Hey everyone, I built an open-source full-stack pipeline (Django + React) that constructs a Knowledge Graph from raw text, detects thematic communities, and uses hybrid search to solve the "lost in the middle" problem in standard vector retrieval. The Pipeline: Ingestion & Chunking: Raw text is cleaned, parsed, and split into overlapping chunks to preserve local context. Graph Construction: spaCy extracts named entities from each chunk. A weighted co-occurrence graph is built using NetworkX, mapping which entities appear together and linking them to their source chunks. Community Detection: The graph is partitioned into thematic clusters using greedy_modularity_communities. For each cluster, random text chunks are sampled and sent to an LLM to generate a high-level summary (preventing "hub node" bias). Indexing: All chunks are embedded into a dense vector store, and a sparse BM25 index is built over the same corpus. Hybrid Retrieval: On query, the system performs a dual search (Dense Vector + BM25). Simultaneously, it extracts entities from the prompt, traverses the graph for 1st-degree neighbors, and retrieves their associated chunks. Fusion & Reranking: Local and Global (community summary) results are merged, deduplicated, and scored using Reciprocal Rank Fusion (RRF). The top-K candidates are then re-scored by a Cross-Encoder for maximum precision. LLM Synthesis: The final curated context is passed to the LLM with strict prompting to generate a concise, well-structured, and cited answer. Why it works: Standard vector search fails at multi-hop queries like: Who ordered the execution of Sansa's father, and how did that person eventually die? By traversing the graph (Sansa -> Ned -> Joffrey -> Poisoning), the system bridges the gap between disconnected text chunks and synthesizes the correct answer. GitHub: https://github.com/mohammad-majoony/graphrag-studio Would love feedback! Thanks. submitted by /u/Future_Caregiver_643 [link] [Kommentare]
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