diff --git a/agentstack/_tools/cognee/__init__.py b/agentstack/_tools/cognee/__init__.py new file mode 100644 index 00000000..d6ea2aa6 --- /dev/null +++ b/agentstack/_tools/cognee/__init__.py @@ -0,0 +1,136 @@ +""" +Implementation of the cognee for AgentStack. +These functions wrap cognee's asynchronous methods and expose them +as synchronous functions with typed parameters & docstrings for use by AI agents. +""" + +import cognee +import asyncio +from cognee.api.v1.search import SearchType + + +def prune_data(metadata: bool = False) -> str: + """ + Prune the cognee data. If metadata is True, also prune system metadata. + + :param metadata: Whether to prune system metadata as well. + :return: A confirmation message. + """ + + async def _prune(): + await cognee.prune.prune_data() + if metadata: + await cognee.prune.prune_system(metadata=True) + return "Data pruned successfully." + + return asyncio.run(_prune()) + + +def add_data(data) -> str: + """ + Add any type of data to cognee's knowledge system for future 'cognify' operations. + + :return: A confirmation message. + """ + + async def _add(): + await cognee.add(data) + return "Data added successfully." + + return asyncio.run(_add()) + + +def cognify() -> str: + """ + Run cognee's 'cognify' pipeline to build the knowledge graph, + summaries, and other metadata from previously added text. + + :return: A confirmation message. + """ + + async def _cognify(): + await cognee.cognify() + return "Cognify process complete." + + return asyncio.run(_cognify()) + + +def search_insights(query_text: str) -> str: + """ + Perform an INSIGHTS search on the knowledge graph for the given query text. + + :param query_text: The query to search for. + :return: The search results as a (stringified) list of matches. + """ + + async def _search(): + results = await cognee.search(SearchType.INSIGHTS, query_text=query_text) + return str(results) + + return asyncio.run(_search()) + + +def search_summaries(query_text: str) -> str: + """ + Perform a SUMMARIES search on the knowledge graph for the given query text. + + :param query_text: The query to search for. + :return: The search results as a (stringified) list of matches. + """ + + async def _search(): + results = await cognee.search(SearchType.SUMMARIES, query_text=query_text) + return str(results) + + return asyncio.run(_search()) + + +def search_chunks(query_text: str) -> str: + """ + Perform a CHUNKS search on the knowledge graph for the given query text. + + :param query_text: The query to search for. + :return: The search results as a (stringified) list of matches. + """ + + async def _search(): + results = await cognee.search(SearchType.CHUNKS, query_text=query_text) + return str(results) + + return asyncio.run(_search()) + + +def search_completion(query_text: str) -> str: + """ + Perform a COMPLETION search on the knowledge graph for the given query text. + + This function retrieves the document chunk most relevant to the user's query + and prompts the LLM to provide an answer using this context. + + :param query_text: The query to search for. + :return: The search results as a (stringified) list of matches. + """ + + async def _search(): + results = await cognee.search(SearchType.COMPLETION, query_text=query_text) + return str(results) + + return asyncio.run(_search()) + + +def search_graph_completion(query_text: str) -> str: + """ + Perform a GRAPH_COMPLETION search on the knowledge graph for the given query text. + + This function identifies the most relevant knowledge graph entities, including document chunks, + related to the user's query and prompts the LLM to generate a response with this enriched context. + + :param query_text: The query to search for. + :return: The search results as a (stringified) list of matches. + """ + + async def _search(): + results = await cognee.search(SearchType.GRAPH_COMPLETION, query_text=query_text) + return str(results) + + return asyncio.run(_search()) diff --git a/agentstack/_tools/cognee/config.json b/agentstack/_tools/cognee/config.json new file mode 100644 index 00000000..24359634 --- /dev/null +++ b/agentstack/_tools/cognee/config.json @@ -0,0 +1,45 @@ +{ + "name": "cognee", + "category": "Memory", + "tools": [ + "prune_data", + "add_data", + "cognify", + "search_insights", + "search_summaries", + "search_chunks", + "search_completion", + "search_graph_completion" + ], + "url": "https://github.com/topoteretes/cognee", + "tools_bundled": true, + "cta": "Cognee installed! Please set your cognee env variables.", + + "env": { + "ENV": "local", + "TOKENIZERS_PARALLELISM": "false", + "LLM_API_KEY": "", + "LLM_MODEL": "openai/gpt-4o-mini", + "LLM_PROVIDER": "openai", + "LLM_ENDPOINT": "", + "LLM_API_VERSION": "", + "EMBEDDING_PROVIDER": "openai", + "EMBEDDING_API_KEY": "", + "EMBEDDING_MODEL": "openai/text-embedding-3-large", + "EMBEDDING_ENDPOINT": "", + "EMBEDDING_API_VERSION": "", + "EMBEDDING_DIMENSIONS": 3072, + "EMBEDDING_MAX_TOKENS": 8191, + "GRAPH_DATABASE_PROVIDER": "networkx", + "VECTOR_DB_PROVIDER": "lancedb", + "DB_PROVIDER": "sqlite", + "DB_NAME": "cognee_db", + }, + + "packages": [ + "cognee" + ], + "post_install": "Now, you can start cognifying!", + "post_remove": "Cognee is removed!" + } + diff --git a/docs/tools/community.mdx b/docs/tools/community.mdx index 62f506c2..79b100ac 100644 --- a/docs/tools/community.mdx +++ b/docs/tools/community.mdx @@ -17,6 +17,7 @@ description: 'AgentStack tools from community contributors' ## Memory / State - [Mem0](/tools/tool/mem0) +- [cognee](/tools/tool/cognee) ## Code Execution diff --git a/docs/tools/tool/cognee.mdx b/docs/tools/tool/cognee.mdx new file mode 100644 index 00000000..967d1112 --- /dev/null +++ b/docs/tools/tool/cognee.mdx @@ -0,0 +1,56 @@ +--- +title: 'cognee' +description: 'AI Memory Engine for Enhanced LLM Accuracy' +--- + +## Overview + +[cognee](https://www.cognee.ai/) is an AI memory engine designed to improve the accuracy of Large Language Models (LLMs). It connects data points to uncover hidden links, providing your LLM applications with a better understanding of your data, leading to more reliable responses. + +## Installation + +1. To install the `cognee` tool in AgentStack, run: + +```bash +agentstack tools add cognee + +2. Set Environment Variables: +Configure the necessary environment variables in your environment or .env file. For the default setup you only need to add your `LLM_API_KEY`. Adjust other configurations as needed based on your setup. + +## Usage +Cognee provides a suite of tools to manage and utilize AI memory effectively. Below is a set of tools available to AgentStack and their functionalities: + +### Available Tools +- prune_data: Cleanses the cognee data store, with an option to include system metadata. +- add_data: Adds any data to cognee's data store for future processing. +- cognify: Processes the added data to build a knowledge graph, summaries, and other metadata. +- search_insights: Performs an insights search on the knowledge graph based on a query. +- search_summaries: Searches for summaries related to the query in the knowledge graph. +- search_chunks: Searches for specific chunks of data related to the query. +- search_completion: Retrieves the document chunk most relevant to the user's query and prompts the LLM to provide an answer using this context. +- search_graph_completion: Identifies the most related knowledge graph entities, including document chunks, related to the user's query and prompts the LLM to generate a response with this enriched context. + + +For more features, please visit [cognee's repo](https://github.com/topoteretes/cognee) +Cognee can be configured for different behaviors by modifying /agentstack/_tools/cognee/__init__.py + + +### Example + +#### Using cognee for your Agent's tasks + +**Description:** + +1. **Prune Data:** Start by pruning existing data in cognee to ensure a clean slate. +2. **Add Data:** Provide text into cognee's knowledge base. Example: "Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval." +3. **Cognify:** Process the added text to build the knowledge graph and related metadata. +4. **Search:** Perform searches using cognee's search functions with a query. Example: "Tell me about NLP". Present each search result separately, specifying the function name used for each search type. + +**Expected Output:** + +The agent should retrieve answers from the provided text based on the query. + + + +For more detailed information and advanced configurations, refer to the official [cognee documentation](https://docs.cognee.ai/). + diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 6f40dfe6..00000000 --- a/poetry.lock +++ /dev/null @@ -1,827 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - -[[package]] -name = "ansicon" -version = "1.89.0" -description = "Python wrapper for loading Jason Hood's ANSICON" -optional = false -python-versions = "*" -files = [ - {file = "ansicon-1.89.0-py2.py3-none-any.whl", hash = "sha256:f1def52d17f65c2c9682cf8370c03f541f410c1752d6a14029f97318e4b9dfec"}, - {file = "ansicon-1.89.0.tar.gz", hash = "sha256:e4d039def5768a47e4afec8e89e83ec3ae5a26bf00ad851f914d1240b444d2b1"}, -] - -[[package]] -name = "arrow" -version = "1.3.0" -description = "Better dates & times for Python" -optional = false -python-versions = ">=3.8" -files = [ - {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"}, - {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"}, -] - -[package.dependencies] -python-dateutil = ">=2.7.0" -types-python-dateutil = ">=2.8.10" - -[package.extras] -doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"] -test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (==3.*)"] - -[[package]] -name = "art" -version = "6.3" -description = "ASCII Art Library For Python" -optional = false -python-versions = ">=3.6" -files = [ - {file = "art-6.3-py3-none-any.whl", hash = "sha256:707ccb5d9c1da33225385e77fbc93a52bedf73a3cb288939979ed709226c4c27"}, - {file = "art-6.3.tar.gz", hash = "sha256:0fbc28864583ba54bcdc17ec24ef6c51a1fc81729a5c68d9b38658bc0afbc598"}, -] - -[package.extras] -dev = ["bandit (>=1.5.1)", "coverage (>=4.1)", "pydocstyle (>=3.0.0)", "vulture (>=1.0)"] - -[[package]] -name = "binaryornot" -version = "0.4.4" -description = "Ultra-lightweight pure Python package to check if a file is binary or text." -optional = false -python-versions = "*" -files = [ - {file = "binaryornot-0.4.4-py2.py3-none-any.whl", hash = "sha256:b8b71173c917bddcd2c16070412e369c3ed7f0528926f70cac18a6c97fd563e4"}, - {file = "binaryornot-0.4.4.tar.gz", hash = "sha256:359501dfc9d40632edc9fac890e19542db1a287bbcfa58175b66658392018061"}, -] - -[package.dependencies] -chardet = ">=3.0.2" - -[[package]] -name = "blessed" -version = "1.20.0" -description = "Easy, practical library for making terminal apps, by providing an elegant, well-documented interface to Colors, Keyboard input, and screen Positioning capabilities." -optional = false -python-versions = ">=2.7" -files = [ - {file = "blessed-1.20.0-py2.py3-none-any.whl", hash = "sha256:0c542922586a265e699188e52d5f5ac5ec0dd517e5a1041d90d2bbf23f906058"}, - {file = "blessed-1.20.0.tar.gz", hash = "sha256:2cdd67f8746e048f00df47a2880f4d6acbcdb399031b604e34ba8f71d5787680"}, -] - -[package.dependencies] -jinxed = {version = ">=1.1.0", markers = "platform_system == \"Windows\""} -six = ">=1.9.0" -wcwidth = ">=0.1.4" - -[[package]] -name = "certifi" -version = "2024.8.30" -description = "Python package for providing Mozilla's CA Bundle." -optional = false -python-versions = ">=3.6" -files = [ - {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"}, - {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"}, -] - -[[package]] -name = "chardet" -version = "5.2.0" -description = "Universal encoding detector for Python 3" -optional = false -python-versions = ">=3.7" -files = [ - {file = "chardet-5.2.0-py3-none-any.whl", hash = "sha256:e1cf59446890a00105fe7b7912492ea04b6e6f06d4b742b2c788469e34c82970"}, - {file = "chardet-5.2.0.tar.gz", hash = "sha256:1b3b6ff479a8c414bc3fa2c0852995695c4a026dcd6d0633b2dd092ca39c1cf7"}, -] - -[[package]] -name = "charset-normalizer" -version = "3.4.0" -description = "The Real First Universal Charset Detector. 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