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### Install RAG Example Dependencies

Source: https://llamahub.ai/l/vector_stores/llama-index-vector-stores-objectbox?from=

Install additional packages required for the complete RAG example.

```bash
pip install llama-index --quiet
pip install llama-index-embeddings-huggingface --quiet
pip install llama-index-llms-gemini --quiet
```

--------------------------------

### End-to-End Example: AzureIndexStore Setup and Index Creation

Source: https://llamahub.ai/l/storage/llama-index-storage-index-store-azure?from=

Demonstrates loading documents, parsing them into nodes, and initializing a `StorageContext` with `AzureIndexStore`. It then adds documents to the docstore and creates a `SimpleKeywordTableIndex` and `VectorStoreIndex`.

```python
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import VectorStoreIndex

reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
nodes = SentenceSplitter().get_nodes_from_documents(documents)

storage_context = StorageContext.from_defaults(
    index_store=AzureIndexStore.from_account_and_key(
        "your_account_name",
        "your_account_key",
        service_mode=ServiceMode.STORAGE,
    ),
)

storage_context.docstore.add_documents(nodes)

keyword_table_index = SimpleKeywordTableIndex(
    nodes, storage_context=storage_context
)

vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
```

--------------------------------

### Load Data from Mangopps Guides

Source: https://llamahub.ai/l/readers/llama-index-readers-mangoapps-guides?from=

Instantiate the MangoppsGuidesReader and load data by providing the base URL of the MangoppsGuides installation and an optional limit for the number of links to crawl.

```python
from llama_index.readers.mangoapps_guides import MangoppsGuidesReader

loader = MangoppsGuidesReader()
documents = loader.load_data(
    domain_url="https://guides.mangoapps.com", limit=1
)

```

--------------------------------

### Run Example Script

Source: https://llamahub.ai/l/embeddings/llama-index-embeddings-heroku?from=

Navigate to the examples directory and run a Python example script using `uvicorn`.

```bash
cd examples
uv run python basic_usage.py
```

--------------------------------

### Minimal Gemini Live Voice Agent Example

Source: https://llamahub.ai/l/voice_agents/llama-index-voice-agents-gemini-live?from=

A basic example demonstrating how to set up and run a Gemini Live Voice Agent with a custom tool. Ensure GOOGLE_API_KEY is set as an environment variable or passed during initialization. The conversation starts automatically.

```python
from llama_index.voice_agents.gemini_live import GeminiLiveVoiceAgent
from llama_index.core.tools import FunctionTool
from llama_index.core.voice_agents import BaseVoiceAgentEvent
from llama_index.core.llms import ChatMessage, TextBlock
from typing import List
import random
import json


# use filter functions to export messages and events without your terminal being swamped by base64-encoded audio bytes :)
def filter_events(
    events: List[BaseVoiceAgentEvent],
) -> List[BaseVoiceAgentEvent]:
    evs = []
    for event in events:
        if not "audio" in event.type_t:
            evs.append(event)
    return evs


def filter_messages(messages: List[ChatMessage]) -> List[ChatMessage]:
    msgs = []
    for message in messages:
        msg = ChatMessage(role=message.role, blocks=[])
        for b in message.blocks:
            if isinstance(b, TextBlock):
                msg.blocks.append(b)
        if len(msg.blocks) > 0:
            msgs.append(msg)
    return msgs


def get_weather(location: str) -> dict:
    """Fetch weather data for a given location."""
    return json.dumps(
        {
            "location": location,
            "temperature_c": round(random.uniform(15, 30), 1),
            "humidity_percent": random.randint(40, 90),
            "wind_speed_kmh": round(random.uniform(5, 25), 1),
            "precipitation_probability_percent": random.randint(0, 100),
        },
        indent=4,
    )


weather_tool = FunctionTool.from_defaults(
    fn=get_weather,
    name="get_weather",
    description="Get the weather at a given location",
)


async def main():
    conversation = GeminiLiveVoiceAgent(tools=[weather_tool])

    await conversation.start()

    if conversation._quitflag:
        print("Events")
        print(conversation.export_events(filter=filter_events))
        print()
        print("Messages")
        print(conversation.export_messages(filter=filter_messages))


if __name__ == "__main__":
    import asyncio

    asyncio.run(main())

```

--------------------------------

### Install Dependencies

Source: https://llamahub.ai/l/readers/llama-index-readers-earnings-call-transcript?from=

Install all necessary dependencies from the requirements.txt file.

```bash
pip install -r requirements.txt
```

--------------------------------

### LMstudio LLM Chat Example

Source: https://llamahub.ai/l/llms/llama-index-llms-lmstudio?from=

Instantiate the LMStudio LLM and perform a chat completion. Ensure LM Studio is running with a model loaded and the local server started.

```python
from llama_index.llms.lmstudio import LMStudio
from llama_index.schema import ChatMessage, MessageRole

llm = LMStudio(
    model_name="Hermes-2-Pro-Llama-3-8B",
    base_url="http://localhost:1234/v1",
    temperature=0.7,
)

messages = [
    ChatMessage(
        role=MessageRole.SYSTEM,
        content="You an expert AI assistant. Help User with their queries.",
    ),
    ChatMessage(
        role=MessageRole.USER,
        content="What is the significance of the number 42?",
    ),
]

response = llm.chat(messages=messages)
print(str(response))
```

--------------------------------

### Install llama-index-postprocessor-alibabacloud-aisearch-rerank

Source: https://llamahub.ai/l/postprocessor/llama-index-postprocessor-alibabacloud-aisearch-rerank?from=

Install the necessary package using pip.

```bash
pip install llama-index-postprocessor-alibabacloud-aisearch-rerank

```

--------------------------------

### Install llama-index-tools-parallel-web-systems

Source: https://llamahub.ai/l/tools/llama-index-tools-parallel-web-systems?from=

Install the necessary package using pip.

```bash
pip install llama-index-tools-parallel-web-systems
```

--------------------------------

### Example Usage: ZyteSerpReader and ZyteWebReader

Source: https://llamahub.ai/l/readers/llama-index-readers-zyte-serp?from=

Demonstrates initializing ZyteSerpReader to get search URLs, then using ZyteWebReader in 'article' mode to extract content from those URLs. Replace 'your_api_key_here' with your actual Zyte API key.

```python
from llama_index.readers.zyte_serp import ZyteSerpReader
from llama_index.readers.web.zyte.base import ZyteWebReader

# Initialize the ZyteSerpReader with your API key
zyte_serp = ZyteSerpReader(
    api_key="your_api_key_here",  # Replace with your actual API key
)

# Get the search results (URLs from google search results)
search_urls = zyte_serp.load_data(query="llama index docs")

# Display the results
print(search_urls)

urls = [result.text for result in search_urls]

# Initialize the ZyteWebReader to load the content from search results
zyte_web = ZyteWebReader(
    api_key="your_api_key_here",  # Replace with your actual API key
    mode="article",
)

documents = zyte_web.load_data(urls)
print(documents)
```

--------------------------------

### Install llama-index-llms-alibabacloud-aisearch

Source: https://llamahub.ai/l/llms/llama-index-llms-alibabacloud-aisearch?from=

Install the necessary package using pip.

```bash
pip install llama-index-llms-alibabacloud-aisearch

```

--------------------------------

### Install MangoppsGuides Reader

Source: https://llamahub.ai/l/readers/llama-index-readers-mangoapps-guides?from=

Install the MangoppsGuides reader using pip.

```bash
pip install llama-index-readers-mangoapps-guides
```

--------------------------------

### Install llama-index-multi-modal-llms-nvidia

Source: https://llamahub.ai/l/multi_modal_llms/llama-index-multi-modal-llms-nvidia?from=

Install the necessary package using pip.

```bash
pip install llama-index-multi-modal-llms-nvidia

```

--------------------------------

### Install llama-index-tools-finance

Source: https://llamahub.ai/l/tools/llama-index-tools-finance?from=

Install the finance tools package using pip.

```bash
pip install llama-index-tools-finance

```

--------------------------------

### Install Dependencies

Source: https://llamahub.ai/l/readers/llama-index-readers-sec-filings?from=

Install project dependencies from the requirements.txt file.

```bash
python install -r requirements.txt
```

--------------------------------

### Install llama-index-protocols-ag-ui

Source: https://llamahub.ai/l/protocols/llama-index-protocols-ag-ui?from=

Install the necessary package using pip.

```bash
pip install llama-index-protocols-ag-ui
```

--------------------------------

### Install llama-index-readers-google

Source: https://llamahub.ai/l/readers/llama-index-readers-google?from=

Install the necessary package for Google readers.

```bash
pip install llama-index-readers-google

```

--------------------------------

### Install llama-index-readers-preprocess

Source: https://llamahub.ai/l/readers/llama-index-readers-preprocess?from=

Install the llama-index-readers-preprocess package using pip.

```bash
pip install llama-index-readers-preprocess
```

--------------------------------

### Install llama-index-retrievers-videodb

Source: https://llamahub.ai/l/retrievers/llama-index-retrievers-videodb?from=

Install the necessary packages for VideoDB integration. Ensure you have llama-index and videodb installed.

```bash
pip install llama-index llama-index-retrievers-videodb videodb

```

--------------------------------

### Install llama-index-callbacks-wandb

Source: https://llamahub.ai/l/callbacks/llama-index-callbacks-wandb?from=

Install the necessary package using pip.

```bash
pip install llama-index-callbacks-wandb

```

--------------------------------

### Install llama-index-agent-llm-compiler

Source: https://llamahub.ai/l/agent/llama-index-agent-llm-compiler?from=

Install the package using pip.

```bash
pip install llama-index-agent-llm-compiler

```

--------------------------------

### Install llama-index-vector-stores-analyticdb

Source: https://llamahub.ai/l/vector_stores/llama-index-vector-stores-analyticdb?from=

Install the necessary package for the AnalyticDB vector store.

```bash
pip install llama-index-vector-stores-analyticdb

```

--------------------------------

### Install LlamaIndex Packages

Source: https://llamahub.ai/l/llms/llama-index-llms-llama-cpp?from=

Install the necessary LlamaIndex packages for Hugging Face embeddings and Llama Cpp LLM integration. Ensure you have followed the performance optimization guide for Llama Cpp installation.

```python
pip install llama-index-embeddings-huggingface
pip install llama-index-llms-llama-cpp
```

--------------------------------

### Setup

Source: https://llamahub.ai/l/indices/llama-index-indices-managed-bge-m3?from=

Install the necessary package for Bge-M3 managed index integration.

```APIDOC
pip install -U llama-index-indices-managed-bge-m3
```

--------------------------------

### Install llama-index-output-parsers-langchain

Source: https://llamahub.ai/l/output_parsers/llama-index-output-parsers-langchain?from=

Install the necessary package using pip.

```bash
pip install llama-index-output-parsers-langchain

```

--------------------------------

### Get GitHub Installation ID via API

Source: https://llamahub.ai/l/readers/llama-index-readers-github?from=

Use this curl command to retrieve your GitHub App installation IDs. Replace YOUR_JWT_TOKEN with your generated JWT.

```bash
curl -H "Authorization: Bearer YOUR_JWT_TOKEN" \
     https://api.github.com/app/installations
```

--------------------------------

### RAG-Style Direct Answers Example

Source: https://llamahub.ai/l/tools/llama-index-tools-airweave?from=

Demonstrates using `search_and_generate_answer` to get a direct natural language answer from a collection.

```APIDOC
## RAG-Style Direct Answers

```python
# Get a direct answer instead of raw documents
answer = airweave_tool.search_and_generate_answer(
    collection_id="finance-data",
    query="What was our Q4 revenue growth?",
    limit=10,
    use_reranking=True,
)
print(answer)  # "Q4 revenue grew by 23% to $45M compared to Q3..."
```
```

--------------------------------

### End-to-End ZepReader Example

Source: https://llamahub.ai/l/readers/llama-index-readers-zep?from=

Demonstrates creating a Zep collection, adding documents, waiting for embedding, and querying with ZepReader. Requires a running Zep instance.

```python
import time
from uuid import uuid4

from llama_index.core.node_parser import SimpleNodeParser
from llama_index.core import Document
from zep_python import ZepClient
from zep_python.document import Document as ZepDocument


from llama_index.readers.zep import ZepReader

# Create a Zep collection
zep_api_url = "http://localhost:8000"  # replace with your Zep API URL
collection_name = f"babbage{uuid4().hex}"
file = "babbages_calculating_engine.txt"

print(f"Creating collection {collection_name}")

client = ZepClient(base_url=zep_api_url, api_key="optional_api_key")
collection = client.document.add_collection(
    name=collection_name,  # required
    description="Babbage's Calculating Engine",  # optional
    metadata={"foo": "bar"},  # optional metadata
    embedding_dimensions=1536,  # this must match the model you've configured in Zep
    is_auto_embedded=True,  # use Zep's built-in embedder. Defaults to True
)

node_parser = SimpleNodeParser.from_defaults(chunk_size=250, chunk_overlap=20)

with open(file) as f:
    raw_text = f.read()

print("Splitting text into chunks and adding them to the Zep vector store.")
docs = node_parser.get_nodes_from_documents(
    [Document(text=raw_text)], show_progress=True
)

# Convert nodes to ZepDocument
zep_docs = [ZepDocument(content=d.get_content()) for d in docs]
uuids = collection.add_documents(zep_docs)
print(f"Added {len(uuids)} documents to collection {collection_name}")

print("Waiting for documents to be embedded")
while True:
    c = client.document.get_collection(collection_name)
    print(
        "Embedding status: "
        f"{c.document_embedded_count}/{c.document_count} documents embedded"
    )
    time.sleep(1)
    if c.status == "ready":
        break

query = "Was Babbage awarded a medal?"

# Using the ZepReader to load data from Zep
reader = ZepReader(api_url=zep_api_url, api_key="optional_api_key")
results = reader.load_data(
    collection_name=collection_name, query=query, top_k=3
)

print("\n\n".join([r.text for r in results]))

```

--------------------------------

### Filter SharePoint Pages with Callback

Source: https://llamahub.ai/l/readers/llama-index-readers-microsoft-sharepoint?from=

Use the process_document_callback to filter which pages are processed. This example only processes pages that do not start with 'Draft'.

```python
def page_filter(page_name: str) -> bool:
    # Only process pages that don't start with "Draft"
    return not page_name.startswith("Draft")


loader = SharePointReader(
    client_id="<Client ID>",
    client_secret="<Client Secret>",
    tenant_id="<Tenant ID>",
    sharepoint_site_name="<Site Name>",
    sharepoint_type=SharePointType.PAGE,
    process_document_callback=page_filter,
)
```

--------------------------------

### Zendesk Support Configuration Schema

Source: https://llamahub.ai/l/readers/llama-index-readers-airbyte-zendesk-support?from=

Example structure for configuring the Airbyte Zendesk Support reader, including subdomain, start date, and credentials.

```json
{
    "subdomain": "<your zendesk subdomain>",
    "start_date": "<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>",
    "credentials": {
        "credentials": "api_token",
        "email": "<your email>",
        "api_token": "<your api token>",
    },
}
```

--------------------------------

### Install from Source

Source: https://llamahub.ai/l/agent/llama-index-agent-azure-foundry?from=

Install the Azure Foundry Agent integration from local source code.

```bash
cd llama_index/llama-index-integrations/agent/llama-index-agent-azure
pip install -e .
```

--------------------------------

### Hubspot Reader Configuration Schema

Source: https://llamahub.ai/l/readers/llama-index-readers-airbyte-hubspot?from=

Example JSON structure for configuring the Airbyte Hubspot reader, including start date and private app credentials.

```json
{
    "start_date": "<date from which to start retrieving records from in ISO format, e.g. 2020-10-20T00:00:00Z>",
    "credentials": {
        "credentials_title": "Private App Credentials",
        "access_token": "<access token of your private app>",
    },
}
```

--------------------------------

### Create FunctionAgent with Yahoo Finance Tool

Source: https://llamahub.ai/l/memory/llama-index-memory-bedrock-agentcore?from=

Create a FunctionAgent with a specific LLM and tools. This example uses YahooFinanceToolSpec and BedrockConverse. Install the finance tool package if needed.

```python
%pip install llama-index-tools-yahoo-finance
```

```python
from llama_index.llms.bedrock_converse import BedrockConverse
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.tools.yahoo_finance import YahooFinanceToolSpec

llm = BedrockConverse(model="us.anthropic.claude-sonnet-4-20250514-v1:0")

finance_tool_spec = YahooFinanceToolSpec()
agent = FunctionAgent(
    tools=finance_tool_spec.to_tool_list(),
    llm=llm,
)
```

--------------------------------

### Install llama-index-agent-openai

Source: https://llamahub.ai/l/agent/llama-index-agent-openai?from=

Install the necessary package using pip.

```bash
pip install llama-index-agent-openai
```

--------------------------------

### Initialize and Use TldwRetriever

Source: https://llamahub.ai/l/retrievers/llama-index-retrievers-tldw?from=

Initialize the TldwRetriever with your API key and collection ID, then create a query engine to search video content.

```python
from llama_index.retrievers.tldw import TldwRetriever
from llama_index.core.query_engine import RetrieverQueryEngine

# Initialize the retriever
retriever = TldwRetriever(
    api_key="YOUT_TLDW_API_KEY",
    collection_id="YOUR_COLLECTION_ID",  # Replace with your actual collection ID
)

# Create a query engine
query_engine = RetrieverQueryEngine(
    retriever=retriever,
)

# Query and summarize response
response = query_engine.query("What are the brands of smart watches reviewed?")
print(
    response
)  # "The brands of smartwatches reviewed in the videos are Apple and Garmin."
```

--------------------------------

### Install llama-index-llms-novita

Source: https://llamahub.ai/l/llms/llama-index-llms-novita?from=

Install the necessary package for NovitaAI integration.

```bash
%pip install llama-index-llms-novita

```

--------------------------------

### Basic GaudiLLM Usage Example

Source: https://llamahub.ai/l/llms/llama-index-llms-gaudi?from=

Demonstrates how to initialize and use the GaudiLLM for text completion. Ensure necessary imports and argument parsing are handled.

```python
import argparse
import os, logging
from llama_index.llms.gaudi import GaudiLLM


def setup_parser(parser):
    parser.add_argument(...) 
    args = parser.parse_args()
    return args


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="GaudiLLM Basic Usage Example"
    )
    args = setup_parser(parser)
    args.model_name_or_path = "HuggingFaceH4/zephyr-7b-alpha"

    llm = GaudiLLM(
        args=args,
        logger=logger,
        model_name="HuggingFaceH4/zephyr-7b-alpha",
        tokenizer_name="HuggingFaceH4/zephyr-7b-alpha",
        query_wrapper_prompt=PromptTemplate(
            "<|system|>
</s>
<|user|>
{query_str}</s>
<|assistant|>
"
        ),
        context_window=3900,
        max_new_tokens=256,
        generate_kwargs={"temperature": 0.7, "top_k": 50, "top_p": 0.95},
        messages_to_prompt=messages_to_prompt,
        device_map="auto",
    )

    query = "Is the ocean blue?"
    print("\n----------------- Complete ------------------")
    completion_response = llm.complete(query)
    print(completion_response.text)
```

--------------------------------

### Install llama-index-node_parser-chonkie

Source: https://llamahub.ai/l/node_parser/llama-index-node-parser-chonkie?from=

Install the necessary package using pip.

```python
pip install llama_index_node_parser-chonkie
```

--------------------------------

### Paginate Confluence Data from a Space

Source: https://llamahub.ai/l/readers/llama-index-readers-confluence?from=

Fetch Confluence pages from a space with pagination. This example shows how to retrieve the first 5 pages and then the next 5 pages by adjusting the `start` and `max_num_results` parameters.

```python
from llama_index.readers.confluence import ConfluenceReader

token = {"access_token": "<access_token>", "token_type": "<token_type>"}
oauth2_dict = {"client_id": "<client_id>", "token": token}

base_url = "https://yoursite.atlassian.com/wiki"

space_key = "<space_key>"

reader = ConfluenceReader(base_url=base_url, oauth2=oauth2_dict)
documents = reader.load_data(
    space_key=space_key,
    include_attachments=True,
    page_status="current",
    start=0,
    max_num_results=5,
)
documents.extend(
    reader.load_data(
        space_key=space_key,
        include_children=True,
        include_attachments=True,
        start=5,
        max_num_results=5,
    )
)
```

--------------------------------

### Usage Example

Source: https://llamahub.ai/l/readers/llama-index-readers-upstage?from=

Demonstrates how to initialize the UpstageDocumentParseReader and load data from a file.

```APIDOC
```python
import os

os.environ["UPSTAGE_API_KEY"] = "YOUR_API_KEY"

from llama_index.readers.upstage import UpstageDocumentParseReader

file_path = "/PATH/TO/YOUR/FILE.pdf"

reader = UpstageDocumentParseReader()
docs = reader.load_data(file_path=file_path)

for doc in docs[:3]:
    print(doc)
```
```

--------------------------------

### Quick Start: SignNow Agent with LlamaIndex

Source: https://llamahub.ai/l/tools/llama-index-tools-signnow?from=

Initialize the SignNowMCPToolSpec, discover tools from the MCP server, and wire them into a LlamaIndex agent. This example shows how to pass credentials via environment overrides and run a query.

```python
import asyncio
from llama_index.tools.signnow import SignNowMCPToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import (
    OpenAI,
)  # or any LLM that supports tool/function calling


async def main():
    # Option A: pass credentials directly (no .env needed)
    spec = SignNowMCPToolSpec.from_env(
        env_overrides={
            # Option 1: token-based auth
            # "SIGNNOW_TOKEN": "your_signnow_token_here",
            # Option 2: credential-based auth
            "SIGNNOW_USER_EMAIL": "login@example.com",
            "SIGNNOW_PASSWORD": "password",
            "SIGNNOW_API_BASIC_TOKEN": "basic_token_base64",
        }
    )

    # Fetch tools from the MCP server
    tools = await spec.to_tool_list_async()
    print({"count": len(tools), "names": [t.metadata.name for t in tools]})

    # Wire them into a LlamaIndex agent
    agent = FunctionAgent(
        name="SignNow Agent",
        description="Query SignNow via MCP tools",
        tools=tools,
        llm=OpenAI(model="gpt-4o"),  # make sure your LLM supports tools
        system_prompt="Be helpful.",
    )

    # Ask for something useful
    resp = await agent.run("Show me the list of templates and their names.")
    print(resp)


if __name__ == "__main__":
    asyncio.run(main())
```

--------------------------------

### Install OCI SDK

Source: https://llamahub.ai/l/embeddings/llama-index-embeddings-oci-genai?from=

Install the Oracle Cloud Infrastructure SDK for Python.

```bash
pip install -U oci
```

--------------------------------

### PandasAI Reader Usage Example

Source: https://llamahub.ai/l/readers/llama-index-readers-pandas-ai?from=

Demonstrates how to use the PandasAIReader with a sample DataFrame. You can either run a PandasAI query directly and get the parsed output by setting `is_conversational_answer=False`, or load the data into Document objects.

```python
from pandasai.llm.openai import OpenAI
import pandas as pd

# Sample DataFrame
df = pd.DataFrame(
    {
        "country": [
            "United States",
            "United Kingdom",
            "France",
            "Germany",
            "Italy",
            "Spain",
            "Canada",
            "Australia",
            "Japan",
            "China",
        ],
        "gdp": [
            21400000,
            2940000,
            2830000,
            3870000,
            2160000,
            1350000,
            1780000,
            1320000,
            516000,
            14000000,
        ],
        "happiness_index": [7.3, 7.2, 6.5, 7.0, 6.0, 6.3, 7.3, 7.3, 5.9, 5.0],
    }
)

llm = OpenAI()

from llama_index.readers.pandas_ai import PandasAIReader

# use run_pandas_ai directly
# set is_conversational_answer=False to get parsed output
loader = PandasAIReader(llm=llm)
response = reader.run_pandas_ai(
    df, "Which are the 5 happiest countries?", is_conversational_answer=False
)
print(response)

# load data with is_conversational_answer=False
# will use our PandasCSVReader under the hood
docs = reader.load_data(
    df, "Which are the 5 happiest countries?", is_conversational_answer=False
)

# load data with is_conversational_answer=True
# will use our PandasCSVReader under the hood
docs = reader.load_data(
    df, "Which are the 5 happiest countries?", is_conversational_answer=True
)
```

--------------------------------

### Initialize MyMagicAI

Source: https://llamahub.ai/l/llms/llama-index-llms-mymagic?from=

Create an instance of MyMagicAI, configuring API key, storage provider, bucket details, and session parameters. Optional parameters include role ARN, system prompt, region, and output handling.

```python
from llama_index.llms.mymagic import MyMagicAI

llm = MyMagicAI(
    api_key="your-api-key",
    storage_provider="s3",  # Options: 's3' or 'gcs'
    bucket_name="your-bucket-name",
    session="your-session-name",  # Directory for batch inference
    role_arn="your-role-arn",
    system_prompt="your-system-prompt",
    region="your-bucket-region",
    return_output=False,  # Set to True to return output JSON
    input_json_file=None,  # Input file stored on the bucket
    list_inputs=None,  # List of inputs for small batch
    structured_output=None,  # JSON schema of the output
)
```

--------------------------------

### Example Usage of AgentCoreBrowserToolSpec

Source: https://llamahub.ai/l/tools/llama-index-tools-aws-bedrock-agentcore?from=

Demonstrates how to use the AgentCoreBrowserToolSpec to create a LlamaIndex agent that can browse websites, extract information, and perform tasks. Includes setup for LLM, agent, and task execution, followed by cleanup.

```python
import asyncio
from llama_index.llms.bedrock_converse import BedrockConverse
from llama_index.tools.aws_bedrock_agentcore import AgentCoreBrowserToolSpec
from llama_index.core.agent.workflow import FunctionAgent

import nest_asyncio

nest_asyncio.apply()  # In case of existing loop (ex. in JupyterLab)


async def main():
    tool_spec = AgentCoreBrowserToolSpec(region="us-west-2")
    tools = tool_spec.to_tool_list()

    llm = BedrockConverse(
        model="us.anthropic.claude-sonnet-4-6-v1",
        region_name="us-west-2",
    )

    agent = FunctionAgent(
        tools=tools,
        llm=llm,
    )

    task = "Go to https://news.ycombinator.com/ and tell me the titles of the top 5 posts."

    response = await agent.run(task)
    print(str(response))

    await tool_spec.cleanup()


if __name__ == "__main__":
    asyncio.run(main())
```

--------------------------------

### End-to-end Example with AzureDocumentStore

Source: https://llamahub.ai/l/storage/llama-index-storage-docstore-azure?from=

Demonstrates loading documents, parsing them into nodes, and initializing a LlamaIndex StorageContext with AzureDocumentStore. The documents are then added to the store.

```python
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex

reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
nodes = SentenceSplitter().get_nodes_from_documents(documents)

storage_context = StorageContext.from_defaults(
    docstore=AzureDocumentStore.from_account_and_key(
        "your_account_name",
        "your_account_key",
        service_mode=ServiceMode.STORAGE,
    ),
)

vector_index = VectorStoreIndex(nodes, storage_context=storage_context)

storage_context.docstore.add_documents(nodes)
```

--------------------------------

### ElevenLabs Voice Agent Basic Usage

Source: https://llamahub.ai/l/voice_agents/llama-index-voice-agents-elevenlabs?from=

A simple example demonstrating how to initialize and start an ElevenLabs voice agent for realtime conversation. Ensure your ElevenLabs API key and agent ID are set as environment variables.

```python
import os

from llama_index.voice_agents.elevenlabs import ElevenLabsVoiceAgent
from dotenv import load_dotenv
from elevenlabs.client import ElevenLabs


load_dotenv()
AGENT_ID = os.environ.get("AGENT_ID")
API_KEY = os.environ.get("ELEVENLABS_API_KEY")


def main():
    client = ElevenLabs(api_key=API_KEY)
    conversation = ElevenLabsVoiceAgent(
        client,
        AGENT_ID,
        requires_auth=bool(API_KEY),
    )
    conversation.start()

    while True:
        try:
            # GET MESSAGES IN llama-index ChatMessage FORMAT
            messages = conversation.export_messages()
            events = conversation.export_events()
            # GET AVERAGE LATENCY
            latency = conversation.average_latency
        except KeyboardInterrupt:
            conversation.interrupt()
            conversation.stop()
            print(f"Messages: {messages}")
            print(f"Events: {events}")
            print(f"Latency: {latency}")


if __name__ == "__main__":
    main()
```

--------------------------------

### Quick Start with Cloudflare AI Gateway

Source: https://llamahub.ai/l/llms/llama-index-llms-cloudflare-ai-gateway?from=

Initialize and use the CloudflareAIGateway LLM. Configure it with a list of fallback LLMs, your Cloudflare account details, and gateway name. This example demonstrates trying OpenAI first, then Anthropic.

```python
from llama_index.llms.cloudflare_ai_gateway import CloudflareAIGateway
from llama_index.llms.openai import OpenAI
from llama_index.llms.anthropic import Anthropic
from llama_index.core.llms import ChatMessage

# Create LLM instances
openai_llm = OpenAI(model="gpt-4o-mini", api_key="your-openai-key")
anthropic_llm = Anthropic(
    model="claude-3-5-sonnet-20241022", api_key="your-anthropic-key"
)

# Create Cloudflare AI Gateway LLM
llm = CloudflareAIGateway(
    llms=[openai_llm, anthropic_llm],  # Try OpenAI first, then Anthropic
    account_id="your-cloudflare-account-id",
    gateway="your-ai-gateway-name",
    api_key="your-cloudflare-api-key",
)

# Use the LLM
messages = [ChatMessage(role="user", content="What is 2+2?")]
response = llm.chat(messages)
print(response.message.content)
```

--------------------------------

### Install Airbyte ZendeskSupport Reader

Source: https://llamahub.ai/l/readers/llama-index-readers-airbyte-zendesk-support?from=

Install the necessary package for the Airbyte ZendeskSupport Reader.

```bash
pip install llama-index-readers-airbyte-zendesk-support
```

--------------------------------

### Example Usage of AgentCoreCodeInterpreterToolSpec

Source: https://llamahub.ai/l/tools/llama-index-tools-aws-bedrock-agentcore?from=

Demonstrates using the AgentCoreCodeInterpreterToolSpec to create a LlamaIndex agent capable of executing code and shell commands. Includes setup for LLM, agent, and running both code and command-based tasks, followed by cleanup.

```python
import asyncio
from llama_index.llms.bedrock_converse import BedrockConverse
from llama_index.tools.aws_bedrock_agentcore import (
    AgentCoreCodeInterpreterToolSpec,
)
from llama_index.core.agent.workflow import FunctionAgent

import nest_asyncio

nest_asyncio.apply()  # In case of existing loop (ex. in JupyterLab)


async def main():
    tool_spec = AgentCoreCodeInterpreterToolSpec(region="us-west-2")
    tools = tool_spec.to_tool_list()

    llm = BedrockConverse(
        model="us.anthropic.claude-sonnet-4-6-v1",
        region_name="us-west-2",
    )

    agent = FunctionAgent(
        tools=tools,
        llm=llm,
    )

    code_task = "Write a Python function that calculates the factorial of a number and test it."

    code_response = await agent.run(code_task)
    print(str(code_response))

    command_task = "Use terminal CLI commands to: 1) Show the environment's Python version. 2) Show me the list of Python package currently installed in the environment."

    command_response = await agent.run(command_task)
    print(str(command_response))

    await tool_spec.cleanup()


if __name__ == "__main__":
    asyncio.run(main())
```

--------------------------------

### Start TextEmbed Server

Source: https://llamahub.ai/l/embeddings/llama-index-embeddings-textembed?from=

Launch the TextEmbed server, specifying the models to use, the number of worker processes, and an API key for authentication.

```bash
python -m textembed.server --models sentence-transformers/all-MiniLM-L12-v2 --workers 4 --api-key TextEmbed
```

--------------------------------

### Install llama-index-readers-make-com

Source: https://llamahub.ai/l/readers/llama-index-readers-make-com?from=

Install the Make-Com reader package using pip.

```python
pip install llama-index-readers-make-com
```

--------------------------------

### Custom Parsers and Callbacks Example

Source: https://llamahub.ai/l/readers/llama-index-readers-service-now?from=

This snippet shows the setup for using custom parsers with the SnowKBReader, including the necessary imports and the structure for defining parsers for different file types. It serves as a template for advanced configurations.

```python
# Example with custom parsers and callbacks
from llama_index.readers.service_now import SnowKBReader, FileType
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from markitdown import MarkItDown
from typing import List, Union
import pathlib



```

--------------------------------

### Basic Usage

Source: https://llamahub.ai/l/llms/llama-index-llms-clarifai?from=

Demonstrates how to set up Clarifai credentials and initialize the Clarifai LLM for text completion.

```APIDOC
## Basic Usage

### Description
This section shows how to set your Clarifai Personal Access Token (PAT) as an environment variable and initialize the `Clarifai` LLM class for text completion.

### Installation
```bash
%pip install llama-index-llms-clarifai
!pip install llama-index
!pip install clarifai
```

### Initialization
```python
import os

os.environ["CLARIFAI_PAT"] = "<YOUR CLARIFAI PAT>"

from llama_index.llms.clarifai import Clarifai

# Method 1: using model_url parameter
llm_model_url = Clarifai(model_url="https://clarifai.com/clarifai/ml/models/llama2-7b-alternative-4k")

# Method 2: using model_name, app_id & user_id parameters
llm_model_params = Clarifai(
    model_name="llama2-7b-alternative-4k",
    app_id="ml",
    user_id="clarifai",
)
```

### Text Completion
```python
# Call complete function
llm_response = llm_model_url.complete(
    prompt="write a 10 line rhyming poem about science"
)
print(llm_response)
```

### Output Example
```
Science is fun, it's true!
From atoms to galaxies, it's all new!
With experiments and tests, we learn so fast,
And discoveries come from the past.
It helps us understand the world around,
And makes our lives more profound.
So let's embrace this wondrous art,
And see where it takes us in the start!
```
```

--------------------------------

### Ray Ingestion Pipeline Setup and Execution

Source: https://llamahub.ai/l/ingestion/llama-index-ingestion-ray?from=

Set up and run a LlamaIndex ingestion pipeline distributed across a Ray cluster. This example demonstrates wrapping transformations like SentenceSplitter, TitleExtractor, and OpenAIEmbedding with RayTransformComponent and configuring batch processing.

```python
import ray
from llama_index.core import Document
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.extractors import TitleExtractor
from llama_index.ingestion.ray import (
    RayIngestionPipeline,
    RayTransformComponent,
)

# Start a new cluster (or connect to an existing one, see https://docs.ray.io/en/latest/ray-core/configure.html)
ray.init()

# Create transformations
transformations = [
    RayTransformComponent(SentenceSplitter, chunk_size=25, chunk_overlap=0),
    RayTransformComponent(
        transform_class=TitleExtractor,
        map_batches_kwargs={
            "batch_size": 10,  # Define the batch size
            # "num_cpus": 4  # The number of CPUs to reserve for each parallel map worker.
            # "num_gpus": 1  # The number of GPUs to reserve for each parallel map worker.
            # See https://docs.ray.io/en/latest/data/api/doc/ray.data.Dataset.map_batches.html for all the available parameters
        },
    ),
    RayTransformComponent(
        transform_class=OpenAIEmbedding,
        map_batches_kwargs={
            "batch_size": 10,
        },
    ),
]

# Create the Ray ingestion pipeline
pipeline = RayIngestionPipeline(transformations=transformations)

# Run the pipeline with many documents
nodes = pipeline.run(documents=[Document.example()] * 10)
```

--------------------------------

### OpenAI Realtime Voice Agent Example

Source: https://llamahub.ai/l/voice_agents/llama-index-voice-agents-openai?from=

A full example demonstrating how to set up and run an OpenAI voice agent with custom tools and instructions. It includes signal handling for graceful shutdown and logging of messages and events.

```python
import os
import signal
import time
import logging

from typing import List
from dotenv import load_dotenv
from llama_index.voice_agents.openai import OpenAIVoiceAgent
from llama_index.core.voice_agents import BaseVoiceAgentEvent
from llama_index.core.llms import ChatMessage, TextBlock
from llama_index.core.tools import FunctionTool

logging.basicConfig(
    level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s"
)

# Load environment variables from a .env file
load_dotenv()

quitFlag = False


# use filter functions to export messages and events without your terminal being swamped by base64-encoded audio bytes :)
def filter_events(
    events: List[BaseVoiceAgentEvent],
) -> List[BaseVoiceAgentEvent]:
    evs = []
    for event in events:
        if "audio" in event.type_t and "transcript" not in event.type_t:
            if "delta" in event.type_t:
                event.delta = ""
                evs.append(event)
        else:
            evs.append(event)
    return evs


def filter_messages(messages: List[ChatMessage]) -> List[ChatMessage]:
    msgs = []
    for message in messages:
        msg = ChatMessage(role=message.role, blocks=[])
        for b in message.blocks:
            if isinstance(b, TextBlock):
                msg.blocks.append(b)
        if len(msg.blocks) > 0:
            msgs.append(msg)
    return msgs


def signal_handler(sig, frame):
    """Handle Ctrl+C and initiate graceful shutdown."""
    global quitFlag
    quitFlag = True


async def main():
    # this is just a mock tool
    def get_weather(location: str):
        """
        Get the weather for a location
        """
        return f"The weather in {location} is sunny, there are 32°C, wind is 3 km/h toward west, and humidity is at 34%."

    api_key = os.getenv("OPENAI_API_KEY")

    if not api_key:
        return
    tools = [
        FunctionTool.from_defaults(
            fn=get_weather,
            name="get_weather",
            description="Get the current weather...",
        )
    ]
    # use custom models and tools!
    conversation = OpenAIVoiceAgent(
        api_key=api_key,
        tools=tools,
        model="gpt-4o-mini-realtime-preview-2024-12-17",
    )

    signal.signal(
        signal.SIGINT,
        lambda sig, frame: signal_handler(sig, frame),
    )

    try:
        # customize the model with instructions and other arguments you can find here: https://platform.openai.com/docs/api-reference/realtime-client-events/session/update
        await conversation.start(
            instructions="You are a very helpful assistant. You can use the 'get_weather' tool to get weather information about a location: you just have to input the location to the tool. Please execute the tool whenever you are asked about the weather of a location.",
        )
        while not quitFlag:
            time.sleep(0.1)
        if quitFlag:
            await conversation.interrupt()
            await conversation.stop()

    except Exception as e:
        logging.error(f"Error in main loop: {e}")
        await conversation.interrupt()
        await conversation.stop()

    finally:
        logging.info("Exiting main.")
        print("Messages:")
        print(conversation.export_messages(filter=filter_messages))
        print("Events:")
        print(conversation.export_events(filter=filter_events))
        await conversation.stop()  # Ensures cleanup if any error occurs


if __name__ == "__main__":
    import asyncio

    asyncio.run(main())
    # remember that YOU have to start the conversation!

```

--------------------------------

### Install GraphQL Reader

Source: https://llamahub.ai/l/readers/llama-index-readers-graphql?from=

Install the necessary package for the GraphQL reader.

```bash
pip install llama-index-readers-graphql
```

--------------------------------

### Install pypreprocess library

Source: https://llamahub.ai/l/readers/llama-index-readers-preprocess?from=

Install the pypreprocess library, which is a requirement for the PreprocessReader.

```bash
pip install pypreprocess
```

--------------------------------

### Quick Start: NotDiamond Selector with RouterQueryEngine

Source: https://llamahub.ai/l/selectors/llama-index-selectors-notdiamond?from=

Set up API keys, create LlamaIndex indexes, define query engine tools, and initialize the NotDiamondSelector with a RouterQueryEngine. This example demonstrates how to use NotDiamond to route queries across different LLMs based on their configurations.

```python
import os
from typing import List

from llama_index.core import (
    SimpleDirectoryReader,
    VectorStoreIndex,
    SummaryIndex,
    Settings,
)
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.tools import QueryEngineTool
from llama_index.selectors.notdiamond.base import NotDiamondSelector

from notdiamond import NotDiamond


# Set up your API keys
os.environ["OPENAI_API_KEY"] = "sk-..."
os.environ["ANTHROPIC_API_KEY"] = "sk-ant-..."
os.environ["NOTDIAMOND_API_KEY"] = "sk-..."


# Create indexes
documents = SimpleDirectoryReader("data/paul_graham").load_data()
nodes = Settings.node_parser.get_nodes_from_documents(documents)

vector_index = VectorStoreIndex.from_documents(documents)
summary_index = SummaryIndex.from_documents(documents)
query_text = "What was Paul Graham's role at Yahoo?"


# Set up Tools for the QueryEngine
list_query_engine = summary_index.as_query_engine(
    response_mode="tree_summarize",
    use_async=True,
)
vector_query_engine = vector_index.as_query_engine()

list_tool = QueryEngineTool.from_defaults(
    query_engine=list_query_engine,
    description=(
        "Useful for summarization questions related to Paul Graham eassy on"
        " What I Worked On."
    ),
)

vector_tool = QueryEngineTool.from_defaults(
    query_engine=vector_query_engine,
    description=(
        "Useful for retrieving specific context from Paul Graham essay on What"
        " I Worked On."
    ),
)


# Create a NotDiamondSelector and RouterQueryEngine
client = NotDiamond(
    api_key=os.environ["NOTDIAMOND_API_KEY"],
    llm_configs=["openai/gpt-4o", "anthropic/claude-3-5-sonnet-20240620"],
)
preference_id = client.create_preference_id()
client.preference_id = preference_id

nd_selector = NotDiamondSelector(client=client)

query_engine = RouterQueryEngine(
    selector=nd_selector,
    query_engine_tools=[
        list_tool,
        vector_tool,
    ],
)


# Use Not Diamond to Query Indexes
response = query_engine.query(
    "Please summarize Paul Graham's working experience."
)
print(str(response))
```

--------------------------------

### Initialize Alibaba Cloud MySQL Vector Store using from_params

Source: https://llamahub.ai/l/vector_stores/llama-index-vector-stores-alibabacloud-mysql?from=

Alternatively, initialize the AlibabaCloudMySQLVectorStore using the from_params class method with your database credentials.

```python
from llama_index.vector_stores.alibabacloud_mysql import \
    AlibabaCloudMySQLVectorStore

vector_store = AlibabaCloudMySQLVectorStore.from_params(
    host="your-instance-endpoint.mysql.rds.aliyuncs.com",
    port=3306,
    user="llamaindex",
    password="password",
    database="vectordb",
)
```

--------------------------------

### Initialize DeepInfraLLM

Source: https://llamahub.ai/l/llms/llama-index-llms-deepinfra?from=

Set up the DeepInfraLLM class with your API key and desired parameters. Replace 'your-deepinfra-api-key' with your actual key.

```python
from llama_index.llms.deepinfra import DeepInfraLLM
import asyncio

llm = DeepInfraLLM(
    model="mistralai/Mixtral-8x22B-Instruct-v0.1",  # Default model name
    api_key="your-deepinfra-api-key",  # Replace with your DeepInfra API key
    temperature=0.5,
    max_tokens=50,
    additional_kwargs={"top_p": 0.9},
)

```

--------------------------------

### Install Rust

Source: https://llamahub.ai/l/llms/llama-index-llms-mistral-rs?from=

Install Rust using the official script. This is a prerequisite for installing mistralrs.

```bash
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source $HOME/.cargo/env
```

--------------------------------

### Install llama-index-llms-friendli

Source: https://llamahub.ai/l/llms/llama-index-llms-friendli?from=

Install the necessary Python packages for the Friendli integration. Ensure llama-index is also installed.

```python
%pip install llama-index-llms-friendli
!pip install llama-index
```

--------------------------------

### Example using a Private Key

Source: https://llamahub.ai/l/llms/llama-index-llms-cortex?from=

Authenticate with Cortex using a private key file. Ensure environment variables for user, account, and the private key file path are set.

```python
import os
from llama_index.llms.cortex import Cortex


llm = Cortex(
    model="llama3.2-1b",
    user=os.environ["YOUR_SF_USER"],
    account=os.environ["YOUR_SF_ACCOUNT"],
    private_key_file=os.environ["PATH_TO_SF_PRIVATE_KEY"],
)

completion_response = llm.complete(
    "write me a haiku about a snowflake", temperature=0.0
)
print(completion_response)
```

--------------------------------

### Initialize and Use VectorDB Tool with Agent

Source: https://llamahub.ai/l/tools/llama-index-tools-vector-db?from=

Demonstrates how to initialize a VectorDB tool with a VectorStoreIndex and integrate it into a FunctionAgent. The tool is configured with metadata for structured queries and then used by the agent to retrieve information.

```python
from llama_index.tools.vector_db import VectorDB
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
from llama_index.core.vector_stores import VectorStoreInfo
from llama_index.core import VectorStoreIndex

index = VectorStoreIndex(nodes=nodes)
tool_spec = VectorDB(index=index)
vector_store_info = VectorStoreInfo(
    content_info="brief biography of celebrities",
    metadata_info=[
        MetadataInfo(
            name="category",
            type="str",
            description="Category of the celebrity, one of [Sports, Entertainment, Business, Music]",
        ),
        MetadataInfo(
            name="country",
            type="str",
            description="Country of the celebrity, one of [United States, Barbados, Portugal]",
        ),
    ],
)

agent = FunctionAgent(
    tools=tool_spec.to_tool_list(
        func_to_metadata_mapping={
            "auto_retrieve_fn": ToolMetadata(
                name="celebrity_bios",
                description=f"""
            Use this tool to look up biographical information about celebrities.
            The vector database schema is given below:

            {vector_store_info.json()}

            {tool_spec.auto_retrieve_fn.__doc__}
        """,
                fn_schema=create_schema_from_function(
                    "celebrity_bios", tool_spec.auto_retrieve_fn
                ),
            )
        }
    ),
    llm=OpenAI(model="gpt-4.1"),
)

print(
    await agent.run("Tell me about two celebrities from the United States. ")
)
```

--------------------------------

### Install llama-index-embeddings-ollama

Source: https://llamahub.ai/l/embeddings/llama-index-embeddings-ollama?from=

Install the necessary package using pip. Ensure Ollama is also installed and running.

```bash
pip install llama-index-embeddings-ollama
```

--------------------------------

### Basic Usage Example

Source: https://llamahub.ai/l/readers/llama-index-readers-genius?from=

An example demonstrating how to initialize the GeniusReader and search for songs by lyrics.

```python
from llama_index.readers.genius import GeniusReader

access_token = "your_generated_access_token"

loader = GeniusReader(access_token)
documents = loader.search_songs_by_lyrics("Imagine")
```

--------------------------------

### Install llama-index-tools-jina

Source: https://llamahub.ai/l/tools/llama-index-tools-jina?from=

Install the Jina search tool for LlamaIndex using pip. Ensure Python is installed on your system.

```bash
pip install llama-index-tools-jina
```
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