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132 lines
5.4 KiB
Text
132 lines
5.4 KiB
Text
---
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title: "Context & Variables"
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description: "How data flows into, through, and out of a conversation"
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---
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Dograh has a simple data model for passing information through a call. Understanding it is key to building agents that feel personalised and to extracting useful results after a call.
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## The three context objects
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```
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initial_context ──► Agent ──► gathered_context
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│
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[template variables](/voice-agent/template-variables)
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(used in prompts)
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```
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### initial_context
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Data available to the agent before the call starts — the contact's name, account details, appointment information, anything the agent should know upfront. It can be set from several places:
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- **API trigger** — pass it in the request body when calling `POST /public/agent/{uuid}` or `POST /telephony/initiate-call`
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- **Campaign CSV** — columns beyond `phone_number` automatically become `initial_context` fields for each contact's call
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- **Dashboard** — set default template context variables on the agent, used when no external context is provided
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```json
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{
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"phone_number": "+14155550100",
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"initial_context": {
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"customer_name": "Jane Smith",
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"plan": "premium",
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"renewal_date": "April 1"
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}
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}
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```
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### Template variables
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Values from `initial_context` are available in your agent's prompt using `{{double_brace}}` syntax.
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```
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You are calling {{customer_name}} about their {{plan}} plan,
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which renews on {{renewal_date}}. Be friendly and confirm
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whether they'd like to continue.
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```
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When the call starts, Dograh substitutes the values before sending the prompt to the LLM — so the agent speaks naturally as if it already knows the contact.
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### Fallback values
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If a variable might be missing or empty, use a pipe (`|`) to provide a default value:
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```
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Hello {{customer_name | there}}, we're calling about your {{plan | current}} plan.
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```
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When `customer_name` is not set, the agent will say "Hello there" instead of leaving a blank. The syntax is:
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```
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{{variable_name | fallback_value}}
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```
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If the variable is present and non-empty, the fallback is ignored and the actual value is used.
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### Default variables
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Built-in variables for current time and weekday, available in any prompt without setting up `initial_context`.
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| Variable | Description | Example output |
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|---|---|---|
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| `{{current_time}}` | Current time in UTC (or inferred timezone) | `2026-04-02 14:30:45 UTC` |
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| `{{current_time_<TIMEZONE>}}` | Current time in the specified timezone | `2026-04-02 20:00:45 IST` |
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| `{{current_weekday}}` | Current weekday name in UTC (or inferred timezone) | `Thursday` |
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| `{{current_weekday_<TIMEZONE>}}` | Current weekday name in the specified timezone | `Thursday` |
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Replace `<TIMEZONE>` with an [IANA timezone name](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) such as `Asia/Kolkata`, `America/New_York`, or `Europe/London`.
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```
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Today is {{current_weekday}} and the current time is {{current_time_America/New_York}}.
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```
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<Note>
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When you use a timezone suffix on **either** `current_time` or `current_weekday`, the other variable without a suffix will automatically use the same timezone instead of UTC. For example, if your prompt contains both `{{current_time_Asia/Kolkata}}` and `{{current_weekday}}`, the weekday will also be resolved in `Asia/Kolkata`.
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</Note>
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### Telephony variables
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For telephony calls (inbound and outbound), Dograh automatically adds these variables to `initial_context`:
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| Variable | Description | Example |
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|---|---|---|
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| `{{caller_number}}` | The phone number that initiated the call | `+14155550100` |
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| `{{called_number}}` | The phone number that received the call | `+18005550199` |
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For **inbound** calls, `caller_number` is the customer's number and `called_number` is your Dograh number. For **outbound** calls, it's the reverse — `caller_number` is your Dograh number and `called_number` is the customer's number.
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```
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You are speaking with the caller at {{caller_number}}.
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```
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### gathered_context
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Data the agent collects *during* the call. You configure what to extract in the agent node's extraction settings — each variable has a name, type, and a prompt that tells the LLM what to look for.
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<img src="../images/extracted_variables.png" alt="Extracted variables" style={{border: "1px solid #d1d5db", borderRadius: "8px", maxWidth: "100%"}} />
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`gathered_context` is returned in the run record after the call completes and is available in [webhook payloads](/developer/webhooks) for downstream processing.
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## Data flow example
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```mermaid
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sequenceDiagram
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participant App as Your System
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participant Dog as Dograh
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participant LLM as LLM
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App->>Dog: initial_context: {customer_name: "Jane", plan: "premium"}
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Dog->>LLM: Prompt with {{customer_name}} and {{plan}} substituted
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LLM-->>Dog: Conversation response
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Note over Dog,LLM: Call progresses...
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Dog->>LLM: Extract: did the customer confirm renewal?
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LLM-->>Dog: gathered_context: {renewal_confirmed: true}
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Dog-->>App: Run record with gathered_context
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```
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## Where variables are available
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| Location | Variables available |
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|---|---|
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| Agent node prompts | `initial_context` fields via `{{variable_name}}` |
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| Edge conditions | Evaluated against the live conversation — no explicit variable syntax needed |
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| Webhook payload templates | All context objects via `{{initial_context.field}}`, `{{gathered_context.field}}` etc. |
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| Campaign CSV columns | CSV columns beyond `phone_number` become `initial_context` fields automatically |
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