diff --git a/README.md b/README.md index f71f34702f..929619be93 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ Try out our MemGPT chatbot on Discord! - ⭐ NEW: You can now run MemGPT with local LLMs and AutoGen! ⭐ + ⭐ NEW: You can now run MemGPT with local LLMs and AutoGen! ⭐ [![Discord](https://img.shields.io/discord/1161736243340640419?label=Discord&logo=discord&logoColor=5865F2&style=flat-square&color=5865F2)](https://discord.gg/9GEQrxmVyE) [![arXiv 2310.08560](https://img.shields.io/badge/arXiv-2310.08560-B31B1B?logo=arxiv&style=flat-square)](https://arxiv.org/abs/2310.08560) @@ -74,16 +74,6 @@ Now, you can run MemGPT with: ```sh memgpt run ``` -The `run` command supports the following optional flags (if set, will override config defaults): -* `--agent`: (str) Name of agent to create or to resume chatting with. -* `--human`: (str) Name of the human to run the agent with. -* `--persona`: (str) Name of agent persona to use. -* `--model`: (str) LLM model to run [gpt-4, gpt-3.5]. -* `--preset`: (str) MemGPT preset to run agent with. -* `--first`: (str) Allow user to sent the first message. -* `--debug`: (bool) Show debug logs (default=False) -* `--no-verify`: (bool) Bypass message verification (default=False) -* `--yes`/`-y`: (bool) Skip confirmation prompt and use defaults (default=False) You can run the following commands in the MemGPT CLI prompt: * `/exit`: Exit the CLI @@ -103,284 +93,11 @@ You can run the following commands in the MemGPT CLI prompt: Once you exit the CLI with `/exit`, you can resume chatting with the same agent by specifying the agent name in `memgpt run --agent `. -### Adding Custom Personas/Humans -You can add new human or persona definitions either by providing a file (using the `-f` flag) or text (using the `--text` flag). -``` -# add a human -memgpt add human [-f ] [--text ] - -# add a persona -memgpt add persona [-f ] [--text ] -``` - -You can view available persona and human files with the following command: -``` -memgpt list [human/persona] -``` - -### Data Sources (i.e. chat with your data) -MemGPT supports pre-loading data into archival memory. You can attach data to your agent (which will place the data in your agent's archival memory) in two ways: - -1. Run `memgpt attach --agent --data-source ` -2. While chatting with the agent, enter the `/attach` command and select the data source. - -#### Loading Data -We currently support loading from a directory and database dumps. We highly encourage contributions for new data sources, which can be added as a new [CLI data load command](https://github.com/cpacker/MemGPT/blob/main/memgpt/cli/cli_load.py). - -Loading from a directory: -``` -# loading a directory -memgpt load directory --name \ - [--input-dir ] [--input-files ...] [--recursive] -``` -Loading from a database dump: -```sh -memgpt load database --name \ - --query \ # Query to run on database to get data - --dump-path \ # Path to dump file - --scheme \ # Database scheme - --host \ # Database host - --port \ # Database port - --user \ # Database user - --password \ # Database password - --dbname # Database name -``` -To encourage your agent to reference its archival memory, we recommend adding phrases like "search your archival memory..." for the best results. - -#### Viewing available data sources -You can view loaded data source with: -``` -memgpt list sources -``` - -### Using other endpoints - -#### Azure -To use MemGPT with Azure, expore the following variables and then re-run `memgpt configure`: -```sh -# see https://github.com/openai/openai-python#microsoft-azure-endpoints -export AZURE_OPENAI_KEY = ... -export AZURE_OPENAI_ENDPOINT = ... -export AZURE_OPENAI_VERSION = ... - -# set the below if you are using deployment ids -export AZURE_OPENAI_DEPLOYMENT = ... -export AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT = ... -``` - -Note: your Azure endpoint must support functions or you will get an error. See https://github.com/cpacker/MemGPT/issues/91 for more information. - -#### Custom Endpoints -To use custom endpoints, run `export OPENAI_API_BASE=` and then re-run `memgpt configure` to set the custom endpoint as the default endpoint. - -
-

Deprecated API

 -
-Debugging command not found - -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 -``` -
- -
-Building from source - -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` -
- - -If you're using Azure OpenAI, set these variables instead: - -```sh -# see https://github.com/openai/openai-python#microsoft-azure-endpoints -export AZURE_OPENAI_KEY = ... -export AZURE_OPENAI_ENDPOINT = ... -export AZURE_OPENAI_VERSION = ... - -# set the below if you are using deployment ids -export AZURE_OPENAI_DEPLOYMENT = ... -export AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT = ... - -# then use the --use_azure_openai flag -memgpt --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: -```sh -# assuming you created a new file ~/.memgpt/humans/me.txt -memgpt -# Select me.txt during configuration process -``` --- OR -- -```sh -# assuming you created a new file ~/.memgpt/humans/me.txt -memgpt --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 -# Select gpt-3.5 during configuration process -``` --- OR -- -```sh -memgpt --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.** - -Please report any bugs you encounter regarding MemGPT running on GPT-3.5 to https://github.com/cpacker/MemGPT/issues/59. - -### Local LLM support -You can run MemGPT with local LLMs too. See [instructions here](/memgpt/local_llm) and report any bugs/improvements here https://github.com/cpacker/MemGPT/discussions/67. - -### `main.py` flags -```text ---first - allows you to send the first message in the chat (by default, MemGPT will send the first message) ---debug - enables debugging output -``` - -
-Configure via legacy flags - -```text ---model - select which model to use ('gpt-4', 'gpt-3.5-turbo-0613', 'gpt-3.5-turbo') ---persona - load a specific persona file ---human - load a specific human file ---archival_storage_faiss_path= - load in document database (backed by FAISS index) ---archival_storage_files="" - pre-load files into archival memory ---archival_storage_files_compute_embeddings="" - pre-load files into archival memory and also compute embeddings for embedding search ---archival_storage_sqldb= - load in SQL database -``` -
- -## Example applications -
-

Use MemGPT to talk to your Database!

 - -MemGPT's archival memory let's you load your database and talk to it! To motivate this use-case, we have included a toy example. - -Consider the `test.db` already included in the repository. - -id | name | age ---- | --- | --- -1 | Alice | 30 -2 | Bob | 25 -3 | Charlie | 35 - -To talk to this database, run: - -```sh -memgpt --archival_storage_sqldb=memgpt/personas/examples/sqldb/test.db -``` - -And then you can input the path to your database, and your query. - -```python -Please enter the path to the database. test.db -... -Enter your message: How old is Bob? -... -🤖 Bob is 25 years old. -``` -
-
-

Loading local files into archival memory

 - 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 [Hugging Face](https://huggingface.co/datasets/MemGPT/example-sec-filings/tree/main) and place them in `memgpt/personas/examples/preload_archival`. - -2. In the root `MemGPT` directory, run - ```bash - memgpt --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="" --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: -``` - To avoid computing embeddings next time, replace --archival_storage_files_compute_embeddings= with - --archival_storage_faiss_path= (if your files haven't changed). -``` - -If you want to reuse these embeddings, run -```bash -memgpt --archival_storage_faiss_path="" --persona=memgpt_doc --human=basic -``` - - -
-
-

Talking to LlamaIndex API Docs

 - -MemGPT also enables you to chat with docs -- try running this example to talk to the LlamaIndex API docs! - -1. - a. Download LlamaIndex API docs and FAISS index from [Hugging Face](https://huggingface.co/datasets/MemGPT/llamaindex-api-docs). - ```bash - # Make sure you have git-lfs installed (https://git-lfs.com) - git lfs install - git clone https://huggingface.co/datasets/MemGPT/llamaindex-api-docs - mv llamaindex-api-docs - ``` - - **-- OR --** - - b. Build the index: - 1. Build `llama_index` API docs with `make text`. Instructions [here](https://github.com/run-llama/llama_index/blob/main/docs/DOCS_README.md). Copy over the generated `_build/text` folder to `memgpt/personas/docqa`. - 2. Generate embeddings and FAISS index. - ```bash - cd memgpt/personas/docqa - python3 scrape_docs.py - python3 generate_embeddings_for_docs.py all_docs.jsonl - python3 build_index.py --embedding_files all_docs.embeddings.jsonl --output_index_file all_docs.index - -3. In the root `MemGPT` directory, run - ```bash - memgpt --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`. - If you built the index yourself, it will be `memgpt/personas/docqa`. -
-
+## Documentation +See full documentation at: https://memgpt.readthedocs.io/ ## Support - -If you have any further questions, or have anything to share, we are excited to hear your feedback! - -* By default MemGPT will use `gpt-4`, so your API key will require `gpt-4` API access -* For issues and feature requests, please [open a GitHub issue](https://github.com/cpacker/MemGPT/issues) or message us on our `#support` channel on [Discord](https://discord.gg/9GEQrxmVyE) +For issues and feature requests, please [open a GitHub issue](https://github.com/cpacker/MemGPT/issues) or message us on our `#support` channel on [Discord](https://discord.gg/9GEQrxmVyE) ## Datasets Datasets used in our [paper](https://arxiv.org/abs/2310.08560) can be downloaded at [Hugging Face](https://huggingface.co/MemGPT). @@ -394,28 +111,3 @@ Datasets used in our [paper](https://arxiv.org/abs/2310.08560) can be downloaded - [x] CLI UI improvements ([issue](https://github.com/cpacker/MemGPT/issues/11)) - [x] Add support for other LLM backends ([issue](https://github.com/cpacker/MemGPT/issues/18), [discussion](https://github.com/cpacker/MemGPT/discussions/67)) - [ ] Release MemGPT family of open models (eg finetuned Mistral) ([discussion](https://github.com/cpacker/MemGPT/discussions/67)) - -## Development - -_Reminder: if you do not plan on modifying the source code, simply install MemGPT with `pip install pymemgpt`!_ - -First, install Poetry using [the official instructions here](https://python-poetry.org/docs/#installing-with-the-official-installer). - -Then, you can install MemGPT from source with: -``` -git clone git@github.com:cpacker/MemGPT.git -poetry shell -poetry install -``` -We recommend installing pre-commit to ensure proper formatting during development: -``` -pip install pre-commit -pre-commit install -pre-commit run --all-files -``` - -### Contributing -We welcome pull requests! Please run the formatter before submitting a pull request: -``` -poetry run black . -l 140 -```