Context graph or vector database for messy project-history search

A retrieval workflow needs to track highly connected project history across a client’s email threads, Slack channels, and project documents. Standard semantic similarity search over small text chunks fails in this setting because it finds text that looks related but misses the chain of decisions. A question like “what was the final resolution of the API migration issue from last quarter?” may return partly relevant code chunks while missing who made the decision in Slack and why the project documents were later updated.

One option is to keep everything as flat text chunks in a vector database and build custom Python middleware to keep metadata filters synced across systems. That path is easier to start, but the metadata sync is fragile and can break down on multi-hop relational questions. Another option is testing a managed context graph platform called 60x over unstructured sources such as Slack.

The main tradeoff is whether the extra development debt of a full context graph is worth it, or whether a vector database with heavy metadata filtering is enough.