advanced-rag-pipelines · diff
v5.0.0 to v6.0.0
66 added, 5 removed. Audit A to A.
---
name: advanced-rag-pipelines
- description: "Use when Production-grade Retrieval-Augmented Generation (RAG) mastery. Semantic chunking, Hybrid Search (Dense + Sparse/BM25), Cross-Encoder Reranking, and architecture-agnostic vector database management."
- version: 5.0.0
- last-updated: 2026-09-13
+ description: "Use when designing, implementing, auditing, and hardening advanced rag pipelines server logic, APIs, background jobs, and error boundaries."
+ version: 6.0.0
+ last-updated: 2026-09-29
skills:
- llm-engineering
- ai-prompt-injection-defense
- database-design
tools: Read, Grep, Glob, Bash, Edit, Write
scripts-binding:
- .agent/scripts/lint_runner.js
- .agent/scripts/verify_all.js
---
# Advanced RAG Pipelines (Production AI Data)
- ---
+ ## Mandatory Pre-Flight Context Inspection
+ Before reading, generating, or refactoring code in the `advanced-rag-pipelines` domain, inspect these 5 critical parameters:
+ 1. **System Boundaries & Dependencies**: Verify that all required dependencies exist in target package manifests and environment paths.
+ 2. **Runtime Context & Platform Invariants**: Confirm target platform constraints (Node.js, Browser, Mobile OS, Edge runtime) before applying APIs.
+ 3. **Execution Guardrails**: Identify potential side-effects, state mutations, and unhandled asynchronous exceptions.
+ 4. **Validation & Type Contracts**: Validate input data schemas and strict type constraints across all module interfaces.
+ 5. **Observability & Proof of Execution**: Ensure execution produces tangible verification signals (terminal output, tests, metrics).
- ## 🛠️ Technical Architecture & Reference Recipes
+ ## Activation Boundaries
+ - **Activate when:** Use when designing, implementing, auditing, and hardening advanced rag pipelines server logic, APIs, background jobs, and error boundaries.
+ - **DO NOT activate when:** The task falls outside the `advanced-rag-pipelines` domain or is managed by a different dedicated specialist agent.
+
+
+ ## 🔁 Multi-Pass Execution Protocol
+
+ | Pass | Phase | Core Action | Adaptive Depth |
+ |:---|:---|:---|:---|
+ | **Pass 1** | **Understand** | Deconstruct the user's explicit objective, implicit requirements, and platform constraints. | Fast / Standard / Deep |
+ | **Pass 2** | **Plan** | Decompose task into smallest logical steps; map dependencies, affected files, and tool calls. | Standard / Deep |
+ | **Pass 3** | **Execute** | Implement solution with production-grade craft, zero placeholders, and strict typing. | All Modes |
+ | **Pass 4** | **Verify** | Run linters, unit tests, or compiler checks to validate structural correctness. | All Modes |
+ | **Pass 5** | **Attack & Falsify** | Perform adversarial search for edge-case failures, counterexamples, race conditions, and traps. | Standard / Deep |
+ | **Pass 6** | **Harden** | Eliminate discovered friction, optimize performance, and harden error boundaries. | Standard / Deep |
+ | **Pass 7** | **Quality Gate** | Enforce Verification-Before-Completion (VBC) with concrete terminal proof before finalizing. | All Modes |
+
+
---
+ ## 🛠️ Technical Architecture & Reference Recipes
+
## 2026 RAG Performance & Vector Invariants
1. **Reciprocal Rank Fusion (RRF)**:
```python
# Combine dense + sparse rankings without normalizing disparate score distributions
def rrf(dense_ranks: dict[str, int], sparse_ranks: dict[str, int], k: int = 60) -> dict[str, float]:
scores = {}
for doc_id, rank in dense_ranks.items():
scores[doc_id] = scores.get(doc_id, 0) + 1 / (k + rank)
for doc_id, rank in sparse_ranks.items():
scores[doc_id] = scores.get(doc_id, 0) + 1 / (k + rank)
return dict(sorted(scores.items(), key=lambda x: x[1], reverse=True))
```
2. **HNSW Indexing with Halfvec (pgvector 0.8+)**:
```sql
-- Halves memory usage with < 1% recall loss
CREATE INDEX idx_docs_embedding ON documents
USING hnsw ((embedding::halfvec(1536)) halfvec_cosine_ops);
```
3. **Aggressive Context Pruning**: Never dump > 5 chunks into the final LLM prompt. Context dilution ("Lost in the Middle") degrades factual recall and spikes latency.
## Hallucination Traps (Read First)
- ❌ Fixed-character chunking (e.g. split every 500 chars) → ✅ AST/Markdown-aware structural chunking
- ❌ Relying only on cosine similarity on raw queries → ✅ Hybrid search (Dense + BM25) with cross-encoder rerank
- ❌ Injecting raw text into system prompt → ✅ Enclose in `<retrieved_context>` tags to prevent indirect prompt injection
- ❌ Full precision FP32 vectors on massive datasets → ✅ Use FP16 (`halfvec`) or scalar quantization
---
## 2. Advanced Architectural Patterns
### A. Semantic Chunking
Instead of splitting text every 1000 characters, split by structural bounds:
- **Code:** Split by Abstract Syntax Tree (AST) nodes (functions, classes).
- **Markdown:** Split by Header levels (`##`).
- **Prose:** Use LLM-assisted proposition extraction (extracting atomic facts from sentences).
### B. Two-Stage Retrieval (Reranking)
```text
1. User Query -> Embed -> Vector DB (Pinecone/Milvus/Pgvector)
2. Retrieve Top K = 50 (Fast, low precision)
3. Pass (Query + 50 Chunks) to Cross-Encoder (e.g., Cohere Rerank, BGE-Reranker)
4. Reranker outputs Top N = 5 (Slow, high precision)
5. Pass Top 5 to LLM Context
```
### C. Query Transformation
Never embed the user's raw query directly. Users write poor queries.
- **HyDE (Hypothetical Document Embeddings):** Have the LLM write a fake answer to the query, then embed that fake answer to search the Vector DB.
- **Query Routing:** Route "summarize" queries to a Graph database, and "how do I" queries to the Vector DB.
## 3. LLM Traps & Pre-Flight Checks
- **TRAP:** Sending 20 chunks to the LLM. This dilutes the context (Lost in the Middle phenomenon) and increases cost.
- **FIX:** Always rerank and aggressively filter down to 3-5 highly relevant chunks before the generation step.
- **TRAP:** Not attaching metadata to chunks.
- **FIX:** Always attach `{ source_file, line_numbers, date, author }` to the vector payload. This allows the Vector DB to pre-filter before calculating cosine similarity.
## Verification Protocol
Before submitting code, ensure:
1. Retrieval pipelines include a Reranking step if accuracy is paramount.
2. BM25 / Sparse search is considered alongside standard dense embeddings.
3. Chunks are injected into the final LLM prompt with explicit `<context>` XML boundaries to prevent prompt injection.
+
+ ## 🚨 Edge-Case & Failure Mode Matrix
+
+ | Scenario | Risk | Production Mitigation |
+ |:---|:---|:---|
+ | **Empty or Null Inputs** | Unhandled exception or unexpected rendering collapse | Enforce fallback guards, optional chaining, and explicit empty state handlers |
+ | **Network Timeout / Latency** | Hanging operations or duplicate side-effects | Implement bounded abort controllers, exponential backoff, and idempotency keys |
+ | **Concurrency / Race Conditions** | Stale state overwrite or inconsistent data mutations | Use atomic transactions, mutex locking, or cancel-on-resubmit controls |
+ | **Invalid Schema / Malformed Payload** | Downstream runtime errors or security injection | Validate boundary payloads with Zod/Pydantic schemas prior to execution |
+ | **Resource / Memory Saturation** | OOM errors, frame drops, or memory leaks | Clean up listeners, cancel active timers, and enforce pagination/virtualization |
+
+
+ ## 🏛️ Tribunal Verification & Guardrails
+
+ **Active Reviewers:** `logic-reviewer` · `security-auditor` · `api-architect` · `resilience-reviewer`
+ **Slash Command:** `/review` or `/tribunal-full`
+
+ ### 🔬 Evidence Standard (Tri-State Verification)
+ Every finding, audit statement, or completion claim must classify its factual certainty:
+ - **`[OBSERVED]`**: Directly confirmed in the codebase or verified via executed terminal command.
+ - **`[INFERRED]`**: Logically deduced from code patterns, architectural data flow, or schema relations.
+ - **`[UNVERIFIED]`**: Speculative hypothesis or runtime possibility requiring active testing or measurement.
+
+ ### ✅ Pre-Flight Self-Audit Checklist
+ ```
+ ✅ Are all inputs and boundary payloads validated against schemas (Zod/Pydantic)?
+ ✅ Are SQL and database queries parameterized with zero string concatenation?
+ ✅ Are error boundaries and timeout/retry policies explicitly declared?
+ ✅ Are authentication and object-level authorization (IDOR/BOLA) checked before business logic?
+ ✅ Did I verify that imported dependencies exist in package manifests?
+ ```
+
+ ### 🛑 Verification-Before-Completion (VBC) Protocol
+ **CRITICAL:** You must follow a strict "evidence-based closeout" state machine.
+ - ❌ **Forbidden:** Declaring a task complete because the output "looks correct."
+ - ✅ **Required:** You are explicitly forbidden from finalizing any task without providing **concrete evidence** (terminal output, passing test suites, compiler success, or equivalent operational proof) that your output works as intended.