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Jun 25, 2024
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Updated docs #1542

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15ed6a1
Update opensource ai
SilasMarvin Jun 25, 2024
c92d107
Updated the Chatbot guide to the 1.0 SDK version
SilasMarvin Jun 25, 2024
9e1ffa4
Update search with docs on re-ranking
SilasMarvin Jun 25, 2024
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116 changes: 116 additions & 0 deletions pgml-cms/docs/api/client-sdk/search.md
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Expand Up @@ -641,3 +641,119 @@ char **results = pgml_collectionc_vector_search(collection, "{\
{% endtabs %}

The above query would filter out all documents that do not have a key `special` with a value `True` or (have a key `user_id` equal to 1 and a key `user_score` less than 100).

## **Re-ranking**

Vector search results can be reranked in the same query they are retrieved in. To enable this, provide the `rerank` key.

{% tabs %}
{% tab title="JavaScript" %}
```javascript
const results = await collection.vector_search(
{
query: {
fields: {
body: {
query: "What is the best database?", parameters: {
prompt:
"Represent this sentence for searching relevant passages: ",
}
},
},
},
rerank: {
model: "mixedbread-ai/mxbai-rerank-base-v1",
query: "What is the best database?",
num_documents_to_rerank: 100,
},
limit: 5,
},
pipeline,
);
```
{% endtab %}

{% tab title="Python" %}
```python
results = await collection.vector_search(
{
"query": {
"fields": {
"body": {
"query": "What is the best database?",
"parameters": {
"prompt": "Represent this sentence for searching relevant passages: ",
},
},
},
},
"rerank": {
"model": "mixedbread-ai/mxbai-rerank-base-v1",
"query": "What is the best database",
"num_documents_to_rerank": 100,
},
"limit": 5,
},
pipeline,
)
```
{% endtab %}

{% tab title="Rust" %}
```rust
let results = collection
.vector_search(
serde_json::json!({
"query": {
"fields": {
"body": {
"query": "What is the best database?",
"parameters": {
"prompt": "Represent this sentence for searching relevant passages: ",
},
},
},
},
"rerank": {
"model": "mixedbread-ai/mxbai-rerank-base-v1",
"query": "What is the best database",
"num_documents_to_rerank": 100,
},
"limit": 5,
})
.into(),
&mut pipeline,
)
.await?;
```
{% endtab %}

{% tab title="C" %}
```cpp
r_size = 0;
char **results = pgml_collectionc_vector_search(collection, "{\
\"query\": {\
\"fields\": {\
\"body\": {\
\"query\": \"What is the best database?\",\
\"parameters\": {\
\"prompt\": \"Represent this sentence for searching relevant passages: \"\
}\
}\
}\
},\
\"rerank\": {\
\"model\": \"mixedbread-ai/mxbai-rerank-base-v1\",\
\"query\": \"What is the best database\",\
\"num_documents_to_rerank\": 100\
},\
\"limit\": 5\
}",
pipeline, &r_size);
```
{% endtab %}
{% endtabs %}

This query will first get the top 100 documents from the initial vector search and then rerank them using the `mixedbread-ai/mxbai-rerank-base-v1` cross-encoder.

You can specify the number of documents to rerank with the `num_documents_to_rerank` parameter. The query returns the top `limit` results after re-ranking.
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