🧙‍♂️ NanoSage: Advanced Recursive Search & Report Generation

Deep Research assistant that runs on your laptop, using tiny models. - all open source!

How is NanoSage different than other Assistant Researchers?

It offers a structured breakdown of a multi-source, relevance-driven, recursive search pipeline. It walks through how the system refines a user query, builds a knowledge base from local and web data, and dynamically explores subqueries—tracking progress through a Table of Contents (TOC).

With Monte Carlo-based exploration, the system balances depth vs. breadth, ranking each branch's relevance to ensure precision and avoid unrelated tangents. The result? A detailed, well-organized report generated using retrieval-augmented generation (RAG), integrating the most valuable insights.

I wanted to experiment with new research methods, so I thought, basically, when we research a topic, we randomly explore new ideas as we search, and NanoSage basically does that! It explores and records its journey, where each (relevant) step is a node... and then sums it up to you in a neat report! Where the table of content is basically its search graph. 🧙

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📅 Latest Update - October 17, 2025

Major System Enhancement: Tavily Integration & Hybrid Embedding Architecture

• Tavily Search API Integration: Replaced unreliable free search engines with Tavily's robust API, enabling access to high-quality academic sources including PubMed, research journals, and scholarly databases
• Hybrid Embedding System: Implemented intelligent model selection where SigLIP/CLIP handle vision tasks (images, PDFs) while all-MiniLM processes text content, eliminating dimension mismatches and optimizing performance
• Enhanced Web Crawler: Added comprehensive web content extraction with metadata generation, domain grouping, and fallback search engines for maximum reliability and coverage

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Example Report

You can find an example report in the following link:
example report output for query: "Create a structure bouldering gym workout to push my climbing from v4 to v6"

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Quick Start Guide

1. Install Dependencies

1. Ensure Python 3.8+ is installed.
2. Install required packages:

pip install -r requirements.txt

3. (Optional) For GPU acceleration, install PyTorch with CUDA:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

(Replace cu118 with your CUDA version.)

4. Make sure to update pyOpenSSL and cryptography:

pip install --upgrade pyOpenSSL cryptography

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2. Set Up Ollama & Pull the Gemma Model

1. Install Ollama:

curl -fsSL https://ollama.com/install.sh | sh
pip install --upgrade ollama

(Windows users: see ollama.com for installer.)

2. Pull Gemma 2B (for RAG-based summaries):

ollama pull gemma2:2b

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3. Configure API Keys

# Copy the example environment file
cp env.example .env

# Edit .env and add your Tavily API key
# Get your free API key at: https://tavily.com/
TAVILY_API_KEY=your_tavily_api_key_here

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4. Run Examples

# Basic web search with SigLIP (vision + text hybrid) - RECOMMENDED
python main.py --query "machine learning algorithms" --retrieval_model siglip --web_search

# Web search with local documents
python main.py --query "quantum computing" --corpus_dir ./my_documents --retrieval_model siglip --web_search

# Fast text-only search
python main.py --query "artificial intelligence" --retrieval_model all-minilm --web_search

# Local documents only (no web search)
python main.py --query "research papers" --corpus_dir ./my_documents --retrieval_model colpali

Parameters:

• --query: Main search query (natural language).
• --web_search: Enables web-based retrieval via Tavily API.
• --retrieval_model: Choose from siglip (recommended), clip, colpali, or all-minilm.
• --device cpu: Uses CPU (swap with cuda for GPU).
• --max_depth: Recursion depth for subqueries (default: 1).

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5. Available Models

• siglip: Vision + text hybrid (recommended for images/PDFs + web content)
• clip: Vision + text hybrid (alternative to SigLIP)
• colpali: Advanced text model (good for documents)
• all-minilm: Fast text model (good for speed)

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6. Supported File Types

• Text: .txt, .md, .py, .json, .yaml, .csv
• PDFs: Converted to images for vision models, OCR for text models
• Images: .png, .jpg, .jpeg (vision models or OCR fallback)

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7. Check Results & Report

A detailed Markdown report will appear in results/<query_id>/.

Example:

results/
└── 389380e2/
    ├── Quantum_computing_in_healthcare_output.md
    ├── web_Quantum_computing/
    ├── web_results/
    └── local_results/

Open the *_output.md file (e.g., Quantum_computing_in_healthcare_output.md) in a Markdown viewer (VSCode, Obsidian, etc.).

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8. Advanced Options

✅ Using Local Files

If you have local PDFs, text files, or images:

python main.py --query "AI in finance" \
               --corpus_dir "my_local_data/" \
               --top_k 5 \
               --device cpu

Now the system searches both local docs and web data (if --web_search is enabled).

🔄 RAG with Gemma 2B

python main.py --query "Climate change impact on economy" \
               --rag_model gemma \
               --personality "scientific"

This uses Gemma 2B to generate LLM-based summaries and the final report.

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9. Troubleshooting

• Missing dependencies? Rerun: pip install -r requirements.txt
• Ollama not found? Ensure it's installed (ollama list shows gemma:2b).
• Memory issues? Use --device cpu.
• Too many subqueries? Lower --max_depth to 1.
• Web search not working? Check your TAVILY_API_KEY in .env file.

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10. Next Steps

• Try different retrieval models (--retrieval_model siglip for best results).
• Tweak recursion (--max_depth).
• Tune config.yaml for web search limits, min_relevance, or Monte Carlo search.

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Hybrid Embedding System

NanoSage uses a hybrid embedding approach for optimal performance:

• Vision Models (SigLIP/CLIP):

  • Use SigLIP/CLIP for images and PDFs → images
  • Use all-MiniLM for text content (web pages, documents)
  • Ensures consistent 384D embeddings for text content

• Text Models (ColPali/all-MiniLM):

  • Use the same model for all content types
  • Consistent embedding dimensions

This approach eliminates dimension mismatches and uses the right tool for each content type.

Web Search Integration

• Primary: Tavily Search API (reliable, academic sources)
• Fallback: DuckDuckGo, SearxNG, Wikipedia
• Sources: PubMed, academic journals, research databases
• Features: Real-time search, content extraction, metadata generation

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Detailed Design: NanoSage Architecture

1. Core Input Parameters

• User Query: E.g. "Quantum computing in healthcare".
• CLI Flags (in main.py):

  --corpus_dir
  --device
  --retrieval_model
  --top_k
  --web_search
  --personality
  --rag_model
  --max_depth
  

• YAML Config (e.g. config.yaml):
  • "results_base_dir", "max_query_length", "web_search_limit", "min_relevance", etc.

2. Configuration & Session Setup

1. Configuration:
   load_config(config_path) to read YAML settings.

   • min_relevance: cutoff for subquery branching.

2. Session Initialization:
   SearchSession.__init__() sets:

   • A unique query_id & base_result_dir.
   • Enhanced query via chain_of_thought_query_enhancement().
   • Retrieval model loaded with load_retrieval_model().
   • Query embedding for relevance checks (embed_text()).
   • Local files (if any) loaded & added to KnowledgeBase.

3. Recursive Web Search & TOC Tracking

1. Subquery Generation:

   • The enhanced query is split with split_query().

2. Monte Carlo Subquery Sampling (Optional):

   • The system can use a Monte Carlo approach to intelligently sample the most relevant subqueries, balancing exploration depth with computational efficiency.
   • Each subquery is scored for relevance against the main query using embedding similarity.
   • Only the most promising subqueries are selected for further exploration.

3. Relevance Filtering:

   • For each subquery, compare embeddings with the main query (via late_interaction_score()).
   • If < min_relevance, skip to avoid rabbit holes.

4. TOCNode Creation:

   • Each subquery → TOCNode, storing the text, summary, relevance, etc.

5. Web Data:

   • If relevant:
     • search_and_download() via Tavily API to fetch results.
     • parse_any_to_text() and embed them.
     • Summarize snippets (summarize_text()).
   • If current_depth < max_depth, optionally expand new sub-subqueries (chain-of-thought on the current subquery).

6. Hierarchy:

   • All subqueries & expansions form a tree of TOC nodes for the final report.

4. Local Retrieval & Summaries

1. Local Documents + Downloaded Web Entries → appended into KnowledgeBase.
2. KnowledgeBase.search(...) for top-K relevant docs.
3. Summaries:
   • Summarize web results & local retrieval with summarize_text().

5. Final RAG Prompt & Report Generation

1. _build_final_answer(...):

   • Constructs a large prompt including:
     • The user query,
     • Table of Contents (with node summaries),
     • Summaries of web & local results,
     • Reference URLs.
   • Asks for a "multi-section advanced markdown report."

2. rag_final_answer(...):

   • Calls call_gemma() (or other LLM) to produce the final text.

3. aggregate_results(...):

   • Saves the final answer plus search data into a .md file in results/<query_id>/.

6. Balancing Exploration vs. Exploitation

• Subqueries with relevance_score < min_relevance are skipped.
• Depth-limited recursion ensures not to blow up on too many expansions.
• Monte Carlo expansions (optional) can sample random subqueries to avoid missing unexpected gems.

7. Final Output

• Markdown report summarizing relevant subqueries, local docs, and a final advanced RAG-based discussion.

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Summary Flow Diagram

User Query
    │
    ▼
main.py:
    └── load_config(config.yaml)
         └── Create SearchSession(...)
              │
              ├── chain_of_thought_query_enhancement()
              ├── load_retrieval_model()
              ├── embed_text() for reference
              ├── load_corpus_from_dir() → KnowledgeBase.add_documents()
              └── run_session():
                  └── perform_recursive_web_searches():
                      ├── For each subquery:
                      │   ├─ Compute relevance_score
                      │   ├─ if relevance_score < min_relevance: skip
                      │   ├─ else:
                      │   │   ├─ search_and_download() (Tavily API)
                      │   │   ├─ parse_any_to_text(), embed
                      │   │   ├─ summarize_text() → store in TOCNode
                      │   │   └─ if depth < max_depth:
                      │   │       └─ recursively expand
                      └── Aggregates web corpus, builds TOC
              │
              ├── KnowledgeBase.search(enhanced_query, top_k)
              ├── Summarize results
              ├── _build_final_answer() → prompt
              ├── rag_final_answer() → call_gemma()
              └── aggregate_results() → saves Markdown

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If you found NanoSage useful for your research or project - or saved you 1 minute of googling, please consider citing it:

BibTeX Citation:

@misc{NanoSage,
  author = {Foad Abo Dahood}, 
  title = {NanoSage: A Recursive, Relevance-Driven Search and RAG Pipeline},
  year = {2025},
  howpublished = {\url{https://github.com/masterFoad/NanoSage}},
  note = {Accessed: \today}
}

APA Citation:
Foad, Abo Dahood. (2025). NanoSage: A Recursive, Relevance-Driven Search and RAG Pipeline. Retrieved from https://github.com/masterFoad/NanoSage