Give LLMs the Context They Need
ContextRAG is a context extension platform for AI systems that enables Large Language Models (LLMs) to understand real project environments by retrieving relevant knowledge before generating responses.
Instead of sending raw prompts directly to an LLM, ContextRAG retrieves project context — such as documentation, architecture notes, code snippets, and developer conversations — and augments the prompt before sending it to the model.
This enables context-aware AI responses grounded in real project knowledge.
Modern AI assistants are powerful, but they lack awareness of the developer’s actual project environment.
Large Language Models typically do not know:
- your project architecture
- your codebase
- internal documentation
- design decisions
- developer discussions
As a result:
- developers must manually paste context into prompts
- important architectural knowledge is lost between conversations
- AI responses lack grounding in real systems
ContextRAG introduces a context retrieval layer between the user and the LLM.
Developer ↓ ContextRAG ↓ LLM ↓ Context-Aware Response
Before sending a prompt to the LLM, ContextRAG:
- Converts the query into embeddings
- Retrieves relevant context from a vector database
- Combines retrieved knowledge with the query
- Sends an augmented prompt to the LLM
This produces responses grounded in real project context.
ContextRAG is built around a Retrieval Augmented Generation (RAG) architecture.
Before sending a prompt to the LLM, ContextRAG:
- Converts the query into embeddings
- Retrieves relevant context from a vector database
- Combines retrieved knowledge with the query
- Sends an augmented prompt to the LLM
This produces responses grounded in real project context.
ContextRAG is built around a Retrieval Augmented Generation (RAG) architecture.
User Query ↓ RAG Service ↓ Embedding Model ↓ Vector Database ↓ Context Retrieval ↓ Prompt Construction ↓ LLM ↓ Response
The system continuously learns from project knowledge and developer interactions.
Use multiple model providers including OpenAI, Claude, Gemini, or local models.
Project documentation, code, and architecture notes become searchable context.
Developer discussions are summarized and stored as reusable knowledge.
Kafka pipelines allow scalable ingestion of documentation, repositories, and logs.
ContextRAG │ ├── common │ ├── rag-service │ ├── docs │ ├── architecture │ └── website
Initial modules:
- common – shared models and utilities
- rag-service – core RAG orchestration service
ContextRAG is currently under active development.
Initial milestones:
- architecture definition
- RAG prototype
- knowledge ingestion pipeline
- conversation memory
- developer platform
The long-term vision of ContextRAG is to build the contextual intelligence layer for AI systems.
Future capabilities may include:
- full codebase awareness
- architecture reasoning
- observability data ingestion
- AI-assisted system design
- developer workflow intelligence
Additional project documentation:
- Whitepaper
- Architecture Document
- System Design Playbook
These documents explain the architecture and long-term design of the platform.
Licensed under the Apache License 2.0.
See the LICENSE file for details.
Nishant