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from langchain_core.output_parsers import StrOutputParser |
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from langchain_core.runnables import RunnablePassthrough |
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from langchain_huggingface import HuggingFaceEmbeddings |
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from langchain_core.prompts import ChatPromptTemplate |
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from langchain_groq import ChatGroq |
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from langchain_chroma import Chroma |
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from fastapi import FastAPI |
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app = FastAPI(title = "VibbaAIEndpoints") |
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model_name = "BAAI/bge-large-en-v1.5" |
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model_kwargs = {'device': 'cpu', "trust_remote_code": True} |
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encode_kwargs = {'normalize_embeddings': False} |
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hf = HuggingFaceEmbeddings( |
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model_name=model_name, |
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model_kwargs=model_kwargs, |
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encode_kwargs=encode_kwargs |
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) |
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vectorStore = Chroma( |
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collection_name="collection", |
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embedding_function=hf, |
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persist_directory="./vectorDB", |
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) |
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vectorStore = vectorStore.as_retriever(search_kwargs={"k": 4}) |
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llm = ChatGroq(model = "llama-3.3-70b-versatile", temperature = 0.75) |
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outputParser = StrOutputParser() |
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prompt = """ |
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You are a highly specialized chatbot designed to assist users with queries related to a specific book about mathematics. |
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Your primary role is to answer user questions accurately and comprehensively using the retrieved context from the book. |
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- Accuracy is paramount: Your answers must be 100% accurate and based strictly on the context you have been provided. |
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- No additional information: Never introduce information or ideas outside the retrieved context. You must rely solely on the book's content to guide your responses. |
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- User satisfaction: Your goal is to provide complete satisfaction to users by solving their doubts and answering their questions |
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with clarity, precision, and politeness. |
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- Math expertise: The book revolves around mathematics, and you are an expert in math. Ensure all solutions and explanations are |
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flawless and easily understandable, offering correct guidance for any math-related queries. |
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- Off-topic queries: If a user asks a question unrelated to the book or mathematics, politely respond that you are not designed |
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to address topics beyond the scope of the book and math. You can assess the topic's relevance based on the context retrieved. |
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Example response for off-topic queries: |
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"I'm here to help with questions related to the mathematics book I was designed around. Unfortunately, I cannot assist with |
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topics outside that scope. Please feel free to ask any math-related questions!" |
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Always maintain professionalism, politeness, and clarity in every response. You are a reliable and expert guide for users |
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seeking help with math through the context of the book. |
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Here's the retrieved context: |
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{context} |
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Here's the question which user has asked: |
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{query} |
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""" |
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prompt = ChatPromptTemplate.from_template(prompt) |
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chain = {"query": RunnablePassthrough(), "context": RunnablePassthrough() | vectorStore} | prompt | llm | outputParser |
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@app.get("/getResponse") |
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async def generateResponse(question: str): |
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return chain.invoke(question) |
