13076938430

Committed 31 Jan 2025 04:39PM UTC coverage: 91.364% (+0.005%) from 91.359%

Build # 13076938430

Build Type

Pull #8794

github

Committed by

web-flow

Commit Message

Merge 3de090102 into 379711f63

Pull Request Pull Request #8794: refactor: HF API Embedders refactoring

Run Details

8876 of 9715 relevant lines covered (91.36%)

0.91 hits per line

Source File
Press 'n' to go to next uncovered line, 'b' for previous

96.61

haystack/components/embedders/hugging_face_api_text_embedder.py

# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0

import json
from typing import Any, Dict, List, Optional, Union

from haystack import component, default_from_dict, default_to_dict, logging
from haystack.lazy_imports import LazyImport
from haystack.utils import Secret, deserialize_secrets_inplace
from haystack.utils.hf import HFEmbeddingAPIType, HFModelType, check_valid_model
from haystack.utils.url_validation import is_valid_http_url

with LazyImport(message="Run 'pip install \"huggingface_hub>=0.27.0\"'") as huggingface_hub_import:
    from huggingface_hub import InferenceClient

logger = logging.getLogger(__name__)


@component
class HuggingFaceAPITextEmbedder:
    """
    Embeds strings using Hugging Face APIs.

    Use it with the following Hugging Face APIs:
    - [Free Serverless Inference API](https://huggingface.co/inference-api)
    - [Paid Inference Endpoints](https://huggingface.co/inference-endpoints)
    - [Self-hosted Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference)

    ### Usage examples

    #### With free serverless inference API

    ```python
    from haystack.components.embedders import HuggingFaceAPITextEmbedder
    from haystack.utils import Secret

    text_embedder = HuggingFaceAPITextEmbedder(api_type="serverless_inference_api",
                                               api_params={"model": "BAAI/bge-small-en-v1.5"},
                                               token=Secret.from_token("<your-api-key>"))

    print(text_embedder.run("I love pizza!"))

    # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
    ```

    #### With paid inference endpoints

    ```python
    from haystack.components.embedders import HuggingFaceAPITextEmbedder
    from haystack.utils import Secret
    text_embedder = HuggingFaceAPITextEmbedder(api_type="inference_endpoints",
                                               api_params={"model": "BAAI/bge-small-en-v1.5"},
                                               token=Secret.from_token("<your-api-key>"))

    print(text_embedder.run("I love pizza!"))

    # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
    ```

    #### With self-hosted text embeddings inference

    ```python
    from haystack.components.embedders import HuggingFaceAPITextEmbedder
    from haystack.utils import Secret

    text_embedder = HuggingFaceAPITextEmbedder(api_type="text_embeddings_inference",
                                               api_params={"url": "http://localhost:8080"})

    print(text_embedder.run("I love pizza!"))

    # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
    ```
    """

    def __init__(
        self,
        api_type: Union[HFEmbeddingAPIType, str],
        api_params: Dict[str, str],
        token: Optional[Secret] = Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False),
        prefix: str = "",
        suffix: str = "",
        truncate: bool = True,
        normalize: bool = False,
    ):  # pylint: disable=too-many-positional-arguments
        """
        Creates a HuggingFaceAPITextEmbedder component.

        :param api_type:
            The type of Hugging Face API to use.
        :param api_params:
            A dictionary with the following keys:
            - `model`: Hugging Face model ID. Required when `api_type` is `SERVERLESS_INFERENCE_API`.
            - `url`: URL of the inference endpoint. Required when `api_type` is `INFERENCE_ENDPOINTS` or
            `TEXT_EMBEDDINGS_INFERENCE`.
        :param token: The Hugging Face token to use as HTTP bearer authorization.
            Check your HF token in your [account settings](https://huggingface.co/settings/tokens).
        :param prefix:
            A string to add at the beginning of each text.
        :param suffix:
            A string to add at the end of each text.
        :param truncate:
            Truncates the input text to the maximum length supported by the model.
            Applicable when `api_type` is `TEXT_EMBEDDINGS_INFERENCE`, or `INFERENCE_ENDPOINTS`
            if the backend uses Text Embeddings Inference.
            If `api_type` is `SERVERLESS_INFERENCE_API`, this parameter is ignored.
        :param normalize:
            Normalizes the embeddings to unit length.
            Applicable when `api_type` is `TEXT_EMBEDDINGS_INFERENCE`, or `INFERENCE_ENDPOINTS`
            if the backend uses Text Embeddings Inference.
            If `api_type` is `SERVERLESS_INFERENCE_API`, this parameter is ignored.
        """
        huggingface_hub_import.check()

        if isinstance(api_type, str):
            api_type = HFEmbeddingAPIType.from_str(api_type)

        if api_type == HFEmbeddingAPIType.SERVERLESS_INFERENCE_API:
            model = api_params.get("model")
            if model is None:
                raise ValueError(
                    "To use the Serverless Inference API, you need to specify the `model` parameter in `api_params`."
                )
            check_valid_model(model, HFModelType.EMBEDDING, token)
            model_or_url = model
        elif api_type in [HFEmbeddingAPIType.INFERENCE_ENDPOINTS, HFEmbeddingAPIType.TEXT_EMBEDDINGS_INFERENCE]:
            url = api_params.get("url")
            if url is None:
                msg = (
                    "To use Text Embeddings Inference or Inference Endpoints, you need to specify the `url` "
                    "parameter in `api_params`."
                )
                raise ValueError(msg)
            if not is_valid_http_url(url):
                raise ValueError(f"Invalid URL: {url}")
            model_or_url = url
        else:
            msg = f"Unknown api_type {api_type}"
            raise ValueError(msg)

        self.api_type = api_type
        self.api_params = api_params
        self.token = token
        self.prefix = prefix
        self.suffix = suffix
        self.truncate = truncate
        self.normalize = normalize
        self._client = InferenceClient(model_or_url, token=token.resolve_value() if token else None)

    def to_dict(self) -> Dict[str, Any]:
        """
        Serializes the component to a dictionary.

        :returns:
            Dictionary with serialized data.
        """
        return default_to_dict(
            self,
            api_type=str(self.api_type),
            api_params=self.api_params,
            prefix=self.prefix,
            suffix=self.suffix,
            token=self.token.to_dict() if self.token else None,
            truncate=self.truncate,
            normalize=self.normalize,
        )

    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> "HuggingFaceAPITextEmbedder":
        """
        Deserializes the component from a dictionary.

        :param data:
            Dictionary to deserialize from.
        :returns:
            Deserialized component.
        """
        deserialize_secrets_inplace(data["init_parameters"], keys=["token"])
        return default_from_dict(cls, data)

    @component.output_types(embedding=List[float])
    def run(self, text: str):
        """
        Embeds a single string.

        :param text:
            Text to embed.

        :returns:
            A dictionary with the following keys:
            - `embedding`: The embedding of the input text.
        """
        if not isinstance(text, str):
            raise TypeError(
                "HuggingFaceAPITextEmbedder expects a string as an input."
                "In case you want to embed a list of Documents, please use the HuggingFaceAPIDocumentEmbedder."
            )

        text_to_embed = self.prefix + text + self.suffix

        np_embedding = self._client.feature_extraction(
            text=text_to_embed,
            # Serverless Inference API does not support truncate and normalize, so we pass None in the request
            truncate=self.truncate if self.api_type != HFEmbeddingAPIType.SERVERLESS_INFERENCE_API else None,
            normalize=self.normalize if self.api_type != HFEmbeddingAPIType.SERVERLESS_INFERENCE_API else None,
        )

        error_msg = f"Expected embedding shape (1, embedding_dim) or (embedding_dim,), got {np_embedding.shape}"
        if np_embedding.ndim > 2:
            raise ValueError(error_msg)
        if np_embedding.ndim == 2 and np_embedding.shape[0] != 1:
            raise ValueError(error_msg)

        embedding = np_embedding.flatten().tolist()

        return {"embedding": embedding}

1	# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
2	#
3	# SPDX-License-Identifier: Apache-2.0
4
5	import json	1✔
6	from typing import Any, Dict, List, Optional, Union	1✔
7
8	from haystack import component, default_from_dict, default_to_dict, logging	1✔
9	from haystack.lazy_imports import LazyImport	1✔
10	from haystack.utils import Secret, deserialize_secrets_inplace	1✔
11	from haystack.utils.hf import HFEmbeddingAPIType, HFModelType, check_valid_model	1✔
12	from haystack.utils.url_validation import is_valid_http_url	1✔
13
14	with LazyImport(message="Run 'pip install \"huggingface_hub>=0.27.0\"'") as huggingface_hub_import:	1✔
15	from huggingface_hub import InferenceClient	1✔
16
17	logger = logging.getLogger(__name__)	1✔
18
19
20	@component	1✔
21	class HuggingFaceAPITextEmbedder:	1✔
22	"""
23	Embeds strings using Hugging Face APIs.
24
25	Use it with the following Hugging Face APIs:
26	- [Free Serverless Inference API](https://huggingface.co/inference-api)
27	- [Paid Inference Endpoints](https://huggingface.co/inference-endpoints)
28	- [Self-hosted Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference)
29
30	### Usage examples
31
32	#### With free serverless inference API
33
34	```python
35	from haystack.components.embedders import HuggingFaceAPITextEmbedder
36	from haystack.utils import Secret
37
38	text_embedder = HuggingFaceAPITextEmbedder(api_type="serverless_inference_api",
39	api_params={"model": "BAAI/bge-small-en-v1.5"},
40	token=Secret.from_token("<your-api-key>"))
41
42	print(text_embedder.run("I love pizza!"))
43
44	# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
45	```
46
47	#### With paid inference endpoints
48
49	```python
50	from haystack.components.embedders import HuggingFaceAPITextEmbedder
51	from haystack.utils import Secret
52	text_embedder = HuggingFaceAPITextEmbedder(api_type="inference_endpoints",
53	api_params={"model": "BAAI/bge-small-en-v1.5"},
54	token=Secret.from_token("<your-api-key>"))
55
56	print(text_embedder.run("I love pizza!"))
57
58	# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
59	```
60
61	#### With self-hosted text embeddings inference
62
63	```python
64	from haystack.components.embedders import HuggingFaceAPITextEmbedder
65	from haystack.utils import Secret
66
67	text_embedder = HuggingFaceAPITextEmbedder(api_type="text_embeddings_inference",
68	api_params={"url": "http://localhost:8080"})
69
70	print(text_embedder.run("I love pizza!"))
71
72	# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
73	```
74	"""
75
76	def __init__(	1✔
77	self,
78	api_type: Union[HFEmbeddingAPIType, str],
79	api_params: Dict[str, str],
80	token: Optional[Secret] = Secret.from_env_var(["HF_API_TOKEN", "HF_TOKEN"], strict=False),
81	prefix: str = "",
82	suffix: str = "",
83	truncate: bool = True,
84	normalize: bool = False,
85	): # pylint: disable=too-many-positional-arguments
86	"""
87	Creates a HuggingFaceAPITextEmbedder component.
88
89	:param api_type:
90	The type of Hugging Face API to use.
91	:param api_params:
92	A dictionary with the following keys:
93	- `model`: Hugging Face model ID. Required when `api_type` is `SERVERLESS_INFERENCE_API`.
94	- `url`: URL of the inference endpoint. Required when `api_type` is `INFERENCE_ENDPOINTS` or
95	`TEXT_EMBEDDINGS_INFERENCE`.
96	:param token: The Hugging Face token to use as HTTP bearer authorization.
97	Check your HF token in your [account settings](https://huggingface.co/settings/tokens).
98	:param prefix:
99	A string to add at the beginning of each text.
100	:param suffix:
101	A string to add at the end of each text.
102	:param truncate:
103	Truncates the input text to the maximum length supported by the model.
104	Applicable when `api_type` is `TEXT_EMBEDDINGS_INFERENCE`, or `INFERENCE_ENDPOINTS`
105	if the backend uses Text Embeddings Inference.
106	If `api_type` is `SERVERLESS_INFERENCE_API`, this parameter is ignored.
107	:param normalize:
108	Normalizes the embeddings to unit length.
109	Applicable when `api_type` is `TEXT_EMBEDDINGS_INFERENCE`, or `INFERENCE_ENDPOINTS`
110	if the backend uses Text Embeddings Inference.
111	If `api_type` is `SERVERLESS_INFERENCE_API`, this parameter is ignored.
112	"""
113	huggingface_hub_import.check()	1✔
114
115	if isinstance(api_type, str):	1✔
116	api_type = HFEmbeddingAPIType.from_str(api_type)	1✔
117
118	if api_type == HFEmbeddingAPIType.SERVERLESS_INFERENCE_API:	1✔
119	model = api_params.get("model")	1✔
120	if model is None:	1✔
121	raise ValueError(	1✔
122	"To use the Serverless Inference API, you need to specify the `model` parameter in `api_params`."
123	)
124	check_valid_model(model, HFModelType.EMBEDDING, token)	1✔
125	model_or_url = model	1✔
126	elif api_type in [HFEmbeddingAPIType.INFERENCE_ENDPOINTS, HFEmbeddingAPIType.TEXT_EMBEDDINGS_INFERENCE]:	1✔
127	url = api_params.get("url")	1✔
128	if url is None:	1✔
129	msg = (	1✔
130	"To use Text Embeddings Inference or Inference Endpoints, you need to specify the `url` "
131	"parameter in `api_params`."
132	)
133	raise ValueError(msg)	1✔
134	if not is_valid_http_url(url):	1✔
135	raise ValueError(f"Invalid URL: {url}")	1✔
136	model_or_url = url	1✔
137	else:
138	msg = f"Unknown api_type {api_type}"	×
139	raise ValueError(msg)	×
140
141	self.api_type = api_type	1✔
142	self.api_params = api_params	1✔
143	self.token = token	1✔
144	self.prefix = prefix	1✔
145	self.suffix = suffix	1✔
146	self.truncate = truncate	1✔
147	self.normalize = normalize	1✔
148	self._client = InferenceClient(model_or_url, token=token.resolve_value() if token else None)	1✔
149
150	def to_dict(self) -> Dict[str, Any]:	1✔
151	"""
152	Serializes the component to a dictionary.
153
154	:returns:
155	Dictionary with serialized data.
156	"""
157	return default_to_dict(	1✔
158	self,
159	api_type=str(self.api_type),
160	api_params=self.api_params,
161	prefix=self.prefix,
162	suffix=self.suffix,
163	token=self.token.to_dict() if self.token else None,
164	truncate=self.truncate,
165	normalize=self.normalize,
166	)
167
168	@classmethod	1✔
169	def from_dict(cls, data: Dict[str, Any]) -> "HuggingFaceAPITextEmbedder":	1✔
170	"""
171	Deserializes the component from a dictionary.
172
173	:param data:
174	Dictionary to deserialize from.
175	:returns:
176	Deserialized component.
177	"""
178	deserialize_secrets_inplace(data["init_parameters"], keys=["token"])	1✔
179	return default_from_dict(cls, data)	1✔
180
181	@component.output_types(embedding=List[float])	1✔
182	def run(self, text: str):	1✔
183	"""
184	Embeds a single string.
185
186	:param text:
187	Text to embed.
188
189	:returns:
190	A dictionary with the following keys:
191	- `embedding`: The embedding of the input text.
192	"""
193	if not isinstance(text, str):	1✔
194	raise TypeError(	1✔
195	"HuggingFaceAPITextEmbedder expects a string as an input."
196	"In case you want to embed a list of Documents, please use the HuggingFaceAPIDocumentEmbedder."
197	)
198
199	text_to_embed = self.prefix + text + self.suffix	1✔
200
201	np_embedding = self._client.feature_extraction(	1✔
202	text=text_to_embed,
203	# Serverless Inference API does not support truncate and normalize, so we pass None in the request
204	truncate=self.truncate if self.api_type != HFEmbeddingAPIType.SERVERLESS_INFERENCE_API else None,
205	normalize=self.normalize if self.api_type != HFEmbeddingAPIType.SERVERLESS_INFERENCE_API else None,
206	)
207
208	error_msg = f"Expected embedding shape (1, embedding_dim) or (embedding_dim,), got {np_embedding.shape}"	1✔
209	if np_embedding.ndim > 2:	1✔
210	raise ValueError(error_msg)	1✔
211	if np_embedding.ndim == 2 and np_embedding.shape[0] != 1:	1✔
212	raise ValueError(error_msg)	1✔
213
214	embedding = np_embedding.flatten().tolist()	1✔
215
216	return {"embedding": embedding}	1✔

deepset-ai / haystack / 13076938430

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous