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various newsletter updates to include optional cta/highlights

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This commit is contained in:
TuanaCelik
2026-03-09 18:04:45 +01:00
parent 852e7554a0
commit 475c07fe7c
3 changed files with 146 additions and 34 deletions
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src/app/api/twitter/route.js
+91 -33
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@@ -18,7 +18,7 @@ If you haven't explored LlamaCloud yet, make sure to [sign up](https://cloud.lla
- **Event-Driven RAG Templates:** Use our event-driven workflows to implement techniques from key RAG papers---LongRAG, CorrectiveRAG, Self-Discover RAG---with added visualization and debugging, available as templates or for custom development. [Tweet](https://x.com/llama_index/status/1824833283928264952).
- **Box Integration in LlamaIndex:** New Box Readers integrated into LlamaIndex workflows facilitate efficient data extraction and authentication for enhanced AI applications. [Blogpost](https://medium.com/box-developer-blog/introducing-box-llama-index-reader-13903442a9e6), [Tweet](https://x.com/llama_index/status/1823464513301307787).
**☁️ LlamaCloud:**
**☁️ LlamaParse:**
- Guide to Building a Multimodal Report Generation Agent using LlamaParse and LlamaIndex workflows to develop a multi-agent system that generates detailed reports with text and images from complex data sources. [Notebook](https://github.com/run-llama/llama_parse/blob/main/examples/multimodal/multimodal_report_generation_agent.ipynb), [Tweet](https://x.com/llama_index/status/1824483475338170541).
@@ -28,18 +28,6 @@ If you haven't explored LlamaCloud yet, make sure to [sign up](https://cloud.lla
2. We have integrated Box documents into LlamaIndex workflows with new Box Readers, enabling efficient data extraction, authentication, and retrieval to enhance your LLM applications with robust, data-driven AI solutions. [Blogpost](https://medium.com/box-developer-blog/introducing-box-llama-index-reader-13903442a9e6), [Tweet](https://x.com/llama_index/status/1823464513301307787).
3. Multi-Agent Concierge as a Workflow, re-implementation of our financial concierge system using LlamaIndex's new Workflows abstraction, which supports looping, branching, debugging, and automatic visualization. [Video](https://www.youtube.com/watch?v=DqiIDMxuoKA&feature=youtu.be), [Tweet](https://x.com/llama_index/status/1823425199704039863).
**✍️ Community:**
- [Dave Bechberger's](https://x.com/bechbd) [tutorial](https://medium.com/@bechbd/knowledge-graphs-and-generative-ai-graphrag-with-amazon-neptune-and-llamaindex-part-1-39cd7255bac4) on Building a Natural Language Querying System for Graph Databases using LlamaIndex with Amazon Neptune to translate natural language into openCypher queries, execute them, and optimize with Amazon Bedrock's LLMs.
- [Ravi Theja's](https://x.com/ravithejads) video [tutorial](https://www.youtube.com/watch?v=Skm70sGaME4) on rebuilding JSONalyze Query Engine using workflows.
- [BeyondLLM](https://github.com/aiplanethub/beyondllm) by AI Planet Hub simplifies the development of advanced RAG pipelines to 5-7 lines of code, with features like auto-retrieval, reranking, and embedding fine-tuning. It integrates with Arize AI Phoenix for comprehensive evaluation and observability.
- [Richmond Alake's](https://x.com/richmondalake) [video tutorial](https://www.youtube.com/watch?v=UfBQxl_Pe1w) on implementing Agentic RAG Using Claude 3.5 Sonnet, LlamaIndex, and MongoDB.
- Rajib Deb's [video tutorial](https://www.youtube.com/watch?v=UFCpF6W2j3w) on Workflows, highlighting decorators for control flow, event-driven chaining, and custom orchestration steps.
- [Tomaz Bratanic's](https://x.com/tb_tomaz) Neo4j [tutorial](https://medium.com/neo4j/entity-linking-and-relationship-extraction-with-relik-in-llamaindex-ca18892c169f) demonstrates using the Relik framework for information extraction, integrating spaCy, Coreferee, LlamaIndex, and Neo4j for entity linking, relationship extraction, and graph-based question answering.
- [Andrei](https://x.com/_nerdai_) [video tutorial](https://www.youtube.com/watch?v=3yG--HKxmi8) on discussing llama-agents, our framework for building multi-agent systems with a focus on production use cases.
- [Ravi Theja's](https://x.com/ravithejads) video [tutorial](https://www.youtube.com/watch?v=P4xHWojIB-M) on re-building our Citation Query Engine using workflows.
- [Farzad Sunavala's](https://hashnode.com/@Farzzy528) [guide](https://farzzy.hashnode.dev/exploring-llamaindex-workflows-a-step-by-step-guide-to-building-a-rag-system-with-azure-ai-search-and-azure-openai) to Building a RAG System with Azure AI Search and Azure OpenAI using LlamaIndex workflows.
- [Benito Martin's](https://medium.com/@benitomartin) [tutorial](https://medium.com/@benitomartin/cooking-with-ai-building-a-smart-multimodal-recipe-recommender-using-qdrant-llamaindex-and-2d6d1fa6566c) on Building a Smart Multimodal Recipe Recommender using Qdrant, LlamaIndex, and Google Gemini.
`
function getDateDaysAgo(days) {
@@ -86,11 +74,29 @@ export async function GET(request) {
Authorization: `Bearer ${session.accessToken}`
}
})
if (!response.ok) {
const errorData = await response.json().catch(() => ({}));
console.log('Twitter API error:', response.status, errorData);
return new Response(JSON.stringify({ error: "Failed to fetch Twitter User ID", details: errorData }), {
status: response.status,
headers: { 'Content-Type': 'application/json' },
})
}
const data = await response.json()
if (!data.data || !data.data.id) {
console.log('Unexpected Twitter API response:', data);
return new Response(JSON.stringify({ error: "Invalid response from Twitter API", details: data }), {
status: 500,
headers: { 'Content-Type': 'application/json' },
})
}
userId = data.data.id
} catch (error) {
console.log(error)
return new Response(JSON.stringify({ error: "Failed to fetch Twitter User ID" }), {
console.log('Error fetching Twitter User ID:', error)
return new Response(JSON.stringify({ error: "Failed to fetch Twitter User ID", details: error.message }), {
status: 500,
headers: { 'Content-Type': 'application/json' },
})
@@ -124,31 +130,62 @@ export async function GET(request) {
}
})
if (response.status !== 200) {
console.log(response)
return new Response(JSON.stringify({ error: "Twitter API call failed" }), {
status: 500,
if (response.status === 429) {
// Rate limit exceeded
const rateLimitReset = response.headers.get('x-rate-limit-reset');
const resetTime = rateLimitReset ? new Date(parseInt(rateLimitReset) * 1000).toISOString() : 'unknown';
const errorData = await response.json().catch(() => ({}));
console.log('Twitter API rate limit exceeded. Reset at:', resetTime);
return new Response(JSON.stringify({
error: "Twitter API rate limit exceeded",
message: `Rate limit exceeded. Please try again after ${resetTime}`,
resetTime: resetTime,
details: errorData
}), {
status: 429,
headers: { 'Content-Type': 'application/json' },
})
}
if (!response.ok) {
const errorData = await response.json().catch(() => ({}));
console.log('Twitter API error:', response.status, errorData);
return new Response(JSON.stringify({
error: "Twitter API call failed",
status: response.status,
details: errorData
}), {
status: response.status,
headers: { 'Content-Type': 'application/json' },
})
}
tweets = await response.json()
} catch (error) {
return new Response(JSON.stringify({ error: "Error fetching recent tweets" }), {
console.log('Error fetching tweets:', error);
return new Response(JSON.stringify({
error: "Error fetching recent tweets",
message: error.message
}), {
status: 500,
headers: { 'Content-Type': 'application/json' },
})
}
function addUtmParams(url) {
if (url.includes('llamaindex.ai')) {
try {
const urlObj = new URL(url);
urlObj.searchParams.set('utm_source', 'newsletter');
urlObj.searchParams.delete('utm_medium');
urlObj.searchParams.delete('utm_campaign');
return urlObj.toString();
const host = urlObj.hostname || '';
// Add utm_source=newsletter to any LlamaIndex-related domains
if (host.includes('llamaindex')) {
urlObj.searchParams.set('utm_source', 'newsletter');
return urlObj.toString();
}
return url;
} catch {
// If URL parsing fails, return original URL
return url;
}
return url;
}
if (tweets.data) {
@@ -195,6 +232,25 @@ export async function GET(request) {
let mostLiked = tweets.data.sort((a, b) => b.public_metrics.like_count - a.public_metrics.like_count).slice(0, 3)
let mostLikedList = listTweets(mostLiked)
// Optional user-provided highlights/CTAs to feature at the very top
const { searchParams } = new URL(request.url);
const highlightsFromRequest = searchParams.get('highlights') || '';
const highlightsPromptSection = highlightsFromRequest.trim()
? `
You have also been given additional "Highlights and CTAs" content that should be surfaced at the very top of the newsletter, immediately after the introductory paragraph.
Here is that additional content (URLs and context):
-----------
${highlightsFromRequest}
-----------
Based on this content, invent a short, attention-grabbing markdown section title (3–8 words) and create a dedicated section directly after the intro paragraph. Do NOT hard-code the title to any fixed phrase; always derive it from the content. Summarize this content into clear bullet points with strong calls to action, and ensure each bullet links to the provided URLs.
`
: `
If no extra "Highlights and CTAs" content is provided, do not add such a special top section.
`;
// const llm = new OpenAI({
// model: "gpt-4o-mini",
// temperature: 0.2,
@@ -203,7 +259,6 @@ export async function GET(request) {
// });
const llm = new Anthropic({
model: "claude-sonnet-4-20250514",
temperature: 0.2,
streaming: true,
apiKey: process.env.ANTHROPIC_API_KEY
})
@@ -225,18 +280,21 @@ export async function GET(request) {
Important features to note:
* Vary the greeting from "Llama Lovers" to something Llama-related, like "Llama Fans" or "Llama Enthusiasts"
* The headings should be:
* The Highlights (most liked tweets, see below)
* LlamaCloud (tweets that mention llamacloud, llamasplit, llamaextract, llamasheets or llamaparse)
* Framework (tweets that are about changes to the llamaindex framework itself)
* Community (everything else)
* Each section should have a bullet point list of items
* Use the following rules for sections and headings:
* If any extra "Highlights and CTAs" content is provided, create a custom-titled section (you choose a short, attention-grabbing title based on the content) immediately after the intro paragraph, using that content.
* Then add a section titled "The Highlights" (most liked tweets, see below).
* Then add a section titled "LlamaParse" for tweets that mention llamacloud, llamasplit, llamaextract, llamasheets or llamaparse.
* Then add a section titled "Framework" for tweets that are about changes to the llamaindex framework itself.
* Do not create a "Community" section.
* Each section should have a bullet point list of items.
* Each item should link to the relevant blog post, tutorial, etc. from the tweet.
The highlights section should focus on the most-liked tweets; these are:
-----------
${mostLikedList}
-----------
${highlightsPromptSection}
`,
stream: true
})
src/app/globals.css
+35
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@@ -92,3 +92,38 @@ a:hover {
overflow-y: auto;
max-height: 1000px;
}
.highlights-input {
margin-bottom: 20px;
padding: 16px 20px;
background-color: #ffffff;
border-radius: 10px;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.08);
border: 1px solid #e2e4e8;
}
.highlights-input h3 {
margin-top: 0;
margin-bottom: 8px;
font-size: 1rem;
color: var(--primary-color);
}
.highlights-input textarea {
width: 100%;
min-height: 120px;
max-height: 240px;
border: 1px solid #d0d4da;
border-radius: 6px;
padding: 10px 12px;
font-family: system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
font-size: 0.9rem;
resize: vertical;
box-sizing: border-box;
}
.highlights-input textarea:focus {
outline: none;
border-color: var(--primary-color);
box-shadow: 0 0 0 2px rgba(29, 161, 242, 0.15);
}
src/app/page.tsx
+20 -1
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@@ -27,13 +27,23 @@ export default function Home() {
const { data: session } = useSession();
const [markdown, setMarkdown] = useState('');
const [isStreaming, setIsStreaming] = useState(false);
const [highlightsInput, setHighlightsInput] = useState('');
const handleClick = async (e: FormEvent) => {
e.preventDefault();
setMarkdown('');
setIsStreaming(true);
const eventSource = new EventSource(`/api/twitter`);
const params = new URLSearchParams();
if (highlightsInput.trim()) {
params.set('highlights', highlightsInput);
}
const url = params.toString()
? `/api/twitter?${params.toString()}`
: `/api/twitter`;
const eventSource = new EventSource(url);
eventSource.onmessage = (event) => {
const data = JSON.parse(event.data);
@@ -62,6 +72,15 @@ export default function Home() {
<LoginStatus />
{session && session.user && session.user.name && (
<div>
<div className="highlights-input">
<h3>Highlights and CTAs (optional)</h3>
<textarea
value={highlightsInput}
onChange={(e) => setHighlightsInput(e.target.value)}
disabled={isStreaming}
placeholder="Paste important URLs or context to highlight at the very top of the newsletter."
/>
</div>
<button onClick={handleClick} disabled={isStreaming}>
{isStreaming ? 'Streaming...' : 'Generate newsletter'}
</button>