I had to write a helper that would batch them into groups of 100 as well as retry if a 500 error hap

D

deo timeOP•11/27/23, 4:34 PM

If you're inserting a lot though itll take quite a while

MMatt Silverlock How are you counting metadata length? The limit is enforced on all bytes (keys, ...

D

divby0•11/27/23, 6:32 PM

Yes, I have a static key (and only one). It's basically

{"content":"..."}

{"content":"..."}

{"content":"..."}

{"content":"..."} with the

...

...

being less than 8'000 for all elements. Which should guarantee that it's less than 10KiB, right?

D

divby0•11/27/23, 6:46 PM

Also it works when I batch the individual vectors into small enough amounts so this is also a proof that the metadata is not too big (or any other single field)

Ddeo time I had to write a helper that would batch them into groups of 100 as well as retr...

D

divby0•11/27/23, 6:47 PM

I already was a bit mad that I need to write batching, but I'd hate to write a retry mechanism for inserting some vectors.

But many thanks, this might be my only option until they support the bindings version for pages functions. Which can't be too far away I guess

D

divby0•11/28/23, 10:28 AM

I am getting error code 2007. What does that mean and where can I look it up? There must be a list of error codes somewhere, otherwise you guys wouldn't have exposed one, right?

Ccfnathan I believe I see your error: `Bad Request: the vector length is incorrect for th...

D

divby0•11/28/23, 10:30 AM

If I recall correct, the reported error improvements that are coming very soon are still not there after 2 months. At least I happened to send the wrong amoutn of dimensions yesterday as well and it showed me a non-useful internal error.

Ddivby0 I am getting error code 2007. What does that mean and where can I look it up? Th...

D

divby0•11/28/23, 10:47 AM

I changed nothing and now, 18mins later the same request went through. I didn't redeploy (so env vars haven't changed), I merely had different vectors that were sent maybe (same openai 1536 embeddings, just for different content).

I am not sure if I am supposed to write so much here, but I'd figure the cloudflare team would be interested in seeing exactly what's not working.

Update: it has failed a few times again and now works a few times again. Not sure what I should learn from this

Ddivby0 I changed nothing and now, 18mins later the same request went through. I didn't ...

J

Jerome•11/28/23, 11:34 AM

Code 2007 is an error whose internals are not exposed; for an upsert it means that there were issues while trying to make the change durable. We are seeing this elevated error rate when write rate on an index reaches some threshold. This is something we are aware of, and we are working on improving the situation.

J

Jerome•11/28/23, 11:36 AM

Thank you for reporting!

D

divby0•11/28/23, 11:37 AM

This specific issue was with the query endpoint in fact. Still something you have on the radar?

J

Jerome•11/28/23, 11:38 AM

Yes, for query operations it means that the durable storage access failed while reading, despite retries

D

divby0•11/28/23, 11:38 AM

I see, okay. So if you guys have those things on your radar and we can expect improvements, that's all I can ask for. I just thought I may be the only one experiencing this and there is a specific bug causing this for me

J

Jerome•11/28/23, 11:38 AM

We have a view on these; we can DM to exchange specifics on your index to help you mitigate if you can

JJerome We have a view on these; we can DM to exchange specifics on your index to help y...

D

divby0•11/28/23, 11:39 AM

that would be awesome. I'll DM you the details in a few minutes

E

Esked•11/29/23, 5:58 PM

Hello! I'm having some issues with Vectorize's query function.
When I run

await env.<INDEX>.query(vector, { topK: 1})

await env.<INDEX>.query(vector, { topK: 1})

await env.<INDEX>.query(vector, { topK: 1})

await env.<INDEX>.query(vector, { topK: 1}), in some cases it simply fails and returns the following error message:

TypeError: Cannot read properties of undefined (reading '0')

TypeError: Cannot read properties of undefined (reading '0')

TypeError: Cannot read properties of undefined (reading '0')

TypeError: Cannot read properties of undefined (reading '0')
It's completely unpredictable, sometimes it works perfectly after a couple of calls, sometimes it fails on the very first one. The

reading '0'

reading '0'

reading '0'

reading '0' isn't constant either, sometimes the number is different, and different userQueries result in different numbers.
Here is the code for the function

export async function getEmbedResponse(env: Environment, userQuery: string, data: string[]): Promise<{ score: number, result: string }> {
  const ai = new Ai(env.AI)

  const queryVector: EmbeddingResponse = await ai.run('@cf/baai/bge-base-en-v1.5', {
    text: [userQuery],
  })

  let matches = await env.<INDEX>.query(queryVector.data[0], { topK: 1 })

  const embedAiResponse = { score: matches.matches[0].score, result: data[parseInt(matches.matches[0].vectorId)] }

  return embedAiResponse
}

export async function getEmbedResponse(env: Environment, userQuery: string, data: string[]): Promise<{ score: number, result: string }> {
  const ai = new Ai(env.AI)

  const queryVector: EmbeddingResponse = await ai.run('@cf/baai/bge-base-en-v1.5', {
    text: [userQuery],
  })

  let matches = await env.<INDEX>.query(queryVector.data[0], { topK: 1 })

  const embedAiResponse = { score: matches.matches[0].score, result: data[parseInt(matches.matches[0].vectorId)] }

  return embedAiResponse
}

export async function getEmbedResponse(env: Environment, userQuery: string, data: string[]): Promise<{ score: number, result: string }> {
  const ai = new Ai(env.AI)

  const queryVector: EmbeddingResponse = await ai.run('@cf/baai/bge-base-en-v1.5', {
    text: [userQuery],
  })

  let matches = await env.<INDEX>.query(queryVector.data[0], { topK: 1 })

  const embedAiResponse = { score: matches.matches[0].score, result: data[parseInt(matches.matches[0].vectorId)] }

  return embedAiResponse
}

export async function getEmbedResponse(env: Environment, userQuery: string, data: string[]): Promise<{ score: number, result: string }> {
  const ai = new Ai(env.AI)

  const queryVector: EmbeddingResponse = await ai.run('@cf/baai/bge-base-en-v1.5', {
    text: [userQuery],
  })

  let matches = await env.<INDEX>.query(queryVector.data[0], { topK: 1 })

  const embedAiResponse = { score: matches.matches[0].score, result: data[parseInt(matches.matches[0].vectorId)] }

  return embedAiResponse
}

EmbeddingResponse is an interface based on the examples on the website

interface EmbeddingResponse {
    shape: number[];
    data: number[][];
}

interface EmbeddingResponse {
    shape: number[];
    data: number[][];
}

interface EmbeddingResponse {
    shape: number[];
    data: number[][];
}

interface EmbeddingResponse {
    shape: number[];
    data: number[][];
}

E

Esked•11/29/23, 5:58 PM

I run the vector upserting before calling this function

E

Esked•11/29/23, 8:46 PM

I can see the error message after the query call too

E

Esked•11/29/23, 8:49 PM

E

Esked•11/29/23, 8:49 PM

(edited image to cover information, oops

)

E

Esked•11/29/23, 9:06 PM

Ok I've tested a bit more and it seems like the problem is on my end.
The

data

data

data

data array is sometimes

undefined

undefined

undefined

undefined, not sure why but that should be my problem

E

Esked•11/29/23, 9:18 PM

Yep, the problem is definitely on my end, sorry

D

divby0•11/30/23, 8:46 AM

@Jerome have you gotten any info on what the plans for vectorize bindings inside pages functions are? Asking here instead of DM because others are probably looking ofr that info as well

Ddivby0 @Jerome have you gotten any info on what the plans for vectorize bindings inside...

M

Matt Silverlock•11/30/23, 3:33 PM

Soon! Ideally before the next holiday period. Prioritizing some stability/scalability work and metadata filtering first, but should be a very fast follow.

C

chand1012•11/30/23, 7:57 PM

Is there a way when querying vectorize to filter by ids as well as the vector themselves? You mentioned metadata filtering, I guess that's pretty close to what I'm looking for. I formatted my IDs in such a way where they contain both an index and the ID of them in my D1 database.

MMatt Silverlock Soon! Ideally before the next holiday period. Prioritizing some stability/scalab...

D

divby0•11/30/23, 9:34 PM

Aweomse!

Cchand1012 Is there a way when querying vectorize to filter by ids as well as the vector th...

H

huw•12/1/23, 3:24 AM

Yeah, this would be supported by metadata filtering or even namespaces. If you only ever want to filter on one of your ‘indices’ at a time, you could totally use namespaces today; for anything more complex you should wait for metadata filtering

Ccfnathan Noted as a feature request! Just so we're on the same page: would metadata filte...

C

chand1012•12/1/23, 3:56 PM

Metadata filtering would be fine, do you have an ETA on when this feature will be ready?

C

chand1012•12/1/23, 4:16 PM

My use case for either feature is I'd like to have one vector index for all the items in a database table, when some of the content from the table may be from both user A and user B, and I only want to query for user A as they're the one making the request.

S

steyblind•12/1/23, 5:08 PM

Not sure if I missed something here:

1. metadata "included in results" was released
2. on an existing worker, I updated the compatibility date
3. on an existing vectorize db there was no change
4. I updated my worker to use the

includeMetadata

includeMetadata

includeMetadata

includeMetadata param

and... no metadata returned?

Exception Thrown @ 12/1/2023, 12:06:34 PM
  (log) {
  count: 2,
  matches: [
    { vectorId: 'hzz2v', score: 0.804275747, vector: [Object] },
    { vectorId: 'aml6o', score: 0.726804477, vector: [Object] }
  ]
}

Exception Thrown @ 12/1/2023, 12:06:34 PM
  (log) {
  count: 2,
  matches: [
    { vectorId: 'hzz2v', score: 0.804275747, vector: [Object] },
    { vectorId: 'aml6o', score: 0.726804477, vector: [Object] }
  ]
}

Exception Thrown @ 12/1/2023, 12:06:34 PM
  (log) {
  count: 2,
  matches: [
    { vectorId: 'hzz2v', score: 0.804275747, vector: [Object] },
    { vectorId: 'aml6o', score: 0.726804477, vector: [Object] }
  ]
}

Exception Thrown @ 12/1/2023, 12:06:34 PM
  (log) {
  count: 2,
  matches: [
    { vectorId: 'hzz2v', score: 0.804275747, vector: [Object] },
    { vectorId: 'aml6o', score: 0.726804477, vector: [Object] }
  ]
}

Cchand1012 Metadata filtering would be fine, do you have an ETA on when this feature will b...

V

Vy•12/1/23, 5:08 PM

we're hoping it'll be out in December, so stay tuned!

Ssteyblind Not sure if I missed something here: 1. metadata "included in results" was rele...

V

Vy•12/1/23, 5:09 PM

metadata filtering is not available yet, we're hoping to make it available for new indexes in Dec, so stay tuend

S

steyblind•12/1/23, 5:09 PM

sorry, I mispoke

S

steyblind•12/1/23, 5:09 PM

I meant metadata in results

S

steyblind•12/1/23, 5:09 PM

per vectorize @vaiton13

S

steyblind•12/1/23, 5:15 PM

let contextResult = await env.DEMO_VECTORDB.query(queryVector, { topK: 5, returnMetadata: true })

let contextResult = await env.DEMO_VECTORDB.query(queryVector, { topK: 5, returnMetadata: true })

let contextResult = await env.DEMO_VECTORDB.query(queryVector, { topK: 5, returnMetadata: true })

let contextResult = await env.DEMO_VECTORDB.query(queryVector, { topK: 5, returnMetadata: true })

S

steyblind•12/1/23, 5:16 PM

(the exception is thrown when I try to access the metadata prop)

S

steyblind•12/1/23, 7:27 PM

thank you! good to know it's not me

S

steyblind•12/1/23, 7:27 PM

S

steyblind•12/1/23, 7:27 PM

godspeed

S

steyblind•12/1/23, 7:27 PM

I'll go back to metadata in KV

D

dave•12/2/23, 8:44 PM

https://github.com/langchain-ai/langchain/issues/14179

GitHub

Cloudflare Vectorize Support · Issue #14179 · langchain-ai/langchain

Feature request Looks like it should be doable to have Cloudflare Vectorize support, as there's a HTTP API for Vectorize. https://developers.cloudflare.com/api/operations/vectorize-list-vectori...

D

dave•12/2/23, 8:44 PM

if anybody official wants to chime in on the ticket to confirm if my assumptions are wrong/right

Ddave https://github.com/langchain-ai/langchain/issues/14179

M

Matt Silverlock•12/4/23, 2:28 PM

See https://js.langchain.com/docs/integrations/text_embedding/cloudflare_ai

Cloudflare Workers AI | 🦜️🔗 Langchain

If you're deploying your project in a Cloudflare worker, you can use Cloudflare's built-in Workers AI embeddings with LangChain.js.

M

Matt Silverlock•12/4/23, 2:28 PM

import { CloudflareVectorizeStore } from "langchain/vectorstores/cloudflare_vectorize";

import { CloudflareVectorizeStore } from "langchain/vectorstores/cloudflare_vectorize";

import { CloudflareVectorizeStore } from "langchain/vectorstores/cloudflare_vectorize";

import { CloudflareVectorizeStore } from "langchain/vectorstores/cloudflare_vectorize";

M

Matt Silverlock•12/4/23, 2:28 PM

and https://js.langchain.com/docs/integrations/vectorstores/cloudflare_vectorize

Cloudflare Vectorize | 🦜️🔗 Langchain

If you're deploying your project in a Cloudflare worker, you can use Cloudflare Vectorize with LangChain.js.

MMatt Silverlock See https://js.langchain.com/docs/integrations/text_embedding/cloudflare_ai

D

dave•12/4/23, 2:28 PM

Oh I know, I just want it to use Vectorize in Python too!

I had to write a helper that would batch them into groups of 100 as well as retry if a 500 error hap

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