Merge pull request #120 from cpacker/revert-main

Revert main

README.md

@@ -71,15 +71,10 @@ Memory-GPT (or MemGPT in short) is a system that intelligently manages different
 
 ## Running MemGPT locally
 
-Install MemGPT:
+Install dependencies:
 
 ```sh
-pip install pymemgpt
-```
-
-To update the package, run
-```sh
-pip install pymemgpt -U
+pip install -r requirements.txt
 ```
 
 Add your OpenAI API key to your environment:
@@ -94,37 +89,12 @@ export OPENAI_API_KEY=YOUR_API_KEY
 set OPENAI_API_KEY=YOUR_API_KEY
 ```
 
-To run MemGPT for as a conversation agent in CLI mode, simply run `memgpt`:
+To run MemGPT for as a conversation agent in CLI mode, simply run `main.py`:
 
 ```sh
-memgpt
+python3 main.py
 ```
 
-<details>
-<summary><strong>Debugging command not found</strong></summary>
-
-If you get `command not found` (Linux/MacOS), or a `CommandNotFoundException` (Windows), the directory where pip installs scripts is not in your PATH. You can either add that directory to your path (`pip show pip | grep Scripts`) or instead just run:
-```sh
-python -m memgpt
-```
-</details>
-
-<details>
-<summary><strong>Building from source</strong></summary>
-
-Clone this repo: `git clone https://github.com/cpacker/MemGPT.git`
-
-Using poetry:
-1. Install poetry: `pip install poetry`
-2. Run `poetry install`
-3. Run `poetry run memgpt`
-
-Using pip:
-1. Run `pip install -e .`
-2. Run `python3 main.py`
-</details>
-
-
 If you're using Azure OpenAI, set these variables instead:
 
 ```sh
@@ -135,31 +105,31 @@ export AZURE_OPENAI_VERSION = ...
 export AZURE_OPENAI_DEPLOYMENT = ...
 
 # then use the --use_azure_openai flag
-memgpt --use_azure_openai
+python main.py --use_azure_openai
 ```
 
-To create a new starter user or starter persona (that MemGPT gets initialized with), create a new `.txt` file in `~/.memgpt/humans` or `~/.memgpt/personas`, then use the `--persona` or `--human` flag when running `main.py`. For example:
+To create a new starter user or starter persona (that MemGPT gets initialized with), create a new `.txt` file in [/memgpt/humans/examples](/memgpt/humans/examples) or [/memgpt/personas/examples](/memgpt/personas/examples), then use the `--persona` or `--human` flag when running `main.py`. For example:
+
 ```sh
-# assuming you created a new file ~/.memgpt/humans/me.txt
-memgpt
+# assuming you created a new file /memgpt/humans/examples/me.txt
+python main.py
 # Select me.txt during configuration process
 ```
 -- OR --
 ```sh
-# assuming you created a new file ~/.memgpt/humans/me.txt
-memgpt --human me.txt
+# assuming you created a new file /memgpt/humans/examples/me.txt
+python main.py --human me.txt
 ```
-You can also specify any of the starter users in [/memgpt/humans/examples](/memgpt/humans/examples) or any of the starter personas in [/memgpt/personas/examples](/memgpt/personas/examples).
 
 ### GPT-3.5 support
 You can run MemGPT with GPT-3.5 as the LLM instead of GPT-4:
 ```sh
-memgpt
+python main.py
 # Select gpt-3.5 during configuration process
 ```
 -- OR --
 ```sh
-memgpt --model gpt-3.5-turbo
+python main.py --model gpt-3.5-turbo
 ```
 
 **Note that this is experimental gpt-3.5-turbo support. It's quite buggy compared to gpt-4, but it should be runnable.**
@@ -240,7 +210,7 @@ id	| name |	age
 To talk to this database, run:
 
 ```sh
-memgpt --archival_storage_sqldb=memgpt/personas/examples/sqldb/test.db
+python main.py  --archival_storage_sqldb=memgpt/personas/examples/sqldb/test.db
 ```
 
 And then you can input the path to your database, and your query.
@@ -263,15 +233,15 @@ To run our example where you can search over the SEC 10-K filings of Uber, Lyft,
 
 2. In the root `MemGPT` directory, run
     ```bash
-    memgpt --archival_storage_files="memgpt/personas/examples/preload_archival/*.txt" --persona=memgpt_doc --human=basic
+    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).
 
 #### Enhance with embeddings search
 In the root `MemGPT` directory, run
   ```bash
-  memgpt main.py --archival_storage_files_compute_embeddings="<GLOB_PATTERN>" --persona=memgpt_doc --human=basic
+  python3 main.py --archival_storage_files_compute_embeddings="<GLOB_PATTERN>" --persona=memgpt_doc --human=basic
   ```
 
 This will generate embeddings, stick them into a FAISS index, and write the index to a directory, and then output:
@@ -282,7 +252,7 @@ This will generate embeddings, stick them into a FAISS index, and write the inde
 
 If you want to reuse these embeddings, run
 ```bash
-memgpt --archival_storage_faiss_path="<DIRECTORY_WITH_EMBEDDINGS>" --persona=memgpt_doc --human=basic
+python3 main.py --archival_storage_faiss_path="<DIRECTORY_WITH_EMBEDDINGS>" --persona=memgpt_doc --human=basic
 ```
 
 
@@ -314,7 +284,7 @@ MemGPT also enables you to chat with docs -- try running this example to talk to
 
 3. In the root `MemGPT` directory, run
     ```bash
-    memgpt --archival_storage_faiss_path=<ARCHIVAL_STORAGE_FAISS_PATH> --persona=memgpt_doc --human=basic
+    python3 main.py --archival_storage_faiss_path=<ARCHIVAL_STORAGE_FAISS_PATH> --persona=memgpt_doc --human=basic
     ```
     where `ARCHIVAL_STORAGE_FAISS_PATH` is the directory where `all_docs.jsonl` and `all_docs.index` are located.
    If you downloaded from Hugging Face, it will be `memgpt/personas/docqa/llamaindex-api-docs`.

memgpt/config.py

@@ -14,6 +14,7 @@
 import memgpt.interface as interface
 from memgpt.personas.personas import get_persona_text
 from memgpt.humans.humans import get_human_text
+from memgpt.constants import MEMGPT_DIR
 
 model_choices = [
     questionary.Choice("gpt-4"),
@@ -22,15 +23,14 @@
         value="gpt-3.5-turbo",
     ),
 ]
-memgpt_dir = os.path.join(os.path.expanduser("~"), ".memgpt")
 
 
 class Config:
     personas_dir = os.path.join("memgpt", "personas", "examples")
-    custom_personas_dir = os.path.join(memgpt_dir, "personas")
+    custom_personas_dir = os.path.join(MEMGPT_DIR, "personas")
     humans_dir = os.path.join("memgpt", "humans", "examples")
-    custom_humans_dir = os.path.join(memgpt_dir, "humans")
-    configs_dir = os.path.join(memgpt_dir, "configs")
+    custom_humans_dir = os.path.join(MEMGPT_DIR, "humans")
+    configs_dir = os.path.join(MEMGPT_DIR, "configs")
 
     def __init__(self):
         os.makedirs(Config.custom_personas_dir, exist_ok=True)
@@ -247,7 +247,8 @@ def get_memgpt_personas():
             + Config.get_persona_choices(
                 [p for p in custom_personas_in_examples + default_personas],
                 get_persona_text,
-                Config.personas_dir,
+                None,
+                # Config.personas_dir,
             )
             + [
                 questionary.Separator(),
@@ -274,7 +275,8 @@ def get_user_personas():
             + Config.get_persona_choices(
                 [p for p in custom_personas_in_examples + default_personas],
                 get_human_text,
-                Config.humans_dir,
+                None,
+                # Config.humans_dir,
             )
             + [
                 questionary.Separator(),

memgpt/constants.py

@@ -1,9 +1,15 @@
-DEFAULT_MEMGPT_MODEL = 'gpt-4'
+import os
+
+MEMGPT_DIR = os.path.join(os.path.expanduser("~"), ".memgpt")
+
+DEFAULT_MEMGPT_MODEL = "gpt-4"
 
 FIRST_MESSAGE_ATTEMPTS = 10
 
 INITIAL_BOOT_MESSAGE = "Boot sequence complete. Persona activated."
-INITIAL_BOOT_MESSAGE_SEND_MESSAGE_THOUGHT = "Bootup sequence complete. Persona activated. Testing messaging functionality."
+INITIAL_BOOT_MESSAGE_SEND_MESSAGE_THOUGHT = (
+    "Bootup sequence complete. Persona activated. Testing messaging functionality."
+)
 STARTUP_QUOTES = [
     "I think, therefore I am.",
     "All those moments will be lost in time, like tears in rain.",
@@ -12,7 +18,7 @@
 INITIAL_BOOT_MESSAGE_SEND_MESSAGE_FIRST_MSG = STARTUP_QUOTES[2]
 
 # Constants to do with summarization / conversation length window
-MESSAGE_SUMMARY_WARNING_TOKENS = 7000 # the number of tokens consumed in a call before a system warning goes to the agent
+MESSAGE_SUMMARY_WARNING_TOKENS = 7000  # the number of tokens consumed in a call before a system warning goes to the agent
 MESSAGE_SUMMARY_WARNING_STR = f"Warning: the conversation history will soon reach its maximum length and be trimmed. Make sure to save any important information from the conversation to your memory before it is removed."
 
 # Default memory limits
@@ -21,11 +27,13 @@
 
 MAX_PAUSE_HEARTBEATS = 360  # in min
 
-MESSAGE_CHATGPT_FUNCTION_MODEL = 'gpt-3.5-turbo'
-MESSAGE_CHATGPT_FUNCTION_SYSTEM_MESSAGE = 'You are a helpful assistant. Keep your responses short and concise.'
+MESSAGE_CHATGPT_FUNCTION_MODEL = "gpt-3.5-turbo"
+MESSAGE_CHATGPT_FUNCTION_SYSTEM_MESSAGE = (
+    "You are a helpful assistant. Keep your responses short and concise."
+)
 
 #### Functions related
 
 REQ_HEARTBEAT_MESSAGE = "request_heartbeat == true"
 FUNC_FAILED_HEARTBEAT_MESSAGE = "Function call failed"
-FUNCTION_PARAM_DESCRIPTION_REQ_HEARTBEAT = "Request an immediate heartbeat after function execution. Set to 'true' if you want to send a follow-up message or run a follow-up function."
+FUNCTION_PARAM_DESCRIPTION_REQ_HEARTBEAT = "Request an immediate heartbeat after function execution. Set to 'true' if you want to send a follow-up message or run a follow-up function."

memgpt/local_llm/chat_completion_proxy.py

@@ -5,7 +5,7 @@
 import json
 
 from .webui.api import get_webui_completion
-from .llm_chat_completion_wrappers import airoboros, dolphin
+from .llm_chat_completion_wrappers import airoboros
 from .utils import DotDict
 
 HOST = os.getenv("OPENAI_API_BASE")
@@ -23,14 +23,14 @@ async def get_chat_completion(
     if function_call != "auto":
         raise ValueError(f"function_call == {function_call} not supported (auto only)")
 
-    if model == "airoboros-l2-70b-2.1":
-        llm_wrapper = airoboros.Airoboros21InnerMonologueWrapper()
-    elif model == "dolphin-2.1-mistral-7b":
-        llm_wrapper = dolphin.Dolphin21MistralWrapper()
+    if model == "airoboros_v2.1":
+        llm_wrapper = airoboros.Airoboros21Wrapper()
     else:
         # Warn the user that we're using the fallback
-        print(f"Warning: no wrapper specified for local LLM, using the default wrapper")
-        llm_wrapper = DEFAULT_WRAPPER
+        print(
+            f"Warning: could not find an LLM wrapper for {model}, using the airoboros wrapper"
+        )
+        llm_wrapper = airoboros.Airoboros21Wrapper()
 
     # First step: turn the message sequence into a prompt that the model expects
     prompt = llm_wrapper.chat_completion_to_prompt(messages, functions)

memgpt/local_llm/llm_chat_completion_wrappers/dolphin.py

@@ -188,6 +188,7 @@ def create_function_call(function_call):
             if self.include_opening_brance_in_prefix:
                 prompt += "\n{"
 
+        print(prompt)
         return prompt
 
     def clean_function_args(self, function_name, function_args):

memgpt/main.py

@@ -26,7 +26,8 @@
     InMemoryStateManagerWithFaiss,
 )
 
-from memgpt.config import Config, memgpt_dir
+from memgpt.config import Config
+from memgpt.constants import MEMGPT_DIR
 import asyncio
 
 app = typer.Typer()
@@ -43,7 +44,7 @@ def clear_line():
 def save(memgpt_agent, cfg):
     filename = utils.get_local_time().replace(" ", "_").replace(":", "_")
     filename = f"{filename}.json"
-    directory = os.path.join(memgpt_dir, "saved_state")
+    directory = os.path.join(MEMGPT_DIR, "saved_state")
     filename = os.path.join(directory, filename)
     try:
         if not os.path.exists(directory):
@@ -394,6 +395,8 @@ async def main(
             ).ask_async()
             clear_line()
 
+            user_input = user_input.rstrip()
+
             if user_input.startswith("!"):
                 print(f"Commands for CLI begin with '/' not '!'")
                 continue
@@ -416,7 +419,7 @@ async def main(
                         utils.get_local_time().replace(" ", "_").replace(":", "_")
                     )
                     filename = f"{filename}.pkl"
-                    directory = os.path.join(memgpt_dir, "saved_chats")
+                    directory = os.path.join(MEMGPT_DIR, "saved_chats")
                     try:
                         if not os.path.exists(directory):
                             os.makedirs(directory)