Do I need Pinecone for a chatbot over our docs?

Not necessarily.

For a chatbot over your docs, Pinecone is one option for the “vector database” piece, but whether you need it depends on your scale and requirements.

You probably don’t need Pinecone if:

• Your docs are relatively small/moderate in size
• You’re just prototyping or building an internal tool
• You’re okay using a simpler setup like:
  • Postgres + pgvector
  • SQLite / local vector store
  • FAISS
  • Chroma
• You don’t need highly scalable, low-latency retrieval across lots of documents/users

Pinecone is useful if:

• You have a large corpus
• You need fast semantic search at scale
• You want a managed service with less ops overhead
• You expect growth and want something production-oriented
• You need features like filtering, indexing, and easy scaling without managing infra

Typical doc chatbot architecture

1. Split docs into chunks
2. Create embeddings for each chunk
3. Store embeddings in a vector store
4. On user question, embed the query
5. Retrieve top relevant chunks
6. Feed them into the LLM to generate the answer

Pinecone is just step 3.

Good alternatives

• Postgres + pgvector: best if you already use Postgres
• Chroma: easy for prototypes
• FAISS: fast local library, but you manage persistence/search plumbing
• Weaviate / Qdrant: solid open-source vector databases

Rule of thumb

• Prototype / internal tool / < ~100k chunks: start without Pinecone
• Production / larger scale / want managed vector search: Pinecone becomes more attractive

If you want, I can recommend the simplest stack for your situation based on:

• number of documents
• expected users
• cloud/on-prem preference
• whether you already use Postgres