Use a Knowledge Graph in a chat - Writer AI Studio

Knowledge Graph Chat Guide

This guide demonstrates how to send questions to a Knowledge Graph during a chat completion. Knowledge Graph chat is a predefined tool you can use to reference a Knowledge Graph during a chat.

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.

Tool Structure

Knowledge Graph chat is a predefined tool supported by Palmyra X4 and later models, to be used with tool calling in the chat endpoint. To use Knowledge Graph chat, add the following object to the tools array when calling the chat endpoint:

Parameter	Type	Description
`type`	string	The type of tool. Must be `graph` for Knowledge Graph chat.
`function`	object	An object containing the `graph_ids`, `description`, `subqueries`, and `query_config` parameters
`function.graph_ids`	array	An array of strings containing the graph IDs you wish to reference
`function.description`	string	A description of the graphs you are referencing. This helps the model understand when to use the Knowledge Graph tool in the chat. If there are multiple graphs, include a description for each, referencing the graph by name.
`function.subqueries`	Boolean	A Boolean indicating whether to include the subqueries used by Palmyra in the response.
`function.query_config`	object	Configuration options for Knowledge Graph queries. See the Query configuration parameters below.

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.
`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.

cURL Example

curl --location 'https://api.writer.com/v1/chat' \
    --header 'Content-Type: application/json' \
    --header "Authorization: Bearer $WRITER_API_KEY" \
    --data '{\
        "model": "palmyra-x5",\
        "messages": [\
            {\
                "role": "user",\
                "content": "Which of our products contain both food coloring and chocolate?"\
            }\
        ],\
        "tool_choice": "auto",\
        "tools": [\
            {\
                "type": "graph",\
                "function": {\
                    "description": "Knowledge Graph containing information about Acme Inc. food products",\
                    "graph_ids": [\
                        "<GRAPH_ID>"\
                    ],\
                    "subqueries": true\
                }\
            }\
        ],\
        "stream": true\
    }'

Python Example

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()

tools = [{\
    "type": "graph",\
    "function": {\
        "description": "Description of the graph(s)",\
        "graph_ids": [\
            "<GRAPH_ID>"\
        ],\
        "subqueries": True\
    }\
}]

messages = [{"role": "user", "content": "Which of our products contain both food coloring and chocolate?"}]

response = client.chat.chat(
    model="palmyra-x5",
    messages=messages,
    tools=tools,  # The tools array defined earlier.
    tool_choice="auto",
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="", flush=True)

JavaScript Example

import { Writer } from "writer-sdk";

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

const tools = [{\
    type: "graph",\
    function: {\
        description: "Description of the graph(s)",\
        graph_ids: [\
            "<GRAPH_ID>"\
        ],\
        subqueries: true\
    }\
}]

let messages = [{role: "user", content: "Which of our products contain both food coloring and chocolate?"}]

const response = await client.chat.chat({
    model: "palmyra-x5",
    messages: messages,
    tools: tools, // The tools array defined earlier.
    tool_choice: "auto",
    stream: true
});

for await (const chunk of response) {
    if (chunk.choices[0].delta.content) {
        process.stdout.write(chunk.choices[0].delta.content);
    }
}

Response Format

When a chat completion uses the Knowledge Graph tool, the response from the Knowledge Graph tool is in the graph_data object. That object contains the following fields:

Parameter	Type	Description
`sources`	array	An array of objects containing the source file IDs and snippets that helped the model answer the question.
`sources.file_id`	string	The ID of the source file.
`sources.snippet`	string	A snippet from the source file that helped the model answer the question.
`status`	string	The status of the query.
`subqueries`	array	An array of objects containing the subqueries used by Palmyra in the response.
`subqueries.query`	string	The query used by Palmyra to answer the question.
`subqueries.answer`	string	The answer to the question.
`subqueries.sources`	array	An array of objects containing the source file IDs and snippets that helped the model answer the question.

The full response has the following structure:

Streaming Response

{
    "id": "1234",
    "object": "chat.completion.chunk",
    "choices": [\
        {\
            "index": 0,\
            "finish_reason": "stop",\
            "delta": {\
                "content": "None of our products contain both chocolate and food coloring. The products containing chocolate are different from those containing food coloring.",\
                "role": "assistant",
                "tool_calls": null,
                "graph_data": {\
                    "sources": [\
                        {\
                            "file_id": "1234",\
                            "snippet": "with cocoa for an extra touch of chocolate…"\
                        },\
                        {\
                            "file_id": "5678",\
                            "snippet": "Sugar, corn syrup, artificial flavors, food coloring…"\
                        }\
                    ],\
                    "status": "finished",\
                    "subqueries": [\
                        {\
                            "query": "Which of our products contain food coloring?",\
                            "answer": "The products that contain food coloring are...",\
                            "sources": [\
                                {\
                                    "file_id": "1234",\
                                    "snippet": "Sugar, citric acid, artificial flavors…"\
                                },\
                                {\
                                    "file_id": "5678",\
                                    "snippet": "Coffee, coconut milk, ice"\
                                }\
                            ]\
                        },\
                        {\
                            "query": "Which of our products contain chocolate?",\
                            "answer": "Several products contain chocolate. These include…",\
                            "sources": [\
                                {\
                                    "file_id": "1234",\
                                    "snippet": "with cocoa for an extra touch of chocolate…"\
                                }\
                            ]\
                        }\
                    ]\
                }\
            },\
        }\
    ]
}

Non-Streaming Response

{
    "id": "1234",
    "object": "chat.completion",
    "choices": [\
        {\
            "index": 0,\
            "finish_reason": "stop",\
            "message": {\
                "content": "None of our products contain both chocolate and food coloring. The products containing chocolate are different from those containing food coloring.",\
                "role": "assistant",
                "tool_calls": null,
                "graph_data": {\
                    "sources": [\
                        {\
                            "file_id": "1234",\
                            "snippet": "with cocoa for an extra touch of chocolate…"\
                        },\
                        {\
                            "file_id": "5678",\
                            "snippet": "Sugar, corn syrup, artificial flavors, food coloring…"\
                        }\
                    ],\
                    "status": "finished",\
                    "subqueries": [\
                        {\
                            "query": "Which of our products contain food coloring?",\
                            "answer": "The products that contain food coloring are...",\
                            "sources": [\
                                {\
                                    "file_id": "1234",\
                                    "snippet": "Sugar, citric acid, artificial flavors…"\
                                },\
                                {\
                                    "file_id": "5678",\
                                    "snippet": "Coffee, coconut milk, ice"\
                                }\
                            ]\
                        },\
                        {\
                            "query": "Which of our products contain chocolate?",\
                            "answer": "Several products contain chocolate. These include…",\
                            "sources": [\
                                {\
                                    "file_id": "1234",\
                                    "snippet": "with cocoa for an extra touch of chocolate…"\
                                }\
                            ]\
                        }\
                    ]\
                }\
            },\
        }\
    ]
}

Usage Example

The following example uses a hypothetical product information Knowledge Graph to answer a question about which food products contain both food coloring and chocolate.

Create the tools array

First, define the tools array with the type set to graph. The function object contains the graph_ids, description, and subqueries parameters. In this example, the subqueries are included in the response. Subqueries can be useful for debugging or for providing additional context to the user about how the model arrived at the answer.