CoreStory conversations API
The conversations API from CoreStory — 11 operation(s) for conversations.
OpenAPI Specification
openapi: 3.1.0
info:
title: Crowdbotics API Documentation admin conversations API
description: "# CoreStory API Overview\n\nThe CoreStory API provides programmatic access to structured insights derived from software source code. It is designed for engineering, architecture, and product teams who need to extract business logic, technical specifications, architectural relationships, and system-level context from complex or legacy codebases.\n\nBy interfacing directly with CoreStory API, users can:\n\n- Generate full Product Requirements Documents (PRDs) from a codebase\n- Extract detailed Technical Specifications for modernization or migration\n- Query a codebase using natural language to surface implementation details, logic paths, dependencies, and architectural insights\n- Construct and visualize knowledge graphs of systems, components, and relationships\n- Ingest, store, and retrieve content from a vector store for AI-powered retrieval-augmented generation (RAG) workflows\n\nCoreStory is especially valuable in contexts where:\n\n- Codebases are large, unstructured, or under-documented\n- Product and engineering teams are onboarding to unfamiliar systems\n- Technical stakeholders require visibility into system design or feature coverage\n- Modernization, replatforming, or M&A due diligence is underway\n\nThe API can be integrated into existing CI/CD pipelines, internal developer portals, architecture review boards, product planning processes, or documentation automation workflows.\n\nAll endpoints support secure authentication and are designed for high-availability, asynchronous workloads. Output formats include JSON, Markdown, and PDF, with flexible formatting options for downstream consumption.\n\nFor step-by-step usage instructions and workflows, refer to the [CoreStory API Quick Start Guide](#section/CoreStory-API-Quick-Start-Guide).\n\n## Features\n\n* **Document Generation**\n * Generate complete Product Requirements Documents (PRD)\n * Generate Technical Specifications\n * Generate individual document sections (executive overview, user personas, etc.)\n * Format documents in JSON or Markdown\n\n* **Vector Store Management**\n * Ingest and manage documents in vector store\n * Query and retrieve document content\n * Manage document embeddings\n\n* **Context Management**\n * Manage context for different document types\n * Set and retrieve PRD context\n * Support for future context types (technical specs, user stories, etc.)\n\n* **Knowledge Graph**\n * Generate knowledge graphs from codebase queries\n * Visualize relationships between code entities\n * Analyze code structure and dependencies\n\n* **API Endpoints**\n * `/api/documents/*` - Document generation and formatting\n * `/api/generate/*` - Document formatting utilities\n * `/api/vector-store/*` - Vector store management\n * `/api/agents/*` - AI agent operations\n * `/api/context/*` - Context management for documents\n * `/api/graph/*` - Knowledge graph generation and analysis\n\nNote: JWT authentication support coming soon. All endpoints currently provide detailed OpenAPI documentation.\n\n* **Observability**\n * OpenTelemetry instrumentation for distributed tracing\n * Prometheus metrics endpoint at `/metrics`\n * Comprehensive request/response tracking\n * Custom business metrics\n\n\n\n# CoreStory System Architecture\n\nThis document provides a high-level overview of how the CoreStory platform ingests, analyzes, and serves structured software intelligence through its API. The system is designed to process codebases and related technical artifacts in order to generate product specifications, technical documents, and knowledge graph representations.\n\n---\n\n## Core Components\n\n### 1. **Ingestion Layer**\n\n- Accepts Git URLs, file uploads, or local archives\n- Extracts source files, documentation, and metadata\n- Initiates chunking and embedding via the vector store pipeline\n\n### 2. **Vector Store and Context Engine**\n\n- Stores chunked and embedded documents\n- Supports semantic search and document retrieval\n- Feeds LLMs with scoped context for generation and reasoning\n\n### 3. **LLM Orchestration Layer**\n\n- Fan-out to multiple large language models\n- Handles prompt templating, context injection, and result normalization\n- Evaluates and scores generated outputs\n\n### 4. **Document Generation Engine**\n\n- Produces:\n - PRDs\n - Technical Specs\n - Individual document sections\n - Markdown and PDF exports\n- Format-flexible output for portals, APIs, and documentation pipelines\n\n### 5. **Graph Construction Module**\n\n- Extracts static and dynamic code relationships\n- Builds DAGs and call graphs of components, services, and files\n- Allows natural-language queries that return graph subviews and LLM explanations\n\n### 6. **Query Engine**\n\n- Accepts free-form questions about codebases\n- Routes queries to code graph, document store, or LLMs\n- Returns structured JSON answers and explanatory text\n\n### 7. **API Gateway**\n\n- Authenticates and rate-limits clients\n- Routes requests to internal services\n- Serves all external-facing endpoints in REST format\n\n---\n\n## Security and Privacy\n\n- All data transfer is encrypted via HTTPS\n- API access is secured using provided auth credentials (JWT tokens coming soon)\n- Uploaded code is only used to generate customer-requested outputs and is not retained indefinitely\n\n---\n\n## Extensibility\n\nThe platform is modular. It can be extended to:\n\n- Add new LLMs for ensemble generation\n- Integrate alternative vector databases\n- Connect to internal developer portals or product management tools via webhook or export endpoints\n\nFor more information, see the Quick Start or contact the CoreStory team.\n\n\n# CoreStory API Quick Start Guide\n\nThe CoreStory API provides programmatic access to structured code intelligence, enabling your team to extract business and technical specifications directly from source code. You can use the API to integrate CoreStory-generated documents into existing workflows, support software modernization and maintenance efforts, or enrich internal platforms with architectural and feature-level metadata.\n\n---\n\n## Overview\n\nThe CoreStory API enables:\n\n- Ingestion of content into a **vector store** for LLM-driven analysis\n- Generation of **Product Requirements Documents (PRDs)** and **Technical Specifications** from code\n- Interrogation of your codebase via a natural language **query engine**\n- Construction of **knowledge graphs** describing code relationships and system architecture\n- Management of **custom document context** and **custom prompts** for improved output relevance and quality\n\nThese capabilities support SDLC workflows such as:\n\n- Application modernization, migration, and maintenance\n- Documentation and auditability of legacy systems\n- Alignment of product, engineering, and architecture teams and systems\n- Developer onboarding and code comprehension\n\n---\n\n**Note for POV customers:** If your codebase has already been ingested as part of a proof-of-value (POV) engagement, you can use your existing authentication credentials and skip step 2 (”Ingest a Codebase or Document Source) of this guide.\n\n## 1. Authentication\n\n```\nPOST /api/auth/login\n```\n\nObtain a JWT for future API calls.\n\n**Request:**\n\n```\n{\n \"email\": \"you@example.com\",\n \"password\": \"your-password\"\n}\n```\n\n**Response:**\n\n```\n{\n \"access_token\": \"...\",\n \"token_type\": \"bearer\"\n}\n```\n\nInclude this token in all subsequent requests:\n\n```\nAuthorization: Bearer <access_token>\n```\n\n---\n\n## 2. Ingest a Codebase or Document Source\n\n```\nPOST /api/vector-store/ingest\n```\n\nTrigger ingestion of code from a Git repo, ZIP file, or local document directory.\n\n**Example Payload (GitHub):**\n\n```\n{\n \"source_type\": \"url\",\n \"source\": \"https://github.com/example/repo.git\",\n \"force\": true\n}\n```\n\nIngestion automatically chunks, embeds, and summarizes source files for downstream analysis.\n\n<details>\n<summary>Supported File Extensions</summary>\n\n### Programming Languages\n- Python: `.py`, `.pyw`, `.pyx`, `.pyi`\n- JavaScript/TypeScript: `.js`, `.mjs`, `.cjs`, `.ts`, `.jsx`, `.tsx`\n- Java: `.java`, `.class`, `.jar`\n- C/C++: `.c`, `.cpp`, `.cc`, `.cxx`, `.c++`, `.h`, `.hpp`, `.hh`, `.hxx`, `.h++`\n- .NET: `.cs`, `.csx`, `.vb`, `.fs`, `.fsx`, `.fsi`\n- PHP: `.php`, `.php3`, `.php4`, `.php5`, `.phtml`\n- Ruby: `.rb`, `.rbw`, `.rake`, `.gemspec`\n- Go: `.go`, `.mod`, `.sum`\n- Rust: `.rs`, `.rlib`\n- Swift: `.swift`\n- Kotlin: `.kt`, `.kts`, `.ktm`\n- Scala: `.scala`, `.sc`\n- Clojure: `.clj`, `.cljs`, `.cljc`, `.edn`\n- Haskell: `.hs`, `.lhs`\n- OCaml: `.ml`, `.mli`, `.mll`, `.mly`\n- R: `.r`, `.R`, `.rmd`, `.rnw`\n- Objective-C: `.m`, `.mm`\n- Perl: `.pl`, `.pm`, `.t`, `.pod`\n- Shell scripts: `.sh`, `.bash`, `.zsh`, `.fish`, `.ksh`, `.csh`, `.tcsh`\n- PowerShell: `.ps1`, `.psm1`, `.psd1`\n- Windows batch: `.bat`, `.cmd`\n- Lua: `.lua`\n- Dart: `.dart`\n- Elm: `.elm`\n- Elixir: `.ex`, `.exs`\n- Erlang: `.erl`, `.hrl`\n- Julia: `.jl`\n- Nim: `.nim`, `.nims`\n- Crystal: `.cr`\n- D: `.d`\n- Pascal: `.pas`, `.pp`, `.inc`\n- Fortran: `.f`, `.f90`, `.f95`, `.f03`, `.f08`, `.for`, `.ftn`, `.fpp`\n- COBOL: `.cob`, `.cbl`, `.cpy`, `.cobol`, `.bms`, `.ctl`, `.jcl`, `.proc`\n- IBM i (AS/400) RPG ILE: `.rpgle`, `.rpgleinc`\n- IBM i (AS/400) CLLE: `.clle`\n- IBM i (AS/400) DDS: `.pf`, `.lf`, `.dspf`\n- PowerBuilder: `.pbt`, `.pbs`, `.pbr`, `.srw`, `.srd`, `.sru`, `.sra`, `.srp`, `.srf`, `.srq`, `.srs`, `.srm`, `.srj`\n- Ada: `.ada`, `.adb`, `.ads`\n- Assembly: `.asm`, `.s`, `.S`\n- Verilog/SystemVerilog: `.v`, `.vh`, `.sv`, `.svh`\n- VHDL: `.vhd`, `.vhdl`\n- Tcl/Tk: `.tcl`, `.tk`\n- Groovy: `.groovy`, `.gvy`, `.gy`, `.gsh`\n- CoffeeScript: `.coffee`, `.litcoffee`\n- PureScript: `.purs`\n- ReasonML: `.reason`, `.re`, `.rei`\n- Racket/Scheme: `.rkt`, `.scm`, `.ss`\n- Lisp: `.lisp`, `.lsp`, `.l`, `.cl`, `.fasl`\n- Prolog: `.prolog`, `.pro`, `.P`\n- MATLAB: `.matlab`, `.fig`\n- Mathematica: `.mathematica`, `.nb`, `.wl`, `.wls`\n- SageMath: `.sage`, `.spyx`\n- Zig: `.zig`\n- Odin: `.odin`\n- V: `.v3`, `.v2`, `.v1`\n- Pony: `.pony`\n- Red: `.red`, `.reds`\n- Io: `.io`\n- Factor: `.factor`\n- Forth: `.forth`, `.fth`, `.4th`\n- J/K/Q/APL: `.j`, `.k`, `.q`, `.apl`\n- Chapel: `.chapel`, `.chpl`\n- X10: `.x10`\n- Arduino/Processing: `.pde`, `.ino`\n\n### Web Technologies\n- HTML: `.html`, `.htm`, `.xhtml`, `.shtml`\n- CSS/Preprocessors: `.css`, `.scss`, `.sass`, `.less`, `.styl`, `.stylus`\n- Frontend frameworks: `.vue`, `.svelte`\n- JSP: `.jsp`, `.jspx`, `.tag`, `.tagx`\n- ASP.NET: `.asp`, `.aspx`, `.ascx`, `.asax`, `.ashx`, `.asmx`\n- Ruby templates: `.erb`, `.haml`, `.slim`\n- PHP templates: `.twig`, `.blade`\n- Template engines: `.mustache`, `.hbs`, `.handlebars`, `.ejs`, `.pug`, `.jade`, `.liquid`, `.ftl`, `.ftlh`, `.vm`, `.vtl`, `.thymeleaf`\n\n### Configuration/Data\n- JSON: `.json`, `.json5`, `.jsonc`, `.jsonl`, `.ndjson`\n- XML: `.xml`, `.xsd`, `.xsl`, `.xslt`, `.dtd`, `.rng`, `.rnc`\n- YAML/TOML: `.yaml`, `.yml`, `.toml`\n- INI/Cfg/Conf: `.ini`, `.cfg`, `.conf`, `.config`, `.properties`\n- Environment: `.env`, `.envrc`, `.env.local`, `.env.development`, `.env.production`\n- Property lists: `.plist`\n- HOCON: `.hocon`\n- RON/HJSON/CSON: `.ron`, `.hjson`, `.cson`\n\n### Documentation/Text\n- Markdown: `.md`, `.markdown`, `.mdown`, `.mkd`, `.mkdn`\n- Plain text: `.txt`, `.text`\n- reStructuredText: `.rst`, `.rest`\n- AsciiDoc: `.asciidoc`, `.adoc`, `.asc`\n- LaTeX: `.tex`, `.latex`, `.ltx`, `.sty`, `.cls`, `.bib`\n- Org mode: `.org`\n- Wiki markup: `.wiki`, `.mediawiki`\n- Textile/Creole/RDoc: `.textile`, `.creole`, `.rdoc`\n\n### Data Formats\n- Delimited: `.csv`, `.tsv`, `.psv`\n- SQL: `.sql`, `.ddl`, `.dml`, `.plsql`, `.psql`, `.mysql`\n- GraphQL: `.graphql`, `.gql`\n- Protocols: `.proto`, `.protobuf`, `.avro`, `.avsc`, `.avdl`, `.thrift`, `.capnp`, `.fbs`\n- Columnar: `.parquet`, `.orc`, `.arrow`\n\n### Build/Dependency\n- Gradle: `.gradle`, `.gradle.kts`\n- Maven: `.maven`, `.mvn`\n- SBT: `.sbt`\n- CMake: `.cmake`, `.cmake.in`\n- Make: `.make`, `.mk`, `.mak`\n- Docker: `.dockerfile`, `.containerfile`, `.dockerignore`\n- Git: `.gitignore`, `.gitattributes`, `.gitmodules`, `.gitkeep`\n- Mercurial/Subversion/Bazaar: `.hgignore`, `.hgrc`, `.svnignore`, `.bzrignore`\n- EditorConfig: `.editorconfig`\n- Linters/Formatters: `.eslintrc`, `.eslintignore`, `.prettierrc`, `.prettierignore`, `.babelrc`, `.babelignore`\n- Node.js: `.npmrc`, `.npmignore`, `.yarnrc`, `.yarnignore`\n- Python: `.pipfile`, `.pipfile.lock`, `.requirements`, `.requirements.txt`, `.poetry.lock`, `.pyproject.toml`, `.setup.py`, `.setup.cfg`, `.manifest.in`, `.tox.ini`, `.noxfile.py`, `.pre-commit-config.yaml`\n- CI/CD: `.github`, `.gitlab-ci.yml`, `.travis.yml`, `.appveyor.yml`, `.circleci`, `.jenkins`, `.jenkinsfile`, `.azure-pipelines.yml`, `.buildkite.yml`\n- Package Managers: `package.json`, `package-lock.json`, `yarn.lock`, `pnpm-lock.yaml`, `composer.json`, `composer.lock`, `gemfile`, `gemfile.lock`, `cargo.toml`, `cargo.lock`, `go.mod`, `go.sum`, `requirements.txt`, `pipfile`, `pipfile.lock`, `poetry.lock`, `pyproject.toml`, `build.gradle`, `build.gradle.kts`, `settings.gradle`, `gradle.properties`, `pom.xml`, `settings.xml`, `build.sbt`, `plugins.sbt`, `makefile`, `gnumakefile`, `makefile.am`, `makefile.in`, `dockerfile`, `containerfile`, `vagrantfile`, `rakefile`, `guardfile`, `capfile`, `gulpfile.js`, `gruntfile.js`, `webpack.config.js`, `rollup.config.js`, `tsconfig.json`, `jsconfig.json`, `deno.json`, `deno.jsonc`, `mix.exs`, `mix.lock`, `rebar.config`, `rebar.lock`, `project.clj`, `deps.edn`, `stack.yaml`, `cabal.config`, `setup.hs`, `dub.json`, `dub.sdl`, `nimble`, `config.nims`, `shard.yml`, `shard.lock`, `pubspec.yaml`, `pubspec.lock`, `elm.json`, `elm-package.json`, `project.json`, `global.json`, `nuget.config`, `packages.config`, `paket.dependencies`, `paket.lock`, `.log`, `.lock`, `.pid`, `.tmp`, `.temp`, `.patch`, `.diff`, `.spec`, `.rpm`, `.deb`, `.control`, `.pkgbuild`, `.ebuild`, `.formula`, `.port`, `.portfile`, `.nix`, `.bzl`, `.bazel`, `.workspace`, `.buck`, `.buckconfig`, `.pants`, `.pants.ini`, `.please`, `.plzconfig`, `.gn`, `.gni`, `.ninja`, `.waf`, `.wscript`, `.scons`, `.sconstruct`, `.sconscript`\n\n</details>\n\n<details>\n<summary>Configuration Files</summary>\n\n- `requirements.txt`\n- `.gitignore`\n- `.dockerignore`\n- `.editorconfig`\n- `.eslintrc.js`\n- `.eslintrc.json`\n- `.prettierrc.js`\n- `.prettierrc.json`\n- `.babelrc.js`\n- `.babelrc.json`\n\n</details>\n\n---\n\n## 3. Generate a Product Requirements Document (PRD)\n\n**Note**: Generating a PRD or technical specification will replace the existing PRD or technical specification. The new outputs will document your codebase as it was when it was most recently ingested.\n\n```\nPOST /api/documents/prd?format=json\n```\n\nGenerates a structured PRD with:\n\n- Executive Overview\n- User Personas\n- High-Level Requirements\n- User Stories\n- Business Value\n\n**Example Response (truncated):**\n\n```\n{\n \"content\": {\n \"executive_overview\": \"Modernize internal authentication platform\",\n \"user_personas\": [...],\n \"user_stories\": [...]\n }\n}\n```\n\n**Tip:** For PRDs or technical specs, use `format=markdown` to return a Markdown-formatted document instead.\n\n---\n\n## 4. Generate a Technical Specification\n\n```\nPOST /api/documents/technical-spec?format=json\n```\n\nReturns system-level implementation details including:\n\n- Architecture and Components\n- Data Models and Relationships\n- User Interface Screens and Components\n- APIs and Integration Points\n- System Interfaces and Specifications\n- Security Considerations\n- Implementation and Deployment Strategy\n\n---\n\n## 5. Query Your Code\n\n```\nPOST /api/graph/query-engine\n```\n\nAsk natural language questions about your codebase to return a structured graph of relationships.\n\n**Example Query:**\n\n```\n{\n \"query\": \"How does the system determine whether a user has admin privileges when accessing the audit log?\"\n}\n```\n\n**Response (truncated):**\n\n```\n{\n \"summary\": \"When a user requests access to the audit log, the system checks their assigned roles in `AuthService.getUserRoles`. If 'admin' is present, access is granted. This check occurs in `AuditLogController` before querying the log database. The middleware `CheckPermissions` is also invoked on this route.\"\n}\n```\n\n---\n\n## 6. Format and Retrieve Document Sections\n\nUse these endpoints to extract or reformat specific document sections:\n\n- `/api/documents/sections/{doc_type}` → generate a specific section\n- `/api/documents/format/markdown-output/{doc_type}` → format JSON as Markdown\n- `/api/documents/format/pdf-output/{doc_type}` → export PDF\n\nExample section names:\n\n- `executive-overview`\n- `data-models`\n- `system-architecture`\n- `integration-points`\n\n---\n\n## Questions?\n\nAfter reviewing this guide and the API docs, we recommend scheduling a short Q&A call if you have integration-specific needs or want assistance mapping this to your environment.\n"
version: 1.26.2
servers:
- url: /
description: Current server
security:
- BearerAuth: []
tags:
- name: conversations
paths:
/api/projects/{project_id}/chat/conversations/:
get:
tags:
- conversations
summary: Get all Project's conversations for a User with pagination
description: Retrieve all conversations related to the Project for a User with pagination support and optional filtering
operationId: list_conversations
parameters:
- name: project_id
in: path
required: true
schema:
type: integer
title: Project Id
- name: limit
in: query
required: false
schema:
type: integer
maximum: 1000
minimum: 1
description: Number of items per page
default: 100
title: Limit
description: Number of items per page
- name: offset
in: query
required: false
schema:
type: integer
minimum: 0
description: Number of items to skip
default: 0
title: Offset
description: Number of items to skip
responses:
'200':
description: Paginated list of conversations
content:
application/json:
schema:
$ref: '#/components/schemas/PaginatedConversationListResponse'
default:
description: Error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorSchema'
post:
tags:
- conversations
summary: Create a new conversation
description: Create a new conversation for a specific project and user
operationId: create_conversation
parameters:
- name: project_id
in: path
required: true
schema:
type: integer
title: Project Id
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/ConversationCreateRequest'
responses:
'201':
description: Details of the created conversation
content:
application/json:
schema:
$ref: '#/components/schemas/ConversationResponse'
default:
description: Error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorSchema'
/api/projects/{project_id}/chat/conversations/list:
get:
tags:
- conversations
summary: 'DEPRECATED: Get all Project''s conversations for a User with pagination'
description: 'DEPRECATED: Use GET / instead. Retrieve all conversations related to the Project for a User.'
operationId: list_conversations_deprecated
deprecated: true
parameters:
- name: project_id
in: path
required: true
schema:
type: integer
title: Project Id
- name: limit
in: query
required: false
schema:
type: integer
maximum: 1000
minimum: 1
description: Number of items per page
default: 100
title: Limit
description: Number of items per page
- name: offset
in: query
required: false
schema:
type: integer
minimum: 0
description: Number of items to skip
default: 0
title: Offset
description: Number of items to skip
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/PaginatedConversationListResponse'
default:
description: Error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorSchema'
/api/projects/{project_id}/chat/conversations/{conversation_id}:
get:
tags:
- conversations
summary: Get conversation details with settings and metadata
description: Retrieve details for a specific conversation including settings, message metadata, and messages (deprecated).
operationId: get_conversation
parameters:
- name: conversation_id
in: path
required: true
schema:
type: integer
title: Conversation Id
- name: project_id
in: path
required: true
schema:
type: integer
title: Project Id
responses:
'200':
description: Conversation details with settings and metadata
content:
application/json:
schema:
$ref: '#/components/schemas/ConversationResponse'
default:
description: Error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorSchema'
patch:
tags:
- conversations
summary: Update conversation
description: Partially update conversation fields (title, read_only status, and/or settings)
operationId: update_conversation
parameters:
- name: project_id
in: path
required: true
schema:
type: integer
title: Project Id
- name: conversation_id
in: path
required: true
schema:
type: integer
title: Conversation Id
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/UpdateConversationRequest'
responses:
'200':
description: Successful Response
content:
application/json:
schema:
$ref: '#/components/schemas/ConversationResponse'
default:
description: Error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorSchema'
delete:
tags:
- conversations
summary: Delete a conversation
description: Delete a specific conversation and its associated chat messages
operationId: delete_conversation
parameters:
- name: project_id
in: path
required: true
schema:
type: integer
title: Project Id
- name: conversation_id
in: path
required: true
schema:
type: integer
title: Conversation Id
responses:
'204':
description: Successful Response
default:
description: Error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorSchema'
/api/projects/{project_id}/chat/conversations/{conversation_id}/messages:
get:
tags:
- conversations
summary: Get all chat messages for a conversation
description: Retrieve all chat messages associated with a specific conversation
operationId: get_conversation_messages
parameters:
- name: project_id
in: path
required: true
schema:
type: integer
title: Project Id
- name: conversation_id
in: path
required: true
schema:
type: integer
title: Conversation Id
responses:
'200':
description: List of chat messages
content:
application/json:
schema:
type: array
items:
$ref: '#/components/schemas/ChatMessageResponse'
title: Response Get Conversation Messages
default:
description: Error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorSchema'
post:
tags:
- conversations
summary: Add Chat Message
description: "Add a new chat message to a specific conversation.\nArgs:\n project_id: The ID of the project the conversation belongs to\n conversation_id: The ID of the conversation to add a message to\n data: QueryRequest object containing the message content (query and optional reasoning_mode)\n user: The authenticated user\n organization: The current Organization of the user\n\nReturns:\n ChatMessageResponse: Details of the created chat message\n\nRaises:\n HTTPException 422: If reasoning_mode=True but REASONING_MODE_MODEL not configured"
operationId: add_chat_message
parameters:
- name: project_id
in: path
required: true
schema:
type: integer
title: Project Id
- name: conversation_id
in: path
required: true
schema:
type: integer
title: Conversation Id
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/QueryRequest'
responses:
'200':
description: 'Server-Sent Events (SSE) stream of JSON objects. Each event is sent as `data: <json>\n\n`. A final named event `done` is emitted at completion.'
content:
text/event-stream:
schema:
$defs:
StreamEventType:
description: 'Types of events streamed to the frontend during chat/agent operations.
These events are emitted as Server-Sent Events (SSE) during orchestrator execution
and provide real-time progress updates to the frontend.'
enum:
- plan
- task_start
- task_complete
- continuation
- token
- reasoning
- reasoning_complete
- streaming_done
- synthesis_corrected
- sources
- title
- results_available
- plan_available
- error
- step_evaluation
- workflow_updated
- done
title: StreamEventType
type: string
description: 'Event model for streaming responses from chat/agent operations.
This model represents various event types that can be streamed to the frontend
during orchestrator execution. The event_type field determines which additional
fields are relevant for each event.'
properties:
event_type:
$ref: '#/$defs/StreamEventType'
default: token
delta:
default: ''
description: Token content (for token/reasoning events)
title: Delta
type: string
seq:
description: Sequence number for ordering events
title: Seq
type: integer
conversation_id:
description: ID of the conversation this event belongs to
title: Conversation Id
type: integer
project_id:
description: ID of the project this event belongs to
title: Project Id
type: integer
title:
anyOf:
- type: string
- type: 'null'
description: Conversation title (for title events)
title: Title
has_sources:
anyOf:
- type: boolean
- type: 'null'
default: false
description: Whether the message has source file references (for done events)
title: Has Sources
summary:
anyOf:
- type: string
- type: 'null'
description: Reasoning summary text (for reasoning_complete events)
title: Summary
message_id:
anyOf:
- type: integer
- type: 'null'
description: ID of the chat message this event belongs to (for done events)
title: Message Id
error_code:
anyOf:
- type: string
- type: 'null'
description: Error code or type (for error events)
title: Error Code
error_message:
anyOf:
- type: string
- type: 'null'
description: Detailed error message (for error events)
title: Error Message
required:
- seq
- conversation_id
- project_id
title: TokenEvent
type: object
examples:
json-sse:
summary: JSON SSE stream example
value: 'data: {"type":"token","delta":"Hel","seq":1,"conversation_id":1,"project_id":1}
data: {"type":"token","delta":"lo","seq":2,"conversation_id":1,"project_id":1}
data: {"type":"done","seq":3, "delta":"","conversation_id":1,"project_id":1}
'
default:
description: Error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorSchema'
'422':
description: Validation Error - reasoning_mode=True requires REASONING_MODE_MODEL to be configured
delete:
tags:
- conversations
summary: Clear all messages from a conversation
description: Delete all chat messages from a specific conversation. Useful for testing or starting fresh while keeping the conversation and its settings.
operationId: clear_conversation_messages
parameters:
- name: conversation_id
in: path
required: true
schema:
type: integer
title: Conversation Id
- name: project_id
in: path
required: true
schema:
type: integer
title: Project Id
responses:
'204':
description: Successful Response
default:
description: Error
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorSchema'
/api/projects/{project_id}/chat/conversations/{conversation_id}/messages/{message_id}/sources:
get:
tags:
- conversations
summary: Get source files for a chat message
description: Retrieve all source file references associated with a specific chat message.
operationId: get_message_sources
parameters:
- name: project_id
in: path
required: true
schema:
type: integer
title: Project Id
- name: conversation_id
in: path
required: true
schema:
type: integer
title: Conversation I
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# Full source: https://raw.githubusercontent.com/api-evangelist/corestory/refs/heads/main/openapi/corestory-conversations-api-openapi.yml