Creating a Document Corpus for AI
Creating a Document Corpus for AI
A guide for building a City government corpus for the Building Department
The data that you currently possess in your organization, becomes invaluable when you leverage it with AI. As a result, the importance of creating a document corpus that can be used by an LLM becomes a massive competitive advantage.
We are going to go walk through a process of creating a document corpus to use with LLMs via Retrieval-Augmented Generation (RAG) in the context of a City Building Department.
Why Create a Document Corpus?
A well-organized document corpus serves as the backbone of any AI system designed to provide accurate, context-aware responses to queries. For municipal governments, where the complexity of regulations, policies, and procedures can be daunting, having an accessible and well-structured corpus can vastly improve efficiency and public service delivery.
Imagine a city’s building department, where staff members, contractors, and even citizens frequently need to access detailed information on building codes, permits, inspections, and zoning regulations. A document corpus tailored for AI can help the department quickly retrieve relevant information, answer queries, and make informed decisions.
Step 1: Define Objectives and Scope
Start by clarifying the goals of your document corpus. For the building department, the primary objective might be to improve the speed and accuracy of responses to queries about building codes and permit processes. The target audience would include department staff, contractors, and the public.
The scope should include all relevant documents, such as building codes, inspection reports, permit applications, zoning regulations, and internal memos.
Step 2: Document Collection
Identify and gather all relevant documents within the building department. These might include:
· Building Codes: The full text of local building codes, updated regularly.
· Permit Applications: Forms and guidelines for various types of permits.
· Inspection Reports: Historical and recent reports on building inspections.
· Zoning Regulations: Documents outlining zoning laws and restrictions.
· Internal Memos: Communications regarding policy changes or new procedures.
Collect these documents in their existing formats, whether they are PDFs, Word documents, or spreadsheets. For now, focus on gathering the content rather than worrying about format consistency. The use of web scrapers can be used to speed up the collection of digital documents.
Step 3: Data Cleaning and Preprocessing
Next, clean and preprocess the documents to ensure they are ready for AI integration. This step involves:
· Deduplication: Remove duplicate documents to ensure the corpus is concise.
· Text Cleaning: Standardize text formatting and remove irrelevant content like disclaimers or redundant headers.
· Document Structuring: Break down large documents into smaller, logically coherent sections or chunks (e.g., chapters, headings). For example, a lengthy building code document might be divided by sections or chapters or subheadings.
Step 4: Content Chunking
Chunking involves dividing documents into manageable pieces that can be effectively processed by the AI. In our building department example, you might chunk the building codes by individual code sections, ensuring each chunk covers a specific regulation or guideline.
Keep chunks between 200-500 words, ensuring that each chunk is semantically coherent. This makes it easier for the AI to retrieve relevant information based on user queries.
Segment the document by using tools like nltk or spaCy (Python libraries) to split text by sentences or paragraphs. Create a script that identifies logical breakpoints in the document (e.g., headings) and splits the content accordingly.
Step 5: Document Categorization and Tagging
Organize the chunks into categories such as “Building Codes,” “Permit Applications,” and “Inspection Reports.” Apply relevant tags to each chunk, like “residential,” “commercial,” “zoning,” or “electrical,” to facilitate search and retrieval.
For example, a section of the building code dealing with residential fire safety could be tagged with “residential,” “fire safety,” and “building codes.” Assign a unique ID to each chunk and store it in a database or spreadsheet along with metadata (e.g., document ID, section title, chunk ID).
Step 6: Embedding and Indexing
Convert the text chunks into vector representations using a pre-trained embedding model. Store these embeddings in a vector database to enable efficient retrieval.
For instance, you might use an embedding model like BERT to convert the text into vectors and store them in a database like Pinecone. This setup allows the AI to quickly retrieve and reference the most relevant chunks when answering queries.
Step 7: Quality Assurance
Perform a manual review of a sample of the processed documents to ensure they meet quality standards. For example, check that a search for “residential zoning regulations” correctly pulls up the relevant sections of the zoning code.
Test the RAG system by posing sample queries and evaluating the accuracy and relevance of the responses. Make iterative adjustments as needed.
Step 8: Deployment and Integration
Integrate the corpus with your AI system, configuring it to use RAG for enhanced retrieval. Train users on how to interact with the system, emphasizing best practices for querying.
For example, a staff member might use the system to quickly find the exact code requirements for installing a commercial elevator, ensuring they provide accurate information to a contractor.
Step 9: Documentation
Document the entire process, including the tools used and any challenges encountered. Create user guides that are easy to follow, helping staff and other stakeholders effectively use the AI system.
Example: Implementing the Process in a City’s Building Department
Let’s bring this to life with a specific example. Suppose the City of Miami’s Building Department wants to create a document corpus to help streamline their response to building code inquiries. Here’s how they could do it:
1. Objective: The goal is to reduce the time it takes for staff to respond to queries about building codes and permit processes.
2. Document Collection: Gather all relevant building codes, permit forms, zoning regulations, and historical inspection reports.
3. Data Cleaning: Remove outdated or duplicate documents, standardize text formatting, and break large documents into logical chunks.
4. Content Chunking: Divide the building code into chunks by section, each covering a specific regulation.
5. Categorization and Tagging: Organize the chunks into categories like “Building Codes” and “Zoning Regulations,” applying tags such as “residential” and “commercial.”
6. Embedding and Indexing: Convert text chunks into vectors using a model like BERT and store them in a vector database.
7. Quality Assurance: Test the system with common queries to ensure accurate and relevant responses.
8. Deployment: Integrate the corpus with the department’s AI system, train staff on its use, and continuously monitor performance.
By following this streamlined process, the City’s Building Department can significantly improve its efficiency, reduce response times, and provide more accurate information to both staff and the public.
Conclusion
Creating a document corpus for AI doesn’t have to be a daunting task. By following a structured process, municipal departments like the City of Miami’s Building Department can leverage AI to enhance their operations, improve public service, and make more informed decisions. As cities continue to embrace digital transformation, building a solid foundation with a well-organized document corpus will be key to unlocking the full potential of AI.