Mintplex-Labs/anything-llm · error · Error

Could not embed document chunks! This document will not be r

Error message

Could not embed document chunks! This document will not be recorded.

What it means

On the novel-document path, pageContent is split into chunks and EmbedderEngine.embedChunks(textChunks) is called. If that returns null/undefined or an empty array (the `!!vectorValues && vectorValues.length > 0` check fails), the code refuses to record the document and throws. The failure is in the embedding engine layer, before Weaviate is ever contacted.

Source

Thrown at server/utils/vectorDbProviders/weaviate/index.js:321

          const vectorRecord = {
            class: camelCase(namespace),
            id: uuidv4(),
            vector: vector,
            // [DO NOT REMOVE]
            // LangChain will be unable to find your text if you embed manually and dont include the `text` key.
            // https://github.com/hwchase17/langchainjs/blob/5485c4af50c063e257ad54f4393fa79e0aff6462/langchain/src/vectorstores/weaviate.ts#L133
            properties: { ...flattenedMetadata, text: textChunks[i] },
          };

          submission.ids.push(vectorRecord.id);
          submission.vectors.push(vectorRecord.values);
          submission.properties.push(metadata);

          vectors.push(vectorRecord);
          documentVectors.push({ docId, vectorId: vectorRecord.id });
        }
      } else {
        throw new Error(
          "Could not embed document chunks! This document will not be recorded."
        );
      }

      const { client } = await this.connect();
      const weaviateClassExits = await this.hasNamespace(namespace);
      if (!weaviateClassExits) {
        await client.schema
          .classCreator()
          .withClass({
            class: camelCase(namespace),
            description: `Class created by AnythingLLM named ${camelCase(
              namespace
            )}`,
            vectorizer: "none",
          })
          .do();
      }

View on GitHub (pinned to 3aec848f28)

Solutions

1. Open Admin settings and re-verify the embedding engine selection and its API key/endpoint, then save
2. Test the embedder directly (e.g. curl the provider /embeddings endpoint) with the configured credentials
3. Check document processing logs for 'Snippets created from document: 0' — an empty extraction also produces no vectors
4. After fixing/changing the embedder, re-upload the document so fresh chunks and vectors are generated

const embedder = getEmbeddingEngineSelection();
if (!embedder) throw new Error("No embedding engine configured — set one in admin settings");
const chunks = await textSplitter.splitText(pageContent);
if (chunks.length === 0) throw new Error("Document produced no text chunks");
const vectors = await embedder.embedChunks(chunks);
if (!vectors?.length) throw new Error("Embedder returned no vectors — check credentials");

const { vectorized, error } = await provider.addDocumentToNamespace(...);
if (!vectorized && /Could not embed document chunks/.test(String(error)))
  haltIngestion("Embedding engine failure — verify provider config before retrying", error);

When it happens

Trigger: Embedding engine unconfigured or misselected in admin settings; embedder API key invalid, quota exhausted, or endpoint unreachable so embedChunks yields nothing; the document produced zero text chunks (empty extraction); local embedding runtime missing on the host.

Common situations: Default OpenAI embedder kept while OPEN_AI_KEY was never set; switching embedder providers without re-verifying credentials; uploading an empty or unparseable file (0 chunks); all-native/local embedder binary not installed; embedder service temporarily down.

Related errors

• Weaviate::Invalid ENV settings
• Error embedding into Weaviate
• Type "${type}" is not a valid type to sync.
• Could not embed document chunks! This document will not be r
• ChromaCloud::Invalid ENV settings

AI-assisted analysis of Mintplex-Labs/anything-llm@3aec848f28 (2026-08-18). Data as JSON: /api/errors/380fb7943ed1e8a4. Report an issue: GitHub.