embed() — langchain Function Reference

Architecture documentation for the embed() function in embeddings.py from the langchain codebase.

Function python LangChainCore MessageInterface called by 2

Entity Profile

LangChainCore→ MessageInterface→ embed() — langchain Function Reference

Dependency Diagram

graph TD
  5d3fe9cb_b3db_545c_9ac3_c09c238b4690["embed()"]
  0646ce15_0ff5_ff53_3e70_3fd27c663144["NomicEmbeddings"]
  5d3fe9cb_b3db_545c_9ac3_c09c238b4690 -->|defined in| 0646ce15_0ff5_ff53_3e70_3fd27c663144
  99a82c59_97c1_5050_aab0_84a9dd45930a["embed_documents()"]
  99a82c59_97c1_5050_aab0_84a9dd45930a -->|calls| 5d3fe9cb_b3db_545c_9ac3_c09c238b4690
  aa8e0525_8e6e_1009_4ff7_dc79b4207f63["embed_query()"]
  aa8e0525_8e6e_1009_4ff7_dc79b4207f63 -->|calls| 5d3fe9cb_b3db_545c_9ac3_c09c238b4690
  style 5d3fe9cb_b3db_545c_9ac3_c09c238b4690 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/nomic/langchain_nomic/embeddings.py lines 97–114

    def embed(self, texts: list[str], *, task_type: str) -> list[list[float]]:
        """Embed texts.

        Args:
            texts: List of texts to embed
            task_type: The task type to use when embedding. One of `'search_query'`,
                `'search_document'`, `'classification'`, `'clustering'`

        """
        output = embed.text(
            texts=texts,
            model=self.model,
            task_type=task_type,
            dimensionality=self.dimensionality,
            inference_mode=self.inference_mode,
            device=self.device,
        )
        return output["embeddings"]