llama-index-tools-airweave 0.1.0

LlamaIndex tools integration for Airweave - making any app searchable for agents

LlamaIndex Tools Integration: Airweave

This tool connects your LlamaIndex agent to Airweave, an open-source platform that makes any app searchable by syncing data from various sources with minimal configuration.

Installation

pip install llama-index-tools-airweave llama-index-llms-openai

Prerequisites

1. An Airweave account and API key
2. At least one collection set up with synced data

Get started at Airweave

Usage

Basic Usage

import os
import asyncio
from llama_index.tools.airweave import AirweaveToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Initialize the Airweave tool
airweave_tool = AirweaveToolSpec(
    api_key=os.environ["AIRWEAVE_API_KEY"],
)

# Create an agent with the Airweave tools
agent = FunctionAgent(
    tools=airweave_tool.to_tool_list(),
    llm=OpenAI(model="gpt-4o-mini"),
    system_prompt="""You are a helpful assistant that can search through
    Airweave collections to answer questions about your organization's data.""",
)


# Use the agent to search your data
async def main():
    response = await agent.run(
        "Search the finance-data collection for Q4 revenue reports"
    )
    print(response)


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

Available Tools

search_collection

Simple search in a collection with default settings (most common use case).

Parameters:

• collection_id (str): The readable ID of the collection
• query (str): Your search query
• limit (int, optional): Max results to return (default: 10)
• offset (int, optional): Pagination offset (default: 0)

advanced_search_collection

Advanced search with full control over retrieval parameters.

Parameters:

• collection_id (str): The readable ID of the collection
• query (str): Your search query
• limit (int, optional): Max results to return (default: 10)
• offset (int, optional): Pagination offset (default: 0)
• retrieval_strategy (str, optional): "hybrid", "neural", or "keyword"
• temporal_relevance (float, optional): Weight recent content (0.0-1.0)
• expand_query (bool, optional): Generate query variations
• interpret_filters (bool, optional): Extract filters from natural language
• rerank (bool, optional): Use LLM-based reranking
• generate_answer (bool, optional): Generate natural language answer

Returns: Dictionary with documents list and optional answer field.

search_and_generate_answer

Convenience method that searches and returns a direct natural language answer (RAG-style).

Parameters:

• collection_id (str): The readable ID of the collection
• query (str): Your question in natural language
• limit (int, optional): Max results to consider (default: 10)
• use_reranking (bool, optional): Use reranking (default: True)

Returns: Natural language answer string.

list_collections

List all collections in your organization.

Parameters:

• skip (int, optional): Pagination skip (default: 0)
• limit (int, optional): Max collections to return (default: 100)

get_collection_info

Get detailed information about a specific collection.

Parameters:

• collection_id (str): The readable ID of the collection

Advanced Examples

Direct Tool Usage

You can use the tools directly without an agent:

from llama_index.tools.airweave import AirweaveToolSpec

airweave_tool = AirweaveToolSpec(api_key="your-key")

# List collections
collections = airweave_tool.list_collections()
print(f"Found {len(collections)} collections")

# Simple search
results = airweave_tool.search_collection(
    collection_id="finance-data", query="Q4 revenue reports", limit=5
)

for doc in results:
    print(f"Score: {doc.metadata.get('score', 'N/A')}")
    print(f"Text: {doc.text[:200]}...")

Advanced Search Options

# Advanced search with all options
result = airweave_tool.advanced_search_collection(
    collection_id="finance-data",
    query="Q4 revenue reports",
    limit=20,
    retrieval_strategy="hybrid",  # hybrid, neural, or keyword
    temporal_relevance=0.3,  # Weight recent content (0.0-1.0)
    expand_query=True,  # Query expansion for better recall
    interpret_filters=True,  # Extract filters from natural language
    rerank=True,  # LLM reranking for better relevance
    generate_answer=True,  # Generate natural language answer
)

# Access results
documents = result["documents"]
if "answer" in result:
    print(f"Generated Answer: {result['answer']}")

RAG-Style Direct Answers

# 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..."

Using Different Retrieval Strategies

# Keyword search for exact term matching
results = airweave_tool.advanced_search_collection(
    collection_id="legal-docs",
    query="GDPR compliance",
    retrieval_strategy="keyword",  # Use BM25 keyword search
)

# Neural search for semantic understanding
results = airweave_tool.advanced_search_collection(
    collection_id="research-papers",
    query="papers about transformer architectures",
    retrieval_strategy="neural",  # Pure semantic search
)

# Hybrid search (default) - best of both worlds
results = airweave_tool.advanced_search_collection(
    collection_id="all-docs",
    query="machine learning best practices",
    retrieval_strategy="hybrid",  # Combines semantic + keyword
)

Temporal Relevance

Weight recent documents higher in results:

# Strongly prefer recent content
results = airweave_tool.advanced_search_collection(
    collection_id="news-articles",
    query="AI breakthroughs",
    temporal_relevance=0.8,  # 0.0 = no recency bias, 1.0 = only recent matters
)

Agent with Advanced Search

Agents can automatically leverage these features:

agent = FunctionAgent(
    tools=airweave_tool.to_tool_list(),
    llm=OpenAI(model="gpt-4o-mini"),
    system_prompt="""You have access to advanced Airweave search capabilities:
    - Use search_collection for simple queries
    - Use advanced_search_collection when you need temporal filtering, reranking, etc.
    - Use search_and_generate_answer to get direct answers from documents

    When searching recent information, use temporal_relevance.
    When you need precise answers, use search_and_generate_answer.
    """,
)


async def main():
    response = await agent.run(
        "Search for recent updates in the engineering-docs collection and summarize them"
    )
    print(response)


asyncio.run(main())

Custom Base URL

If you're self-hosting Airweave:

airweave_tool = AirweaveToolSpec(
    api_key="your-api-key",
    base_url="https://your-airweave-instance.com",
)

Using with Local Models

If you want to use local models instead of OpenAI:

from llama_index.llms.ollama import Ollama

agent = FunctionAgent(
    tools=airweave_tool.to_tool_list(),
    llm=Ollama(model="llama3.1", request_timeout=360.0),
)

Learn More

• Airweave Documentation
• Airweave GitHub
• LlamaIndex Documentation

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This integration is released under the MIT License.