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Helped a Client Overcome Challenges in Building an AI Chatbot – Part 2

In Part 1: Overcoming AI Chatbot Challenges, we explored the challenges we faced with Meta AI’s Llama model and how we decided to implement an alternative and more efficient solution. Now, we will explore how we practically implemented Deepseek R1 to overcome those challenges and create the chatbot. We will also discuss the key tools and libraries that contributed to the development of the chatbot.

Building Chatbot with Deepseek R1

Deepseek’s R1 14b model, an alternative solution, was complemented by Nomic Ember Text, Chroma DB, Flask Framework, and PyPDF2. Let’s have a look at the architecture:

How we built the chatbot

Using the tools mentioned above, we created a strong, high-performing chatbot capable of processing and responding to complex queries. Following is the breakdown of the architecture and steps we followed to build the chatbot:

1. Why Deepseek R1 14b?

We chose Deepseek R1 for its efficiency and compatibility with our client’s existing hardware. Deepseek R1 could deliver high-quality performance on more modest setups. Deepseek R1’s optimized architecture (learn more in their official whitepaper) allowed us to run inference smoothly without compromising the quality of responses.

2. Data Handling with PyPDF2

To extract textual content from company policies and guidelines, we utilized PyPDF2, a lightweight and reliable library for processing PDFs. This allowed us to implement data parsing by converting unstructured data from various documents into clean, plain text. Additionally, we used the Pandoc library to convert different file formats, such as Word, Text, etc, into PDFs, ensuring consistency in document processing.

Workflow:

• Loaded PDFs using PyPDF2.
• Extracted text in chunks, ensuring no information was lost.
• Converted other file formats to PDF using Pandoc for uniform handling.

3. Vector Embeddings with Nomic Embed Text

To represent the extracted text as meaningful vectors, we used Nomic Embed Text, part of the Nomic AI ecosystem. This embedding model was instrumental in converting the raw textual data into vector representations that captured the semantics of the content. These embeddings formed the backbone of the chatbot’s search and response capabilities.

Steps:

• Processed text chunks from PyPDF2.
• Passed the cleaned text into Nomic Embed Text for vectorization.
• Stored the resulting embeddings for efficient retrieval.

4. Efficient Storage and Retrieval with ChromaDB

To handle the large volume of vectorized data, we relied on ChromaDB, a high-performance vector database. ChromaDB allowed us to index and query vector data quickly, enabling the chatbot to deliver accurate responses in real time.

5. Backend Integration with Deepseek R1

Deepseek R1 acted as the core language model, providing intelligent responses based on user queries and the processed data stored in ChromaDB. We integrated this model into the Python/Flask backend, allowing the chatbot to:

• Retrieve relevant vector data from ChromaDB.
• Generate context-aware answers based on Deepseek’s language understanding.
• Deliver responses through the user interface.

6. Final Results

With Deepseek R1 and our optimized tech stack, NextChatbot was able to:

• Provide employees with instant, accurate answers to policy-related queries.
• Operate seamlessly within existing hardware constraints.

7. Next Steps

In our next phase, we plan to enhance the chatbot further by integrating voice-based interactions.

Related read: chatbot vs. voicebot analysis

Stay tuned as we continue to push the boundaries of what the chatbot can achieve.