The Problem

Organizations often store important information in documents such as PDFs, offer letters, contracts, policies, and reports. Manually searching these documents to find specific information is time-consuming and inefficient. Existing cloud-based AI solutions also raise concerns regarding data privacy and internet dependency.

The objective of this Proof of Concept (POC) was to build a completely local AI-powered document question answering system that allows users to upload documents and ask natural language questions without relying on external APIs.

Our Solution

A Retrieval-Augmented Generation (RAG) application was developed using Langflow to provide an intuitive low-code workflow for document ingestion and querying.

The solution performs the following steps:

  • Reads uploaded PDF or text documents. 
  • Splits documents into smaller chunks. 
  • Generates vector embeddings using Ollama’s local embedding model. 
  • Stores embeddings in ChromaDB. 
  • Retrieves the most relevant document chunks based on the user’s query. 
  • Uses a local Llama 3.1 model through Ollama to generate accurate responses using the retrieved context. 

The entire pipeline runs locally without sending any data to external cloud services.

Solution Architecture

User

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Chat Input

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Agent (Llama 3.1 via Ollama)

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ChromaDB Search Tool

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Relevant Document Chunks

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Generated Answer

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Read File

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Split Text

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Ollama Embeddings

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ChromaDB

Deliverables

  • Local RAG application built using Langflow. 
  • PDF/Text document ingestion workflow. 
  • ChromaDB vector database integration. 
  • Ollama embedding generation. 
  • Local Llama 3.1 inference. 
  • Interactive question-answering interface. 
  • Reusable Langflow workflow. 

Tech Stack

  • Langflow 
  • Ollama 
  • Llama 3.1 8B 
  • Nomic Embed Text 
  • ChromaDB 
  • Python 
  • LangChain 
  • Windows 
  • PDF/Text Parser

Business Impact

This solution enables organizations to quickly retrieve information from large document repositories without manual searching.

Potential business benefits include:

  • Faster document search and information retrieval. 
  • Improved employee productivity. 
  • Reduced dependency on cloud AI services. 
  • Better data privacy since all processing occurs locally. 
  • Lower operational costs by using open-source local models. 
  • Can be extended for HR documents, legal contracts, SOPs, research papers, and company policies. 

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