similarity_search_by_image() — langchain Function Reference

Architecture documentation for the similarity_search_by_image() function in vectorstores.py from the langchain codebase.

Function python CoreAbstractions RunnableInterface calls 1

Entity Profile

CoreAbstractions→ RunnableInterface→ similarity_search_by_image() — langchain Function Reference

Dependency Diagram

graph TD
  c0b7a4cd_c01b_9d43_bd62_7b531e7e6b42["similarity_search_by_image()"]
  d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab["Chroma"]
  c0b7a4cd_c01b_9d43_bd62_7b531e7e6b42 -->|defined in| d25f9e94_3ec0_b9ca_7d2f_eb7ef487ccab
  76615968_7cd6_d5f7_a619_c032edfc26cb["similarity_search_by_vector()"]
  c0b7a4cd_c01b_9d43_bd62_7b531e7e6b42 -->|calls| 76615968_7cd6_d5f7_a619_c032edfc26cb
  style c0b7a4cd_c01b_9d43_bd62_7b531e7e6b42 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/chroma/langchain_chroma/vectorstores.py lines 945–984

    def similarity_search_by_image(
        self,
        uri: str,
        k: int = DEFAULT_K,
        filter: dict[str, str] | None = None,  # noqa: A002
        **kwargs: Any,
    ) -> list[Document]:
        """Search for similar images based on the given image URI.

        Args:
            uri: URI of the image to search for.
            k: Number of results to return.
            filter: Filter by metadata.
            **kwargs: Additional arguments to pass to function.


        Returns:
            List of Images most similar to the provided image. Each element in list is a
            LangChain Document Object. The page content is b64 encoded image, metadata
            is default or as defined by user.

        Raises:
            ValueError: If the embedding function does not support image embeddings.
        """
        if self._embedding_function is not None and hasattr(
            self._embedding_function, "embed_image"
        ):
            # Obtain image embedding
            # Assuming embed_image returns a single embedding
            image_embedding = self._embedding_function.embed_image(uris=[uri])[0]

            # Perform similarity search based on the obtained embedding
            return self.similarity_search_by_vector(
                embedding=image_embedding,
                k=k,
                filter=filter,
                **kwargs,
            )
        msg = "The embedding function must support image embedding."
        raise ValueError(msg)