# 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](/files/MlUkTh3mKfG5aczqBILT)

## 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

```py
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

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

## Instantiate the LLM

````py
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

```py
chromadb = ChromaDB.from_kwargs()
chromadb
```

## Instantiate the Retriver

```py
retriever = LangChainRetriever.from_kwargs()
```

## Instantiate the Prompt engine

Prompt engine constructs the prompt template for instructing the LLM

```py
promptengine = PromptEngine.from_kwargs(should_validate=False)
```

## Instantiate Memory

```py
memory = ConversationBufferMemory.from_kwargs()
```

## Setup the GenAI Stack

```py
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)

```py
etl.run()
```

## Ask a question

```py
question = input("Ask a question: ")

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

## Helper Function to generate response

```py
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")
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://genaistack.aiplanet.com/example-use-cases/rag_pipelines.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.