ReasoningBlock
Collapsible 'thinking' panel for extended-reasoning models (Claude extended thinking, OpenAI o-series, DeepSeek R1). Auto-scrolls while streaming, shows token count + duration, and tucks itself away when reasoning finishes.
Source
src/components/ai/reasoning-block.tsxLive streaming
The header pulses while the model is still emitting reasoning tokens. The content auto-scrolls to follow the stream. When done, it auto-collapses 1.5s later.
No reasoning emitted yet.
Finished reasoning, default-collapsed
When the model emits a long reasoning trace before its final answer, render it collapsed by default — the user clicks to expand if they want to audit.
The user is asking me to compare pgvector and Pinecone for their AI startup.
Let me think about what they actually need to know:
1. They're a small team (4 engineers) running on Postgres already.
2. They want a recommendation, not a feature matrix.
The deciding factor for teams of this size is operational complexity. pgvector
keeps embeddings inside the same database they already operate; Pinecone is a
separate data plane with its own credentials, monitoring, and failure modes.
For their stated scale (~5M vectors), pgvector is the safer default. Pinecone
becomes worth the trade once they pass roughly 10M vectors and start hitting
HNSW memory limits on their RDS instance.
Final answer: recommend pgvector, name Pinecone as the migration target if
their volume crosses the threshold.
Default-expanded
Pass defaultOpen if you want the panel always visible on render — useful for review surfaces (eval dashboards, prompt-debugging tools).
The user is asking me to compare pgvector and Pinecone for their AI startup.
Let me think about what they actually need to know:
1. They're a small team (4 engineers) running on Postgres already.
2. They want a recommendation, not a feature matrix.
The deciding factor for teams of this size is operational complexity. pgvector
keeps embeddings inside the same database they already operate; Pinecone is a
separate data plane with its own credentials, monitoring, and failure modes.
For their stated scale (~5M vectors), pgvector is the safer default. Pinecone
becomes worth the trade once they pass roughly 10M vectors and start hitting
HNSW memory limits on their RDS instance.
Final answer: recommend pgvector, name Pinecone as the migration target if
their volume crosses the threshold.