TypeError using similaritySearchWithScore method from XataVectorSearch with the xata client

[kappita]

Checked other resources

• I added a very descriptive title to this issue.
• I searched the LangChain.js documentation with the integrated search.
• I used the GitHub search to find a similar question and didn't find it.
• I am sure that this is a bug in LangChain.js rather than my code.
• The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

Example Code

import { getXataClient } from "../xata"; // client created using `xata init` command, suggested by the langchain documentation
import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama";
import { XataVectorSearch } from "@langchain/community/vectorstores/xata";

const client = getXataClient();
const table = "scholarships";
const embeddings = new OllamaEmbeddings({ model: "mxbai-embed-large" });
const store = new XataVectorSearch(embeddings, { client, table });

const results = await store.similaritySearchWithScore("pascuense", 6, {
    university: "USACH",
  });

Error Message and Stack Trace (if applicable)

TypeError: Cannot read properties of undefined (reading 'score')
at C:\Users<user>\Desktop\fwa\ai\node_modules.pnpm@langchain+community@0.2.20_@xata.io+client@0.0.0-next.v64f10283eb9a098c575590d3ea17600e35032_ocbcrk4os3vohexqsbp5nhgrva\node_modules@langchain\community\dist\vectorstores\xata.cjs:106:25
at Array.map ()
at XataVectorSearch.similaritySearchVectorWithScore (C:\Users<user>\Desktop\fwa\ai\node_modules.pnpm@langchain+community@0.2.20_@xata.io+client@0.0.0-next.v64f10283eb9a098c575590d3ea17600e35032_ocbcrk4os3vohexqsbp5nhgrva\node_modules@langchain\community\dist\vectorstores\xata.cjs:98:18)
at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
at example (c:\Users<user>\Desktop\fwa\ai\src\scholarships\example.ts:69:19)

Description

I'm trying to use Xata to store the embeddings and documents, and then use its similarity search.

I use the similaritySearchWithScore method, from the XataVectorSearch object, but it gets a typeError, stopping execution of the code.

System Info

yarn info v1.22.22
warning package.json: No license field
{
name: 'langchain',
'dist-tags': {
next: '0.2.3-rc.0',
latest: '0.2.11'
},
versions: [
'0.1.20',
'0.1.21',
'0.1.22',
'0.1.23',
'0.1.24',
'0.1.25',
'0.1.26',
'0.1.27',
'0.1.28',
'0.1.29-rc.0',
'0.1.29',
'0.1.30',
'0.1.31',
'0.1.32',
'0.1.33',
'0.1.34',
'0.1.35',
'0.1.36',
'0.1.37',
'0.2.0-rc.0',
'0.2.0-rc.1',
'0.2.0',
'0.2.1',
'0.2.2',
'0.2.3-rc.0',
'0.2.3',
'0.2.4',
'0.2.5',
'0.2.6',
'0.2.7',
'0.2.8',
'0.2.9',
'0.2.10',
'0.2.11'
],

CUT OUT MID HERE BECAUSE ERROR BODY TOO LONG

platform: windows

node v20.11.0

yarn v1.22.22

[dosubot]

Hey there, @kappita! I'm a bot here to help you with your coding conundrums. Let's squash those bugs together while we wait for a human maintainer to join in.

To resolve the TypeError when using the similaritySearchWithScore method from the XataVectorSearch class with the Xata client, ensure that the metadata parameter in the similaritySearchWithScore method is correctly structured. The correct method name is similaritySearchVectorWithScore, not similaritySearchWithScore.

Here is the corrected code snippet:

import { getXataClient } from "../xata"; // client created using `xata init` command, suggested by the langchain documentation
import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama";
import { XataVectorSearch } from "@langchain/community/vectorstores/xata";

const client = getXataClient();
const table = "scholarships";
const embeddings = new OllamaEmbeddings({ model: "mxbai-embed-large" });
const store = new XataVectorSearch(embeddings, { client, table });

const results = await store.similaritySearchVectorWithScore("pascuense", 6, {
    university: "USACH",
  });

Additionally, ensure that the university field exists in the documents' metadata and that the similaritySearchVectorWithScore method is being called with the correct parameters.

If you are still encountering the TypeError, it might be due to null or undefined records. This issue was addressed in a recent pull request that fixed null or undefined records causing errors in the similaritySearchVectorWithScore method. The relevant change ensures that if records is null or undefined, the method returns an empty array instead of attempting to map over records and causing an error ^[1].

Here is the relevant code change:

const { records } = await this.client.db[this.table].vectorSearch(
  "embedding",
  query,
  {
    size: k,
    filter,
  }
);

return (
  records?.map((record: any) => [
    new Document({
      pageContent: record.content,
      metadata: Object.fromEntries(
        Object.entries(record).filter(
          ([key]) =>
            key !== "content" &&
            key !== "embedding" &&
            key !== "xata" &&
            key !== "id"
        )
      ),
    }),
    record.xata.score,
  ]) ?? []
);

This ensures that if records is null or undefined, the method returns an empty array instead of causing an error ^[1].

To continue talking to Dosu, mention @dosu.