PinRAG

Overview

PinRAG is for when you want to learn about something and your materials are scattered—PDFs and ebooks, GitHub repos, YouTube videos, Discord discussions, and plain notes. You index those materials into one shared RAG index, then ask questions from Cursor, VS Code (GitHub Copilot), or any MCP-capable assistant and get answers with citations pointing back to pages, timestamps, files, or threads.

Under the hood it is Retrieval-Augmented Generation built with LangChain and exposed as an MCP (Model Context Protocol) server: add documents from the editor, query with natural language, list or remove what you indexed. Supported inputs include PDFs, local text files and directories, Discord exports, YouTube (transcript from URL, playlist, or ID), and GitHub repo URLs. For YouTube you can optionally add vision so on-screen code, diagrams, and UI text are merged with the transcript in the same chunks—see YouTube vision enrichment.

Features

• Multi-format indexing — PDF (.pdf), local files or directories, plain text (.txt), Discord export (.txt), YouTube (video or playlist URL, or video ID), GitHub repo (URL)
• Optional YouTube vision — Off by default. When enabled, runs a vision model (OpenAI, Anthropic, or OpenRouter native video) and merges structured on-screen context with the transcript so RAG chunks carry searchable code names, labels, and diagrams—not speech alone. OpenRouter mode avoids local ffmpeg/video download; openai/anthropic use scene keyframes and require pinrag[vision] + ffmpeg (see YouTube vision enrichment)
• RAG with citations — Answers cite source context: PDF page, YouTube timestamp, document name for plain text and Discord, file path for GitHub repos
• Document tags — Tag documents at index time (e.g. AMIGA, PI_PICO) for filtered search
• Metadata filtering — query_tool supports document_id, tag, document_type, PDF page_min/page_max, GitHub file_path, and response_style (thorough or concise)
• MCP tools — add_document_tool (files, dirs, or URLs), query_tool, list_documents_tool, remove_document_tool
• MCP resources — pinrag://documents (indexed documents) and pinrag://server-config (env vars and config); click in Cursor’s MCP panel to view
• MCP prompt — use_pinrag (parameter: request) for querying, indexing, listing, or removing documents
• Configurable LLM — OpenRouter (default, free openrouter/free router), OpenAI, Anthropic, or Cerebras Inference (OpenAI-compatible API); set via PINRAG_LLM_PROVIDER and PINRAG_LLM_MODEL in MCP env or your shell
• Local embeddings — Nomic (PINRAG_EMBEDDING_MODEL, default nomic-embed-text-v1.5); no API key; first run downloads model weights (~270 MB, cached)
• Retrieval & chunking options — Structure-aware chunking (on by default); optional FlashRank re-ranking, multi-query expansion, and parent-child chunks for PDFs (see Configuration)
• Observability — MCP tool notifications (ctx.log) plus optional LangSmith tracing
• Built with — LangChain, Chroma; optional OpenRouter, OpenAI, Anthropic, FlashRank

Installation

Add PinRAG as an MCP server in your editor. Install uv and ensure uvx is on your PATH—that runs PinRAG from PyPI without a prior pip install.

Cursor: add this under mcpServers in ~/.cursor/mcp.json:

{
  "mcpServers": {
    "pinrag": {
      "command": "uvx",
      "args": ["--refresh", "pinrag"],
      "env": {
        "OPENROUTER_API_KEY": "your-openrouter-api-key-here",
        "PINRAG_PERSIST_DIR": "/absolute/path/to/your/pinrag-data"
      }
    }
  }
}

VS Code (GitHub Copilot): run MCP: Open User Configuration from the Command Palette (or add .vscode/mcp.json in a workspace), then merge this shape—top-level key is servers:

{
  "servers": {
    "pinrag": {
      "command": "uvx",
      "args": ["--refresh", "pinrag"],
      "env": {
        "OPENROUTER_API_KEY": "your-openrouter-api-key-here",
        "PINRAG_PERSIST_DIR": "/absolute/path/to/your/pinrag-data"
      }
    }
  }
}

Quick Start

Configure MCP server

Put API keys and any PinRAG settings in the MCP entry’s env block. The server does not load .env files when the editor launches it.

Use in chat

Action	Tool
Index files, directories, or URLs	add_document_tool — required paths: list of local paths (PDFs, plain or DiscordChatExporter .txt, directories) or URLs (YouTube videos, playlist URLs, GitHub repos; bare YouTube video IDs allowed). Optional tags (one per path). For GitHub URLs only: branch, include_patterns, exclude_patterns.
List indexed documents	list_documents_tool — returns documents (IDs), total_chunks, and optional tag filter. document_details may include document_type, tags, page / message / segment counts, titles, GitHub files / file_count, and upload_timestamp when present in metadata.
Query with filters	query_tool — required query. Optional document_id, tag, document_type, page_min / page_max (PDF ranges), file_path (path inside a GitHub doc), response_style (thorough or concise; leave empty to use PINRAG_RESPONSE_STYLE).
Remove a document	remove_document_tool — required document_id (exact value from list_documents_tool).
View resources (read-only)	In the MCP panel, open Resources and choose pinrag://documents (indexed docs) or pinrag://server-config (effective config, including PINRAG_VERSION).

Ask in chat: "Add /path/to/amiga-book.pdf with tag AMIGA", "Index https://youtu.be/xyz and ask what it says", or "Index https://github.com/owner/repo and ask about the codebase". The AI will invoke the tools for you. Citations show page numbers for PDFs, timestamps (e.g. t. 1:23) for YouTube, document names for plain text and Discord exports, and file paths for GitHub.

GitHub indexing

Index a repo with add_document_tool and a URL in paths, e.g. https://github.com/owner/repo, https://github.com/owner/repo/tree/branch, or github.com/owner/repo (scheme optional).

GitHub-only options: branch, include_patterns / exclude_patterns — defaults already favor common text and source files and skip bulky artifacts; use patterns when you need files outside that set. Files over PINRAG_GITHUB_MAX_FILE_BYTES (default 512 KiB) are skipped.

Auth: Set GITHUB_TOKEN in MCP env (or the shell) for private repos or fewer rate-limit hits on big indexes; small public runs often work without it. Use a classic or fine-grained PAT with repo read access; there is no OAuth in PinRAG.

YouTube indexing and IP blocking

Transcript-heavy indexing—especially from cloud or high-volume IPs—may return errors like "YouTube is blocking requests from your IP". Point youtube-transcript-api at a proxy via MCP env (or your shell):

PINRAG_YT_PROXY_HTTP_URL=http://user:[email protected]:80
PINRAG_YT_PROXY_HTTPS_URL=http://user:[email protected]:80

PINRAG_YT_PROXY_* affects transcript fetches only; yt-dlp steps (titles, playlists) do not use it. Residential or rotating proxies usually fare better than raw datacenter IPs.

When some paths fail (e.g. a few videos in a playlist), add_document_tool includes fail_summary with counts keyed by blocked, disabled, missing_transcript, and other.

YouTube vision enrichment (optional)

Default indexing is transcript-only. Set PINRAG_YT_VISION_ENABLED=true to add vision captions for on-screen content, time-aligned with the transcript and chunked with metadata such as has_visual, frame_count, and visual_source.

PINRAG_YT_VISION_PROVIDER:

• openai (default) or anthropic: yt-dlp download → scene-based frames → one multimodal call per frame. Needs pinrag[vision], ffmpeg/ffprobe on PATH, and OPENAI_API_KEY or ANTHROPIC_API_KEY (install the extra in the same env as pinrag, e.g. uv sync --extra vision or pip install 'pinrag[vision]').
• openrouter: one OpenRouter request per video via video_url (default google/gemini-2.5-flash). OPENROUTER_API_KEY only—no download, ffmpeg, or pinrag[vision]; choose a video-capable model if you override PINRAG_YT_VISION_MODEL.

Ops: Re-index after changing vision settings. For openai/anthropic, tune cost and timeouts with PINRAG_YT_VISION_MAX_FRAMES and optional PINRAG_YT_VISION_IMAGE_DETAIL=high (clearer small text, more tokens). MCP stdio: yt-dlp progress goes to stderr so stdout stays JSON-clean. Downloading video can breach YouTube ToS or local rules—your call. Docker: build with BUILD_WITH_VISION=1 for ffmpeg + pinrag[vision] (see Dockerfile).

Tips

• pinrag not found: MCP inherits your login PATH. After pipx or uv tool install, restart the editor and confirm which pinrag.
• PINRAG_PERSIST_DIR: Use a stable absolute path in MCP env (e.g. ~/.pinrag/chroma_db) so the vector store does not depend on the server process cwd.
• FlashRank: Install pinrag[rerank] in the same tool env (pipx install 'pinrag[rerank]' / uv tool install 'pinrag[rerank]'); tunables are in Configuration.
• YouTube vision: Follow YouTube vision enrichment for env and deps; re-index after changing vision settings.
• pinrag://server-config: MCP Resources → this URI for PINRAG_VERSION, effective LLM/embeddings/chunking, and API key set / not set status.

Configuration

The pinrag://server-config MCP resource prints PINRAG_VERSION (package version, not an env var you set) and effective values for the variables below, plus which API keys are set. Use the table as the full env reference.

Environment variables:

Variable	Default	Description
LLM
Provider & model
PINRAG_LLM_PROVIDER	openrouter	openrouter, openai, anthropic, or cerebras
PINRAG_LLM_MODEL	(provider default)	When unset: OpenRouter openrouter/free, OpenAI gpt-4o-mini, Anthropic claude-haiku-4-5, Cerebras llama3.1-8b. Override with any model id (e.g. OpenRouter anthropic/claude-sonnet-4-6, Cerebras gpt-oss-120b).
OpenRouter
PINRAG_OPENROUTER_MODEL_FALLBACKS	(unset)	Comma-separated fallback model slugs sent as OpenRouter’s models list. The gateway tries the next slug when the primary (PINRAG_LLM_MODEL) fails (rate limits, downtime, etc.). Use extra free models here to stay zero-cost. Legacy alias: PINRAG_LLM_MODEL_FALLBACKS.
PINRAG_OPENROUTER_SORT	(unset)	Optional provider.sort — price, throughput, or latency. When unset, OpenRouter uses its default provider selection. Prefer leaving this unset if you set PINRAG_OPENROUTER_PROVIDER_ORDER to pin a specific backend (avoids conflicting routing signals).
PINRAG_OPENROUTER_PROVIDER_ORDER	(unset)	Comma-separated provider names for provider.order (tried in sequence). Example: Cerebras with PINRAG_LLM_MODEL=openai/gpt-oss-120b to prefer Cerebras-backed routing. Use exact labels from the model’s provider list on OpenRouter.
OPENROUTER_APP_URL	https://github.com/ndjordjevic/pinrag	App attribution (HTTP-Referer). Override with your site URL (see OpenRouter app attribution). PinRAG copies this into OPENROUTER_HTTP_REFERER for the OpenRouter Python SDK.
OPENROUTER_APP_TITLE	PinRAG	App title (X-Title). Override to label usage in the OpenRouter dashboard. PinRAG copies this into OPENROUTER_X_OPEN_ROUTER_TITLE for the SDK.
API keys
OPENROUTER_API_KEY	(required when using OpenRouter for LLM, evaluators, or YouTube vision)	Required when PINRAG_LLM_PROVIDER=openrouter, PINRAG_EVALUATOR_PROVIDER=openrouter, or YouTube vision with PINRAG_YT_VISION_PROVIDER=openrouter.
OPENAI_API_KEY	(required for OpenAI LLM or OpenAI YouTube vision)	Required when PINRAG_LLM_PROVIDER=openai, or when PINRAG_YT_VISION_ENABLED=true and PINRAG_YT_VISION_PROVIDER=openai.
OPENAI_BASE_URL	(optional)	Override OpenAI API base URL (e.g. https://openrouter.ai/api/v1 with OPENAI_API_KEY set to your OpenRouter key for vision or other OpenAI-compatible calls).
CEREBRAS_API_KEY	(required for Cerebras LLM)	Required when PINRAG_LLM_PROVIDER=cerebras. Get a key from the Cerebras cloud console.
PINRAG_CEREBRAS_BASE_URL	https://api.cerebras.ai/v1	Override the OpenAI-compatible base URL for Cerebras (e.g. dedicated inference endpoints).
ANTHROPIC_API_KEY	(required for Anthropic LLM or Anthropic YouTube vision)	Required when PINRAG_LLM_PROVIDER=anthropic, PINRAG_EVALUATOR_PROVIDER=anthropic, or YouTube vision with PINRAG_YT_VISION_PROVIDER=anthropic.
Embeddings
PINRAG_EMBEDDING_MODEL	nomic-embed-text-v1.5	Local Nomic model id (via langchain-nomic). First run downloads weights (~270 MB, cached). No API key.
Storage & chunking
PINRAG_PERSIST_DIR	chroma_db	Chroma vector store directory (default is relative to the server process cwd unless you set an absolute path; e.g. ~/.pinrag/chroma_db for a fixed location)
PINRAG_CHUNK_SIZE	1000	Text chunk size (chars)
PINRAG_CHUNK_OVERLAP	200	Chunk overlap (chars)
PINRAG_STRUCTURE_AWARE_CHUNKING	true	Apply structure-aware chunking heuristics for code/table boundaries
PINRAG_COLLECTION_NAME	pinrag	Chroma collection name. Single shared collection by default.
ANONYMIZED_TELEMETRY	False via setdefault when unset	Chroma telemetry flag. PinRAG’s MCP logging setup calls os.environ.setdefault("ANONYMIZED_TELEMETRY", "False") so empty/unset behaves like opt-out; set true in env if you want Chroma’s telemetry on.
Retrieval
PINRAG_RETRIEVE_K	20	Retrieval pool size when re-ranking is off. When re-ranking is on, PINRAG_RERANK_RETRIEVE_K falls back to this value if unset, then results are cut to PINRAG_RERANK_TOP_N.
Parent-child retrieval
PINRAG_USE_PARENT_CHILD	false	Set to true to embed small chunks and return larger parent chunks (supported for PDF, GitHub, YouTube, and Discord indexing—not plain .txt). Requires re-indexing.
PINRAG_PARENT_CHUNK_SIZE	2000	Parent chunk size (chars) when PINRAG_USE_PARENT_CHILD=true.
PINRAG_CHILD_CHUNK_SIZE	800	Child chunk size (chars) when PINRAG_USE_PARENT_CHILD=true.
Re-ranking
PINRAG_USE_RERANK	false	Set to true to enable FlashRank re-ranking: fetch more chunks, re-score locally, pass top N to the LLM. Requires pip install pinrag[rerank] and no API key.
PINRAG_RERANK_RETRIEVE_K	(inherits PINRAG_RETRIEVE_K)	Chunks to fetch before FlashRank when PINRAG_USE_RERANK=true. If unset, equals PINRAG_RETRIEVE_K (not a separate hard-coded 20).
PINRAG_RERANK_TOP_N	10	Chunks passed to the LLM after re-ranking when PINRAG_USE_RERANK=true (capped by the pre-rerank fetch size).
Multi-query
PINRAG_USE_MULTI_QUERY	false	Set to true to generate alternative phrasings of the user query via LLM, retrieve per variant, and merge (unique union). Improves recall for terse or ambiguous queries.
PINRAG_MULTI_QUERY_COUNT	4	Number of alternative queries to generate (default 4, max 10). The original query is still included in retrieval when merging.
Response style
PINRAG_RESPONSE_STYLE	thorough	RAG answer style: thorough (detailed) or concise. Used by evaluation target and as default when MCP query omits response_style.
MCP notifications
PINRAG_VERBOSE_LOGGING	false	Set true to emit detailed per-phase MCP notifications for tool/resource execution (format detection, transcript load, vision path/steps, chunk upserts). Default keeps concise start/ok/error lifecycle logs.
GitHub indexing
GITHUB_TOKEN	(optional)	Personal access token for GitHub API. Required for private repos; increases rate limits for public repos.
PINRAG_GITHUB_MAX_FILE_BYTES	524288 (512 KB)	Skip files larger than this when indexing GitHub repos.
PINRAG_GITHUB_DEFAULT_BRANCH	main	Default branch when not specified in the GitHub URL.
Plain text indexing
PINRAG_PLAINTEXT_MAX_FILE_BYTES	524288 (512 KB)	Skip plain .txt files larger than this when indexing.
YouTube transcript proxy
PINRAG_YT_PROXY_HTTP_URL	(none)	HTTP proxy URL for transcript fetches (e.g. http://user:pass@proxy:80). Use when YouTube blocks your IP.
PINRAG_YT_PROXY_HTTPS_URL	(none)	HTTPS proxy URL for transcript fetches. Same as HTTP when using a generic proxy.
YouTube vision (optional)
PINRAG_YT_VISION_ENABLED	false	true / 1 / yes / on enables on-screen enrichment for YouTube. openai / anthropic: needs pinrag[vision], ffmpeg on PATH, and the matching API key. openrouter: needs OPENROUTER_API_KEY only (native video_url path; no local download).
PINRAG_YT_VISION_PROVIDER	openai	openai, anthropic, or openrouter. Independent of PINRAG_LLM_PROVIDER (RAG LLM and vision can use different providers). Legacy alias: PINRAG_VISION_PROVIDER.
PINRAG_YT_VISION_MODEL	(per provider)	If unset: OpenAI gpt-4o-mini, Anthropic claude-sonnet-4-6, OpenRouter google/gemini-2.5-flash. Use a vision-capable id. Legacy alias: PINRAG_VISION_MODEL.
PINRAG_YT_VISION_MAX_FRAMES	8	Download + frame path only (openai / anthropic): cap analyzed keyframes after scene detect. Ignored for openrouter (single video request).
PINRAG_YT_VISION_MIN_SCENE_SCORE	27.0	Download + frame path only: PySceneDetect AdaptiveDetector threshold (higher → fewer cuts). Ignored for openrouter.
PINRAG_YT_VISION_IMAGE_DETAIL	low	OpenAI frame path only: low, high, or auto for image_url.detail. Ignored for anthropic (full frames) and openrouter (video_url).
LangSmith (optional)
LANGSMITH_TRACING	(off)	Set true to send traces to LangSmith. Requires LANGSMITH_API_KEY.
LANGSMITH_API_KEY	(none)	API key from LangSmith Settings → API keys.
LANGSMITH_PROJECT	(LangChain default)	Project name for traces (e.g. pinrag).
LANGSMITH_ENDPOINT	US API (implicit)	EU workspaces: set https://eu.api.smith.langchain.com so traces land in your EU project. If your account uses eu.smith.langchain.com in the browser, you need this. US-region workspaces can omit it (default API host).
Evaluators (LLM-as-judge)
PINRAG_EVALUATOR_PROVIDER	openai	openai, anthropic, or openrouter — which LLM runs LLM-as-judge graders. Used only during evaluation runs (LangSmith experiments).
PINRAG_EVALUATOR_MODEL	(provider default)	Model for correctness grading (e.g. gpt-4o, claude-sonnet-4-6, openrouter/free when evaluator provider is OpenRouter). With OpenRouter, the default free router may rotate models; graders use strict JSON schema—set this to a specific free slug from openrouter.ai/models if you need stable structured output. OpenRouter routing env vars below also apply to graders when PINRAG_EVALUATOR_PROVIDER=openrouter.
PINRAG_EVALUATOR_MODEL_CONTEXT	(provider default)	Model for groundedness grading (large retrieved context; e.g. gpt-4o-mini, claude-haiku-4-5, openrouter/free when evaluator provider is OpenRouter). Same OpenRouter note as PINRAG_EVALUATOR_MODEL. When the evaluator provider is OpenRouter, PINRAG_OPENROUTER_MODEL_FALLBACKS, PINRAG_OPENROUTER_SORT, and PINRAG_OPENROUTER_PROVIDER_ORDER apply to the grader client.

Re-indexing when changing embedding model: Changing PINRAG_EMBEDDING_MODEL requires re-indexing; vector dimensions must match the model used at index time (including indexes created under an older default embedding).

Re-indexing when enabling parent-child: Setting PINRAG_USE_PARENT_CHILD=true requires re-indexing; the new structure (child chunks in Chroma, parent chunks in docstore) is created only during indexing for supported document types (not plain .txt).

Re-indexing when toggling YouTube vision: Turning PINRAG_YT_VISION_ENABLED on or off, changing PINRAG_YT_VISION_PROVIDER, or changing vision model / PINRAG_YT_VISION_IMAGE_DETAIL / frame limits, requires re-indexing affected YouTube documents for chunks to reflect the new behavior.

Monitoring & Observability

For query performance metrics (latency, timing, token usage) and debugging, use LangSmith. Set LANGSMITH_TRACING=true and LANGSMITH_API_KEY in MCP env or your shell; optionally set LANGSMITH_PROJECT (see table above). If your LangSmith workspace is in the EU region (you use eu.smith.langchain.com in the browser), you must also set LANGSMITH_ENDPOINT=https://eu.api.smith.langchain.com; without it, traces may not show up in the EU deployment. US-region accounts use the default API host and do not need LANGSMITH_ENDPOINT. See notes/langsmith-setup.md for more detail.

For MCP-side introspection, set PINRAG_VERBOSE_LOGGING=true to surface detailed phase events in notifications/message (e.g., YouTube transcript load, whether vision runs, and chunk upsert milestones).

Multiple providers and collections

Vector dimension is fixed per Chroma collection and must match the PINRAG_EMBEDDING_MODEL used when chunks were written. The default id nomic-embed-text-v1.5 is a 768-d Nomic model; another PINRAG_EMBEDDING_MODEL value may imply a different size—check that model’s documentation.

• Default: PINRAG_COLLECTION_NAME defaults to pinrag. Do not change PINRAG_EMBEDDING_MODEL for an existing collection without re-indexing into a new collection (or wiping the old one); otherwise adds/queries can fail with embedding dimension errors.
• Per-model collections: Use a stable pair of PINRAG_EMBEDDING_MODEL + PINRAG_COLLECTION_NAME (+ PINRAG_PERSIST_DIR if you isolate stores) for each index. To query a collection, set the same env values you used when indexing it. You can index the same sources again under another pair (change env, restart MCP if needed, run add_document_tool).
• MCP tools: server.py wires every tool to config.get_persist_dir() and config.get_collection_name() only—there is no per-call collection or persist argument (tools.query / add_files accept optional collection in Python, but MCP does not expose that).

MCP reference

Tools, prompt, and read-only resources from the pinrag MCP server (FastMCP("PinRAG")). Tool results are JSON objects that always include _server_version; with PINRAG_VERBOSE_LOGGING=true they may include _verbose_log.

add_document_tool returns indexed, failed, counts, persist_directory, collection_name, and fail_summary when any path failed. query_tool returns answer and sources (each entry: document_id, page—PDF page, often 0 for non-PDF—plus optional start in seconds for YouTube and file_path for GitHub).

query_tool

Natural-language question; optional filters narrow retrieval ("" / omit when unused):

Parameter	Description
query	Question (required)
document_id	Limit to this document (id from list_documents_tool, e.g. PDF file name or YouTube video id)
page_min, page_max	Inclusive PDF page range (must pass both; one page: same value twice)
tag	Only chunks with this tag
document_type	pdf, youtube, discord, github, or plaintext
file_path	Path inside a doc (GitHub; see document_details / files from list_documents_tool)
response_style	thorough or concise. Empty (the schema default) or any other string → resolved via PINRAG_RESPONSE_STYLE (see server.py: only those two literals override env).

Filters can be combined. The sources list uses page for PDFs, start (seconds) for YouTube, file_path for GitHub; answers may show t. M:SS labels derived from start.

Example: "What is OpenOCD? In the Pico doc, pages 16–17 only" → query_tool(query="What is OpenOCD?", document_id="RP-008276-DS-1-getting-started-with-pico.pdf", page_min=16, page_max=17).

add_document_tool

Index locals (PDF, plain or Discord .txt, directories), YouTube (video URL, playlist URL, or bare id), or GitHub URLs (scheme optional). paths batches work items; one failed path does not roll back others. Persists to PINRAG_PERSIST_DIR / PINRAG_COLLECTION_NAME only (no MCP parameters for those).

Parameter	Description
paths	Required list: files, dirs, URLs, or video ids
tags	Optional; one per paths entry, same order
branch	GitHub only: branch override
include_patterns	GitHub only: glob include list
exclude_patterns	GitHub only: glob exclude list

list_documents_tool

Returns documents, total_chunks, persist_directory, collection_name, and document_details (tags, titles, counts, upload_timestamp when present, GitHub files / file_count, etc.). If tag is set, total_chunks counts only chunks with that tag (not the whole collection).

Parameter	Description
tag	Optional: only docs that have this tag

remove_document_tool

Deletes every chunk for document_id (exact id from list_documents_tool).

Parameter	Description
document_id	Required

MCP prompt: use_pinrag

Built-in routing blurb: request is interpolated as the first line; the rest lists when to use each tool and their parameters (matches use_pinrag in server.py). Listed wherever the client exposes MCP prompts (e.g. Cursor).

Parameter	Description
request	Optional user goal (may be empty)

MCP resources

Resource	Description
pinrag://documents	Plain-text listing for the server’s configured collection (from format_documents_list)
pinrag://server-config	Printable dump of effective env/config (includes PINRAG_VERSION, key operational vars, API key presence)

Running tests

From the repo root, install dev extras (e.g. uv sync --extra dev).

• Fast (no integration):
  uv run pytest tests/ -q -m "not integration"
  Skips anything marked integration in pyproject.toml (network, API keys, optional assets, MCP stdio). Any test that uses the sample_pdf_path fixture gets that marker automatically in tests/conftest.py, so the sample PDF under data/pdfs/ is only needed for the full run.

• Full suite:
  uv run pytest tests/ -q

  Secrets: For MCP stdio tests, the subprocess env starts from your shell, then any missing OPENAI_API_KEY / ANTHROPIC_API_KEY are filled from tests/.mcp_stdio_integration.env (copy from tests/mcp_stdio_integration.env.example; only those keys are read from the file—already-set env wins). Override the file with PINRAG_MCP_ITEST_ENV_FILE. After merge, test_mcp_stdio_repo.py requires OPENAI_API_KEY, and PINRAG_LLM_PROVIDER must pass its credential check (e.g. export OPENROUTER_API_KEY when using OpenRouter). test_mcp_stdio_pypi.py also requires a working OpenAI key.

  PDF / stdio: Default PDF is data/pdfs/sample-text.pdf (not in git). Override with PINRAG_MCP_ITEST_PDF / PINRAG_MCP_ITEST_QUERY. Stdio tests need uv on PATH, or set PINRAG_TEST_UV to the binary path.

  PyPI MCP test: Marked pypi_mcp; skip with -m "not pypi_mcp" or PINRAG_MCP_ITEST_SKIP_PYPI=1. Pin the install with PINRAG_MCP_ITEST_PYPI_SPEC (default pinrag = latest on PyPI).

  Verbose: --log-cli-level=INFO.

The data/ directory is gitignored—create data/pdfs/ (and similar) locally; nothing under data/ is committed.

License

MIT License. Full text in LICENSE.