minicoil tutorial

qdrant-landing/content/documentation/headless/snippets/fastembed/bm25/create-collection/_description.md

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+This code snippet creates a collection configured for BM25-based lexical retrieval. It defines a sparse named vector with the IDF modifier enabled,ensuring that the Inverse Document Frequency, a core component of the BM25 scoring formula, is calculated on Qdrant’s side. This setup allows Qdrant to perform BM25-style retrieval based on keyword frequency and rarity.

qdrant-landing/content/documentation/headless/snippets/fastembed/bm25/create-collection/python.md

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+```python
+client.create_collection(
+    collection_name="{bm25_collection_name}",
+    sparse_vectors_config={
+        "bm25": models.SparseVectorParams(
+            modifier=models.Modifier.IDF
+        )
+    }
+)
+```

qdrant-landing/content/documentation/headless/snippets/fastembed/bm25/query-points/_description.md

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+This code snippet performs a search in a collection configured for BM25 sparse vectors using the Qdrant and FastEmbed integration. It infers a sparse BM25 vector for the query and retrieves the most relevant document (limit=1) based on the BM25 scoring. The response includes the top-matching document’s ID, score, and payload.

qdrant-landing/content/documentation/headless/snippets/fastembed/bm25/query-points/python.md

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+```python
+query = "Vectors in Medicine"
+
+client.query_points(
+    collection_name="{bm25_collection_name}",
+    query=models.Document(
+        text=query, 
+        model="Qdrant/bm25"
+    ),
+    using="bm25",
+    limit=1,
+)
+```

qdrant-landing/content/documentation/headless/snippets/fastembed/bm25/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 BM25 sparse vectors. Each document is converted into a sparse BM25 vector, with the conversion incorporating the avg_len parameter of the BM25 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/bm25/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="{bm25_collection_name}",
+    points=[
+        models.PointStruct(
+            id=i,
+            payload={
+                "text": documents[i]
+            },
+            vector={
+                # Sparse vector from BM25
+                "bm25": models.Document(
+                    text=documents[i],
+                    model="Qdrant/bm25",
+                    options={"avg_len": avg_documents_length}   
+                    #Average length of documents in the corpus 
+                    # (a part of the BM25 formula)
+                )
+            },
+        )
+        for i in range(len(documents))
+    ],
+)
+```