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Hi, why can't we use BGE-M3 embedding
Hi, why can't we use BGE-M3 embedding model (@cf/baai/bge-m3) to create vector index?
Only these model are available to choose from: [ "@cf/baai/bge-small-en-v1.5", "@cf/baai/bge-base-en-v1.5", "@cf/baai/bge-large-en-v1.5", "openai/text-embedding-ada-002", "cohere/embed-multilingual-v2.0" ]...
Anyone have any repos/guides/videos of
Anyone have any repos/guides/videos of RAG pipelines being built fully on Clouldflare stack? Looking for some inspo
**Quick question: **For Vectorize
Quick question: For Vectorize billing calculation, is the total count of vectors is used? Or does namespace and metadata filtering reduced the number of billable
Queried Vector Dimensions for that request? I have multiple tenants with different sets of vector, so looking for an efficient way to manage these. Creating one index per tenant would work, but it seems to complicate the bindings a bit.
https://developers.cloudflare.com/vectorize/platform/pricing/...AutoRAG today doesn’t support metadata
AutoRAG today doesn’t support metadata filtering — but can I just append metadata anyway and manually query the database with these outside of the AutoRAG endpoint?
Hi Team, Getting these errors very often
Hi Team, Getting these errors very often for some time.
Vectorize API error: upstream request timeout
Can anyone verify?...
First time reading about precise scores
First time reading about precise scores when querying. How much would be the latency increase?
hi does vectorize behave in a similar
hi does vectorize behave in a similar way to r2? in the sense that we can use preview_index_name in the wrangler.toml? just like preview_bucket_name?
I found an issue in my similarity
I found an issue in my similarity pipeline that started like a day ago. The issue is now, the similarity scores have been downleveved significantly - for eg, earlier I was getting the score of 0.7 for a few content pieces and now the processing of the same content again are sending the score of ~ 0.4 hence my check is not returning results. Has this behaviour been changed recently?
Getting "message": "vectorize.upstream_
Getting "message": "vectorize.upstream_error - failed to parse insert vectors request in ndjson format: line Some(0) was not expected format"
Hey,
Hey,
I’m encountering a persistent issue when querying a Cloudflare vector index using env.VECTORS.query() in a worker. Despite passing a valid 768-dimensional vector, I consistently receive the error.
VECTOR_QUERY_ERROR (code = 40006): invalid query vector, expected 768 dimensions, and got 0 dimensions
I have validated the entries with:
console.log(“Vector length:”, queryVectorRaw.length); // 768...Can I have some questions? 1) What's the
Can I have some questions? 1) What's the best way to get all metadata from vectorize? For example i have 1000 topK i want to return 1000 metadata texts, right now limit is just 20 2) Is it good idea to have namespace per user, or i should go with separate vectorize per user? Thanks for help 🙂
Hi Vectorize team,
Hi Vectorize team,
I’m really enjoying Vectorize—great work on building it! I’m running into issues though with metadata filtering on a number field.
Issue: I have two indexes with ~3M vectors each, with each vector representing a document. I have a metadata index on the field "authored" which contains a UNIX timestamp representing the date of authorship. Doing a query on date ranges like 1970-1979 or 1939-1955 consistently causes a 504 error (code 7009: upstream unavailable) after a long wait. Without the filter, queries work fine. Ranges of 5 years or less usually work, but not always....
**delete vectors from AutoRAG vector
delete vectors from AutoRAG vector store when file is deleted
We have a new AutoRAG projects and we regularly add new files and delete old files from the data store.
it was observed that when we delete some file from data rource i.e. R2 bucket. the vectors fro these files is still available in the vector store and search results return those vectors.
...
Hey all, I’m experiencing unexpectedly
Hey all, I’m experiencing unexpectedly slow insert performance with Cloudflare Vectorize during a large-scale vector insertion. Over 12 hours, I successfully inserted about 2.5 million documents individually or in very small groups (1-2 vectors at a time). However, after about 36 hours, my process is still at around 1.9 million vectors total. It appears that Vectorize is batching inserts at about 1,000 vectors each, rather than the advertised batches of up to 200,000 vectors for improved throughput.
My understanding was that Vectorize would automatically batch inserts at these larger sizes to optimize performance, but this doesn’t seem to be happening. Do I need to explicitly batch my inserts (e.g., in groups of 5,000 vectors) to achieve better efficiency, or is there something else going on here?
Could anyone from Cloudflare clarify how batching works internally with Vectorize and suggest the best practices or architecture adjustments for optimizing large-scale vector insert operations?...
Hi, is there a way of accessing each
Hi, is there a way of accessing each vecor to add metadata tags to each vector? Used standard R2 ingest but did not add any meta-data and cannot seem to recover a vector based upon id. ID appears to be random value (hash?) I see that each chunk returns the same basic format:
Chunk 4 Cosine Sim. 0.6107 Relevancy...
Chunk 4 Cosine Sim. 0.6107 Relevancy...
@yevgen Is there any way to resync error
@yevgen Is there any way to resync error files in vector db?
Hey, is there any doc / link about using
Hey, is there any doc / link about using Cloudflare Vectorize with llamaindex ?