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What is a knowledge base?

A knowledge base consists of a single vector store along with several data connectors that point to different external data sources. As shown in the diagram below, knowledge bases are a fundamental component of the SGP API ecosystem as you need a knowledge base to ingest data through a data connector.
How are knowledge bases related to vector stores? Every knowledge base has one vector store under the hood. Any data that is imported or ingested into a knowledge base will be embedded and stored in its vector store. How are knowledge bases related to data connectors? When you create a data connector, you associated it with a single knowledge base. Any data that is ingested via that data connector will be stored in its associated knowledge base.

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Creating a new knowledge base in the UI

To create a new knowledge base, you need to:
  1. Define a Knowledge Base Name.
  2. Select an Embedding Model.
  3. Select a Data Source.
  4. Configure Upload Source.

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Knowledge Base Name

This will be used to locate your knowledge base after you create it.

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Embedding Model

Embedding models are ML models that convert text into a numerical representation. The representation is then used to compare and match text data. You can select an existing model on the SGP Platform or install a new model and use the new model.

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Data Source

Data sources provide the data for knowledge bases to ingest. When a knowledge base upload is created from a data source, it will read data from it, extract text from relevant files, split it into chunks, embed the chunks, and store the embeddings in a vector database for future retrieval.

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Configure Upload

Upload configurations define how the data source is split into chunks. The chunking configuration affects the relevance of the content retrieved from a knowledge base when the LLM is used to embed content. You can choose to go with the default configuration or choose your own Chunk Strategy, Size, Overlap, and Separator.

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Knowledge Base API

The starter code below creates an SGP knowledge base using the Create Knowledge Base endpoint. Fill in your API key and choose a name for your knowledge base, and try out the starter code below!
Python

import requests

# Replace this with your SGP API key
# See instructions for getting your API key here: scale-egp.readme.io/docs/getting-started
API_KEY = '[Your API key]'

# Choose a name for your knowledge base
KNOWLEDGE_BASE_NAME = "my_knowledge_base_1"
# Select an embedding model of the two options listed below
EMBEDDING_MODEL = "openai/text-embedding-ada-002"  # or "sentence-transformers/all-MiniLM-L12-v2"

print(f"Creating a knowledge base named {KNOWLEDGE_BASE_NAME}...")
url = "https://api.egp.scale.com/v3/knowledge-bases"

payload = {
    "embedding_config": {
        "type": "base",
        "embedding_model": "openai/text-embedding-3-large"
    },
    "knowledge_base_name": "myknowledge_base"
}

headers = {
    "accept": "application/json",
	  "content-type": "application/json",
    "x-api-key": API_KEY
}
response = requests.post(url, json=payload, headers=headers)
print(f"Response: {response.text}")
You should see a response like the following, which will show your knowledge base ID. {"knowledge_base_id":"clk123456789"} Keep this value—you will need it later when getting information about your knowledge base, creating data connectors, and more.