Search Jira - Semantic Search for Jira Content

Search the jira-data collection for issues and comments using semantic similarity with advanced filtering.

Activation

# Basic semantic search
/aim-jira-search "authentication bug"

# Filter by project
/aim-jira-search "API errors" --project BMAD

# Filter by type (issue or comment)
/aim-jira-search "implementation details" --type jira_comment

# Filter by issue type
/aim-jira-search "bugs" --issue-type Bug

# Filter by status
/aim-jira-search "in progress work" --status "In Progress"

# Filter by priority
/aim-jira-search "critical issues" --priority High

# Filter by author (comments) or reporter (issues)
/aim-jira-search "alice's comments" --author alice@company.com

# Issue lookup mode (issue + all comments)
/aim-jira-search --issue BMAD-42

# Combine filters
/aim-jira-search "database" --project BMAD --issue-type Bug --status Done --limit 10

Options

• --project <key> - Filter by Jira project key (e.g., BMAD, PROJ)
• --type <type> - Filter by document type (jira_issue or jira_comment)
• --issue-type <type> - Filter by issue type (Bug, Story, Task, Epic)
• --status <status> - Filter by issue status (To Do, In Progress, Done, etc.)
• --priority <priority> - Filter by priority (Highest, High, Medium, Low, Lowest)
• --author <email> - Filter by comment author or issue reporter
• --issue <key> - Lookup mode: retrieve issue + all comments (e.g., BMAD-42)
• --limit <n> - Maximum results to return (default: 5)

Result Format

Each result includes:

• Jira URL - Direct link to issue/comment
• Metadata badges - Type, Status, Priority, Author/Reporter
• Content snippet - First ~300 characters
• Relevance score - Semantic similarity (0-100%)

---

Qdrant Connection Details

The jira-data collection is stored in the local Qdrant instance:

Parameter	Value
Host	localhost
Port	26350 (NOT the default 6333)
API Key	Required. Read from env: QDRANT_API_KEY
Collection	jira-data
URL	http://localhost:26350

---

Qdrant Payload Schema

Every point in jira-data has the following payload fields. Use these exact names for filtering — do NOT guess field names like project_key or issue_key.

Common Fields (all points)

Field	Type	Description	Example
content	string	Full text content of issue/comment	"[PROJ-123] Fix login bug..."
type	string	Document type	"jira_issue" or "jira_comment"
group_id	string	Jira instance hostname (tenant isolation)	"hidden-history.atlassian.net"
session_id	string	Always "jira_sync"	"jira_sync"
jira_project	string	Project key	"BMAD"
jira_issue_key	string	Full issue key	"BMAD-42"
jira_issue_type	string	Issue type name	"Bug", "Story", "Task", "Epic"
jira_status	string	Issue status	"To Do", "In Progress", "Done"
jira_priority	string or null	Priority level	"High", "Medium", "Low", null
jira_updated	string	ISO 8601 timestamp	"2026-02-10T14:30:00.000+0000"
jira_url	string	Full Jira URL	"https://company.atlassian.net/browse/BMAD-42"

Issue-Only Fields (type: "jira_issue")

Field	Type	Description	Example
jira_reporter	string	Issue reporter display name	"Alice Smith"
jira_labels	list[string]	Issue labels	["backend", "auth"]

Comment-Only Fields (type: "jira_comment")

Field	Type	Description	Example
jira_comment_id	string	Jira comment ID	"10042"
jira_author	string	Comment author display name	"Bob Jones"

Chunking Metadata (if content was chunked)

Field	Type	Description
chunk_index	int	Chunk sequence number (0-based)
total_chunks	int	Total chunks for this document
chunking_strategy	string	Strategy used (e.g., "topical")

---

Direct Query Examples

Use query.py via run-with-env.sh for all direct Qdrant queries. Auth and connection are handled by the standard memory.* config layer — no manual API key export required.

INSTALL="${AI_MEMORY_INSTALL_DIR:-$HOME/.ai-memory}"
QUERY="$INSTALL/_ai-memory/skills/aim-jira-search/scripts/query.py"

# Search by project key (table output)
"$INSTALL/scripts/memory/run-with-env.sh" "$QUERY" \
  --project BMAD --limit 10

# Filter by issue type and status
"$INSTALL/scripts/memory/run-with-env.sh" "$QUERY" \
  --project BMAD --issue-type Bug --status Done --limit 20

# Count points and vectors in the collection
"$INSTALL/scripts/memory/run-with-env.sh" "$QUERY" --count

# Get all comments for a specific issue
"$INSTALL/scripts/memory/run-with-env.sh" "$QUERY" \
  --issue-key BMAD-42 --doc-type jira_comment --limit 50

# JSON output for programmatic use
"$INSTALL/scripts/memory/run-with-env.sh" "$QUERY" \
  --project BMAD --format json --limit 5

Available flags (use exact Qdrant payload field values — see schema above):

• --project — project key (e.g., BMAD)
• --issue-type — issue type (e.g., Bug, Story, Task, Epic)
• --status — status (e.g., "In Progress", Done)
• --issue-key — full issue key (e.g., BMAD-42)
• --doc-type — document type (jira_issue or jira_comment)
• --limit — max results (default: 10)
• --format — table (default) or json
• --count — return collection info counts instead of scroll

---

Python Implementation Reference

The src/memory/connectors/jira/search.py module is not importable from external scripts — use query.py (above) for direct Qdrant access.

Technical Details

• Semantic Search: Uses jina-embeddings-v2-base-en for vector similarity
• Tenant Isolation: Mandatory group_id filter prevents cross-instance leakage
• Performance: < 2s for typical searches
• Collection: jira-data (issues and comments)
• Score Threshold: Configurable via SIMILARITY_THRESHOLD (default 0.7)
• Port: 26350 (NOT the Qdrant default of 6333)
• API Key: Required — stored in ~/.ai-memory/docker/.env as QDRANT_API_KEY

Notes

• Jira instance URL is auto-detected from project configuration
• Results sorted by relevance score (highest first)
• Issue lookup mode returns chronologically sorted comments
• All filters are optional except query (or --issue for lookup mode)
• Use exact field names from the schema above — jira_project NOT project_key, jira_issue_key NOT issue_key