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openai[patch]: fix embedding float precision issue (#21736)

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also clean up + comment some of the embedding batching code
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Erick Friis 2024-05-15 19:06:51 -07:00 committed by GitHub
parent 38c297a025
commit e41d801369
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1 changed files with 109 additions and 63 deletions
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172
libs/partners/openai/langchain_openai/embeddings/base.py
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@ -37,6 +37,67 @@ from langchain_core.utils import (
logger = logging.getLogger(__name__)
def _process_batched_chunked_embeddings(
num_texts: int,
tokens: List[Union[List[int], str]],
batched_embeddings: List[List[float]],
indices: List[int],
skip_empty: bool,
) -> List[Optional[List[float]]]:
# for each text, this is the list of embeddings (list of list of floats)
# corresponding to the chunks of the text
results: List[List[List[float]]] = [[] for _ in range(num_texts)]
# for each text, this is the token length of each chunk
# for transformers tokenization, this is the string length
# for tiktoken, this is the number of tokens
num_tokens_in_batch: List[List[int]] = [[] for _ in range(num_texts)]
for i in range(len(indices)):
if skip_empty and len(batched_embeddings[i]) == 1:
continue
results[indices[i]].append(batched_embeddings[i])
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
# for each text, this is the final embedding
embeddings: List[Optional[List[float]]] = []
for i in range(num_texts):
# an embedding for each chunk
_result: List[List[float]] = results[i]
if len(_result) == 0:
# this will be populated with the embedding of an empty string
# in the sync or async code calling this
embeddings.append(None)
continue
elif len(_result) == 1:
# if only one embedding was produced, use it
embeddings.append(_result[0])
continue
else:
# else we need to weighted average
# should be same as
# average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
total_weight = sum(num_tokens_in_batch[i])
average = [
sum(
val * weight
for val, weight in zip(embedding, num_tokens_in_batch[i])
)
/ total_weight
for embedding in zip(*_result)
]
# should be same as
# embeddings.append((average / np.linalg.norm(average)).tolist())
magnitude = sum(val**2 for val in average) ** 0.5
embeddings.append([val / magnitude for val in average])
return embeddings
class OpenAIEmbeddings(BaseModel, Embeddings):
"""OpenAI embedding models.
@ -248,9 +309,29 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
def _tokenize(
self, texts: List[str], chunk_size: int
) -> Tuple[Iterable[int], List[List[float]], List[int]]:
tokens = []
indices = []
) -> Tuple[Iterable[int], List[Union[List[int], str]], List[int]]:
"""
Take the input `texts` and `chunk_size` and return 3 iterables as a tuple:
We have `batches`, where batches are sets of individual texts
we want responses from the openai api. The length of a single batch is
`chunk_size` texts.
Each individual text is also split into multiple texts based on the
`embedding_ctx_length` parameter (based on number of tokens).
This function returns a 3-tuple of the following:
_iter: An iterable of the starting index in `tokens` for each *batch*
tokens: A list of tokenized texts, where each text has already been split
into sub-texts based on the `embedding_ctx_length` parameter. In the
case of tiktoken, this is a list of token arrays. In the case of
HuggingFace transformers, this is a list of strings.
indices: An iterable of the same length as `tokens` that maps each token-array
to the index of the original text in `texts`.
"""
tokens: List[Union[List[int], str]] = []
indices: List[int] = []
model_name = self.tiktoken_model_name or self.model
# If tiktoken flag set to False
@ -269,14 +350,16 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
)
for i, text in enumerate(texts):
# Tokenize the text using HuggingFace transformers
tokenized = tokenizer.encode(text, add_special_tokens=False)
tokenized: List[int] = tokenizer.encode(text, add_special_tokens=False)
# Split tokens into chunks respecting the embedding_ctx_length
for j in range(0, len(tokenized), self.embedding_ctx_length):
token_chunk = tokenized[j : j + self.embedding_ctx_length]
token_chunk: List[int] = tokenized[
j : j + self.embedding_ctx_length
]
# Convert token IDs back to a string
chunk_text = tokenizer.decode(token_chunk)
chunk_text: str = tokenizer.decode(token_chunk)
tokens.append(chunk_text)
indices.append(i)
else:
@ -351,43 +434,23 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
response = response.model_dump()
batched_embeddings.extend(r["embedding"] for r in response["data"])
results: List[List[List[float]]] = [[] for _ in range(len(texts))]
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
for i in range(len(indices)):
if self.skip_empty and len(batched_embeddings[i]) == 1:
continue
results[indices[i]].append(batched_embeddings[i])
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
embeddings = _process_batched_chunked_embeddings(
len(texts), tokens, batched_embeddings, indices, self.skip_empty
)
_cached_empty_embedding: Optional[List[float]] = None
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
for i in range(len(texts)):
_result = results[i]
if len(_result) == 0:
def empty_embedding() -> List[float]:
nonlocal _cached_empty_embedding
if _cached_empty_embedding is None:
average_embedded = self.client.create(
input="", **self._invocation_params
)
if not isinstance(average_embedded, dict):
average_embedded = average_embedded.model_dump()
average = average_embedded["data"][0]["embedding"]
else:
# should be same as
# average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
total_weight = sum(num_tokens_in_batch[i])
average = [
sum(
val * weight
for val, weight in zip(embedding, num_tokens_in_batch[i])
)
/ total_weight
for embedding in zip(*_result)
]
_cached_empty_embedding = average_embedded["data"][0]["embedding"]
return _cached_empty_embedding
# should be same as
# embeddings[i] = (average / np.linalg.norm(average)).tolist()
magnitude = sum(val**2 for val in average) ** 0.5
embeddings[i] = [val / magnitude for val in average]
return embeddings
return [e if e is not None else empty_embedding() for e in embeddings]
# please refer to
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
@ -423,40 +486,23 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
response = response.model_dump()
batched_embeddings.extend(r["embedding"] for r in response["data"])
results: List[List[List[float]]] = [[] for _ in range(len(texts))]
num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))]
for i in range(len(indices)):
results[indices[i]].append(batched_embeddings[i])
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
embeddings = _process_batched_chunked_embeddings(
len(texts), tokens, batched_embeddings, indices, self.skip_empty
)
_cached_empty_embedding: Optional[List[float]] = None
embeddings: List[List[float]] = [[] for _ in range(len(texts))]
for i in range(len(texts)):
_result = results[i]
if len(_result) == 0:
async def empty_embedding() -> List[float]:
nonlocal _cached_empty_embedding
if _cached_empty_embedding is None:
average_embedded = await self.async_client.create(
input="", **self._invocation_params
)
if not isinstance(average_embedded, dict):
average_embedded = average_embedded.model_dump()
average = average_embedded["data"][0]["embedding"]
else:
# should be same as
# average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
total_weight = sum(num_tokens_in_batch[i])
average = [
sum(
val * weight
for val, weight in zip(embedding, num_tokens_in_batch[i])
)
/ total_weight
for embedding in zip(*_result)
]
# should be same as
# embeddings[i] = (average / np.linalg.norm(average)).tolist()
magnitude = sum(val**2 for val in average) ** 0.5
embeddings[i] = [val / magnitude for val in average]
_cached_empty_embedding = average_embedded["data"][0]["embedding"]
return _cached_empty_embedding
return embeddings
return [e if e is not None else await empty_embedding() for e in embeddings]
def embed_documents(
self, texts: List[str], chunk_size: Optional[int] = 0