llama-index llms openai integration
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Author: llama-index
Requires Python: <4.0,>=3.10
Classifiers
LlamaIndex Llms Integration: Openai
Installation
To install the required package, run:
%pip install llama-index-llms-openai
Setup
- Set your OpenAI API key as an environment variable. You can replace
"sk-..."with your actual API key:
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
Basic Usage
Generate Completions
To generate a completion for a prompt, use the complete method:
from llama_index.llms.openai import OpenAI
resp = OpenAI().complete("Paul Graham is ")
print(resp)
Chat Responses
To send a chat message and receive a response, create a list of ChatMessage instances and use the chat method:
from llama_index.core.llms import ChatMessage
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality."
),
ChatMessage(role="user", content="What is your name?"),
]
resp = OpenAI().chat(messages)
print(resp)
Streaming Responses
Stream Complete
To stream responses for a prompt, use the stream_complete method:
from llama_index.llms.openai import OpenAI
llm = OpenAI()
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
Stream Chat
To stream chat responses, use the stream_chat method:
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
llm = OpenAI()
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="")
Configure Model
You can specify a particular model when creating the OpenAI instance:
llm = OpenAI(model="gpt-3.5-turbo")
resp = llm.complete("Paul Graham is ")
print(resp)
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality."
),
ChatMessage(role="user", content="What is your name?"),
]
resp = llm.chat(messages)
print(resp)
Asynchronous Usage
You can also use asynchronous methods for completion:
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")
resp = await llm.acomplete("Paul Graham is ")
print(resp)
Set API Key at a Per-Instance Level
If desired, you can have separate LLM instances use different API keys:
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo", api_key="BAD_KEY")
resp = OpenAI().complete("Paul Graham is ")
print(resp)
LLM Implementation example
0.7.5
Mar 30, 2026
0.7.4
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0.7.3
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0.6.9
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0.6.7
Nov 03, 2025
0.6.6
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0.6.5
Oct 15, 2025
0.6.4
Oct 07, 2025
0.6.3
Oct 07, 2025
0.6.2
Oct 06, 2025
0.6.1
Sep 25, 2025
0.6.0
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0.5.6
Sep 08, 2025
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Sep 08, 2025
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0.3.37
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0.3.33
Apr 10, 2025
0.3.32
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0.3.31
Apr 08, 2025
0.3.30
Apr 02, 2025
0.3.29
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0.3.28
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0.3.27
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0.3.26
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0.3.25
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0.3.24
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0.3.19
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0.3.18
Feb 06, 2025
0.3.17
Feb 03, 2025
0.3.16
Feb 01, 2025
0.3.15
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0.3.14
Jan 22, 2025
0.3.13
Jan 08, 2025
0.3.12
Dec 19, 2024
0.3.11
Dec 18, 2024
0.3.10
Dec 11, 2024
0.3.9
Dec 11, 2024
0.3.8
Dec 11, 2024
0.3.7
Dec 11, 2024
0.3.6
Dec 11, 2024
0.3.5
Dec 11, 2024
0.3.4
Dec 10, 2024
0.3.3
Dec 08, 2024
0.3.2
Nov 25, 2024
0.3.1
Nov 20, 2024
0.3.0
Nov 18, 2024
0.2.16
Oct 22, 2024
0.2.15
Oct 16, 2024
0.2.14
Oct 15, 2024
0.2.13
Oct 09, 2024
0.2.12
Oct 07, 2024
0.2.11
Oct 03, 2024
0.2.10
Oct 02, 2024
0.2.9
Sep 18, 2024
0.2.8
Sep 17, 2024
0.2.7
Sep 14, 2024
0.2.6
Sep 13, 2024
0.2.5
Sep 12, 2024
0.2.4
Sep 12, 2024
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Sep 07, 2024
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Sep 04, 2024
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Sep 02, 2024
0.2.0
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0.1.31
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Feb 20, 2024
0.1.2
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0.1.1
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0.1.0
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0.0.1
Feb 02, 2024
