An end-to-end Retrieval-Augmented Generation pipeline engineered with two-stage neural reranking, verified character citations, and strict anti-hallucination guardrails.
Click any node below to inspect real-time data payloads and Python code implementation.
URL or Raw Document (HTML/PDF/Text)
Clean sanitized text + metadata (Title, URL)
def scrap_return_text(url: str) -> str:
res = requests.get(url, headers={'User-Agent': 'Mozilla/5.0...'})
soup = BeautifulSoup(res.text, 'html.parser')
paragraphs = [p.get_text().strip() for p in soup.find_all('p')]
return "\n\n".join([p for p in paragraphs if len(p) > 20])The model receives direct document context chunks and strict grounding instructions. If the retrieved context does not contain sufficient facts to answer the question, the system clearly states the lack of documentation rather than confabulating plausible claims.
Cohere's fine-grained citation engine produces exact start-and-end character spans matching specific retrieved documents. Our frontend replaces these tokens with interactive React popover badges linked to corresponding source cards below.
Single-stage vector search alone often surfaces false positives due to cosine distance limitations. Adding Cohere's cross-encoder reranker ensures only the highest-scoring paragraphs enter the LLM's prompt window.