Oreag is an Experience-Augmented Generation (EAG) system that goes beyond traditional RAG by incorporating contextual user experience signals into the retrieval and generation pipeline — producing personalized, context-aware responses that adapt to individual user history, preferences, and interaction patterns using LangChain.
Oreag extends the standard RAG paradigm by injecting user experience signals — past queries, preferred topics, interaction depth, and session memory — into both the retrieval scoring and the generation prompt. This creates a self-improving loop where the system becomes more accurate and relevant with every interaction, without fine-tuning the base model.
Retrieval scoring is weighted by user experience signals — surfacing documents that are not just semantically similar but contextually relevant to the user.
User profiles store interaction history, topic preferences, and engagement signals — feeding into every subsequent query for adaptive personalization.
Built on LangChain for modular, composable retrieval chains — enabling easy swapping of retrievers, LLMs, and memory backends.
Connects to vector databases for semantic document search, augmented with metadata filters driven by the user's experience profile.
Built with production observability in mind — prompt versioning, response logging, and evaluation hooks for continuous quality monitoring.
Each interaction refines the user's experience graph — making retrieval more accurate and generation more relevant over time without retraining.
User query is encoded into a semantic vector. The user's experience profile (history, preferences, recent topics) is encoded as a context signal alongside it.
Vector similarity search is re-ranked using experience weights — documents aligned with the user's expertise level and topic history score higher.
Retrieved documents are injected into a prompt template that also includes the user's experience context — guiding the LLM to generate personalized responses.
The LangChain pipeline passes the augmented prompt to the LLM, which generates a response calibrated to the user's knowledge level and interests.
Post-generation, the interaction is logged and the user's profile is updated — feeding back into future retrieval to continuously improve relevance.