Feature/add local llama tut #278
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❌ Changes requested.
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| @@ -51,21 +49,22 @@ def search(self, query): | |||
| print(body[:500]) | |||
| print("\n") | |||
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| def rag_completion(self, query): | |||
| def rag_completion(self, query, model="gpt-4-turbo-preview"): | |||
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The model parameter is hardcoded to 'gpt-4-turbo-preview'. Consider making this a configurable parameter to allow users to specify the model they want to use.
Suggested change
| def rag_completion(self, query, model="gpt-4-turbo-preview"): | |
| def rag_completion(self, query, model): |
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Summary:
This PR adds support for local LLM providers
ollamaandLlama.cppin the R2R framework, along with updates to the documentation, example code, and configuration files to guide users on how to use this new feature.Key points:
ollamaandLlama.cppin/r2r/llms/__init__.pyand/r2r/llms/llama_cpp/base.py.config.jsonexamples to include configurations for local LLM providers in/r2r/examples/configs/local_ollama.jsonand/r2r/examples/configs/local_llama_cpp.json./r2r/examples/servers/basic_pipeline.pyto allow selection of configuration based on the desired LLM provider./r2r/examples/clients/run_basic_client.pyto demonstrate how to use the new local LLM providers./docs/pages/getting-started/basic-example.mdx,/docs/pages/getting-started/configure-your-pipeline.mdx,/docs/pages/providers/evals.mdx, and/docs/pages/providers/llms.mdxto reflect the new feature./docs/pages/tutorials/local_rag.mdxon how to run a local RAG pipeline with R2R.Generated with ❤️ by ellipsis.dev