Ask questions to Knowledge Graphs

The Knowledge Graph question endpoint allows you to ask questions directly to one or more Knowledge Graphs. This provides a direct way to query your Knowledge Graphs without needing to use chat completions. You can also enable subqueries to break down complex questions into smaller subqueries and provide answers for each one.

You need an API key to access the Writer API. Get an API key by following the steps in the API quickstart.We recommend setting the API key as an environment variable in a .env file with the name WRITER_API_KEY.

Question endpoint

Endpoint: POST /v1/graphs/question

curl --location --request POST https://api.writer.com/v1/graphs/question \
  --header "Authorization: Bearer $WRITER_API_KEY" \
  --header "Content-Type: application/json" \
  --data-raw '{
    "graph_ids": ["<GRAPH_ID>"],
    "question": "<QUESTION>"
  }'

from writerai import Writer

# Initialize the Writer client. If you don't pass the `api_key` parameter,
# the client looks for the `WRITER_API_KEY` environment variable.
client = Writer()

response = client.graphs.question(
  graph_ids=["<GRAPH_ID>"],
  question="<QUESTION>"
)

print(f"Answer: {response.answer}")
print(f"Sources: {response.sources}")

import { Writer } from "writer-sdk";

// Initialize the Writer client. If you don't pass the `api_key` parameter,
// the client looks for the `WRITER_API_KEY` environment variable.
const client = new Writer();

const response = await client.graphs.question({
  graph_ids: ["<GRAPH_ID>"],
  question: "<QUESTION>"
});

console.log(`Answer: ${response.answer}`);
console.log(`Sources: ${response.sources}`);

Request body

The request body is a JSON object with the following fields:

Parameter	Type	Required	Description
`graph_ids`	array[string]	Yes	The unique identifiers of the Knowledge Graphs to query. You can specify multiple Knowledge Graph IDs to search across multiple graphs.
`question`	string	Yes	The question to answer using the Knowledge Graph.
`subqueries`	Boolean	No	Specify whether to include subqueries. Defaults to `false`.
`stream`	Boolean	No	Determines whether to stream the response. If `true`, the output is sent as it is generated, which can be useful for real-time applications. Defaults to `false`. See how to handle streaming responses below.
`query_config`	object	No	Configuration options for Knowledge Graph queries. See Query configuration parameters below for detailed parameter descriptions.

Find your Knowledge Graph ID using one of these methods:

Call the Knowledge Graph list endpoint to retrieve all Knowledge Graphs with their IDs.
Locate the ID in the URL of the Knowledge Graph page in AI Studio.

Knowledge Graphs deployed to a specific team aren’t accessible via the API or SDK. To use a Knowledge Graph via the API or SDK, configure it with “All Teams” access in AI Studio.

Query configuration parameters

Parameter	Type	Range	Default	Description
`max_subquestions`	integer	1-10	6	Maximum number of sub-questions to generate when processing complex queries.
`search_weight`	integer	0-100	50	Controls the balance between keyword and semantic search in ranking results.
`grounding_level`	number	0.0-1.0	0.0	Controls how closely responses must match to source material. Set lower for grounded outputs, higher for creativity.
`max_snippets`	integer	5-25 (recommended)	30	Maximum number of text snippets to retrieve from the Knowledge Graph for context. Works in concert with `search_weight` to control best matches vs broader coverage. Note: While technically supports 1-60, values below 5 may return no results due to RAG implementation. Recommended range is 5-25. Due to RAG system behavior, you may see more snippets than requested.
`max_tokens`	integer	100-8000	4000	Maximum number of tokens the model can generate in the response.
`keyword_threshold`	number	0.0-1.0	0.7	Threshold for keyword-based matching when searching Knowledge Graph content.
`semantic_threshold`	number	0.0-1.0	0.7	Threshold for semantic similarity matching when searching Knowledge Graph content. Set higher for stricter relevance, lower for broader range.
`inline_citations`	Boolean	true/false	false	Whether to include inline citations within the response text.

For detailed explanations and usage examples, see the Knowledge Graph query configuration guide.

Response format

The response is a JSON object with the following fields:

Parameter	Type	Description
`question`	string	The question you asked.
`answer`	string	The answer to the question based on the Knowledge Graph content.
`sources`	array	An array of source objects that contain the file information and text snippets used to generate the answer.
`sources[].file_id`	string	If the source is a file, this is the unique identifier of the file that contains the source information.
`sources[].snippet`	string	If the source is a file, this is a snippet of text from the source file used to answer the question.
`subqueries`	array	An array of subquery objects. Only included if `subqueries` is `true`.
`subqueries[].query`	string	The subquery the model generated to answer the question.
`subqueries[].answer`	string	The answer to the subquery.
`subqueries[].sources`	array	An array of source objects for the subquery.

Non-streaming response
Streaming response

{
  "question": "What was our company's revenue growth in Q4 2023?",
  "answer": "The company achieved 23% year-over-year revenue growth in Q4 2023, reaching $142 million in total revenue.",
  "sources": [
    {
      "file_id": "1234",
      "snippet": "In the fourth quarter of 2023, we saw strong performance with revenue reaching $142 million, representing a 23% increase compared to Q4 2022."
    }
  ]
}

data: {
    "question": "What was our company's revenue growth in Q4 2023?",
    "answer": "The",
    "sources": [],
    "subqueries": null,
    "references": null
}

Examples

Query multiple knowledge graphs

You can query multiple Knowledge Graphs at once by providing multiple graph IDs in the graph_ids array.

curl --location --request POST https://api.writer.com/v1/graphs/question \
  --header "Authorization: Bearer $WRITER_API_KEY" \
  --header "Content-Type: application/json" \
  --data-raw '{
    "graph_ids": [
      "<GRAPH_ID_1>",
      "<GRAPH_ID_2>"
    ],
    "question": "What are the key findings from our research documents?"
  }'

from writerai import Writer

client = Writer()

response = client.graphs.question(
  graph_ids=[
    "<GRAPH_ID_1>",
    "<GRAPH_ID_2>"
  ],
  question="What are the key findings from our research documents?"
)

print(f"Answer: {response.answer}")

import { Writer } from "writer-sdk";

const client = new Writer();

const response = await client.graphs.question({
  graph_ids: [
    "<GRAPH_ID_1>",
    "<GRAPH_ID_2>"
  ],
  question: "What are the key findings from our research documents?"
});

console.log(`Answer: ${response.answer}`);

Enable subqueries

When you enable subqueries, the model breaks down complex questions into smaller subqueries and provides answers for each one from the Knowledge Graphs.

curl --location --request POST https://api.writer.com/v1/graphs/question \
  --header "Authorization: Bearer $WRITER_API_KEY" \
  --header "Content-Type: application/json" \
  --data-raw '{
    "graph_ids": ["<GRAPH_ID>"],
    "question": "What are the main products and their market performance?",
    "subqueries": true
  }'

from writerai import Writer

client = Writer()

response = client.graphs.question(
  graph_ids=["<GRAPH_ID>"],
  question="What are the main products and their market performance?",
  subqueries=True
)

print(f"Main answer: {response.answer}")
for subquery in response.subqueries:
    print(f"Subquery: {subquery.query}")
    print(f"Answer: {subquery.answer}")

import { Writer } from "writer-sdk";

const client = new Writer();

const response = await client.graphs.question({
  graph_ids: ["<GRAPH_ID>"],
  question: "What are the main products and their market performance?",
  subqueries: true
});

console.log(`Main answer: ${response.answer}`);
response.subqueries.forEach(subquery => {
    console.log(`Subquery: ${subquery.query}`);
    console.log(`Answer: ${subquery.answer}`);
});

Stream the response

You can stream the response from the Knowledge Graphs by setting the stream parameter to true. This is useful for real-time applications that need to see the response as it is generated.

curl --location --request POST https://api.writer.com/v1/graphs/question \
  --header "Authorization: Bearer $WRITER_API_KEY" \
  --header "Content-Type: application/json" \
  --data-raw '{
    "graph_ids": ["<GRAPH_ID>"],
    "question": "<QUESTION>",
    "stream": true
  }'

from writerai import Writer

client = Writer()

response = client.graphs.question(
  graph_ids=["<GRAPH_ID>"],
  question="<QUESTION>",
  stream=True
)

for chunk in response:
    print(chunk.answer, end="", flush=True)

import { Writer } from "writer-sdk";

const client = new Writer();

const response = await client.graphs.question({
  graph_ids: ["<GRAPH_ID>"],
  question: "<QUESTION>",
  stream: true
});

for await (const chunk of response) {
    process.stdout.write(chunk.answer);
}

Next steps

Learn how to create and manage Knowledge Graphs
Use Knowledge Graphs in chat completions with tool calling
Explore Knowledge Graph API reference

Getting started

Monitor and manage

Core concepts

Models and pricing

Connect to your data

Chat completions

No-code agents

Knowledge Graphs

Tool calling

Additional capabilities

Integrations

Security and compliance

Resources

Ask questions to Knowledge Graphs

Question endpoint

Request body

Query configuration parameters

Response format

Examples

Query multiple knowledge graphs

Enable subqueries

Stream the response

Next steps

​Question endpoint

​Request body

​Query configuration parameters

​Response format

​Examples

​Query multiple knowledge graphs

​Enable subqueries

​Stream the response

​Next steps

Question endpoint

Request body

Query configuration parameters

Response format

Examples

Query multiple knowledge graphs

Enable subqueries

Stream the response

Next steps