Semantic Document Querying

Collect all relevant documents (PDFs, Word files, plain text) and convert them into a uniform plain-text format. Remove headers, footers, and extraneous formatting to ensure clean input for downstream processing. Store each document with a unique identifier and metadata (e.g., date, author, source).

Why NucliaDB: NucliaDB provides automated document ingestion and indexing for multi-modal documents, directly covering the ingest and normalize step with built-in support for various formats.

Split each document into coherent, meaning-rich chunks (e.g., paragraphs, sections, or topic-based segments) using natural language processing. Use sentence boundary detection and topic segmentation algorithms to ensure each chunk represents a self-contained unit of meaning. Store chunks with their parent document ID and sequence number.

Why Gensim: Gensim offers unsupervised topic extraction and semantic similarity calculation, which can be used to segment documents into semantic chunks via topic modeling or text segmentation.

Convert each semantic chunk into a dense vector representation using a pre-trained language model (e.g., Sentence-BERT or OpenAI embeddings). This captures the meaning of the text in a high-dimensional space. Store embeddings in a vector database (e.g., Pinecone, FAISS) along with chunk metadata for fast retrieval.

Why Superlinked: Superlinked explicitly generates text embeddings for semantic search, directly matching the embedding generation requirement for each chunk.

Create a simple interface (e.g., a command-line or web form) that accepts natural language queries from the user. Preprocess the query by removing stop words and optionally expanding abbreviations. Convert the query into an embedding using the same model used for chunks.

Why Humata: Humata provides semantic document querying and automated summary generation, which aligns with building a query interface and processing user input for document search.

Perform a nearest-neighbor search in the vector database using the query embedding. Retrieve the top-k chunks (e.g., top 5) that are most semantically similar to the query. Return these chunks along with their similarity scores and source document references.

Why Superlinked: Superlinked performs similarity search across large document collections, directly executing the semantic similarity search step.

Display the retrieved chunks to the user in a readable format, including the original document name, chunk text, and a link to the full document. Optionally, allow the user to expand a chunk to see surrounding context (e.g., previous and next chunks). This step ensures the query result is actionable and verifiable.

Why Humata: Humata offers semantic document querying and summary generation, which can present results with context, though it lacks explicit source link display; it is the best fit from the menu.

Allow the user to provide feedback on the relevance of results (e.g., thumbs up/down) or to rephrase the query. Use this feedback to adjust the query embedding (e.g., by re-weighting terms) or to expand the search with synonyms. This step improves accuracy over time and adapts to user intent.

Why Superlinked: Superlinked can classify documents by topic or intent, enabling query refinement through semantic reclassification or feedback-based adjustments.