Error when doing similaritysearch with libsql and langchain

[binaryArrow]

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Example Code

import {createClient} from "@libsql/client";
import path from "node:path";
import {LlamaCppEmbeddings} from "@langchain/community/embeddings/llama_cpp";
import {join} from "path";
import {PDFLoader} from "@langchain/community/document_loaders/fs/pdf";
import {RecursiveCharacterTextSplitter} from "@langchain/textsplitters";
import {Document} from '@langchain/core/documents';
import {fileURLToPath} from "node:url";
import {LibSQLVectorStore} from "@langchain/community/vectorstores/libsql";

// we need this function to pad the embeddings to the correct size, somehow vectorstres addDocument function does not do this
function padOrTruncateEmbedding(embedding: number[], targetSize: number): number[] {
    if (embedding.length < targetSize) {
        return [...embedding, ...new Array(targetSize - embedding.length).fill(0)];
    }
    return embedding;
}
// create DB and indexes, the vector size for llamacpp is 4096
const client = createClient({
    url: 'file:local.db'
});
await client.batch(
    [
        `CREATE TABLE IF NOT EXISTS vectors
         (
             id        INTEGER PRIMARY KEY AUTOINCREMENT,
             content   TEXT,
             metadata  TEXT,
             embedding F32_BLOB(4096)
         );`,
        `CREATE INDEX IF NOT EXISTS vector_idx ON vectors (libsql_vector_idx(embedding));`,
    ],
    'write'
);

// create embeddingmodel
const __filename = fileURLToPath(import.meta.url);
const __dirname = path.dirname(__filename);
const llamaEmbeddings = await LlamaCppEmbeddings.initialize({
    modelPath: path.join(join(__dirname, '/llm-model'), 'Mistral-7B-Instruct-v0.3.Q4_K_M.gguf')
});

// get pdfloader and split the pdf into chunks
const loader = new PDFLoader('testpdf');
const docs = await loader.load();
const textSplitter = new RecursiveCharacterTextSplitter({
    chunkSize: 2000,
    chunkOverlap: 500
});
const splits: Document[] = await textSplitter.splitDocuments(docs);
console.log('Splits:', splits.length);

// create the vectorstore and embed the document
const testVectorStore = new LibSQLVectorStore(llamaEmbeddings, {
    db: client,
});
const embeddings = await llamaEmbeddings.embedDocuments(splits.map((doc) => doc.pageContent));
embeddings.forEach((embedding, i) => {
    embeddings[i] = padOrTruncateEmbedding(embedding, 4096);
});
await testVectorStore.addVectors(embeddings, splits);
const question = 'give me all words in the documents that relate to the word drugs'
console.log('Retrieving documents for question:', question);
const retrievedDocs = await testVectorStore.similaritySearch(question);
console.log(retrievedDocs)

### Description

I am trying to use Libsql vectorstore to embed pdf content into it and do a RAG on the documents. It fails to even do the similaritysearch. I get the following Error: `Error: vector index(search): failed to parse vector index parameters`.
For the code to work you need the LLM Model, i opened a repo reproducing this error https://github.com/binaryArrow/libsql-index-error-reproduction.
The code does create the database and creates the embeddings, but using it fails.
My resources i used are:
https://js.langchain.com/docs/integrations/vectorstores/libsql/
https://docs.turso.tech/features/ai-and-embeddings#vector-index

### System Info

    "@langchain/community": "^0.3.24",
    "@langchain/core": "^0.3.29",
    "@libsql/client": "^0.14.0",
    "node-llama-cpp": "^3.4.0",
    "pdf-parse": "^1.1.1"
    
    Node version 20.18.0
    System: Windows 10
   

[dosubot[bot]]

Hey @binaryArrow! I'm here to help you with this bug. I'm a bot that can assist with bug fixes, answer questions, and guide you on how to become a contributor. Let's squash this bug together! 🤖

The error message "Error: vector index(search): failed to parse vector index parameters" suggests there might be an issue with how the vector index parameters are being handled or configured in your setup. Here are a few things to check and consider:

1. Vector Index Creation: Ensure that the vector index is created correctly. The libsql_vector_idx function should be used properly in the CREATE INDEX statement. Make sure the index is created on the correct column and that the column type matches the expected format. The correct SQL statement should look like this:

   CREATE INDEX IF NOT EXISTS vector_idx ON vectors (libsql_vector_idx(embedding));

2. Embedding Dimensionality: Verify that the embeddings you are using have the correct dimensionality. In your case, you are using a dimensionality of 4096 for the LlamaCppEmbeddings. Ensure that this matches the dimensionality expected by the vector index and that the embeddings are padded or truncated to this size before being added to the vector store.

3. Parameter Parsing: The error might also be related to how parameters are being passed to the similaritySearch method. Ensure that the query and any optional parameters like k or filter are correctly formatted. The method signature expects a query string, an optional number of results (k), and an optional filter object ^[1].

4. Library Versions: Double-check that the versions of the libraries you are using are compatible with each other. Sometimes, issues can arise from version mismatches or bugs in specific versions.

If these suggestions do not resolve the issue, consider checking the detailed logs or debugging the code to identify where the parameter parsing might be failing.

To continue talking to Dosu, mention @dosu.

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