doubleo7/README.md

# deep_research

A local-first, multi-agent deep-research CLI: give it a topic, it searches
the web, cross-checks what it finds, and writes up a cited report — entirely
on infrastructure you control, with no cloud LLM API key and no query ever
leaving your machine.

```
$ deep_research "trends in AI customer-support chatbots"
🔎 Researching...
🧐 Reviewing findings...
✍️  Writing report...

# AI Customer-Support Chatbots: 2025–2026 Trends
...
```

## Why this exists

This started as a "does deep research actually work end-to-end" exercise and
turned into a small case study in building an *agentic* system that survives
contact with reality: models that hit their turn budget mid-task, search
providers that rate-limit, and reviewers that reject good-faith work. The
[case study](./docs/case-study.md) walks through what broke and how each
failure was fixed, not just papered over.

## Architecture

Four small agents, each with one job, coordinated by plain Rust control
flow — not a framework's agent graph, not an LLM deciding when to stop:

```
┌─────────────┐  approve/reject  ┌──────────┐
│  researcher │ ───────────────► │ reviewer │
│ (tool-using)│ ◄─────────────── │          │
└──────┬──────┘   gaps/feedback  └────┬─────┘
       │ turn budget exhausted        │ approved,
       │ mid-investigation            │ or out of rounds
       ▼                              ▼
┌──────────────┐              ┌──────────────┐
│ summarizer   │──findings───►│    writer    │──► report
│ (recovery)   │              │              │
└──────────────┘              └──────────────┘
```

- **researcher** — a tool-calling agent (`search_web`, `fetch_page`) that
  gathers and cross-checks evidence, capped at a fixed model-call budget so
  a confused model can't loop forever.
- **reviewer** — a separate, fresh-context agent that checks the researcher's
  conclusions actually follow from its cited sources, and either approves
  the findings or hands back concrete gaps for another pass.
- **writer** — turns approved (or partial) findings into a structured,
  footnoted report, streamed to the terminal as it's generated.
- **summarizer** (recovery path) — only runs when the researcher exhausts
  its turn budget before concluding on its own. It reconstructs a proper
  findings dump from the raw tool-call transcript rather than the run
  simply failing; see the case study for why this exists and how it
  degrades gracefully if the summarizer call itself fails.

Everything runs against local models via [Ollama](https://ollama.com) and a
self-hosted [SearXNG](https://searx.space) instance for search — no OpenAI/
Anthropic/Google API key, no third-party search API, nothing about the
research topic leaves the host it runs on. That's a deliberate constraint,
not a limitation: it's the same shape a privacy-sensitive customer
deployment would need.

## Running it

Prerequisites:
- [Ollama](https://ollama.com) running locally with a tool-calling-capable
  model pulled (the researcher and reviewer/writer models are configured in
  [`src/models.rs`](./src/models.rs))
- A local [SearXNG](https://docs.searxng.org/) instance with its JSON API
  enabled (defaults to `http://localhost:8080`, overridable via
  `SEARXNG_URL`)

```
cargo run -- "your research topic"

# or, with tracing spans on stderr instead of the progress spinner:
cargo run -- -l info "your research topic"

# give the researcher your own documents to draw on, alongside the web —
# repeatable, and a directory contributes every file directly inside it
# (one level deep, not recursive):
cargo run -- --doc ./notes.txt --doc ./research-docs/ "your research topic"
```

Uploaded documents are embedded with a dedicated embedding model (see
`EMBEDDING_MODEL` in [`src/models.rs`](./src/models.rs)) into an in-memory
vector index, then the excerpts most relevant to the topic are retrieved and
folded into the researcher's task alongside anything it finds on the web —
the same footnote-citation scheme applies to both.

## Project layout

Split one concern per file rather than one large module:

| File | Responsibility |
|---|---|
| `main.rs` | Argument parsing, logging setup, and the single top-level call — no orchestration logic |
| `research.rs` | The research/review round loop |
| `researcher.rs` | The tool-calling research phase |
| `review.rs` | The reviewer agent |
| `summarizer.rs` | Max-turns recovery: reconstructs findings via a model call |
| `writer.rs` | Turns findings into the final streamed report |
| `history.rs` | Pure, unit-tested helpers for parsing a rig chat history into usable text |
| `documents.rs` | Resolves `--doc` paths into embeddable documents |
| `retrieval.rs` | Embeds documents into an in-memory vector index and retrieves relevant excerpts |
| `tools.rs` | `search_web` (SearXNG) and `fetch_page` tool implementations |
| `stream.rs` | Drains a streaming prompt response to the terminal |
| `progress.rs` | The terminal spinner and per-phase emoji |
| `models.rs`, `cli.rs`, `observability.rs` | Small shared config: model names, CLI args, tracing setup |

## Testing

```
cargo test               # unit tests — pure functions, no network
cargo test -- --ignored  # + a live smoke test against SearXNG
cargo clippy --all-targets
```

CI (`.forgejo/workflows/ci.yml`) runs formatting, lint, build, and the unit
test suite on every push and PR.