From 7291fab5d4fe219c7ce2b09d6341f70d89278273 Mon Sep 17 00:00:00 2001
From: ayush chaurasia <ayush.chaurarsia@gmail.com>
Date: Wed, 15 May 2024 14:59:28 +0530
Subject: [PATCH] update

---
 README.md | 44 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 44 insertions(+)

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+# Ragged
+
+Simple utilities for piece-wise evaluation of LLM based chat and retrieval system.
+
+### Setup
+Build from source
+```
+pip install -e .
+```
+
+## GUI quickstart 
+### VectorDB retrieval eval
+```
+ragged --quickstart vectordb
+```
+
+## API Usage
+### VectorDB retrieval eval
+```python
+from ragged.dataset import LlamaIndexDataset
+from ragged.metrics.retriever import HitRate
+from ragged.search_utils import QueryType
+from lancedb.rerankers import CrossEncoderReranker
+
+# 1. Select dataset
+# Automatically download the dataset from llama-hub or pass existing path="/path/to/dataset"
+dataset = LlamaIndexDataset("Uber10KDataset2021")
+
+# 2. Select eval metrics
+hit_rate = HitRate(
+            dataset,
+            embedding_registry_id="sentence-transformers",
+            embed_model_kwarg={"name":"BAAI/bge-small-en-v1.5"},
+            reranker=CohereReranker(),
+            )
+
+# 3. Evaluate on desired query types
+
+#print(hit_rate.evaluate(top_k=5, query_type=QueryType.VECTOR)) # Evaluate vector search
+print(hit_rate.evaluate(top_k=5, query_type="all")) # Evaliate all possible query types
+```
+
+## Create custom Dataset, Metrics, Reranking connectors
+# TODO
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