minicoil tutorial

qdrant-landing/content/documentation/headless/snippets/fastembed/minicoil/upsert-points/_description.md

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+This code snippet uses the Qdrant and FastEmbed integration to infer and upsert documents into a collection configured for miniCOIL sparse vectors. Each document is converted into a sparse miniCOIL vector, with the conversion incorporating the avg_len parameter of the BM25-based miniCOIL scoring formula — the average document length in the corpus — which must be provided by the user. The resulting vector and the document’s text, stored as payload, are then upserted to the collection.

qdrant-landing/content/documentation/headless/snippets/fastembed/minicoil/upsert-points/python.md

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+```python
+#Estimating the average length of the documents in the corpus
+avg_documents_length = sum(len(document.split()) for document in documents) / len(documents)
+
+client.upsert(
+    collection_name="{minicoil_collection_name}",
+    points=[
+        models.PointStruct(
+            id=i,
+            payload={
+                "text": documents[i]
+            },
+            vector={
+                # Sparse miniCOIL vectors
+                "minicoil": models.Document(
+                    text=documents[i],
+                    model="Qdrant/minicoil-v1",
+                    options={"avg_len": avg_documents_length}   
+                    #Average length of documents in the corpus 
+                     # (a part of the BM25 formula on which miniCOIL is built)
+                )
+            },
+        )
+        for i in range(len(documents))
+    ],
+)
+```