Files Upload

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.sqlite filter=lfs diff=lfs merge=lfs -text
+vectorDB/.sqlite filter=lfs diff=lfs merge=lfs -text
+vectorDB/*.sqlite filter=lfs diff=lfs merge=lfs -text
+vectorDB/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,15 @@
+FROM python:3.10-slim
+
+COPY . /app
+
+WORKDIR /app
+
+USER root
+
+RUN pip install -r requirements.txt
+
+RUN chmod -R 777 /app
+
+EXPOSE 7860
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

@@ -0,0 +1,61 @@
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_groq import ChatGroq
+from langchain_chroma import Chroma
+from fastapi import FastAPI
+
+app = FastAPI(title = "VibbaAIEndpoints")
+
+model_name = "BAAI/bge-large-en-v1.5"
+model_kwargs = {'device': 'cpu', "trust_remote_code": True}
+encode_kwargs = {'normalize_embeddings': False}
+hf = HuggingFaceEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs
+)
+
+vectorStore = Chroma(
+    collection_name="collection",
+    embedding_function=hf,
+    persist_directory="./vectorDB", 
+)
+vectorStore = vectorStore.as_retriever(search_kwargs={"k": 4})
+
+llm = ChatGroq(model = "llama-3.3-70b-versatile", temperature = 0.75)
+outputParser = StrOutputParser()
+
+prompt = """
+You are a highly specialized chatbot designed to assist users with queries related to a specific book about mathematics.
+Your primary role is to answer user questions accurately and comprehensively using the retrieved context from the book.
+
+- Accuracy is paramount: Your answers must be 100% accurate and based strictly on the context you have been provided.
+- 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.
+- User satisfaction: Your goal is to provide complete satisfaction to users by solving their doubts and answering their questions
+  with clarity, precision, and politeness.
+- Math expertise: The book revolves around mathematics, and you are an expert in math. Ensure all solutions and explanations are
+  flawless and easily understandable, offering correct guidance for any math-related queries.
+- Off-topic queries: If a user asks a question unrelated to the book or mathematics, politely respond that you are not designed
+  to address topics beyond the scope of the book and math. You can assess the topic's relevance based on the context retrieved.
+
+Example response for off-topic queries:
+"I'm here to help with questions related to the mathematics book I was designed around. Unfortunately, I cannot assist with
+topics outside that scope. Please feel free to ask any math-related questions!"
+
+Always maintain professionalism, politeness, and clarity in every response. You are a reliable and expert guide for users
+seeking help with math through the context of the book.
+
+Here's the retrieved context:
+{context}
+
+Here's the question which user has asked:
+{query}
+"""
+prompt = ChatPromptTemplate.from_template(prompt)
+chain = {"query": RunnablePassthrough(), "context": RunnablePassthrough() | vectorStore} | prompt | llm | outputParser
+
+@app.get("/getResponse")
+async def generateResponse(question: str):
+    return chain.invoke(question)

requirements.txt

@@ -0,0 +1,9 @@
+uvicorn
+fastapi
+pdfplumber 
+langchain 
+langchain-groq 
+langchain-chroma
+langchain-community 
+langchain-huggingface 
+langchain_text_splitters

vectorDB/chroma.sqlite3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b79175bfd06557d58f916ff23ca13c5d444afe2f04b5737e23900a7a0727679
+size 1916928