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#
# SPDX-License-Identifier: Apache-2.0
import os
from typing import Any, Dict, List, Optional, Tuple
from openai import OpenAI
from tqdm import tqdm
from haystack import Document, component, default_from_dict, default_to_dict
from haystack.utils import Secret, deserialize_secrets_inplace
@component
class OpenAIDocumentEmbedder:
"""
Computes document embeddings using OpenAI models.
### Usage example
```python
from haystack import Document
from haystack.components.embedders import OpenAIDocumentEmbedder
doc = Document(content="I love pizza!")
document_embedder = OpenAIDocumentEmbedder()
result = document_embedder.run([doc])
print(result['documents'][0].embedding)
# [0.017020374536514282, -0.023255806416273117, ...]
```
"""
def __init__(
self,
api_key: Secret = Secret.from_env_var("OPENAI_API_KEY"),
model: str = "text-embedding-ada-002",
dimensions: Optional[int] = None,
api_base_url: Optional[str] = None,
organization: Optional[str] = None,
prefix: str = "",
suffix: str = "",
batch_size: int = 32,
progress_bar: bool = True,
meta_fields_to_embed: Optional[List[str]] = None,
embedding_separator: str = "\n",
timeout: Optional[float] = None,
max_retries: Optional[int] = None,
):
"""
Creates an OpenAIDocumentEmbedder component.
Before initializing the component, you can set the 'OPENAI_TIMEOUT' and 'OPENAI_MAX_RETRIES'
environment variables to override the `timeout` and `max_retries` parameters respectively
in the OpenAI client.
:param api_key:
The OpenAI API key.
You can set it with an environment variable `OPENAI_API_KEY`, or pass with this parameter
during initialization.
:param model:
The name of the model to use for calculating embeddings.
The default model is `text-embedding-ada-002`.
:param dimensions:
The number of dimensions of the resulting embeddings. Only `text-embedding-3` and
later models support this parameter.
:param api_base_url:
Overrides the default base URL for all HTTP requests.
:param organization:
Your OpenAI organization ID. See OpenAI's
[Setting Up Your Organization](https://platform.openai.com/docs/guides/production-best-practices/setting-up-your-organization)
for more information.
: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 batch_size:
Number of documents to embed at once.
:param progress_bar:
If `True`, shows a progress bar when running.
:param meta_fields_to_embed:
List of metadata fields to embed along with the document text.
:param embedding_separator:
Separator used to concatenate the metadata fields to the document text.
:param timeout:
Timeout for OpenAI client calls. If not set, it defaults to either the
`OPENAI_TIMEOUT` environment variable, or 30 seconds.
:param max_retries:
Maximum number of retries to contact OpenAI after an internal error.
If not set, it defaults to either the `OPENAI_MAX_RETRIES` environment variable, or 5 retries.
"""
self.api_key = api_key
self.model = model
self.dimensions = dimensions
self.api_base_url = api_base_url
self.organization = organization
self.prefix = prefix
self.suffix = suffix
self.batch_size = batch_size
self.progress_bar = progress_bar
self.meta_fields_to_embed = meta_fields_to_embed or []
self.embedding_separator = embedding_separator
if timeout is None:
timeout = float(os.environ.get("OPENAI_TIMEOUT", 30.0))
if max_retries is None:
max_retries = int(os.environ.get("OPENAI_MAX_RETRIES", 5))
self.client = OpenAI(
api_key=api_key.resolve_value(),
organization=organization,
base_url=api_base_url,
timeout=timeout,
max_retries=max_retries,
)
def _get_telemetry_data(self) -> Dict[str, Any]:
"""
Data that is sent to Posthog for usage analytics.
"""
return {"model": self.model}
def to_dict(self) -> Dict[str, Any]:
"""
Serializes the component to a dictionary.
:returns:
Dictionary with serialized data.
"""
return default_to_dict(
self,
model=self.model,
dimensions=self.dimensions,
organization=self.organization,
api_base_url=self.api_base_url,
prefix=self.prefix,
suffix=self.suffix,
batch_size=self.batch_size,
progress_bar=self.progress_bar,
meta_fields_to_embed=self.meta_fields_to_embed,
embedding_separator=self.embedding_separator,
api_key=self.api_key.to_dict(),
)
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> "OpenAIDocumentEmbedder":
"""
Deserializes the component from a dictionary.
:param data:
Dictionary to deserialize from.
:returns:
Deserialized component.
"""
deserialize_secrets_inplace(data["init_parameters"], keys=["api_key"])
return default_from_dict(cls, data)
def _prepare_texts_to_embed(self, documents: List[Document]) -> List[str]:
"""
Prepare the texts to embed by concatenating the Document text with the metadata fields to embed.
"""
texts_to_embed = []
for doc in documents:
meta_values_to_embed = [
str(doc.meta[key]) for key in self.meta_fields_to_embed if key in doc.meta and doc.meta[key] is not None
]
text_to_embed = (
self.prefix + self.embedding_separator.join(meta_values_to_embed + [doc.content or ""]) + self.suffix
)
# copied from OpenAI embedding_utils (https://github.com/openai/openai-python/blob/main/openai/embeddings_utils.py)
# replace newlines, which can negatively affect performance.
text_to_embed = text_to_embed.replace("\n", " ")
texts_to_embed.append(text_to_embed)
return texts_to_embed
def _embed_batch(self, texts_to_embed: List[str], batch_size: int) -> Tuple[List[List[float]], Dict[str, Any]]:
"""
Embed a list of texts in batches.
"""
all_embeddings = []
meta: Dict[str, Any] = {}
for i in tqdm(
range(0, len(texts_to_embed), batch_size), disable=not self.progress_bar, desc="Calculating embeddings"
):
batch = texts_to_embed[i : i + batch_size]
if self.dimensions is not None:
response = self.client.embeddings.create(model=self.model, dimensions=self.dimensions, input=batch)
else:
response = self.client.embeddings.create(model=self.model, input=batch)
embeddings = [el.embedding for el in response.data]
all_embeddings.extend(embeddings)
if "model" not in meta:
meta["model"] = response.model
if "usage" not in meta:
meta["usage"] = dict(response.usage)
else:
meta["usage"]["prompt_tokens"] += response.usage.prompt_tokens
meta["usage"]["total_tokens"] += response.usage.total_tokens
return all_embeddings, meta
@component.output_types(documents=List[Document], meta=Dict[str, Any])
def run(self, documents: List[Document]):
"""
Embeds a list of documents.
:param documents:
A list of documents to embed.
:returns:
A dictionary with the following keys:
- `documents`: A list of documents with embeddings.
- `meta`: Information about the usage of the model.
"""
if not isinstance(documents, list) or documents and not isinstance(documents[0], Document):
raise TypeError(
"OpenAIDocumentEmbedder expects a list of Documents as input."
"In case you want to embed a string, please use the OpenAITextEmbedder."
)
texts_to_embed = self._prepare_texts_to_embed(documents=documents)
embeddings, meta = self._embed_batch(texts_to_embed=texts_to_embed, batch_size=self.batch_size)
for doc, emb in zip(documents, embeddings):
doc.embedding = emb
return {"documents": documents, "meta": meta}
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