Bring your own vectors

Store embeddings you generate yourself in a document index, then search them by vector similarity with a query vector from the same model.

If you already generate embeddings, store them in a dense_vector field on a document index. This fits when you use a model Pinecone doesn’t host, a model you’ve fine-tuned, or vectors you’ve already computed. You embed every document before you upsert it and every query before you search. To have Pinecone embed your text instead, use integrated embedding.

For a legacy vector index, see Create a vector index, Upsert vectors, and Query vectors.

The steps use an SDK or the REST API, with your API key in the PINECONE_API_KEY environment variable. To set them up, see the quickstart. The JavaScript examples use top-level await, so run them as an ES module. The Go examples build one main function: the first block is the full program, and each later block adds to it.

1. Generate embeddings for your documents

   Embed each document’s text with the model of your choice. So the example runs as is, it uses Pinecone’s hosted llama-text-embed-v2 model through the standalone embedding API, which returns 1,024-dimension vectors. Set input_type to passage for documents.

   Python

   import os, time
   from pinecone import Pinecone, SchemaBuilder
   
   pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])
   
   texts = [
       "Refund requests must be submitted within 30 days of purchase.",
       "Enterprise customers get support with a 4-hour response time.",
       "Error E1042: the connection timed out after 30 seconds. Retry with a longer timeout.",
       "To reset your password, open Settings and choose Security.",
       "Order 88231 shipped on 2026-08-20 via express courier.",
   ]
   
   embeddings = pc.inference.embed(
       model="llama-text-embed-v2",
       inputs=texts,
       parameters={"input_type": "passage"},
   )
   
2. Create an index with a dense_vector field

   Declare a dense_vector field whose dimension matches your model’s output and whose metric matches what the model was trained for. llama-text-embed-v2 uses cosine. This schema also declares chunk_text with full_text_search, so the index stores each document’s text and can rank it by keyword too. Fields you don’t declare are stored as metadata.

   Python

   schema = (
       SchemaBuilder()
         .add_dense_vector_field("embedding", dimension=1024, metric="cosine")
         .add_string_field("chunk_text", full_text_search={"language": "en"})
         .build()
   )
   if not pc.indexes.exists(name="byo-vectors"):
       pc.indexes.create(name="byo-vectors", schema=schema)
   
   while not pc.indexes.describe(name="byo-vectors").status.ready:
       time.sleep(2)
   
   index = pc.index(name="byo-vectors")
   
3. Upsert documents with their vectors

   Put each vector on the embedding field, next to the text it came from. Each vector must have exactly 1,024 values. Documents are indexed asynchronously, so the code waits 10 seconds before you search.

   Python

   index.documents.upsert(
       namespace="__default__",
       documents=[
           {"_id": str(i + 1), "chunk_text": text, "embedding": e.values}
           for i, (text, e) in enumerate(zip(texts, embeddings))
       ],
   )
   
   time.sleep(10)  # documents are indexed asynchronously, so wait a moment
   
4. Embed your query

   Embed the query with the same model you used for the documents. Vectors from different models aren’t comparable, so a different model returns meaningless rankings. For llama-text-embed-v2, set input_type to query.

   Python

   query = pc.inference.embed(
       model="llama-text-embed-v2",
       inputs=["how do I get my money back?"],
       parameters={"input_type": "query"},
   )
   
5. Search by vector similarity

   Score by dense_vector and pass the query vector in values. Pinecone ranks documents by the field’s metric.

   Python

   results = index.documents.search(
       namespace="__default__",
       top_k=3,
       score_by=[{"type": "dense_vector", "fields": ["embedding"], "values": query.data[0].values}],
       include_fields=["chunk_text"],
   )
   
   for m in results.matches:
       print(m.id, f"{m.score:.3f}", m.chunk_text)
   

   The refund policy ranks first:

   Output

   1 0.417 Refund requests must be submitted within 30 days of purchase.
   4 0.119 To reset your password, open Settings and choose Security.
   2 0.089 Enterprise customers get support with a 4-hour response time.
   

Because chunk_text declares full_text_search, the same index also supports a full-text search on the text, and either search accepts a metadata filter.