Twilio Enterprise Knowledge

Overview

Enterprise Knowledge gives AI agents access to your organization's source material during conversations — FAQs, warranty policies, support scripts, product catalogs. It closes the gap between general model knowledge and how your business actually operates.

Your content (web/PDF/text) → Knowledge Base → Indexed chunks
Agent query → Search → Ranked chunks → Inject into LLM prompt

Enterprise Knowledge is shared across your organization — it captures institutional content. It is distinct from Conversation Memory (twilio-conversation-memory), which is per-customer context. The two are designed to be combined: enterprise content for accuracy, customer memory for personalization.

Base URL: https://knowledge.twilio.com

Authentication: HTTP Basic — Authorization: Basic {base64(accountSid:authToken)}

Rules for agents:

• Always poll statusUrl after any 202 response — all writes are async
• Always wait for Knowledge Base status COMPLETED before adding sources
• Always wait for source processing to complete before searching
• Never use /v1/ paths — all routes use /v2/ prefix
• Never include auth headers when uploading to presigned URLs — they're already signed
• Never use spaces or underscores in displayName — pattern is ^[a-zA-Z0-9-]+$
• Never exceed 16MB per file upload or 1,048,576 chars per text source

Prerequisites

• Twilio account with Enterprise Knowledge enabled — A credit card must be added to the account — See twilio-account-setup for initial setup — See twilio-iam-auth-setup for credential best practices
• Environment variables:
  • TWILIO_ACCOUNT_SID
  • TWILIO_AUTH_TOKEN
• SDK: pip install twilio / npm install twilio

Quickstart

Step 1 — Create a Knowledge Base

Python

import os, requests, time

account_sid = os.environ["TWILIO_ACCOUNT_SID"]
auth_token = os.environ["TWILIO_AUTH_TOKEN"]
base_url = "https://knowledge.twilio.com"
auth = (account_sid, auth_token)

res = requests.post(
    f"{base_url}/v2/ControlPlane/KnowledgeBases",
    auth=auth,
    json={"displayName": "product-docs", "description": "Support agent knowledge"}
)

status_url = res.json()["statusUrl"]

while True:
    op = requests.get(status_url, auth=auth).json()
    if op["status"] == "COMPLETED":
        kb_id = op["result"]["id"]
        break
    if op["status"] == "FAILED":
        raise Exception(op["error"]["detail"])
    time.sleep(2)

print(kb_id)  # know_knowledgebase_xxx

Node.js

const accountSid = process.env.TWILIO_ACCOUNT_SID;
const authToken = process.env.TWILIO_AUTH_TOKEN;
const baseUrl = "https://knowledge.twilio.com";
const authHeader = "Basic " + btoa(`${accountSid}:${authToken}`);
const headers = { "Authorization": authHeader, "Content-Type": "application/json" };

const res = await fetch(`${baseUrl}/v2/ControlPlane/KnowledgeBases`, {
    method: "POST",
    headers,
    body: JSON.stringify({ displayName: "product-docs", description: "Support agent knowledge" }),
});

const { statusUrl } = await res.json();

let kbId;
while (true) {
    const op = await fetch(statusUrl, { headers: { "Authorization": authHeader } }).then(r => r.json());
    if (op.status === "COMPLETED") { kbId = op.result.id; break; }
    if (op.status === "FAILED") throw new Error(op.error.detail);
    await new Promise(r => setTimeout(r, 2000));
}

Step 2 — Add a Knowledge Source

Three source types: Web (crawl a URL), File (upload PDF/CSV/Markdown/text, max 16MB), Text (inline, max 1,048,576 chars).

Python

knowledge = requests.post(
    f"{base_url}/v2/KnowledgeBases/{kb_id}/Knowledge",
    auth=auth,
    json={
        "name": "Product Documentation",
        "description": "Public product docs",
        "source": {"type": "Web", "url": "https://docs.example.com", "crawlDepth": 3}
    }
).json()

knowledge_id = knowledge["id"]

Node.js

const knowledge = await fetch(`${baseUrl}/v2/KnowledgeBases/${kbId}/Knowledge`, {
    method: "POST",
    headers,
    body: JSON.stringify({
        name: "Product Documentation",
        description: "Public product docs",
        source: { type: "Web", url: "https://docs.example.com", crawlDepth: 3 },
    }),
}).then(r => r.json());

const knowledgeId = knowledge.id;

Step 3 — Wait for processing

Sources are processed asynchronously. Poll until status is COMPLETED.

Python

while True:
    k = requests.get(
        f"{base_url}/v2/KnowledgeBases/{kb_id}/Knowledge/{knowledge_id}", auth=auth
    ).json()
    if k["status"] == "COMPLETED":
        break
    if k["status"] == "FAILED":
        raise Exception(f"Processing failed: {k}")
    time.sleep(3)

Node.js

while (true) {
    const k = await fetch(
        `${baseUrl}/v2/KnowledgeBases/${kbId}/Knowledge/${knowledgeId}`,
        { headers: { "Authorization": authHeader } }
    ).then(r => r.json());
    if (k.status === "COMPLETED") break;
    if (k.status === "FAILED") throw new Error(JSON.stringify(k));
    await new Promise(r => setTimeout(r, 3000));
}

Statuses: SCHEDULED → QUEUED → PROCESSING → COMPLETED / FAILED

Step 4 — Search and inject into LLM prompt

Python

results = requests.post(
    f"{base_url}/v2/KnowledgeBases/{kb_id}/Search",
    auth=auth,
    json={"query": "How do I reset my password?", "top": 5}
).json()

chunks = "\n\n".join(c["content"] for c in results["chunks"])

system_prompt = f"""You are a helpful support agent.

Relevant knowledge:
{chunks}

Answer using only the above content."""

Node.js

const results = await fetch(`${baseUrl}/v2/KnowledgeBases/${kbId}/Search`, {
    method: "POST",
    headers,
    body: JSON.stringify({ query: "How do I reset my password?", top: 5 }),
}).then(r => r.json());

const chunks = results.chunks.map(c => c.content).join("\n\n");
const systemPrompt = `You are a helpful support agent.\n\nRelevant knowledge:\n${chunks}`;

Key Patterns

Combine with Conversation Memory

For the best agent responses, combine Enterprise Knowledge (company content) with Conversation Memory Recall (individual customer history).

Python

recall_res = requests.post(
    f"https://memory.twilio.com/v1/Stores/{store_id}/Profiles/{profile_id}/Recall",
    auth=auth,
    json={"query": user_query, "observationsLimit": 5}
).json()

search_res = requests.post(
    f"{base_url}/v2/KnowledgeBases/{kb_id}/Search",
    auth=auth,
    json={"query": user_query, "top": 3}
).json()

customer_context = "\n".join(o["content"] for o in recall_res.get("observations", []))
knowledge = "\n\n".join(c["content"] for c in search_res.get("chunks", []))

system_prompt = f"""Customer history:\n{customer_context}\n\nDocumentation:\n{knowledge}"""

Node.js

const [recallRes, searchRes] = await Promise.all([
    fetch(`https://memory.twilio.com/v1/Stores/${storeId}/Profiles/${profileId}/Recall`, {
        method: "POST",
        headers,
        body: JSON.stringify({ query: userQuery, observationsLimit: 5 }),
    }).then(r => r.json()),
    fetch(`${baseUrl}/v2/KnowledgeBases/${kbId}/Search`, {
        method: "POST",
        headers,
        body: JSON.stringify({ query: userQuery, top: 3 }),
    }).then(r => r.json()),
]);

const customerContext = recallRes.observations.map(o => o.content).join("\n");
const knowledge = searchRes.chunks.map(c => c.content).join("\n\n");

File Upload (PDF/CSV/Markdown)

File sources return a presigned URL. Upload the file there — do not include auth headers.

Python

knowledge = requests.post(
    f"{base_url}/v2/KnowledgeBases/{kb_id}/Knowledge",
    auth=auth,
    json={"name": "Handbook", "description": "Employee handbook", "source": {"type": "File"}}
).json()

upload_url = knowledge["source"]["importUrl"]

with open("handbook.pdf", "rb") as f:
    requests.put(upload_url, data=f, headers={"Content-Type": "application/pdf"})

Node.js

const knowledge = await fetch(`${baseUrl}/v2/KnowledgeBases/${kbId}/Knowledge`, {
    method: "POST",
    headers,
    body: JSON.stringify({ name: "Handbook", description: "Employee handbook", source: { type: "File" } }),
}).then(r => r.json());

const file = await fs.promises.readFile("handbook.pdf");
await fetch(knowledge.source.importUrl, {
    method: "PUT",
    headers: { "Content-Type": "application/pdf" },
    body: file,
});

Filter Search to Specific Sources

When your Knowledge Base has multiple sources, target search to specific ones:

Python

results = requests.post(
    f"{base_url}/v2/KnowledgeBases/{kb_id}/Search",
    auth=auth,
    json={"query": "cancellation policy", "top": 5, "knowledgeIds": [policy_source_id]}
).json()

for chunk in results["chunks"]:
    print(f"[{chunk['score']:.3f}] {chunk['content'][:100]}")

Node.js

const results = await fetch(`${baseUrl}/v2/KnowledgeBases/${kbId}/Search`, {
    method: "POST",
    headers,
    body: JSON.stringify({ query: "cancellation policy", top: 5, knowledgeIds: [policySourceId] }),
}).then(r => r.json());

for (const chunk of results.chunks) {
    console.log(`[${chunk.score.toFixed(3)}] ${chunk.content.slice(0, 100)}`);
}

Omit knowledgeIds to search across all sources. Max 100 IDs per request.

Refresh a Web Source

Re-crawl without changing config. Set crawlPeriod for automatic recrawling.

Python

requests.patch(
    f"{base_url}/v2/KnowledgeBases/{kb_id}/Knowledge/{knowledge_id}?refresh=true",
    auth=auth,
    json={"name": "Product Documentation"}
)

requests.patch(
    f"{base_url}/v2/KnowledgeBases/{kb_id}/Knowledge/{knowledge_id}",
    auth=auth,
    json={"name": "Product Documentation", "source": {"type": "Web", "crawlPeriod": "WEEKLY"}}
)

Node.js

await fetch(`${baseUrl}/v2/KnowledgeBases/${kbId}/Knowledge/${knowledgeId}?refresh=true`, {
    method: "PATCH",
    headers,
    body: JSON.stringify({ name: "Product Documentation" }),
});

await fetch(`${baseUrl}/v2/KnowledgeBases/${kbId}/Knowledge/${knowledgeId}`, {
    method: "PATCH",
    headers,
    body: JSON.stringify({ name: "Product Documentation", source: { type: "Web", crawlPeriod: "WEEKLY" } }),
});

Crawl period options: WEEKLY | BIWEEKLY | MONTHLY | NEVER

Inspect Chunks

Audit what was indexed from a source:

Python

chunks = requests.get(
    f"{base_url}/v2/KnowledgeBases/{kb_id}/Knowledge/{knowledge_id}/Chunks",
    auth=auth,
    params={"pageSize": 50}
).json()

for chunk in chunks["chunks"]:
    print(f"[{chunk['metadata']['sourceType']}] {chunk['content'][:100]}")

Node.js

const chunks = await fetch(
    `${baseUrl}/v2/KnowledgeBases/${kbId}/Knowledge/${knowledgeId}/Chunks?pageSize=50`,
    { headers: { "Authorization": authHeader } }
).then(r => r.json());

for (const chunk of chunks.chunks) {
    console.log(`[${chunk.metadata.sourceType}] ${chunk.content.slice(0, 100)}`);
}

Paginate with pageToken from chunks.meta.nextToken.

CANNOT

• Cannot exceed 5 Knowledge Bases per account
• Cannot exceed 10 knowledge sources per Knowledge Base
• Cannot add sources before Knowledge Base is active — poll statusUrl until COMPLETED
• Cannot use v1 endpoints — all routes use /v2/ prefix on knowledge.twilio.com
• Cannot include auth header when uploading to presigned URL — importUrl is already signed
• Cannot search before source processing completes — poll source status first
• Cannot exceed 16 MiB (16,777,216 bytes) per file upload
• Cannot exceed 1,048,576 characters (~1MB) per text source pushed via API
• Cannot exceed 2048 characters in a search query
• Cannot exceed 2048 characters in a web source URL
• Cannot retrieve more than 20 search results per query (top max is 20)
• Cannot exceed 100 knowledgeIds in a search filter
• Cannot set crawl depth beyond 1–10 levels for web sources
• Cannot use custom crawl schedules — locked to fixed intervals: WEEKLY, BIWEEKLY, MONTHLY, or NEVER
• Cannot use spaces or underscores in displayName — alphanumeric and hyphens only
• Cannot use name longer than 30 characters for knowledge sources
• Cannot modify immutable fields (id, type, status, url, createdAt, updatedAt) via PATCH
• Cannot use Knowledge for per-customer context — use twilio-conversation-memory for that

Next Steps

• Per-customer context: twilio-conversation-memory — combine with Enterprise Knowledge for full agent context
• Background transcript intelligence: twilio-conversation-intelligence
• Voice agent with ConversationRelay: twilio-voice-conversation-relay
• TAC SDK integration: twilio-agent-connect
• Debug integration issues: twilio-debugging-observability