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Use Hanzo AI with LlamaIndex

Use LlamaIndex with Hanzo AI to build powerful RAG applications, data agents, and query pipelines. Drop in Hanzo as your LLM provider with zero friction.

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Basic setuppython
pip install llama-index llama-index-llms-openai
 
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
 
Settings.llm = OpenAI(
    model="zen4-pro",
    api_key="your-hanzo-api-key",
    api_base="https://api.hanzo.ai/v1",
)
2 of 8 lines point at Hanzo

RAG pipeline

python

4 of 16 lines point at Hanzo
RAG pipelinepython
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
 
Settings.llm = OpenAI(
    model="zen4-pro",
    api_key="your-hanzo-api-key",
    api_base="https://api.hanzo.ai/v1",
)
Settings.embed_model = OpenAIEmbedding(
    api_key="your-hanzo-api-key",
    api_base="https://api.hanzo.ai/v1",
)
 
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What is in these docs?")

Chat engine

python

Chat enginepython
chat_engine = index.as_chat_engine(
    chat_mode="condense_plus_context",
    verbose=True,
)
response = chat_engine.chat("Tell me more about this topic")

Streaming query

python

Streaming querypython
streaming_response = query_engine.query(
    "Summarize the key points",
    streaming=True,
)
streaming_response.print_response_stream()

Created by LlamaIndex

License: MIT · View source on GitHub →

Hanzo AI is OpenAI-compatible, so existing LlamaIndex code works with zero refactoring. We deeply appreciate the LlamaIndex team for building and maintaining this open-source project.

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