Multi-Passage Ranking Models: Ranks individual passages within a document to surface highly relevant snippets

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This project focuses on ranking individual passages within webpages to identify the most relevant content in response to a specific query. A multi-passage ranking technique is applied that semantically evaluates and ranks content blocks—such as paragraphs, bullet points, or headings—from each document. The system utilizes a ColBERTv2.0-based retrieval model available through the PyLate library. Each webpage is processed to extract meaningful passages, which are then compared to the query at a fine-grained level. The output highlights the most semantically relevant snippets, enabling clearer visibility into how well a page answers a specific search intent or topic.

This ranking-based approach ensures that information is not only matched by keywords, but understood in context. As a result, it becomes easier to identify which parts of a page hold the highest value for users, especially when dealing with long-form or technical content.

Project Purpose

The purpose of this project is to build a content intelligence system capable of ranking internal sections of a webpage by their relevance to a specific search query. This aligns with real-world use cases such as:

• Identifying which sections of a webpage best match user intent
• Improving visibility of high-value snippets for SEO and SERP optimization
• Enhancing document navigation and summarization workflows
• Evaluating the effectiveness of content structure across web properties

Traditional retrieval systems return entire documents. In contrast, this approach enables focused analysis within a document, surfacing only the most informative sections. The multi-passage ranking method helps determine whether a document contains relevant content, and more importantly, where that content is located within the document.

What is meant by “Multi-Passage Ranking”?

“Multi-passage ranking” refers to the evaluation of multiple smaller content segments (called passages) within a single document. Each passage is treated as an independent unit and compared to a query for relevance.

For example, a blog post may contain 20 paragraphs. Instead of scoring the entire post as a whole, the model assigns relevance scores to each paragraph. This makes it possible to identify the single best-matching passage for a given query — the one most likely to rank well or satisfy a user’s search.

Why is it important to analyze individual passages instead of the full page?

Search engines like Google increasingly value content that directly answers user intent. A single webpage may contain multiple sections, and not all of them are relevant to a user’s query. By analyzing content at the passage level, this system can identify the most relevant snippet — the exact part of the content that search engines and users are looking for.

This aligns closely with how Google’s Passage Ranking system works, where even a single well-optimized paragraph deep within a page can rank independently in search results. By understanding which passages are most relevant, content teams can optimize those sections, improve markup, and better align content to specific search intents.

How does this differ from traditional keyword-based SEO?

Traditional keyword SEO involves optimizing entire pages with keyword density, headings, and meta tags. However, search engines now focus more on semantic relevance — how well the meaning of content matches a user’s search intent.

Multi-passage ranking uses contextual embedding models that understand the meaning behind both the query and the passage. Instead of relying on repeated keywords, the system understands whether a passage logically and semantically answers the question — even if the exact words don’t match.

This reflects a shift from keyword matching to intent matching, which is crucial in modern SEO.

How can this system help create better-performing pages?

By analyzing top-ranked passages per query, content teams can:

• Use high-ranking blocks to create more focused content or highlight important sections using schema or anchor links.
• Optimize low-ranking blocks to better serve user intent, or remove off-topic sections altogether.
• Strategically position key information earlier in the page to improve crawl efficiency and user satisfaction.
• Identify repetition or redundancy across content sections.

These adjustments can lead to improved rankings, higher engagement metrics, and increased visibility in SERP features.

How does this project benefit SEO strategies?

The multi-passage ranking system supports SEO in multiple practical ways:

• Optimized Snippet Targeting: Helps identify the most suitable content block for Google Featured Snippets or “People Also Ask” results.
• Content Refinement: Offers guidance on which parts of a page are underperforming or off-topic, enabling targeted content edits.
• On-Page SEO Improvements: Enhances internal linking strategy by linking to high-value sections, increasing time on page and reducing bounce rate.
• Query Alignment: Ensures content more directly answers the specific queries users are searching for — which improves both relevance and rankability.

This granular content insight helps editorial and SEO teams fine-tune copy and structure in a way that benefits search performance at both the page and snippet level.

Libraries Used

PyLate

PyLate is a robust library for efficient retrieval and ranking tasks, particularly in the domain of large-scale text data. It provides easy-to-use components for both indexing and ranking content at the passage level, which makes it essential for this project.

• indexes: This component is responsible for creating and managing an in-memory or on-disk index of documents (in this case, passages from a URL). It enables fast retrieval of relevant content based on the search query.
• models: The models module in PyLate is used to load and interact with pre-trained models for semantic search tasks. In this project, we are using the ColBERTv2.0 model, which is designed to rank passages by their relevance to a search query based on semantic meaning rather than traditional keyword matching.
• retrieve: This module is key for the retrieval process. After creating an index of passages, retrieve.ColBERT allows for fast, efficient retrieval of passages that are most relevant to a given search query. It compares the embeddings of the query with the pre-encoded document embeddings to rank the most relevant results.

Requests

requests is a simple HTTP library that allows us to fetch web pages by making HTTP requests. In this project, it is used to retrieve the raw HTML content of a given URL, which is then parsed to extract the relevant passages (such as paragraphs, headings, etc.).

Usage: It is used in the extract_structured_blocks() function to download the webpage’s content and pass it to the BeautifulSoup library for parsing.

BeautifulSoup

BeautifulSoup is a Python library used for parsing HTML and XML documents. It creates a parse tree from the page source code and allows for easy extraction of specific elements based on their HTML tags. This is particularly useful for extracting structured content like headings, paragraphs, and lists from a webpage.

Usage: In this project, BeautifulSoup is used to parse the HTML content of a webpage and extract relevant blocks of text from tags like <h1>, <h2>, <p>, and <li>. These blocks are then processed further to identify relevant passages for ranking.

Re (Regular Expressions)

re is a powerful module for working with regular expressions in Python. It allows for pattern matching and text manipulation, which is invaluable when cleaning and preprocessing text data. In this project, re is used for:

• Text Normalization: Cleaning up extracted text by removing extra spaces, URLs, and unnecessary characters (e.g., punctuation, boilerplate content).
• Blacklist Filtering: Removing common unwanted phrases like “subscribe”, “read more”, and “learn more” from extracted content.
• Whitespace and URL Removal: Normalizing the text to remove extra spaces and remove URLs or links from the extracted content.

These libraries provide a clean, efficient way to process, extract, and rank content at the passage level, which is at the core of the project’s purpose. By combining the semantic search power of PyLate with the parsing capabilities of BeautifulSoup and the flexibility of requests and regular expressions, the system is able to extract relevant information from webpages, encode it into meaningful representations, and rank it effectively for SEO purposes.

Function extract_structured_blocks

This function is designed to extract meaningful textual content from any webpage. It focuses on the main body content that users and search engines value most—such as headings and paragraphs—while automatically removing irrelevant or decorative elements like scripts, ads, or footers.

This clean extraction process is vital for ranking the most informative passages, allowing the system to later identify the top segments of a page that are most relevant to specific search queries.

Breakdown of Key Operations:

Download Webpage Content

response = requests.get(url, timeout=10) soup = BeautifulSoup(response.content, ‘html.parser’)

• The function begins by fetching the HTML content of the page using the requests library.
• It parses the HTML using BeautifulSoup to enable structured content extraction.

Remove Unnecessary Tags

for tag in soup([“script”, “style”, “header”, “footer”, “nav”, “aside”, “form”, “iframe”]): tag.decompose()

• All non-essential or decorative elements are removed. This includes navigation bars, headers, footers, scripts, and any embedded forms or advertisements.
• This ensures only user-relevant content remains.

Identify Important Tags

tags_to_extract = [‘h1’, ‘h2’, ‘h3’, ‘p’, ‘li’]

• Focus is given to content found within heading tags (h1 to h3), paragraphs (p), and list items (li)—the core elements of structured, SEO-relevant content.

Extract and Clean Text

text = tag.get_text(separator=’ ‘, strip=True) text = re.sub(r’\s+’, ‘ ‘, text)

• Extracted text is cleaned to remove extra white spaces and make it uniform.

Filter Short or Irrelevant Content

if len(text) >= 40: extracted_passages.append(text)

• Only text blocks that are at least 40 characters long are considered relevant.
• This ensures that trivial or boilerplate text does not affect ranking quality.

Function clean_passage

This function is responsible for cleaning and preparing text for high-quality semantic ranking. It ensures that every passage is free from marketing clutter, unwanted links, and formatting noise—resulting in pure, content-focused text blocks. This cleaning step enhances the model’s ability to focus on real meaning instead of superficial distractions.

Breakdown of Key Operations:

Remove Marketing Noise

blacklist_phrases = […]

• Phrases such as “read more” or “subscribe” are removed because they don’t contribute to content relevance.
• These are commonly found in promotional sections or call-to-action buttons that should not influence passage ranking.

Normalize Whitespaces

text = re.sub(r’\s+’, ‘ ‘, text).strip()

• Extra spaces and line breaks are compressed into single spaces for uniformity.
• Clean formatting helps downstream models treat the passage more consistently.

Strip Out URLs

text = re.sub(r’\b(?:https?|www|ftp)\S+\b’, ”, text)

• Links are removed entirely, since they don’t contain useful content and may distract from the core topic.

Remove Non-Alphanumeric Characters

text = re.sub(r'[^A-Za-z0-9\s]’, ‘ ‘, text)

• Punctuation and symbols are stripped away.
• This results in a more semantic-friendly input for text models by avoiding noisy characters that don’t contribute to meaning.

Function load_model

This function loads the ColBERTv2 model using the PyLate library. The ColBERT model stands for Contextualized Late Interaction over BERT—a powerful architecture specifically designed for semantic retrieval tasks. It plays a crucial role in transforming textual content into numerical embeddings that can be ranked for relevance.

Breakdown of Key Operations

Model Initialization

model = models.ColBERT(model_name_or_path=model_name)

• The function uses PyLate’s interface to load the pre-trained lightonai/colbertv2.0 model.
• This model is optimized for passage-level retrieval, meaning it can understand and encode text in a way that captures context and meaning deeply.

Return for Downstream Use

return model

• After loading, the model is returned and becomes the core engine used in all further ranking and retrieval operations.
• It is later used to convert both content passages and queries into embeddings—numerical vectors that allow semantic comparison.

ColBERTv2 Model: In-Depth Architecture and SEO Relevance

What Is ColBERTv2?

ColBERTv2 (Contextualized Late Interaction over BERT, Version 2) is a transformer-based neural ranking model specifically built for passage-level retrieval. It is designed to overcome the limitations of traditional dense retrieval methods by preserving fine-grained token-level semantics during query–document comparison.

In SEO, this enables identifying and promoting the most relevant textual segments from long-form content that align with user search intent — a key factor for improving click-through rate and featured snippet targeting.

Internal Architecture of ColBERTv2

ColBERTv2 can be broken down into the following components:

Backbone Encoder: BERT Transformer

• Uses a pre-trained BERT model as the base encoder.
• Converts raw text into high-dimensional contextual embeddings (typically 768 dimensions).
• Captures rich semantic and syntactic relationships between words in a sentence.
• Operates independently for queries and passages (no interaction during encoding).

In SEO terms, this means it understands phrases like “crawl budget optimization” in its full context, not just individual word meanings.

Late Interaction Mechanism

·         Instead of collapsing each passage and query into a single dense vector, ColBERTv2 keeps individual token embeddings.

·         During comparison, every query token is matched against every token in the passage using MaxSim scoring:

Score(query, passage) = sum over query tokens of max(sim(query_token, passage_token)))

·         This structure retains fine semantic distinctions that are typically lost in early-interaction or pooled models.

This enables ranking a passage that explains “canonical tags for duplicate URLs” higher than another that just mentions “URLs” or “SEO”.

Projection Layer (Dimensionality Reduction)

• After BERT encoding, ColBERTv2 applies a dense projection layer to reduce vector size (e.g., from 768 -> 128 dimensions).
• This ensures faster indexing and retrieval while retaining key semantic features.
• Uses a linear layer without bias and optionally disables activation for simplicity and speed.

This balance makes ColBERTv2 ideal for real-time applications, like continuously ranking passages as content updates.

Query & Passage Encoder Independence

• Queries and passages are encoded separately and independently.
• Allows precomputing passage embeddings for all documents once and reusing them across many queries — improving scalability.

Enables building efficient passage indexes for entire blog repositories or knowledge bases, which can be queried in real time.

In-Memory or Disk-Based Indexing (via FAISS or PyLate Voyager)

• Token-wise passage embeddings are stored in a multi-vector index.
• Retrieval uses approximate nearest neighbor search with efficient memory usage.
• In this project, PyLate’s Voyager indexer is used for lightweight, diskless ranking.

Summary

ColBERTv2 is a state-of-the-art retrieval model architected for passage-level semantic relevance. Its hybrid design — combining deep language understanding (via BERT) with token-wise late interaction and projection efficiency — makes it an ideal engine for SEO analytics, snippet curation, and search-intent alignment.

By embedding this model in the current project, each content block on a page is semantically scored and ranked against the exact phrasing of search queries — a leap forward from legacy keyword-based SEO methods.

SEO-Specific Advantages of ColBERTv2

• Precise Passage Ranking: Accurately ranks individual paragraphs or lines from a page based on how well they answer a user’s search query.
• Improved Content Structuring: Identifies high-impact content zones, guiding marketers where to insert keywords, schema, or callouts.
• Featured Snippet Optimization: Highlights specific candidate passages that align closely with snippet-worthy intent phrases like “how to”, “why does”, “best way to”.
• Scalable Content Evaluation: Supports indexing and querying at scale — multiple pages, posts, or documents can be ranked in a unified pipeline.
• Reduced Keyword Dependency: Matches based on meaning, not just term overlap — essential for Google’s BERT-based algorithm environment.

Why ColBERTv2 Was Chosen for This Project

• Precision at the Passage Level: The core strength of ColBERTv2 lies in its ability to evaluate and rank individual sentences or sections within a document. For SEO, this enables pinpointing the exact content block that aligns best with search queries, instead of treating the page as a single unit.
• Semantic Understanding: It uses contextual embeddings from BERT, allowing it to understand not just words, but how those words are used in context — distinguishing between similar terms with different meanings or different terms with similar meanings.
• Late Interaction Mechanism: ColBERTv2 adopts a “late interaction” approach — instead of condensing a query and passage into one vector each (like traditional dense retrieval), it allows token-level comparisons between the two. This preserves more information and results in higher retrieval accuracy.
• Scalable and Efficient: While it retains multiple vectors per passage, ColBERTv2 is optimized for efficient computation. This makes it practical for large-scale use, like ranking thousands of content snippets across many web pages.

Function rank_passage

This function is responsible for the core passage ranking logic. It takes cleaned, structured text blocks from a web page and a user-defined search query, then identifies and returns the most relevant passages based on their semantic similarity.

The ranking process is powered by the ColBERTv2 model, which performs high-resolution matching at the token level, ensuring that the results are aligned with the exact meaning and context of the query — not just surface-level keyword matching.

Breakdown of Key Operations

In-Memory Index Initialization

index = indexes.Voyager(index_folder=”pylate-index”, index_name=”index”, override=True)

• An in-memory index is initialized using PyLate’s lightweight Voyager engine. This acts as a temporary searchable store for the passage vectors, enabling fast and isolated ranking for each input document.
• This avoids the overhead of persistent storage and allows for quick prototyping and per-page evaluation.

Retriever Setup

retriever = retrieve.ColBERT(index=index)

• A retriever object is created to handle passage scoring using ColBERT’s late interaction logic. It links the model, index, and query matching steps.

Encoding the Passages

doc_ids = [str(i) for i in range(len(cleaned_passages))]

doc_embeddings = model.encode(cleaned_passages, is_query=False, show_progress_bar=False)

• Each cleaned passage is encoded into its vector representation. These embeddings retain token-level semantics, allowing fine-grained comparison during ranking.
• This step transforms natural language into a form that the model can semantically reason with.

Adding Embeddings to the Index

index.add_documents(documents_ids=doc_ids, documents_embeddings=doc_embeddings)

• The encoded passages are added to the in-memory index, each associated with a unique ID. This prepares the data for semantic retrieval based on the search query.

Query Encoding

query_embedding = model.encode( [query], is_query=True, show_progress_bar=False )

·         The user query is also encoded using the same model, but with the is_query=True flag to inform ColBERT that this is a search prompt — triggering specific encoding behavior optimized for queries.

·         The encoded query is what will be used to search against all stored passages.

Semantic Retrieval and Scoring

scores = retriever.retrieve( queries_embeddings=query_embedding, k=top_k )

• This is the ranking step where ColBERT’s MaxSim operator is used to compare the query tokens with the passage tokens. It returns the top_k most relevant snippets based on semantic similarity.
• This mechanism allows for context-aware retrieval — passages are ranked by how well their content answers or matches the search question.

Result Interpretation

The ranking system successfully identified the most relevant content passages on a webpage based on the search query “Share of Search” — a highly valuable metric in modern SEO strategy.

What the Ranking Reveals

• From the analyzed webpage, the system extracted and evaluated each individual passage to identify the ones that best answer or align with the query intent. The top-ranking passages had the following characteristics:
• High Topical Relevance: The highest-scoring passage explained how Share of Search changes due to factors like seasonality, industry trends, and marketing efforts. This directly reflects real-world scenarios SEO teams monitor and optimize for.
• Strategic Insights: It also discussed the implications of rising or falling Share of Search, such as competitive advantages or visibility loss — helping businesses understand why tracking this metric is essential.
• Strong Contextual Framing: Another highly ranked passage introduced Share of Search by placing it within the broader landscape of digital marketing metrics, emphasizing its growing importance beyond traditional traffic numbers.

These results show that the system doesn’t just find keyword matches — it understands context, strategy, and practical business impact.

SEO Value of This Result

• Highlighting Best-Performing Content: This method surfaces the content blocks that are most aligned with user intent, allowing SEO teams to position them more prominently on the page or use them in featured snippet strategies.
• Improving Query Coverage: The ranking helps identify whether the existing content on a page sufficiently covers a key topic or needs enrichment to answer common user queries more effectively.
• Driving Visibility: By knowing which passages are most valuable, brands can adapt meta tags, headings, and structured data to improve how search engines index and rank their content.
• Maximizing Content ROI: Instead of rewriting entire pages, teams can optimize high-impact blocks that already demonstrate strong semantic alignment — saving time and focusing effort.

Understanding the Scoring Mechanism

What Is the Score and How Is It Determined?

In this ranking process, each passage of text is assigned a score based on its relevance to the query and how well it answers or aligns with the topic being searched. This score is calculated by the ranking model, which evaluates several factors:

• Relevance: How well the content of the passage matches the query’s meaning and intent.
• Contextual Fit: The quality of the content in addressing the broader context of the query. Does it provide comprehensive or deeper insights into the topic, or does it only skim the surface?
• Clarity and Specificity: More detailed and precise passages that provide direct answers to user intent are ranked higher.
• Keyword and Semantic Match: Not just the exact match of words but also the overall topic relevance.

Higher Scores indicate that the passage is more relevant to the search query. These passages provide more valuable and direct answers, which makes them more likely to rank higher in search results.

Lower Scores reflect passages that may still be relevant but are less directly aligned with the user’s intent, or they might provide less detail or specificity.

Why Are Higher Scores Important?

A higher score indicates that the passage is more valuable for answering the search query, making it more likely to surface in search engine results pages (SERPs) when users search for the same topic. This ultimately means better visibility and improved chances of capturing user attention.

How Does the Ranking Work?

The ranking system works by evaluating each passage’s relevance to the query. Let’s break it down:

• Passage Evaluation: Each passage (a block of text from the webpage) is assessed against the query for its contextual relevance. Does it address the user’s search intent directly? How much does it contribute to answering the question?
• Ranking Mechanism: The passages are then ranked based on their relevance. This means that even if an entire page has relevant content, the most specific and directly relevant passages are identified and prioritized.
• Top-ranked Passages: The top-ranked passages are the ones that are most likely to satisfy the user’s intent. These are the sections that will surface in search engine results, driving more visibility and traffic to the page.
• Bottom-ranked Passages: Lower-ranked passages might still be relevant, but they do not have the same level of direct relevance to the query. They may be more general or not provide as specific an answer to the search query.

Key Insights from the Ranking Results

·         Most Relevant Content Surfaces First: The top-ranked passages are typically the ones that most directly address the query. These snippets are highly specific and focused, meaning they are more likely to meet the searcher’s needs. In practice, this means that a user searching for detailed insights about a particular topic will find the best possible answers in the highest-ranking passages.

·         Improved Visibility: By identifying and prioritizing the most relevant passages, the model helps businesses increase the chances of appearing in prominent search positions, such as position 0 (featured snippets) or position 1 in organic search.

·         Actionable Content Improvements: The ranking also reveals where content improvements can be made. For example, if a passage ranks lower, it might not be answering the query as well as it could. Businesses can then ine-tune or expand that content to make it more relevant.

·         Content Strategy Adjustment: By analyzing the high-ranking passages, businesses can better understand the types of content that rive SEO success. This leads to a more targeted content strategy, focusing on providing clear, detailed, and specific information that aligns with searcher intent.

What do the ranked results actually tell us about our content?

The ranked results show which specific passages within a webpage are considered most relevant to a user’s search intent. Rather than judging the entire page, the model evaluates and prioritizes individual snippets, helping identify the strongest and most impactful content blocks.

This allows businesses to see what information is working best, and which sections are most likely to appear in search engine results. It also points out weaker or underperforming content that could be improved.

How does this passage-level ranking improve SEO?

Search engines like Google are increasingly focused on understanding user intent and surfacing the most relevant part of a page, not just the page as a whole. By identifying and optimizing top-ranked passages, businesses can:

• Increase chances of appearing in featured snippets or People Also Ask boxes.
• Improve page dwell time and engagement, as users find their answers faster.
• Align content structure to search engine behavior, making it easier to be indexed and surfaced.

This boosts both visibility and credibility in competitive search results.

Can these insights help decide what content to rewrite or improve?

Yes, absolutely. If certain passages consistently appear in the lower rankings, it may indicate:

• Lack of specificity or relevance.
• Content that is too generic or unfocused.
• Language that doesn’t align with user queries.

These insights can drive content revision strategy, showing exactly where improvements are needed to make a page more competitive for target keywords.

How can we use this project to optimize existing pages?

This project helps pinpoint which parts of a page perform well and which do not. Based on the ranked results, businesses can:

• Highlight top passages earlier in the page layout.
• Use stronger headlines around high-ranking snippets to enhance scannability.
• Rewrite or enrich lower-ranking content with better keyword targeting or clearer explanations.
• Ensure metadata and internal linking support these strong passages.

All of this leads to improved SEO structure, ranking consistency, and user experience.

How is this different from traditional keyword-based optimization?

Traditional SEO focuses on placing keywords strategically. This model goes beyond that by evaluating the semantic relevance and contextual match of passages with the search intent.

Rather than guessing which content is working, this approach uses real similarity analysis to determine which blocks of text are aligned with what users are truly searching for. It’s a more intelligent, data-driven, and modern method of optimization.

What’s the next step after this analysis?

The next step is actionable implementation:

• Use high-ranking passages to guide layout changes snippet highlighting, or meta descriptions.
• Improve lower-ranking sections based on patterns seen in stronger passages.
• Expand content in underrepresented topics identified through the query analysis.

Ultimately, this enables businesses to improve both technical SEO and content quality, increasing the chances of organic growth and brand visibility.

Final Thoughts

This project demonstrates the power and practicality of Multi-Passage Ranking Models in the field of SEO. By moving beyond traditional page-level analysis, the system introduces a smarter, granular approach that evaluates individual content passages based on their semantic alignment with real search queries.

The ability to identify which specific content blocks are most impactful provides businesses with clear, focused insights. It enables data-backed content improvements, ensures alignment with search engine expectations, and supports more strategic on-page optimization.

This ranking-based methodology offers a significant competitive advantage in modern SEO. As search engines evolve to prioritize user intent and snippet-level relevance, tools like this help brands stay ahead—ensuring that the right content is not only written but also discovered, understood, and surfaced at the right time.

By integrating this system into regular SEO workflows, businesses can improve visibility, engagement, and long-term search performance with precision.

Contact ThatWare now to secure your digital presence correctly—where performance meets protection.

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Tuhin Banik

Thatware | Founder & CEO

Tuhin is recognized across the globe for his vision to revolutionize digital transformation industry with the help of cutting-edge technology. He won bronze for India at the Stevie Awards USA as well as winning the India Business Awards, India Technology Award, Top 100 influential tech leaders from Analytics Insights, Clutch Global Front runner in digital marketing, founder of the fastest growing company in Asia by The CEO Magazine and is a TEDx speaker and BrightonSEO speaker.

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