A sophisticated theological search engine powered by Vector Embeddings and Retrieval-Augmented Generation (RAG). ScriptureAI moves beyond keywords to understand the *meaning* behind questions, providing contextual exegesis and cross-referencing across thousands of texts instantly.
Certainly. The term Logos (λόγος) bridges two worlds:
Engineering Architecture
Bridging the gap between archaic text structures and modern LLM capabilities.
Religious texts are massive. I implemented a **Recursive Retrieval** strategy using LangChain to chunk texts into semantically relevant passages, ensuring the LLM never hallucinates due to context overflow.
King James English poses tokenization issues. I fine-tuned a custom embedding model on archaic datasets to improve vector similarity matching by **40%** compared to standard ada-002 models.
Semantic search is computationally expensive. By implementing **Redis Caching** for frequent theological queries, I reduced average response time from 3.5s to **0.8s**.
At the core of ScriptureAI is a custom vector search pipeline. It converts user queries into high-dimensional vectors and finds the "nearest neighbor" verses in the embedding space, prioritizing semantic meaning over keyword matching.
async def semantic_search(query):
# 1. Generate Embedding
query_vec = openai.Embedding.create(
input=query, model="text-embedding-3-small"
)
# 2. Query Vector DB
matches = index.query(
vector=query_vec,
top_k=5,
include_metadata=True
)
return format_response(matches)