📌 Description
The current Retrieval-Augmented Generation (RAG) pipeline retrieves relevant documents successfully but lacks an actual LLM synthesis layer to generate contextualized and coherent responses.
At present, retrieved documents are only concatenated and returned directly, which limits answer quality, reasoning capability, and natural-language summarization.
The backend generator.py module should integrate Gemini API-based synthesis so retrieved context and user queries are processed through a large language model before generating the final response.
Additionally, the implementation should gracefully handle missing API keys by falling back to the existing document concatenation mechanism.
🎯 Objective
This feature aims to improve the intelligence and usability of the RAG system by enabling context-aware response generation using LLM synthesis.
This will:
Improve response quality
Enable better contextual reasoning
Produce human-readable summaries
Improve conversational AI experience
Maintain graceful fallback reliability
🛠️ Proposed Solution
Integrate Gemini API inside generator.py
Pass:
User query
Retrieved documents
Context metadata
Add fallback logic when API key is unavailable
Add google-generativeai dependency to requirements.txt
Improve error handling and API response validation
📌 Features to Include
Gemini-powered synthesis pipeline
Context-aware answer generation
API key fallback handling
Error handling and retry logic
Modular prompt construction
Improved backend logging
🔄 Alternatives Considered
Returning raw retrieved documents only
Using static summarization templates
Client-side synthesis generation
These alternatives provide lower-quality responses and weaker contextual reasoning.
🧪 Acceptance Criteria
📷 Screenshots / References (if any)
N/A
📢 Contribution Guidelines
- Comment "assign me" to work on this issue
- Wait for assignment before starting
- Follow project coding standards
- Submit a clean PR with description
📌 Description
The current Retrieval-Augmented Generation (RAG) pipeline retrieves relevant documents successfully but lacks an actual LLM synthesis layer to generate contextualized and coherent responses.
At present, retrieved documents are only concatenated and returned directly, which limits answer quality, reasoning capability, and natural-language summarization.
The backend generator.py module should integrate Gemini API-based synthesis so retrieved context and user queries are processed through a large language model before generating the final response.
Additionally, the implementation should gracefully handle missing API keys by falling back to the existing document concatenation mechanism.
🎯 Objective
This feature aims to improve the intelligence and usability of the RAG system by enabling context-aware response generation using LLM synthesis.
This will:
Improve response quality
Enable better contextual reasoning
Produce human-readable summaries
Improve conversational AI experience
Maintain graceful fallback reliability
🛠️ Proposed Solution
Integrate Gemini API inside generator.py
Pass:
User query
Retrieved documents
Context metadata
Add fallback logic when API key is unavailable
Add google-generativeai dependency to requirements.txt
Improve error handling and API response validation
📌 Features to Include
Gemini-powered synthesis pipeline
Context-aware answer generation
API key fallback handling
Error handling and retry logic
Modular prompt construction
Improved backend logging
🔄 Alternatives Considered
Returning raw retrieved documents only
Using static summarization templates
Client-side synthesis generation
These alternatives provide lower-quality responses and weaker contextual reasoning.
🧪 Acceptance Criteria
📷 Screenshots / References (if any)
N/A
📢 Contribution Guidelines