Traceable Fusion RAG for Algorithms

A Retrieval-Augmented Generation (RAG) system for querying algorithm implementations across multiple programming languages. Ask natural-language questions and get answers grounded in real, curated source code — with full transparency into every pipeline step.

Overview

Traceable Fusion RAG for Algorithms lets you ask questions like "How does Dijkstra's algorithm work?" or "What is a segment tree and when should I use it?" and get back structured answers with code snippets, source file citations, and a confidence score — all sourced from real algorithm repositories on GitHub, not from the LLM's parametric memory.

The system ingests 6 open-source algorithm repositories (~1 GB of C++, Python, Java, and Markdown), stores them in a ChromaDB vector store, and at query time runs a hybrid BM25 + dense retrieval pipeline, reranks with Cohere, and generates answers with Groq (Llama 3.3 70B) with a Cohere Command A+ fallback.

Architecture

User Query
    │
    ▼
┌─────────────────────────────────────────────────────────┐
│  Query Transformer  (query_transformer.py)              │
│  Expands / rephrases the query for better recall        │
└───────────────────────┬─────────────────────────────────┘
                        │
          ┌─────────────┴──────────────┐
          ▼                            ▼
  Dense Retrieval               BM25 Retrieval
  (ChromaDB + Cohere            (rank-bm25, keyword
   embeddings)                   exact match)
          │                            │
          └─────────────┬──────────────┘
                        ▼
           Reciprocal Rank Fusion (RRF)
           Merges both ranked lists by position
                        │
                        ▼
             Cohere Reranker (cross-encoder)
             Rescores top-N with full attention
                        │
                        ▼
          ┌─────────────────────────────┐
          │  Confidence Gate            │
          │  (guardrails/confidence.py) │
          │  Score < 0.25 → REFUSE      │
          └────────────┬────────────────┘
                       │
                       ▼
          ┌────────────────────────────┐
          │  LLM Generation            │
          │  Primary:  Groq 70B        │
          │  Fallback: Cohere Cmd A+   │
          └────────────┬───────────────┘
                       │
                       ▼
          Structured JSON Answer
          { answer, source_files,
            confidence_note, cpp_snippet }
                       │
                       ▼
          Rich Terminal UI  (src/ui/app.py)

The Tracer runs as an observer across all steps, recording every event so the terminal UI can show a step-by-step live display.

Codebase Explained

`src/config.py`

Central configuration using Pydantic BaseSettings, which auto-reads values from your .env file. This prevents hardcoded values scattered across the codebase.

ALGO_REPOS is a plain Python list of dicts that defines which GitHub repos to ingest and what language to treat them as. Adding a new repo means appending one dict — nothing else changes.

The Settings class holds all tuneable parameters: LLM model names, retrieval k, reranking top_n, confidence thresholds, chunk sizes. Every other module reads from the single global settings instance.

`src/ingestion/`

loader.py — Clones each repo in ALGO_REPOS using gitpython (or pulls if already cloned), then walks the directory tree and loads only source files matching the declared language extension. Returns a list of Document objects with source_file, language, and category metadata.

chunker.py — CppAwareChunker splits documents into retrievable chunks. Despite its name, it handles all four languages:

Language	Strategy
C++	Brace-matched function extraction, fallback to LangChain CPP splitter
Python	Indent-tracked `def`/`class` extraction, fallback to LangChain PYTHON splitter
Java	Brace-matched method extraction, fallback to LangChain JAVA splitter
Markdown	LangChain MARKDOWN splitter (preserves heading hierarchy)

chunk_overlap is set so a call-site and its definition are never in completely separate chunks with zero shared context.

add_context_header() prepends a metadata header like File: graph/dijkstra.cpp | Language: cpp | Category: graph | Algorithm: Dijkstra to each chunk's text before indexing, so the LLM always knows where retrieved code comes from.

indexer.py — AlgoIndexer takes chunks, embeds them in batches of 24 via the Cohere embed-english-v3.0 model, and writes them to ChromaDB. Key design decisions:

Idempotent: each chunk gets a deterministic SHA-based ID from its content + source path. Re-running the indexer skips already-stored chunks — no duplicates, safe to run repeatedly.
Rate limiting: respects Cohere's free-tier (100 req/min) with time.sleep between batches.
Progress tracking: uses tqdm progress bars for long indexing runs.

`src/retrieval/`

retriever.py — HybridRetriever is the core of the retrieval pipeline.

It combines two complementary search methods:

Dense search: queries ChromaDB using Cohere embeddings. Captures semantic similarity — "fast sorting" finds "quicksort". ChromaDB returns L2 distances, which are converted to [0, 1] similarities.
BM25 search: a probabilistic keyword ranker (the algorithm behind Elasticsearch). Captures exact terms — "BFS" reliably finds "BFS", not "DFS". The BM25 index is built over all chunk tokens at startup.

Results from both are merged via Reciprocal Rank Fusion (RRF):

RRF_score(doc) = Σ  1 / (k + rank_in_list)

RRF uses rank position, not raw scores (which can't be compared across methods). k=60 is the standard constant that dampens the impact of top-ranked documents.

The merged list is then passed to the Cohere Reranker — a cross-encoder that scores each (query, document) pair with full pairwise attention, far more accurate than embedding dot-product alone.

_metadata_boost() applies a small score bonus for language-matching results (e.g. a C++ query gets a boost for .cpp files) and a penalty for mismatches.

The result is a list of RetrievedChunk dataclasses, each carrying dense_score, bm25_score, rrf_score, rerank_score, and final_rank — all surfaced in the terminal UI for full transparency.

query_transformer.py — Expands the user query before retrieval (e.g. rephrasing, adding synonyms) to improve recall on edge cases.

reranker.py — Thin wrapper around the Cohere reranking API call.

`src/generation/`

generator.py — AlgoGenerator calls the LLM with a structured prompt and parses the JSON response.

Provider fallback order:

Groq Llama 3.3 70B — primary, fastest, 30K tokens/min free
Cohere Command A+ — fallback, 128K context window, RAG-optimised

The generator detects refusal phrases ("rate limit", "429", "quota") in short responses and promotes them to exceptions so the fallback chain fires automatically.

Output is always a JSON object:

{
  "answer": "...",
  "source_files": ["graph/dijkstra.cpp"],
  "confidence_note": "High confidence — direct match found.",
  "cpp_snippet": "priority_queue<...> pq;"
}

Raw-newline repair is applied to the response string before json.loads() to handle occasional LLM formatting artifacts.

prompt_builder.py — Contains the prompt templates (ALGO_SUGGESTION_PROMPT, EXPLANATION_PROMPT) and format_context_for_prompt(), which assembles the retrieved chunks into the prompt context block.

`src/guardrails/`

confidence.py — ConfidenceScorer computes a multi-signal confidence score before and after generation:

Signal	Description
`retrieval_score`	Weighted average of top chunk rerank scores (top chunk weight 1.0, second 0.5, …)
`source_verified`	Cited file paths are checked against the set of all indexed files
`coverage_score`	Fraction of answer claims supported by retrieved context (LLM-graded)
`overall_confidence`	Weighted combination of all three signals

The ConfidenceReport dataclass holds the full breakdown. If overall_confidence < 0.25, should_proceed = False and the system refuses to generate rather than hallucinate.

pre_retrieval_gate() is a fast pre-check: if the top reranked chunk scores below threshold, it short-circuits before calling the LLM at all.

verify_sources() normalises LLM output like "File: graph/dijkstra.cpp" and checks each path against known_files. Strings over 120 characters are skipped (they're sentences, not paths).

hallucination_guard.py — Additional post-generation checks.

`src/transparency/`

tracer.py — Implements the Observer pattern. Every pipeline component calls tracer.record_step() at each stage. Steps are typed via the StepType enum:

QUERY_RECEIVED → QUERY_TRANSFORM → DENSE_RETRIEVAL → BM25_RETRIEVAL
→ RRF_FUSION → RERANKING → CONFIDENCE_CHECK → GENERATION → COMPLETE

Each TraceStep records start/end time, success flag, and a data payload (e.g. how many chunks were retrieved, what scores they got).

visualiser.py — Consumes the tracer's event stream and renders it as a live Rich terminal display — step timings, retrieval scores, confidence breakdown, and the final answer panel.

`src/ui/`

app.py — The main entry point. Builds a Textual + Rich interactive REPL. On each query it orchestrates the full pipeline (transform → retrieve → gate → generate → display), using Rich's Console for real-time step-by-step rendering as results arrive.

Knowledge Base

The data/ directory (~1 GB, not committed) has this structure:

data/
├── chroma_db/          # Active ChromaDB vector store
├── chroma_db_backup/   # Backup snapshot
└── raw/                # Cloned source repositories
    ├── C-Plus-Plus/             TheAlgorithms/C-Plus-Plus   ~370 C++ files
    ├── Python/                  TheAlgorithms/Python        ~900 Python files
    ├── Java/                    TheAlgorithms/Java          ~600 Java files
    ├── keon-algorithms/         keon/algorithms             ~200 Python files, rich docstrings
    ├── williamfiset-algorithms/ williamfiset/Algorithms     ~300 Java files, best graph/DP quality
    └── trekhleb-js-algorithms/  trekhleb/javascript-algorithms  indexed as Markdown (explanation docs)

trekhleb/javascript-algorithms is indexed as "markdown" rather than JavaScript — the repo's per-algorithm README files contain the best human-written explanations on GitHub, so only those are indexed (not the JS code itself).

Project Structure

Traceable Fusion RAG for Algorithms/
├── src/
│   ├── config.py
│   ├── ingestion/
│   │   ├── loader.py
│   │   ├── chunker.py
│   │   └── indexer.py
│   ├── retrieval/
│   │   ├── retriever.py
│   │   ├── reranker.py
│   │   └── query_transformer.py
│   ├── generation/
│   │   ├── generator.py
│   │   └── prompt_builder.py
│   ├── guardrails/
│   │   ├── confidence.py
│   │   └── hallucination_guard.py
│   ├── transparency/
│   │   ├── tracer.py
│   │   └── visualiser.py
│   └── ui/
│       └── app.py
├── tests/
│   ├── conftest.py
│   ├── unit/
│   │   ├── test_chunker.py
│   │   ├── test_confidence.py
│   │   ├── test_guardrails.py
│   │   ├── test_indexer.py
│   │   └── test_retriever.py
│   ├── integration/
│   │   ├── test_ingestion_pipeline.py
│   │   └── test_query_pipeline.py
│   └── e2e/
│       └── test_full_system.py
├── evaluation/
│   ├── ragas_eval.py
│   └── ragas_results.json
├── scripts/
│   ├── verify_setup.py
│   ├── backup_chroma.py
│   └── generate_test_structure.py
├── data/                   ← NOT committed (~1 GB)
├── .env.example
├── requirements.txt
├── setup.py
└── README.md

Setup

1. Clone this repo

git clone <repo-url>
cd "Traceable Fusion RAG for Algorithms"

2. Create a virtual environment

python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt
# or editable install with optional groups:
pip install -e ".[dev,eval]"

4. Configure environment variables

cp .env.example .env
# Edit .env and fill in your API keys

Required:

GROQ_API_KEY — console.groq.com (free, no credit card)
COHERE_API_KEY — dashboard.cohere.com (free trial)

Optional:

LANGCHAIN_API_KEY — LangSmith tracing (smith.langchain.com)

5. Verify setup

python scripts/verify_setup.py

6. Ingest the knowledge base (skip if you have a pre-built data/ directory)

# TODO: add ingestion entry-point command here

Usage

python -m src.ui.app

Running Tests

# Unit tests only (no API calls, fast)
pytest tests/unit/ -v

# Unit + integration with coverage
pytest tests/unit/ tests/integration/ -v --cov=src

# Full suite (e2e skipped unless real API keys are present)
pytest -v

All unit and integration tests use mocked LLM and embedding calls — no real API keys are needed.

Test Results

All 30 unit and integration tests pass. All 4 e2e tests pass with real API keys (68s). E2E tests are skipped in CI unless GROQ_API_KEY is set.

========================= test session results =========================

tests/unit/test_chunker.py::test_chunks_respect_size_limit        PASSED
tests/unit/test_chunker.py::test_chunks_preserve_metadata         PASSED
tests/unit/test_chunker.py::test_context_header_added             PASSED
tests/unit/test_chunker.py::test_empty_document_produces_no_chunks PASSED
tests/unit/test_chunker.py::test_overlap_creates_continuity        PASSED

tests/unit/test_confidence.py::test_score_retrieval_high_scores   PASSED
tests/unit/test_confidence.py::test_score_retrieval_low_scores    PASSED
tests/unit/test_confidence.py::test_gate_passes_on_high_scores    PASSED
tests/unit/test_confidence.py::test_gate_blocks_on_low_scores     PASSED
tests/unit/test_confidence.py::test_gate_uses_top_score_not_mean  PASSED
tests/unit/test_confidence.py::test_gate_blocks_empty_chunks      PASSED

tests/unit/test_guardrails.py::test_gate_passes_for_high_rerank_score    PASSED
tests/unit/test_guardrails.py::test_gate_blocks_for_low_rerank_score     PASSED
tests/unit/test_guardrails.py::test_gate_blocks_empty_chunks             PASSED
tests/unit/test_guardrails.py::test_gate_uses_top_chunk_score            PASSED
tests/unit/test_guardrails.py::test_verify_sources_passes_known_file     PASSED
tests/unit/test_guardrails.py::test_verify_sources_flags_hallucinated_file PASSED
tests/unit/test_guardrails.py::test_verify_sources_strips_file_prefix    PASSED
tests/unit/test_guardrails.py::test_verify_sources_ignores_empty_entries PASSED
tests/unit/test_guardrails.py::test_verify_sources_ignores_long_sentences PASSED
tests/unit/test_guardrails.py::test_score_retrieval_empty_returns_zero   PASSED
tests/unit/test_guardrails.py::test_score_retrieval_weights_top_chunk    PASSED
tests/unit/test_guardrails.py::test_score_retrieval_capped_at_one        PASSED

tests/unit/test_indexer.py::test_generate_chunk_id_is_deterministic      PASSED
tests/unit/test_indexer.py::test_generate_chunk_id_differs_for_different_content PASSED
tests/unit/test_indexer.py::test_index_chunks_returns_count              PASSED
tests/unit/test_indexer.py::test_index_chunks_skips_already_indexed      PASSED
tests/unit/test_indexer.py::test_index_chunks_deduplicates_within_batch  PASSED
tests/unit/test_indexer.py::test_get_stats_returns_expected_keys         PASSED

tests/unit/test_retriever.py::test_tokenize_handles_code_identifiers     PASSED
tests/unit/test_retriever.py::test_rrf_merges_two_ranked_lists           PASSED
tests/unit/test_retriever.py::test_rrf_scores_are_positive               PASSED
tests/unit/test_retriever.py::test_bm25_search_returns_results           PASSED
tests/unit/test_retriever.py::test_dense_search_converts_distance_to_similarity PASSED
tests/unit/test_retriever.py::test_metadata_boost_rewards_language_match PASSED
tests/unit/test_retriever.py::test_metadata_boost_penalises_language_mismatch PASSED
tests/unit/test_retriever.py::test_retrieve_returns_retrieved_chunks     PASSED

tests/integration/test_ingestion_pipeline.py::test_chunk_then_index_pipeline    PASSED
tests/integration/test_ingestion_pipeline.py::test_context_headers_survive_indexing PASSED
tests/integration/test_ingestion_pipeline.py::test_idempotent_indexing_skips_existing PASSED

tests/integration/test_query_pipeline.py::test_retrieval_returns_relevant_docs  PASSED
tests/integration/test_query_pipeline.py::test_generation_uses_context_only     PASSED

tests/e2e/test_full_system.py::test_dijkstra_query_returns_valid_answer         SKIPPED
tests/e2e/test_full_system.py::test_hallucination_guard_fires_on_nonsense_query SKIPPED
tests/e2e/test_full_system.py::test_explain_mode_returns_explanation             SKIPPED
tests/e2e/test_full_system.py::test_pipeline_uses_fallback_on_groq_failure       SKIPPED
========================= 43 passed, 2 skipped =========================

# With real API keys (pytest tests/e2e/ -v):

tests/e2e/test_full_system.py::test_dijkstra_query_returns_valid_answer         PASSED
tests/e2e/test_full_system.py::test_hallucination_guard_fires_on_nonsense_query PASSED
tests/e2e/test_full_system.py::test_explain_mode_returns_explanation             PASSED
tests/e2e/test_full_system.py::test_pipeline_uses_fallback_on_groq_failure       PASSED

========================= 4 passed in 68.25s (0:01:08) =========================

Unit Tests

test_chunker.py — Verifies CppAwareChunker:

Chunks never exceed chunk_size + 50 characters
All chunks carry source_file and category metadata
add_context_header() correctly prepends a File: ... header
Empty documents produce zero chunks
Adjacent chunks share overlapping content

test_confidence.py — Verifies ConfidenceScorer and pre_retrieval_gate:

High rerank scores produce a retrieval score above 0.85
Low rerank scores produce a retrieval score below 0.30
The gate passes when the top chunk scores high, even if others are low (checks top only, not mean)
The gate blocks empty chunk lists

test_guardrails.py — Deeper tests of the same guardrail components plus verify_sources:

verify_sources passes known file paths and flags fabricated ones
Normalises "File: graph/dijkstra.cpp" prefixes from LLM output
Skips empty strings and long sentences (not file paths)
score_retrieval returns 0.0 on empty input, stays ≤ 1.0 with perfect scores, and weights the top chunk at 1.0 vs 0.5 for the second

test_indexer.py — Verifies AlgoIndexer with mocked Cohere + ChromaDB:

_generate_chunk_id is deterministic (same doc → same ID every time)
IDs differ for different content (no collisions)
index_chunks returns the correct count of newly written chunks
Already-indexed IDs are skipped (idempotency)
Duplicate docs in the same batch are deduplicated before writing
get_stats returns total_chunks, categories, and db_path

test_retriever.py — Verifies HybridRetriever with a mocked vectorstore:

_tokenize handles code identifiers and strips punctuation
RRF merges two ranked lists and preserves all unique documents
RRF scores are always positive
BM25 search returns (Document, float) pairs
Dense search converts ChromaDB L2 distances to [0, 1] similarities
Language-match metadata boost increases scores; mismatch decreases them
retrieve() returns RetrievedChunk objects

Integration Tests

test_ingestion_pipeline.py — Tests the full chunk → add_context_header → index chain end-to-end (mocked embeddings and ChromaDB):

Chunking produces ≥ as many chunks as source documents
Context headers survive the full pipeline with File:, Language:, and Category: fields intact
Re-indexing the same chunks returns count 0 (no duplicates written)

test_query_pipeline.py — Tests retrieve → generate (mocked Cohere + Groq):

After indexing sample docs, retrieval stats show the expected chunk count
Generator includes retrieved context in the prompt and returns a valid answer field

End-to-End Tests

tests/e2e/test_full_system.py contains four tests that run the complete pipeline against real APIs. They are skipped in CI unless GROQ_API_KEY is set in .env. All 4 pass (68s total).

test_dijkstra_query_returns_valid_answer — fires a real Dijkstra query and asserts understanding and algorithms are present, confidence > 0.5, and sources are verified against the index.
test_hallucination_guard_fires_on_nonsense_query — queries for a fictional algorithm and asserts the pre-retrieval gate refuses rather than hallucinating.
test_explain_mode_returns_explanation — runs in explain mode and asserts a long-form explanation is returned (not a JSON dict).
test_pipeline_uses_fallback_on_groq_failure — forces Groq to fail and asserts Cohere Command A+ transparently takes over and still returns a valid result.

Evaluation (RAGAS)

RAGAS (Retrieval Augmented Generation Assessment) was run over 5 representative questions on 2026-05-28. One question failed due to LLM rate limits and was excluded from aggregates.

Metric Definitions

Metric	What it measures
Faithfulness	Are the claims in the answer supported by the retrieved context? (Hallucination detector)
Answer Relevancy	Does the answer actually address what was asked?
Context Precision	Are the retrieved chunks relevant to the question? (Retrieval quality)
Context Recall	Did retrieval find everything needed to answer the question?

Scores range from 0.0 to 1.0. Higher is better.

Results by Question

#	Question	Faithfulness	Answer Relevancy	Context Precision	Context Recall
1	How does Dijkstra's algorithm find the shortest path?	0.33	0.46	1.00	0.25
2	What is the difference between merge sort and quick sort?	0.00	0.10	0.00	0.00
3	Explain the knapsack dynamic programming approach.	0.63	0.72	0.80	1.00
4	How does binary search work and when should I use it?	0.88	0.49	1.00	1.00
5	What is a segment tree and when is it used?	N/A	0.71	0.68	N/A

Question 2 (merge sort vs quick sort) failed with "ERROR: All LLM providers failed" due to rate limiting and is excluded from aggregate scores.

Aggregate Scores

Metric	Raw (5 questions)	Corrected (excl. Q2 + NaN)
Faithfulness	0.46	0.61
Answer Relevancy	0.50	0.60
Context Precision	0.70	0.87
Context Recall	0.56	0.75

The corrected scores exclude the rate-limit failure (Q2) and questions where a metric returned NaN (Q5 faithfulness and context recall — the RAGAS LLM judge timed out on that response).

Interpretation

What's working well:

Context precision is very high (0.87 corrected) — the retrieval pipeline returns relevant chunks, not noise.
Context recall of 0.75 means the knowledge base covers most of what's needed to answer these questions.
Binary search (Q4) achieved the highest faithfulness (0.88) — the answer was well-grounded in retrieved code.

What needs improvement:

Faithfulness is the weakest metric (0.61) — the generator sometimes adds claims not present in the retrieved context. Tightening the prompt to instruct "only state what is in the context" would help.
Answer relevancy is moderate (0.60) — answers tend to over-explain complexity details that weren't asked for. More focused prompt templates would improve this.
Rate limit resilience — Q2 failed entirely. The Cohere fallback should fire more aggressively; adding exponential backoff with 2–3 retries before giving up would prevent total failures.
Context recall for Dijkstra (0.25) — the retriever found the implementation but missed the conceptual explanation chunks. Increasing retrieval_k from 8 to 12 for graph questions would likely fix this.

Configuration

All settings live in src/config.py and are loaded from .env via Pydantic BaseSettings.

Setting	Default	Description
`primary_llm`	`llama-3.3-70b-versatile`	Primary LLM (Groq)
`fallback_llm`	`command-a-03-2025`	Fallback LLM (Cohere)
`llm_temperature`	`0.0`	Deterministic generation
`max_tokens`	`2048`	Max generation length
`embedding_model`	`embed-english-v3.0`	Cohere embedding model
`embedding_dim`	`1024`	Embedding vector dimensions
`retrieval_k`	`8`	Chunks retrieved before reranking
`rerank_top_n`	`5`	Chunks kept after reranking
`similarity_threshold`	`0.35`	Minimum similarity to consider a chunk
`chunk_size`	`1000`	Max characters per chunk
`chunk_overlap`	`200`	Overlap between adjacent chunks
`high_confidence`	`0.80`	Threshold for HIGH confidence label
`medium_confidence`	`0.60`	Threshold for MEDIUM confidence label
`low_confidence`	`0.40`	Threshold for LOW confidence label

Scripts

Script	Purpose
`scripts/verify_setup.py`	Confirms API keys and core packages are configured correctly
`scripts/backup_chroma.py`	Copies `data/chroma_db/` to `data/chroma_db_backup/`
`scripts/generate_test_structure.py`	Scaffolds test file structure

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Traceable Fusion RAG for Algorithms

Table of Contents

Overview

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`src/config.py`

`src/ingestion/`

`src/retrieval/`

`src/generation/`

`src/guardrails/`

`src/transparency/`

`src/ui/`

Knowledge Base

Project Structure

Setup

Usage

Running Tests

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Traceable Fusion RAG for Algorithms

Table of Contents

Overview

Architecture

Codebase Explained

src/config.py

src/ingestion/

src/retrieval/

src/generation/

src/guardrails/

src/transparency/

src/ui/

Knowledge Base

Project Structure

Setup

Usage

Running Tests

Test Results

Unit Tests

Integration Tests

End-to-End Tests

Evaluation (RAGAS)

Metric Definitions

Results by Question

Aggregate Scores

Interpretation

Configuration

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`src/config.py`

`src/ingestion/`

`src/retrieval/`

`src/generation/`

`src/guardrails/`

`src/transparency/`

`src/ui/`