A company is considering migrating its database infrastructure to Pinecone to overcome the vector dimension limits of its current pgvector setup and support Google's Gemini embeddings.

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  "company": {
    "name": "Pinecone",
    "domain": "pinecone.io"
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    "stage": "evaluating",
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    "topics": [
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    "summary": "A company is considering migrating its database infrastructure to Pinecone to overcome the vector dimension limits of its current pgvector setup and support Google's Gemini embeddings.",
    "category": "Vector Database",
    "comments": [
      {
        "url": "https://www.reddit.com/r/Rag/comments/1wq1tfx/comment/pc1fsl0/",
        "depth": 0,
        "score": 1,
        "author": "24iqSuperGenius",
        "excerpt": "With only 2k docs, I'd first test the truncated Gemini vectors and see how much retrieval quality drops. You already have BM25 + HNSW, so it may be fine.\n\n If the dimension limit is still an issue, I'd use a separate vector search engine instead of moving the whole DB. Keep Supabase for your main data.\n\n I'm building one myself. It's been tested on 10M Cohere Wikipedia vectors (1024D, 41GB raw dat",
        "posted_at": "2026-09-25T20:05:04.000Z",
        "author_url": "https://www.reddit.com/user/24iqSuperGenius/"
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      {
        "url": "https://www.reddit.com/r/Rag/comments/1wq1tfx/comment/pc1kyv4/",
        "depth": 0,
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        "author": "searchblox_searchai",
        "excerpt": "You can use https://index-server.searchblox.com/blog/semantic-search-dialects.html and https://inference-server.searchblox.com/blog/multimodal-embeddings-reranker-local.html for free and run locally.",
        "posted_at": "2026-09-25T20:28:04.000Z",
        "author_url": "https://www.reddit.com/user/searchblox_searchai/"
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      {
        "url": "https://www.reddit.com/r/Rag/comments/1wq1tfx/comment/pc1sdvg/",
        "depth": 0,
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        "author": "OkFlan504",
        "excerpt": "Before changing databases, I’d separate missing evidence from poor retrieval. For a few failed image-dependent questions, check whether the required visual content reaches the embedding step at all. Then compare embedding options against the same questions and expected evidence. A database migration won’t help if the relevant information never entered the index.",
        "posted_at": "2026-09-25T21:01:21.000Z",
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        "author": "Clean_Research_2583",
        "excerpt": "I’d be hesitant to move the whole database before proving the larger vectors are buying you enough quality to justify it. With 2k docs and no external users yet, this feels like a good point to build a small eval set from your harder queries and test the truncated Gemini vectors against the other embedding option first.\n\n A DB migration is a lot easier to justify when you can point to failures the",
        "posted_at": "2026-09-25T22:27:17.000Z",
        "author_url": "https://www.reddit.com/user/Clean_Research_2583/"
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        "url": "https://www.reddit.com/r/Rag/comments/1wq1tfx/comment/pc2p8eb/",
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        "author": "Interesting-Town-433",
        "excerpt": "I built www.embedding-adapters.com for this reason",
        "posted_at": "2026-09-25T23:45:00.000Z",
        "author_url": "https://www.reddit.com/user/Interesting-Town-433/"
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        "url": "https://www.reddit.com/r/Rag/comments/1wq1tfx/comment/pcdvzth/",
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        "author": "Lumpy_Fisherman_5352",
        "excerpt": "Migrating to Pinecone for 2000 docs is overkill - stay on pgvector. Two real options: (1) pgvector 0.7+ has halfvec, which indexes up to 4000 dims, so a 3072-dim Gemini vector fits with HNSW at half the storage; or (2) both Gemini and OpenAI-3 are Matryoshka-trained, so setting a smaller output dimensionality (e.g. 1536) is a designed truncation, not the lossy kind - usually a tiny recall hit. I'd",
        "posted_at": "2026-09-27T15:42:46.000Z",
        "author_url": "https://www.reddit.com/user/Lumpy_Fisherman_5352/"
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      {
        "url": "https://www.reddit.com/r/Rag/comments/1wq1tfx/comment/pc0wpeo/",
        "depth": 0,
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        "author": "onur90",
        "excerpt": "Why don't you try it with a graph database (Neo4j)? I installed Neo4j with the MCP server. The RAG can use MCP and Cypher (it's like SQL for graph databases).\nThe RAG can perform similarity searches to find the first node in the graph. From there, it searches node by node until it finds the information it needs.\nTo create the graph, I used a custom script with Gemini AI.",
        "posted_at": "2026-09-25T18:40:51.000Z",
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      "[post] My coworker wants to migrate our entire DB to something like pinecone but something tells me migrating Databases for something like this isn't worth doing."
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    "post_date": "2026-09-25T17:04:23.000Z",
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    "post_text": "Hey! Im usually active on these feeds but never comment but this problems made me stress out.\n\nI've built a rag across 2000 documents for my company right now and i was originally using the Text-embeddings-3-model from OpenAI and realized it wasn't able to gather nuanced contexts like images so i decided to migrate to the Gemini multi modal.\n\nOur current DB runs on supabase and uses pg vector as our vector db. Currently we use HNSW search + BM 25 in our search algorithm and hit a constraint during migration as Geminis vectors are bigger than the 2000 limit we get using postgreSQL.\n\nWe can either use truncated vectors and accept some loss of information or migrate to a cohereV4 multimodal tech that fits in the vector constraints allowed. My coworker wants to migrate our entire DB to something like pinecone but something tells me migrating Databases for something like this isn't worth doing. (We aren't in production for other users yet and have a small blast radius).\n\nI would love to know your suggestions!",
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