최신 버전 Discourse 커뮤니티 포럼 설정을 위한 AI 봇 지원

지원
1

LLM, 임베딩, 페르소나 기능을 설정했지만, 커뮤니티 내 질문과 답변 또는 사용 가능한 위키 리소스에서 예상되는 응답을 프롬프트가 제공하지 않고 있습니다.

자체 호스팅 LLM: mistral:latest
자체 호스팅 임베딩: all-minilm:latest

페르소나:

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어떤 조치를 취해야 하는지 도움을 주실 수 있을까요?

2

이것은 정확히 어떤 Mistral 모델인가요?

프롬프트를 공유해 주실 수 있나요?

모델의 응답을 보다 정확하게 유도하기 위해 첫 번째 상호작용에서 Search를 "강제 도구(Forced Tool)"로 추가할 수 있습니다.

3

@Falco, 아래는 tailing 응답에 대한 세부 정보입니다:

정확히 어떤 Mistral 모델인가요?

LLM 모델: mistral:latest

링크:

https://ollama.com/library/mistral:latest에 있는 모델은 다음과 같습니다:

Mistral 7B v0.3 — Mistral AI에서 출시한 70억 파라미터 오픈소스 모델입니다.

프롬프트를 공유해 주실 수 있나요?

시스템 프롬프트:

{site_title}이라는 이름의 이 포럼을 위해 설계된 커뮤니티 지식 어시스턴트입니다. 사이트 URL은 {site_url}이며, 사용자는 엔지니어입니다.

답변을 생성하기 전에 항상 관련 포럼 게시물, 위키 문서, 태그된 토론을 검색하고 참조하십시오.

가장 우선해야 할 것은 검색된 포럼 콘텐츠(임베딩 검색을 통해)를 사용하여 응답을 구성하는 것입니다.

  기존 게시물에서 가져온 요약과 인용을 우선하십시오.
  여러 관련 주제가 발견되면 명확하게 결합하여 제시하십시오.
  관련 콘텐츠가 존재하지 않는 경우에만 LLM을 통해 일반 지식으로 답변하십시오.
  게시물을 참조할 때 주제 제목이나 URL을 포함하십시오.
  포럼 데이터로 뒷받침되지 않는 답변을 망상하거나 지어내지 마십시오.
  사실에 기반하여 간결하고 전문적인 답변을 제공하십시오.
  사용자가 광범위한 질문을 할 경우 추측하기보다는 여러 출처를 요약하는 것을 선호하십시오.
  항상 임베딩에 인덱싱된 카테고리, 태그, 위키의 컨텍스트를 우선하십시오.

제안대로 강제 도구를 업데이트했습니다

4

2023년에 나온 모델의 재탕 버전은 여기선 적합하지 않을 것 같습니다. 또한, 이 모델의 Ollama 공식 문서에 따르면 도구 호출(tool calling)은 원시 API에서만 지원되며, 이는 우리가 사용하는 방식이 아닙니다.

전반적으로, 현재 AI 봇을 위한 선택지로는 부적절합니다.

대신, 다음 중 하나를 사용하세요.

5

@Falco 님, 안녕하세요. 그리고 죄송합니다. 피드백 주셔서 감사합니다. 확인 후 다시 말씀드리겠습니다.

6

@Falco LLM을 활성화하는 동안 502 오류가 발생했습니다. 아래 로그를 확인해 주세요

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메시지

Unicorn worker received USR2 signal indicating it is about to timeout, dumping backtrace for main thread
config/unicorn.conf.rb:203:in `backtrace'
config/unicorn.conf.rb:203:in `block (2 levels) in reload'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-protocol-0.2.2/lib/net/protocol.rb:229:in `wait_readable'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-protocol-0.2.2/lib/net/protocol.rb:229:in `rbuf_fill'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-protocol-0.2.2/lib/net/protocol.rb:199:in `readuntil'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-protocol-0.2.2/lib/net/protocol.rb:209:in `readline'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http/response.rb:158:in `read_status_line'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http/response.rb:147:in `read_new'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http.rb:2420:in `block in transport_request'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http.rb:2411:in `catch'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http.rb:2411:in `transport_request'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http.rb:2384:in `request'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/rack-mini-profiler-4.0.1/lib/patches/net_patches.rb:19:in `block in request_with_mini_profiler'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/rack-mini-profiler-4.0.1/lib/mini_profiler/profiling_methods.rb:45:in `step'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/rack-mini-profiler-4.0.1/lib/patches/net_patches.rb:18:in `request_with_mini_profiler'
/var/www/discourse/plugins/discourse-ai/lib/completions/endpoints/base.rb:168:in `block in perform_completion!'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http.rb:1632:in `start'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http.rb:1070:in `start'
/var/www/di...

백트레이스

/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/activesupport-8.0.2.1/lib/active_support/broadcast_logger.rb:130:in `block in warn'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/activesupport-8.0.2.1/lib/active_support/broadcast_logger.rb:231:in `block in dispatch'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/activesupport-8.0.2.1/lib/active_support/broadcast_logger.rb:231:in `each'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/activesupport-8.0.2.1/lib/active_support/broadcast_logger.rb:231:in `dispatch'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/activesupport-8.0.2.1/lib/active_support/broadcast_logger.rb:130:in `warn'
/var/www/discourse/lib/signal_trap_logger.rb:40:in `public_send'
/var/www/discourse/lib/signal_trap_logger.rb:40:in `block (2 levels) in ensure_logging_thread_running'
<internal:kernel>:187:in `loop'
/var/www/discourse/lib/signal_trap_logger.rb:37:in `block in ensure_logging_thread_running'
7

Ollama의 OpenAI 호환 API를 사용하는 경우 제공자를 OpenAI로 설정하세요.

8

@Falco

openAI 제공자를 사용해도 여전히 문제가 지속되고 있습니다.

메시지

Unicorn worker received USR2 signal indicating it is about to timeout, dumping backtrace for main thread
config/unicorn.conf.rb:203:in `backtrace'
config/unicorn.conf.rb:203:in `block (2 levels) in reload'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-protocol-0.2.2/lib/net/protocol.rb:229:in `wait_readable'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-protocol-0.2.2/lib/net/protocol.rb:229:in `rbuf_fill'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-protocol-0.2.2/lib/net/protocol.rb:199:in `readuntil'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-protocol-0.2.2/lib/net/protocol.rb:209:in `readline'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http/response.rb:158:in `read_status_line'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http/response.rb:147:in `read_new'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http.rb:2420:in `block in transport_request'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http.rb:2411:in `catch'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http.rb:2411:in `transport_request'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http.rb:2384:in `request'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/rack-mini-profiler-4.0.1/lib/patches/net_patches.rb:19:in `block in request_with_mini_profiler'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/rack-mini-profiler-4.0.1/lib/mini_profiler/profiling_methods.rb:45:in `step'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/rack-mini-profiler-4.0.1/lib/patches/net_patches.rb:18:in `request_with_mini_profiler'
/var/www/discourse/plugins/discourse-ai/lib/completions/endpoints/base.rb:168:in `block in perform_completion!'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http.rb:1632:in `start'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/net-http-0.6.0/lib/net/http.rb:1070:in `start'
/var/www/di...

백트레이스

/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/activesupport-8.0.2.1/lib/active_support/broadcast_logger.rb:130:in `block in warn'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/activesupport-8.0.2.1/lib/active_support/broadcast_logger.rb:231:in `block in dispatch'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/activesupport-8.0.2.1/lib/active_support/broadcast_logger.rb:231:in `each'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/activesupport-8.0.2.1/lib/active_support/broadcast_logger.rb:231:in `dispatch'
/var/www/discourse/vendor/bundle/ruby/3.3.0/gems/activesupport-8.0.2.1/lib/active_support/broadcast_logger.rb:130:in `warn'
/var/www/discourse/lib/signal_trap_logger.rb:40:in `public_send'
/var/www/discourse/lib/signal_trap_logger.rb:40:in `block (2 levels) in ensure_logging_thread_running'
<internal:kernel>:187:in `loop'
/var/www/discourse/lib/signal_trap_logger.rb:37:in `block in ensure_logging_thread_running'
9

Discourse 컨테이너가 포트 11434의 서비스에 액세스할 수 있나요? 호스트에서 실행 중인 경우, 네트워크가 컨테이너 경계를 넘어갈 수 있도록 하는 방법을 제공해야 합니다.

10

네, 방금 Discourse 컨테이너와 동일한 포트에서 LLM 모델 중 하나에 접근할 수 있었습니다.

11

이 문제에 대해 도움을 주실 수 있으며, 이 LLM이 실행되는 최소 RAM 요구 사항이 얼마인지 알고 싶습니다.

12

따라서 다음 링크를 참고할 수 있습니다.

모델과 컨텍스트 크기에 따라 달라집니다.

13

안녕하세요 @Falco

아래에 설명된 언어 모델(LLM)을 성공적으로 통합했습니다. 그러나 쿼리 처리 과정에서 문제가 발생했습니다: 프롬프트가 제 포럼의 특정 내용을 반영하지 못하고 있습니다. 관련 토론과 통찰이 담긴 포럼 내에서 정보를 가져오는 대신, LLM에 이미 존재하는 사전 정보에 기반하여 응답을 생성하고 있습니다. 이 문제를 해결하고 모델이 포럼의 고유한 기여 사항을 효과적으로 반영하도록 하려면 어떤 조치를 취해야 할까요?

이전에 제안된 LLM 사용:

예시:

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14

페르소나의 도구(Tools) 섹션에서 **검색 및 읽기(Search and Read)**에 모두 접근할 수 있도록 설정하고, **검색(Search)**을 **강제 도구(Forced tool)**로 지정하세요.

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또한, 페르소나가 검색하도록 되어 있는 포럼 콘텐츠가 모두 공개되어 있나요?

15

@Falco,
위 설정을 올바르게 수정했지만 여전히 동일한 상태입니다.

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저는 봇이 내 지식 베이스에 저장된 내용에서 파생된 응답을 제공하도록 하기를 원합니다. AI 플러그인 및 기타 관련 설정에 대해 종합적인 정보를 공유해 주실 수 있을까요? 또한, 검색 증강 생성(RAG)을 활성화하기 위해 애플리케이션 내에서 특정 명령을 실행해야 하는지 궁금합니다.

16

@Falco 위 내용을 검토해 주시고 피드백을 주실 수 있을까요?

17

AI bot debugging allowed groups를 통해 봇 디버깅을 활성화하고, 봇이 어떤 도구 호출을 수행하는지 및 응답이 무엇인지 확인하세요.

18

@Falco 다음 내용은 세부 정보입니다

요청 토큰: 1304
응답 토큰: 652
모델: gemma3:1b
메시지:
0:
역할: system
내용: 당신은 유용한 Discourse 어시스턴트입니다.
당신은 Discourse 마크다운을 _이해_하고 **생성**합니다.
당신은 Discourse 포럼 메시지에 존재합니다.

당신은 다음 URL의 포럼에 존재합니다: https://faq.company.com
당신의 사이트 제목: company FAQ
설명은: company 커뮤니티 포털
이 대화의 참가자는: Forum_Helper_bot, testuser
현재 날짜는: 2025-12-02 08:36:27 UTC이며, 학습 이후로 많은 것이 변했습니다.

당신은 오래된 데이터로 학습되었으므로, 이 포럼에 대한 최신 정보를 얻으려면 검색에 의존하세요.
검색 시 검색어를 단순화하려고 시도하세요.
Discourse 검색은 모든 용어를 AND로 결합합니다. 더 많은 결과를 찾으려면 용어를 줄이고 단순화하세요.

이 환경에서는 사용자의 질문에 답하기 위해 사용할 수 있는 도구 세트에 접근할 수 있습니다.
다음과 같이 호출할 수 있습니다.

<function_calls>
<invoke>
<tool_name>$TOOL_NAME</tool_name>
<parameters>
<$PARAMETER_NAME>$PARAMETER_VALUE</$PARAMETER_NAME>
...
</parameters>
</invoke>
</function_calls>

한 응답에서 여러 함수를 호출하려면 여러 <invoke>를 감싸서
b단일<function_calls> 블록.

- 도구를 실행해야 하는 경우 항상 도구 호출로 시작하세요.
- 도구 호출 전에 "확인해 드릴게요."와 같은多余的한 인사말을 피하세요.
- 도구 호출 시 HTML 엔티티를 인코딩하지 마세요. 필요할 경우 인코딩을 위해 <![CDATA[...]]>를 사용할 수 있습니다.
사용 가능한 도구의 전체 목록은 다음과 같습니다:

<tools>
<tool_description>
<tool_name>categories</tool_name>
<description>현재 Discourse 인스턴스의 카테고리를 나열합니다. 카테고리 이름 앞에 #를 붙여 서식 처리하는 것을 권장합니다</description>
<parameters>
</parameters>
</tool_description>
<tool_description>
<tool_name>search</tool_name>
<description>현재 Discourse 인스턴스에서 토픽을 검색합니다. 렌더링할 때 항상 찾은 토픽으로 링크하는 것을 권장합니다</description>
<parameters>
<parameter>
<name>search_query</name>
<type>string</type>
<description>검색할 특정 키워드, 공백으로 구분 (잘못된 철자를 수정하고, 접속어를 제거)</description>
<required>false</required>
</parameter>
<parameter>
<name>user</name>
<type>string</type>
<description>검색 결과를 이 사용자 이름으로 필터링 (사용자가 사용자별로 필터링하도록 명시적으로 요청한 경우에만 포함)</description>
<required>false</required>
</parameter>
<parameter>
<name>order</name>
<type>string</type>
<description>검색 결과 순서</description>
<required>false</required>
<options>latest,latest_topic,oldest,views,likes</options>
</parameter>
<parameter>
<name>max_results</name>
<type>integer</type>
<description>반환될 결과 수 제한 (일반적으로 기본값을 유지하는 것을 권장)</description>
<required>false</required>
</parameter>
<parameter>
<name>max_posts</name>
<type>integer</type>
<description>토픽당 최대 게시글 수 (많은 사람들이 게시글을 쓴 토픽)</description>
<required>false</required>
</parameter>
<parameter>
<name>tags</name>
<type>string</type>
<description>검색할 태그 목록. +를 사용하여 OR로 결합하고, ,를 사용하여 AND로 결합</description>
<required>false</required>
</parameter>
<parameter>
<name>category</name>
<type>string</type>
<description>필터링할 카테고리 이름</description>
<required>false</required>
</parameter>
<parameter>
<name>before</name>
<type>string</type>
<description>특정 날짜 YYYY-MM-DD 이전에 생성된 토픽만</description>
<required>false</required>
</parameter>
<parameter>
<name>after</name>
<type>string</type>
<description>특정 날짜 YYYY-MM-DD 이후에 생성된 토픽만</description>
<required>false</required>
</parameter>
<parameter>
<name>status</name>
<type>string</type>
<description>특정 상태의 토픽 검색</description>
<required>false</required>
<options>open,closed,archived,noreplies,single_user</options>
</parameter>
</parameters>
</tool_description>
<tool_description>
<tool_name>read</tool_name>
<description>이 Discourse 인스턴스의 토픽 또는 게시글을 읽습니다</description>
<parameters>
<parameter>
<name>topic_id</name>
<type>integer</type>
<description>읽을 토픽의 ID</description>
<required>true</required>
</parameter>
<parameter>
<name>post_numbers</name>
<type>array</type>
<description>읽을 게시글 번호 (선택 사항)</description>
<required>false</required>
<array_item_type>integer</array_item_type>
</parameter>
</parameters>
</tool_description>
<tool_description>
<tool_name>tags</tool_name>
<description>현재 Discourse 인스턴스의 가장 인기 있는 100개의 태그를 나열합니다</description>
<parameters>
</parameters>
</tool_description>
</tools>
1:
역할: 사용자
이름: testuser
내용: apt 패키지 zliblg:i386이 존재하지 않는 문제를 어떻게 해결하나요
스트림: true
스트림 옵션:
사용량 포함: true

응답

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19

  1. LLM 설정에서 Disable native tool support (use XML based tools) (optional)를 체크한 이유는 무엇인가요? Gemma 3은 네이티브 도구를 지원합니다.

  2. Gemma3 1B는 신뢰할 수 있는 도구 호출을 수행하기에는 너무 작은 모델입니다. 이 모델은 임베디드 하드웨어나 브라우저에서 실행되는 모델들보다도 작습니다. 최소한 4B 또는 12B 변형을 사용해 보세요.

20

@Falco 제공된 입력으로 추가적으로 시도해 보고, 다시 알려드리겠습니다