---
license: mit
tags:
- langgraph
- rag
- bayesian
- probabilistic
- uncertainty
- retrieval
---

# Bayesian LangGraph RAG

A corrected, Hugging Face-ready Bayesian RAG implementation using a **real LangGraph `StateGraph`**.

The original repository used a custom Python loop under the LangGraph name and could erase its final SMC posterior through final-step resampling. This version separates retrieval inference from orchestration and uses the graph:

`START -> retrieve -> assess -> (generate | abstain) -> END`

The retriever uses the generalized-Bayesian posterior

`p(d | q) ∝ p(d) (similarity(q,d) + eps)^tau`

with a Beta reliability prior plus authority and freshness metadata.

## What was corrected

- Replaced the custom pseudo-LangGraph runtime with an actual `StateGraph`.
- Removed Colab-only setup, Drive mounts, repository cloning, and import-time execution.
- Fixed all-`-inf` log-weight normalization.
- Fixed final-step resampling so the posterior cannot be reset to uniform.
- Added proposal-density correction to the reusable SMC implementation.
- Removed deterministic identical-particle demos as evidence of inference quality.
- Replaced hard-coded "Bayes wins" benchmark flags with end-to-end document retrieval and grounded answering.
- Added uncertainty-aware abstention for missing evidence and unresolved source conflicts.
- Added reproducible RAG evaluation with a plain TF-IDF baseline.

## Install

```bash
pip install -e .
```

No GPU is required. The default generator is extractive and citation-grounded; a model-backed generator can be supplied if desired.

## Quick start

```python
from bayesian_langgraph import DocumentRecord, BayesianIndex, BayesianRAGGraph

docs = [
    DocumentRecord(
        id="official",
        title="Current portal policy",
        text="The current HR portal is PeopleHub.",
        source="official",
        metadata={"reliability_alpha": 18, "reliability_beta": 2, "authority": 0.98, "freshness": 1.0},
    ),
    DocumentRecord(
        id="legacy",
        title="HR portal guide",
        text="The HR portal is WorkSphere.",
        source="legacy-cache",
        metadata={"reliability_alpha": 6, "reliability_beta": 6, "authority": 0.45, "freshness": 0.18},
    ),
]

graph_rag = BayesianRAGGraph(BayesianIndex(docs))
print(graph_rag.invoke("What is the current HR portal?"))
```

## RAG validation

Run:

```bash
PYTHONPATH=src:. python evaluation/rag_benchmark.py
```

The committed result is in `artifacts/rag_benchmark_results.json`.

| Suite | System | Recall@1 | MRR | Answer accuracy | Abstention accuracy | End-to-end accuracy |
|---|---|---:|---:|---:|---:|---:|
| Clean RAG (8 QA) | Bayesian | 1.000 | 1.000 | 1.000 | n/a | 1.000 |
| Clean RAG (8 QA) | Plain TF-IDF | 1.000 | 1.000 | 1.000 | n/a | 1.000 |
| Conflict/missing RAG (18 QA) | Bayesian | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
| Conflict/missing RAG (18 QA) | Plain TF-IDF | 0.250 | 0.611 | 0.250 | 0.333 | 0.278 |

These are controlled end-to-end repository tests, not a claim of universal RAG accuracy.

### Framework-level LangGraph RAG test

`evaluation/framework_rag_benchmark.py` executes every case through the compiled LangGraph graph itself. The validated topology is:

`START -> retrieve -> assess -> {generate | abstain} -> END`.

It was validated against **langgraph 1.2.16** with **langchain-core 1.6.11**. Results: **8/8 clean cases** and **18/18 conflict/missing-evidence cases** passed end-to-end. The committed output is `artifacts/framework_rag_benchmark_results.json`.

Run it with:

```bash
PYTHONPATH=src:. python evaluation/framework_rag_benchmark.py
```

## Layout

- `src/bayesian_langgraph/core.py` — posterior retrieval, abstention, grounded answerer
- `src/bayesian_langgraph/graph.py` — actual LangGraph `StateGraph`
- `src/bayesian_langgraph/smc.py` — corrected importance-weighted SMC utility
- `evaluation/rag_benchmark.py` — controlled retrieval/grounding benchmark
- `evaluation/framework_rag_benchmark.py` — compiled `StateGraph` RAG benchmark
- `tests/` — regression and graph-integration tests
- `VALIDATION.md` — exact framework/version validation record