feat: Add OpenAIEmbeddingEncoder to EmbeddingRetriever

haystack/json-schemas/haystack-pipeline-1.10.0rc0.schema.json

@@ -2975,6 +2975,17 @@
               "items": {
                 "type": "string"
               }
+            },
+            "api_key": {
+              "title": "Api Key",
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ]
             }
           },
           "required": [

haystack/json-schemas/haystack-pipeline-main.schema.json

@@ -2975,6 +2975,17 @@
               "items": {
                 "type": "string"
               }
+            },
+            "api_key": {
+              "title": "Api Key",
+              "anyOf": [
+                {
+                  "type": "string"
+                },
+                {
+                  "type": "null"
+                }
+              ]
             }
           },
           "required": [

haystack/nodes/retriever/_embedding_encoder.py

@@ -1,16 +1,19 @@
+import json
 import logging
 from abc import abstractmethod
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Callable, Dict, List, Union
 
 import numpy as np
+import requests
 import torch
 from sentence_transformers import InputExample
 from torch.utils.data import DataLoader
 from torch.utils.data.sampler import SequentialSampler
 from tqdm.auto import tqdm
 from transformers import AutoModel, AutoTokenizer
 
+from haystack.errors import OpenAIError
 from haystack.modeling.data_handler.dataloader import NamedDataLoader
 from haystack.modeling.data_handler.dataset import convert_features_to_dataset, flatten_rename
 from haystack.modeling.infer import Inferencer
@@ -20,7 +23,6 @@
 if TYPE_CHECKING:
     from haystack.nodes.retriever import EmbeddingRetriever
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -374,9 +376,82 @@ def save(self, save_dir: Union[Path, str]):
         )
 
 
+class _OpenAIEmbeddingEncoder(_BaseEmbeddingEncoder):
+    def __init__(self, retriever: "EmbeddingRetriever"):
+        # pretrained embedding models coming from:
+        self.max_seq_len = retriever.max_seq_len
+        self.url = "https://api.openai.com/v1/embeddings"
+        self.api_key = retriever.api_key
+        self.batch_size = retriever.batch_size
+        self.progress_bar = retriever.progress_bar
+        model_class: str = next(
+            (m for m in ["ada", "babbage", "davinci", "curie"] if m in retriever.embedding_model), "babbage"
+        )
+        self.query_model_encoder_engine = f"text-search-{model_class}-query-001"
+        self.doc_model_encoder_engine = f"text-search-{model_class}-doc-001"
+        self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
+
+    def _ensure_text_limit(self, text: str) -> str:
+        """
+        Ensure that length of the text is within the maximum length of the model.
+        OpenAI embedding models have a limit of 2048 tokens
+        """
+        tokenized_payload = self.tokenizer(text)
+        return self.tokenizer.decode(tokenized_payload["input_ids"][: self.max_seq_len])
+
+    def embed(self, model: str, text: List[str]) -> np.ndarray:
+        payload = {"model": model, "input": text}
+        headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
+        response = requests.request("POST", self.url, headers=headers, data=json.dumps(payload), timeout=30)
+        res = json.loads(response.text)
+
+        if response.status_code != 200:
+            raise OpenAIError(
+                f"OpenAI returned an error.\n"
+                f"Status code: {response.status_code}\n"
+                f"Response body: {response.text}"
+            )
+
+        unordered_embeddings = [(ans["index"], ans["embedding"]) for ans in res["data"]]
+        ordered_embeddings = sorted(unordered_embeddings, key=lambda x: x[0])
+        generated_embeddings = [emb[1] for emb in ordered_embeddings]
+        return np.array(generated_embeddings)
+
+    def embed_batch(self, model: str, text: List[str]) -> np.ndarray:
+        all_embeddings = []
+        for i in tqdm(
+            range(0, len(text), self.batch_size), disable=not self.progress_bar, desc="Calculating embeddings"
+        ):
+            batch = text[i : i + self.batch_size]
+            batch_limited = [self._ensure_text_limit(content) for content in batch]
+            generated_embeddings = self.embed(model, batch_limited)
+            all_embeddings.append(generated_embeddings)
+        return np.concatenate(all_embeddings)
+
+    def embed_queries(self, queries: List[str]) -> np.ndarray:
+        return self.embed_batch(self.query_model_encoder_engine, queries)
+
+    def embed_documents(self, docs: List[Document]) -> np.ndarray:
+        return self.embed_batch(self.doc_model_encoder_engine, [d.content for d in docs])
+
+    def train(
+        self,
+        training_data: List[Dict[str, Any]],
+        learning_rate: float = 2e-5,
+        n_epochs: int = 1,
+        num_warmup_steps: int = None,
+        batch_size: int = 16,
+    ):
+        raise NotImplementedError(f"Training is not implemented for {self.__class__}")
+
+    def save(self, save_dir: Union[Path, str]):
+        raise NotImplementedError(f"Saving is not implemented for {self.__class__}")
+
+
 _EMBEDDING_ENCODERS: Dict[str, Callable] = {
     "farm": _DefaultEmbeddingEncoder,
     "transformers": _DefaultEmbeddingEncoder,
     "sentence_transformers": _SentenceTransformersEmbeddingEncoder,
     "retribert": _RetribertEmbeddingEncoder,
+    "openai": _OpenAIEmbeddingEncoder,
 }

haystack/nodes/retriever/dense.py

@@ -1495,6 +1495,7 @@ def __init__(
         use_auth_token: Optional[Union[str, bool]] = None,
         scale_score: bool = True,
         embed_meta_fields: List[str] = [],
+        api_key: Optional[str] = None,
     ):
         """
         :param document_store: An instance of DocumentStore from which to retrieve documents.
@@ -1511,6 +1512,7 @@ def __init__(
                              - ``'transformers'`` (will use `_DefaultEmbeddingEncoder` as embedding encoder)
                              - ``'sentence_transformers'`` (will use `_SentenceTransformersEmbeddingEncoder` as embedding encoder)
                              - ``'retribert'`` (will use `_RetribertEmbeddingEncoder` as embedding encoder)
+                             - ``'openai'``: (will use `_OpenAIEmbeddingEncoder` as embedding encoder)
         :param pooling_strategy: Strategy for combining the embeddings from the model (for farm / transformers models only).
                                  Options:
 
@@ -1541,6 +1543,9 @@ def __init__(
                                   This approach is also used in the TableTextRetriever paper and is likely to improve
                                   performance if your titles contain meaningful information for retrieval
                                   (topic, entities etc.).
+        :param api_key: The OpenAI API key. Required if one wants to use OpenAI embeddings. For more
+                        details see https://beta.openai.com/account/api-keys
+
         """
         super().__init__()
 
@@ -1561,6 +1566,7 @@ def __init__(
         self.progress_bar = progress_bar
         self.use_auth_token = use_auth_token
         self.scale_score = scale_score
+        self.api_key = api_key
         self.model_format = (
             self._infer_model_format(model_name_or_path=embedding_model, use_auth_token=use_auth_token)
             if model_format is None
@@ -1869,6 +1875,8 @@ def _preprocess_documents(self, docs: List[Document]) -> List[Document]:
 
     @staticmethod
     def _infer_model_format(model_name_or_path: str, use_auth_token: Optional[Union[str, bool]]) -> str:
+        if any(m in model_name_or_path for m in ["ada", "babbage", "davinci", "curie"]):
+            return "openai"
         # Check if model name is a local directory with sentence transformers config file in it
         if Path(model_name_or_path).exists():
             if Path(f"{model_name_or_path}/config_sentence_transformers.json").exists():

test/conftest.py

@@ -818,6 +818,13 @@ def get_retriever(retriever_type, document_store):
         retriever = EmbeddingRetriever(
             document_store=document_store, embedding_model="yjernite/retribert-base-uncased", use_gpu=False
         )
+    elif retriever_type == "openai":
+        retriever = EmbeddingRetriever(
+            document_store=document_store,
+            embedding_model="ada",
+            use_gpu=False,
+            api_key=os.environ.get("OPENAI_API_KEY", ""),
+        )
     elif retriever_type == "dpr_lfqa":
         retriever = DensePassageRetriever(
             document_store=document_store,

test/nodes/test_retriever.py

@@ -1,4 +1,5 @@
 import logging
+import os
 from math import isclose
 
 import numpy as np
@@ -10,6 +11,7 @@
 
 from haystack.document_stores import WeaviateDocumentStore
 from haystack.nodes.retriever.base import BaseRetriever
+from haystack.pipelines import DocumentSearchPipeline
 from haystack.schema import Document
 from haystack.document_stores.elasticsearch import ElasticsearchDocumentStore
 from haystack.document_stores.faiss import FAISSDocumentStore
@@ -216,6 +218,45 @@ def test_retribert_embedding(document_store, retriever, docs_with_ids):
         assert isclose(embedding[0], expected_value, rel_tol=0.001)
 
 
+@pytest.mark.integration
+@pytest.mark.parametrize("document_store", ["memory"], indirect=True)
+@pytest.mark.parametrize("retriever", ["openai"], indirect=True)
+@pytest.mark.embedding_dim(1024)
+@pytest.mark.skipif(
+    not os.environ.get("OPENAI_API_KEY", None),
+    reason="Please export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
+)
+def test_openai_embedding(document_store, 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 = sorted(docs, key=lambda d: d.id)
+
+    for doc in docs:
+        assert len(doc.embedding) == 1024
+
+
+@pytest.mark.integration
+@pytest.mark.parametrize("document_store", ["memory"], indirect=True)
+@pytest.mark.parametrize("retriever", ["openai"], indirect=True)
+@pytest.mark.embedding_dim(1024)
+@pytest.mark.skipif(
+    not os.environ.get("OPENAI_API_KEY", None),
+    reason="Please export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
+)
+def test_retriever_basic_search(document_store, retriever, docs_with_ids):
+    document_store.return_embedding = True
+    document_store.write_documents(docs_with_ids)
+    document_store.update_embeddings(retriever=retriever)
+
+    p_retrieval = DocumentSearchPipeline(retriever)
+    res = p_retrieval.run(query="Madrid", params={"Retriever": {"top_k": 1}})
+    assert len(res["documents"]) == 1
+    assert "Madrid" in res["documents"][0].content
+
+
 @pytest.mark.integration
 @pytest.mark.parametrize("retriever", ["table_text_retriever"], indirect=True)
 @pytest.mark.parametrize("document_store", ["elasticsearch", "memory"], indirect=True)