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client for little big brain: search, graph queries, and data import","maintainers":[{"name":"rinofmc","email":"rino@littlebigbrain.com"}],"readme":"# @littlebigbrain/client\n\nTypeScript client for [little big brain](https://littlebigbrain.com), a search\nplatform for AI applications such as chatbots, search tools, and agents.\nLoad facts, query their relationships, and keep the data version behind an answer\nso you can check it later.\n\nThe client has no runtime dependencies and includes generated request and response\ntypes. It uses `fetch` and supports Node.js 18+, browsers, and edge workers.\nKeep stack API keys on your server or local machine, outside browser bundles.\n\n[Documentation](https://docs.littlebigbrain.com/sdks/typescript/) ·\n[Quickstart](https://docs.littlebigbrain.com/start/quickstart/) ·\n[Issues](https://github.com/littlebigbrains/lbb-typescript/issues)\n\n## Install\n\n```sh\nnpm install @littlebigbrain/client\n```\n\n## Load facts and run a query\n\nCreate a stack in the [console](https://cloud.littlebigbrain.com) and open\n**Connect**. Copy its complete endpoint and a stack API key:\n\n```sh\nexport LBB_URL=\"https://<your-complete-stack-host>\"\nexport LBB_API_KEY=\"<your-stack-api-key>\"\n```\n\nThis example creates a graph named `quickstart` on its first write. It stores\nthree facts: a service writes to a database, and each has a label. The data uses\nResource Description Framework (RDF), where each line names a subject, a\nrelationship, and a value or another record. SPARQL is the query language for\nthose facts.\n\nSave as `quickstart.mts`, then run `npx tsx quickstart.mts`:\n\n```ts\nimport { LbbClient } from \"@littlebigbrain/client\";\n\nconst lbb = new LbbClient({\n  baseUrl: process.env.LBB_URL!,\n  apiKey: process.env.LBB_API_KEY!,\n  graph: \"quickstart\",\n});\n\nconst facts = `\n<https://example.org/auth-service> <https://example.org/writesTo> <https://example.org/user-db> .\n<https://example.org/auth-service> <http://www.w3.org/2000/01/rdf-schema#label> \"Auth Service\" .\n<https://example.org/user-db> <http://www.w3.org/2000/01/rdf-schema#label> \"User Database\" .\n`;\n\nconst imported = await lbb.graph(\"quickstart\").facts.importRdf(facts, {\n  format: \"ntriples\",\n  idempotencyKey: \"sdk-quickstart-v1\",\n});\nconst commitSeq = imported.committed_commit_seq;\nif (commitSeq == null) throw new Error(\"The import did not return a commit sequence.\");\n\nconst query = `\n  SELECT ?service ?database WHERE {\n    ?s <https://example.org/writesTo> ?db .\n    ?s <http://www.w3.org/2000/01/rdf-schema#label> ?service .\n    ?db <http://www.w3.org/2000/01/rdf-schema#label> ?database .\n  } ORDER BY ?service ?database LIMIT 10\n`;\n\nconst { rows } = await lbb.sparqlRows(\n  { query },\n  { consistency: \"strong\", minIndexedSeq: commitSeq },\n);\n\nfor (const row of rows) console.log(`${row.service} -> ${row.database}`);\n```\n\nOn a fresh graph, this prints:\n\n```text\nAuth Service -> User Database\n```\n\nThe query follows the stored relationship between the service and database.\n`consistency: \"strong\"` makes the new facts available to this read without\nwaiting for a background index job. Reads default to eventual consistency, so\nomit this option only when an earlier version is acceptable.\n\nThe idempotency key makes repeating the same import safe. Use a new key if you\nchange the data.\n\n## Read the same version again\n\nRun the query at the commit returned by the import:\n\n```ts\nconst replay = await lbb.sparqlRows({\n  query,\n  as_of_commit_seq: commitSeq,\n});\nconsole.log(replay.rows);\n```\n\nSave the query, its options, and the commit sequence with any answer you need to\ncheck later. See [history and replay](https://docs.littlebigbrain.com/guides/time-travel-audit/)\nfor retention and evidence handling.\n\n## Ask a question in plain words\n\n`query.ask` turns a question into a SPARQL query, runs it, and returns the rows.\nA router model selects the kind of query. A rewriter model writes the query from\na description of the graph. The server checks the query before it runs it.\n\n```ts\nconst answer = await lbb.query.ask(\n  \"Which services write to the user database?\",\n  { context: \"Services and databases of the platform team.\" },\n);\n\nconsole.log(answer.route.kind, answer.query?.sparql);\nfor (const row of answer.rows) console.log(row);\n```\n\n`answer.error` holds the error when the query did not run. `answer.traceId`\nnames the eval trace of the run, so you can label its rows. `query.rewrite`\nreturns the query without a run, and `mode: \"route\"` returns only the kind of\nquery. Each call uses model tokens, so the client does not retry a failed call.\n\n## Next steps\n\n- [Search by meaning](https://docs.littlebigbrain.com/guides/search-by-meaning/): choose which facts to embed, find records from a text description, and search inside a SPARQL query with `search:similarTo`.\n- [Load your own RDF](https://docs.littlebigbrain.com/guides/load-rdf/): import Turtle, N-Triples, N-Quads, or TriG.\n- [Work with JSON records](https://docs.littlebigbrain.com/guides/without-rdf/): define a schema and write records without writing RDF.\n- [Validate writes](https://docs.littlebigbrain.com/guides/sparql-and-shacl/): define constraints with the Shapes Constraint Language (SHACL).\n\nThe RDF and JSON guides use different write workflows. Choose one when creating\na graph; a graph first written through RDF import does not accept\n`facts.create` or JSON record imports.\n\n## Integrations for your customers\n\n`client.integrations` connects each of your customers' HubSpot, Linear,\nSharePoint or Google Drive to a graph of their own. Its routes are on the\nintegrations API, `https://api.littlebigbrain.com` by default\n(`integrationsUrl`). They take the\nsame stack API key. An operator turns on developer access per stack; ask us\nfirst.\n\n```ts\nconst graph = \"c-5f1c9a0e3b7d2c4a8e6f1b0d\"; // one opaque graph per customer\n\nawait lbb.integrations.create({\n  graph,\n  id: \"hubspot\",\n  kind: \"hubspot\",\n  credentials: { HUBSPOT_TOKEN: token },\n});\nconst { connections } = await lbb.integrations.list({ graph });\nconst open = await lbb.integrations.suggestions(\"hubspot\", { graph, status: \"open\" });\nawait lbb.integrations.accept(open.suggestions[0].suggestion_id, { graph, sync: true });\nawait lbb.integrations.erase(graph, { confirm: graph });\n```\n\nGoogle Drive connects through your own Google app. `googleDrive` builds the\nconsent URL and turns the callback's code into the connection's credentials:\n\n```ts\nimport { googleDrive } from \"@littlebigbrain/client\";\n\nconst url = googleDrive.authorizeUrl({ clientId, redirectUri, state });\n// On the callback, after `state` matches:\nconst { credentials } = await googleDrive.exchangeCode({\n  clientId,\n  clientSecret,\n  redirectUri,\n  code,\n});\nawait lbb.integrations.create({ graph, id: \"google-drive\", kind: \"google_drive\", credentials });\n```\n\nSee [Integrations for your customers](https://docs.littlebigbrain.com/guides/integrations-for-your-customers/).\n\n## Errors and retries\n\nFailed HTTP requests throw `LbbError`, with a status, error code, message, and\nrequest ID. Use `rawRequest()` when you also need response headers or timing.\n\nSafe reads and writes with an idempotency key retry rate limits, retryable server\nerrors, and network failures. Retries respect `Retry-After` and use a 60-second\nbudget by default. See the [client reference](https://docs.littlebigbrain.com/sdks/typescript/)\nfor timeout and retry options.\n\n## Development\n\nFrom a clone of this repository:\n\n```sh\nnpm ci\nnpm run typecheck\nnpm test\n```\n\nThe request and response types are generated from the API contract. See\n[CONTRIBUTING.md](CONTRIBUTING.md) for changes to generated types.\n\n## License\n\n[Apache-2.0](LICENSE).\n","readmeFilename":"README.md"}