embed_query() — langchain Function Reference

Architecture documentation for the embed_query() function in fake_embeddings.py from the langchain codebase.

Function python LangChainCore MessageInterface calls 1 called by 1

Entity Profile

LangChainCore→ MessageInterface→ embed_query() — langchain Function Reference

Dependency Diagram

graph TD
  76f25d73_04c2_6b9d_ccc7_c1b1d6d106d8["embed_query()"]
  bbd47f76_d825_e868_c481_441b9a41a376["AngularTwoDimensionalEmbeddings"]
  76f25d73_04c2_6b9d_ccc7_c1b1d6d106d8 -->|defined in| bbd47f76_d825_e868_c481_441b9a41a376
  119760c2_93a9_f08e_4423_1af7744c75bd["embed_documents()"]
  119760c2_93a9_f08e_4423_1af7744c75bd -->|calls| 76f25d73_04c2_6b9d_ccc7_c1b1d6d106d8
  f91f62e2_f3f4_c3b1_fa56_7ac7544ca8e1["embed_query()"]
  76f25d73_04c2_6b9d_ccc7_c1b1d6d106d8 -->|calls| f91f62e2_f3f4_c3b1_fa56_7ac7544ca8e1
  style 76f25d73_04c2_6b9d_ccc7_c1b1d6d106d8 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/langchain/tests/integration_tests/cache/fake_embeddings.py lines 98–117

    def embed_query(self, text: str) -> list[float]:
        """Embed query text.

        Convert input text to a 'vector' (list of floats).
        If the text is a number, use it as the angle for the
        unit vector in units of pi.
        Any other input text becomes the singular result [0, 0] !

        Args:
            text: Text to embed.

        Returns:
            Embedding.
        """
        try:
            angle = float(text)
            return [math.cos(angle * math.pi), math.sin(angle * math.pi)]
        except ValueError:
            # Assume: just test string, no attention is paid to values.
            return [0.0, 0.0]