Problem
Business users needed a way to query document-heavy knowledge sources naturally in Sinhala and English, while preserving answer relevance and conversational context across a session.
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Project
RAG Based Natural Language Agentic Business Chatbot
A multilingual business assistant built with retrieval-augmented generation and agentic workflow patterns to support contextual question answering over document knowledge bases.
Gemini APILangChainFAISSPineconePyPDF
Timeline
August 2025 - October 2025
Outcome
Built a multilingual business chatbot with retrieval, session memory, and agentic orchestration.
Outcome achieved
- Enabled multilingual querying across Sinhala and English.
- Added session-based memory for more contextual follow-up conversations.
- Combined dense retrieval with agentic flow design for practical business question answering.
Challenges faced
- Keeping retrieval quality reliable across multilingual queries.
- Balancing session memory with accurate source retrieval.
- Designing the system to work with both local and hosted vector storage choices.
How I solved them
- Used LangChain to orchestrate the retrieval and response flow around Gemini 2.5 Flash.
- Integrated FAISS and Pinecone to support efficient vector search patterns.
- Added session-scoped conversation memory to preserve context without overloading the prompt.
Technical details
- Retrieval-augmented generation pipeline for business document question answering.
- Multilingual query support for Sinhala and English prompts.
- Session memory layer for ongoing conversational context.
- Vector retrieval support through FAISS and Pinecone backends.