I developed a user-friendly web application that delivers semantic search results based on open-access financial publications from various websites. My approach involved three key phases: data extraction, semantic indexing, and backend search integration.

I began by identifying and analyzing the target websites where financial publications were available. Collaborating with the client, I pinpointed the most relevant sources and studied their structures to determine the optimal methods for data extraction. I wrote custom web scraping scripts in Python using Scrapy. The scripts were tailored to handle challenges such as pagination, dynamic content, and diverse HTML layouts. Throughout this process, I ensured that my methods complied with each site's policies and respected their robots.txt files. Once the data was extracted, I focused on cleaning and preprocessing the information. I removed duplicate entries, normalized formats, and stored the processed data in a structured format for easy access in later stages.

With the cleaned data ready, I moved on to the semantic indexing phase. I used pre-trained language models, including OpenAI’s embedding models, to convert the textual data into high-dimensional vector representations. This transformation was crucial to capturing the semantic nuances of the financial content. I chose Qdrant as the vector database because of its efficiency in handling similarity searches. I designed an indexing pipeline that inserted each vectorized document into Qdrant, ensuring that the data was organized for quick and accurate retrieval. I also built in scalability from the outset, implementing batch processing techniques to update the index continuously as new publications became available.

The final phase involved developing a backend service to support the search functionality. I built a RESTful API using FastAPI, which provided a robust and efficient framework for integrating the various components of the application. When a user submitted a query, my backend service first vectorized the query using the same pre-trained model to maintain consistency with the indexed documents. It then executed a similarity search within Qdrant to identify the most relevant documents. To generate a concise and coherent answer for the user, I aggregated the retrieved documents and interfaced with OpenAI’s API with a custom prompt. This allowed me to produce a summarized response that was both informative and easy to understand. I implemented extensive error handling and fallback procedures to ensure that communication with the OpenAI API remained reliable, and I worked on optimizing the performance of the entire pipeline through careful testing and caching of frequent queries.

Throughout the project, I maintained regular consultations with the client to refine the requirements and iterate on the design. This collaborative approach helped me align the technical solutions with the client’s vision and business objectives. I documented the code thoroughly and prepared a comprehensive deployment guide, ensuring that the client could transition the solution into production smoothly.

By integrating advanced web scraping techniques, semantic vectorization, and intelligent search capabilities, I successfully delivered a solution that not only met the client’s requirements but also enhanced the user experience. This project was an exciting opportunity to work on a problem that combined data engineering, natural language processing, and backend development, and it stands as a testament to my ability to tackle complex, real-world challenges.