File size: 4,160 Bytes
6ff2979 198bd6d 6ff2979 198bd6d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | ---
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
|