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deepgram-sdk-patterns Apply production-ready Deepgram SDK patterns for TypeScript and Python.
Use when implementing Deepgram integrations, refactoring SDK usage,
or establishing team coding standards for Deepgram.
Trigger: "deepgram SDK patterns", "deepgram best practices",
"deepgram code patterns", "idiomatic deepgram", "deepgram typescript".
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langchain-deploy-integration Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".
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2 archivos name deepgram-sdk-patterns description Apply production-ready Deepgram SDK patterns for TypeScript and Python.
Use when implementing Deepgram integrations, refactoring SDK usage,
or establishing team coding standards for Deepgram.
Trigger: "deepgram SDK patterns", "deepgram best practices",
"deepgram code patterns", "idiomatic deepgram", "deepgram typescript".
allowed-tools Read, Write, Edit version 1.13.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","deepgram","python","typescript","patterns"] compatibility Designed for Claude Code
Deepgram SDK Patterns
Overview
Production patterns for @deepgram/sdk (TypeScript) and deepgram-sdk (Python). Covers singleton client, typed wrappers, text-to-speech with Aura, audio intelligence pipeline, error handling, and SDK v5 migration path.
Prerequisites
npm install @deepgram/sdk or pip install deepgram-sdkDEEPGRAM_API_KEY environment variable configured
Instructions
Step 1: Singleton Client (TypeScript)
import { createClient, DeepgramClient } from '@deepgram/sdk' ;
class DeepgramService {
private static instance : DeepgramService ;
private client : DeepgramClient ;
private constructor (const apiKey = process.env .DEEPGRAM_API_KEY ;
if (!apiKey) throw new Error ('DEEPGRAM_API_KEY is required' );
this .client = createClient (apiKey);
}
static getInstance (): DeepgramService {
if (!this .instance ) this .instance = new DeepgramService ();
return this .instance
getClient
DeepgramClient
return
this
client
export
const
DeepgramService
getInstance
getClient
Step 2: Text-to-Speech with Aura import { createClient } from '@deepgram/sdk' ;
import { writeFileSync } from 'fs' ;
const deepgram = createClient (process.env .DEEPGRAM_API_KEY !);
async function textToSpeech (text : string , outputPath : string const response = await deepgram.speak .request (
{ text },
{
model : 'aura-2-thalia-en' ,
encoding : 'linear16' ,
container : 'wav' ,
sample_rate : 24000 ,
}
);
const stream = await response.getStream ();
if (!stream) throw new Error ('No audio stream returned' );
const reader = stream.getReader ();
const chunks : Uint8Array [] = [];
while (true ) {
const { done, value } = await reader.read ();
if (done) break ;
chunks.push (value);
}
const buffer = Buffer .concat (chunks);
writeFileSync (outputPath, buffer);
console .log (`Audio saved: ${outputPath} (${buffer.length} bytes)` );
return buffer;
}
Step 3: Audio Intelligence Pipeline async function analyzeConversation (audioUrl : string const { result, error } = await deepgram.listen .prerecorded .transcribeUrl (
{ url : audioUrl },
{
model : 'nova-3' ,
smart_format : true ,
diarize : true ,
utterances : true ,
summarize : 'v2' ,
detect_topics : true ,
sentiment : true ,
intents : true ,
}
);
if (error) throw error;
return {
transcript : result.results .channels [0 ].alternatives [0 ].transcript ,
summary : result.results .summary ?.short ,
topics : result.results .topics ?.segments ?.map ((s : any ({
text : s.text ,
topics : s.topics .map ((t : any t.topic ),
})),
sentiments : result.results .sentiments ?.segments ?.map ((s : any ({
text : s.text ,
sentiment : s.sentiment ,
confidence : s.sentiment_score ,
})),
intents : result.results .intents ?.segments ?.map ((s : any ({
text : s.text ,
intent : s.intents [0 ]?.intent ,
confidence : s.intents [0 ]?.confidence_score ,
})),
};
}
Step 4: Python Production Patterns from deepgram import DeepgramClient, PrerecordedOptions, LiveOptions, SpeakOptions
import os
class DeepgramService :
_instance = None
def __new__ (cls ):
if cls._instance is None :
cls._instance = super ().__new__(cls)
cls._instance.client = DeepgramClient(os.environ["DEEPGRAM_API_KEY" ])
return cls._instance
def transcribe_url (self, url: str , **kwargs ):
options = PrerecordedOptions(
model=kwargs.get("model" , "nova-3" ),
smart_format=True ,
diarize=kwargs.get("diarize" , False ),
summarize=kwargs.get("summarize" , False ),
)
source = {"url" : url}
return self .client.listen.rest.v("1" ).transcribe_url(source, options)
def transcribe_file (self, path: str , **kwargs ):
with open (path, "rb" ) as f:
source = {"buffer" : f.read(), "mimetype" : self ._mimetype(path)}
options = PrerecordedOptions(
model=kwargs.get("model" , "nova-3" ),
smart_format=True ,
diarize=kwargs.get("diarize" , False ),
)
return self .client.listen.rest.v("1" ).transcribe_file(source, options)
def text_to_speech (self, text: str , output_path: str ):
options = SpeakOptions(model="aura-2-thalia-en" , encoding="linear16" )
response = self .client.speak.rest.v("1" ).save(output_path, {"text" : text}, options)
return response
@staticmethod
def _mimetype (path: str ) -> str :
ext = path.rsplit("." , 1 )[-1 ].lower()
return {"wav" : "audio/wav" , "mp3" : "audio/mpeg" , "flac" : "audio/flac" ,
"ogg" : "audio/ogg" , "m4a" : "audio/mp4" }.get(ext, "audio/wav" )
Step 5: Typed Response Helpers
interface TranscriptWord {
word : string ;
start : number ;
end : number ;
confidence : number ;
speaker ?: number ;
punctuated_word ?: string ;
}
interface TranscriptResult {
transcript : string ;
confidence : number ;
words : TranscriptWord [];
duration : number ;
requestId : string ;
}
function parseResult (result : any TranscriptResult {
const alt = result.results .channels [0 ].alternatives [0 ];
return {
transcript : alt.transcript ,
confidence : alt.confidence ,
words : alt.words ?? [],
duration : result.metadata .duration ,
requestId : result.metadata .request_id ,
};
}
Step 6: SDK v5 Migration Notes
import { createClient } from '@deepgram/sdk' ;
const dg = createClient (apiKey);
await dg.listen .prerecorded .transcribeUrl (source, options);
await dg.listen .live (options);
await dg.speak .request ({ text }, options);
import { DeepgramClient } from '@deepgram/sdk' ;
const dg = new DeepgramClient ({ apiKey });
await dg.listen .v1 .media .transcribeUrl (source, options);
await dg.listen .v1 .connect (options);
await dg.speak .v1 .audio .generate ({ text }, options);
Output
Singleton client pattern with environment validation
Text-to-speech (Aura-2) with stream-to-file
Audio intelligence pipeline (summary, topics, sentiment, intents)
Python production service class
Typed response helpers
v5 migration reference
Error Handling Error Cause Solution 401 UnauthorizedInvalid API key Check DEEPGRAM_API_KEY value 400 Unsupported formatBad audio codec Convert to WAV/MP3/FLAC speak.request is not a functionSDK version mismatch Check import, v5 uses speak.v1.audio.generate Empty TTS response Empty text input Validate text is non-empty before calling summarize returns nullFeature not enabled Pass summarize: 'v2' (string, not boolean)
Resources
Next Steps Proceed to deepgram-data-handling for transcript storage and processing patterns.
