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Fetch the complete documentation index at: https://docs.datalinks.com/llms.txt

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datalinks.api package

Bases: object Class for interfacing with the DataLinks API. Provides methods for ingesting data, managing namespaces, and querying data from DataLinks. Designed to interact with a configurable backend, providing flexibility for deployment environments.
  • Variables: config – Configuration object containing API key, host, index, namespace, and object name.

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ingest(data, inference_steps=None, entity_resolution=None, batch_size=0, max_attempts=3, curate=None, data_description=None, schema_definition=None, additional_instructions=None)

Ingests data into the namespace by batching the given data and performing multiple retries in case of failures. This function sends data in chunks (batches), to be processed through configured inference steps, and to resolve entities based on the provided configuration. If a batch fails, it is retried up to a maximum number of attempts.
  • Parameters:
    • data (List[Dict[str, Any]]) – List of dictionaries, where each dictionary represents a data block to be ingested.
    • inference_steps (Pipeline | None) – Pipeline of inference steps to be applied for processing the data. If None the data will be ingested as is.
    • entity_resolution (MatchTypeConfig | None) – Configuration specifying how entity resolution is to be performed.
    • batch_size – Number of data blocks to be included in each batch. Defaults to the size of the entire dataset if not provided.
    • max_attempts – Maximum number of retry attempts for failed batches. Defaults to the provided constant MAX_INGEST_ATTEMPTS.
    • curate (Optional *[*bool ]) – If True, automatically curate ontology links after ingestion.
    • data_description (Optional *[*str ]) – Free-text description of the dataset to guide the AI during ingestion.
    • schema_definition (Optional *[*Dict *[*str , str ] ]) – Field-name-to-description mapping to guide the AI in structuring extracted data.
    • additional_instructions (Optional *[*str ]) – Additional free-text instructions to guide the AI during ingestion.
  • Return type: IngestionResult
  • Returns: An IngestionResult object containing lists of successfully ingested data blocks and data blocks that failed to be ingested.

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create_space(is_private=True, data_description=None, schema_definition=None)

Creates a new space with the specified privacy settings. This function sends a POST request to create a namespace with the given privacy status. Information about the namespace creation will be logged, including the HTTP status code and response reason. If the namespace already exists, a warning will be logged.
  • Parameters:
    • is_private (bool) – Determines whether the created namespace will be private or public.
    • data_description (Optional *[*str ]) – Free-text description of the dataset (max 10,000 chars).
    • schema_definition (Optional *[*Dict *[*str , str ] ]) – Field-name-to-description mapping to guide the AI in structuring data.
  • Return type: None
  • Returns: None
  • Raises: HTTPError – If the HTTP request fails due to connectivity issues or server-side problems.

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update_infer_definition(data_description, field_definition)

Update the saved inference definition for the configured dataset. The inference definition is used automatically on future ingest calls to guide field extraction and normalization.
  • Parameters:
    • data_description (str) – Free-text description of the dataset (max 10,000 chars).
    • field_definition (str) – Field-level definitions, one per line as field=description (max 10,000 chars).
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None

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infer_dataset_description(sample, model=None, provider=None, current_description=None, current_schema=None)

Ask an agent to infer a data description and field schema from sampled data.
  • Parameters:
    • sample (Dataset) – A sample of data rows to analyse.
    • model (Optional *[*str ]) – LLM model name.
    • provider (Optional *[*str ]) – LLM provider (e.g. "openai", "ollama").
    • current_description (Optional *[*str ]) – Existing description to refine.
    • current_schema (Optional *[*Dict *[*str , str ] ]) – Existing field schema to refine (field → description mapping).
  • Returns: Inferred dataDescription and fieldDefinition.
  • Return type: Dict
  • Raises: DataLinksRequestError – If the HTTP request fails.

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update_sort_order(order)

Update the display order of columns for the configured dataset.
  • Parameters: order (List *[*str ]) – Ordered list of all column names in the desired sequence.
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None

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prepare_multipart_upload(filename, size)

Initiate a multipart upload and receive presigned URLs for each part. Use this for large files. Upload each part directly to its presigned URL, then call finish_multipart_upload() with the returned ETags.
  • Parameters:
    • filename (str) – Name of the file being uploaded.
    • size (int) – File size in bytes.
  • Returns: Response containing uploadId, key, and presigned part URLs.
  • Return type: Dict
  • Raises: DataLinksRequestError – If the HTTP request fails.

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finish_multipart_upload(upload_id, key, parts, name=None)

Complete a multipart upload after all parts have been uploaded.
  • Parameters:
    • upload_id (str) – Upload ID from prepare_multipart_upload().
    • key (str) – S3 object key from prepare_multipart_upload().
    • parts (List *[*Dict *[*str , Any ] ]) – List of completed parts, each with partNumber (int) and etag (str) returned by S3.
    • name (Optional *[*str ]) – Optional label for the ingestion (e.g. original filename).
  • Returns: Ingestion result from the server.
  • Return type: Dict
  • Raises: DataLinksRequestError – If the HTTP request fails.

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abort_multipart_upload(upload_id, key)

Abort a multipart upload and clean up partial data.

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list_ingestions(page_size=25)

List ingestion attempts for the configured dataset, most recent first. Each record contains id, status, statusMessage, processedBytes, expectedTotalBytes, processedRows, and attributes.
  • Parameters: page_size (int) – Number of records to return (1-100, default 25).
  • Returns: List of ingestion attempt dicts, or None on failure.
  • Return type: Optional[List[Dict]]

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wait_for_ingestion(ingestion_id, poll_interval=5, timeout=1200)

Poll until the given ingestion reaches a terminal status. Polls list_ingestions() every poll_interval seconds until the ingestion with ingestion_id is no longer in a pending/processing state, or until timeout seconds have elapsed.
  • Parameters:
    • ingestion_id (str) – Ingestion ID returned by finish_multipart_upload().
    • poll_interval (int) – Seconds between polls (default 5).
    • timeout (int) – Maximum seconds to wait before raising (default 600).
  • Returns: The final ingestion record dict.
  • Return type: Dict
  • Raises:
    • TimeoutError – If timeout is exceeded before a terminal status.
    • DataLinksRequestError – If polling requests fail.

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get_dataset_info()

Retrieve metadata for the configured dataset. Returns a dict with dataset, metadata, and inferDefinition keys, or None if the request fails.
  • Returns: Dataset metadata dict, or None on failure.
  • Return type: Optional[Dict]

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delete_dataset()

Permanently delete the configured dataset, including all data, links, and metadata. This action is irreversible (balefire).

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rename_dataset(new_name)

Rename the configured dataset.
  • Parameters: new_name (str) – The new dataset name.
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None

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clear_dataset()

Remove all data and links from the configured dataset. The dataset itself (metadata, schema) is preserved. This action is irreversible. Create a manual link between two dataset columns.
  • Parameters:
    • from_namespace (str) – Source namespace.
    • from_dataset (str) – Source dataset name.
    • from_column (str) – Source column name.
    • to_namespace (str) – Target namespace.
    • to_dataset (str) – Target dataset name.
    • to_column (str) – Target column name.
    • match_type (str) – Match type — "ExactMatch" or "GeoMatch".
    • options (Optional[Dict[str, Any]]) – Optional match configuration (e.g. minDistinct, distance).
  • Returns: True if the link was successfully created, False if already exists. None if failure.
  • Return type: bool
  • Raises: DataLinksRequestError – If the HTTP request fails.
Preview what recalculating links would produce without saving changes.
  • Parameters:
    • data (Dataset) – Array of ontology data objects (e.g. from query_data()).
    • entity_resolution (Optional [MatchTypeConfig ]) – Optional link matching configuration.
  • Returns: Preview of link objects, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.
Recalculate links for the configured dataset based on current data.
  • Parameters:
    • data (Dataset) – Array of ontology data objects (e.g. from query_data()).
    • entity_resolution (Optional [MatchTypeConfig ]) – Optional link matching configuration.
  • Returns: Updated list of link objects, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.
Retrieve active and suggested links for the configured dataset.
  • Returns: A list of link objects, or None on failure.
  • Return type: Optional[List[Dict]]

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list_datasets(namespace=None)

Retrieves the list of datasets for the user, optionally filtered by a specific namespace.
  • Parameters: namespace (Optional *[*AnyStr ]) – Optional namespace to filter the datasets by. If provided, only datasets associated with the given namespace will be returned. If not provided, all datasets are retrieved.
  • Returns: A list of datasets represented as dictionaries if the query is successful and returns a status code of 200, or None if the query fails or encounters an error.
  • Return type: List[Dict] | None

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query_data(query=None, is_natural_language=False, model=None, provider=None, include_metadata=False, explain=False)

Queries data from a specified data source and processes the response. The method allows querying with a specific query string or with a wildcard (“*”) for all data. The response from the query can be filtered to exclude metadata fields if include_metadata is set to False. Metadata fields are identified by key names starting with an underscore.
  • Parameters:
    • query (str) – The query string to use for fetching data. Defaults to “*”, which retrieves all data.
    • is_natural_language (bool) – If True, the query is treated as a natural language query.
    • model (str) – The model name to use for inference.
    • provider (str) – The provider of the LLM model (ollama, openai, etc)
    • include_metadata (bool) – Specifies whether to include metadata fields in the returned data. Defaults to False.
    • explain (bool) – If True, request an explanation of how the query was resolved.
  • Returns: A list of records represented as dictionaries, or None if the query fails or an exception occurs during the request.
  • Return type: List[Dict] | None
  • Raises: requests.exceptions.RequestException – If a request-related error occurs during querying.

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ask(query, model=None, provider=None, helper_prompt=None)

Talk to your data with natural language using the DataLinks AutoRAG agent. Streams the agent’s reasoning and final answer as Server-Sent Events. Events are yielded in order: one plan event, one or more step events, then either an answer event or an error event.
  • Parameters:
    • query (str) – The natural language question to answer.
    • model (str) – The model name to use for inference.
    • provider (str) – The LLM provider (e.g. openai, ollama).
    • helper_prompt (str) – Optional custom system prompt.
  • Returns: An iterator of AskEvent objects.
  • Return type: Iterator[AskEvent]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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preview_ingest(data, inference_steps=None)

Process data through the ingestion pipeline without saving it to a dataset.
  • Parameters:
    • data (Dataset) – List of data records to preview.
    • inference_steps (Optional [Pipeline ]) – Optional pipeline of inference steps to apply.
  • Returns: List of processed preview records, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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infer_schema(sample, model=None, provider=None, current_schema=None)

Ask an agent to infer a field type schema from sampled data.
  • Parameters:
    • sample (Dataset) – A sample of data rows to analyse.
    • model (Optional *[*str ]) – LLM model name.
    • provider (Optional *[*str ]) – LLM provider (e.g. "openai", "ollama").
    • current_schema (Optional *[*Dict *[*str , str ] ]) – Existing field schema to refine (field → description mapping).
  • Returns: Dict with schema key mapping field names to their inferred types.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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retry_ingestion(ingestion_id)

Retry a failed ingestion by creating a new ingestion record from the original.
  • Parameters: ingestion_id (str) – The ID of the ingestion to retry.
  • Returns: Dict with the new ingestion id, or None on failure.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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mark_ingestion_seen(ingestion_id)

Mark an ingestion as seen, updating its seenAt timestamp.
  • Parameters: ingestion_id (str) – The ID of the ingestion to mark as seen.
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None

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autorag(query, model=None, provider=None, helper_prompt=None)

Answer a natural language question using the AutoRAG agent (non-streaming). Returns the final answer and all intermediate steps once the agent completes. For incremental streaming results, use ask() instead.
  • Parameters:
    • query (str) – The natural language question to answer.
    • model (Optional *[*str ]) – LLM model name.
    • provider (Optional *[*str ]) – LLM provider (e.g. "openai", "ollama").
    • helper_prompt (Optional *[*str ]) – Optional custom system prompt.
  • Returns: Dict with response (str) and steps (list) keys, or None on failure.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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request_cleaning(prompts, output_namespace, output_dataset_name)

Request a cleaning job for the configured dataset.
  • Parameters:
    • prompts (List *[*str ]) – 1–10 prompts describing each cleaning step in order.
    • output_namespace (str) – Target namespace for the cleaned dataset.
    • output_dataset_name (str) – Name for the cleaned dataset (must be unused in target namespace).
  • Returns: The cleaningTaskId UUID string, or None on failure.
  • Return type: Optional[str]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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get_cleaning_code(cleaning_task_id)

Retrieve code files generated by the cleaning agent for a task.
  • Parameters: cleaning_task_id (str) – UUID of the cleaning task.
  • Returns: List of dicts with name and content keys, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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get_ontology()

Load the ontology (active links) for the configured dataset.
  • Returns: List of link dicts, or None if no ontology exists or the request fails.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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save_ontology(add=None, remove=None)

Save (update) the ontology for the configured dataset.
  • Parameters:
    • add (Optional *[*List *[*Dict ] ]) – Links to add to the ontology.
    • remove (Optional *[*List *[*Dict ] ]) – Links to remove from the ontology.
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None
Run the OntologyCurator agent to analyse computed links and optionally activate them. When activate=False (default), the curated links are returned without being saved. When activate=True, the curated links are added to the ontology.
  • Parameters:
    • namespace (Optional *[*str ]) – Namespace to curate. Defaults to the configured namespace.
    • dataset (Optional *[*str ]) – Dataset to curate. If omitted, all datasets in the namespace are curated.
    • model (Optional *[*str ]) – LLM model name.
    • provider (Optional *[*str ]) – LLM provider (e.g. "openai", "anthropic").
    • activate (bool) – If True, add curated links to the ontology.
  • Returns: Dict with datasetsProcessed, totalSelected, and optionally curatedLinks.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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rename_namespace(new_name)

Rename the configured namespace.
  • Parameters: new_name (str) – The new namespace name.
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None

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list_namespaces(user=‘self’)

Retrieve namespaces for a user.
  • Parameters: user (str) – Username or "self" for the current user.
  • Returns: List of namespace dicts, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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list_all_datasets_schema()

Retrieve all datasets visible to the authenticated user (schema endpoint).
  • Returns: List of dataset dicts, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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list_datasets_in_namespace_schema(namespace=None, user=‘self’)

Retrieve datasets within a specific namespace (schema endpoint).
  • Parameters:
    • namespace (Optional *[*str ]) – Namespace to list. Defaults to the configured namespace.
    • user (str) – Username or "self" for the current user.
  • Returns: List of dataset dicts, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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list_tokens()

List all API tokens for the authenticated user.
  • Returns: List of token dicts, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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add_token(name, expires_at=None, access_restricted_to=None)

Create a new API token for the authenticated user.
  • Parameters:
    • name (str) – Display name for the token.
    • expires_at (Optional *[*str ]) – Optional expiry timestamp (ISO 8601 string).
    • access_restricted_to (Optional *[*List *[*Dict ] ]) – Optional list of permission entries restricting access (each dict with username, namespace, and optionally dataset).
  • Returns: Created token dict (includes the token secret), or None on failure.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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delete_token(token_id)

Delete an API token.
  • Parameters: token_id (str) – ID of the token to delete.
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None

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list_token_permissions(token_id)

List permissions assigned to a token.
  • Parameters: token_id (str) – ID of the token.
  • Returns: Dict with restricted (bool) and permissions (list) keys, or None on failure.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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get_usage_history(on_or_after=None, before=None, page_size=25, page_cursor=None)

Retrieve historical usage data for the authenticated user.
  • Parameters:
    • on_or_after (Optional *[*str ]) – Return records on or after this ISO 8601 timestamp.
    • before (Optional *[*str ]) – Return records before this ISO 8601 timestamp.
    • page_size (int) – Number of records per page (default 25).
    • page_cursor (Optional *[*Dict ]) – Pagination cursor dict from a previous response.
  • Returns: Dict with data and meta keys, or None on failure.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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get_usage_by_day(on_or_after=None, before=None, timezone=‘UTC’)

Retrieve usage data aggregated by day for the authenticated user.
  • Parameters:
    • on_or_after (Optional *[*str ]) – Return records on or after this ISO 8601 timestamp.
    • before (Optional *[*str ]) – Return records before this ISO 8601 timestamp.
    • timezone (str) – Timezone for date aggregation (e.g. "America/New_York"). Defaults to UTC.
  • Returns: Dict with data and meta keys, or None on failure.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.
Bases: Exception Bases: object DLConfig class is a configuration container for managing the required settings to interact with DataLinks. It loads configuration values from environment variables to provide flexibility across different environments. This class is designed to simplify the initialization and storage of connection and namespace details required to communicate with DataLinks.
  • Variables:
    • host – The host URL for the data layer connection.
    • apikey – The API key for authentication with the data layer.
    • index – The index name to be used in the data layer operations.
    • namespace – The namespace for organizing data in the data layer.
    • objectname – The name of the object associated with the configuration. Defaults to an empty string.

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host : str

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apikey : str

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index : str

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namespace : str

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objectname : str

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classmethod from_env(load_dotenv=True)

Bases: object Represents a single SSE event from the /query/ask streaming endpoint.
  • Variables:
    • type – Event type — one of plan, step, answer, or error.
    • data – Parsed JSON payload for the event.

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type : str

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data : Dict[str, Any]

Bases: object Represents the result of a data ingestion process into DataLinks. This class is a data structure used to store the results of a data ingestion operation. It separates the successfully ingested items from the failed ones, enabling users to track and handle both cases effectively.
  • Variables:
    • successful – A list of records successfully ingested. Each record is represented as a dictionary.
    • failed – A list of records that failed ingestion. Each record is represented as a dictionary.

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successful : List[Dict[str, Any]]

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failed : List[Dict[str, Any]]

Bases: object Client for the DataLinks ingestion proxy (auto-modelling) service. Wraps the POST /api/pipeline, GET /api/pipeline/{runId}/stream, GET /api/pipeline/{runId}/trace, and POST /api/pipeline/{runId}/hook endpoints.
  • Variables: config – Proxy configuration.

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run_pipeline(, data=None, data_url=None, data_blob_url=None, namespace=None, user_prompt=None, model=True, ingest=True, ontology=True, max_eval_retries=3, max_rows_for_modeling=20, max_sample_rows=10, enable_human_in_the_loop=False, predefined_schema=None, explosion_helper_prompt=None, coalescence_helper_prompt=None, llm=None, datalinks_inference_settings=None)

Start a full pipeline run (auto-modelling + ingest). Exactly one of data, data_url, or data_blob_url must be provided. Returns a PipelineRun whose run_id attribute is the workflow run identifier and which can be iterated to receive NDJSON progress events.
  • Parameters:
    • data (Optional[List[Dict[str, Any]]]) – Inline JSON array of row objects.
    • data_url (Optional[str]) – Remote URL returning a JSON array (fetched by the pipeline).
    • data_blob_url (Optional[str]) – Pre-uploaded Vercel Blob URL.
    • namespace (Optional[str]) – Target namespace; defaults to config.namespace.
    • user_prompt (Optional[str]) – Domain goals; inferred from data when omitted.
    • model (bool) – Run the model phase (default True).
    • ingest (bool) – Run the ingest phase (default True).
    • ontology (bool) – Run namespace curation after ingest (default True).
    • max_eval_retries (int) – Max modelling iterations (default 3).
    • max_rows_for_modeling (int) – Rows sent to the LLM for schema modelling (default 20).
    • max_sample_rows (int) – Sample rows generated for preview (default 10).
    • enable_human_in_the_loop (bool) – Surface clarification + schema review hooks (default False).
    • predefined_schema (Optional[Dict[str, Any]]) – Skip model phase when provided.
    • explosion_helper_prompt (Optional[str]) – Extra context injected into the explode step.
    • coalescence_helper_prompt (Optional[str]) – Extra context injected into the coalesce step.
    • llm (Optional[Dict[str, Any]]) – LLM configuration dict with optional keys: provider, model, explosionTemperature, coalescenceTemperature, evaluationTemperature, ontologyTemperature.
    • datalinks_inference_settings (Optional[Dict[str, Any]]) – DataLinks inference settings dict with optional keys: provider, model, ontologyCurationProvider, ontologyCurationModel.
  • Return type: PipelineRun
  • Returns: A PipelineRun instance.
  • Raises: DataLinksRequestError – If the HTTP request fails.

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stream_pipeline(run_id, start_index=0)

Stream progress events for an existing pipeline run.
  • Parameters:
    • run_id (str) – Workflow run identifier returned by run_pipeline().
    • start_index (int) – Resume from this event index (default 0). Pass the number of events already received to skip replaying them on reconnect.
  • Return type: Iterator[Dict[str, Any]]
  • Returns: An iterator of NDJSON event dicts.
  • Raises: DataLinksRequestError – If the HTTP request fails.

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get_pipeline_trace(run_id)

Download the full trace for a completed pipeline run.
  • Parameters: run_id (str) – Workflow run identifier.
  • Return type: Optional[Dict[str, Any]]
  • Returns: Dict with LLM calls, token usage, and step durations, or None on failure.
  • Raises: DataLinksRequestError – If the HTTP request fails.

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resume_pipeline_hook(run_id, payload)

Resume a human-in-the-loop hook (clarification, schema review, or token refresh).
  • Parameters:
    • run_id (str) – Workflow run identifier.
    • payload (Dict[str, Any]) – Hook response payload.
  • Return type: Optional[Dict[str, Any]]
  • Returns: Response dict, or None on failure.
  • Raises: DataLinksRequestError – If the HTTP request fails.
Bases: object Configuration for the DataLinks ingestion proxy (auto-modelling) service.
  • Variables:
    • host – Base URL of the ingestion proxy (e.g. http://localhost:3003).
    • datalinks_token – DataLinks JWT token sent as Authorization: Bearer (DL_API_KEY).
    • datalinks_username – DataLinks username included in the request body (DL_USERNAME).
    • namespace – Default target namespace (DL_NAMESPACE).

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host : str

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datalinks_token : str

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datalinks_username : str

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namespace : str

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classmethod from_env(load_dotenv=True)

Bases: object Wraps a pipeline run with automatic stream reconnection. Provides the workflow run_id (from the x-workflow-run-id response header) and an iterable interface over the NDJSON progress events. The iterator reconnects transparently on connection drops, resuming from the last received event via the startIndex query parameter. Iteration ends only when an explicit complete or error event is received. Usage: 
run = proxy.run_pipeline(data=[...])
print(run.run_id)
for event in run:
    print(event)
Can also be used as a context manager: 
with proxy.run_pipeline(data=[...]) as run:
    for event in run:
        process(event)

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close()

  • Return type: None

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Submodules

Bases: object DLConfig class is a configuration container for managing the required settings to interact with DataLinks. It loads configuration values from environment variables to provide flexibility across different environments. This class is designed to simplify the initialization and storage of connection and namespace details required to communicate with DataLinks.
  • Variables:
    • host – The host URL for the data layer connection.
    • apikey – The API key for authentication with the data layer.
    • index – The index name to be used in the data layer operations.
    • namespace – The namespace for organizing data in the data layer.
    • objectname – The name of the object associated with the configuration. Defaults to an empty string.

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host : str

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apikey : str

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index : str

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namespace : str

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objectname : str

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classmethod from_env(load_dotenv=True)

Bases: object Represents a single SSE event from the /query/ask streaming endpoint.
  • Variables:
    • type – Event type — one of plan, step, answer, or error.
    • data – Parsed JSON payload for the event.

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type : str

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data : Dict[str, Any]

Bases: object Represents the result of a data ingestion process into DataLinks. This class is a data structure used to store the results of a data ingestion operation. It separates the successfully ingested items from the failed ones, enabling users to track and handle both cases effectively.
  • Variables:
    • successful – A list of records successfully ingested. Each record is represented as a dictionary.
    • failed – A list of records that failed ingestion. Each record is represented as a dictionary.

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successful : List[Dict[str, Any]]

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failed : List[Dict[str, Any]]

Bases: Exception Bases: object Class for interfacing with the DataLinks API. Provides methods for ingesting data, managing namespaces, and querying data from DataLinks. Designed to interact with a configurable backend, providing flexibility for deployment environments.
  • Variables: config – Configuration object containing API key, host, index, namespace, and object name.

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config : DLConfig

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ingest(data, inference_steps=None, entity_resolution=None, batch_size=0, max_attempts=3, curate=None, data_description=None, schema_definition=None, additional_instructions=None)

Ingests data into the namespace by batching the given data and performing multiple retries in case of failures. This function sends data in chunks (batches), to be processed through configured inference steps, and to resolve entities based on the provided configuration. If a batch fails, it is retried up to a maximum number of attempts.
  • Parameters:
    • data (List[Dict[str, Any]]) – List of dictionaries, where each dictionary represents a data block to be ingested.
    • inference_steps (Pipeline | None) – Pipeline of inference steps to be applied for processing the data. If None the data will be ingested as is.
    • entity_resolution (MatchTypeConfig | None) – Configuration specifying how entity resolution is to be performed.
    • batch_size – Number of data blocks to be included in each batch. Defaults to the size of the entire dataset if not provided.
    • max_attempts – Maximum number of retry attempts for failed batches. Defaults to the provided constant MAX_INGEST_ATTEMPTS.
    • curate (Optional *[*bool ]) – If True, automatically curate ontology links after ingestion.
    • data_description (Optional *[*str ]) – Free-text description of the dataset to guide the AI during ingestion.
    • schema_definition (Optional *[*Dict *[*str , str ] ]) – Field-name-to-description mapping to guide the AI in structuring extracted data.
    • additional_instructions (Optional *[*str ]) – Additional free-text instructions to guide the AI during ingestion.
  • Return type: IngestionResult
  • Returns: An IngestionResult object containing lists of successfully ingested data blocks and data blocks that failed to be ingested.

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create_space(is_private=True, data_description=None, schema_definition=None)

Creates a new space with the specified privacy settings. This function sends a POST request to create a namespace with the given privacy status. Information about the namespace creation will be logged, including the HTTP status code and response reason. If the namespace already exists, a warning will be logged.
  • Parameters:
    • is_private (bool) – Determines whether the created namespace will be private or public.
    • data_description (Optional *[*str ]) – Free-text description of the dataset (max 10,000 chars).
    • schema_definition (Optional *[*Dict *[*str , str ] ]) – Field-name-to-description mapping to guide the AI in structuring data.
  • Return type: None
  • Returns: None
  • Raises: HTTPError – If the HTTP request fails due to connectivity issues or server-side problems.

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update_infer_definition(data_description, field_definition)

Update the saved inference definition for the configured dataset. The inference definition is used automatically on future ingest calls to guide field extraction and normalization.
  • Parameters:
    • data_description (str) – Free-text description of the dataset (max 10,000 chars).
    • field_definition (str) – Field-level definitions, one per line as field=description (max 10,000 chars).
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None

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infer_dataset_description(sample, model=None, provider=None, current_description=None, current_schema=None)

Ask an agent to infer a data description and field schema from sampled data.
  • Parameters:
    • sample (Dataset) – A sample of data rows to analyse.
    • model (Optional *[*str ]) – LLM model name.
    • provider (Optional *[*str ]) – LLM provider (e.g. "openai", "ollama").
    • current_description (Optional *[*str ]) – Existing description to refine.
    • current_schema (Optional *[*Dict *[*str , str ] ]) – Existing field schema to refine (field → description mapping).
  • Returns: Inferred dataDescription and fieldDefinition.
  • Return type: Dict
  • Raises: DataLinksRequestError – If the HTTP request fails.

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update_sort_order(order)

Update the display order of columns for the configured dataset.
  • Parameters: order (List *[*str ]) – Ordered list of all column names in the desired sequence.
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None

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prepare_multipart_upload(filename, size)

Initiate a multipart upload and receive presigned URLs for each part. Use this for large files. Upload each part directly to its presigned URL, then call finish_multipart_upload() with the returned ETags.
  • Parameters:
    • filename (str) – Name of the file being uploaded.
    • size (int) – File size in bytes.
  • Returns: Response containing uploadId, key, and presigned part URLs.
  • Return type: Dict
  • Raises: DataLinksRequestError – If the HTTP request fails.

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finish_multipart_upload(upload_id, key, parts, name=None)

Complete a multipart upload after all parts have been uploaded.
  • Parameters:
    • upload_id (str) – Upload ID from prepare_multipart_upload().
    • key (str) – S3 object key from prepare_multipart_upload().
    • parts (List *[*Dict *[*str , Any ] ]) – List of completed parts, each with partNumber (int) and etag (str) returned by S3.
    • name (Optional *[*str ]) – Optional label for the ingestion (e.g. original filename).
  • Returns: Ingestion result from the server.
  • Return type: Dict
  • Raises: DataLinksRequestError – If the HTTP request fails.

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abort_multipart_upload(upload_id, key)

Abort a multipart upload and clean up partial data.

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list_ingestions(page_size=25)

List ingestion attempts for the configured dataset, most recent first. Each record contains id, status, statusMessage, processedBytes, expectedTotalBytes, processedRows, and attributes.
  • Parameters: page_size (int) – Number of records to return (1-100, default 25).
  • Returns: List of ingestion attempt dicts, or None on failure.
  • Return type: Optional[List[Dict]]

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wait_for_ingestion(ingestion_id, poll_interval=5, timeout=1200)

Poll until the given ingestion reaches a terminal status. Polls list_ingestions() every poll_interval seconds until the ingestion with ingestion_id is no longer in a pending/processing state, or until timeout seconds have elapsed.
  • Parameters:
    • ingestion_id (str) – Ingestion ID returned by finish_multipart_upload().
    • poll_interval (int) – Seconds between polls (default 5).
    • timeout (int) – Maximum seconds to wait before raising (default 600).
  • Returns: The final ingestion record dict.
  • Return type: Dict
  • Raises:
    • TimeoutError – If timeout is exceeded before a terminal status.
    • DataLinksRequestError – If polling requests fail.

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get_dataset_info()

Retrieve metadata for the configured dataset. Returns a dict with dataset, metadata, and inferDefinition keys, or None if the request fails.
  • Returns: Dataset metadata dict, or None on failure.
  • Return type: Optional[Dict]

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delete_dataset()

Permanently delete the configured dataset, including all data, links, and metadata. This action is irreversible (balefire).

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rename_dataset(new_name)

Rename the configured dataset.
  • Parameters: new_name (str) – The new dataset name.
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None

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clear_dataset()

Remove all data and links from the configured dataset. The dataset itself (metadata, schema) is preserved. This action is irreversible. Create a manual link between two dataset columns.
  • Parameters:
    • from_namespace (str) – Source namespace.
    • from_dataset (str) – Source dataset name.
    • from_column (str) – Source column name.
    • to_namespace (str) – Target namespace.
    • to_dataset (str) – Target dataset name.
    • to_column (str) – Target column name.
    • match_type (str) – Match type — "ExactMatch" or "GeoMatch".
    • options (Optional[Dict[str, Any]]) – Optional match configuration (e.g. minDistinct, distance).
  • Returns: True if the link was successfully created, False if already exists. None if failure.
  • Return type: bool
  • Raises: DataLinksRequestError – If the HTTP request fails.
Preview what recalculating links would produce without saving changes.
  • Parameters:
    • data (Dataset) – Array of ontology data objects (e.g. from query_data()).
    • entity_resolution (Optional [MatchTypeConfig ]) – Optional link matching configuration.
  • Returns: Preview of link objects, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.
Recalculate links for the configured dataset based on current data.
  • Parameters:
    • data (Dataset) – Array of ontology data objects (e.g. from query_data()).
    • entity_resolution (Optional [MatchTypeConfig ]) – Optional link matching configuration.
  • Returns: Updated list of link objects, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.
Retrieve active and suggested links for the configured dataset.
  • Returns: A list of link objects, or None on failure.
  • Return type: Optional[List[Dict]]

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list_datasets(namespace=None)

Retrieves the list of datasets for the user, optionally filtered by a specific namespace.
  • Parameters: namespace (Optional *[*AnyStr ]) – Optional namespace to filter the datasets by. If provided, only datasets associated with the given namespace will be returned. If not provided, all datasets are retrieved.
  • Returns: A list of datasets represented as dictionaries if the query is successful and returns a status code of 200, or None if the query fails or encounters an error.
  • Return type: List[Dict] | None

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query_data(query=None, is_natural_language=False, model=None, provider=None, include_metadata=False, explain=False)

Queries data from a specified data source and processes the response. The method allows querying with a specific query string or with a wildcard (“*”) for all data. The response from the query can be filtered to exclude metadata fields if include_metadata is set to False. Metadata fields are identified by key names starting with an underscore.
  • Parameters:
    • query (str) – The query string to use for fetching data. Defaults to “*”, which retrieves all data.
    • is_natural_language (bool) – If True, the query is treated as a natural language query.
    • model (str) – The model name to use for inference.
    • provider (str) – The provider of the LLM model (ollama, openai, etc)
    • include_metadata (bool) – Specifies whether to include metadata fields in the returned data. Defaults to False.
    • explain (bool) – If True, request an explanation of how the query was resolved.
  • Returns: A list of records represented as dictionaries, or None if the query fails or an exception occurs during the request.
  • Return type: List[Dict] | None
  • Raises: requests.exceptions.RequestException – If a request-related error occurs during querying.

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ask(query, model=None, provider=None, helper_prompt=None)

Talk to your data with natural language using the DataLinks AutoRAG agent. Streams the agent’s reasoning and final answer as Server-Sent Events. Events are yielded in order: one plan event, one or more step events, then either an answer event or an error event.
  • Parameters:
    • query (str) – The natural language question to answer.
    • model (str) – The model name to use for inference.
    • provider (str) – The LLM provider (e.g. openai, ollama).
    • helper_prompt (str) – Optional custom system prompt.
  • Returns: An iterator of AskEvent objects.
  • Return type: Iterator[AskEvent]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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preview_ingest(data, inference_steps=None)

Process data through the ingestion pipeline without saving it to a dataset.
  • Parameters:
    • data (Dataset) – List of data records to preview.
    • inference_steps (Optional [Pipeline ]) – Optional pipeline of inference steps to apply.
  • Returns: List of processed preview records, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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infer_schema(sample, model=None, provider=None, current_schema=None)

Ask an agent to infer a field type schema from sampled data.
  • Parameters:
    • sample (Dataset) – A sample of data rows to analyse.
    • model (Optional *[*str ]) – LLM model name.
    • provider (Optional *[*str ]) – LLM provider (e.g. "openai", "ollama").
    • current_schema (Optional *[*Dict *[*str , str ] ]) – Existing field schema to refine (field → description mapping).
  • Returns: Dict with schema key mapping field names to their inferred types.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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retry_ingestion(ingestion_id)

Retry a failed ingestion by creating a new ingestion record from the original.
  • Parameters: ingestion_id (str) – The ID of the ingestion to retry.
  • Returns: Dict with the new ingestion id, or None on failure.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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mark_ingestion_seen(ingestion_id)

Mark an ingestion as seen, updating its seenAt timestamp.
  • Parameters: ingestion_id (str) – The ID of the ingestion to mark as seen.
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None

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autorag(query, model=None, provider=None, helper_prompt=None)

Answer a natural language question using the AutoRAG agent (non-streaming). Returns the final answer and all intermediate steps once the agent completes. For incremental streaming results, use ask() instead.
  • Parameters:
    • query (str) – The natural language question to answer.
    • model (Optional *[*str ]) – LLM model name.
    • provider (Optional *[*str ]) – LLM provider (e.g. "openai", "ollama").
    • helper_prompt (Optional *[*str ]) – Optional custom system prompt.
  • Returns: Dict with response (str) and steps (list) keys, or None on failure.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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request_cleaning(prompts, output_namespace, output_dataset_name)

Request a cleaning job for the configured dataset.
  • Parameters:
    • prompts (List *[*str ]) – 1–10 prompts describing each cleaning step in order.
    • output_namespace (str) – Target namespace for the cleaned dataset.
    • output_dataset_name (str) – Name for the cleaned dataset (must be unused in target namespace).
  • Returns: The cleaningTaskId UUID string, or None on failure.
  • Return type: Optional[str]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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get_cleaning_code(cleaning_task_id)

Retrieve code files generated by the cleaning agent for a task.
  • Parameters: cleaning_task_id (str) – UUID of the cleaning task.
  • Returns: List of dicts with name and content keys, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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get_ontology()

Load the ontology (active links) for the configured dataset.
  • Returns: List of link dicts, or None if no ontology exists or the request fails.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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save_ontology(add=None, remove=None)

Save (update) the ontology for the configured dataset.
  • Parameters:
    • add (Optional *[*List *[*Dict ] ]) – Links to add to the ontology.
    • remove (Optional *[*List *[*Dict ] ]) – Links to remove from the ontology.
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None
Run the OntologyCurator agent to analyse computed links and optionally activate them. When activate=False (default), the curated links are returned without being saved. When activate=True, the curated links are added to the ontology.
  • Parameters:
    • namespace (Optional *[*str ]) – Namespace to curate. Defaults to the configured namespace.
    • dataset (Optional *[*str ]) – Dataset to curate. If omitted, all datasets in the namespace are curated.
    • model (Optional *[*str ]) – LLM model name.
    • provider (Optional *[*str ]) – LLM provider (e.g. "openai", "anthropic").
    • activate (bool) – If True, add curated links to the ontology.
  • Returns: Dict with datasetsProcessed, totalSelected, and optionally curatedLinks.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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rename_namespace(new_name)

Rename the configured namespace.
  • Parameters: new_name (str) – The new namespace name.
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None

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list_namespaces(user=‘self’)

Retrieve namespaces for a user.
  • Parameters: user (str) – Username or "self" for the current user.
  • Returns: List of namespace dicts, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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list_all_datasets_schema()

Retrieve all datasets visible to the authenticated user (schema endpoint).
  • Returns: List of dataset dicts, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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list_datasets_in_namespace_schema(namespace=None, user=‘self’)

Retrieve datasets within a specific namespace (schema endpoint).
  • Parameters:
    • namespace (Optional *[*str ]) – Namespace to list. Defaults to the configured namespace.
    • user (str) – Username or "self" for the current user.
  • Returns: List of dataset dicts, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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list_tokens()

List all API tokens for the authenticated user.
  • Returns: List of token dicts, or None on failure.
  • Return type: Optional[List[Dict]]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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add_token(name, expires_at=None, access_restricted_to=None)

Create a new API token for the authenticated user.
  • Parameters:
    • name (str) – Display name for the token.
    • expires_at (Optional *[*str ]) – Optional expiry timestamp (ISO 8601 string).
    • access_restricted_to (Optional *[*List *[*Dict ] ]) – Optional list of permission entries restricting access (each dict with username, namespace, and optionally dataset).
  • Returns: Created token dict (includes the token secret), or None on failure.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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delete_token(token_id)

Delete an API token.
  • Parameters: token_id (str) – ID of the token to delete.
  • Raises: DataLinksRequestError – If the HTTP request fails.
  • Return type: None

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list_token_permissions(token_id)

List permissions assigned to a token.
  • Parameters: token_id (str) – ID of the token.
  • Returns: Dict with restricted (bool) and permissions (list) keys, or None on failure.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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get_usage_history(on_or_after=None, before=None, page_size=25, page_cursor=None)

Retrieve historical usage data for the authenticated user.
  • Parameters:
    • on_or_after (Optional *[*str ]) – Return records on or after this ISO 8601 timestamp.
    • before (Optional *[*str ]) – Return records before this ISO 8601 timestamp.
    • page_size (int) – Number of records per page (default 25).
    • page_cursor (Optional *[*Dict ]) – Pagination cursor dict from a previous response.
  • Returns: Dict with data and meta keys, or None on failure.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.

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get_usage_by_day(on_or_after=None, before=None, timezone=‘UTC’)

Retrieve usage data aggregated by day for the authenticated user.
  • Parameters:
    • on_or_after (Optional *[*str ]) – Return records on or after this ISO 8601 timestamp.
    • before (Optional *[*str ]) – Return records before this ISO 8601 timestamp.
    • timezone (str) – Timezone for date aggregation (e.g. "America/New_York"). Defaults to UTC.
  • Returns: Dict with data and meta keys, or None on failure.
  • Return type: Optional[Dict]
  • Raises: DataLinksRequestError – If the HTTP request fails.
Bases: object Configuration for the DataLinks ingestion proxy (auto-modelling) service.
  • Variables:
    • host – Base URL of the ingestion proxy (e.g. http://localhost:3003).
    • datalinks_token – DataLinks JWT token sent as Authorization: Bearer (DL_API_KEY).
    • datalinks_username – DataLinks username included in the request body (DL_USERNAME).
    • namespace – Default target namespace (DL_NAMESPACE).

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host : str

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datalinks_token : str

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datalinks_username : str

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namespace : str

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classmethod from_env(load_dotenv=True)

Bases: object Wraps a pipeline run with automatic stream reconnection. Provides the workflow run_id (from the x-workflow-run-id response header) and an iterable interface over the NDJSON progress events. The iterator reconnects transparently on connection drops, resuming from the last received event via the startIndex query parameter. Iteration ends only when an explicit complete or error event is received. Usage: 
run = proxy.run_pipeline(data=[...])
print(run.run_id)
for event in run:
    print(event)
Can also be used as a context manager: 
with proxy.run_pipeline(data=[...]) as run:
    for event in run:
        process(event)

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close()

  • Return type: None
Bases: object Client for the DataLinks ingestion proxy (auto-modelling) service. Wraps the POST /api/pipeline, GET /api/pipeline/{runId}/stream, GET /api/pipeline/{runId}/trace, and POST /api/pipeline/{runId}/hook endpoints.
  • Variables: config – Proxy configuration.

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config : IngestProxyConfig

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run_pipeline(, data=None, data_url=None, data_blob_url=None, namespace=None, user_prompt=None, model=True, ingest=True, ontology=True, max_eval_retries=3, max_rows_for_modeling=20, max_sample_rows=10, enable_human_in_the_loop=False, predefined_schema=None, explosion_helper_prompt=None, coalescence_helper_prompt=None, llm=None, datalinks_inference_settings=None)

Start a full pipeline run (auto-modelling + ingest). Exactly one of data, data_url, or data_blob_url must be provided. Returns a PipelineRun whose run_id attribute is the workflow run identifier and which can be iterated to receive NDJSON progress events.
  • Parameters:
    • data (Optional[List[Dict[str, Any]]]) – Inline JSON array of row objects.
    • data_url (Optional[str]) – Remote URL returning a JSON array (fetched by the pipeline).
    • data_blob_url (Optional[str]) – Pre-uploaded Vercel Blob URL.
    • namespace (Optional[str]) – Target namespace; defaults to config.namespace.
    • user_prompt (Optional[str]) – Domain goals; inferred from data when omitted.
    • model (bool) – Run the model phase (default True).
    • ingest (bool) – Run the ingest phase (default True).
    • ontology (bool) – Run namespace curation after ingest (default True).
    • max_eval_retries (int) – Max modelling iterations (default 3).
    • max_rows_for_modeling (int) – Rows sent to the LLM for schema modelling (default 20).
    • max_sample_rows (int) – Sample rows generated for preview (default 10).
    • enable_human_in_the_loop (bool) – Surface clarification + schema review hooks (default False).
    • predefined_schema (Optional[Dict[str, Any]]) – Skip model phase when provided.
    • explosion_helper_prompt (Optional[str]) – Extra context injected into the explode step.
    • coalescence_helper_prompt (Optional[str]) – Extra context injected into the coalesce step.
    • llm (Optional[Dict[str, Any]]) – LLM configuration dict with optional keys: provider, model, explosionTemperature, coalescenceTemperature, evaluationTemperature, ontologyTemperature.
    • datalinks_inference_settings (Optional[Dict[str, Any]]) – DataLinks inference settings dict with optional keys: provider, model, ontologyCurationProvider, ontologyCurationModel.
  • Return type: PipelineRun
  • Returns: A PipelineRun instance.
  • Raises: DataLinksRequestError – If the HTTP request fails.

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stream_pipeline(run_id, start_index=0)

Stream progress events for an existing pipeline run.
  • Parameters:
    • run_id (str) – Workflow run identifier returned by run_pipeline().
    • start_index (int) – Resume from this event index (default 0). Pass the number of events already received to skip replaying them on reconnect.
  • Return type: Iterator[Dict[str, Any]]
  • Returns: An iterator of NDJSON event dicts.
  • Raises: DataLinksRequestError – If the HTTP request fails.

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get_pipeline_trace(run_id)

Download the full trace for a completed pipeline run.
  • Parameters: run_id (str) – Workflow run identifier.
  • Return type: Optional[Dict[str, Any]]
  • Returns: Dict with LLM calls, token usage, and step durations, or None on failure.
  • Raises: DataLinksRequestError – If the HTTP request fails.

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resume_pipeline_hook(run_id, payload)

Resume a human-in-the-loop hook (clarification, schema review, or token refresh).
  • Parameters:
    • run_id (str) – Workflow run identifier.
    • payload (Dict[str, Any]) – Hook response payload.
  • Return type: Optional[Dict[str, Any]]
  • Returns: Response dict, or None on failure.
  • Raises: DataLinksRequestError – If the HTTP request fails.