论文公式提取工具

Import from BibTeX file (requires bibtexparser)

papi add --from-file papers.bib

Installation

```bash

or install with BibTeX support:

uv tool install paperpipe --with "paperpipe[bibtex]"

**Title Search:**

Install MCP servers for all supported agents (user scope)

papi install mcp

Install for specific agents

papi install mcp --claude papi install mcp --codex papi install mcp --gemini

Install repo-local MCP configs (Claude + Gemini) and Codex globally

papi install mcp --repo

Index builds

```bash papi index --backend leann

Override common LEANN build knobs (maps to `leann build ...`):

papi index --backend leann --leann-embedding-mode ollama --leann-embedding-model nomic-embed-text papi index --backend leann --leann-embedding-mode ollama --leann-embedding-host http://localhost:11434 papi index --backend leann --leann-doc-chunk-size 350 --leann-doc-chunk-overlap 128 ```

By default, papi ask --backend leann auto-builds the index if missing (disable with --leann-no-auto-index). For explicit derived names such as papers_openai_voyage-4, auto-build infers the embedding mode/model from the name.

</details>

MCP usage

1. Build indexes: papi index --backend pqa --pqa-embedding text-embedding-3-small
2. In your agent: leann_search() (fast) or retrieve_chunks() (with citations)
3. For PaperQA2: embedding model is automatically inferred from index metadata (or index name for backward compatibility)

</details>

MCP environment variables

Common options

Any additional arguments are passed through to pqa (e.g., --agent.search_count 10).

API keys should be in env

export OPENAI_API_KEY=... export ANTHROPIC_API_KEY=... export GEMINI_API_KEY=... export VOYAGE_API_KEY=... export OPENROUTER_API_KEY=...

Common options

papi ask "..." --backend leann --leann-provider ollama --leann-model qwen3:8b
papi ask "..." --backend leann --leann-host http://localhost:11434
papi ask "..." --backend leann --leann-top-k 12 --leann-complexity 64

Notes: - If you use --leann-provider anthropic, your leann install must include the anthropic Python package (pip install anthropic in the same environment that runs leann). - You can pass through extra leann CLI flags after -- (useful for debugging), e.g.: papi -v ask "..." --backend leann -- ...

API keys should be in env

export OPENAI_API_KEY=... export ANTHROPIC_API_KEY=... export GEMINI_API_KEY=... export VOYAGE_API_KEY=... export OPENROUTER_API_KEY=...

LLM configuration

paperpipe uses LLMs for generating summaries, extracting equations, and tagging. Without an LLM, it falls back to regex extraction and metadata-based summaries.

```bash

Either env var name works (paperpipe normalizes both):

export OLLAMA_HOST=http://localhost:11434

Configuration file

For persistent settings, create ~/.paperpipe/config.toml (override location with PAPERPIPE_CONFIG_PATH):

```toml [llm] model = "gemini/gemini-2.5-flash" temperature = 0.3

Optional: override LEANN separately (otherwise it follows [llm]/[embedding] for openai/ollama model ids)

[leann] llm_provider = "ollama" llm_model = "qwen3:8b" embedding_model = "nomic-embed-text" embedding_mode = "ollama"

[tags.aliases] cv = "computer-vision" nlp = "natural-language-processing" ```

Precedence: CLI flags > env vars > config.toml > built-in defaults.

(or let your coding agent do this via the /papi skill)

papi show lora --level tex # exact LaTeX definitions

RAG backends (`papi ask`)

paperpipe supports two RAG backends for cross-paper questions:

```bash

Voyage embeddings (OpenAI-compatible) — endpoint + key auto-routed from VOYAGE_API_KEY

papi index --backend leann --leann-embedding-mode openai --leann-embedding-model voyage-4

**Asking:**

Voyage (OpenAI-compatible) — endpoint + key auto-routed from VOYAGE_API_KEY

export VOYAGE_API_KEY=... papi index --backend leann --leann-embedding-mode openai --leann-embedding-model voyage-4 ```

Gemini notes: - May hit quota/rate limits (HTTP 429). Retry after suggested delay. - Some LEANN versions batch too many inputs per request for Gemini (hard limit: 100 inputs/request) and fail with HTTP 400; update LEANN or reduce chunk counts (e.g., larger --leann-doc-chunk-size).

</details>

Set your API key (pick one)

export GEMINI_API_KEY=... # default provider export OPENAI_API_KEY=... export ANTHROPIC_API_KEY=... export VOYAGE_API_KEY=... # for Voyage embeddings (recommended with Claude) export OPENROUTER_API_KEY=... # 200+ models

export OLLAMA_API_BASE=http://localhost:11434

Ollama defaults to a small context window (~4k tokens) and **silently truncates** longer
prompts, so a full paper's LaTeX would be cut off. paperpipe avoids this by sizing the
context window to each prompt, capped at 32768 tokens. Lower the cap to save memory, or raise
it for very long papers:

This applies to local and Ollama Cloud (`ollama/<model>:cloud`, after `ollama signin`) models.

Many reasoning-capable models "think" by default and can spend their whole output budget on
hidden reasoning, returning empty content for extraction prompts. paperpipe disables thinking
for Ollama models by default since it does structured extraction, not reasoning. Re-enable it
if you want:

Via the Claude Code CLI (no API key)

If you have the Claude Code CLI installed and signed in (including subscription/OAuth logins), paperpipe can route summary/equation/tag/title generation through it instead of an API key:

export PAPERPIPE_LLM_MODEL=claude-cli/sonnet   # or claude-cli/opus, claude-cli/<full-model-name>

paperpipe shells out to claude -p for each generation step using the CLI's own authentication. Notes:

• Summarization only. papi ask (PaperQA2/RAG) and embeddings still need an API key or local Ollama — the CLI backend cannot drive them.
• Calls count against your Claude Code usage/rate limits, not API billing.
• Each invocation boots the CLI runtime (~3s), so generation is slower than an API backend.
• temperature is not configurable on this backend.

Check which models work with your keys:

papi models                    # probe default models for your configured keys
papi models latest             # probe latest model candidates (gpt-5, Gemini via OpenRouter/Gemini, Claude, Voyage 4)
papi models last-gen           # probe previous generation
papi models all                # probe broader superset
papi models --verbose          # show underlying provider errors

Typical workflow

```bash

Agent integration

paperpipe is designed to work with coding agents. Install the skill and MCP servers:

```bash papi install # installs skill + MCP for detected CLIs

4. Ask cross-paper questions (requires RAG backend)

papi ask "How does LoRA differ from full fine-tuning in terms of parameter count?"

PaperQA2 (default if installed)

papi ask "What regularization techniques do these papers use?"

agent_llm drives PaperQA2's search agent. PaperQA2's own default is gpt-4o; paperpipe