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fix post_process_function on rerank_pipeline_with_bge-rerank-m3-v2_model_deployed_on_Sagemaker.md (opensearch-project#3296)
* fix post_process_function bug on sort results for rerank_pipeline_with_bge-rerank-m3-v2_model_deployed_on_Sagemaker.md (opensearch-project#3247) Signed-off-by: tkykenmt <[email protected]> * fix typo Signed-off-by: Yaliang Wu <[email protected]> --------- Signed-off-by: tkykenmt <[email protected]> Signed-off-by: Yaliang Wu <[email protected]> Co-authored-by: Yaliang Wu <[email protected]>
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‎docs/tutorials/rerank/rerank_pipeline_with_bge-rerank-m3-v2_model_deployed_on_Sagemaker.md‎

Lines changed: 127 additions & 14 deletions
Original file line numberDiff line numberDiff line change
@@ -59,10 +59,39 @@ result = predictor.predict(data={
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]
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})
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print(json.dumps(sorted(result, key=lambda x: x['index']), indent=2))
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print(json.dumps(result, indent=2))
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```
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The reranking results are as follows:
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The reranking result is ordering by the highest score first:
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```
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[
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{
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"index": 2,
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"score": 0.92879725
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},
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{
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"index": 0,
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"score": 0.013636836
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},
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{
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"index": 1,
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"score": 0.000593021
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},
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{
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"index": 3,
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"score": 0.00012148176
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}
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]
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```
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You can sort the result by index number.
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```python
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print(json.dumps(sorted(result, key=lambda x: x['index']),indent=2))
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```
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The results are as follows:
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```
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[
@@ -121,9 +150,51 @@ POST /_plugins/_ml/connectors/_create
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"headers": {
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"content-type": "application/json"
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},
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"request_body": "{ \"query\": \"${parameters.query}\", \"texts\": ${parameters.texts} }",
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"pre_process_function": "\n def query_text = params.query_text;\n def text_docs = params.text_docs;\n def textDocsBuilder = new StringBuilder('[');\n for (int i=0; i<text_docs.length; i++) {\n textDocsBuilder.append('\"');\n textDocsBuilder.append(text_docs[i]);\n textDocsBuilder.append('\"');\n if (i<text_docs.length - 1) {\n textDocsBuilder.append(',');\n }\n }\n textDocsBuilder.append(']');\n def parameters = '{ \"query\": \"' + query_text + '\", \"texts\": ' + textDocsBuilder.toString() + ' }';\n return '{\"parameters\": ' + parameters + '}';\n",
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"post_process_function": "\n \n def dataType = \"FLOAT32\";\n \n \n if (params.result == null)\n {\n return 'no result generated';\n //return params.response;\n }\n def outputs = params.result;\n \n def sorted_outputs = outputs;\n for (int i=0; i<outputs.length; i++) {\n def idx = new BigDecimal(outputs[i].index.toString()).intValue();\n sorted_outputs[idx] = outputs[i];\n }\n def resultBuilder = new StringBuilder('[');\n for (int i=0; i<outputs.length; i++) {\n resultBuilder.append(' {\"name\": \"similarity\", \"data_type\": \"FLOAT32\", \"shape\": [1],');\n //resultBuilder.append('{\"name\": \"similarity\"}');\n \n resultBuilder.append('\"data\": [');\n resultBuilder.append(outputs[i].score);\n resultBuilder.append(']}');\n if (i<outputs.length - 1) {\n resultBuilder.append(',');\n }\n }\n resultBuilder.append(']');\n \n return resultBuilder.toString();\n "
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"pre_process_function": """
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def query_text = params.query_text;
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def text_docs = params.text_docs;
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def textDocsBuilder = new StringBuilder('[');
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for (int i=0; i<text_docs.length; i++) {
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textDocsBuilder.append('"');
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textDocsBuilder.append(text_docs[i]);
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textDocsBuilder.append('"');
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if (i<text_docs.length - 1) {
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textDocsBuilder.append(',');
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}
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}
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textDocsBuilder.append(']');
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def parameters = '{ "query": "' + query_text + '", "texts": ' + textDocsBuilder.toString() + ' }';
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return '{"parameters": ' + parameters + '}';
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""",
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"request_body": """
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{
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"query": "${parameters.query}",
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"texts": ${parameters.texts}
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}
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""",
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"post_process_function": """
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if (params.result == null || params.result.length == 0) {
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throw new IllegalArgumentException("Post process function input is empty.");
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}
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def outputs = params.result;
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def scores = new Double[outputs.length];
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for (int i=0; i<outputs.length; i++) {
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def index = new BigDecimal(outputs[i].index.toString()).intValue();
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scores[index] = outputs[i].score;
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}
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def resultBuilder = new StringBuilder('[');
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for (int i=0; i<scores.length; i++) {
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resultBuilder.append(' {"name": "similarity", "data_type": "FLOAT32", "shape": [1],');
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resultBuilder.append('"data": [');
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resultBuilder.append(scores[i]);
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resultBuilder.append(']}');
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if (i<outputs.length - 1) {
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resultBuilder.append(',');
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}
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}
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resultBuilder.append(']');
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return resultBuilder.toString();
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"""
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}
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]
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}
@@ -152,9 +223,51 @@ POST /_plugins/_ml/connectors/_create
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"headers": {
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"content-type": "application/json"
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},
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"request_body": "{ \"query\": \"${parameters.query}\", \"texts\": ${parameters.texts} }",
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"pre_process_function": "\n def query_text = params.query_text;\n def text_docs = params.text_docs;\n def textDocsBuilder = new StringBuilder('[');\n for (int i=0; i<text_docs.length; i++) {\n textDocsBuilder.append('\"');\n textDocsBuilder.append(text_docs[i]);\n textDocsBuilder.append('\"');\n if (i<text_docs.length - 1) {\n textDocsBuilder.append(',');\n }\n }\n textDocsBuilder.append(']');\n def parameters = '{ \"query\": \"' + query_text + '\", \"texts\": ' + textDocsBuilder.toString() + ' }';\n return '{\"parameters\": ' + parameters + '}';\n",
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"post_process_function": "\n \n def dataType = \"FLOAT32\";\n \n \n if (params.result == null)\n {\n return 'no result generated';\n //return params.response;\n }\n def outputs = params.result;\n \n def sorted_outputs = outputs;\n for (int i=0; i<outputs.length; i++) {\n def idx = new BigDecimal(outputs[i].index.toString()).intValue();\n sorted_outputs[idx] = outputs[i];\n }\n def resultBuilder = new StringBuilder('[');\n for (int i=0; i<outputs.length; i++) {\n resultBuilder.append(' {\"name\": \"similarity\", \"data_type\": \"FLOAT32\", \"shape\": [1],');\n //resultBuilder.append('{\"name\": \"similarity\"}');\n \n resultBuilder.append('\"data\": [');\n resultBuilder.append(outputs[i].score);\n resultBuilder.append(']}');\n if (i<outputs.length - 1) {\n resultBuilder.append(',');\n }\n }\n resultBuilder.append(']');\n \n return resultBuilder.toString();\n "
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"pre_process_function": """
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def query_text = params.query_text;
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def text_docs = params.text_docs;
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def textDocsBuilder = new StringBuilder('[');
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for (int i=0; i<text_docs.length; i++) {
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textDocsBuilder.append('"');
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textDocsBuilder.append(text_docs[i]);
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textDocsBuilder.append('"');
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if (i<text_docs.length - 1) {
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textDocsBuilder.append(',');
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}
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}
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textDocsBuilder.append(']');
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def parameters = '{ "query": "' + query_text + '", "texts": ' + textDocsBuilder.toString() + ' }';
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return '{"parameters": ' + parameters + '}';
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""",
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"request_body": """
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{
244+
"query": "${parameters.query}",
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"texts": ${parameters.texts}
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}
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""",
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"post_process_function": """
249+
if (params.result == null || params.result.length == 0) {
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throw new IllegalArgumentException("Post process function input is empty.");
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}
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def outputs = params.result;
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def scores = new Double[outputs.length];
254+
for (int i=0; i<outputs.length; i++) {
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def index = new BigDecimal(outputs[i].index.toString()).intValue();
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scores[index] = outputs[i].score;
257+
}
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def resultBuilder = new StringBuilder('[');
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for (int i=0; i<scores.length; i++) {
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resultBuilder.append(' {"name": "similarity", "data_type": "FLOAT32", "shape": [1],');
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resultBuilder.append('"data": [');
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resultBuilder.append(scores[i]);
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resultBuilder.append(']}');
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if (i<outputs.length - 1) {
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resultBuilder.append(',');
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}
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}
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resultBuilder.append(']');
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return resultBuilder.toString();
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"""
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}
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]
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}
@@ -188,7 +301,7 @@ POST _plugins/_ml/models/your_model_id/_predict
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}
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```
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Each item in the `inputs` array comprises a `query_text` and a `text_docs` string, separated by a ` . `
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Each item in the array comprises a `query_text` and a `text_docs` string, separated by a ` . `
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Alternatively, you can test the model as follows:
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```json
@@ -209,6 +322,10 @@ The connector `pre_process_function` transforms the input into the format requir
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By default, the SageMaker model output has the following format:
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```json
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[
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{
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"index": 2,
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"score": 0.92879725
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},
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{
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"index": 0,
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"score": 0.013636836
@@ -217,18 +334,14 @@ By default, the SageMaker model output has the following format:
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"index": 1,
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"score": 0.000593021
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},
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{
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"index": 2,
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"score": 0.92879725
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},
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{
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"index": 3,
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"score": 0.00012148176
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}
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]
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```
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The connector `post_process_function` transforms the model's output into a format that the [Reranker processor](https://opensearch.org/docs/latest/search-plugins/search-pipelines/rerank-processor/) can interpret. This adapted format is as follows:
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The connector `post_process_function` transforms the model's output into a format that the [Reranker processor](https://opensearch.org/docs/latest/search-plugins/search-pipelines/rerank-processor/) can interpret, and order result by index. This adapted format is as follows:
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```json
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{
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"inference_results": [

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