Add DotProduct Method and README Example for Embedding Similarity Search (#492)

* Add DotProduct Method and README Example for Embedding Similarity Search

- Implement a DotProduct() method for the Embedding struct to calculate the dot product between two embeddings.
- Add a custom error type for vector length mismatch.
- Update README.md with a complete example demonstrating how to perform an embedding similarity search for user queries.
- Add unit tests to validate the new DotProduct() method and error handling.

* Update README to focus on Embedding Semantic Similarity

embeddings.go

@@ -4,10 +4,13 @@ import (
 	"context"
 	"encoding/base64"
 	"encoding/binary"
+	"errors"
 	"math"
 	"net/http"
 )
 
+var ErrVectorLengthMismatch = errors.New("vector length mismatch")
+
 // EmbeddingModel enumerates the models which can be used
 // to generate Embedding vectors.
 type EmbeddingModel int
@@ -124,6 +127,23 @@ type Embedding struct {
 	Index     int       `json:"index"`
 }
 
+// DotProduct calculates the dot product of the embedding vector with another
+// embedding vector. Both vectors must have the same length; otherwise, an
+// ErrVectorLengthMismatch is returned. The method returns the calculated dot
+// product as a float32 value.
+func (e *Embedding) DotProduct(other *Embedding) (float32, error) {
+	if len(e.Embedding) != len(other.Embedding) {
+		return 0, ErrVectorLengthMismatch
+	}
+
+	var dotProduct float32
+	for i := range e.Embedding {
+		dotProduct += e.Embedding[i] * other.Embedding[i]
+	}
+
+	return dotProduct, nil
+}
+
 // EmbeddingResponse is the response from a Create embeddings request.
 type EmbeddingResponse struct {
 	Object string         `json:"object"`