formatting, section highlighting, and summary generation. By organizing the retrieved information and presenting it in a structured manner, users can easily navigate and comprehend the answers to their questions.

Why This isn’t Practical

Complexity and Variability

Extracting and preprocessing text from PDFs can be challenging, especially due to the complex structures of PDFs, the unique fonts, layouts, how they’re encoded, and how they’re formatted, making it tough to extract text accurately. In addition, interpreting user queries about PDF content is challenging due to ambiguity, domain-specific language, query formulation issues, syntax variations, and contextual understanding. Additional preprocessing, NLP techniques, and interactive clarification may be necessary to bypass these hurdles and retrieve relevant information precisely.

Information Accuracy

The quality and relevance of the information that ChatGPT retrieves relies on the accuracy of the preprocessed text. If there are errors or inaccuracies that are introduced during the extraction process, the integrity of the retrieved information could significantly be impacted. Additionally, extracting and incorporating complex structures like footnotes, citations, and cross-references within PDFs could be a challenge, possibly resulting in inaccurate information in responses.

Scalability and Performance

Due to how resource-intensive and time-consuming ChatGPT’s response generation can be, when faced with a large number of PDFs, scalability can become a huge issue. As the workload begins to increase, generating responses for multiple queries can tighten computational resources and result in much slower response times as well as performance degradation. Due to the time-consuming nature of ChatGPT’s response generation process, being able to get responses in near-real-time is quite a challenge also, and would require optimizing system architecture and allotting additional computational resources.

Alternatives for ChatGPT for PDFs

There are some solutions available today which allow you to have a conversation with your PDFs, such as ChatPDF, PDFgear Chatbot, PDF ChatBot, and Chat with PDF (by HiPDF). Each of them have unique limitations:

    • ChatPDF: Utilizes GPT 3.5, which, as discussed previously, doesn’t natively support a conversational experience with PDFs due to lacking specialized knowledge of PDF structures and the capacity to analyze PDF material. It doesn’t allow you to chat with multiple PDFs in one chat. Also, the free version only allows you to chat with 3 PDFs a day. In the paid version, a user is limited to 50 PDFs/day and 1,000 questions/day. On the flip side, Vectara’s Scale Plan can support enterprise-grade amounts of PDFs and queries.

    • PDFgear Chatbot: Requires you to install PDFgear onto a Mac or Windows computer in order to use the software. Also utilizes ChatGPT-3.5, which lacks native support for a ‘chat-like’ experience with PDFs. Vectara doesn’t require an installation and can be operated directly from its admin console.

    • PDF ChatBot: Only allows you to upload one PDF document at a time, limiting its ability to scale for companies with hundreds and thousands of PDFs. It also functions as a plugin for ChatGPT (only available to ChatGPT Plus users), and like PDFgear Chatbot, lacks native support for a conversational experience with PDFs.

    • Chat with PDF (by HiPDF): Just like PDFgear Chatbot and PDF ChatBot, Chat with PDF is powered by ChatGPT and is similarly limited in its ability to provide native information retrieval. The free version limits a user to 50 chats, while the paid version limits a user to 500 chats a month.

For the aforementioned reasons you should consider a more out-of-the-box solution, called Vectara.

Vectara: ChatGPT-like Functionality for PDFs, Out-of-the-box

Vectara is an LLM-powered answer as a service platform that works out of the box for informational retrieval and conversational AI, and it performs exceptionally well with PDFs. With a full ML search pipeline seamlessly stitched into an easy to use platform, the complexity of building everything from scratch is taken out of the equation. Vectara is kind of like a ChatGPT, but for your data. All it takes is a quick drag and drop of your PDFs into Vectara’s console, and you can ask questions and chat with the information in your document in a matter of seconds.

Vectara is built for information retrieval, and built for scale. From a single document to millions, the platform is capable of handling as little or as much of the information you want to have a conversation with. Vectara performs extremely well at quickly indexing documents and PDFs, and finding the relevant bits and pieces of the question you’re asking before surfacing a precise and accurate answer, and its relevance is unmatched. Moreover, every step of the process, from indexing to reranking, happens in near-real-time. Simply drag in your PDFs, submit your query, and get your results in seconds. It’s really that simple.

An additional key benefit to using the platform is that Vectara utilizes a ‘zero-shot’ machine learning (ML) approach, enabling the models to continuously learn from new data, without the need to consume additional data, fine-tune, or retrain. This means Vectara does not train on your proprietary data, and retrieves the most relevant answers with a broad understanding of any user’s question, regardless of the language used or context.

How to use Vectara for PDFs

Overall, while possible, the practical implementation and maintenance of a system for chatting with PDFs is complex and resource-intensive, posing challenges to achieving seamless and efficient interactions. While some solutions do exist, they fall short in providing an easy-to-use, AI-first, all-in-one platform built for information retrieval. Vectara provides just that: a full ML search pipeline that’s easily API addressable, with a process as simple as a drag & drop, letting you search and ask questions of your PDFs within seconds.


If you’re looking for a PDF information retrieval solution, be sure to pay close attention to your organization’s specific needs and requirements, the time it could take to build something from the ground up, and consider platforms specialized and optimized for handling PDFs and all types of documents in a seamless manner. Give Vectara a try here! Your queries will thank you 🙂

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In the AI era, how people interact with information has changed. Users expect relevant answers to questions in natural language, not a shopping list of hit or miss search results. They expect the best semantic or exact matches regardless of typos, colloquialisms, or context. Additionally, it is Vectara's mission to remove language as a barrier by allowing cross-language hybrid search that delivers summarized answers in the language of your choice. The Internet, mobile, and AI have made information accessible, now Vectara helps you find meaning quickly through the most relevant answers. Get to know Vectara, and if you have a question, just ask.

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