Yes, you can use vector databases (DBs) with legacy SharePoint-based legal archives, but it requires careful integration to bridge the gap between unstructured document storage and vector-based search capabilities. SharePoint, especially older on-premises versions, is designed primarily for document management, metadata tagging, and basic keyword searches. Vector DBs, which specialize in storing and querying high-dimensional vectors (numerical representations of data), enable semantic search by comparing the “meaning” of content rather than relying on exact keyword matches. To connect them, you’ll need to extract text from SharePoint documents, convert it into vectors using machine learning models, and then index those vectors in a dedicated database.
The first step involves data extraction and preprocessing. Legacy SharePoint systems often store legal documents in formats like PDFs, Word files, or emails, which may require text extraction tools (e.g., Apache Tika, Python’s PyPDF2). Once extracted, the text is processed into vectors using embedding models like BERT, OpenAI’s embeddings, or open-source alternatives (e.g., Sentence Transformers). For example, a legal contract stored in SharePoint could be converted into a 768-dimensional vector representing its semantic content. These vectors are then stored in a vector DB such as Pinecone, FAISS, or Milvus, which can efficiently perform similarity searches. This setup allows queries like “Find all non-disclosure agreements amended after 2020” to return results based on conceptual relevance, even if the exact keywords aren’t present.
However, challenges arise in maintaining scalability and security. Legacy SharePoint systems may lack modern APIs, requiring custom scripts (e.g., PowerShell or .NET tools) to automate data export. Additionally, legal archives often have strict access controls, so permissions must be mirrored in the vector DB or applied during post-processing to filter results. For instance, if a user lacks access to confidential case files in SharePoint, the vector DB query should exclude those documents. Performance can also be a concern: indexing millions of legal documents might require distributed processing frameworks like Apache Spark. Despite these hurdles, integrating vector DBs with SharePoint unlocks powerful use cases, such as identifying precedent cases with similar legal arguments or detecting compliance risks in unstructured text.