partners: add similarity search by image functionality to langchain_chroma partner package (#22982)

- **Description:** This pull request introduces two new methods to the
Langchain Chroma partner package that enable similarity search based on
image embeddings. These methods enhance the package's functionality by
allowing users to search for images similar to a given image URI. Also
introduces a notebook to demonstrate it's use.
  - **Issue:** N/A
  - **Dependencies:** None
  - **Twitter handle:** @mrugank9009

---------

Co-authored-by: ccurme <chester.curme@gmail.com>

libs/community/langchain_community/vectorstores/chroma.py

@@ -478,6 +478,93 @@ class Chroma(VectorStore):
                 "Consider providing relevance_score_fn to Chroma constructor."
             )
 
+    def similarity_search_by_image(
+        self,
+        uri: str,
+        k: int = DEFAULT_K,
+        filter: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Search for similar images based on the given image URI.
+
+        Args:
+            uri (str): URI of the image to search for.
+            k (int, optional): Number of results to return. Defaults to DEFAULT_K.
+            filter (Optional[Dict[str, str]], optional): Filter by metadata.
+            **kwargs (Any): 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 None or not hasattr(
+            self._embedding_function, "embed_image"
+        ):
+            raise ValueError("The embedding function must support image embedding.")
+
+        # Obtain image embedding
+        # Assuming embed_image returns a single embedding
+        image_embedding = self._embedding_function.embed_image(uris=[uri])
+
+        # Perform similarity search based on the obtained embedding
+        results = self.similarity_search_by_vector(
+            embedding=image_embedding,
+            k=k,
+            filter=filter,
+            **kwargs,
+        )
+
+        return results
+
+    def similarity_search_by_image_with_relevance_score(
+        self,
+        uri: str,
+        k: int = DEFAULT_K,
+        filter: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Search for similar images based on the given image URI.
+
+        Args:
+            uri (str): URI of the image to search for.
+            k (int, optional): Number of results to return.
+            Defaults to DEFAULT_K.
+            filter (Optional[Dict[str, str]], optional): Filter by metadata.
+            **kwargs (Any): Additional arguments to pass to function.
+
+        Returns:
+            List[Tuple[Document, float]]: List of tuples containing documents similar
+            to the query image and their similarity scores.
+            0th element in each tuple is a Langchain Document Object.
+            The page content is b64 encoded img, metadata is default or defined by user.
+
+        Raises:
+            ValueError: If the embedding function does not support image embeddings.
+        """
+        if self._embedding_function is None or not hasattr(
+            self._embedding_function, "embed_image"
+        ):
+            raise ValueError("The embedding function must support image embedding.")
+
+        # Obtain image embedding
+        # Assuming embed_image returns a single embedding
+        image_embedding = self._embedding_function.embed_image(uris=[uri])
+
+        # Perform similarity search based on the obtained embedding
+        results = self.similarity_search_by_vector_with_relevance_scores(
+            embedding=image_embedding,
+            k=k,
+            filter=filter,
+            **kwargs,
+        )
+
+        return results
+
     def max_marginal_relevance_search_by_vector(
         self,
         embedding: List[float],

libs/partners/chroma/langchain_chroma/vectorstores.py

@@ -607,6 +607,94 @@ class Chroma(VectorStore):
                 "Consider providing relevance_score_fn to Chroma constructor."
             )
 
+    def similarity_search_by_image(
+        self,
+        uri: str,
+        k: int = DEFAULT_K,
+        filter: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Search for similar images based on the given image URI.
+
+        Args:
+            uri (str): URI of the image to search for.
+            k (int, optional): Number of results to return. Defaults to DEFAULT_K.
+            filter (Optional[Dict[str, str]], optional): Filter by metadata.
+            **kwargs (Any): 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 None or not hasattr(
+            self._embedding_function, "embed_image"
+        ):
+            raise ValueError("The embedding function must support image embedding.")
+
+        # Obtain image embedding
+        # Assuming embed_image returns a single embedding
+        image_embedding = self._embedding_function.embed_image(uris=[uri])
+
+        # Perform similarity search based on the obtained embedding
+        results = self.similarity_search_by_vector(
+            embedding=image_embedding,
+            k=k,
+            filter=filter,
+            **kwargs,
+        )
+
+        return results
+
+    def similarity_search_by_image_with_relevance_score(
+        self,
+        uri: str,
+        k: int = DEFAULT_K,
+        filter: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Search for similar images based on the given image URI.
+
+        Args:
+            uri (str): URI of the image to search for.
+            k (int, optional): Number of results to return.
+            Defaults to DEFAULT_K.
+            filter (Optional[Dict[str, str]], optional): Filter by metadata.
+            **kwargs (Any): Additional arguments to pass to function.
+
+        Returns:
+            List[Tuple[Document, float]]: List of tuples containing documents similar
+            to the query image and their similarity scores.
+            0th element in each tuple is a Langchain Document Object.
+            The page content is b64 encoded img, metadata is default or defined by user.
+
+        Raises:
+            ValueError: If the embedding function does not support image embeddings.
+        """
+        if self._embedding_function is None or not hasattr(
+            self._embedding_function, "embed_image"
+        ):
+            raise ValueError("The embedding function must support image embedding.")
+
+        # Obtain image embedding
+        # Assuming embed_image returns a single embedding
+        image_embedding = self._embedding_function.embed_image(uris=[uri])
+
+        # Perform similarity search based on the obtained embedding
+        results = self.similarity_search_by_vector_with_relevance_scores(
+            embedding=image_embedding,
+            k=k,
+            filter=filter,
+            **kwargs,
+        )
+
+        return results
+
     def max_marginal_relevance_search_by_vector(
         self,
         embedding: List[float],