Create README.md

README.md

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+---
+license: apache-2.0
+model-index:
+- name: vstackai-law-1
+  results:
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+      name: MTEB AILACasedocs (default)
+      revision: 4106e6bcc72e0698d714ea8b101355e3e238431a
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+      value: 6.356000000000001
+    - type: precision_at_3
+      value: 27.346999999999998
+    - type: precision_at_5
+      value: 18.521
+    - type: recall_at_1
+      value: 50.583999999999996
+    - type: recall_at_10
+      value: 80.833
+    - type: recall_at_100
+      value: 96.467
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_20
+      value: 87.543
+    - type: recall_at_3
+      value: 65.515
+    - type: recall_at_5
+      value: 71.47200000000001
+    task:
+      type: Retrieval
+tags:
+- mteb
+---
+
+# VectorStack AI | vstackai-law-1
+
+This repository describes the steps of using the VectorStack AI's `vstackai-law-1` model. 
+You can use the embedding model via our API or via private deployments in your private cloud.
+
+## Usage via private deployment
+If you are interested in running this model in your own private cloud/ own hardware, [contact us](https://www.vectorstack.ai/contact) to learn more.
+
+## Usage via API
+The following code snippet shows the steps required to generate embeddings via the API. Install the SDK via:
+```
+pip install -U vectorstackai
+```
+Get your free API key on www.vectorstack.ai
+
+```python
+import vectorstackai
+import numpy as np
+api_key = "{API_KEY}"  # Get your API key from www.vectorstack.ai
+client = vectorstackai.Client(api_key=api_key)
+
+# Documents related to law domain (e.g., court cases, consumer contracts, etc.)
+documents = [
+    "The defendant was charged with violation of contract terms in the lease agreement signed on January 1, 2022.",
+    "This contract stipulates that the consumer has 30 days to return the product in case of any manufacturing defects.",
+    "In the case of Smith v. Johnson, the court ruled that the plaintiff had the right to claim damages under section 12 of the Consumer Protection Act."
+]
+
+# Get embeddings for the legal documents
+doc_embeddings = client.embed(texts=documents, model='vstackai-law-1', is_query=False) # EmbeddingsObject(num_embeddings=3, embedding_dims=1536)
+doc_embeddings = np.array(doc_embeddings.embeddings)
+
+# Encode the query
+query = "How many days does the consumer have to return the product?"
+query_embedding = client.embed(
+    texts=[query], 
+    model='vstackai-law-1', 
+    is_query=True, 
+    instruction='Represent the query for searching legal documents'
+) # EmbeddingsObject(num_embeddings=1, embedding_dims=1536)
+query_embedding = np.array(query_embedding.embeddings) # (1, 1536)
+
+# To check if the embeddings work, you can compute similarity between the query and documents
+similarities = np.dot(doc_embeddings, query_embedding.T)
+print(similarities)
+# array([[0.355],
+#        [0.772],
+#        [0.433]])
+```