💬RAG pipeline

Provide direct, dynamic answers from databases, making data access swift and user-friendly.

GenAI Stack Workflow for Knowledge Base Question & Answer

rag_genai_stack

Installing GenAI Stack

!pip install genai_stack

Componets used for implementation

• gpt-3.5-turbo as LLM

• Chromadb as Vectorstore

• sentence-transformers/all-mpnet-base-v2 sentence transformer for text embeddings

• Langchain Framework

Import Required GenAI Stack Components

from genai_stack.stack.stack import Stack
from genai_stack.etl.langchain import LangchainETL
from genai_stack.embedding.langchain import LangchainEmbedding
from genai_stack.vectordb.chromadb import ChromaDB
from genai_stack.prompt_engine.engine import PromptEngine
from genai_stack.model.gpt3_5 import OpenAIGpt35Model
from genai_stack.retriever import LangChainRetriever
from genai_stack.memory.langchain import ConversationBufferMemory

Instantiate ETL component by providing configuration according to source data type.

• Here the input source is .pdf file

etl = LangchainETL.from_kwargs(name="PyPDFLoader", fields={"file_path": "YOUR PDF DOCUMENT PATH"})

Instantiate the LLM

llm = OpenAIGpt35Model.from_kwargs(parameters={"openai_api_key": "YOUR OPENAI API KEY"})

## Instantiate Embedding component using open source Huggingface Model

```py
config = {
    "model_name": "sentence-transformers/all-mpnet-base-v2",
    "model_kwargs": {"device": "cpu"},
    "encode_kwargs": {"normalize_embeddings": False},
}
embedding = LangchainEmbedding.from_kwargs(name="HuggingFaceEmbeddings", fields=config)

Instantiate the Vectorstore

chromadb = ChromaDB.from_kwargs()
chromadb

Instantiate the Retriver

retriever = LangChainRetriever.from_kwargs()

Instantiate the Prompt engine

Prompt engine constructs the prompt template for instructing the LLM

promptengine = PromptEngine.from_kwargs(should_validate=False)

Instantiate Memory

memory = ConversationBufferMemory.from_kwargs()

Setup the GenAI Stack

stack = Stack(
    etl=etl,
    embedding=embedding,
    vectordb=chromadb,
    model=llm,
    prompt_engine=promptengine,
    retriever=retriever,
    memory=memory
)

Performing the ETL operations

1. extracting the content

2. transforrmation(creating embeddings),

3. load(storing the knowledge base in the vectordb)

etl.run()

Ask a question

question = input("Ask a question: ")

response = retriever.retrieve(question)
print(response['output'])

Helper Function to generate response

process = "y"
while process == 'y':
  question = input("Ask a question: ")
  print(f"Question : {question}")
  response = retriever.retrieve(question)
  print(f"Response : {response}")
  print("\n\n")
  process =  input("Do you want to continue : y -to continue, n - to exit :")
  print("\n")