> ## Documentation Index > Fetch the complete documentation index at: https://docs.co-mind.ai/llms.txt > Use this file to discover all available pages before exploring further. # Get session message history > Get conversation history for a chat session. Returns all messages in chronological order. Example use case: A customer support session where the user asks multiple questions about a product. ## OpenAPI ````yaml /openapi.yaml get /v1/chat/sessions/{id}/messages openapi: 3.1.0 info: title: Co-mind.ai Private AI Platform API version: 1.2.4 description: > Co-mind.ai Private AI Platform API. ## Features - OpenAI-compatible endpoints - Tool/function calling support - Streaming responses - Multiple backend support - Personal Access Tokens (PAT) for programmatic access ## Authentication The API supports two authentication methods: **1. JWT Authentication** — Login with email/password to get short-lived access tokens. Use for interactive sessions (web apps, Postman). **2. Personal Access Tokens (PAT)** — Long-lived tokens for programmatic/API access. Create via `POST /v1/api-tokens` after authenticating with JWT. PAT format: `cmnd_.` Both methods use the `Authorization: Bearer ` header. contact: email: support@co-mind.ai servers: - url: http://co-mind-platform-host description: Co-mind.ai AI Platform security: - BearerAuth: [] tags: - name: Discovery description: List available models - name: Authentication description: >- JWT login, refresh, logout, SSO, registration, password reset, and `/v1/auth/me` - name: API Tokens description: Personal Access Token (PAT) management - name: Completions description: Text completion endpoints (single-shot, no conversation state) - name: Chat description: > OpenAI-compatible chat completions. **Stateless** — the client sends the full conversation history on every request; the server does not persist messages. - name: Responses description: > OpenAI-Responses-compatible endpoint. Wire-shape passthrough to the Inference Engine — every field the caller sends flows through, so the caller can use the full Responses API surface. The server does not persist responses server-side; use the `/v1/responses/{resp_id}/continue` variant to extend a prior response by id. - name: Embeddings description: > Text-embedding endpoint. Runs against the platform's configured embedding model (see `GET /v1/models`); the resulting vectors are returned to the caller and are not persisted server-side. - name: Translation description: > Text translation between languages. Ships with `POST /v1/translate/text`; async document translation is on the roadmap for a future release. - name: Knowledge Base description: > Knowledge base management (create / update / delete KBs, upload files, query for context) and **stateless** retrieval-augmented chat (`/v1/knowledgebase/chat/completions`). The RAG chat endpoint is stateless — the client sends the full conversation history on every request; the server injects retrieved KB context and returns the completion. - name: Chat Sessions (Preview) description: > **Preview — not currently enabled.** **Stateful** chat sessions. The server manages conversation history and supports tool orchestration (knowledge base retrieval, research, agents). These endpoints are documented for integrator preparation but are not exposed on any deployment today; they will be enabled in an upcoming release. Request and response schemas may change before general availability. - name: Researcher description: Web search, research sessions, analysis, synthesis, and source credibility - name: Quota description: Usage quota tracking paths: /v1/chat/sessions/{id}/messages: get: tags: - Chat Sessions (Preview) summary: Get session message history description: > Get conversation history for a chat session. Returns all messages in chronological order. Example use case: A customer support session where the user asks multiple questions about a product. operationId: getSessionMessages parameters: - name: id in: path required: true schema: type: string description: The session ID example: session_xyz789 - name: limit in: query schema: type: integer default: 50 description: Maximum number of messages to return - name: before in: query schema: type: string description: Message ID to paginate before - name: after in: query schema: type: string description: Message ID to paginate after responses: '200': description: Message history content: application/json: schema: $ref: '#/components/schemas/ChatSessionMessageList' examples: multi_turn_conversation: summary: Multi-turn conversation with 6 messages value: object: list data: - id: msg_001 object: chat.message session_id: session_xyz789 role: user content: What are the main features of your product? created_at: 1699564800 - id: msg_002 object: chat.message session_id: session_xyz789 role: assistant content: >- Our product has three main features - advanced analytics, real-time collaboration, and automated workflows. created_at: 1699564801 knowledge_base_context: sources: - knowledgebase_id: kb_abc123 document_id: doc_123 title: Product Overview relevance_score: 0.95 page: 2 - id: msg_003 object: chat.message session_id: session_xyz789 role: user content: Can you tell me more about the analytics feature? created_at: 1699564820 - id: msg_004 object: chat.message session_id: session_xyz789 role: assistant content: >- The analytics feature provides real-time dashboards, custom reports, and predictive insights using machine learning algorithms. created_at: 1699564821 knowledge_base_context: sources: - knowledgebase_id: kb_abc123 document_id: doc_456 title: Analytics Documentation relevance_score: 0.92 page: 5 - id: msg_005 object: chat.message session_id: session_xyz789 role: user content: What machine learning algorithms do you support? created_at: 1699564840 - id: msg_006 object: chat.message session_id: session_xyz789 role: assistant content: >- We support several ML algorithms including linear regression, decision trees, random forests, gradient boosting, and neural networks for deep learning tasks. created_at: 1699564841 has_more: false '404': $ref: '#/components/responses/NotFound' components: schemas: ChatSessionMessageList: type: object properties: object: type: string example: list data: type: array items: $ref: '#/components/schemas/ChatSessionMessage' has_more: type: boolean example: false example: object: list data: - id: msg_001 object: chat.message session_id: session_xyz789 role: user content: What are the main features of your product? created_at: 1699564800 - id: msg_002 object: chat.message session_id: session_xyz789 role: assistant content: >- Our product has three main features - advanced analytics, real-time collaboration, and automated workflows. created_at: 1699564801 knowledge_base_context: sources: - knowledgebase_id: kb_abc123 document_id: doc_123 title: Product Overview relevance_score: 0.95 page: 2 - id: msg_003 object: chat.message session_id: session_xyz789 role: user content: Can you tell me more about the analytics feature? created_at: 1699564820 - id: msg_004 object: chat.message session_id: session_xyz789 role: assistant content: >- The analytics feature provides real-time dashboards, custom reports, and predictive insights using machine learning algorithms. created_at: 1699564821 knowledge_base_context: sources: - knowledgebase_id: kb_abc123 document_id: doc_456 title: Analytics Documentation relevance_score: 0.92 page: 5 - id: msg_005 object: chat.message session_id: session_xyz789 role: user content: What machine learning algorithms do you support? created_at: 1699564840 - id: msg_006 object: chat.message session_id: session_xyz789 role: assistant content: >- We support several ML algorithms including linear regression, decision trees, random forests, gradient boosting, and neural networks for deep learning tasks. created_at: 1699564841 has_more: false ChatSessionMessage: type: object properties: id: type: string example: msg_abc123 object: type: string example: chat.message session_id: type: string example: session_xyz789 role: type: string enum: - user - assistant - system example: assistant content: type: string example: The main features include... created_at: type: integer description: Unix timestamp example: 1699564800 knowledge_base_context: type: object properties: sources: type: array items: type: object properties: knowledgebase_id: type: string document_id: type: string title: type: string content: type: string page: type: integer relevance_score: type: number Error: type: object properties: error: type: object properties: message: type: string type: type: string code: type: string responses: NotFound: description: Resource not found content: application/json: schema: $ref: '#/components/schemas/Error' securitySchemes: BearerAuth: type: http scheme: bearer bearerFormat: JWT or PAT description: > Bearer token authentication. Supports two token types: - **JWT Access Token** — obtained via `POST /v1/auth/login` - **Personal Access Token (PAT)** — created via `POST /v1/api-tokens`, format: `cmnd_.` ````