Package: megabots
🤖 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/2The 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 GradioComing soon:🗣️ accept voice as an input usingwhisper👍 validate and correct the outputs of LLMs usingguardrails💰 semanticly cache LLM Queries and reduce Costs by 10x usingGPTCache🏋️ mega-easy LLM training🚀 mega-easy deployment🤖 Megabots is backed by some of the most famous tools for productionalising AI. It usesLangChainfor managing LLM chains,FastAPIto create a production ready API,Gradioto create a UI. At the moment it usesOpenAIto generate answers, but we plan to support other LLMs in the future.Getting startedNote: This is a work in progress. The API might change.pipinstallmegabotsfrommegabotsimportbotimportosos.environ["OPENAI_API_KEY"]="my key"# Create a bot 👉 with one line of code. Automatically loads your data from ./index or index.pkl.qnabot=bot("qna-over-docs")# Ask a questionanswer=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 runqnabot=bot("qna-over-docs",index="./index.pkl")# Or create the index from a directory of documentsqnabot=bot("qna-over-docs",index="./index")# Change the modelqnabot=bot("qna-over-docs",model="text-davinci-003")Changing the bot's promptYou can change the bots promnpt to customize it to your needs. In theqna-over-docstype of bot you will need to pass 2 variables for thecontext(knwoledge searched from the index) and thequestion(the human question).frommegabotsimportbotprompt="""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 memoryYou can easily add memory to yourbotusing thememoryparameter. 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 thememoryfunction and pass the type of memory and the configuration you need.frommegabotsimportbotqnabot=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 thememoryfactory functionfrommegabotsimportbot,memorymem("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 theqna-over-docsbot, 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 ishistory.frommegabotsimportbotprompt="""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)Megabotsbotcan 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 followthis guideto download a docker compose file and run it.
The command is:wgethttps://raw.githubusercontent.com/milvus-io/pymilvus/v2.2.7/examples/hello_milvus.pyYou can theninstall Attuas a management tool for Milvusfrommegabotsimportbot# 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 vectorstorebot=bot("qna-over-docs",index="./examples/files/",vectorstore="milvus")bot.ask("what was the first roster of the avengers?")Or use thevectorstorefactory function for more customisationfrommegabotsimportbot,vectorstoremilvus=vectorstore("milvus",host="localhost",port=19530)bot=bot("qna-over-docs",index="./examples/files/",vectorstore=milvus)Exposing an API with FastAPIYou can also create a FastAPI app that will expose the bot as an API using the create_app function.
Assuming you file is calledmain.pyrunuvicorn main:app --reloadto run the API locally.
You should then be able to visithttp://localhost:8000/docsto see the API documentation.frommegabotsimportbot,create_apiapp=create_app(bot("qna-over-docs"))Exposing a Gradio chat-like interfaceYou can expose a gradio UI for the bot usingcreate_interfacefunction.
Assuming your file is calledui.pyrungradio qnabot/ui.pyto run the UI locally.
You should then be able to visithttp://127.0.0.1:7860to see the API documentation.frommegabotsimportbot,create_interfacedemo=create_interface(bot("qna-over-docs"))Customising botThebotfunction should serve as the starting point for creating and customising your bot. Below is a list of the available arguments inbot.ArgumentDescriptiontaskThe type of bot to create. Available options:qna-over-docs. More comming soonindexSpecifies 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 specifiedbotwill look forindex.pklor./indexmodelThe 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-003More comming soon.promptA 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 memoryhistory.memoryThe type of memory to be used by the bot. Can be a string with the type of the memory or you can usememoryfactory function. Supported memories:conversation-buffer,conversation-buffer-windowvectorstoreThe vectorstore to be used for the index. Can be a string with the name of the databse or you can usevectorstorefactory function. Supported DBs:milvus.| sources | WhensourcesisTruethe bot will also include sources in the response. A knownissueexists, where if you pass a custom prompt with sources the code breaks. |How QnA bot worksLarge 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-docsuses FAISS to create an index of documents and GPT to generate answers.sequenceDiagram
    actor User
    participant API
    participant LLM
    participant Vectorstore
    participant IngestionEngine
    participant DataLake
    autonumber

    Note over API, DataLake: Ingestion phase
    loop Every X time
    IngestionEngine ->> DataLake: Load documents
    DataLake -->> IngestionEngine: Return data
    IngestionEngine -->> IngestionEngine: Split documents and Create embeddings
    IngestionEngine ->> Vectorstore: Store documents and embeddings
    end

    Note over API, DataLake: Generation phase

    User ->> API: Receive user question
    API ->> Vectorstore: Lookup documents in the index relevant to the question
    API ->> API: Construct a prompt from the question and any relevant documents
    API ->> LLM: Pass the prompt to the model
    LLM -->> API: Get response from model
    API -->> User: Return response