Integrations

Langchain

LangChain is a framework for developing context-aware, reasoning applications powered by language models.

To install the LangChain x Together library, run:

pip install --upgrade langchain-together

Here's sample code to get you started with Langchain + Together AI:

from langchain_together import ChatTogether

chat = ChatTogether(model="meta-llama/Llama-3-70b-chat-hf")

for m in chat.stream("Tell me fun things to do in NYC"):
    print(m.content, end="", flush=True)

Output:

The city that never sleeps! New York City is a hub of entertainment, culture, and adventure, offering countless fun things to do for visitors of all ages and interests. Here are some ideas to get you started:

**Iconic Landmarks and Attractions:**

1. **Statue of Liberty and Ellis Island**: Take a ferry to Liberty Island to see the iconic statue up close and visit the Ellis Island Immigration Museum.
2. **Central Park**: Explore the 843-acre green oasis in the middle of Manhattan, featuring lakes, gardens, and plenty of walking paths.
3. **Empire State Building**: Enjoy panoramic views of the city from the observation deck of this iconic skyscraper.
4. **The Metropolitan Museum of Art**: One of the world's largest and most famous museums, with a collection that spans over 5,000 years of human history.

LlamaIndex

LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs).

Here's sample code to get you started with Llama Index + Together AI:

!pip install llama-index langchain

from llama_index.llms import OpenAILike

llm = OpenAILike(
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
    api_base="https://api.together.xyz/v1",
    api_key="TOGETHER_API_KEY",
    is_chat_model=True,
    is_function_calling_model=True,
    temperature=0.1,
)

response = llm.complete("Write up to 500 words essay explaining Large Language Models")

print(response)

Pinecone

Pinecone is a vector database that helps companies build RAG applications.

Here's some sample code to get you started with Pinecone + Together AI:

from pinecone import Pinecone, ServerlessSpec
from together import Together

pc = Pinecone(
  api_key="PINECONE_API_KEY", 
  source_tag="TOGETHER_AI"
)
client = Together()

# Create an index in pinecone
index = pc.create_index(
    name="serverless-index",
    dimension=1536,
    metric="cosine",
    spec=ServerlessSpec(cloud="aws", region="us-west-2"),
)

# Create an embedding on Together AI
textToEmbed = "Our solar system orbits the Milky Way galaxy at about 515,000 mph"
embeddings = client.embeddings.create(
    model="togethercomputer/m2-bert-80M-8k-retrieval", 
  	input=textToEmbed
)

# Use index.upsert() to insert embeddings and index.query() to query for similar vectors