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559dd34 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | """Batch embedder abstraction and implementations."""
import json
import logging
import os
from abc import ABC, abstractmethod
from collections import Counter
from typing import Dict, Generator, List, Tuple
from openai import OpenAI
from chunker import Chunk, Chunker
from repo_manager import RepoManager
Vector = Tuple[Dict, List[float]] # (metadata, embedding)
class BatchEmbedder(ABC):
"""Abstract class for batch embedding of a repository."""
@abstractmethod
def embed_repo(self, chunks_per_batch: int):
"""Issues batch embedding jobs for the entire repository."""
@abstractmethod
def embeddings_are_ready(self) -> bool:
"""Checks whether the batch embedding jobs are done."""
@abstractmethod
def download_embeddings(self) -> Generator[Vector, None, None]:
"""Yields (chunk_metadata, embedding) pairs for each chunk in the repository."""
class OpenAIBatchEmbedder(BatchEmbedder):
"""Batch embedder that calls OpenAI. See https://platform.openai.com/docs/guides/batch/overview."""
def __init__(
self, repo_manager: RepoManager, chunker: Chunker, local_dir: str
):
self.repo_manager = repo_manager
self.chunker = chunker
self.local_dir = local_dir
# IDs issued by OpenAI for each batch job mapped to metadata about the chunks.
self.openai_batch_ids = {}
self.client = OpenAI()
def embed_repo(self, chunks_per_batch: int):
"""Issues batch embedding jobs for the entire repository."""
if self.openai_batch_ids:
raise ValueError("Embeddings are in progress.")
batch = []
chunk_count = 0
repo_name = self.repo_manager.repo_id.split("/")[-1]
for filepath, content in self.repo_manager.walk():
chunks = self.chunker.chunk(filepath, content)
chunk_count += len(chunks)
batch.extend(chunks)
if len(batch) > chunks_per_batch:
for i in range(0, len(batch), chunks_per_batch):
batch = batch[i : i + chunks_per_batch]
openai_batch_id = self._issue_job_for_chunks(
batch, batch_id=f"{repo_name}/{len(self.openai_batch_ids)}"
)
self.openai_batch_ids[openai_batch_id] = self._metadata_for_chunks(
batch
)
batch = []
# Finally, commit the last batch.
if batch:
openai_batch_id = self._issue_job_for_chunks(
batch, batch_id=f"{repo_name}/{len(self.openai_batch_ids)}"
)
self.openai_batch_ids[openai_batch_id] = self._metadata_for_chunks(batch)
logging.info(
"Issued %d jobs for %d chunks.", len(self.openai_batch_ids), chunk_count
)
# Save the job IDs to a file, just in case this script is terminated by mistake.
metadata_file = os.path.join(self.local_dir, "openai_batch_ids.json")
with open(metadata_file, "w") as f:
json.dump(self.openai_batch_ids, f)
logging.info("Job metadata saved at %s", metadata_file)
def embeddings_are_ready(self) -> bool:
"""Checks whether the embeddings jobs are done (either completed or failed)."""
if not self.openai_batch_ids:
raise ValueError("No embeddings in progress.")
job_ids = self.openai_batch_ids.keys()
statuses = [self.client.batches.retrieve(job_id.strip()) for job_id in job_ids]
are_ready = all(status.status in ["completed", "failed"] for status in statuses)
status_counts = Counter(status.status for status in statuses)
logging.info("Job statuses: %s", status_counts)
return are_ready
def download_embeddings(self) -> Generator[Vector, None, None]:
"""Yield a (chunk_metadata, embedding) pair for each chunk in the repository."""
job_ids = self.openai_batch_ids.keys()
statuses = [self.client.batches.retrieve(job_id.strip()) for job_id in job_ids]
for idx, status in enumerate(statuses):
if status.status == "failed":
logging.error("Job failed: %s", status)
continue
if not status.output_file_id:
error = self.client.files.content(status.error_file_id)
logging.error("Job %s failed with error: %s", status.id, error.text)
continue
batch_metadata = self.openai_batch_ids[status.id]
file_response = self.client.files.content(status.output_file_id)
data = json.loads(file_response.text)["response"]["body"]["data"]
logging.info("Job %s generated %d embeddings.", status.id, len(data))
for datum in data:
idx = int(datum["index"])
metadata = batch_metadata[idx]
embedding = datum["embedding"]
yield (metadata, embedding)
def _issue_job_for_chunks(self, chunks: List[Chunk], batch_id: str) -> str:
"""Issues a batch embedding job for the given chunks. Returns the job ID."""
logging.info("*" * 100)
logging.info("Issuing job for batch %s with %d chunks.", batch_id, len(chunks))
# Create a .jsonl file with the batch.
request = OpenAIBatchEmbedder._chunks_to_request(chunks, batch_id)
input_file = os.path.join(self.local_dir, f"batch_{batch_id}.jsonl")
OpenAIBatchEmbedder._export_to_jsonl([request], input_file)
# Uplaod the file and issue the embedding job.
batch_input_file = self.client.files.create(file=open(input_file, "rb"), purpose="batch")
batch_status = self._create_batch_job(batch_input_file.id)
logging.info("Created job with ID %s", batch_status.id)
return batch_status.id
def _create_batch_job(self, input_file_id: str):
"""Creates a batch embedding job for OpenAI."""
try:
return self.client.batches.create(
input_file_id=input_file_id,
endpoint="/v1/embeddings",
completion_window="24h", # This is the only allowed value for now.
timeout=3 * 60, # 3 minutes
metadata={},
)
except Exception as e:
print(
f"Failed to create batch job with input_file_id={input_file_id}. Error: {e}"
)
return None
@staticmethod
def _export_to_jsonl(list_of_dicts: List[Dict], output_file: str):
"""Exports a list of dictionaries to a .jsonl file."""
directory = os.path.dirname(output_file)
if not os.path.exists(directory):
os.makedirs(directory)
with open(output_file, "w") as f:
for item in list_of_dicts:
json.dump(item, f)
f.write("\n")
@staticmethod
def _chunks_to_request(chunks: List[Chunk], batch_id: str):
"""Convert a list of chunks to a batch request."""
return {
"custom_id": batch_id,
"method": "POST",
"url": "/v1/embeddings",
"body": {
"model": "text-embedding-ada-002",
"input": [chunk.content for chunk in chunks],
},
}
@staticmethod
def _metadata_for_chunks(chunks):
metadata = []
for chunk in chunks:
filename_ascii = chunk.filename.encode("ascii", "ignore").decode("ascii")
metadata.append(
{
# Some vector stores require the IDs to be ASCII.
"id": f"{filename_ascii}_{chunk.start_byte}_{chunk.end_byte}",
"filename": chunk.filename,
"start_byte": chunk.start_byte,
"end_byte": chunk.end_byte,
# Note to developer: When choosing a large chunk size, you might exceed the vector store's metadata
# size limit. In that case, you can simply store the start/end bytes above, and fetch the content
# directly from the repository when needed.
"text": chunk.content,
}
)
return metadata
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