Python

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)

{
  "knowledge_base_id": "<string>"
}

Knowledge Bases

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.

POST

knowledge-bases

Python

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)

{
  "knowledge_base_id": "<string>"
}

Authorizations

x-api-key

string

header

required

Body

application/json

embedding_config

EmbeddingConfigModelsAPI · object

required

The configuration of the embedding

EmbeddingConfigModelsAPI
EmbeddingConfigBase

Show child attributes

knowledge_base_name

string

required

A unique name for the knowledge base

account_id

string

Account to create knowledge base in. If you have access to more than one account, you must specify an account_id

metadata

Metadata · object

Metadata associated with the knowledge base

kb_index_configuration

KBIndexConfiguration · object

Configuration for metadata schema in the knowledge base, including field definitions and index settings

Show child attributes

Response

Successful Response

knowledge_base_id

string

required

The unique ID of the created knowledge base

List Knowledge Bases Get Knowledge Base

⌘I

Knowledge Bases

Chunks

Agents

Completions

Chat Completions

Models

Users

Accounts

Organizations

Question Sets

Evaluations

Evaluation Configs

Evaluation Datasets

Studio Projects

Application Specs

Questions

Knowledge Base Data Sources

Model Templates V3 (Beta)

Model server

API Reference

Fine Tuning Jobs V3 (Beta)

Training Datasets V3 (Beta)

package deployments

Beta

Applications

ChatThreads

Interactions

MonitoringDashboard

Chat Themes

account groups

Create Knowledge Base

Description

Details

Differences from V1

Backwards Compatibility

Authorizations

Body

Response