Optimize LLM tool call execution and multi-repo searches

[ishaanxgupta]

💡 What: Optimized the execution of LLM tool calls and multi-repository searches in the CodeRetrievalPipeline and RetrievalPipeline. Replaced sequential for loops with concurrent execution via asyncio.gather, while carefully preserving the required sequential processing of the gathered results to avoid race conditions on shared lists (sources, tool_messages).

🎯 Why: When the LLM decided to invoke multiple tools (e.g., retrieving different files simultaneously) or when a query targeted multiple repositories, the pipeline previously awaited each I/O-bound operation sequentially. This created a significant bottleneck where independent network/database requests were waiting on each other.

📊 Impact: Reduces latency significantly during multi-tool execution and global searches. For example, executing 5 tool calls that take 200ms each will now take ~200ms in total instead of ~1000ms.

🔬 Measurement: Measure the total execution time of the run method in RetrievalPipeline and CodeRetrievalPipeline when the LLM makes multiple tool calls in a single turn, or when searching across multiple repositories without specifying a repo. Observe the timing logs to verify concurrent execution.

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PR created automatically by Jules for task 16276008160634690662 started by @ishaanxgupta

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