Dependency management

.github/workflows/tests.yml

@@ -43,7 +43,7 @@ jobs:
         PGVECTOR_TEST_DB_URL: ${{ secrets.PGVECTOR_TEST_DB_URL }}
         OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
       run: |
-        poetry install
+        poetry install -E postgres -E dev
 
     - name: Set Poetry config
       env:

docs/contributing.md

@@ -6,14 +6,12 @@ First, install Poetry using [the official instructions here](https://python-poet
 Then, you can install MemGPT from source with:
 ```sh
 git clone [email protected]:cpacker/MemGPT.git
-poetry shell
-poetry install
+poetry install -E dev
 ```
 We recommend installing pre-commit to ensure proper formatting during development:
 ```sh
-pip install pre-commit
-pre-commit install
-pre-commit run --all-files
+poetry run pre-commit install
+poetry run pre-commit run --all-files
 ```
 
 ### Formatting

docs/local_llm.md

@@ -6,6 +6,12 @@
 
     Make sure to check the [local LLM troubleshooting page](../local_llm_faq) to see common issues before raising a new issue or posting on Discord.
 
+### Installing dependencies
+To install dependencies required for running local models, run:
+```
+pip install 'pymemgpt[local]'
+```
+
 ### Quick overview
 
 1. Put your own LLM behind a web server API (e.g. [oobabooga web UI](https://github.com/oobabooga/text-generation-webui#starting-the-web-ui))

docs/storage.md

@@ -1,16 +1,19 @@
-# Configuring Storage Backends 
-MemGPT supports both local and database storage for archival memory. You can configure which storage backend to use via `memgpt configure`. For larger datasets, we recommend using a database backend. 
+# Configuring Storage Backends
+MemGPT supports both local and database storage for archival memory. You can configure which storage backend to use via `memgpt configure`. For larger datasets, we recommend using a database backend.
 
 !!! warning "Switching storage backends"
 
-    MemGPT can only use one storage backend at a time. If you switch from local to database storage, you will need to re-load data and start agents from scratch. We currently do not support migrating between storage backends.   
+    MemGPT can only use one storage backend at a time. If you switch from local to database storage, you will need to re-load data and start agents from scratch. We currently do not support migrating between storage backends.
 
 ## Local
-MemGPT will default to using local storage (saved at `~/.memgpt/archival/` for loaded data sources, and `~/.memgpt/agents/` for agent storage). 
+MemGPT will default to using local storage (saved at `~/.memgpt/archival/` for loaded data sources, and `~/.memgpt/agents/` for agent storage).
 
 ## Postgres
-In user to us the Postgres backend, you must have a running Postgres database that MemGPT can write to. You can enable the Postgres backend by running `memgpt configure` and selecting `postgres` for archival storage, which will then prompt for the database URI (e.g. `postgresql+pg8000://<USER>:<PASSWORD>@<IP>:5432/<DB_NAME>`)
+In user to us the Postgres backend, you must have a running Postgres database that MemGPT can write to. You can enable the Postgres backend by running `memgpt configure` and selecting `postgres` for archival storage, which will then prompt for the database URI (e.g. `postgresql+pg8000://<USER>:<PASSWORD>@<IP>:5432/<DB_NAME>`). To enable the Postgres backend, make sure to install the required dependencies with:
+```
+pip install 'pymemgpt[postgres]'
+```
 
 
-## Chroma 
+## Chroma
 (Coming soon)

memgpt/memory.py

@@ -2,8 +2,6 @@
 import os
 import datetime
 import re
-import faiss
-import numpy as np
 from typing import Optional, List, Tuple
 
 from .constants import MESSAGE_SUMMARY_WARNING_TOKENS, MEMGPT_DIR
@@ -353,6 +351,8 @@ class DummyArchivalMemoryWithFaiss(DummyArchivalMemory):
 
     def __init__(self, index=None, archival_memory_database=None, embedding_model="text-embedding-ada-002", k=100):
         if index is None:
+            import faiss
+
             self.index = faiss.IndexFlatL2(1536)  # openai embedding vector size.
         else:
             self.index = index
@@ -366,6 +366,8 @@ def __len__(self):
         return len(self._archive)
 
     def _insert(self, memory_string, embedding):
+        import numpy as np
+
         print(f"Got an embedding, type {type(embedding)}, len {len(embedding)}")
 
         self._archive.append(
@@ -394,6 +396,8 @@ def _search(self, query_embedding, query_string, count=None, start=None):
 
         # query_embedding = get_embedding(query_string, model=self.embedding_model)
         # our wrapped version supports backoff/rate-limits
+        import numpy as np
+
         if query_string in self.embeddings_dict:
             search_result = self.search_results[query_string]
         else:

memgpt/utils.py

@@ -7,7 +7,6 @@
 import json
 import pytz
 import os
-import faiss
 import tiktoken
 import glob
 import sqlite3
@@ -104,6 +103,8 @@ def parse_json(string):
 
 
 def prepare_archival_index(folder):
+    import faiss
+
     index_file = os.path.join(folder, "all_docs.index")
     index = faiss.read_index(index_file)
 
@@ -308,6 +309,8 @@ async def prepare_archival_index_from_files_compute_embeddings(
             f.write("\n")
 
     # make the faiss index
+    import faiss
+
     index = faiss.IndexFlatL2(1536)
     data = np.array(embedding_data).astype("float32")
     try: