LlamaIndex | Airweave

The llama-index-tools-airweave package currently uses the legacy search API. It will be updated to support the new three-tier search API (instant, classic, agentic) in a future release. The methods and parameters documented below still work but do not expose the new search tiers.

The llama-index-tools-airweave package provides an AirweaveToolSpec that gives your LlamaIndex agents access to Airweave’s search capabilities.

Prerequisites

Before you start you’ll need:

A collection with data: at least one source connection must have completed its initial sync. See the Quickstart if you need to set this up.
An API key: Create one in the Airweave dashboard under API Keys.

Installation

pip install llama-index llama-index-tools-airweave

Quick Start

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

The AirweaveToolSpec provides five tools that your agent can use:

`search_collection`

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

Parameter	Type	Description
`collection_id`	str	The readable ID of the collection
`query`	str	Your search query
`limit`	int	Max results to return (default: 10)
`offset`	int	Pagination offset (default: 0)

`advanced_search_collection`

Advanced search with full control over retrieval parameters.

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

Returns a 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).

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

`list_collections`

List all collections in your organization.

Parameter	Type	Description
`skip`	int	Pagination skip (default: 0)
`limit`	int	Max collections to return (default: 100)

`get_collection_info`

Get detailed information about a specific collection.

Parameter	Type	Description
`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 with All Options

result = airweave_tool.advanced_search_collection(
    collection_id="finance-data",
    query="Q4 revenue reports",
    limit=20,
    retrieval_strategy="hybrid",
    temporal_relevance=0.3,
    expand_query=True,
    interpret_filters=True,
    rerank=True,
    generate_answer=True,
)
documents = result["documents"]
if "answer" in result:
    print(f"Generated Answer: {result['answer']}")

RAG-Style Direct Answers

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

Temporal Relevance

Weight recent documents higher in results:

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
)

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

pip install llama-index-llms-ollama

from llama_index.llms.ollama import Ollama
agent = FunctionAgent(
    tools=airweave_tool.to_tool_list(),
    llm=Ollama(model="llama3.1", request_timeout=360.0),
)