Merge pull request #85 from cpacker/fast-embeddings

Parallelize embedding generation

memgpt/utils.py

@@ -1,5 +1,6 @@
 from datetime import datetime
 
+import asyncio
 import csv
 import difflib
 import demjson3 as demjson
@@ -194,6 +195,31 @@ def chunk_files_for_jsonl(files, tkns_per_chunk=300, model='gpt-4'):
         ret.append(curr_file)
     return ret
 
+async def process_chunk(i, chunk, model):
+    try:
+        return i, await async_get_embedding_with_backoff(chunk['content'], model=model)
+    except Exception as e:
+        print(chunk)
+        raise e
+
+async def process_concurrently(archival_database, model, concurrency=10):
+    # Create a semaphore to limit the number of concurrent tasks
+    semaphore = asyncio.Semaphore(concurrency)
+
+    async def bounded_process_chunk(i, chunk):
+        async with semaphore:
+            return await process_chunk(i, chunk, model)
+
+    # Create a list of tasks for chunks
+    embedding_data = [0 for _ in archival_database]
+    tasks = [bounded_process_chunk(i, chunk) for i, chunk in enumerate(archival_database)]
+
+    for future in tqdm(asyncio.as_completed(tasks), total=len(archival_database), desc="Processing file chunks"):
+        i, result = await future
+        embedding_data[i] = result
+
+    return embedding_data
+
 async def prepare_archival_index_from_files_compute_embeddings(glob_pattern, tkns_per_chunk=300, model='gpt-4', embeddings_model='text-embedding-ada-002'):
     files = sorted(glob.glob(glob_pattern))
     save_dir = "archival_index_from_files_" + get_local_time().replace(' ', '_').replace(':', '_')
@@ -206,15 +232,7 @@ async def prepare_archival_index_from_files_compute_embeddings(glob_pattern, tkn
 
     # chunk the files, make embeddings
     archival_database = chunk_files(files, tkns_per_chunk, model)
-    embedding_data = []
-    for chunk in tqdm(archival_database, desc="Processing file chunks", total=len(archival_database)):
-        # for chunk in tqdm(f, desc=f"Embedding file {i+1}/{len(chunks_by_file)}", total=len(f), leave=False):
-        try:
-            embedding = await async_get_embedding_with_backoff(chunk['content'], model=embeddings_model)
-        except Exception as e:
-            print(chunk)
-            raise e
-        embedding_data.append(embedding)
+    embedding_data = await process_concurrently(archival_database, embeddings_model)
     embeddings_file = os.path.join(save_dir, "embeddings.json")
     with open(embeddings_file, 'w') as f:
         print(f"Saving embeddings to {embeddings_file}")