langchain

e3ea3657 - core: use friendlier names for duplicated nodes in mermaid output (#27747)

core: use friendlier names for duplicated nodes in mermaid output (#27747) Thank you for contributing to LangChain! - [x] **PR title**: "core: use friendlier names for duplicated nodes in mermaid output" - **Description:** When generating the Mermaid visualization of a chain, if the chain had multiple nodes of the same type, the reid function would replace their names with the UUID node_id. This made the generated graph difficult to understand. This change deduplicates the nodes in a chain by appending an index to their names. - **Issue:** None - **Discussion:** https://github.com/langchain-ai/langchain/discussions/27714 - **Dependencies:** None - [ ] **Add tests and docs**: - Currently this functionality is not covered by unit tests, happy to add tests if you'd like - [x] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. # Example Code: ```python from langchain_core.runnables import RunnablePassthrough def fake_llm(prompt: str) -> str: # Fake LLM for the example return "completion" runnable = { 'llm1': fake_llm, 'llm2': fake_llm, } | RunnablePassthrough.assign( total_chars=lambda inputs: len(inputs['llm1'] + inputs['llm2']) ) print(runnable.get_graph().draw_mermaid(with_styles=False)) ``` # Before ```mermaid graph TD; Parallel_llm1_llm2_Input --> 0b01139db5ed4587ad37964e3a40c0ec; 0b01139db5ed4587ad37964e3a40c0ec --> Parallel_llm1_llm2_Output; Parallel_llm1_llm2_Input --> a98d4b56bd294156a651230b9293347f; a98d4b56bd294156a651230b9293347f --> Parallel_llm1_llm2_Output; Parallel_total_chars_Input --> Lambda; Lambda --> Parallel_total_chars_Output; Parallel_total_chars_Input --> Passthrough; Passthrough --> Parallel_total_chars_Output; Parallel_llm1_llm2_Output --> Parallel_total_chars_Input; ``` # After ```mermaid graph TD; Parallel_llm1_llm2_Input --> fake_llm_1; fake_llm_1 --> Parallel_llm1_llm2_Output; Parallel_llm1_llm2_Input --> fake_llm_2; fake_llm_2 --> Parallel_llm1_llm2_Output; Parallel_total_chars_Input --> Lambda; Lambda --> Parallel_total_chars_Output; Parallel_total_chars_Input --> Passthrough; Passthrough --> Parallel_total_chars_Output; Parallel_llm1_llm2_Output --> Parallel_total_chars_Input; ```