To search your GitHub stars by what a project does rather than by its name, fetch your authenticated account’s starred repositories, turn useful repository text into embeddings, and rank those vectors against an embedding of your query. GitHub does not automatically build this semantic index for you: you supply the indexing, storage, and refresh logic.
What you are building
The workflow has four parts: retrieve the repositories you starred, create one searchable text document for each repository, embed those documents, and retrieve the closest matches for a query. Keep repository IDs and display fields alongside the vectors so results can link back to GitHub and support filters.
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This guide covers an individual’s own starred list, not a search for everyone who starred a particular repository. A practical self-managed storage option is PostgreSQL with pgvector; other managed vector-capable databases can fill the same role, with different operational and data-handling trade-offs.
The Tool Desk
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Use GitHub’s authenticated-user endpoint, GET /user/starred. Send Accept: application/vnd.github+json, an explicit supported X-GitHub-Api-Version header, and a bearer token when authentication is required. The documented fine-grained token permission for this endpoint is Starring: read. Follow the pagination links in the response until there are no more pages; GitHub allows at most 100 repositories per page.
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If you need the date each repository was starred, request the star media type application/vnd.github.star+json. Public resources can be requested without authentication, but access to private profile data requires authentication as that user. See GitHub’s starring API documentation for the endpoint, headers, permissions, and response details.
Do not confuse this route with the separate endpoint for listing people who starred a repository. GitHub’s documentation describes a July 2026 restriction on that other endpoint; it does not change the purpose of listing your own starred repositories.
2. Choose what repository text to index
Start with one compact text document per repository. A useful initial combination is the owner and repository name, description, topics, and a bounded amount of README text. This is a practical starting point, not a universally proven best selection.
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- Repository ID and full name, for stable identity and display.
- GitHub URL, language, topics, and star timestamp, where available.
- The source text used to produce the embedding and the embedding model/version.
For long README files, chunking may make focused passages easier to retrieve, but it adds complexity and more vectors. Compare whole-repository documents with chunks using queries that reflect how you actually search; there is no established universal chunk size.
3. Generate embeddings
An embedding is a vector of floating-point values representing text. To search, embed repository documents during ingestion and embed each query at search time with a compatible model and vector dimension. Similarity between the query vector and stored vectors provides a ranking; it does not guarantee that the first result is the right answer.
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OpenAI’s embeddings guide describes search as a common use and lists text-embedding-3-small and text-embedding-3-large among its newer embedding models. Check the current embeddings documentation for model and API details when implementing, since offerings can change. Store the model/version associated with each vector so a model change can be handled with an intentional re-index rather than silently mixing incompatible vectors.
4. Store vectors and query for matches
For a local or self-managed implementation, pgvector adds vector storage and similarity search to PostgreSQL. Enable the extension with CREATE EXTENSION vector;, then create a vector column whose dimensions match the embedding model. Store the repository identity, metadata, source text, and vector together or in related tables.
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For example, a query can order by cosine distance using pgvector’s <=> operator and return the closest rows with a limit. The pgvector project documentation describes exact search, approximate HNSW and IVFFlat indexes, and supported distance operators. If vectors are normalized, its documentation notes that inner product can offer the best performance.
Begin with exact nearest-neighbor search for a modest personal collection. It provides a straightforward baseline against which to judge relevance. Add an approximate index only if your latency or collection size warrants it: approximate search can be faster but may return different results. Compare its results with exact search on representative queries so you understand any recall trade-off.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Keep the index synchronized and protect credentials
Refresh by reconciling the latest complete star list against stored records. Upsert a vector when the text used to create it changes; update display-only fields independently; and remove records for repositories that are no longer starred if the index is meant to mirror the current list. pgvector supports inserts, upserts, updates, and deletes, while the reconciliation policy is your application’s responsibility.
Keep GitHub tokens and embedding API secrets on a server or in another protected execution environment, never in browser-exposed code. Avoid needless polling and handle rate-limit responses with the response headers and retry/backoff behavior appropriate to the response. GitHub documents primary REST limits of 60 requests per hour for unauthenticated requests and 5,000 per hour for authenticated users; app installations, Actions GITHUB_TOKEN, and secondary limits have additional rules. See GitHub’s REST API rate-limit guidance.
Choose components for your workload
| Decision | Option A | Option B | What to weigh |
|---|---|---|---|
| Vector storage | PostgreSQL with pgvector, self-managed | Managed vector-capable database | Operational burden, service dependence, privacy and data handling, and the features you need. No head-to-head benchmark or current cost comparison is established here. |
| Embedding generation | Hosted embedding API | Local model | API/service dependence, data handling, operating effort, and cost for your usage. Verify current provider terms and model details. |
| Repository text | Metadata and description | README text or chunks | More text can expose useful concepts but may add ingestion and retrieval complexity. Test using your own searches. |
| Nearest-neighbor retrieval | Exact search | Approximate HNSW or IVFFlat index | Use exact search as a baseline; assess whether approximate search’s speed is worth any change in result recall. |
There is no universal best combination established for every personal star list. Begin with the simplest setup that answers your queries, then change one choice at a time and compare relevance, latency, privacy implications, and operating cost for your workload.
Quick Recap
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