Update README.md

README.md

@@ -15,53 +15,66 @@ inference: false
 
 # colbertv1-camembert-base-mmarcoFR
 
-This is a [ColBERTv1](https://github.com/stanford-futuredata/ColBERT) model: it encodes queries & passages into matrices of token-level embeddings and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators. It can be used for tasks like clustering or semantic search. The model was trained on the **French** portion of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset.
+This is a [ColBERTv1](https://github.com/stanford-futuredata/ColBERT) model for semantic search. It encodes queries & passages into matrices of token-level embeddings and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators. The model was trained on the **French** portion of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset.
 
-## Installation
+## Usage
 
-To use this model, you will need to install the following libraries:
-```
+Here are some examples for using the model with [colbert-ai](https://github.com/stanford-futuredata/ColBERT) or [RAGatouille](https://github.com/bclavie/RAGatouille).
+
+### Using ColBERT-AI
+
+First, you will need to install the following libraries:
+
+```bash
 pip install git+https://github.com/stanford-futuredata/ColBERT.git torch faiss-gpu==1.7.2
 ```
 
-## Usage
+Then, you can use the model like this:
 
-**Step 1: Indexing.** This step encodes all passages into matrices, stores them on disk, and builds data structures for efficient search. ⚠️ ColBERT indexing requires a GPU!
-```
-from colbert import Indexer
+```python
+from colbert import Indexer, Searcher
 from colbert.infra import Run, RunConfig
 
 n_gpu: int = 1 # Set your number of available GPUs
-experiment: str = "" # Name of the folder where the logs and created indices will be stored
-index_name: str = "" # The name of your index, i.e. the name of your vector database
+experiment: str = "colbert" # Name of the folder where the logs and created indices will be stored
+index_name: str = "my_index" # The name of your index, i.e. the name of your vector database
+documents: list = ["Ceci est un premier document.", "Voici un second document.", "etc."] # Corpus
 
+# Step 1: Indexing. This step encodes all passages into matrices, stores them on disk, and builds data structures for efficient search.
 with Run().context(RunConfig(nranks=n_gpu,experiment=experiment)):
     indexer = Indexer(checkpoint="antoinelouis/colbertv1-camembert-base-mmarcoFR")
-    documents = [
-      "Ceci est un premier document.",
-      "Voici un second document.",
-      ...
-    ]
     indexer.index(name=index_name, collection=documents)
 
+# Step 2: Searching. Given the model and index, you can issue queries over the collection to retrieve the top-k passages for each query.
+with Run().context(RunConfig(nranks=n_gpu,experiment=experiment)):
+    searcher = Searcher(index=index_name) # You don't need to specify checkpoint again, the model name is stored in the index.
+    results = searcher.search(query="Comment effectuer une recherche avec ColBERT ?", k=10)
+    # results: tuple of tuples of length k containing ((passage_id, passage_rank, passage_score), ...)
 ```
 
-**Step 2: Searching.** Given the model and index, you can issue queries over the collection to retrieve the top-k passages for each query.
+### Using RAGatouille
+
+First, you will need to install the following libraries:
+
+```bash
+pip install -U ragatouille
 ```
-from colbert import Searcher
-from colbert.infra import Run, RunConfig
 
-n_gpu: int = 0
-experiment: str = "" # Name of the folder where the logs and created indices will be stored
-index_name: str = "" # Name of your previously created index where the documents you want to search are stored.
-k: int = 10 # how many results you want to retrieve
+Then, you can use the model like this:
 
-with Run().context(RunConfig(nranks=n_gpu,experiment=experiment)):
-    searcher = Searcher(index=index_name) # You don't need to specify checkpoint again, the model name is stored in the index.
-    query = "Comment effectuer une recherche avec ColBERT ?"
-    results = searcher.search(query, k=k)
-    # results: tuple of tuples of length k containing ((passage_id, passage_rank, passage_score), ...)
+```python
+from ragatouille import RAGPretrainedModel
+
+index_name: str = "my_index" # The name of your index, i.e. the name of your vector database
+documents: list = ["Ceci est un premier document.", "Voici un second document.", "etc."] # Corpus
+
+# Step 1: Indexing.
+RAG = RAGPretrainedModel.from_pretrained("antoinelouis/colbertv1-camembert-base-mmarcoFR")
+RAG.index(name=index_name, collection=documents)
 
+# Step 2: Searching.
+RAG = RAGPretrainedModel.from_index(index_name) # if not already loaded
+RAG.search(query="Comment effectuer une recherche avec ColBERT ?", k=10)
 ```
 
 ## Evaluation