pgContext AGENTS.md

Installation and usage instructions for pgContext, a PostgreSQL extension that adds vector and combined text-and-vector search inside the database.

In plain words
What is it for?
Install pgContext with Docker or from source, then add vector or hybrid retrieval to PostgreSQL applications.
Why use it?
It explains how to install the extension reliably and why keeping search beside the source tables helps preserve database permissions and data consistency.

Instructions file for CodexOpenCode

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/evokoa/pgcontext/agents-md
Clone the repo
git clone --depth 1 https://github.com/Evokoa/pgContext

Made for: Codex, OpenCode.

Per session 1,455 This file is loaded in full into every session.
When invoked 1,455 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.01455 $0.01455
Opus 5 $0.00727 $0.00727
Sonnet 5 $0.00291 $0.00291
Haiku 4.5 $0.00145 $0.00145

Measured 2d ago against content hash f8fc35643657, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

pgContext AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 136 lines

How it starts

The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — installing & using pgContext with an AI agent

Instructions for an AI coding agent (or an automated environment) that needs to install pgContext, verify it, and wire it into an application. Everything here is non-interactive and copy-paste safe. For human-oriented docs see README.md and docs/.

What pgContext is (and why it's worth using)

pgContext is an Apache-2.0 PostgreSQL 17 and 18 extension (Rust + pgrx) that adds vector and hybrid retrieval inside PostgreSQL, beside the data it searches:

  • One system of record. Your ordinary PostgreSQL tables stay authoritative. HNSW and other acceleration artifacts are derived and rebuildable — never a second, drifting copy of your data.
  • Fast answers that are still correct. Exact search is the oracle: every approximate (HNSW) candidate is resolved back to the live row and scored exactly before it is returned.
  • PostgreSQL's rules still hold. MVCC visibility, ACL/RLS, and SQL predicates apply to every result, so retrieval cannot surface a row the caller isn't allowed to see.
  • Competitive performance. Page-native HNSW with SIMD distance kernels matches pgvector's recall and serves it several times faster on the recognized GloVe-100-angular benchmark. See docs/benchmarks/pgvector.md.

Wiring guidance for an application: treat the source PostgreSQL table as the truth, add embeddings as a vector column, create a pgcontext_hnsw index for approximate search, and order by the schema-qualified distance operator (ORDER BY embedding OPERATOR(pgcontext.<=>) $query) when you need exact ranking. Never persist the index as if it were primary data — it can always be rebuilt with REINDEX.

Environment facts (pins — do not guess)

Fact Value
Extension name pgcontext
Supported PostgreSQL majors 17 and 18
Docker images ghcr.io/evokoa/pgcontext:pgMAJOR-v0.3.0 (multi-arch amd64/arm64; default aliases use PG17)
Rust (source build) 1.96.0
cargo-pgrx (source build) 0.19.1 (pin exactly)
License Apache-2.0

Read the full file on GitHub · 136 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 136 lines · 1,455 tokens per session scan A f8fc35643657

Subscribe to this mod's changes

pgContext AGENTS.md is an instructions file published in the GitHub repository Evokoa/pgContext (251 stars, last pushed 17d ago), licensed Apache-2.0. It adds 1,455 tokens to every session, about $0.0073 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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