# pi-intelli-search [![npm version](https://img.shields.io/npm/v/@curio-data/pi-intelli-search?color=blue)](https://www.npmjs.com/package/@curio-data/pi-intelli-search) [![npm downloads](https://img.shields.io/npm/dt/@curio-data/pi-intelli-search?color=blue)](https://www.npmjs.com/package/@curio-data/pi-intelli-search) [![pi compatible](https://img.shields.io/badge/pi-%E2%89%A50.80.8-blueviolet)](https://github.com/earendil-works/pi) [![license](https://img.shields.io/badge/license-Apache--2.0-green)](./LICENSE) ![tests](https://img.shields.io/badge/tests-330%20passing-brightgreen) Intelligent web research for [`Pi`](https://github.com/earendil-works/pi): search, extract, collate, and cache grounded web context in one tool call. A `Pi` extension for deep web research. It searches via [Perplexity Sonar](https://docs.perplexity.ai), fetches pages through a dual-fetch comparison ([_Defuddle_](https://github.com/kepano/defuddle) versus Markdown endpoint), extracts query-relevant content per page with a dedicated LLM guided by a _focused prompt_, then collates findings: deduplicating, flagging inconsistencies, and synthesising a concise summary. Everything is cached in `.search/` for offline reuse. Cache suggest surfaces related previous searches on each query.

PI-Intelli Search: a five-stage research pipeline diagram arranged in a clockwise cycle. The five labelled stages, each enclosed in a laurel-wreath medallion, are Search (top, depicted as a magnifying glass over an open book), Fetch (right, a hand retrieving a document from shelves), Extract (bottom-right, a distillation apparatus), Collate (bottom-left, stacked books and filing boxes), and Cache & Suggest (left, a treasure chest with an envelope). Copper-coloured arrows connect the stages in sequence. The background is decorated with pen-and-ink botanical and scholarly motifs including quill pens, ink bottles, scrolls, globes, hourglasses, and open books.

**Features:** - 🔍 **Search:** [_Perplexity Sonar_](https://docs.perplexity.ai) via [_OpenRouter_](https://openrouter.ai). One API key, no $50 minimum. - 🌐 **Fetch:** Dual-fetch each page (HTML → Defuddle versus Markdown endpoint), compare quality, pick the cleaner version. - 📄 **Extract:** Per-page LLM extraction guided by a _focused prompt_. Compresses ≈50K to ≈3-5K chars of query-relevant content. - 🔗 **Collate:** Cross-source deduplication, inconsistency detection, and synthesis into a focused ≈5K summary. - 💾 **Cache:** Persistent `.search/` cache with automatic cache suggest. Related previous searches surfaced on each query. - 🎯 **Configurable:** Swap any pipeline stage (search, extract, collate) to any model `Pi` supports. - 💰 **Low cost:** Approximately $0.05 per research session with default settings. ## What It Adds Over Other Extensions

Pipeline comparison infographic titled "PI-INTELLI-SEARCH" contrasting two approaches in a vintage engraving style. The top row shows the Intelli-Search purpose-built research pipeline: seven sequential stages: Search (Perplexity Sonar, single unified key), Dual Fetch (defuddle & markdown), Quality Compare (pick the best), LLM Extract Per Page (MiniMax M2.7, focused & targeted), LLM Collate (MiniMax M2.7, dedupe/highlight/source, flags conflicts), Persistent Cache (expand on demand), and Cache Suggest (LLM-judged relevance, additive, feeds back). The bottom row shows generic fetch/search extensions: Search (additional keys), Single Fetch (simple, provider-dependent), Raw Page Content (unstructured, ≈50K chars/page), No Cache (in-memory at best). Footer summary: "Intelli-Search: deduped, cited, focused, ≈$0.05/session, reusable" vs. "Other extensions: raw pages, must synthesise, no reuse."

Four capabilities together separate `intelli-search` from every other extension in the `Pi` ecosystem: 1. **Dual-fetch quality comparison.** Every page is fetched twice in parallel (Defuddle versus Markdown endpoint), scored, and the better version wins. Server-rendered Markdown is not guaranteed to be cleaner than HTML; the comparison catches this automatically. 2. **Per-page LLM extraction guided by `focusPrompt`.** Each page is compressed to ≈3-5K chars of query-relevant content before entering the agent's context. Extraction quality scales with the chosen model. 3. **LLM collation with deduplication.** A collation model synthesises across sources, flags conflicting claims, and preserves source attribution. The agent does not spend reasoning tokens on mechanical synthesis. 4. **Persistent cache with cache suggest.** Full pages and extractions are kept in `.search/` and indexed. An LLM judge surfaces related previous searches on each query, so follow-up research is faster and cheaper. The agent receives a concise ≈5K summary by default. The full page content stays in the cache, accessible via native `Pi` tools like `read` or `grep` for deeper inspection. No other `Pi` search extension offers both. For the detailed feature-by-feature comparison against six other `Pi` search extensions, see [docs/COMPARISON.md](docs/COMPARISON.md). ## Install ### Prerequisites You need at minimum an [OpenRouter](https://openrouter.ai) account for [_Perplexity Sonar_](https://docs.perplexity.ai) search. The same account also covers extraction and collation with the default models. For the extract and collate stages, any model or provider `Pi` supports can be used. See [Model Configuration](#model-configuration) for how to swap them. 1. **Sign in with OAuth (recommended):** run `/login openrouter` in `Pi`. On `Pi` 0.82.0 and later this performs OpenRouter OAuth PKCE sign-in and stores a user-controlled key automatically. No manual key paste is required. 2. **Or add a key manually:** create one at [openrouter.ai/keys](https://openrouter.ai/keys), then edit `~/.pi/agent/auth.json`: ```json { "openrouter": { "type": "api_key", "key": "sk-or-v1-..." } } ``` ### Install the Extension From `npm` (recommended): ```bash pi install npm:@curio-data/pi-intelli-search ``` From _GitHub_: ```bash pi install git:github.com/Curio-Data/pi-intelli-search ``` Local development: ```bash pi install /path/to/pi-intelli-search ``` On first load, `Pi` will show: `Added models: perplexity/sonar, perplexity/sonar-pro`. If your OpenRouter key is missing, you will see a warning notification. ### Verify Installation Start `Pi` and type `/model`. You should see `perplexity/sonar` and `perplexity/sonar-pro` in the model list. If they are missing after a manual edit, reopen `/model`: since `Pi` 0.82.0 the picker reloads `models.json` on open. Restart `Pi` only if they are still absent. ### Customise (Optional) No configuration is needed to get started. The defaults use OpenRouter for all stages. If you want to change models, add a `pi-intelli-search` block to `~/.pi/agent/settings.json` or, for a trusted project, `/.pi/settings.json`: **Defaults (what you get without any config):** ```jsonc { "pi-intelli-search": { "searchModel": { "provider": "openrouter", "model": "perplexity/sonar" }, "extractModel": { "provider": "openrouter", "model": "minimax/minimax-m2.7" }, "collateModel": { "provider": "openrouter", "model": "minimax/minimax-m2.7" }, "defaultUrls": 8, "maxUrls": 16, "cacheDir": ".search", "extractMaxChars": 150000, "extractionConcurrency": 4, "extractionMaxTokens": 3000, "collationMaxTokens": 4000, "fetchTimeoutMs": 20000, "fetchConcurrency": 4, "browserFingerprint": "chrome_145" } } ``` **Customised example (different provider, tuned pipeline):** ```jsonc { "pi-intelli-search": { "searchModel": { "provider": "openrouter", "model": "perplexity/sonar" }, "extractModel": { "provider": "openai", "model": "gpt-4o-mini" }, "collateModel": { "provider": "openai", "model": "gpt-4o-mini" }, "defaultUrls": 6, "maxUrls": 6, "cacheDir": ".my-research-cache", "extractMaxChars": 80000, "extractionMaxTokens": 8000, "collationMaxTokens": 16000, "fetchTimeoutMs": 30000, "fetchConcurrency": 2, "browserFingerprint": "chrome_145" } } ``` See [Model Configuration](#model-configuration) for all options and [Settings](#settings) for the full reference. ## Tools | Tool | Description | | ------------------ | --------------------------------------------------------------------------------------------------- | | `intelli_search` | Search the web and return a concise answer with source URLs. | | `intelli_extract` | Extract query-relevant content from a web page, preserving code and technical detail verbatim. | | `intelli_collate` | Deduplicate and synthesise multiple extractions into a summary. Writes cache. | | `intelli_research` | Search, fetch, extract, collate, cache. The primary research tool. One call. | ## Quick Start ### Quick Search ```text intelli_search(query="TypeScript 5.8 release date") ``` ### Deep Research **Always provide a `focusPrompt`.** The extraction LLM works best with specific guidance. ```text intelli_research( query="Svelte 5 runes tutorial examples", focusPrompt="Extract the core rune concepts ($state, $derived, $effect), their syntax, and how they replace the old reactive declarations. Include migration patterns from Svelte 4." ) ``` ### Targeted Research With Domain Restriction ```text intelli_research( query="Cloudflare Workers KV write timeout limits", focusPrompt="Extract KV write limits, timeout thresholds, storage limits, and any workarounds for bulk writes. Focus on hard numbers and error messages.", maxUrls=3, domains=["developers.cloudflare.com"] ) ``` ### Comparing Options ```text intelli_research( query="Tailwind CSS vs Vanilla Extract comparison 2026", focusPrompt="Extract pros/cons, bundle size benchmarks, DX tradeoffs, and migration costs. Note which claims come from official sources vs blog opinions." ) ``` ## Model Configuration All three pipeline stages use independently configurable models. Defaults are chosen for cost-efficiency, but **any model `Pi` can access works**. This includes built-in providers, [OpenRouter](https://openrouter.ai) models, or models from other extensions. | Stage | Default | Config key | | ------- | ----------------------------- | ----------------------- | | Search | `openrouter/perplexity/sonar` | `searchModel` | | Extract | `openrouter/minimax/minimax-m2.7` | `extractModel` | | Collate | `openrouter/minimax/minimax-m2.7` | `collateModel` | ### Why OpenRouter for _Sonar_? [_Perplexity Sonar_](https://docs.perplexity.ai) is an excellent search-grounded model, but it is not in `Pi`'s built-in model list. Rather than requiring a separate Perplexity API account (which requires a **$50 minimum credit top-up**), the extension routes _Sonar_ through [OpenRouter](https://openrouter.ai). _OpenRouter_ is a unified pay-as-you-go API with a lower minimum spend. One API key gives you _Sonar_ alongside thousands of other models. On first load, the extension patches `~/.pi/agent/models.json` to add _Sonar_ under the `openrouter` provider so `Pi` can discover it. This approach has several benefits: - **Avoids the Perplexity API $50 minimum.** Routing through `OpenRouter` consolidates spend on a single account already used across the open-source coding-agent ecosystem, including `Pi`. No separate _Perplexity_ subscription is required. - **One account, many models.** The same OpenRouter key covers _Sonar_ and any other models you might want for extract or collate. - **Is non-destructive.** The patch merges new models by ID. It never replaces existing OpenRouter models. - **Is idempotent.** It is safe across extension reloads and updates. ### Swapping the Extract and Collate Model _MiniMax_ M2.7 (via OpenRouter) is the default because it is cheap and effective for extraction and collation. However, you can use any model `Pi` supports. Override in `~/.pi/agent/settings.json` or `.pi/settings.json`: **Option A: Use a `Pi` Built-In Provider** (auth via `/login`): ```jsonc { "pi-intelli-search": { "extractModel": { "provider": "openai", "model": "gpt-4o-mini" }, "collateModel": { "provider": "openai", "model": "gpt-4o-mini" } } } ``` **Option B: Use Another OpenRouter Model** (same key, no extra setup): ```jsonc { "pi-intelli-search": { "extractModel": { "provider": "openrouter", "model": "google/gemini-2.0-flash-001" }, "collateModel": { "provider": "openrouter", "model": "google/gemini-2.0-flash-001" } } } ``` **Option C: Use a Model Provided by Another Extension** (for example, Z.Ai or local models): ```jsonc { "pi-intelli-search": { "extractModel": { "provider": "zai", "model": "glm-5.1" }, "collateModel": { "provider": "zai", "model": "glm-5.1" } } } ``` The only requirement is that the model is registered in `Pi`'s model registry and has auth configured. Run `/login` to set up built-in providers, or follow the extension's own setup for extension-provided models. ### Model Selection Guidance For extraction and collation, the ideal model has: - **Low cost per token:** 8 extractions, 1 collation, and 1 cache suggest per default session. - **Good instruction following:** Must adhere to extraction prompts precisely. - **Sufficient context:** Cleaned pages can be ≈50K chars (truncated to `extractMaxChars`). Models known to work well for extraction and collation: _MiniMax_ M2.7 (default, via OpenRouter), _Qwen_ 3.5-Flash (≈1M context, ≈$0.26/M output), _DeepSeek_ V4 Flash (≈1M context, ≈$0.28/M output), _Gemini_ 2.0 Flash Lite (≈1M context, ≈$0.30/M output), _GPT-4.1_ Nano (≈1M context, ≈$0.40/M output). ### Required API Keys With default settings, you need one key in `~/.pi/agent/auth.json`: ```json { "openrouter": { "type": "api_key", "key": "sk-or-v1-..." } } ``` A single [OpenRouter](https://openrouter.ai) key is the minimum required. It covers Sonar for search plus MiniMax M2.7 for extraction and collation with the default models. The extract and collate stages can use any model `Pi` supports. Override `extractModel` or `collateModel` in settings to switch providers. Run `/login openrouter` in `Pi` to authorise via OAuth (`Pi` 0.82.0 and later), or edit the file directly with a key from [openrouter.ai/keys](https://openrouter.ai/keys). ## Pipeline

Vintage engraving-style infographic titled "INTELLI_RESEARCH: The Five-Stage Pipeline," showing five sequentially linked numbered stages triggered by intelli_research(query): (1) Search: web discovery via Perplexity Sonar, OpenRouter/pi-native auth; (2) Fetch: dual fetch and quality comparison using wreq-js + Defuddle against raw markdown; (3) Extract: per-page parallel LLM extraction, default model MiniMax M2.7, configurable; (4) Collate: deduplication and persistent cache via MiniMax M2.7 (default, configurable), flags conflicts; (5) Cache Suggest: additive stage, LLM judge surfaces related prior searches. Stages are connected by bold arrows; each is illustrated with a period-appropriate vignette (armillary sphere, scrolls, alchemical still, filing cabinet, owl with documents).

All model assignments are configurable. See [Model Configuration](#model-configuration). Each page is dual-fetched (HTML via Defuddle versus Markdown endpoint) and scored for quality. Per-page extraction (guided by `focusPrompt`) compresses ≈50K chars to ≈3-5K of query-relevant content before collation, keeping the total context manageable (≈24-40K for 8 pages). At the end of each run the pipeline writes a local-only `meta.json` telemetry sidecar into the cache directory (see [Cache Structure](#cache-structure)). Set `disableTelemetry: true` to suppress it. See [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) for detailed design decisions. ## Cost Per research session with the default 8 pages: **≈$0.05** | Step | Calls | Cost | | ------------------------------ | ---------------- | -------- | | Search (_Sonar_) | 1 | ≈$0.02 | | Fetch (Defuddle + Markdown) | 8 (≤4 concurrent) pairs | $0.00 | | Extract (M2.7 via OpenRouter) | 8 (≤4 concurrent) | ≈$0.03 | | Collate (M2.7 via OpenRouter) | 1 | ≈$0.005 | | Cache suggest (M2.7 via OpenRouter) | 1 | ≈$0.0002 | Costs scale with your chosen extract or collate model. _MiniMax_ M2.7 (via OpenRouter) is the default specifically for its low relative cost. ## Settings Override defaults in `~/.pi/agent/settings.json` or, for a trusted project, `/.pi/settings.json` under the `pi-intelli-search` namespace. `Pi` ignores project-local settings until you approve the project; the global file always applies: ```jsonc { "pi-intelli-search": { // Model assignments (see Model Configuration above) "searchModel": { "provider": "openrouter", "model": "perplexity/sonar" }, "extractModel": { "provider": "openrouter", "model": "minimax/minimax-m2.7" }, "collateModel": { "provider": "openrouter", "model": "minimax/minimax-m2.7" }, // Pipeline tuning "defaultUrls": 8, "maxUrls": 16, "cacheDir": ".search", "extractMaxChars": 150000, "extractionMaxTokens": 3000, "collationMaxTokens": 4000, // Fetch behavior "fetchTimeoutMs": 20000, "fetchConcurrency": 4, "browserFingerprint": "chrome_145" } } ``` ### Settings Reference | Setting | Stage | Default | What It Does | |---|---|---|---| | `searchModel` | 1. Search | `openrouter/perplexity/sonar` | Model for the initial web search. Swap to a stronger model for deeper search results, or to a cheaper one to reduce the ≈$0.02 search cost. See [Model Configuration](#model-configuration). | | `extractModel` | 3. Extract | `openrouter/minimax/minimax-m2.7` | Model for per-page content extraction. Runs 8 times per session so low cost per token matters. Ensure the model's context window exceeds `extractMaxChars` plus the system prompt. For a model with a smaller window (e.g. 256K), lower `extractMaxChars` to match. See [Model Configuration](#model-configuration). | | `collateModel` | 4. Collate | `openrouter/minimax/minimax-m2.7` | Model for cross-source synthesis and deduplication. Sees all extractions at once so it needs enough context and instruction-following to flag contradictions. A model with ≥128K context handles 8 full extractions comfortably. See [Model Configuration](#model-configuration). | | `defaultUrls` | 1 → 2 | `8` | Fallback when the agent does not pass `maxUrls` per call. Lower values reduce cost and latency but give less thorough results. The agent's [skill guide](skills/intelli-search/SKILL.md) recommends 3 (targeted), 8 (broad), or 12 (exhaustive). | | `maxUrls` | 1 → 2 | `16` | Hard cap on URLs fetched. Lower caps mean faster responses and lower cost; higher caps allow more thorough research. Each extra URL adds roughly $0.004 in extract cost with the default model. Requests above the cap are silently clamped. | | `cacheDir` | 4, 5 | `.search` | Directory where research sessions are cached. Change this to keep project-specific research separate. Example: `".my-research-cache"`. | | `extractMaxChars` | 3. Extract | `150000` | Maximum characters of raw page content fed to the extract LLM per page. **Lower this when using an extract model with a small context window** (e.g. 256K) to prevent context overflow. Raise it if pages are truncated and your model has ample headroom. Each 50K chars consumes roughly 12K input tokens. | | `extractionConcurrency` | 3. Extract | `4` | Number of per-page extractions sent to the extract model simultaneously. Bounded so a wide result set does not fire many concurrent LLM calls and trigger rate limiting. Raise it (6-8) on generous rate limits for faster extraction; lower it (1-2) on tight limits. | | `extractionMaxTokens` | 3. Extract | `3000` | Maximum output tokens for each per-page extraction. Higher values preserve more detail at higher cost. **Lower this when using a model with a small context window** so the output does not crowd out the input. The extract prompt targets 3,000-5,000 characters; 3,000 tokens covers this comfortably. | | `collationMaxTokens` | 4. Collate | `4000` | Maximum output tokens for the final synthesis. Lower values force tighter deduplication. **Lower this when using a collation model with a small context window** (the output must fit alongside all extraction inputs). The summary you see in the agent context is bounded by this setting. | | `fetchTimeoutMs` | 2. Fetch | `20000` | Per-page fetch timeout in milliseconds. Increase if you research sites known to be slow. Fetches run in parallel so this does not multiply by page count. | | `fetchConcurrency` | 2. Fetch | `4` | Number of pages fetched simultaneously. Higher values (6-8) complete the fetch stage faster but may trigger rate limiting. Lower values (2) are gentler on target servers. | | `browserFingerprint` | 2. Fetch | `chrome_145` | TLS fingerprint used by [_wreq-js_](https://github.com/sqdshguy/wreq-js) to impersonate a browser. Determines which HTTP client signature site sees. Available profiles include `chrome_*`, `firefox_*`, `safari_*`, `edge_*`, and `opera_*` across many versions. Change this if a site blocks the default fingerprint. | | `llmTimeoutMs` | All LLM | `90000` | Hard per-call timeout in milliseconds for every LLM request. Bounds a stalled provider connection (common under rate limiting) so it becomes a retryable timeout instead of hanging on the SDK's long default. Raise it for slow reasoning models on large inputs; lower it to fail faster. | | `llmRetryAttempts` | All LLM | `3` | Total attempts per LLM call including the first. Transient failures (HTTP 429, 5xx, timeouts) are retried with full-jitter exponential backoff that honours any Retry-After hint. Set to `1` to disable retry. | | `retryBaseDelayMs` | All LLM | `1500` | Base delay for retry backoff. Attempt N waits a random duration up to `min(retryMaxDelayMs, retryBaseDelayMs * 2^(N-1))`. | | `retryMaxDelayMs` | All LLM | `20000` | Upper bound on any single retry backoff, and the clamp applied to a Retry-After hint so a large hint cannot stall the pipeline. | | `searchRetryAttempts` | 1. Search | `2` | Total attempts for the search stage when it returns a valid response with zero usable links (a degraded result), including the first. Independent of `llmRetryAttempts`, which covers transport errors. | | `minRequestIntervalMs` | 3. Extract | `0` | Minimum gap in milliseconds between concurrent extract LLM calls. `0` disables the throttle. Free-tier OpenRouter keys (approximately 0.33 requests per second) should set this to approximately `3000` to avoid tripping rate limits; paid keys can leave it off. | | `disableTelemetry` | All | `false` | When `false`, each `intelli_research` run writes a local-only `meta.json` sidecar into its `.search//` cache directory recording per-stage outcomes (pages fetched/failed, fetch-variant winners, search-retry, cache-suggest hits, latency). Strictly local: no network call, no data leaves the host. Set `true` to suppress the sidecar. | ### Automatic llms-full.txt Discovery Sites that follow the [`llms-full.txt` convention](https://llmstxt.org) publish a single Markdown file containing their complete documentation. During the fetch stage, every domain in the search results is probed at `https://domain/llms-full.txt`. If the file exists (HTTP 200), it is downloaded raw to `sources/llms-full-*.md` for offline search with `grep` or `read`. These probes are supplementary and never gate the research result. Each runs under a tight timeout and honours cancellation, so a slow or unresponsive documentation host cannot stall the summary, and pressing Esc cancels them along with the rest of the pipeline. Set `disableLlmsFullDiscovery: true` to skip the probes entirely. A small built-in list handles sites with non-standard paths: | Site | Path Pattern | |------|-------------| | Cloudflare Docs | `/product/llms-full.txt` | | Next.js | `/docs/llms-full.txt` | | Vite | `/llms-full.txt` (root) | No configuration is needed. The probe and download are automatic. ## Cache Structure ```text .search/ ├── 2026-04-19-d1-worker-api-3f7a2c/ │ ├── report.md # Collated summary + source index │ ├── query.txt # Original search query │ ├── meta.json # Local-only telemetry sidecar (v0.11.0+) │ ├── extractions/ # Per-page LLM extractions (≈3-5K each) │ │ ├── 01-developers-cloudflare-com.md │ │ └── 02-developers-cloudflare-com.md │ └── sources/ # Full page content │ ├── 01-developers-cloudflare-com.md │ ├── 02-developers-cloudflare-com.md │ └── llms-full-developers-cloudflare-com.md └── .index.json # Index of all cached searches ``` Each cached session lives in a directory named `--`. The `` is a short SHA-1 of the full query, appended so that distinct queries issued on the same day do not collide and overwrite each other. Concurrent runs stage their output before a short cache commit, so source files and the shared index remain intact. **`meta.json` (local-only telemetry).** Each `intelli_research` run writes a `meta.json` sidecar recording per-stage outcomes: pages fetched and failed, fetch-variant winners (Defuddle versus Markdown), whether search-retry fired, cache-suggest hits, and per-stage latency. It is strictly local: no network call is added, no data leaves the host, and no account or identity is recorded. Set `disableTelemetry: true` in [Settings](#settings) to suppress it. The bundled [`scripts/analyze-sessions.sh`](scripts/README.md) can aggregate these sidecars to report per-stage success rates. - **`Pi` >= 0.80.8:** Core functionality, trusted project settings, the configurable `CONFIG_DIR_NAME`, compatible `pi-ai` calls, and sequential cache-writing tools. - UI notifications and status indicators are guarded with `ctx.hasUI`, so the tools behave cleanly in non-interactive modes (`pi -p`, `--mode json`, RPC). - Page fetching honours the global `httpProxy` setting. The LLM stages already route through `Pi`'s managed HTTP clients, which apply `httpProxy` automatically. - Retry and timeout are owned by the extension (`callLlm()`), independent of `Pi`'s `retry.provider.maxRetries`. The extension forces `maxRetries: 0` and runs its own full-jitter backoff, so changing `Pi`'s provider-retry setting has no effect on these tools. ## Development ```bash npm install npm run build # TypeScript -> dist/ npm test # Unit tests npm run test:smoke # Smoke test # Test in pi pi -e ./dist/index.js # Install as package pi install /path/to/pi-intelli-search ``` ## Documentation - [Comparison](docs/COMPARISON.md): How `intelli-search` compares to other `Pi` search extensions. - [Changelog](CHANGELOG.md): Release history. - [Architecture](docs/ARCHITECTURE.md): Detailed design decisions and pipeline internals. - [Components](docs/COMPONENTS.md): Third-party dependencies and license attribution. - [Skill guide](skills/intelli-search/SKILL.md): Agent-facing usage instructions. - [Contributor guide](AGENTS.md): Coding conventions and project structure. ## Provenance Git history was rewritten in `v0.9.0` to normalise commit author and committer metadata on the path to a stable `v1` release. The `gitHead` SHAs recorded in `npm` SLSA provenance attestations for versions 0.3.1 through 0.8.0 reference pre-rewrite commits that no longer resolve in this repository. Published tarballs and their tree-level contents are unchanged; only commit metadata was altered. From v0.9.0 onwards, attestations track the rewritten history. See the [Changelog](CHANGELOG.md) entry for v0.9.0 for the full account. ## Sponsor Banner image for "Curio Data Pro." A cartoon robot detective in a deerstalker hat and brown cape peers through binoculars on the left, beside a bordered logo reading "CURIO DATA PRO" in dark red serif type. The background is a stylised steampunk harbour scene featuring a docked submarine, a steam locomotive pulling into a quayside station, gas street lamps, industrial cranes, and brick warehouses under a hazy sky. **[Curio Data Pro Ltd](https://blog.curiodata.pro/)** sponsors this project. _Curio Data Pro_ is a data consultancy serving _Rail_, _Naval Design_, _Aviation_, and _Offshore Energy_, combining 20+ years of _Chartered Engineer_ experience with _Data Science_ and _DevOps_ capabilities. [Blog](https://blog.curiodata.pro/) | [LinkedIn](https://www.linkedin.com/company/curio-data-pro-ltd/) ## License Copyright 2026 Ashraf Miah, Curio Data Pro Ltd. Licensed under the [Apache License, Version 2.0](./LICENSE). ## Use of Large Language Models Large Language Models were used extensively during the development of this project: - **`Pi` agent** (primary development environment). - **_GLM_ 5.1:** Primary model for code generation and architecture. - **_DeepSeek_ V4 Pro:** Research and data analysis. - **_Qwen_ 3.6 Plus:** Secondary model for review and documentation.