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---
stage: Create
group: Code Review
info: To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/product/ux/technical-writing/#assignments
---
# Code Intelligence **(FREE)**
> [Introduced](https://gitlab.com/groups/gitlab-org/-/epics/1576) in GitLab 13.1.
This document describes the design behind [Code Intelligence](../../user/project/code_intelligence.md).
The built-in Code Intelligence in GitLab is powered by
[LSIF](https://lsif.dev) and comes down to generating an LSIF document for a
project in a CI job, processing the data, uploading it as a CI artifact and
displaying this information for the files in the project.
Here is a sequence diagram for uploading an LSIF artifact:
```mermaid
sequenceDiagram
participant Runner
participant Workhorse
participant Rails
participant Object Storage
Runner->>+Workhorse: POST /v4/jobs/:id/artifacts
Workhorse->>+Rails: POST /:id/artifacts/authorize
Rails-->>-Workhorse: Respond with ProcessLsif header
Note right of Workhorse: Process LSIF file
Workhorse->>+Object Storage: Put file
Object Storage-->>-Workhorse: request results
Workhorse->>+Rails: POST /:id/artifacts
Rails-->>-Workhorse: request results
Workhorse-->>-Runner: request results
```
1. The CI/CD job generates a document in an LSIF format (usually `dump.lsif`) using
[an indexer](https://lsif.dev) for the language of a project. The format
[describes](https://github.com/sourcegraph/sourcegraph/blob/main/doc/code_intelligence/explanations/writing_an_indexer.md)
interactions between a method or function and its definitions or references. The
document is marked to be stored as an LSIF report artifact.
1. After receiving a request for storing the artifact, Workhorse asks
GitLab Rails to authorize the upload.
1. GitLab Rails validates whether the artifact can be uploaded and sends
`ProcessLsif: true` header if the LSIF artifact can be processed.
1. Workhorse reads the LSIF document line by line and generates code intelligence
data for each file in the project. The output is a zipped directory of JSON
files which imitates the structure of the project:
Project:
```code
app
controllers
application_controller.rb
models
application.rb
```
Generated data:
```code
app
controllers
application_controller.rb.json
models
application.rb.json
```
1. The zipped directory is stored as a ZIP artifact. Workhorse replaces the
original LSIF document with a set of JSON files in the ZIP artifact and
generates metadata for it. The metadata makes it possible to view a single
file in a ZIP file without unpacking or loading the whole file. That allows us
to access code intelligence data for a single file.
1. When a file is viewed in the GitLab application, frontend fetches code
intelligence data for the file directly from the object storage. The file
contains information about code units in the file. For example:
```json
[
{
"definition_path": "cmd/check/main.go#L4",
"hover": [
{
"language": "go",
"tokens": [
[
{
"class": "kn",
"value": "package"
},
{
"value": " "
},
{
"class": "s",
"value": "\"fmt\""
}
]
]
},
{
"value": "Package fmt implements formatted I/O with functions analogous to C's printf and scanf. The format 'verbs' are derived from C's but are simpler. \n\n### hdr-PrintingPrinting\nThe verbs: \n\nGeneral: \n\n```\n%v\tthe value in a default format\n\twhen printing st..."
}
],
"start_char": 2,
"start_line": 33
}
...
]
```
|
