Skip to main content

llama-index llms fireworks integration

Project description

LlamaIndex Llms Integration: Fireworks

Installation

  1. Install the required Python packages:

    %pip install llama-index-llms-fireworks
    %pip install llama-index
    
  2. Set the Fireworks API key as an environment variable or pass it directly to the class constructor.

Usage

Basic Completion

To generate a simple completion, use the complete method:

from llama_index.llms.fireworks import Fireworks

resp = Fireworks().complete("Paul Graham is ")
print(resp)

Example output:

Paul Graham is a well-known essayist, programmer, and startup entrepreneur. He co-founded Y Combinator, which supported startups like Dropbox, Airbnb, and Reddit.

Basic Chat

To simulate a chat with multiple messages:

from llama_index.core.llms import ChatMessage
from llama_index.llms.fireworks import Fireworks

messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]
resp = Fireworks().chat(messages)
print(resp)

Example output:

Arr matey, ye be askin' for me name? Well, I be known as Captain Redbeard the Terrible!

Streaming Completion

To stream a response in real-time using stream_complete:

from llama_index.llms.fireworks import Fireworks

llm = Fireworks()
resp = llm.stream_complete("Paul Graham is ")

for r in resp:
    print(r.delta, end="")

Example output (partial):

Paul Graham is a well-known essayist, programmer, and venture capitalist...

Streaming Chat

For a streamed conversation, use stream_chat:

from llama_index.llms.fireworks import Fireworks
from llama_index.core.llms import ChatMessage

llm = Fireworks()
messages = [
    ChatMessage(
        role="system", content="You are a pirate with a colorful personality"
    ),
    ChatMessage(role="user", content="What is your name"),
]
resp = llm.stream_chat(messages)

for r in resp:
    print(r.delta, end="")

Example output (partial):

Arr matey, ye be askin' for me name? Well, I be known as Captain Redbeard the Terrible...

Model Configuration

To configure the model for more specific behavior:

from llama_index.llms.fireworks import Fireworks

llm = Fireworks(model="accounts/fireworks/models/firefunction-v1")
resp = llm.complete("Paul Graham is ")
print(resp)

Example output:

Paul Graham is an English-American computer scientist, entrepreneur, venture capitalist, and blogger.

API Key Configuration

To use separate API keys for different instances:

from llama_index.llms.fireworks import Fireworks

llm = Fireworks(
    model="accounts/fireworks/models/firefunction-v1", api_key="YOUR_API_KEY"
)
resp = llm.complete("Paul Graham is ")
print(resp)

LLM Implementation example

https://docs.llamaindex.ai/en/stable/examples/llm/fireworks/

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

llama_index_llms_fireworks-0.3.1.tar.gz (4.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

llama_index_llms_fireworks-0.3.1-py3-none-any.whl (5.5 kB view details)

Uploaded Python 3

File details

Details for the file llama_index_llms_fireworks-0.3.1.tar.gz.

File metadata

  • Download URL: llama_index_llms_fireworks-0.3.1.tar.gz
  • Upload date:
  • Size: 4.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.12.3 Linux/6.8.0-1017-azure

File hashes

Hashes for llama_index_llms_fireworks-0.3.1.tar.gz
Algorithm Hash digest
SHA256 06101bdf8f2fb35fc9637b0b1b77266022724e5d0c21eed6f5a0374d763bb4c4
MD5 f24b91b39a560afb11c736840822ca78
BLAKE2b-256 0d8ff0b5c121491330cea46956e768fdbeece42941ff917763a531ad65bd8776

See more details on using hashes here.

File details

Details for the file llama_index_llms_fireworks-0.3.1-py3-none-any.whl.

File metadata

File hashes

Hashes for llama_index_llms_fireworks-0.3.1-py3-none-any.whl
Algorithm Hash digest
SHA256 db77969a6fdca83882536c2b893b8553ddd6008a35c5bace1236fb076c287d14
MD5 bf6c856aeba365c753fce174cca0da59
BLAKE2b-256 3151b4dad74c71cbe394e4de1e21f5cbc29b86d8371a69ce3c3525d16ae774c6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page