mirror of
https://github.com/hwchase17/langchain
synced 2024-11-04 06:00:26 +00:00
ed58eeb9c5
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
27 lines
1.6 KiB
Python
27 lines
1.6 KiB
Python
STEAM_GET_GAMES_DETAILS = """
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This tool is a wrapper around python-steam-api's steam.apps.search_games API and
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steam.apps.get_app_details API, useful when you need to search for a game.
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The input to this tool is a string specifying the name of the game you want to
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search for. For example, to search for a game called "Counter-Strike: Global
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Offensive", you would input "Counter-Strike: Global Offensive" as the game name.
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This input will be passed into steam.apps.search_games to find the game id, link
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and price, and then the game id will be passed into steam.apps.get_app_details to
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get the detailed description and supported languages of the game. Finally the
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results are combined and returned as a string.
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"""
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STEAM_GET_RECOMMENDED_GAMES = """
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This tool is a wrapper around python-steam-api's steam.users.get_owned_games API
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and steamspypi's steamspypi.download API, useful when you need to get a list of
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recommended games. The input to this tool is a string specifying the steam id of
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the user you want to get recommended games for. For example, to get recommended
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games for a user with steam id 76561197960435530, you would input
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"76561197960435530" as the steam id. This steamid is then utilized to form a
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data_request sent to steamspypi's steamspypi.download to retrieve genres of user's
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owned games. Then, calculates the frequency of each genre, identifying the most
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popular one, and stored it in a dictionary. Subsequently, use steamspypi.download
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to returns all games in this genre and return 5 most-played games that is not owned
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by the user.
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"""
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