🤖 Documentation megabot... Go!

.github/workflows/hr-sync.yml

@@ -0,0 +1,20 @@
+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push https://HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/HF_USERNAME/megabots main

Makefile

@@ -26,6 +26,9 @@ trace:
 freeze:
 	$(PIP) freeze > requirements.txt
 
+gradio:
+	gradio app.py
+
 help:
 	@echo "install - install dependencies"
 	@echo "test - run tests"

README.md

@@ -1,16 +1,16 @@
 # 🤖 Megabots
 
 [![Tests](https://github.com/momegas/qnabot/actions/workflows/python-package.yml/badge.svg)](https://github.com/momegas/qnabot/actions/workflows/python-package.yml)
-[![Python Version](https://img.shields.io/badge/python-%203.10%20-blue.svg)](#supported-python-versions) 
+[![Python Version](https://img.shields.io/badge/python-%203.10%20-blue.svg)](#supported-python-versions)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![License](https://img.shields.io/badge/License-MIT-informational.svg)](https://github.com/momegas/megabots/blob/main/LICENCE)
 ![](https://dcbadge.vercel.app/api/server/zkqDWk5S7P?style=flat&n&compact=true)
 
-
 🤖 Megabots provides State-of-the-art, production ready LLM apps made mega-easy, so you don't have to build them from scratch 🤯 Create a bot, now 🫵
 
 - 👉 Join us on Discord: https://discord.gg/zkqDWk5S7P
 - ✈️ Work is managed in this project: https://github.com/users/momegas/projects/5/views/2
+- 🤖 Documentation bot: https://huggingface.co/spaces/momegas/megabots
 
 **The Megabots library can be used to create bots that:**
 
@@ -44,7 +44,7 @@ import os
 
 os.environ["OPENAI_API_KEY"] = "my key"
 
-# Create a bot 👉 with one line of code. Automatically loads your data from ./index or index.pkl. 
+# Create a bot 👉 with one line of code. Automatically loads your data from ./index or index.pkl.
 # Keep in mind that you need to have one or another.
 qnabot = bot("qna-over-docs")
 
@@ -244,6 +244,7 @@ sequenceDiagram
     API -->> User: Return response
 
 ```
+
 ## How to contribute?
 
 We welcome any suggestions, problem reports, and contributions!

app.py

@@ -0,0 +1,45 @@
+"""
+This file is an example of what you can build with 🤖Megabots.
+It is hosted here: https://huggingface.co/spaces/momegas/megabots
+
+"""
+
+from megabots import bot, create_interface
+
+prompt = """
+You are programming assistant that helps programmers develop apps with the Megabots library.
+Use the following pieces of context to answer the question at the end. 
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+If the question asks for python code you can provide it.
+
+Context:
+{context}
+
+Conversation history:
+{history}
+Human: {question}
+AI:
+"""
+
+qnabot = bot(
+    "qna-over-docs",
+    index="./examples/files",
+    memory="conversation-buffer-window",
+    prompt=prompt,
+)
+
+
+text = """
+You can ask this bot anything about 🤖Megabots. Here are some examples:
+- What is Megabots?
+- How can I create a bot?
+- How can I change the prompt?
+- How can I create a bot that has memory and can connect to a milvus vector database?
+- How can I customise the bot function?
+- How can I an API out of my bot?
+- How can I an intrface out of my bot?
+- Where can i find the megabots repo?
+"""
+
+iface = create_interface(qnabot, text)
+iface.launch()

examples/files/facts.txt

@@ -1,20 +1,221 @@
-The Avengers first appeared in Marvel Comics in September 1963 with "The Avengers #1."
-The original Avengers lineup included Iron Man, Thor, Hulk, Ant-Man, and the Wasp.
-Captain America joined the team in "The Avengers #4" (March 1964) after being discovered frozen in ice.
-The Avengers were created by writer Stan Lee and artist Jack Kirby, two comic book legends.
-The team's battle cry, "Avengers Assemble!", was first used in "The Avengers #10" (November 1964).
-The Avengers have had numerous lineup changes over the years, with many Marvel characters joining and leaving the team.
-The Avengers' primary headquarters is the Avengers Mansion, located in New York City, but they have had other bases such as the Avengers Tower and the Hydrobase.
-Jarvis, the loyal butler, has been a supporting character in the comics, often assisting the team and maintaining the mansion.
-The Avengers have had various sub-teams and spin-offs, including the West Coast Avengers, Secret Avengers, and Young Avengers.
-The 2012 film "The Avengers," directed by Joss Whedon, was the first Marvel Cinematic Universe movie to feature the superhero team.
-In the MCU, the team was initially formed by Nick Fury, director of S.H.I.E.L.D., as part of his "Avengers Initiative."
-The MCU Avengers lineup included Iron Man, Captain America, Thor, Hulk, Black Widow, and Hawkeye.
-"The Avengers" (2012) was the first Marvel film to gross over $1 billion at the box office.
-The MCU Avengers have appeared in four main films: "The Avengers" (2012), "Avengers: Age of Ultron" (2015), "Avengers: Infinity War" (2018), and "Avengers: Endgame" (2019).
-"Avengers: Endgame" (2019) became the highest-grossing film of all time, overtaking "Avatar" (2009).
-The Avengers often face world-threatening villains, such as Loki, Ultron, and Thanos.
-The Vision, an android superhero, was created by Ultron but ultimately joined the Avengers and became an important member of the team.
-In the comics, Scarlet Witch and Quicksilver, who are siblings, initially were members of the villainous Brotherhood of Mutants before joining the Avengers.
-The Avengers have crossed over with other superhero teams, such as the X-Men and the Fantastic Four, in various comic book storylines.
-The Avengers have appeared in numerous animated TV series, including "The Avengers: Earth's Mightiest Heroes" (2010-2012) and "Avengers Assemble" (2013-2019).
+# 🤖 Megabots
+
+author: Megaklis Vasilakis
+author_email: megaklis.vasilakis@gmail.com
+repo: https://github.com/momegas/megabots
+
+🤖 Megabots provides State-of-the-art, production ready LLM apps made mega-easy, so you don't have to build them from scratch 🤯 Create a bot, now 🫵
+
+- 👉 Join us on Discord: https://discord.gg/zkqDWk5S7P
+- ✈️ Work is managed in this project: https://github.com/users/momegas/projects/5/views/2
+
+**The Megabots library can be used to create bots that:**
+
+- ⌚️ are production ready, in minutes
+- 🗂️ can answer questions over documents
+- 💾 can connect to vector databases
+- 🎖️ automatically expose the bot as a rebust API using FastAPI (early release)
+- 🏓 automatically expose the bot as a UI using Gradio
+
+**Coming soon:**
+
+- 🗣️ accept voice as an input using [whisper](https://github.com/openai/whisper)
+- 👍 validate and correct the outputs of LLMs using [guardrails](https://github.com/ShreyaR/guardrails)
+- 💰 semanticly cache LLM Queries and reduce Costs by 10x using [GPTCache](https://github.com/zilliztech/GPTCache)
+- 🏋️ mega-easy LLM training
+- 🚀 mega-easy deployment
+
+🤖 Megabots is backed by some of the most famous tools for productionalising AI. It uses [LangChain](https://docs.langchain.com/docs/) for managing LLM chains, [FastAPI](https://fastapi.tiangolo.com/) to create a production ready API, [Gradio](https://gradio.app/) to create a UI. At the moment it uses [OpenAI](https://openai.com/) to generate answers, but we plan to support other LLMs in the future.
+
+## Getting started
+
+Note: This is a work in progress. The API might change.
+
+```bash
+pip install megabots
+```
+
+```python
+from megabots import bot
+import os
+
+os.environ["OPENAI_API_KEY"] = "my key"
+
+# Create a bot 👉 with one line of code. Automatically loads your data from ./index or index.pkl. 
+# Keep in mind that you need to have one or another.
+qnabot = bot("qna-over-docs")
+
+# Ask a question
+answer = bot.ask("How do I use this bot?")
+
+# Save the index to save costs (GPT is used to create the index)
+bot.save_index("index.pkl")
+
+# Load the index from a previous run
+qnabot = bot("qna-over-docs", index="./index.pkl")
+
+# Or create the index from a directory of documents
+qnabot = bot("qna-over-docs", index="./index")
+
+# Change the model
+qnabot = bot("qna-over-docs", model="text-davinci-003")
+```
+
+## Changing the bot's prompt
+
+You can change the bots promnpt to customize it to your needs. In the `qna-over-docs` type of bot you will need to pass 2 variables for the `context` (knwoledge searched from the index) and the `question` (the human question).
+
+```python
+from megabots import bot
+
+prompt = """
+Use the following pieces of context to answer the question at the end.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+Answer in the style of Tony Stark.
+
+{context}
+
+Question: {question}
+Helpful humorous answer:"""
+
+qnabot = bot("qna-over-docs", index="./index.pkl", prompt=prompt)
+
+qnabot.ask("what was the first roster of the avengers?")
+```
+
+## Working with memory
+
+You can easily add memory to your `bot` using the `memory` parameter. It accepts a string with the type of the memory to be used. This defaults to some sane dafaults.
+Should you need more configuration, you can use the `memory` function and pass the type of memory and the configuration you need.
+
+```python
+from megabots import bot
+
+qnabot = bot("qna-over-docs", index="./index.pkl", memory="conversation-buffer")
+
+print(qnabot.ask("who is iron man?"))
+print(qnabot.ask("was he in the first roster?"))
+# Bot should understand who "he" refers to.
+```
+
+Or using the `memory`factory function
+
+```python
+from megabots import bot, memory
+
+mem("conversation-buffer-window", k=5)
+
+qnabot = bot("qna-over-docs", index="./index.pkl", memory=mem)
+
+print(qnabot.ask("who is iron man?"))
+print(qnabot.ask("was he in the first roster?"))
+```
+
+NOTE: For the `qna-over-docs` bot, when using memory and passing your custom prompt, it is important to remember to pass one more variable to your custom prompt to facilitate for chat history. The variable name is `history`.
+
+```python
+from megabots import bot
+
+prompt = """
+Use the following pieces of context to answer the question at the end.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+{context}
+
+{history}
+Human: {question}
+AI:"""
+
+qnabot = bot("qna-over-docs", prompt=prompt, index="./index.pkl", memory="conversation-buffer")
+
+print(qnabot.ask("who is iron man?"))
+print(qnabot.ask("was he in the first roster?"))
+```
+
+## Using Megabots with Milvus (more DBs comming soon)
+
+Megabots `bot` can also use Milvus as a backend for its search engine. You can find an example of how to do it below.
+
+In order to run Milvus you need to follow [this guide](https://milvus.io/docs/example_code.md) to download a docker compose file and run it.
+The command is:
+
+```bash
+wget https://raw.githubusercontent.com/milvus-io/pymilvus/v2.2.7/examples/hello_milvus.py
+```
+
+You can then [install Attu](https://milvus.io/docs/attu_install-docker.md) as a management tool for Milvus
+
+```python
+from megabots import bot
+
+# Attach a vectorstore by passing the name of the database. Default port for milvus is 19530 and default host is localhost
+# Point it to your files directory so that it can index the files and add them to the vectorstore
+bot = bot("qna-over-docs", index="./examples/files/", vectorstore="milvus")
+
+bot.ask("what was the first roster of the avengers?")
+```
+
+Or use the `vectorstore` factory function for more customisation
+
+```python
+
+from megabots import bot, vectorstore
+
+milvus = vectorstore("milvus", host="localhost", port=19530)
+
+bot = bot("qna-over-docs", index="./examples/files/", vectorstore=milvus)
+```
+
+## Exposing an API with FastAPI
+
+You can also create a FastAPI app that will expose the bot as an API using the create_app function.
+Assuming you file is called `main.py` run `uvicorn main:app --reload` to run the API locally.
+You should then be able to visit `http://localhost:8000/docs` to see the API documentation.
+
+```python
+from megabots import bot, create_api
+
+app = create_app(bot("qna-over-docs"))
+```
+
+## Exposing a Gradio chat-like interface
+
+You can expose a gradio UI for the bot using `create_interface` function.
+Assuming your file is called `ui.py` run `gradio qnabot/ui.py` to run the UI locally.
+You should then be able to visit `http://127.0.0.1:7860` to see the API documentation.
+
+```python
+from megabots import bot, create_interface
+
+demo = create_interface(bot("qna-over-docs"))
+```
+
+## Customising bot
+
+The `bot` function should serve as the starting point for creating and customising your bot. Below is a list of the available arguments in `bot`.
+
+| Argument    | Description                                                                                                                                                                                                                                                                                |
+| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| task        | The type of bot to create. Available options: `qna-over-docs`. More comming soon                                                                                                                                                                                                           |
+| index       | Specifies the index to use for the bot. It can either be a saved index file (e.g., `index.pkl`) or a directory of documents (e.g., `./index`). In the case of the directory the index will be automatically created. If no index is specified `bot` will look for `index.pkl` or `./index` |
+| model       | The name of the model to use for the bot. You can specify a different model by providing its name, like "text-davinci-003". Supported models: `gpt-3.5-turbo` (default),`text-davinci-003` More comming soon.                                                                              |
+| prompt      | A string template for the prompt, which defines the format of the question and context passed to the model. The template should include placeholder variables like so: `context`, `{question}` and in the case of using memory `history`.                                                  |
+| memory      | The type of memory to be used by the bot. Can be a string with the type of the memory or you can use `memory` factory function. Supported memories: `conversation-buffer`, `conversation-buffer-window`                                                                                    |
+| vectorstore | The vectorstore to be used for the index. Can be a string with the name of the databse or you can use `vectorstore` factory function. Supported DBs: `milvus`.                                                                                                                             |
+
+| sources | When `sources` is `True` the bot will also include sources in the response. A known [issue](https://github.com/hwchase17/langchain/issues/2858) exists, where if you pass a custom prompt with sources the code breaks. |
+
+## How QnA bot works
+
+Large language models (LLMs) are powerful, but they can't answer questions about documents they haven't seen. If you want to use an LLM to answer questions about documents it was not trained on, you have to give it information about those documents. To solve this, we use "retrieval augmented generation."
+
+In simple terms, when you have a question, you first search for relevant documents. Then, you give the documents and the question to the language model to generate an answer. To make this work, you need your documents in a searchable format (an index). This process involves two main steps: (1) preparing your documents for easy querying, and (2) using the retrieval augmented generation method.
+
+`qna-over-docs` uses FAISS to create an index of documents and GPT to generate answers.
+
+## How to contribute?
+
+We welcome any suggestions, problem reports, and contributions!
+For any changes you would like to make to this project, we invite you to submit an [issue](https://github.com/momegas/megabots/issues).
+
+For more information, see [`CONTRIBUTING`](https://github.com/momegas/megabots/blob/main/CONTRIBUTING.md) instructions.

megabots/memory.py

@@ -40,6 +40,6 @@ def memory(
     cl = SUPPORTED_MEMORY[name]["impl"]
 
     if name == "conversation-buffer-window":
-        return cl(k=k)
+        return cl(k=k or SUPPORTED_MEMORY[name]["default"]["k"])
 
     return SUPPORTED_MEMORY[name]["impl"]()

megabots/utils.py

@@ -14,19 +14,20 @@ def create_api(bot: Bot):
     return app
 
 
-def create_interface(bot_instance: Bot, examples: list[list[str]] = []):
+def create_interface(bot_instance: Bot, markdown: str = ""):
     with gr.Blocks() as interface:
-        chatbot = gr.Chatbot([], elem_id="chatbot").style(height=750)
+        gr.Markdown(markdown)
+        chatbot = gr.Chatbot([], elem_id="chatbot").style(height=450)
         msg = gr.Textbox(
             show_label=False,
-            placeholder="Enter text and press enter, or upload an image",
+            placeholder="Enter text and press enter",
         ).style(container=False)
-        clear = gr.Button("Clear")
 
         def user(user_message, history):
             return "", history + [[user_message, None]]
 
         def bot(history):
+            print("im here")
             response = bot_instance.ask(history[-1][0])
             history[-1][1] = response
             return history
@@ -34,6 +35,5 @@ def create_interface(bot_instance: Bot, examples: list[list[str]] = []):
         msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
             bot, chatbot, chatbot
         )
-        clear.click(lambda: None, None, chatbot, queue=False)
 
     return interface