Build RAG Chatbot with LangChain, Milvus, GPT-4o mini, and text-embedding-3-large

Retrieval-Augmented Generation (RAG) is a game-changer for GenAI applications, especially in conversational AI. It combines the power of pre-trained large language models (LLMs) like OpenAI’s GPT with external knowledge sources stored in vector databases such as Milvus and Zilliz Cloud, allowing for more accurate, contextually relevant, and up-to-date response generation.

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Milvus Lite

Import bs4 From langchain import hub From langchain_community.document_loaders import WebBaseLoader From langchain_core.documents import Document From langchain_text_splitters import RecursiveCharacterTextSplitter From langgraph.graph import START, StateGraph From typing_extensions import List, TypedDict # Load and chunk contents of the blog Loader = WebBaseLoader( web_paths=("https://milvus.io/docs/overview.md",), bs_kwargs=dict( parse_only=bs4.SoupStrainer( class_=("doc-style doc-post-content") ) ), ) Docs = loader.load() Text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) All_splits = text_splitter.split_documents(docs) # Index chunks _ = vector_store.add_documents(documents=all_splits) # Define prompt for question-answering Prompt = hub.pull("rlm/rag-prompt") # Define state for application Class State(TypedDict): question: str context: List[Document] answer: str # Define application steps Def retrieve(state: State): retrieved_docs = vector_store.similarity_search(state["question"]) return {"context": retrieved_docs} Def generate(state: State): docs_content = "\n\n".join(doc.page_content for doc in state["context"]) messages = prompt.invoke({"question": state["question"], "context": docs_content}) response = llm.invoke(messages) return {"answer": response.content} # Compile application and test Graph_builder = StateGraph(State).add_sequence([retrieve, generate]) Graph_builder.add_edge(START, "retrieve") Graph = graph_builder.compile().

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Milvus

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#Vector Databases #Retrieval Augmented Generation #Search Engine 38 social mentions