I am working on a chatbot connected to the OpenAI API that is responsible for answering questions related to documents that have previously been processed and finally saved in a vector database.
The process that the PDF documents have followed consists of dividing the entire document into chunks and finally vectorizing them and saving them to a vector database that also connects to the interface where the user asks about the information of said document. I am also using python for the web interface and langchain as the framework to connect to the OpenAI APIs.
However, when a user asks a question on the interface they sometimes get confused and give a very similar answer. This is because the PDF document has subtopics and among them some concepts are similar. So the question the user asks must be very specific. But what happens if the user doesn't know the topic and starts asking general questions? you will get wrong answers (Although for the model they will be fine)
So now I want to improve the quality of my chatbot's responses. I want him to be smarter. I am thinking that the PDF document should be pre-treated, organized or divided into each subtopic. So that it is saved in an orderly manner in the vector database. Or maybe another tool is what I should modify? like python code.
Some of you have had the same problem of poor quality answers due to many overlaps in various sections of the PDF document, and would like better quality and specific answers. Maybe you can comment and share a solution to this problem.
I appreciate it very much.
Your challenge is a common one in the realm of document-based chatbots, especially when dealing with dense and overlapping content. Improving the quality of responses requires a combination of preprocessing the documents, refining the querying mechanism, and potentially post-processing the model's outputs. Here are some strategies you can employ:
1. Document Preprocessing:
Subtopic Identification: Use topic modeling techniques, such as Latent Dirichlet Allocation (LDA) or Non-Negative Matrix Factorization (NMF), to identify distinct subtopics within the document. This can help in segmenting the document more effectively.
Hierarchical Chunking: Instead of dividing the document into arbitrary chunks, consider a hierarchical approach. Start with sections, then subsections, and so on. This ensures that each chunk is contextually coherent.
Metadata Annotation: For each chunk or segment, add metadata such as the identified subtopic, section title, or any other relevant information. This metadata can assist in refining search queries later.
2. Query Refinement:
Contextual Prompts: Before a user asks a question, provide them with a brief overview or a table of contents of the document. This can guide them in framing more specific questions.
Follow-up Questions: If a user's query is too general, the chatbot can ask clarifying questions to narrow down the search. For instance, if a user asks about "benefits," the bot can clarify, "Are you asking about benefits of X or benefits of Y?"
3. Vector Database Improvements:
Semantic Search: Instead of simple vector matching, consider using semantic search techniques. This involves understanding the intent behind the user's query and finding the most contextually relevant chunk in the database.
Weighted Vectorization: When vectorizing chunks, give more weight to titles, subheadings, or keywords. This ensures that these crucial elements play a significant role in the matching process.
4. Post-processing:
Response Ranking: Instead of providing a single answer, retrieve a set of potential answers and rank them based on relevance. Present the top-ranked answer to the user.
Response Summarization: If a matched chunk is too long, use summarization techniques to provide a concise answer to the user.
5. Feedback Loop:
User Feedback: Allow users to rate the quality of the chatbot's response. This feedback can be used to continuously train and improve the model.
Iterative Refinement: Regularly analyze the chatbot's performance. Identify common areas where it fails or provides suboptimal answers and refine the system accordingly.
6. Consider External Tools:
Document Parsing Tools: Tools like Apache Tika or PDFMiner can help in extracting structured information from PDFs, making the chunking process more accurate.
Advanced Search Libraries: Consider using libraries like Elasticsearch, which offer powerful full-text search capabilities and can handle complex queries.
In conclusion, improving the chatbot's performance is an iterative process that involves refining multiple components of the system. By enhancing the preprocessing, query mechanism, and post-processing steps, you can significantly boost the quality and specificity of the chatbot's responses.