> ## Documentation Index > Fetch the complete documentation index at: https://docs.gp.scale.com/llms.txt > Use this file to discover all available pages before exploring further. # Create Knowledge Base > ### Description Creates an EGP knowledge base. ### Details A knowledge base is a storage device for all data that needs to be accessible to EGP models. Users can upload data from a variety of data sources into a knowledge base, and then query the knowledge base for chunks that are semantically relevant to the query. Every knowledge base must be associated with a fixed embedding model. This embedding model will be used to embed all data that is stored in the knowledge base. The embedding model cannot be changed once the knowledge base is created. Only the embedding models in the dropdown menu below are supported. #### Differences from V1 - V1 data ingestion consisted of knowledge bases, vector stores, and data connectors. V1 Knowledge bases interacted with natural language, V1 vector stores interacted with chunks and embeddings, and V1 data connectors set up automatic ingestion pipelines with third party data sources. - In V2, all data ingestion is done through knowledge bases. Low level configuration such as chunking strategies and data sources are now handled by this unified knowledge base v2 upload API. - The way data is stores in V2 allows for better observability on the ingestion progress and content of the knowledge base. - Reliability and scalability is also improved via distributed temporal workflows. #### Backwards Compatibility V2 and V1 Knowledge Bases are entirely separate and not backwards compatible. Users who have existing V1 knowledge bases will need to migrate their data to V2 knowledge bases. ## OpenAPI ````yaml https://app.stainlessapi.com/api/spec/documented/sgp/openapi.yml post /v4/knowledge-bases openapi: 3.1.0 info: title: EGP API V4 description: >- This is the parent API for all EGP APIs. If you are looking for the EGP API, please go to https://api.egp.scale.com/docs. contact: name: Scale Generative AI Platform url: https://scale.com/genai-platform version: 0.1.0 servers: - url: https://api.egp.scale.com security: [] tags: - name: Models description: Model API. paths: /v4/knowledge-bases: post: tags: - Knowledge Bases summary: Create Knowledge Base description: >- ### Description Creates an EGP knowledge base. ### Details A knowledge base is a storage device for all data that needs to be accessible to EGP models. Users can upload data from a variety of data sources into a knowledge base, and then query the knowledge base for chunks that are semantically relevant to the query. Every knowledge base must be associated with a fixed embedding model. This embedding model will be used to embed all data that is stored in the knowledge base. The embedding model cannot be changed once the knowledge base is created. Only the embedding models in the dropdown menu below are supported. #### Differences from V1 - V1 data ingestion consisted of knowledge bases, vector stores, and data connectors. V1 Knowledge bases interacted with natural language, V1 vector stores interacted with chunks and embeddings, and V1 data connectors set up automatic ingestion pipelines with third party data sources. - In V2, all data ingestion is done through knowledge bases. Low level configuration such as chunking strategies and data sources are now handled by this unified knowledge base v2 upload API. - The way data is stores in V2 allows for better observability on the ingestion progress and content of the knowledge base. - Reliability and scalability is also improved via distributed temporal workflows. #### Backwards Compatibility V2 and V1 Knowledge Bases are entirely separate and not backwards compatible. Users who have existing V1 knowledge bases will need to migrate their data to V2 knowledge bases. operationId: POST-V4-/v2/knowledge-bases parameters: - name: x-selected-account-id in: header required: false schema: anyOf: - type: string - type: 'null' title: Account ID Header requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/CreateKnowledgeBaseRequestV2' responses: '200': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/CreateKnowledgeBaseResponse' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - APIKeyHeader: [] x-codeSamples: - lang: Python source: |- import os from scale_gp import SGPClient client = SGPClient( api_key=os.environ.get("SGP_API_KEY"), # This is the default and can be omitted ) create_knowledge_base_response = client.knowledge_bases.create( embedding_config={ "model_deployment_id": "model_deployment_id", "type": "models_api", }, knowledge_base_name="knowledge_base_name", ) print(create_knowledge_base_response.knowledge_base_id) - lang: Go source: "package main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\n\t\"github.com/stainless-sdks/sgp-go\"\n\t\"github.com/stainless-sdks/sgp-go/option\"\n)\n\nfunc main() {\n\tclient := sgp.NewClient(\n\t\toption.WithAPIKey(\"My API Key\"),\n\t)\n\tcreateKnowledgeBaseResponse, err := client.KnowledgeBases.New(context.TODO(), sgp.KnowledgeBaseNewParams{\n\t\tEmbeddingConfig: sgp.F[sgp.KnowledgeBaseNewParamsEmbeddingConfigUnion](sgp.KnowledgeBaseNewParamsEmbeddingConfigEmbeddingConfigModelsAPI{\n\t\t\tModelDeploymentID: sgp.F(\"model_deployment_id\"),\n\t\t\tType: sgp.F(sgp.KnowledgeBaseNewParamsEmbeddingConfigEmbeddingConfigModelsAPITypeModelsAPI),\n\t\t}),\n\t\tKnowledgeBaseName: sgp.F(\"knowledge_base_name\"),\n\t})\n\tif err != nil {\n\t\tpanic(err.Error())\n\t}\n\tfmt.Printf(\"%+v\\n\", createKnowledgeBaseResponse.KnowledgeBaseID)\n}\n" components: schemas: CreateKnowledgeBaseRequestV2: properties: embedding_config: $ref: '#/components/schemas/EmbeddingConfig' description: The configuration of the embedding account_id: title: Account Id description: >- Account to create knowledge base in. If you have access to more than one account, you must specify an account_id type: string knowledge_base_name: type: string title: Knowledge Base Name description: A unique name for the knowledge base metadata: title: Metadata description: Metadata associated with the knowledge base additionalProperties: true type: object kb_index_configuration: $ref: '#/components/schemas/KBIndexConfiguration' description: >- Configuration for metadata schema in the knowledge base, including field definitions and index settings type: object required: - embedding_config - knowledge_base_name title: CreateKnowledgeBaseRequestV2 CreateKnowledgeBaseResponse: properties: knowledge_base_id: type: string title: Knowledge Base Id description: The unique ID of the created knowledge base type: object required: - knowledge_base_id title: CreateKnowledgeBaseResponse HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError EmbeddingConfig: anyOf: - $ref: '#/components/schemas/EmbeddingConfigModelsAPI' - $ref: '#/components/schemas/EmbeddingConfigBase' title: EmbeddingConfig KBIndexConfiguration: properties: fields: additionalProperties: true type: object title: Fields description: >- Schema defining metadata fields for the knowledge base. Each field can have properties: type (string, int32, int64, double, boolean, complex), filterable (bool), searchable (bool). Complex types can have nested 'fields' with the same structure. example: author: fields: age: filterable: true type: int32 name: filterable: true searchable: true type: string type: complex priority: filterable: true type: int32 max_index_size: title: Max Index Size description: >- Maximum size (in bytes) for the index. If not provided, uses the default. type: integer exclusiveMinimum: 0 additionalProperties: true type: object required: - fields title: KBIndexConfiguration description: >- Configuration for knowledge base metadata schema. This configuration determines how metadata fields are created in the search index. ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type input: title: Input ctx: type: object title: Context additionalProperties: true type: object required: - loc - msg - type title: ValidationError EmbeddingConfigModelsAPI: properties: type: type: string const: models_api title: Type description: The type of the embedding configuration. model_deployment_id: type: string title: Model Deployment Id description: The ID of the deployment of the created model in the Models API V3. type: object required: - type - model_deployment_id title: EmbeddingConfigModelsAPI EmbeddingConfigBase: properties: type: type: string const: base title: Type description: The type of the embedding configuration. default: base embedding_model: $ref: '#/components/schemas/EmbeddingModelName' description: >- The name of the base embedding model to use. To use custom models, change to type 'models'. type: object required: - embedding_model title: EmbeddingConfigBase EmbeddingModelName: type: string enum: - sentence-transformers/all-MiniLM-L12-v2 - sentence-transformers/all-mpnet-base-v2 - sentence-transformers/multi-qa-distilbert-cos-v1 - sentence-transformers/paraphrase-multilingual-mpnet-base-v2 - openai/text-embedding-ada-002 - openai/text-embedding-3-small - openai/text-embedding-3-large - embed-english-v3.0 - embed-english-light-v3.0 - embed-multilingual-v3.0 - gemini/text-embedding-005 - gemini/text-multilingual-embedding-002 - gemini/gemini-embedding-001 title: EmbeddingModelName securitySchemes: APIKeyHeader: type: apiKey in: header name: x-api-key ````