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Update default model to TheBloke/Mistral-7B-Instruct-v0.2-GGUF #1415

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merged 3 commits into from
Dec 17, 2023
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Update default model to TheBloke/Mistral-7B-Instruct-v0.2-GGUF #1415

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557dea8
Update LlamaCPP dependency
imartinez Dec 17, 2023
b3a1575
Default to TheBloke/Mistral-7B-Instruct-v0.2-GGUF
imartinez Dec 17, 2023
136f123
Fix API docs
imartinez Dec 17, 2023
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13 changes: 13 additions & 0 deletions fern/docs/pages/api-reference/api-reference.mdx
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@@ -1 +1,14 @@
# API Reference

The API is divided in two logical blocks:

1. High-level API, abstracting all the complexity of a RAG (Retrieval Augmented Generation) pipeline implementation:
- Ingestion of documents: internally managing document parsing, splitting, metadata extraction,
embedding generation and storage.
- Chat & Completions using context from ingested documents: abstracting the retrieval of context, the prompt
engineering and the response generation.

2. Low-level API, allowing advanced users to implement their own complex pipelines:
- Embeddings generation: based on a piece of text.
- Contextual chunks retrieval: given a query, returns the most relevant chunks of text from the ingested
documents.
15 changes: 0 additions & 15 deletions fern/docs/pages/overview/welcome.mdx
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Expand Up @@ -32,21 +32,6 @@ The installation guide will help you in the [Installation section](/installation
/>
</Cards>

## API Organization

The API is divided in two logical blocks:

1. High-level API, abstracting all the complexity of a RAG (Retrieval Augmented Generation) pipeline implementation:
- Ingestion of documents: internally managing document parsing, splitting, metadata extraction,
embedding generation and storage.
- Chat & Completions using context from ingested documents: abstracting the retrieval of context, the prompt
engineering and the response generation.

2. Low-level API, allowing advanced users to implement their own complex pipelines:
- Embeddings generation: based on a piece of text.
- Contextual chunks retrieval: given a query, returns the most relevant chunks of text from the ingested
documents.

<Callout intent = "info">
A working **Gradio UI client** is provided to test the API, together with a set of useful tools such as bulk
model download script, ingestion script, documents folder watch, etc.
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