feat: add Airweave tool integration with advanced search features

llama-index-integrations/tools/llama-index-tools-airweave/LICENSE

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+MIT License
+
+Copyright (c) 2024 Airweave
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

llama-index-integrations/tools/llama-index-tools-airweave/Makefile

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+GIT_ROOT ?= $(shell git rev-parse --show-toplevel)
+
+help:	## Show all Makefile targets.
+	@grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}'
+
+format:	## Run code autoformatters (black).
+	pre-commit install
+	git ls-files | xargs pre-commit run black --files
+
+lint:	## Run linters: pre-commit (black, ruff, codespell) and mypy
+	pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files
+
+test:	## Run tests via pytest.
+	pytest tests
+
+watch-docs:	## Build and watch documentation.
+	sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/

llama-index-integrations/tools/llama-index-tools-airweave/README.md

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+# LlamaIndex Tools Integration: Airweave
+
+This tool connects your LlamaIndex agent to [Airweave](https://airweave.ai/), an open-source platform that makes any app searchable by syncing data from various sources with minimal configuration.
+
+## Installation
+
+```bash
+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](https://airweave.ai/)
+
+## Usage
+
+### Basic Usage
+
+```python
+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:
+
+```python
+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
+
+```python
+# 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
+
+```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..."
+```
+
+### Using Different Retrieval Strategies
+
+```python
+# 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:
+
+```python
+# 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:
+
+```python
+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:
+
+```python
+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:
+
+```python
+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](https://docs.airweave.ai/)
+- [Airweave GitHub](https://github.com/airweave-ai/airweave)
+- [LlamaIndex Documentation](https://docs.llamaindex.ai/)
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+## License
+
+This integration is released under the MIT License.

llama-index-integrations/tools/llama-index-tools-airweave/examples/airweave_search.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Airweave Tool Integration Example\n",
+    "\n",
+    "This notebook demonstrates how to use the Airweave tool integration with LlamaIndex.\n",
+    "\n",
+    "Airweave is an open-source platform that makes any app searchable by syncing data from various sources.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

llama-index-integrations/tools/llama-index-tools-airweave/llama_index/tools/airweave/__init__.py

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+"""Airweave tool integration for LlamaIndex."""
+
+from llama_index.tools.airweave.base import AirweaveToolSpec
+
+__all__ = ["AirweaveToolSpec"]