https://github.com/sdivyanshu90/large-language-models-with-semantic-search
https://github.com/sdivyanshu90/large-language-models-with-semantic-search
ai cohere cohere-ai deeplearning-ai deeplearning-ai-courses
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- Host: GitHub
- URL: https://github.com/sdivyanshu90/large-language-models-with-semantic-search
- Owner: sdivyanshu90
- Created: 2025-09-14T17:12:14.000Z (21 days ago)
- Default Branch: main
- Last Pushed: 2025-09-14T18:26:49.000Z (21 days ago)
- Last Synced: 2025-09-14T20:31:58.787Z (20 days ago)
- Topics: ai, cohere, cohere-ai, deeplearning-ai, deeplearning-ai-courses
- Language: Jupyter Notebook
- Homepage:
- Size: 404 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Large Language Models with Semantic Search
*Course by [DeepLearning.AI](https://www.deeplearning.ai/) in collaboration with [Cohere](https://cohere.com/)*
---
## 📌 Overview
This repository provides a detailed summary of the **"Large Language Models with Semantic Search"** course.
The course explores how **Large Language Models (LLMs)**, when combined with **semantic search techniques**, can go far beyond traditional keyword search.
You will learn how to:
* Represent text as embeddings,
* Retrieve relevant documents,
* Re-rank results for higher accuracy,
* And finally, generate meaningful answers.
---
## 🎯 Course Topics
### 1️⃣ Keyword Search
🔎 **Definition**:
A traditional search approach that matches query terms with words in documents.
⚙️ **How it works**:
* Breaks a query into tokens (keywords).
* Looks for exact matches in the document corpus.
* Ranks documents by keyword frequency (e.g., TF-IDF, BM25).
📌 **Strengths**:
* Simple, efficient, and interpretable.
* Works well when queries and documents share the same words.
⚠️ **Limitations**:
* Cannot capture **synonyms** or **semantic meaning**.
* Sensitive to spelling variations.
* Example: Searching "AI" won’t return "Artificial Intelligence" unless explicitly included.
💡 **Takeaway**: Keyword search is the foundation but limited for modern, meaning-driven queries.
---
### 2️⃣ Embeddings
🧩 **Definition**:
Embeddings are **vector representations** of text (words, sentences, or documents) that capture semantic meaning.
⚙️ **How it works**:
* Text is passed through a model (like word2vec, GloVe, or modern transformer-based models).
* Each word/sentence is mapped to a high-dimensional vector.
* Semantically similar texts end up close together in the vector space.
📌 **Key Properties**:
* Capture **contextual meaning**, not just surface forms.
* Enable comparison between texts using similarity metrics (cosine similarity, dot product).
📊 **Example**:
* "king" - "man" + "woman" ≈ "queen"
* "doctor" and "physician" will be close in vector space.
💡 **Takeaway**: Embeddings make it possible to search based on **meaning**, not just words.
---
### 3️⃣ Dense Retrieval
📖 **Definition**:
Dense retrieval uses **embeddings** to fetch relevant documents.
⚙️ **How it works**:
1. Encode the **query** into a vector.
2. Encode all **documents** into vectors.
3. Compute similarity between query and document vectors.
4. Return the documents with the highest similarity scores.
📌 **Benefits**:
* Captures semantic relationships.
* Works even if the query and document don’t share exact words.
📊 **Example**:
Query: *"How does a neural network learn?"*
Retrieved doc: *"Neural nets adjust their weights during training to minimize error."*
⚠️ **Challenges**:
* Requires efficient storage and search (ANN: Approximate Nearest Neighbors).
* May retrieve loosely relevant docs if embeddings are poor.
💡 **Takeaway**: Dense retrieval powers **semantic search** by matching concepts, not just terms.
---
### 4️⃣ Re-Rank
📖 **Definition**:
Re-ranking improves retrieval accuracy by **ordering results more intelligently**.
⚙️ **How it works**:
* Step 1: Dense retrieval fetches a candidate set (e.g., top 50 docs).
* Step 2: A stronger model (cross-encoder or LLM) re-evaluates each doc against the query.
* Step 3: Produces a new ranking, with the most relevant results on top.
📌 **Benefits**:
* Improves **precision** of search.
* Handles subtle differences in context.
📊 **Example**:
Query: *"Apple revenue 2023"*
* Initial retrieval: docs about fruit 🍎 and company 🍏.
* Re-rank: prioritizes docs about **Apple Inc. financial reports**.
💡 **Takeaway**: Re-ranking adds an extra layer of **intelligence** to search.
---
### 5️⃣ Generating Answers
📖 **Definition**:
Instead of returning documents, LLMs can **synthesize answers** from retrieved content.
⚙️ **How it works**:
1. Retrieve documents using dense retrieval.
2. Provide retrieved content as **context** to the LLM.
3. LLM generates a **natural language response**.
📌 **Benefits**:
* Provides **direct answers** rather than links.
* Handles summarization, reasoning, and rephrasing.
📊 **Example**:
Query: *"What is machine learning?"*
* Retrieval: Articles containing definitions.
* Generated Answer:
*"Machine learning is a subset of artificial intelligence where algorithms learn patterns from data to make predictions without being explicitly programmed."*
💡 **Takeaway**: Answer generation is the final step — transforming retrieval into **user-friendly outputs**.
---
## 🔄 End-to-End Workflow
```mermaid
flowchart LR
A[User Query] --> B[Convert to Embedding]
B --> C[Dense Retrieval]
C --> D[Candidate Documents]
D --> E[Re-Rank with Model]
E --> F[Top Relevant Docs]
F --> G[Answer Generation with LLM]
G --> H[Final Answer]
```
---
## 🙌 Acknowledgements
This learning material is based on the course:
📘 **[Large Language Models with Semantic Search](https://learn.deeplearning.ai/)**
* Provider: [DeepLearning.AI](https://www.deeplearning.ai/)
* Partner: [Cohere](https://cohere.com/)
Special thanks to **Andrew Ng** and the course contributors for creating this learning pathway.
---
## 📚 Resources
* [DeepLearning.AI Courses](https://learn.deeplearning.ai/)
* [Cohere Documentation](https://docs.cohere.com/)
* [Weaviate Semantic Search Guide](https://docs.weaviate.io/academy/py/starter_text_data/text_searches/semantic)