TavilySearchAPIRetriever
Tavily’s Search API is a search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.
Overview
This will help you getting started with the Tavily Search API
retriever. For detailed documentation of
all TavilySearchAPIRetriever
features and configurations head to the
API
reference.
Integration details
Retriever | Source | Package |
---|---|---|
TavilySearchAPIRetriever | Information on the web. | @langchain/community |
Setup
You will need to populate a TAVILY_API_KEY
environment variable with
your Tavily API key or pass it into the constructor as apiKey
. Obtain
a key by signing up on their website.
If you want to get automated tracing from individual queries, you can also set your LangSmith API key by uncommenting below:
// process.env.LANGSMITH_API_KEY = "<YOUR API KEY HERE>";
// process.env.LANGSMITH_TRACING = "true";
Installation
This retriever lives in the @langchain/community
package:
- npm
- yarn
- pnpm
npm i @langchain/community @langchain/core
yarn add @langchain/community @langchain/core
pnpm add @langchain/community @langchain/core
Instantiation
Now we can instantiate our retriever:
import { TavilySearchAPIRetriever } from "@langchain/community/retrievers/tavily_search_api";
const retriever = new TavilySearchAPIRetriever({
k: 3,
});
For a full list of allowed arguments, see the official
documentation.
You can pass any param to the SDK via a kwargs
object.
Usage
const query = "what is the current weather in SF?";
await retriever.invoke(query);
[
Document {
pageContent: "{'location': {'name': 'San Francisco', 'region': 'California', 'country': 'United States of America', 'lat': 37.78, 'lon': -122.42, 'tz_id': 'America/Los_Angeles', 'localtime_epoch': 1722900266, 'localtime': '2024-08-05 16:24'}, 'current': {'last_updated_epoch': 1722899700, 'last_updated': '2024-08-05 16:15', 'temp_c': 16.8, 'temp_f': 62.2, 'is_day': 1, 'condition': {'text': 'Partly Cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}, 'wind_mph': 13.2, 'wind_kph': 21.2, 'wind_degree': 261, 'wind_dir': 'W', 'pressure_mb': 1014.0, 'pressure_in': 29.94, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 74, 'cloud': 60, 'feelslike_c': 16.8, 'feelslike_f': 62.2, 'windchill_c': 16.8, 'windchill_f': 62.2, 'heatindex_c': 16.8, 'heatindex_f': 62.2, 'dewpoint_c': 12.3, 'dewpoint_f': 54.1, 'vis_km': 10.0, 'vis_miles': 6.0, 'uv': 5.0, 'gust_mph': 17.3, 'gust_kph': 27.8}}",
metadata: {
title: 'Weather in San Francisco',
source: 'https://www.weatherapi.com/',
score: 0.9947009,
images: []
},
id: undefined
},
Document {
pageContent: 'Current Weather for Popular Cities . San Francisco, CA 56 ° F Mostly Cloudy; Manhattan, NY warning 85 ° F Fair; Schiller Park, IL (60176) 71 ° F Mostly Cloudy; Boston, MA warning 84 ° F Partly ...',
metadata: {
title: 'San Francisco, CA Hourly Weather Forecast | Weather Underground',
source: 'https://www.wunderground.com/hourly/us/ca/san-francisco/date/2024-08-02',
score: 0.9859904,
images: []
},
id: undefined
},
Document {
pageContent: 'San Francisco CA 37.77°N 122.41°W (Elev. 131 ft) Last Update: 2:42 pm PDT Aug 4, 2024. Forecast Valid: 5pm PDT Aug 4, 2024-6pm PDT Aug 11, 2024 . Forecast Discussion . Additional Resources. Radar & Satellite Image. Hourly Weather Forecast. ... Severe Weather ; Current Outlook Maps ; Drought ; Fire Weather ; Fronts/Precipitation Maps ; Current ...',
metadata: {
title: 'National Weather Service',
source: 'https://forecast.weather.gov/zipcity.php?inputstring=San+Francisco,CA',
score: 0.98141783,
images: []
},
id: undefined
}
]
Use within a chain
Like other retrievers, TavilySearchAPIRetriever
can be incorporated
into LLM applications via chains.
We will need a LLM or chat model:
Pick your chat model:
- OpenAI
- Anthropic
- FireworksAI
- MistralAI
- Groq
- VertexAI
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/openai
yarn add @langchain/openai
pnpm add @langchain/openai
Add environment variables
OPENAI_API_KEY=your-api-key
Instantiate the model
import { ChatOpenAI } from "@langchain/openai";
const llm = new ChatOpenAI({
model: "gpt-4o-mini",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/anthropic
yarn add @langchain/anthropic
pnpm add @langchain/anthropic
Add environment variables
ANTHROPIC_API_KEY=your-api-key
Instantiate the model
import { ChatAnthropic } from "@langchain/anthropic";
const llm = new ChatAnthropic({
model: "claude-3-5-sonnet-20240620",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/community
yarn add @langchain/community
pnpm add @langchain/community
Add environment variables
FIREWORKS_API_KEY=your-api-key
Instantiate the model
import { ChatFireworks } from "@langchain/community/chat_models/fireworks";
const llm = new ChatFireworks({
model: "accounts/fireworks/models/llama-v3p1-70b-instruct",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/mistralai
yarn add @langchain/mistralai
pnpm add @langchain/mistralai
Add environment variables
MISTRAL_API_KEY=your-api-key
Instantiate the model
import { ChatMistralAI } from "@langchain/mistralai";
const llm = new ChatMistralAI({
model: "mistral-large-latest",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/groq
yarn add @langchain/groq
pnpm add @langchain/groq
Add environment variables
GROQ_API_KEY=your-api-key
Instantiate the model
import { ChatGroq } from "@langchain/groq";
const llm = new ChatGroq({
model: "mixtral-8x7b-32768",
temperature: 0
});
Install dependencies
- npm
- yarn
- pnpm
npm i @langchain/google-vertexai
yarn add @langchain/google-vertexai
pnpm add @langchain/google-vertexai
Add environment variables
GOOGLE_APPLICATION_CREDENTIALS=credentials.json
Instantiate the model
import { ChatVertexAI } from "@langchain/google-vertexai";
const llm = new ChatVertexAI({
model: "gemini-1.5-flash",
temperature: 0
});
import { ChatPromptTemplate } from "@langchain/core/prompts";
import {
RunnablePassthrough,
RunnableSequence,
} from "@langchain/core/runnables";
import { StringOutputParser } from "@langchain/core/output_parsers";
import type { Document } from "@langchain/core/documents";
const prompt = ChatPromptTemplate.fromTemplate(`
Answer the question based only on the context provided.
Context: {context}
Question: {question}`);
const formatDocs = (docs: Document[]) => {
return docs.map((doc) => doc.pageContent).join("\n\n");
};
// See https://js.langchain.com/docs/tutorials/rag
const ragChain = RunnableSequence.from([
{
context: retriever.pipe(formatDocs),
question: new RunnablePassthrough(),
},
prompt,
llm,
new StringOutputParser(),
]);
await ragChain.invoke(query);
The current weather in San Francisco is partly cloudy with a temperature of 16.8°C (62.2°F). The wind is coming from the west at 13.2 mph (21.2 kph), and the humidity is at 74%. There is no precipitation, and visibility is 10 km (6 miles).
API reference
For detailed documentation of all TavilySearchAPIRetriever
features
and configurations head to the API
reference.
Related
- Retriever conceptual guide
- Retriever how-to guides