feat: embedding instructions for dense retrieval (#6372)

* Embedding instructions in EmbeddingRetriever

Query and documents embeddings are prefixed with instructions, useful
for retrievers finetuned on specific tasks, such as Q&A.

* Tests

Checking vectors 0th component vs. reference, using different stores.

* Normalizing vectors

* Release notes

haystack/nodes/retriever/dense.py

@@ -1453,6 +1453,8 @@ class EmbeddingRetriever(DenseRetriever):
         max_seq_len: int = 512,
         model_format: Optional[str] = None,
         pooling_strategy: str = "reduce_mean",
+        query_prompt: Optional[str] = None,
+        passage_prompt: Optional[str] = None,
         emb_extraction_layer: int = -1,
         top_k: int = 10,
         progress_bar: bool = True,
@@ -1495,7 +1497,8 @@ class EmbeddingRetriever(DenseRetriever):
         2. `reduce_mean` (sentence vector)
         3. `reduce_max` (sentence vector)
         4. `per_token` (individual token vectors)
-
+        :param query_prompt: Model instruction for embedding texts to be used as queries.
+        :param passage_prompt: Model instruction for embedding texts to be retrieved.
         :param emb_extraction_layer: Number of layer from which the embeddings shall be extracted (for farm / transformers models only).
                                      Default: -1 (very last layer).
         :param top_k: How many documents to return per query.
@@ -1550,6 +1553,8 @@ class EmbeddingRetriever(DenseRetriever):
         self.max_seq_len = max_seq_len
         self.pooling_strategy = pooling_strategy
         self.emb_extraction_layer = emb_extraction_layer
+        self.query_prompt = query_prompt
+        self.passage_prompt = passage_prompt
         self.top_k = top_k
         self.progress_bar = progress_bar
         self.use_auth_token = use_auth_token
@@ -1830,6 +1835,8 @@ class EmbeddingRetriever(DenseRetriever):
         if isinstance(queries, str):
             queries = [queries]
         assert isinstance(queries, list), "Expecting a list of texts, i.e. create_embeddings(texts=['text1',...])"
+        if self.query_prompt:
+            queries = [self.query_prompt + " " + q for q in queries]
         return self.embedding_encoder.embed_queries(queries)
 
     def embed_documents(self, documents: List[Document]) -> np.ndarray:
@@ -1840,6 +1847,9 @@ class EmbeddingRetriever(DenseRetriever):
         :return: Embeddings, one per input document, shape: (docs, embedding_dim)
         """
         documents = self._preprocess_documents(documents)
+        if self.passage_prompt:
+            for doc in documents:
+                doc.content = self.passage_prompt + " " + doc.content
         return self.embedding_encoder.embed_documents(documents)
 
     def _preprocess_documents(self, docs: List[Document]) -> List[Document]:

releasenotes/notes/embedding-instructions-4feb216cbf796678.yaml

@@ -0,0 +1,4 @@
+---
+features:
+  - |
+    Support for dense embedding instructions, used in retrieval models such as BGE and LLM-Embedder.

test/conftest.py

@@ -647,6 +647,15 @@ def get_retriever(retriever_type, document_store):
             model_format="sentence_transformers",
             use_gpu=False,
         )
+    elif retriever_type == "embedding_sbert_instructions":
+        retriever = EmbeddingRetriever(
+            document_store=document_store,
+            embedding_model="sentence-transformers/msmarco-distilbert-dot-v5",
+            model_format="sentence_transformers",
+            query_prompt="Embed this query for retrieval:",
+            passage_prompt="Embed this passage for retrieval:",
+            use_gpu=False,
+        )
     elif retriever_type == "retribert":
         retriever = EmbeddingRetriever(
             document_store=document_store, embedding_model="yjernite/retribert-base-uncased", use_gpu=False

test/nodes/test_retriever.py

@@ -311,6 +311,28 @@ def test_dpr_embedding(document_store: BaseDocumentStore, retriever, docs_with_i
         assert isclose(embedding[0], expected_value, rel_tol=0.01)
 
 
+@pytest.mark.integration
+@pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory", "weaviate", "pinecone"], indirect=True)
+@pytest.mark.parametrize("retriever", ["embedding_sbert_instructions"], indirect=True)
+def test_embedding_with_instructions(document_store: BaseDocumentStore, retriever, docs_with_ids):
+    document_store.return_embedding = True
+    document_store.write_documents(docs_with_ids)
+    document_store.update_embeddings(retriever=retriever)
+
+    docs = document_store.get_all_documents()
+    docs.sort(key=lambda d: d.id)
+
+    print([doc.id for doc in docs])
+
+    expected_values = [0.00484978, 0.02258789, 0.03414359, -0.01461711, 0.01784192]
+    for doc, expected_value in zip(docs, expected_values):
+        embedding = doc.embedding
+        # always normalize vector as faiss returns normalized vectors and other document stores do not
+        embedding /= np.linalg.norm(embedding)
+        assert len(embedding) == 768
+        assert isclose(embedding[0], expected_value, rel_tol=0.01)
+
+
 @pytest.mark.integration
 @pytest.mark.parametrize("document_store", ["elasticsearch", "faiss", "memory", "weaviate", "pinecone"], indirect=True)
 @pytest.mark.parametrize("retriever", ["retribert"], indirect=True)