v1.0.2

Parallelized parsing with ColBERT and BM25 embeddings

What's Changed

We have parallelized the parsing process and packaged the entire parsing into one sub package for parsing.So the old create_graph_rs Rust module has been replaced with a new app/src/parsing/ module with ColBERT/BM25 hybrid vector indexing via Qdrant for superior code search quality.

Core Infrastructure

• New Parsing module (app/src/parsing/): Complete rewrite with tree-sitter for multi-language support, 800+ lines of tag extraction, and Python bindings via PyO3. Moved parsing queries to the new module structure.
• Qdrant Hybrid Search: Added dense (ColBERT) + sparse (BM25) vector indexing to the code graph pipeline. Non-fatal indexing failures don't block graph creation. Includes deletion handling on project cleanup.
• Qdrant configuration: Added Qdrant configuration support (host, port, gRPC port, API key, HTTPS) alongside existing Neo4j config.

Multimodal & Conversation Improvements

• Attachment Handling: Extended conversation creation to accept attachment_ids. First-message attachments are now stored in Redis and applied retroactively if the client redirects without re-sending them.
• Document Attachments: Multimodal processing now handles both images and documents (AttachmentType.DOCUMENT), not just images.
• Agent Updates: Added update_agent endpoint to conversation controller for dynamic agent switching.

PR's merged

• mode switch backend API added with update in DB by @shmbhvi101 in #728
• Fix rust build for docker by @Dsantra92 in #744
• fix(auth): send Slack alerts for email/password signups by @yashkrishan in #747
• unbound local error while parsing using priority 3 github token by @BrhKmr23 in #748
• Add faster parsing + colbert embeddings by @Dsantra92 in #746

New Contributors

• @BrhKmr23 made their first contribution in #748

Full Changelog: v1.0.1...1.0.2