Add Inference concept page

qdrant-landing/content/documentation/headless/snippets/inference/cohere/http.md

@@ -0,0 +1,43 @@
+```http
+// Create the collection for vectors with 512 dimensions.
+PUT /collections/<your-collection_name>
+{
+  "vectors": {
+    "size": 512,
+    "distance": "Cosine"
+  }
+}
+
+// Ingest a point. Provide the model name, prepended with "cohere/". 
+// Provide the Cohere API key in the "options" object.
+PUT /collections/<your-collection_name>/points?wait=true
+{
+  "points": [
+    {
+      "id": 1,
+      "vector": {
+        "image": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mNk+M9Qz0AEYBxVSF+FAAhKDveksOjmAAAAAElFTkSuQmCC",
+        "model": "cohere/embed-v4.0",
+        "options": {
+          "cohere-api-key": "<YOUR_COHERE_API_KEY>",
+          "output_dimension": 512
+        }
+      }
+    }
+  ]
+}
+
+// Query the data by providing the model name, prepended with "cohere/" 
+// and the Cohere API key.
+POST /collections/<your-collection_name>/points/query
+{
+  "query": {
+    "text": "a green square",
+    "model": "cohere/embed-v4.0",
+    "options": {
+      "cohere-api-key": "<YOUR_COHERE_API_KEY>",
+      "output_dimension": 512
+    }
+  }
+}
+```

qdrant-landing/content/documentation/headless/snippets/inference/create-512-dims/http.md

@@ -0,0 +1,9 @@
+```http
+PUT /collections/<your-collection_name>
+{
+  "vectors": {
+    "size": 512,
+    "distance": "Cosine"
+  }
+}
+```

qdrant-landing/content/documentation/headless/snippets/inference/ingest/http.md

@@ -0,0 +1,16 @@
+```http
+PUT /collections/<your-collection_name>/points
+{
+  "points": [
+    {
+      "id": 1,
+      "vector": {
+        "my-bm25-vector": {
+          "text": "Recipe for baking chocolate chip cookies",
+          "model": "qdrant/bm25"
+        }
+      }
+    }
+  ]
+}
+```

qdrant-landing/content/documentation/headless/snippets/inference/jinaai/http.md

@@ -0,0 +1,43 @@
+```http
+// Create the collection for vectors with 512 dimensions.
+PUT /collections/<your-collection_name>
+{
+  "vectors": {
+    "size": 512,
+    "distance": "Cosine"
+  }
+}
+
+// Ingest a point. Provide the model name, prepended with "jinaai/". 
+// Provide the Jina AI API key in the "options" object.
+PUT /collections/<your-collection_name>/points?wait=true
+{
+  "points": [
+    {
+      "id": 1,
+      "vector": {
+        "image": "https://qdrant.tech/example.png",
+        "model": "jinaai/jina-clip-v2",
+        "options": {
+          "jina-api-key": "<YOUR_JINAAI_API_KEY>",
+          "dimensions": 512
+        }
+      }
+    }
+  ]
+}
+
+// Query the data by providing the model name, prepended with "jinaai/" 
+// and the Jina AI API key.
+POST /collections/<your-collection_name>/points/query
+{
+  "query": {
+    "text": "Mission to Mars",
+    "model": "jinaai/jina-clip-v2",
+    "options": {
+      "jina-api-key": "<YOUR_JINAAI_API_KEY>",
+      "dimensions": 512
+    }
+  }
+}
+```

qdrant-landing/content/documentation/headless/snippets/inference/openai/http.md

@@ -0,0 +1,46 @@
+```http
+// Configure the collection for vectors with 512 dimensions.
+PUT /collections/<your-collection_name>
+{
+  "vectors": {
+    "size": 512,
+    "distance": "Cosine"
+  }
+}
+
+// Ingest a point. Provide the model name, prepended with "openai/". 
+// Provide the OpenAI API key in the "options" object.
+PUT /collections/<your-collection_name>/points?wait=true
+{
+  "points": [
+    {
+      "id": 1,
+      "vector": {
+        "text": "Recipe for baking chocolate chip cookies",
+        "model": "openai/text-embedding-3-large",
+        "options": {
+          "openai-api-key": "<YOUR_OPENAI_API_KEY>",
+          "dimensions": 512
+        }
+      }
+    }
+  ]
+}
+
+// Retrieve the point to see the generated embeddings
+GET /collections/<your-collection_name>/points/1
+
+// Query the data by providing the model name, prepended with "openai/" 
+// and the OpenAI API key.
+POST /collections/<your-collection_name>/points/query
+{
+  "query": {
+    "text": "How to bake cookies?",
+    "model": "openai/text-embedding-3-large",
+    "options": {
+      "openai-api-key": "<YOUR_OPENAI_API_KEY>",
+      "dimensions": 512
+    }
+  }
+}
+```

qdrant-landing/content/documentation/headless/snippets/inference/query/http.md

@@ -0,0 +1,10 @@
+```http
+POST /collections/<your-collection_name>/points/query
+{
+  "query": {
+    "text": "How to bake cookies?",
+    "model": "qdrant/bm25"
+  },
+  "using": "my-bm25-vector"
+}
+```