> ## Documentation Index > Fetch the complete documentation index at: https://www.dataframer.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Get context > Retrieve the knowledge and rubric content that failures reference ## OpenAPI ````yaml GET /api/dataframer/context/ openapi: 3.0.0 info: title: DataFramer API version: 0.1.0 description: '' termsOfService: https://www.aimon.ai/docs/privacy-policy.pdf contact: name: DataFramer Support email: info@dataframer.ai license: name: Proprietary x-logo: url: https://dataframer.ai/logo.png altText: DataFramer AI x-stainless: package-name: aimon-dataframer namespace: - aimon - dataframer servers: - url: https://df-api.dataframer.ai description: Production server security: - BearerAuth: [] externalDocs: description: Complete API Guide url: https://docs.dataframer.ai/dataframer paths: /api/dataframer/context/: get: summary: Get context description: >- Retrieve the grounding context failures reference: knowledge distilled from your expert reviews, and the content of the rubrics your failures were graded against. operationId: api_dataframer_context_retrieve parameters: - name: project_id in: query required: false description: >- Narrow the rubrics to one connected observability platform's failures. It narrows only the rubrics — knowledge stays workspace-wide by design, so a project with no rubrics of its own returns knowledge and an empty rubrics list schema: type: string format: uuid - name: rubric_id in: query required: false description: >- Serve only this rubric, with every example included and no size budget. Ignores `project_id`, and the response carries `rubrics` alone — no `knowledge` key, since naming a rubric says what is wanted schema: type: string format: uuid responses: '200': description: Workspace knowledge and rubric content content: application/json: schema: $ref: '#/components/schemas/ContextResponse' '400': description: Bad request - validation error content: application/json: schema: type: object properties: error: type: string example: bad_uuid: value: error: 'Invalid rubric_id format: "abc" is not a valid UUID' '401': description: Unauthorized '404': description: Rubric not found content: application/json: schema: type: object properties: error: type: string example: not_found: value: error: Rubric not found x-codeSamples: - lang: Python source: |- import os from dataframer import Dataframer client = Dataframer( api_key=os.environ.get("DATAFRAMER_API_KEY"), # This is the default and can be omitted ) context = client.dataframer.context.retrieve() print(context.knowledge) - lang: cURL source: |- curl https://df-api.dataframer.ai/api/dataframer/context/ \ -H "Authorization: Bearer $DATAFRAMER_API_KEY" components: schemas: ContextResponse: type: object properties: knowledge: allOf: - $ref: '#/components/schemas/ContextKnowledge' description: >- Knowledge distilled from your expert reviews. Always the whole workspace, never narrowed by `project_id`: policy, terminology and domain facts are meant to carry across projects. Absent entirely when `rubric_id` is given rubric_count: type: integer description: >- Rubrics your failures were graded against. When it exceeds the length of `rubrics`, that list is a trimmed view rubrics: type: array items: $ref: '#/components/schemas/ContextRubric' description: Those rubrics by name, each at its current version ContextKnowledge: type: object description: Distilled knowledge for the whole workspace, never scoped to one project properties: facts: type: array maxItems: 100 items: $ref: '#/components/schemas/KnowledgeFact' description: >- Facts, newest first, and a sample rather than the whole set — compare against `total` total: type: integer description: >- Facts that exist, before the newest-first trim — the true total behind the `facts` sample ContextRubric: type: object description: One rubric's current content properties: id: type: string format: uuid description: Referenced by `rubrics[].id` on failures name: type: string description: Name of the rubric project: allOf: - $ref: '#/components/schemas/FailureProject' description: The project the rubric belongs to version: type: integer description: Version number served description: type: string description: Description of the rubric instructions: type: string description: The team's grading guidance dimensions: type: array items: $ref: '#/components/schemas/ContextRubricDimension' description: Scoring axes of the rubric examples_omitted: type: integer description: >- Examples left out of this response. Re-request this rubric with the `rubric_id` query parameter to get all of them KnowledgeFact: type: object description: One claim about your domain, distilled by an LLM from an expert review properties: id: type: string format: uuid kind: type: string example: policy_rule description: What sort of claim this is text: type: string description: The claim itself grounding: type: string description: Excerpts from the source trace that support the claim tags: type: array items: type: string description: Tags on the fact confidence: type: number minimum: 0 maximum: 1 description: Confidence in the claim created_at: type: string format: date-time description: When the fact was distilled source: type: object nullable: true description: The trace the fact came from (null if it no longer resolves) properties: source_id: type: string description: The trace's ID in your observability platform source_url: type: string format: uri nullable: true description: Deep link to the trace (null if no link can be built) FailureProject: type: object description: The project a failure's traces come from properties: id: type: string format: uuid name: type: string description: Name of the project in DataFramer platform: type: string enum: - langfuse - langsmith description: Observability platform the traces are pulled from ContextRubricDimension: type: object description: One scoring axis of a rubric properties: name: type: string description: Name of the dimension scoring_model: type: string enum: - pass_fail - scale_1_5 - single_select - checklist description: How the dimension is scored required: type: boolean description: Whether reviewers must grade this dimension levels: type: array description: The choosable verdicts for this dimension items: type: object properties: label: type: string description: Label shown to reviewers description: type: string nullable: true description: Guidance for the level (null if none) value: type: number nullable: true description: Numeric value for scale levels (null for other scoring models) positive_examples: type: array items: $ref: '#/components/schemas/RubricExample' description: Examples of a pass (pass/fail and scale dimensions) negative_examples: type: array items: $ref: '#/components/schemas/RubricExample' description: Examples of a fail (pass/fail and scale dimensions) examples: type: array items: $ref: '#/components/schemas/RubricExample' description: >- Neutral calibration examples (checklist and single-select dimensions) RubricExample: type: object description: One calibration example from a reviewed trace properties: query: type: string description: The example trace's input answer: type: string description: The example trace's output source_id: type: string nullable: true description: >- The trace's ID in your observability platform (null if it no longer resolves) source_url: type: string format: uri nullable: true description: Deep link to the trace (null if no link can be built) securitySchemes: BearerAuth: type: http scheme: bearer bearerFormat: API Key description: 'API Key authentication. Format: "Bearer YOUR_API_KEY"' ```` This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.