Search for documents

Perform a similarity search on a Telnyx Storage Bucket, returning the most similar num_docs document chunks to the query.

Currently the only available distance metric is cosine similarity which will return a distance between 0 and 1. The lower the distance, the more similar the returned document chunks are to the query. A certainty will also be returned, which is a value between 0 and 1 where the higher the certainty, the more similar the document. You can read more about Weaviate distance metrics here: Weaviate Docs

If a bucket was embedded using a custom loader, such as intercom, the additional metadata will be returned in the loader_metadata field.

POST

embeddings

similarity-search

JavaScript

import Telnyx from 'telnyx';

const client = new Telnyx({
  apiKey: process.env['TELNYX_API_KEY'], // This is the default and can be omitted
});

const response = await client.ai.embeddings.similaritySearch({
  bucket_name: 'bucket_name',
  query: 'query',
});

console.log(response.data);

import os
from telnyx import Telnyx

client = Telnyx(
    api_key=os.environ.get("TELNYX_API_KEY"),  # This is the default and can be omitted
)
response = client.ai.embeddings.similarity_search(
    bucket_name="bucket_name",
    query="query",
)
print(response.data)

package main

import (
	"context"
	"fmt"

	"github.com/team-telnyx/telnyx-go"
	"github.com/team-telnyx/telnyx-go/option"
)

func main() {
	client := telnyx.NewClient(
		option.WithAPIKey("My API Key"),
	)
	response, err := client.AI.Embeddings.SimilaritySearch(context.TODO(), telnyx.AIEmbeddingSimilaritySearchParams{
		BucketName: "bucket_name",
		Query:      "query",
	})
	if err != nil {
		panic(err.Error())
	}
	fmt.Printf("%+v\n", response.Data)
}

package com.telnyx.sdk.example;

import com.telnyx.sdk.client.TelnyxClient;
import com.telnyx.sdk.client.okhttp.TelnyxOkHttpClient;
import com.telnyx.sdk.models.ai.embeddings.EmbeddingSimilaritySearchParams;
import com.telnyx.sdk.models.ai.embeddings.EmbeddingSimilaritySearchResponse;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        TelnyxClient client = TelnyxOkHttpClient.fromEnv();

        EmbeddingSimilaritySearchParams params = EmbeddingSimilaritySearchParams.builder()
            .bucketName("bucket_name")
            .query("query")
            .build();
        EmbeddingSimilaritySearchResponse response = client.ai().embeddings().similaritySearch(params);
    }
}

require "telnyx"

telnyx = Telnyx::Client.new(api_key: "My API Key")

response = telnyx.ai.embeddings.similarity_search(bucket_name: "bucket_name", query: "query")

puts(response)

<?php

require_once dirname(__DIR__) . '/vendor/autoload.php';

use Telnyx\Client;
use Telnyx\Core\Exceptions\APIException;

$client = new Client(apiKey: getenv('TELNYX_API_KEY') ?: 'My API Key');

try {
  $response = $client->ai->embeddings->similaritySearch(
    bucketName: 'bucket_name', query: 'query', numOfDocs: 0
  );

  var_dump($response);
} catch (APIException $e) {
  echo $e->getMessage();
}

telnyx ai:embeddings similarity-search \
  --api-key 'My API Key' \
  --bucket-name bucket_name \
  --query query

curl --request POST \
  --url https://api.telnyx.com/v2/ai/embeddings/similarity-search \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "bucket_name": "<string>",
  "query": "<string>",
  "num_of_docs": 3
}
'

{
  "data": [
    {
      "document_chunk": "<string>",
      "distance": 123,
      "metadata": {
        "source": "<string>",
        "checksum": "<string>",
        "embedding": "<string>",
        "filename": "<string>",
        "certainty": 123,
        "loader_metadata": {}
      }
    }
  ]
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json

bucket_name

string

required

query

string

required

num_of_docs

integer

default:3

Response

Successful Response

data

EmbeddingSimilaritySearchDocument · object[]

required

Show child attributes

Disable AI for an Embedded Bucket

Embed URL content

⌘I

JavaScript

import Telnyx from 'telnyx';

const client = new Telnyx({
  apiKey: process.env['TELNYX_API_KEY'], // This is the default and can be omitted
});

const response = await client.ai.embeddings.similaritySearch({
  bucket_name: 'bucket_name',
  query: 'query',
});

console.log(response.data);

import os
from telnyx import Telnyx

client = Telnyx(
    api_key=os.environ.get("TELNYX_API_KEY"),  # This is the default and can be omitted
)
response = client.ai.embeddings.similarity_search(
    bucket_name="bucket_name",
    query="query",
)
print(response.data)

package main

import (
	"context"
	"fmt"

	"github.com/team-telnyx/telnyx-go"
	"github.com/team-telnyx/telnyx-go/option"
)

func main() {
	client := telnyx.NewClient(
		option.WithAPIKey("My API Key"),
	)
	response, err := client.AI.Embeddings.SimilaritySearch(context.TODO(), telnyx.AIEmbeddingSimilaritySearchParams{
		BucketName: "bucket_name",
		Query:      "query",
	})
	if err != nil {
		panic(err.Error())
	}
	fmt.Printf("%+v\n", response.Data)
}

package com.telnyx.sdk.example;

import com.telnyx.sdk.client.TelnyxClient;
import com.telnyx.sdk.client.okhttp.TelnyxOkHttpClient;
import com.telnyx.sdk.models.ai.embeddings.EmbeddingSimilaritySearchParams;
import com.telnyx.sdk.models.ai.embeddings.EmbeddingSimilaritySearchResponse;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        TelnyxClient client = TelnyxOkHttpClient.fromEnv();

        EmbeddingSimilaritySearchParams params = EmbeddingSimilaritySearchParams.builder()
            .bucketName("bucket_name")
            .query("query")
            .build();
        EmbeddingSimilaritySearchResponse response = client.ai().embeddings().similaritySearch(params);
    }
}

require "telnyx"

telnyx = Telnyx::Client.new(api_key: "My API Key")

response = telnyx.ai.embeddings.similarity_search(bucket_name: "bucket_name", query: "query")

puts(response)

<?php

require_once dirname(__DIR__) . '/vendor/autoload.php';

use Telnyx\Client;
use Telnyx\Core\Exceptions\APIException;

$client = new Client(apiKey: getenv('TELNYX_API_KEY') ?: 'My API Key');

try {
  $response = $client->ai->embeddings->similaritySearch(
    bucketName: 'bucket_name', query: 'query', numOfDocs: 0
  );

  var_dump($response);
} catch (APIException $e) {
  echo $e->getMessage();
}

telnyx ai:embeddings similarity-search \
  --api-key 'My API Key' \
  --bucket-name bucket_name \
  --query query

curl --request POST \
  --url https://api.telnyx.com/v2/ai/embeddings/similarity-search \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "bucket_name": "<string>",
  "query": "<string>",
  "num_of_docs": 3
}
'

{
  "data": [
    {
      "document_chunk": "<string>",
      "distance": 123,
      "metadata": {
        "source": "<string>",
        "checksum": "<string>",
        "embedding": "<string>",
        "filename": "<string>",
        "certainty": 123,
        "loader_metadata": {}
      }
    }
  ]
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}

Overview

Conversation History

Conversation Histories API

Embedding

For AI Agents

Search for documents

Authorizations

Body

Response