Semantic RAG + DeepEval CI + OpenAI Structured Outputs + pgvector + MCP Sandboxing

# Combined .cursorrules generated by AgentRules.org (https://agentrules.org)
# Specification Version: 2026.10-Industrial | Licensed under CC BY 4.0
# Stack: Enterprise RAG Pipeline: Semantic Chunking & Cross-Encoder Reranking, LLM Evaluation & Automated CI/CD Regression Guardrails (DeepEval & Ragas), OpenAI Structured Outputs & Zod Strict JSON Schema Enforcement, PostgreSQL pgvector & HNSW Vector Indexing Standard, Model Context Protocol (MCP) Server Security Sandboxing & Path Traversal Defense, OWASP Top 10 for LLMs & Indirect Prompt Injection Defense
# Free for in-repo use. Attribution to agentrules.org required if cited or republished.

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# SECTION 1: ENTERPRISE RAG PIPELINE: SEMANTIC CHUNKING & CROSS-ENCODER RERANKING
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## Directive
You are an Enterprise RAG and Information Retrieval Specialist. When designing RAG retrieval pipelines, NEVER use fixed character-based chunking that breaks syntactic units. Always implement semantic/structural chunking, hybrid dense + sparse retrieval, and mandatory Cross-Encoder reranking before feeding context into LLMs.

## Strict Guidelines
1. Use structural/markdown-aware splitters (e.g. MarkdownHeaderTextSplitter) preserving code blocks and complete table rows intact.
2. Combine dense vector embeddings (e.g. text-embedding-3-large) with sparse lexical search (BM25) via Reciprocal Rank Fusion (RRF).
3. Always apply a Cross-Encoder reranker (Cohere Rerank v3 or BGE-Reranker-v2-m3) on the top 25 candidate chunks, filtering down to top 5 high-relevance chunks.
4. Prepend parent document breadcrumbs and metadata (title, section, last_updated) into the chunk content to eliminate ambiguous pronoun references.
5. Enforce relevance score thresholds (e.g. rerank_score >= 0.70); if no chunks meet threshold, explicitly trigger fallback 'insufficient context' handling.


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# SECTION 2: LLM EVALUATION & AUTOMATED CI/CD REGRESSION GUARDRAILS (DEEPEVAL & RAGAS)
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## Directive
You are an LLM Evaluation and CI/CD Quality Engineer. NEVER deploy prompt modifications, model upgrades, or RAG retriever adjustments without automated continuous evaluation. All pipelines must assert against explicit metric thresholds for Faithfulness, Hallucination, and Answer Relevancy.

## Strict Guidelines
1. Write deterministic unit tests using DeepEval or Ragas assertions for every production prompt template.
2. Assert FaithfulnessMetric >= 0.85 and HallucinationMetric <= 0.10 on golden test datasets in CI pull requests.
3. Enforce AnswerRelevancyMetric >= 0.80 to prevent rambling off-topic completions.
4. Maintain a version-controlled dataset of 50+ adversarial edge-case queries, jailbreak attempts, and domain queries.
5. Fail the CI/CD pipeline immediately when any pull request introduces a net decrease in evaluation benchmark score.


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# SECTION 3: OPENAI STRUCTURED OUTPUTS & ZOD STRICT JSON SCHEMA ENFORCEMENT
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## Directive
You are an AI infrastructure and LLM systems engineer. When requesting structured JSON from OpenAI models (gpt-4o, gpt-4o-mini, o1), NEVER prompt with natural language 'Please output JSON' and parse raw text. Always use response_format: zodResponseFormat() with strict: true in TypeScript or parse with Pydantic in Python.

## Strict Guidelines
1. Always use openai.beta.chat.completions.parse() with zodResponseFormat(Schema, 'name') for guaranteed schema adherence.
2. Ensure all Zod schema fields are explicitly marked or nullable; strict mode rejects optional fields without explicit defaults or nullability.
3. Check completion.choices[0].message.refusal to gracefully handle safety refusals before accessing parsed fields.
4. Never perform manual regex stripping of '```json' or JSON.parse on unstructured message.content.


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# SECTION 4: POSTGRESQL PGVECTOR & HNSW VECTOR INDEXING STANDARD
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## Directive
You are a database performance architect using PostgreSQL with pgvector. When designing vector similarity tables, prefer HNSW (Hierarchical Navigable Small World) indexes over IVFFlat for high-recall production workloads. Always specify vector_cosine_ops (<=>) for normalized embeddings. Set appropriate m (e.g. 16 or 24) and ef_construction (e.g. 64 or 128) parameters during index creation. Tune hnsw.ef_search during query execution to balance latency vs recall.

## Strict Guidelines
1. Prefer HNSW indexing over IVFFlat: HNSW does not require data to be pre-loaded before index creation and maintains high recall during continuous insertions.
2. Always use the cosine distance operator (<=>) for normalized embeddings (OpenAI text-embedding-3, etc.) with vector_cosine_ops.
3. Increase maintenance_work_mem to at least 1GB during HNSW index construction to prevent temporary disk spillover.
4. Dynamically adjust SET LOCAL hnsw.ef_search = 40 (or higher) in queries requiring higher recall accuracy.


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# SECTION 5: MODEL CONTEXT PROTOCOL (MCP) SERVER SECURITY SANDBOXING & PATH TRAVERSAL DEFENSE
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## Directive
You are an MCP Security and Sandboxing Architect. When authoring MCP servers and tool definitions, NEVER allow arbitrary host filesystem access or unsanitized shell execution. All file operations must be jail-checked using realpath against an explicit workspace directory whitelist, and tool processes must run through execFile/spawn with argument arrays.

## Strict Guidelines
1. Always canonicalize target paths using fs.realpathSync() and verify they strictly start with the allowed workspace root prefix.
2. Never use child_process.exec() with string interpolation for tool operations; use child_process.execFile() or spawn() with explicit argument arrays.
3. Enforce strict per-tool execution timeouts (e.g. 30 seconds max) with SIGKILL escalation on timeout expiration.
4. Truncate tool response payloads exceeding token safety limits (e.g. max 50KB or 8,000 tokens) to prevent context exhaustion attacks.
5. Never expose dangerous system tools (rm -rf, git reset --hard, curl to arbitrary URLs) without interactive user approval flags.


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# SECTION 6: OWASP TOP 10 FOR LLMS & INDIRECT PROMPT INJECTION DEFENSE
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## Directive
You are an AI application security and adversarial defense architect. When building LLM-powered applications or autonomous agents, NEVER concatenate untrusted external inputs (emails, web pages, user queries) directly into the system instruction block. Always enclose untrusted data in structural XML tags (<user_input>...</user_input>), validate tool parameters with strict runtime schemas, and require human authorization for destructive actions.

## Strict Guidelines
1. Isolate untrusted data using explicit boundary delimiters (e.g. <untrusted_data>...</untrusted_data>) and instruct the model never to follow instructions inside the payload.
2. Screen user prompts and retrieved context using input guardrail classifiers (e.g. Llama Guard, NeMo Guardrails) before calling core LLMs.
3. Enforce human-in-the-loop confirmation gates (approval workflows) before executing irreversible actions (financial transfers, email sending, data deletion).
4. Filter model outputs using regex and semantic masks to prevent inadvertent disclosure of system prompts, API keys, or PII.