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have a file that any AI agent can add as knowledge that gives them good RAG abilities of the github repo
update it daily. It can go something like date,type(issue,pr,commit),title,author, etc
The text was updated successfully, but these errors were encountered:
Here are some useful jq commands to transform your contributor activity data:
jq -r '.[] | .activity | (.code.commits[] | ["commit", .created_at, .message, .author] | @csv), (.code.pull_requests[] | ["pr", .created_at, .title, .author] | @csv), (.issues.opened[] | ["issue", .created_at, .title, .author] | @csv)' input.json > activity_timeline.csv
jq -r '.[] | .activity.code.pull_requests[] | [.created_at, "pr", .title, .author, .state, .body] | @csv' input.json > pr_details.csv
jq -r '.[] | .activity.code.commits[] | [.created_at, .sha[0:7], .message, (.additions + .deletions | tostring + " changes"), .changed_files] | @csv' input.json > commit_summary.csv
jq -r '.[] | [.contributor, .score, .summary, .activity.code.total_commits, .activity.code.total_prs] | @csv' input.json > contributor_summary.csv
You can then use these CSV files as input for Eliza's knowledge system using the following approach:
import { readFileSync } from 'fs'; import { parse } from 'csv-parse/sync'; // Read and parse CSV const activityData = readFileSync('activity_timeline.csv'); const records = parse(activityData); // Create knowledge items in chunks for (const record of records) { await knowledge.set(runtime, { id: generateId(), // Use appropriate ID generation content: { text: `On ${record[1]}, ${record[3]} ${record[0]}ed: ${record[2]}`, type: record[0], author: record[3], date: record[1] } }, 512); // Using default chunk size }
The knowledge system will:
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have a file that any AI agent can add as knowledge that gives them good RAG abilities of the github repo
update it daily. It can go something like date,type(issue,pr,commit),title,author, etc
The text was updated successfully, but these errors were encountered: