KeploreAI-Lab/MindAct

1. 1
   Define Your Agent's Domain

   Identify the specific problem or context your AI agent will operate within. This determines the scope of necessary domain knowledge, shifting focus from general models to specialized applications.
2. 2
   Curate Domain-Specific Knowledge

   Collect and structure high-quality, relevant information unique to your agent's domain. This forms the foundation for reliable and accurate responses, replacing broad, uncontextualized data.
3. 3
   Implement Knowledge Integration (RAG)

   Utilize Retrieval Augmented Generation (RAG) or similar techniques to connect your agent to the curated knowledge base. This allows the agent to retrieve and incorporate specific facts into its responses before generating output.
4. 4
   Engineer Retrieval Mechanisms

   Design and optimize how your agent queries and retrieves information from the knowledge base. Focus on efficient indexing, semantic search, and context-aware retrieval to ensure the most relevant data is accessed.
5. 5
   Test and Refine for Autonomy

   Rigorously test the agent's performance within its domain. Evaluate its reliability, accuracy, and ability to handle complex tasks with the integrated knowledge, iterating on knowledge sources and retrieval methods to enhance autonomy.

from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA

# 1. Load your domain-specific documents
loader = TextLoader("your_domain_knowledge.txt") # Replace with your actual knowledge source
documents = loader.load()

# 2. Split documents into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

# 3. Create embeddings and store in a vector database
embeddings = OpenAIEmbeddings() # Ensure OPENAI_API_KEY is set
db = Chroma.from_documents(texts, embeddings)

# 4. Set up a RetrievalQA chain
rqa = RetrievalQA.from_chain_type(llm=OpenAI(), retriever=db.as_retriever())

# 5. Query your agent with domain knowledge
query = "What is the main principle of MindAct?"
print(rqa.run(query))

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