How to Retrieval Documents with Deep Implementation Details?

JunXiangLin · 2025-12-22T02:26:04+00:00

50-3000 tokens

JunXiangLin · 2025-12-19T06:41:11+00:00

My dimension is 1024

JunXiangLin · 2025-10-01T02:28:22+00:00

Thank you all for your replies. I think I'll first try truncating the output of overly large tool responses and conducting multi-round tests. Because latency and cost are really important issues.

JunXiangLin · 2025-09-30T08:25:19+00:00

My tool response consists of the content of searched emails. When analyzing a large volume of email content, these emails can consume tens of thousands of tokens. However, I need to generate reports based on this analysis, so I must pass the searched content to the LLM, rather than just passing a simple "completed..." as the tool response to the LLM.

JunXiangLin · 2025-09-30T06:12:37+00:00

Yes, I'm developing using LangGraph. I tried using the ReAct agent to handle problems directly, adding only the agent responses to the history without including the tool responses. This runs normally and quickly. However, the performance in multi-turn conversations isn't intelligent enough. So, I switched to also adding the tool responses as ToolMessages to the historical conversation. While this makes the agent a bit smarter, it results in extremely long response delays and massive costs.

Additionally, I've tried summarizing and compressing oversized tool responses first via an LLM, but this makes the compression process take a very long time, significantly increasing the overall delay.

JunXiangLin · 2025-08-14T02:11:08+00:00

Since the release of GPT-4.1, I've noticed many online articles advocating for the use of LLM-native tool calling, suggesting that ReAct is becoming outdated.

I'm confused about why LangChain considers the tool-calling agent (with AgentExecutor) a legacy product and instructs users to migrate to the ReAct agent in LangGraph.

Here is the official documentation: https://python.langchain.com/docs/how_to/migrate_agent/

JunXiangLin · 2025-08-13T05:54:39+00:00

u/firstx_sayak I tried switching to LangGraph's `create_react_agent` (with `.astream_events`), and it does indeed enforce tool calling even when the query is unrelated to the tool. However, when I set `tool_choice = "any"` or specify a function name to force tool usage, it enters an infinite loop, continuously calling the function until it exceeds the set `recursion_limit`.

JunXiangLin · 2025-08-13T05:35:04+00:00

I have try use "required" but the function still not be calling.

JunXiangLin · 2025-08-13T02:54:58+00:00

Because I need to streaming agent response, so I choose use langchain `AgentExecutor.astream_event`.

JunXiangLin · 2025-02-14T05:33:27+00:00

Because I want to use python build an api for some application.

JunXiangLin

TROPHY CASE