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

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())

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",  # 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']}")

# 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..."

# 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
)

# 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 = 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())

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

from llama_index.llms.ollama import Ollama

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