{"record":{"id":"9dfea62755b83005","repo":"langchain-ai/langgraph","slug":"embedding-configuration-is-required-for-vector-ope-9dfea6","errorCode":null,"errorMessage":"Embedding configuration is required for vector operations (for semantic search). Please provide an Embeddings when initializing the {self.__class__.__name__}.","messagePattern":"Embedding configuration is required for vector operations \\(for semantic search\\)\\. Please provide an Embeddings when initializing the (.+?)\\.","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"libs/checkpoint-sqlite/langgraph/store/sqlite/base.py","lineNumber":1343,"sourceCode":"                        row = key_to_row.get(key)\n                        if row:\n                            results[idx] = _row_to_item(\n                                namespace, row, loader=self._deserializer\n                            )\n                        else:\n                            results[idx] = None\n\n    def _batch_put_ops(\n        self,\n        put_ops: Sequence[tuple[int, PutOp]],\n        cur: sqlite3.Cursor,\n    ) -> None:\n        queries, embedding_request = self._prepare_batch_PUT_queries(put_ops)\n        if embedding_request:\n            if self.embeddings is None:\n                # Should not get here since the embedding config is required\n                # to return an embedding_request above\n                raise ValueError(\n                    \"Embedding configuration is required for vector operations \"\n                    f\"(for semantic search). \"\n                    f\"Please provide an Embeddings when initializing the {self.__class__.__name__}.\"\n                )\n            query, txt_params = embedding_request\n            # Update the params to replace the raw text with the vectors\n            vectors = self.embeddings.embed_documents(\n                [param[-1] for param in txt_params]\n            )\n\n            # Convert vectors to SQLite-friendly format\n            vector_params = []\n            for (ns, k, pathname, _), vector in zip(txt_params, vectors, strict=False):\n                vector_params.extend(\n                    [ns, k, pathname, sqlite_vec.serialize_float32(vector)]\n                )\n\n            queries.append((query, vector_params))","sourceCodeStart":1325,"sourceCodeEnd":1361,"githubUrl":"https://github.com/langchain-ai/langgraph/blob/38031739e551638e373fb553453256c23feeb41f/libs/checkpoint-sqlite/langgraph/store/sqlite/base.py#L1325-L1361","documentation":"Error \"Embedding configuration is required for vector operations (for semantic search). Please provide an Embeddings when initializing the {self.__class__.__name__}.\" thrown in langchain-ai/langgraph.","triggerScenarios":"Thrown at libs/checkpoint-sqlite/langgraph/store/sqlite/base.py:1343 when the library encounters an invalid state.","commonSituations":"See trigger scenarios.","solutions":[],"exampleFix":null,"handlingStrategy":null,"validationCode":null,"typeGuard":null,"tryCatchPattern":null,"preventionTips":[],"tags":[],"backgroundTag":null,"analyzedSha":"38031739e551638e373fb553453256c23feeb41f","analyzedAt":"2026-08-26T18:02:49.312Z","schemaVersion":2},"datasetVersion":"2026-08-26T21:11:00.512Z"}