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
AND
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.
