Generate en docs

Author: Milvus-doc-bot

localization/v2.4.x/site/en/embeddings/embed-with-cohere.json

@@ -1 +1 @@
-{"codeList":["pip install --upgrade pymilvus\npip install \"pymilvus[model]\"\n","cohere_ef = CohereEmbeddingFunction(\n    model_name=\"embed-english-light-v3.0\",\n    api_key=\"YOUR_COHERE_API_KEY\",\n    input_type=\"search_document\",\n    embedding_types=[\"float\"]\n)\n","docs = [\n    \"Artificial intelligence was founded as an academic discipline in 1956.\",\n    \"Alan Turing was the first person to conduct substantial research in AI.\",\n    \"Born in Maida Vale, London, Turing was raised in southern England.\",\n]\n\ndocs_embeddings = cohere_ef.encode_documents(docs)\n\n# Print embeddings\nprint(\"Embeddings:\", docs_embeddings)\n# Print dimension and shape of embeddings\nprint(\"Dim:\", cohere_ef.dim, docs_embeddings[0].shape)\n","Embeddings: [array([ 3.43322754e-02,  1.16252899e-03, -5.25207520e-02,  1.32846832e-03,\n       -6.80541992e-02,  6.10961914e-02, -7.06176758e-02,  1.48925781e-01,\n        1.54174805e-01,  1.98516846e-02,  2.43835449e-02,  3.55224609e-02,\n        1.82952881e-02,  7.57446289e-02, -2.40783691e-02,  4.40063477e-02,\n...\n        0.06359863, -0.01971436, -0.02253723,  0.00354195,  0.00222015,\n        0.00184727,  0.03408813, -0.00777817,  0.04919434,  0.01519775,\n       -0.02862549,  0.04760742, -0.07891846,  0.0124054 ], dtype=float32)]\nDim: 384 (384,)\n","queries = [\"When was artificial intelligence founded\", \n           \"Where was Alan Turing born?\"]\n\nquery_embeddings = cohere_ef.encode_queries(queries)\n\nprint(\"Embeddings:\", query_embeddings)\nprint(\"Dim\", cohere_ef.dim, query_embeddings[0].shape)\n","Embeddings: [array([-1.33361816e-02,  9.79423523e-04, -7.28759766e-02, -1.93786621e-02,\n       -9.71679688e-02,  4.34875488e-02, -9.81445312e-02,  1.16882324e-01,\n        5.89904785e-02, -4.19921875e-02,  4.95910645e-02,  5.83496094e-02,\n        3.47595215e-02, -5.87463379e-03, -7.30514526e-03,  2.92816162e-02,\n...\n        0.00749969, -0.01192474,  0.02719116,  0.03347778,  0.07696533,\n        0.01409149,  0.00964355, -0.01681519, -0.0073204 ,  0.00043154,\n       -0.04577637,  0.03591919, -0.02807617, -0.04812622], dtype=float32)]\nDim 384 (384,)\n"],"headingContent":"Cohere","anchorList":[{"label":"Cohere","href":"Cohere","type":1,"isActive":false}]}
+{"codeList":["pip install --upgrade pymilvus\npip install \"pymilvus[model]\"\n","from pymilvus.model.dense import CohereEmbeddingFunction\n\ncohere_ef = CohereEmbeddingFunction(\n    model_name=\"embed-english-light-v3.0\",\n    api_key=\"YOUR_COHERE_API_KEY\",\n    input_type=\"search_document\",\n    embedding_types=[\"float\"]\n)\n","docs = [\n    \"Artificial intelligence was founded as an academic discipline in 1956.\",\n    \"Alan Turing was the first person to conduct substantial research in AI.\",\n    \"Born in Maida Vale, London, Turing was raised in southern England.\",\n]\n\ndocs_embeddings = cohere_ef.encode_documents(docs)\n\n# Print embeddings\nprint(\"Embeddings:\", docs_embeddings)\n# Print dimension and shape of embeddings\nprint(\"Dim:\", cohere_ef.dim, docs_embeddings[0].shape)\n","Embeddings: [array([ 3.43322754e-02,  1.16252899e-03, -5.25207520e-02,  1.32846832e-03,\n       -6.80541992e-02,  6.10961914e-02, -7.06176758e-02,  1.48925781e-01,\n        1.54174805e-01,  1.98516846e-02,  2.43835449e-02,  3.55224609e-02,\n        1.82952881e-02,  7.57446289e-02, -2.40783691e-02,  4.40063477e-02,\n...\n        0.06359863, -0.01971436, -0.02253723,  0.00354195,  0.00222015,\n        0.00184727,  0.03408813, -0.00777817,  0.04919434,  0.01519775,\n       -0.02862549,  0.04760742, -0.07891846,  0.0124054 ], dtype=float32)]\nDim: 384 (384,)\n","queries = [\"When was artificial intelligence founded\", \n           \"Where was Alan Turing born?\"]\n\nquery_embeddings = cohere_ef.encode_queries(queries)\n\nprint(\"Embeddings:\", query_embeddings)\nprint(\"Dim\", cohere_ef.dim, query_embeddings[0].shape)\n","Embeddings: [array([-1.33361816e-02,  9.79423523e-04, -7.28759766e-02, -1.93786621e-02,\n       -9.71679688e-02,  4.34875488e-02, -9.81445312e-02,  1.16882324e-01,\n        5.89904785e-02, -4.19921875e-02,  4.95910645e-02,  5.83496094e-02,\n        3.47595215e-02, -5.87463379e-03, -7.30514526e-03,  2.92816162e-02,\n...\n        0.00749969, -0.01192474,  0.02719116,  0.03347778,  0.07696533,\n        0.01409149,  0.00964355, -0.01681519, -0.0073204 ,  0.00043154,\n       -0.04577637,  0.03591919, -0.02807617, -0.04812622], dtype=float32)]\nDim 384 (384,)\n"],"headingContent":"Cohere","anchorList":[{"label":"Cohere","href":"Cohere","type":1,"isActive":false}]}

localization/v2.4.x/site/en/embeddings/embed-with-cohere.md

@@ -28,7 +28,9 @@ title: Embed Cohere
 pip install <span class="hljs-string">&quot;pymilvus[model]&quot;</span>
 <button class="copy-code-btn"></button></code></pre>
 <p>Then, instantiate the <code translate="no">CohereEmbeddingFunction</code>:</p>
-<pre><code translate="no" class="language-python">cohere_ef = <span class="hljs-title class_">CohereEmbeddingFunction</span>(
+<pre><code translate="no" class="language-python"><span class="hljs-keyword">from</span> pymilvus.<span class="hljs-property">model</span>.<span class="hljs-property">dense</span> <span class="hljs-keyword">import</span> <span class="hljs-title class_">CohereEmbeddingFunction</span>
+
+cohere_ef = <span class="hljs-title class_">CohereEmbeddingFunction</span>(
     model_name=<span class="hljs-string">&quot;embed-english-light-v3.0&quot;</span>,
     api_key=<span class="hljs-string">&quot;YOUR_COHERE_API_KEY&quot;</span>,
     input_type=<span class="hljs-string">&quot;search_document&quot;</span>,