Aim Github Search

Search GitHub - Semantic Search for GitHub Content

Search the discussions collection for GitHub-sourced content using semantic similarity with advanced filtering.

Usage

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

# Filter by type
/aim-github-search "API refactoring" --type github_pr
/aim-github-search "CI failures" --type github_ci_result

# Filter by state
/aim-github-search "deployment fix" --state merged

# Combine filters
/aim-github-search "security" --type github_issue --limit 10

Options

• --type <type> - Filter by GitHub document type: github_issue, github_pr, github_commit, github_ci_result, github_code_blob, github_issue_comment, github_pr_review, github_pr_diff
• --state <state> - Filter by state: open, closed, merged
• --limit <n> - Maximum results to return (default: 5)

Result Format

Each result includes:

• GitHub URL - Direct link
• Metadata badges - Type, State, Date
• Content snippet - First ~300 characters
• Relevance score - Semantic similarity with decay (0-100%)

Qdrant Connection Details

GitHub content is stored in the discussions collection:

To get the API key:

export QDRANT_API_KEY="$(grep QDRANT_API_KEY ~/.ai-memory/docker/.env | cut -d= -f2)"

Qdrant Payload Schema

Every GitHub point in discussions has the following payload fields. Use these exact names for filtering.

Common Fields (all GitHub points)

Direct Query Examples

Use the curl-to-file-to-python pattern for direct Qdrant queries.

Important: Save curl output to a temp file first, then process with Python. Do NOT pipe directly to python3.

Search by source

# Step 1: Get API key
export QDRANT_API_KEY="$(grep QDRANT_API_KEY ~/.ai-memory/docker/.env | cut -d= -f2)"

# Step 2: Query Qdrant and save to temp file
# Replace "owner/repo-name" with your GITHUB_REPO value (e.g., "Hidden-History/ai-memory")
curl -s -H "Api-Key: $QDRANT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "filter": {
      "must": [
        {"key": "source", "match": {"value": "github"}},
        {"key": "group_id", "match": {"value": "owner/repo-name"}}
      ]
    },
    "limit": 10,
    "with_payload": true
  }' \
  http://localhost:26350/collections/discussions/points/scroll > /tmp/github_results.json

# Step 3: Process with Python
python3 -c "
import json
data = json.load(open('/tmp/github_results.json'))
points = data.get('result', {}).get('points', [])
print(f'Found {len(points)} points')
for p in points:
    pl = p.get('payload', {})
    print(f\"  [{pl.get('type', '?')}] {pl.get('state', '?')} - {pl.get('url', '?')} - {pl.get('content', '')[:80]}...\")
"

Filter by type and state

# Replace "owner/repo-name" with your GITHUB_REPO value
curl -s -H "Api-Key: $QDRANT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "filter": {
      "must": [
        {"key": "source", "match": {"value": "github"}},
        {"key": "group_id", "match": {"value": "owner/repo-name"}},
        {"key": "type", "match": {"value": "github_pr"}},
        {"key": "state", "match": {"value": "merged"}}
      ]
    },
    "limit": 20,
    "with_payload": true
  }' \
  http://localhost:26350/collections/discussions/points/scroll > /tmp/github_results.json

Count GitHub points in collection

# Replace "owner/repo-name" with your GITHUB_REPO value
curl -s -H "Api-Key: $QDRANT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "filter": {
      "must": [
        {"key": "source", "match": {"value": "github"}},
        {"key": "group_id", "match": {"value": "owner/repo-name"}}
      ]
    },
    "exact": true
  }' \
  http://localhost:26350/collections/discussions/points/count | python3 -c "
import json, sys
data = json.load(sys.stdin)
print(f\"GitHub points: {data.get('result', {}).get('count', 0)}\")
"

Python Implementation Reference

This skill uses the source="github" filter pattern against the discussions collection:

# Key filter patterns
from qdrant_client.models import FieldCondition, Filter, MatchValue

must_conditions = [
    FieldCondition(key="source", match=MatchValue(value="github")),
    FieldCondition(key="group_id", match=MatchValue(value=group_id)),
]
if type_filter:
    must_conditions.append(
        FieldCondition(key="type", match=MatchValue(value=type_filter))
    )
if state_filter:
    must_conditions.append(
        FieldCondition(key="state", match=MatchValue(value=state_filter))
    )

Technical Details

• Semantic Search: Uses jina-embeddings-v2-base-en for vector similarity
• Tenant Isolation: Mandatory group_id filter prevents cross-repo leakage
• Performance: < 2s for typical searches
• Collection: discussions (GitHub content stored alongside conversation data)
• 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
• Decay Scoring: Applied to results via existing search path

Notes

• GitHub repo is auto-detected from project configuration
• Results sorted by relevance score (highest first)
• All filters are optional except the search query
• Use exact field names from the schema above -- source NOT namespace, type NOT doc_type
• The source="github" filter is always applied to restrict results to GitHub content
• The group_id filter is always applied for mandatory tenant isolation (prevents cross-repo leakage)