フォーラム研究者AIペルソナガイド

:bookmark: This guide explains the Forum Researcher persona in Discourse AI, how it works, and how to configure it for in-depth forum content analysis.

:person_raising_hand: Required user level: Administrator (to enable and configure), All users (to interact, if granted access)

Understanding and using the Forum Researcher persona

The Discourse AI plugin includes the Forum Researcher persona, a powerful tool designed for conducting in-depth research on the content within your forum. This persona can help you uncover insights, summarize discussions, and analyze trends across your community.

Summary

This document will cover:

  • How the Forum Researcher persona functions.
  • Steps to configure the Forum Researcher.
  • Best practices for interacting with the persona.
  • The distinction between the Forum Researcher and standard forum helper tools.
  • Guidance on selecting an appropriate Large Language Model (LLM).
  • Debugging tips for research tasks.
  • Current limitations of the persona.

How it works

The Forum Researcher persona uses a dedicated Researcher tool. This tool is engineered to:

  1. Access forum content: It can read through various sections of your forum.
  2. Apply advanced filters: A flexible filter system allows the tool to target relevant information precisely. You can specify content by:
    • Specific categories (e.g., category:support)
    • Users or groups (e.g., usernames:sam,jane, group:moderators)
    • Keywords in posts or topic titles (e.g., keywords:regression,bug, topic_keywords:"feature request")
    • Date ranges for posts or topics (e.g., after:2024-01-01 before:2024-06-30)
    • Topic status (e.g., status:open, status:closed)
    • Post type (e.g., post_type:first)
    • Filters can be combined using AND logic (space-separated) or OR logic (using OR between filter groups). For example: category:bugs status:open after:2024-05-01 OR tag:critical usernames:sally.
  3. Analyze content with Large Language Models (LLMs): After retrieving the filtered content, it uses an LLM to analyze the information, extract insights, and answer your specific questions or achieve your research goals.
  4. Follow a structured process: To ensure efficiency and accuracy, especially considering potential costs, the Forum Researcher is designed to:
    • Understand: It will work with you to clarify your research goals at the beginning.
    • Plan: Based on your goals, it designs a comprehensive research approach using the available filters.
    • Test (Dry Run): Before executing the full analysis, the persona typically performs a “dry run.” This involves calculating how many posts match your filter criteria without immediately processing them with the LLM. The persona will then inform you of this count.
    • Refine: Based on the dry run results, if the number of posts is too large (risking high costs or overly broad results) or too small (potentially missing key information), the persona can help you adjust the filters.
    • Execute: Once you confirm the scope is appropriate (after the dry run), the persona runs the final analysis, sending the content to the LLM.
    • Summarize: It presents the findings, typically using Discourse Markdown, with links back to the original forum posts and topics as supporting evidence.

This methodical approach means you can ask the researcher to perform tasks like:

  • “Summarize the most frequently discussed unresolved bugs in the ‘mobile-app’ category from the last quarter, and identify any proposed solutions or workarounds mentioned in the discussions.”
  • “Help me identify the main arguments for and against the ‘New User Onboarding’ proposal topic (link), and list the key proponents of each side.”
  • “Review activity by the ‘documentation-team’ group in the past year and provide a report on their key contributions to how-to articles, highlighting any tutorials that received significant positive feedback.”

Configuring the Forum Researcher

The Forum Researcher is disabled by default because its usage can incur LLM costs.

  1. Enable Persona: Activate it by navigating to Admin → AI → Personas.
  2. Control Access: It is strongly recommended to limit this persona to specific groups to manage LLM costs. You can also use AI quotas for finer control.

Once enabled, the tool has several configuration options:

  • LLM: Select a specific LLM for research. This defaults to the bot’s current LLM. This option allows you to balance quality and cost.
  • Maximum number of results: This limits the number of posts processed per query to control costs. The default is 1000.
  • Include private: This allows searching in secure categories and private messages, using the interacting user’s permissions.
  • Maximum tokens per post: This truncates long posts to save token costs. It defaults to 2000 tokens, with a minimum of 50.
  • Maximum tokens per batch: This controls the data chunk size sent to the LLM. It’s useful for LLMs with large context windows or to maintain focus. If set below 8000, it defaults to the LLM’s maximum prompt tokens minus a 2000 token buffer.

Best practices for interaction

To get the most out of the Forum Researcher while managing costs:

  • Be specific with goals: Clearly define what you want to find out before you start. The persona works best when it has precise objectives.
  • Confirm scope after dry run: The persona will typically perform a ‘dry run’ first and inform you how many posts it found based on your request. Pay close attention to this number. If it’s too high (risking high costs or unfocused results) or too low (potentially missing crucial information), discuss refining your filters with the persona before committing to the full analysis.
  • Iterate on filters: If the initial dry run isn’t targeting the right information, work with the persona to adjust filter criteria. Add more specific keywords, narrow date ranges, or specify categories/tags.
  • Consolidate queries: The persona is designed to handle multiple related goals in a single research execution. Try to group related questions into one comprehensive research request to the persona.

Relationship to standard forum helper and related tools

The Forum Researcher persona is distinct from a general Forum Helper that uses standard tools like Search and Read.

  • Standard Search and Read Tools:

    • The Search tool primarily identifies relevant topics. It does this by matching keywords against post content and other criteria (tags, categories, etc.). For each matching topic, it returns a link and a brief snippet from a relevant post, not the full post content.
    • The Read tool is used to access the full content of a specific topic (or selected posts within it) that Search has identified.
    • These tools work in tandem for targeted retrieval: Search finds topics, Read digests their content.
  • Forum Researcher’s researcher Tool:

    • Direct, deep content analysis: The researcher tool doesn’t just identify topics; it directly processes and analyzes the full content of potentially many posts (up to its configured Maximum number of results) that match its comprehensive filter criteria.
    • Advanced filtering and synthesis: It uses a more complex filtering language to build a dataset of posts from across the forum (potentially spanning hundreds of topics), and then synthesizes information from this entire dataset to answer complex questions. This is fundamentally different from reading individual topics one by one.

In essence, while a Forum Helper uses Search to pinpoint topics (presenting snippets) and Read to delve into one, the Forum Researcher conducts broad analysis across the actual text of many posts simultaneously to uncover deeper, synthesized insights.

What LLM should I use?

LLM technology is rapidly evolving, with models continually improving in capability and cost-effectiveness. During the development of the Forum Researcher, models like Gemini 2.5 Flash, Gemini 2.5 Pro, GPT-4.1, and Claude 4 Sonnet provided excellent results for complex research plans.

The best choice depends on your specific needs:

  • High-quality, nuanced analysis: More advanced models might be preferable, though they usually come with higher costs.
  • Broad overviews or cost-sensitive tasks: Faster, more economical models can be very effective.

Here are some point-in-time examples from internal testing at Discourse for a very specific, complex query:

Look at the top 1000 open topics in the feature category - ordered by like (first post only) - all time … make me an executive report of the:

  • Top 20 features CDCK should build
  • Easiest 20 features CDCK could build
  • Obvious duplicates
  • Things that are very poorly defined

ask me no more questions, just run the research

  1. Gemini 2.0 Flash Example
  2. Gemini 2.5 Flash (with thinking) Example
  3. GPT-4.1 Example
  4. Claude 4 Sonnet Example
  5. Gemini 2.5 Pro Example

Hybrid example: Driver is Gemini 2.5 Pro and Researcher LLM is Gemini 2.0 Flash
Hybrid example

Debugging research

In Discourse, you can enable advanced AI debugging by adding groups to the ai_bot_debugging_allowed_groups site setting. With that in place, you are able to see the actual payloads sent to the LLM.

Limitations

Currently, there is no option to send images to the research LLM. This will be considered in future versions.

FAQs

  • Is the Forum Researcher available on all Discourse plans?
    The Forum Researcher is part of the Discourse AI plugin, which is available for self-hosted sites and on our Enterprise hosting plan.

  • Can the Forum Researcher access content from private categories or messages?
    Yes, if the “Include private” option is enabled in its configuration and the user interacting with the persona has the necessary permissions to access those areas.

  • How can I control the cost of using the Forum Researcher?

    • Limit access to specific, trusted groups.
    • Use the “Maximum number of results” and “Maximum tokens per post” settings to cap processing.
    • Choose cost-effective LLMs.
    • Pay close attention to the “dry run” estimates before executing full research.
    • Utilize AI quotas.

Additional resources

「いいね!」 17

@sam 素晴らしい出来栄えと、Discourse AIペルソナの着実な進歩に感謝します。本当に素晴らしい仕事です。

複数のペルソナが有効になっている場合、コンポーザーのドロップダウンはユーザーにとって混雑して混乱する可能性があります。最適な方法についてガイダンスを求めています。

  • ユーザーが選択できるドロップダウンに多数のペルソナを表示するのが正しい使い方ですか?

  • デフォルトのペルソナが、背後で専門的なペルソナを利用できますか?

  • 権限を通じて可視性を制御することで、ヘルパーペルソナが一般ユーザーの目に触れないようにし、自動化で使用すると、複数の応答投稿が発生すると思います。これらがツールとして使用できると素晴らしいでしょう。

構成のヒントや、デプロイメントガイドラインの例があれば助かります。

「いいね!」 2

皆さん、こんにちは。

まず、素晴らしい仕事ぶりです!フォーラムの知識をすべてキュレーションできるようになるというのは、まさに私たちが待ち望んでいたことです。

見つけた小さな問題点です。

  • 私たちのフォーラムはドイツ語で実行されているため、LLMはドイツ語の引用符「このように」を使用して検索を試みたようですが、その結果、検索結果は空になりました。補足:リサーチャーのデフォルトシステムプロンプトをドイツ語に翻訳しました。
「いいね!」 1

どのLLMを使用していますか?ペルソナをコピーして、ヒント付きでシステムプロンプトをドイツ語でやり直す価値があるかもしれません

すでにそのようにしましたが、さらに指示を追加しました。

- フォーラムで検索パラメータを絞り込むには、引用符 ``„“`` ではなく、引用符 ``\"`` のみを使用してください。

しかし、問題はまだ解決していません。gpt 4.1 で発生し、gemini 2.5 pro および flash で時折発生しました。

ところで、topic_keywords: および keywords: パラメータの使用方法に関する詳細情報はどこで入手できますか? meta や ask.discourse.com のどちらでも見つけることができませんでした。LLM が実行しようとしている検索を再現したいと思います。検索結果は、フォーラム検索 (バージョン 3.5.0.beta8-dev を使用しています) でこれらのパラメータを使用しても得られません。

gemini 2.5 researcher で奇妙な動作に遭遇しました:

LLMは次のように応答します:

これらの貢献およびその他の貢献から情報を収集し、系統の説明を作成します。少々お時間をいただきます。完了したら改めてご連絡いたします。

しかし、応答は実際には終了しており、ここから続かず、続行するには手動で再トリガーする必要があります。

研究者ペルソナは、Discourseのコア検索実装を使用せず、カスタム実装を使用しています。これは解析され、その後フルテキスト検索を直接呼び出します。

なるほど。プロンプトを使って検索の動作をよりきめ細かく制御できるようなドキュメントがまだあると嬉しいです。

「いいね!」 1

これはLLMによる1000%の幻覚です。

トレーニングデータのコーパスでは、これは「一般的な」応答なので、注意しないとこのようなものを作り出す可能性があります:frowning:

「いいね!」 2

Nueva actualización para Minecraft añade templos submarinos

リサーチャーペルソナがカスタムかつ高度な検索パラメータを利用できるのは素晴らしいと思いますが、この状況では、フロントエンドで同じ検索パラメータや値を使用できないため、検索クエリを手動で再現し、システムプロンプトをカスタマイズまたは改良したり、検索結果がゼロの場合にデバッグしたりすることが困難です。

カスタム検索をAPI経由で再現する方法はありますか?

「いいね!」 1

今のところはそうではありませんが、とても良いアイデアです。要するに、これはフィルターの一種です。

「いいね!」 2

素晴らしい記事ですね、サムさん。Discourse AI を使って、これほど簡単に独自の Deep Research エージェントを構築できるようになったのは本当に素晴らしいです!\nただ、一つ心配なことがあります。\nimage :open_mouth:

「いいね!」 3