mirror of
https://github.com/arc53/DocsGPT
synced 2024-11-08 01:10:24 +00:00
49 lines
2.2 KiB
Markdown
49 lines
2.2 KiB
Markdown
# Setting Up Local Language Models for Your App
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Your app relies on two essential models: Embeddings and Text Generation. While OpenAI's default models work seamlessly, you have the flexibility to switch providers or even run the models locally.
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## Step 1: Configure Environment Variables
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Navigate to the `.env` file or set the following environment variables:
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```env
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LLM_NAME=<your Text Generation model>
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API_KEY=<API key for Text Generation>
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EMBEDDINGS_NAME=<LLM for Embeddings>
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EMBEDDINGS_KEY=<API key for Embeddings>
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VITE_API_STREAMING=<true or false>
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```
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You can omit the keys if users provide their own. Ensure you set `LLM_NAME` and `EMBEDDINGS_NAME`.
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## Step 2: Choose Your Models
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**Options for `LLM_NAME`:**
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- openai ([More details](https://platform.openai.com/docs/models))
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- anthropic ([More details](https://docs.anthropic.com/claude/reference/selecting-a-model))
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- manifest ([More details](https://python.langchain.com/docs/integrations/llms/manifest))
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- cohere ([More details](https://docs.cohere.com/docs/llmu))
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- llama.cpp ([More details](https://python.langchain.com/docs/integrations/llms/llamacpp))
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- huggingface (Arc53/DocsGPT-7B by default)
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- sagemaker ([Mode details](https://aws.amazon.com/sagemaker/))
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Note: for huggingface you can choose any model inside application/llm/huggingface.py or pass llm_name on init, loads
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**Options for `EMBEDDINGS_NAME`:**
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- openai_text-embedding-ada-002
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- huggingface_sentence-transformers/all-mpnet-base-v2
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- huggingface_hkunlp/instructor-large
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- cohere_medium
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If you want to be completely local, set `EMBEDDINGS_NAME` to `huggingface_sentence-transformers/all-mpnet-base-v2`.
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For llama.cpp Download the required model and place it in the `models/` folder.
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Alternatively, for local Llama setup, run `setup.sh` and choose option 1. The script handles the DocsGPT model addition.
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## Step 3: Local Hosting for Privacy
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If working with sensitive data, host everything locally by setting `LLM_NAME`, llama.cpp or huggingface, use any model available on Hugging Face, for llama.cpp you need to convert it into gguf format.
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That's it! Your app is now configured for local and private hosting, ensuring optimal security for critical data.
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