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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".
الانتقال إلى التثبيت سوق المهارات اكتشف واستكشف مهارات الذكاء الاصطناعي التي بناها المجتمع.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
عرض تفاصيل Prompt يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill deepgram-sdk-patternsيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
تحميل Zip جاري التحميل... المزيد من هذا المستودع 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".
langchain-langgraph-agents Build a correct LangGraph 1.0 ReAct agent with create_react_agent — typed tools, error propagation, recursion caps, and stop conditions that actually stop. Use when writing a first tool-calling agent, migrating from AgentExecutor or initialize_agent, or diagnosing an agent that loops on vague prompts. Trigger with "langgraph agent", "create_react_agent", "langgraph tool calling", "AgentExecutor migration", or "agent loop cost".
langchain-langgraph-human-in-loop Build LangGraph 1.0 human-in-the-loop approval flows with interrupt_before /
interrupt_after and Command(resume=...) — JSON-serializable state, clean
resume semantics, and UI wiring for approval decisions. Use when adding an
approval gate before an expensive tool call, wiring a Slack/web UI for agent
approvals, or debugging a graph that crashes on interrupt.
Trigger with "langgraph human in loop", "langgraph interrupt_before",
"langgraph approval flow", "Command resume", "langgraph HITL".
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.
