add flag for preloading files

README.md

@@ -76,6 +76,8 @@ python main.py --human me.txt
   enables debugging output
 --archival_storage_faiss_path=<ARCHIVAL_STORAGE_FAISS_PATH>
   load in document database (backed by FAISS index)
+--archival_storage_files="<ARCHIVAL_STORAGE_FILES_GLOB>"
+  pre-load files into archival memory
 ```
 
 ### Interactive CLI commands

main.py

@@ -16,7 +16,7 @@ import memgpt.presets as presets
 import memgpt.constants as constants
 import memgpt.personas.personas as personas
 import memgpt.humans.humans as humans
-from memgpt.persistence_manager import InMemoryStateManager, InMemoryStateManagerWithFaiss
+from memgpt.persistence_manager import InMemoryStateManager, InMemoryStateManagerWithPreloadedArchivalMemory, InMemoryStateManagerWithFaiss
 
 FLAGS = flags.FLAGS
 flags.DEFINE_string("persona", default=personas.DEFAULT, required=False, help="Specify persona")
@@ -24,7 +24,8 @@ flags.DEFINE_string("human", default=humans.DEFAULT, required=False, help="Speci
 flags.DEFINE_string("model", default=constants.DEFAULT_MEMGPT_MODEL, required=False, help="Specify the LLM model")
 flags.DEFINE_boolean("first", default=False, required=False, help="Use -first to send the first message in the sequence")
 flags.DEFINE_boolean("debug", default=False, required=False, help="Use -debug to enable debugging output")
-flags.DEFINE_string("archival_storage_faiss_path", default="", required=False, help="Specify archival storage to load (a folder with a .index and .json describing documents to be loaded)")
+flags.DEFINE_string("archival_storage_faiss_path", default="", required=False, help="Specify archival storage with FAISS index to load (a folder with a .index and .json describing documents to be loaded)")
+flags.DEFINE_string("archival_storage_files", default="", required=False, help="Specify files to pre-load into archival memory (glob pattern)")
 
 
 def clear_line():
@@ -47,6 +48,10 @@ async def main():
     if FLAGS.archival_storage_faiss_path:
         index, archival_database = utils.prepare_archival_index(FLAGS.archival_storage_faiss_path)
         persistence_manager = InMemoryStateManagerWithFaiss(index, archival_database)
+    elif FLAGS.archival_storage_files:
+        archival_database = utils.prepare_archival_index_from_files(FLAGS.archival_storage_files)
+        print(f"Preloaded {len(archival_database)} chunks into archival memory.")
+        persistence_manager = InMemoryStateManagerWithPreloadedArchivalMemory(archival_database)
     else:
         persistence_manager = InMemoryStateManager()
     memgpt_agent = presets.use_preset(presets.DEFAULT, FLAGS.model, personas.get_persona_text(FLAGS.persona), humans.get_human_text(FLAGS.human), interface, persistence_manager)

memgpt/persistence_manager.py

@@ -85,11 +85,29 @@ class InMemoryStateManager(PersistenceManager):
         self.memory = new_memory
 
 
-class InMemoryStateManagerWithEmbeddings(InMemoryStateManager):
+class InMemoryStateManagerWithPreloadedArchivalMemory(InMemoryStateManager):
+    archival_memory_cls = DummyArchivalMemory
+    recall_memory_cls = DummyRecallMemory
 
+    def __init__(self, archival_memory_db):
+        self.archival_memory_db = archival_memory_db
+    
+    def init(self, agent):
+        print(f"Initializing InMemoryStateManager with agent object")
+        self.all_messages = [{'timestamp': get_local_time(), 'message': msg} for msg in agent.messages.copy()]
+        self.messages = [{'timestamp': get_local_time(), 'message': msg} for msg in agent.messages.copy()]
+        self.memory = agent.memory
+        print(f"InMemoryStateManager.all_messages.len = {len(self.all_messages)}")
+        print(f"InMemoryStateManager.messages.len = {len(self.messages)}")
+        self.recall_memory = self.recall_memory_cls(message_database=self.all_messages)
+        self.archival_memory = self.archival_memory_cls(archival_memory_database=self.archival_memory_db)
+
+
+class InMemoryStateManagerWithEmbeddings(InMemoryStateManager):
     archival_memory_cls = DummyArchivalMemoryWithEmbeddings
     recall_memory_cls = DummyRecallMemoryWithEmbeddings
 
+
 class InMemoryStateManagerWithFaiss(InMemoryStateManager):
     archival_memory_cls = DummyArchivalMemoryWithFaiss
     recall_memory_cls = DummyRecallMemoryWithEmbeddings

memgpt/personas/examples/preload_archival/README.md

@@ -0,0 +1,16 @@
+# Preloading Archival Memory with Files
+MemGPT enables you to chat with your data locally -- this example gives the workflow for loading documents into MemGPT's archival memory.
+
+To run our example where you can search over the SEC 10-K filings of Uber, Lyft, and Airbnb,
+
+1. Download the .txt files from [HuggingFace](https://huggingface.co/datasets/MemGPT/example-sec-filings/tree/main) and place them in this directory.
+
+2. In the root `MemGPT` directory, run
+    ```bash
+    python3 main.py --archival_storage_files="memgpt/personas/examples/preload_archival/*.txt" --persona=memgpt_doc --human=basic
+    ```
+
+
+If you would like to load your own local files into MemGPT's archival memory, run the command above but replace `--archival_storage_files="memgpt/personas/examples/preload_archival/*.txt"` with your own file glob expression (enclosed in quotes).
+
+## Demo

memgpt/utils.py

@@ -7,6 +7,7 @@ import pytz
 import os
 import faiss
 import tiktoken
+import glob
 
 def count_tokens(s: str, model: str = "gpt-4") -> int:
     encoding = tiktoken.encoding_for_model(model)
@@ -83,4 +84,56 @@ def prepare_archival_index(folder):
                 'content': f"[Title: {passage['title']}, {i}/{total}] {passage['text']}",
                 'timestamp': get_local_time(),
             })  
-    return index, archival_database
+    return index, archival_database
+
+def read_in_chunks(file_object, chunk_size):
+    while True:
+        data = file_object.read(chunk_size)
+        if not data:
+            break
+        yield data
+
+def prepare_archival_index_from_files(glob_pattern, tkns_per_chunk=300, model='gpt-4'):
+    encoding = tiktoken.encoding_for_model(model)
+    files = glob.glob(glob_pattern)
+    archival_database = []
+    for file in files:
+        timestamp = os.path.getmtime(file)
+        formatted_time = datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %I:%M:%S %p %Z%z")
+        with open(file, 'r') as f:
+            lines = [l for l in read_in_chunks(f, tkns_per_chunk*4)]
+        chunks = [] 
+        curr_chunk = []
+        curr_token_ct = 0
+        for line in lines:
+            line = line.rstrip()
+            line = line.lstrip()
+            try:
+                line_token_ct = len(encoding.encode(line))
+            except Exception as e:
+                line_token_ct = len(line.split(' ')) / .75
+                print(f"Could not encode line {line}, estimating it to be {line_token_ct} tokens") 
+            if line_token_ct > tkns_per_chunk:
+                if len(curr_chunk) > 0:
+                    chunks.append(''.join(curr_chunk))
+                    curr_chunk = []
+                    curr_token_ct = 0
+                chunks.append(line[:3200])
+                continue
+            curr_token_ct += line_token_ct
+            curr_chunk.append(line)
+            if curr_token_ct > tkns_per_chunk:
+                chunks.append(''.join(curr_chunk))
+                curr_chunk = []
+                curr_token_ct = 0
+
+        if len(curr_chunk) > 0:
+            chunks.append(''.join(curr_chunk))
+
+        file_stem = file.split('/')[-1]
+        for i, chunk in enumerate(chunks):
+            archival_database.append({
+                'content': f"[File: {file_stem} Part {i}/{len(chunks)}] {chunk}",
+                'timestamp': formatted_time,
+            })
+    return archival_database