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* add pre-commit config Signed-off-by: Sun, Xuehao <xuehao.sun@intel.com> * add prettier Signed-off-by: Sun, Xuehao <xuehao.sun@intel.com> * update prettier Signed-off-by: Sun, Xuehao <xuehao.sun@intel.com> * fix codespell Signed-off-by: Sun, Xuehao <xuehao.sun@intel.com> --------- Signed-off-by: Sun, Xuehao <xuehao.sun@intel.com>
21 lines
1.2 KiB
Markdown
21 lines
1.2 KiB
Markdown
## Performance measurements of chain with langsmith
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Pre-requisite: Signup in langsmith [https://www.langchain.com/langsmith] and get the api token <br />
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### Steps to run perf measurements
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1. Build langchain-rag container with most updated Dockerfile
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2. Start tgi server on system with Gaudi
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3. Statr redis container with docker-compose-redis.yml
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4. Add your hugging face access token in docker-compose-langchain.yml and start langchain-rag-server container
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5. enter into langchain-rag-server container and start jupyter notebook server (can specify needed IP address and jupyter will run on port 8888)
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```
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docker exec -it langchain-rag-server bash
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cd /test
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jupyter notebook --allow-root --ip=X.X.X.X
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```
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6. Launch jupyter notebook in your browser and open the tgi_gaudi.ipynb notebook
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7. Add langsmith api key in first cell of the notebook [os.environ["LANGCHAIN_API_KEY"] = "add-your-langsmith-key" # Your API key]
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8. Clear all the cells and run all the cells
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9. The output of the last cell which calls client.run_on_dataset() will run the langchain Q&A test and captures measurements in the langsmith server. The URL to access the test result can be obtained from the output of the command
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