openova/platform/librechat/README.md
talent-mesh c9d04a53b4 refactor: flatten platform/ structure (41 components)
Remove hierarchical grouping (networking/, security/, etc.) and use flat
structure for all 41 platform components.

Changes:
- All components now directly under platform/ (no subfolders)
- AI Hub components moved from meta-platforms/ai-hub/components/ to platform/
- Open Banking components (lago, openmeter) moved to platform/
- meta-platforms/ now only contains README files that reference platform/
- Open Banking custom services remain in meta-platforms/open-banking/services/

Structure:
- platform/ (41 components, flat)
- meta-platforms/ai-hub/ (README only, references platform/)
- meta-platforms/open-banking/ (README + 6 custom services)

All documentation links updated.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-02-08 15:19:48 +00:00

4.9 KiB

LibreChat

Open-source chat UI with multi-model support and file uploads.

Status: Accepted | Updated: 2026-02-07


Overview

LibreChat provides a ChatGPT-like interface supporting multiple AI backends, file uploads, and customizable agent presets.

flowchart LR
    subgraph LibreChat["LibreChat"]
        UI[Chat UI]
        Presets[Agent Presets]
        Files[File Handling]
    end

    subgraph Backends["AI Backends"]
        OpenAI[OpenAI API]
        Custom[Custom Endpoints]
        RAG[RAG Service]
    end

    subgraph Storage["Storage"]
        MongoDB[MongoDB]
        FileStore[File Storage]
    end

    User[User] --> UI
    UI --> Presets
    UI --> Files
    Presets --> Backends
    Files --> FileStore
    UI --> MongoDB

Why LibreChat?

Feature Benefit
Multi-model Switch between AI backends
Agent presets Pre-configured assistants
File uploads Document analysis
Conversation history Persistent chat storage
SSO integration Enterprise authentication

Configuration

Helm Values

librechat:
  replicas: 2

  config:
    endpoints:
      custom:
        - name: "AI Hub"
          apiKey: "${RAG_SERVICE_API_KEY}"
          baseURL: "http://rag-service.ai-hub.svc:8000/v1"
          models:
            default: ["deep-thinker", "quick-thinker", "compliance-advisor"]
          titleModel: "quick-thinker"
          dropParams: ["stop", "user"]

    registration:
      socialLogins: ["openid"]

    fileConfig:
      endpoints:
        custom:
          fileLimit: 10
          fileSizeLimit: 50  # MB
          supportedMimeTypes:
            - "application/pdf"
            - "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
            - "text/plain"

mongodb:
  enabled: true
  auth:
    rootPassword: ""  # From ESO
  persistence:
    size: 10Gi

Agent Presets

Deep Thinker

{
  "name": "Deep Thinker",
  "model": "deep-thinker",
  "description": "Complex reasoning with visible chain-of-thought",
  "systemPrompt": "You are a thoughtful analyst. Think step by step and show your reasoning.",
  "temperature": 0.7,
  "maxTokens": 8192
}

Quick Thinker

{
  "name": "Quick Thinker",
  "model": "quick-thinker",
  "description": "Fast responses for simple queries",
  "systemPrompt": "You are a helpful assistant. Be concise and direct.",
  "temperature": 0.3,
  "maxTokens": 2048
}

Compliance Advisor

{
  "name": "Compliance Advisor",
  "model": "compliance-advisor",
  "description": "Regulatory knowledge with citations",
  "systemPrompt": "You are a compliance expert. Always cite your sources with document references.",
  "temperature": 0.1,
  "maxTokens": 4096
}

SSO Configuration

Azure AD OIDC

socialLogins:
  - openid

openidConfig:
  issuer: "https://login.microsoftonline.com/${TENANT_ID}/v2.0"
  clientId: "${CLIENT_ID}"
  clientSecret: "${CLIENT_SECRET}"
  scope: ["openid", "profile", "email"]
  callbackURL: "https://chat.ai-hub.<domain>/oauth/openid/callback"

Keycloak

openidConfig:
  issuer: "https://keycloak.<domain>/realms/ai-hub"
  clientId: "librechat"
  clientSecret: ""  # From ESO
  scope: ["openid", "profile", "email"]

File Upload Flow

sequenceDiagram
    participant User
    participant LibreChat
    participant RAG as RAG Service
    participant Milvus

    User->>LibreChat: Upload PDF
    LibreChat->>RAG: POST /ingest/file
    RAG->>RAG: Parse & chunk
    RAG->>Milvus: Store vectors (ephemeral)
    RAG-->>LibreChat: file_id

    User->>LibreChat: Ask question about file
    LibreChat->>RAG: Query with file_id context
    RAG->>Milvus: Search ephemeral partition
    RAG-->>LibreChat: Response with citations

Environment Variables

Variable Purpose
MONGO_URI MongoDB connection string
OPENID_CLIENT_ID SSO client ID
OPENID_CLIENT_SECRET SSO client secret
CREDS_KEY Encryption key for credentials
CREDS_IV Encryption IV
JWT_SECRET JWT signing secret

Custom Endpoints

endpoints:
  custom:
    - name: "RAG Service"
      baseURL: "http://rag-service.ai-hub.svc:8000/v1"
      apiKey: "${API_KEY}"
      models:
        default:
          - deep-thinker
          - quick-thinker
          - compliance-advisor
          - aiops-advisor
          - dev-advisor
          - internet-search

Monitoring

Metric Description
Active users Concurrent chat sessions
Message count Total messages sent
File uploads Documents processed
Response time Backend latency

Consequences

Positive:

  • ChatGPT-like experience
  • Multi-model switching
  • File upload support
  • Enterprise SSO
  • Customizable presets

Negative:

  • Requires MongoDB
  • Complex configuration
  • UI customization limited

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