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c29fdbc9fa5a-34 | format for a human, also make sure to represent numbers in readable ways, like 1M instead of 1000000. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I can first ask which tables I have, then how each table is defined and then ask the query tool the question I need, and finally create a nice sentence that answers the question.\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', examples: Optional[str] = None, input_variables: Optional[List[str]] = None, top_k: int = | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-35 | Optional[List[str]] = None, top_k: int = 10, verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-36 | Construct a pbi agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-37 | langchain.agents.create_pbi_chat_agent(llm: langchain.chat_models.base.BaseChatModel, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, prefix: str = 'Assistant is a large language model built to help users interact with a PowerBI Dataset.\n\nAssistant has access to a tool that can write a query based on the question and then run those against PowerBI, Microsofts business intelligence tool. The questions from the users should be interpreted as related to the dataset that is available and not general questions about the world. If the question does not seem related to the dataset, just return "This does not appear to be part of this dataset." as the answer.\n\nGiven an input question, ask to run the questions against the dataset, then look at the results and return the answer, the answer should be a complete sentence that answers the question, if multiple rows are asked find a way to write that in | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-38 | rows are asked find a way to write that in a easily readible format for a human, also make sure to represent numbers in readable ways, like 1M instead of 1000000. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\n', suffix: str = "TOOLS\n------\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\n\n{{tools}}\n\n{format_instructions}\n\nUSER'S INPUT\n--------------------\nHere is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\n\n{{{{input}}}}\n", examples: Optional[str] = None, input_variables: Optional[List[str]] = None, memory: Optional[langchain.memory.chat_memory.BaseChatMemory] = None, top_k: int = 10, verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-39 | Construct a pbi agent from an Chat LLM and tools.
If you supply only a toolkit and no powerbi dataset, the same LLM is used for both.
langchain.agents.create_spark_dataframe_agent(llm: langchain.llms.base.BaseLLM, df: Any, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = '\nYou are working with a spark dataframe in Python. The name of the dataframe is `df`.\nYou should use the tools below to answer the question posed of you:', suffix: str = '\nThis is the result of `print(df.first())`:\n{df}\n\nBegin!\nQuestion: {input}\n{agent_scratchpad}', input_variables: Optional[List[str]] = None, verbose: bool = False, return_intermediate_steps: bool = False, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]#
Construct a spark agent from an LLM and dataframe. | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-40 | langchain.agents.create_spark_sql_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.spark_sql.toolkit.SparkSQLToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with Spark SQL.\nGiven an input question, create a syntactically correct Spark SQL query to run, then look at the results of the query and return the answer.\nUnless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\nYou can order the results by a relevant column to return the most interesting examples in the database.\nNever query for all the columns from a specific table, only ask for the relevant columns given the question.\nYou have access to tools for interacting with the database.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nYou MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.\n\nDO NOT make | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-41 | rewrite the query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return "I don\'t know" as the answer.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should look at the tables in the database to see what I can query.\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None, top_k: int = 10, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = False, | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-42 | str = 'force', verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-43 | Construct a sql agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-44 | langchain.agents.create_sql_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with a SQL database.\nGiven an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.\nUnless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\nYou can order the results by a relevant column to return the most interesting examples in the database.\nNever query for all the columns from a specific table, only ask for the relevant columns given the question.\nYou have access to tools for interacting with the database.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nYou MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.\n\nDO NOT make | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-45 | rewrite the query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return "I don\'t know" as the answer.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should look at the tables in the database to see what I can query.\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None, top_k: int = 10, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = False, | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-46 | str = 'force', verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-47 | Construct a sql agent from an LLM and tools.
langchain.agents.create_vectorstore_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to answer questions about sets of documents.\nYou have access to tools for interacting with the documents, and the inputs to the tools are questions.\nSometimes, you will be asked to provide sources for your questions, in which case you should use the appropriate tool to do so.\nIf the question does not seem relevant to any of the tools provided, just return "I don\'t know" as the answer.\n', verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]#
Construct a vectorstore agent from an LLM and tools.
langchain.agents.create_vectorstore_router_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreRouterToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to answer questions.\nYou have access to tools for interacting with different sources, and the inputs to the tools are questions.\nYour main task is to decide which of the tools is relevant for answering question at hand.\nFor complex questions, you can break the question down into sub questions and use tools to answers the sub questions.\n', verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]#
Construct a vectorstore router agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-48 | Construct a vectorstore router agent from an LLM and tools.
langchain.agents.get_all_tool_names() → List[str][source]#
Get a list of all possible tool names.
langchain.agents.initialize_agent(tools: Sequence[langchain.tools.base.BaseTool], llm: langchain.base_language.BaseLanguageModel, agent: Optional[langchain.agents.agent_types.AgentType] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, agent_path: Optional[str] = None, agent_kwargs: Optional[dict] = None, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
Load an agent executor given tools and LLM.
Parameters
tools – List of tools this agent has access to.
llm – Language model to use as the agent.
agent – Agent type to use. If None and agent_path is also None, will default to
AgentType.ZERO_SHOT_REACT_DESCRIPTION.
callback_manager – CallbackManager to use. Global callback manager is used if
not provided. Defaults to None.
agent_path – Path to serialized agent to use.
agent_kwargs – Additional key word arguments to pass to the underlying agent
**kwargs – Additional key word arguments passed to the agent executor
Returns
An agent executor
langchain.agents.load_agent(path: Union[str, pathlib.Path], **kwargs: Any) → langchain.agents.agent.BaseSingleActionAgent[source]#
Unified method for loading a agent from LangChainHub or local fs.
langchain.agents.load_huggingface_tool(task_or_repo_id: str, model_repo_id: Optional[str] = None, token: Optional[str] = None, remote: bool = False, **kwargs: Any) → langchain.tools.base.BaseTool[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-49 | langchain.agents.load_tools(tool_names: List[str], llm: Optional[langchain.base_language.BaseLanguageModel] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → List[langchain.tools.base.BaseTool][source]#
Load tools based on their name.
Parameters
tool_names – name of tools to load.
llm – Optional language model, may be needed to initialize certain tools.
callbacks – Optional callback manager or list of callback handlers.
If not provided, default global callback manager will be used.
Returns
List of tools.
langchain.agents.tool(*args: Union[str, Callable], return_direct: bool = False, args_schema: Optional[Type[pydantic.main.BaseModel]] = None, infer_schema: bool = True) → Callable[source]#
Make tools out of functions, can be used with or without arguments.
Parameters
*args – The arguments to the tool.
return_direct – Whether to return directly from the tool rather
than continuing the agent loop.
args_schema – optional argument schema for user to specify
infer_schema – Whether to infer the schema of the arguments from
the function’s signature. This also makes the resultant tool
accept a dictionary input to its run() function.
Requires:
Function must be of type (str) -> str
Function must have a docstring
Examples
@tool
def search_api(query: str) -> str:
# Searches the API for the query.
return
@tool("search", return_direct=True)
def search_api(query: str) -> str:
# Searches the API for the query.
return
previous
Agents
next
Tools
By Harrison Chase
© Copyright 2023, Harrison Chase. | https://python.langchain.com/en/latest/reference/modules/agents.html |
c29fdbc9fa5a-50 | Tools
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/reference/modules/agents.html |
bca714657b2c-0 | .rst
.pdf
Experimental Modules
Contents
Autonomous Agents
Generative Agents
Experimental Modules#
This module contains experimental modules and reproductions of existing work using LangChain primitives.
Autonomous Agents#
Here, we document the BabyAGI and AutoGPT classes from the langchain.experimental module.
class langchain.experimental.BabyAGI(*, memory: Optional[langchain.schema.BaseMemory] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, verbose: bool = None, task_list: collections.deque = None, task_creation_chain: langchain.chains.base.Chain, task_prioritization_chain: langchain.chains.base.Chain, execution_chain: langchain.chains.base.Chain, task_id_counter: int = 1, vectorstore: langchain.vectorstores.base.VectorStore, max_iterations: Optional[int] = None)[source]#
Controller model for the BabyAGI agent.
model Config[source]#
Configuration for this pydantic object.
arbitrary_types_allowed = True#
execute_task(objective: str, task: str, k: int = 5) → str[source]#
Execute a task.
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, vectorstore: langchain.vectorstores.base.VectorStore, verbose: bool = False, task_execution_chain: Optional[langchain.chains.base.Chain] = None, **kwargs: Dict[str, Any]) → langchain.experimental.autonomous_agents.baby_agi.baby_agi.BabyAGI[source]#
Initialize the BabyAGI Controller.
get_next_task(result: str, task_description: str, objective: str) → List[Dict][source]#
Get the next task. | https://python.langchain.com/en/latest/reference/modules/experimental.html |
bca714657b2c-1 | Get the next task.
property input_keys: List[str]#
Input keys this chain expects.
property output_keys: List[str]#
Output keys this chain expects.
prioritize_tasks(this_task_id: int, objective: str) → List[Dict][source]#
Prioritize tasks.
class langchain.experimental.AutoGPT(ai_name: str, memory: langchain.vectorstores.base.VectorStoreRetriever, chain: langchain.chains.llm.LLMChain, output_parser: langchain.experimental.autonomous_agents.autogpt.output_parser.BaseAutoGPTOutputParser, tools: List[langchain.tools.base.BaseTool], feedback_tool: Optional[langchain.tools.human.tool.HumanInputRun] = None)[source]#
Agent class for interacting with Auto-GPT.
Generative Agents#
Here, we document the GenerativeAgent and GenerativeAgentMemory classes from the langchain.experimental module.
class langchain.experimental.GenerativeAgent(*, name: str, age: Optional[int] = None, traits: str = 'N/A', status: str, memory: langchain.experimental.generative_agents.memory.GenerativeAgentMemory, llm: langchain.base_language.BaseLanguageModel, verbose: bool = False, summary: str = '', summary_refresh_seconds: int = 3600, last_refreshed: datetime.datetime = None, daily_summaries: List[str] = None)[source]#
A character with memory and innate characteristics.
model Config[source]#
Configuration for this pydantic object.
arbitrary_types_allowed = True#
field age: Optional[int] = None#
The optional age of the character.
field daily_summaries: List[str] [Optional]#
Summary of the events in the plan that the agent took. | https://python.langchain.com/en/latest/reference/modules/experimental.html |
bca714657b2c-2 | Summary of the events in the plan that the agent took.
generate_dialogue_response(observation: str, now: Optional[datetime.datetime] = None) → Tuple[bool, str][source]#
React to a given observation.
generate_reaction(observation: str, now: Optional[datetime.datetime] = None) → Tuple[bool, str][source]#
React to a given observation.
get_full_header(force_refresh: bool = False, now: Optional[datetime.datetime] = None) → str[source]#
Return a full header of the agent’s status, summary, and current time.
get_summary(force_refresh: bool = False, now: Optional[datetime.datetime] = None) → str[source]#
Return a descriptive summary of the agent.
field last_refreshed: datetime.datetime [Optional]#
The last time the character’s summary was regenerated.
field llm: langchain.base_language.BaseLanguageModel [Required]#
The underlying language model.
field memory: langchain.experimental.generative_agents.memory.GenerativeAgentMemory [Required]#
The memory object that combines relevance, recency, and ‘importance’.
field name: str [Required]#
The character’s name.
field status: str [Required]#
The traits of the character you wish not to change.
summarize_related_memories(observation: str) → str[source]#
Summarize memories that are most relevant to an observation.
field summary: str = ''#
Stateful self-summary generated via reflection on the character’s memory.
field summary_refresh_seconds: int = 3600#
How frequently to re-generate the summary.
field traits: str = 'N/A'#
Permanent traits to ascribe to the character. | https://python.langchain.com/en/latest/reference/modules/experimental.html |
bca714657b2c-3 | field traits: str = 'N/A'#
Permanent traits to ascribe to the character.
class langchain.experimental.GenerativeAgentMemory(*, llm: langchain.base_language.BaseLanguageModel, memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever, verbose: bool = False, reflection_threshold: Optional[float] = None, current_plan: List[str] = [], importance_weight: float = 0.15, aggregate_importance: float = 0.0, max_tokens_limit: int = 1200, queries_key: str = 'queries', most_recent_memories_token_key: str = 'recent_memories_token', add_memory_key: str = 'add_memory', relevant_memories_key: str = 'relevant_memories', relevant_memories_simple_key: str = 'relevant_memories_simple', most_recent_memories_key: str = 'most_recent_memories', now_key: str = 'now', reflecting: bool = False)[source]#
add_memory(memory_content: str, now: Optional[datetime.datetime] = None) → List[str][source]#
Add an observation or memory to the agent’s memory.
field aggregate_importance: float = 0.0#
Track the sum of the ‘importance’ of recent memories.
Triggers reflection when it reaches reflection_threshold.
clear() → None[source]#
Clear memory contents.
field current_plan: List[str] = []#
The current plan of the agent.
fetch_memories(observation: str, now: Optional[datetime.datetime] = None) → List[langchain.schema.Document][source]#
Fetch related memories.
field importance_weight: float = 0.15#
How much weight to assign the memory importance.
field llm: langchain.base_language.BaseLanguageModel [Required]#
The core language model. | https://python.langchain.com/en/latest/reference/modules/experimental.html |
bca714657b2c-4 | The core language model.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]#
Return key-value pairs given the text input to the chain.
field memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever [Required]#
The retriever to fetch related memories.
property memory_variables: List[str]#
Input keys this memory class will load dynamically.
pause_to_reflect(now: Optional[datetime.datetime] = None) → List[str][source]#
Reflect on recent observations and generate ‘insights’.
field reflection_threshold: Optional[float] = None#
When aggregate_importance exceeds reflection_threshold, stop to reflect.
save_context(inputs: Dict[str, Any], outputs: Dict[str, Any]) → None[source]#
Save the context of this model run to memory.
previous
Utilities
next
Integrations
Contents
Autonomous Agents
Generative Agents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/reference/modules/experimental.html |
6a4e22dd1a6a-0 | .rst
.pdf
Memory
Memory#
class langchain.memory.CassandraChatMessageHistory(contact_points: List[str], session_id: str, port: int = 9042, username: str = 'cassandra', password: str = 'cassandra', keyspace_name: str = 'chat_history', table_name: str = 'message_store')[source]#
Chat message history that stores history in Cassandra.
:param contact_points: list of ips to connect to Cassandra cluster
:param session_id: arbitrary key that is used to store the messages
of a single chat session.
Parameters
port – port to connect to Cassandra cluster
username – username to connect to Cassandra cluster
password – password to connect to Cassandra cluster
keyspace_name – name of the keyspace to use
table_name – name of the table to use
add_ai_message(message: str) → None[source]#
Add an AI message to the store
add_user_message(message: str) → None[source]#
Add a user message to the store
append(message: langchain.schema.BaseMessage) → None[source]#
Append the message to the record in Cassandra
clear() → None[source]#
Clear session memory from Cassandra
property messages: List[langchain.schema.BaseMessage]#
Retrieve the messages from Cassandra
pydantic model langchain.memory.ChatMessageHistory[source]#
field messages: List[langchain.schema.BaseMessage] = []#
add_ai_message(message: str) → None[source]#
Add an AI message to the store
add_user_message(message: str) → None[source]#
Add a user message to the store
clear() → None[source]#
Remove all messages from the store
pydantic model langchain.memory.CombinedMemory[source]#
Class for combining multiple memories’ data together.
Validators
check_input_key » memories
check_repeated_memory_variable » memories | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-1 | Validators
check_input_key » memories
check_repeated_memory_variable » memories
field memories: List[langchain.schema.BaseMemory] [Required]#
For tracking all the memories that should be accessed.
clear() → None[source]#
Clear context from this session for every memory.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]#
Load all vars from sub-memories.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Save context from this session for every memory.
property memory_variables: List[str]#
All the memory variables that this instance provides.
pydantic model langchain.memory.ConversationBufferMemory[source]#
Buffer for storing conversation memory.
field ai_prefix: str = 'AI'#
field human_prefix: str = 'Human'#
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]#
Return history buffer.
property buffer: Any#
String buffer of memory.
pydantic model langchain.memory.ConversationBufferWindowMemory[source]#
Buffer for storing conversation memory.
field ai_prefix: str = 'AI'#
field human_prefix: str = 'Human'#
field k: int = 5#
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]#
Return history buffer.
property buffer: List[langchain.schema.BaseMessage]#
String buffer of memory.
pydantic model langchain.memory.ConversationEntityMemory[source]#
Entity extractor & summarizer to memory.
field ai_prefix: str = 'AI'#
field chat_history_key: str = 'history'#
field entity_cache: List[str] = []# | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-2 | field entity_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nThe conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-3 | a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I\'m working with Person #2.\nOutput: Langchain, Person #2\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:', template_format='f-string', validate_template=True)# | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-4 | field entity_store: langchain.memory.entity.BaseEntityStore [Optional]#
field entity_summarization_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['entity', 'summary', 'history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant helping a human keep track of facts about relevant people, places, and concepts in their life. Update the summary of the provided entity in the "Entity" section based on the last line of your conversation with the human. If you are writing the summary for the first time, return a single sentence.\nThe update should only include facts that are relayed in the last line of conversation about the provided entity, and should only contain facts about the provided entity.\n\nIf there is no new information about the provided entity or the information is not worth noting (not an important or relevant fact to remember long-term), return the existing summary unchanged.\n\nFull conversation history (for context):\n{history}\n\nEntity to summarize:\n{entity}\n\nExisting summary of {entity}:\n{summary}\n\nLast line of conversation:\nHuman: {input}\nUpdated summary:', template_format='f-string', validate_template=True)#
field human_prefix: str = 'Human'#
field k: int = 3#
field llm: langchain.base_language.BaseLanguageModel [Required]#
clear() → None[source]#
Clear memory contents.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]#
Return history buffer.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Save context from this conversation to buffer.
property buffer: List[langchain.schema.BaseMessage]#
pydantic model langchain.memory.ConversationKGMemory[source]#
Knowledge graph memory for storing conversation memory. | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-5 | Knowledge graph memory for storing conversation memory.
Integrates with external knowledge graph to store and retrieve
information about knowledge triples in the conversation.
field ai_prefix: str = 'AI'# | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-6 | field entity_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last line of conversation. As a guideline, a proper noun is generally capitalized. You should definitely extract all names and places.\n\nThe conversation history is provided just in case of a coreference (e.g. "What do you know about him" where "him" is defined in a previous line) -- ignore items mentioned there that are not in the last line.\n\nReturn the output as a single comma-separated list, or NONE if there is nothing of note to return (e.g. the user is just issuing a greeting or having a simple conversation).\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-7 | a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff.\nOutput: Langchain\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: how\'s it going today?\nAI: "It\'s going great! How about you?"\nPerson #1: good! busy working on Langchain. lots to do.\nAI: "That sounds like a lot of work! What kind of things are you doing to make Langchain better?"\nLast line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I\'m working with Person #2.\nOutput: Langchain, Person #2\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:', template_format='f-string', validate_template=True)# | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-8 | field human_prefix: str = 'Human'#
field k: int = 2#
field kg: langchain.graphs.networkx_graph.NetworkxEntityGraph [Optional]# | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-9 | field knowledge_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template="You are a networked intelligence helping a human track knowledge triples about all relevant people, things, concepts, etc. and integrating them with your knowledge stored within your weights as well as that stored in a knowledge graph. Extract all of the knowledge triples from the last line of conversation. A knowledge triple is a clause that contains a subject, a predicate, and an object. The subject is the entity being described, the predicate is the property of the subject that is being described, and the object is the value of the property.\n\nEXAMPLE\nConversation history:\nPerson #1: Did you hear aliens landed in Area 51?\nAI: No, I didn't hear that. What do you know about Area 51?\nPerson #1: It's a secret military base in Nevada.\nAI: What do you know about Nevada?\nLast line of conversation:\nPerson #1: It's a state in the US. It's also the number 1 producer of gold in | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-10 | It's also the number 1 producer of gold in the US.\n\nOutput: (Nevada, is a, state)<|>(Nevada, is in, US)<|>(Nevada, is the number 1 producer of, gold)\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: Hello.\nAI: Hi! How are you?\nPerson #1: I'm good. How are you?\nAI: I'm good too.\nLast line of conversation:\nPerson #1: I'm going to the store.\n\nOutput: NONE\nEND OF EXAMPLE\n\nEXAMPLE\nConversation history:\nPerson #1: What do you know about Descartes?\nAI: Descartes was a French philosopher, mathematician, and scientist who lived in the 17th century.\nPerson #1: The Descartes I'm referring to is a standup comedian and interior designer from Montreal.\nAI: Oh yes, He is a comedian and an interior designer. He has been in the industry for 30 years. His favorite food is baked bean pie.\nLast line of conversation:\nPerson #1: Oh huh. I know Descartes likes to drive antique | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-11 | huh. I know Descartes likes to drive antique scooters and play the mandolin.\nOutput: (Descartes, likes to drive, antique scooters)<|>(Descartes, plays, mandolin)\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for extraction):\nHuman: {input}\n\nOutput:", template_format='f-string', validate_template=True)# | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-12 | field llm: langchain.base_language.BaseLanguageModel [Required]#
field summary_message_cls: Type[langchain.schema.BaseMessage] = <class 'langchain.schema.SystemMessage'>#
Number of previous utterances to include in the context.
clear() → None[source]#
Clear memory contents.
get_current_entities(input_string: str) → List[str][source]#
get_knowledge_triplets(input_string: str) → List[langchain.graphs.networkx_graph.KnowledgeTriple][source]#
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]#
Return history buffer.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Save context from this conversation to buffer.
pydantic model langchain.memory.ConversationStringBufferMemory[source]#
Buffer for storing conversation memory.
field ai_prefix: str = 'AI'#
Prefix to use for AI generated responses.
field buffer: str = ''#
field human_prefix: str = 'Human'#
field input_key: Optional[str] = None#
field output_key: Optional[str] = None#
clear() → None[source]#
Clear memory contents.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]#
Return history buffer.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Save context from this conversation to buffer.
property memory_variables: List[str]#
Will always return list of memory variables.
:meta private:
pydantic model langchain.memory.ConversationSummaryBufferMemory[source]#
Buffer with summarizer for storing conversation memory.
field max_token_limit: int = 2000#
field memory_key: str = 'history'#
field moving_summary_buffer: str = ''# | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-13 | field memory_key: str = 'history'#
field moving_summary_buffer: str = ''#
clear() → None[source]#
Clear memory contents.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]#
Return history buffer.
prune() → None[source]#
Prune buffer if it exceeds max token limit
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Save context from this conversation to buffer.
property buffer: List[langchain.schema.BaseMessage]#
pydantic model langchain.memory.ConversationSummaryMemory[source]#
Conversation summarizer to memory.
field buffer: str = ''#
clear() → None[source]#
Clear memory contents.
classmethod from_messages(llm: langchain.base_language.BaseLanguageModel, chat_memory: langchain.schema.BaseChatMessageHistory, *, summarize_step: int = 2, **kwargs: Any) → langchain.memory.summary.ConversationSummaryMemory[source]#
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]#
Return history buffer.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Save context from this conversation to buffer.
pydantic model langchain.memory.ConversationTokenBufferMemory[source]#
Buffer for storing conversation memory.
field ai_prefix: str = 'AI'#
field human_prefix: str = 'Human'#
field llm: langchain.base_language.BaseLanguageModel [Required]#
field max_token_limit: int = 2000#
field memory_key: str = 'history'#
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]#
Return history buffer.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]# | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-14 | Save context from this conversation to buffer. Pruned.
property buffer: List[langchain.schema.BaseMessage]#
String buffer of memory.
class langchain.memory.CosmosDBChatMessageHistory(cosmos_endpoint: str, cosmos_database: str, cosmos_container: str, session_id: str, user_id: str, credential: Any = None, connection_string: Optional[str] = None, ttl: Optional[int] = None, cosmos_client_kwargs: Optional[dict] = None)[source]#
Chat history backed by Azure CosmosDB.
add_ai_message(message: str) → None[source]#
Add a AI message to the memory.
add_user_message(message: str) → None[source]#
Add a user message to the memory.
clear() → None[source]#
Clear session memory from this memory and cosmos.
load_messages() → None[source]#
Retrieve the messages from Cosmos
prepare_cosmos() → None[source]#
Prepare the CosmosDB client.
Use this function or the context manager to make sure your database is ready.
upsert_messages(new_message: Optional[langchain.schema.BaseMessage] = None) → None[source]#
Update the cosmosdb item.
class langchain.memory.DynamoDBChatMessageHistory(table_name: str, session_id: str)[source]#
Chat message history that stores history in AWS DynamoDB.
This class expects that a DynamoDB table with name table_name
and a partition Key of SessionId is present.
Parameters
table_name – name of the DynamoDB table
session_id – arbitrary key that is used to store the messages
of a single chat session.
add_ai_message(message: str) → None[source]#
Add an AI message to the store
add_user_message(message: str) → None[source]#
Add a user message to the store | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-15 | add_user_message(message: str) → None[source]#
Add a user message to the store
append(message: langchain.schema.BaseMessage) → None[source]#
Append the message to the record in DynamoDB
clear() → None[source]#
Clear session memory from DynamoDB
property messages: List[langchain.schema.BaseMessage]#
Retrieve the messages from DynamoDB
class langchain.memory.FileChatMessageHistory(file_path: str)[source]#
Chat message history that stores history in a local file.
Parameters
file_path – path of the local file to store the messages.
add_ai_message(message: str) → None[source]#
Add an AI message to the store
add_user_message(message: str) → None[source]#
Add a user message to the store
append(message: langchain.schema.BaseMessage) → None[source]#
Append the message to the record in the local file
clear() → None[source]#
Clear session memory from the local file
property messages: List[langchain.schema.BaseMessage]#
Retrieve the messages from the local file
class langchain.memory.InMemoryEntityStore[source]#
Basic in-memory entity store.
clear() → None[source]#
Delete all entities from store.
delete(key: str) → None[source]#
Delete entity value from store.
exists(key: str) → bool[source]#
Check if entity exists in store.
get(key: str, default: Optional[str] = None) → Optional[str][source]#
Get entity value from store.
set(key: str, value: Optional[str]) → None[source]#
Set entity value in store.
store: Dict[str, Optional[str]] = {}# | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-16 | Set entity value in store.
store: Dict[str, Optional[str]] = {}#
class langchain.memory.MomentoChatMessageHistory(session_id: str, cache_client: momento.CacheClient, cache_name: str, *, key_prefix: str = 'message_store:', ttl: Optional[timedelta] = None, ensure_cache_exists: bool = True)[source]#
Chat message history cache that uses Momento as a backend.
See https://gomomento.com/
add_ai_message(message: str) → None[source]#
Store an AI message in the cache.
Parameters
message (str) – The message to store.
add_user_message(message: str) → None[source]#
Store a user message in the cache.
Parameters
message (str) – The message to store.
clear() → None[source]#
Remove the session’s messages from the cache.
Raises
SdkException – Momento service or network error.
Exception – Unexpected response.
classmethod from_client_params(session_id: str, cache_name: str, ttl: timedelta, *, configuration: Optional[momento.config.Configuration] = None, auth_token: Optional[str] = None, **kwargs: Any) → MomentoChatMessageHistory[source]#
Construct cache from CacheClient parameters.
property messages: list[langchain.schema.BaseMessage]#
Retrieve the messages from Momento.
Raises
SdkException – Momento service or network error
Exception – Unexpected response
Returns
List of cached messages
Return type
list[BaseMessage]
class langchain.memory.MongoDBChatMessageHistory(connection_string: str, session_id: str, database_name: str = 'chat_history', collection_name: str = 'message_store')[source]#
Chat message history that stores history in MongoDB.
Parameters
connection_string – connection string to connect to MongoDB
session_id – arbitrary key that is used to store the messages | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-17 | session_id – arbitrary key that is used to store the messages
of a single chat session.
database_name – name of the database to use
collection_name – name of the collection to use
add_ai_message(message: str) → None[source]#
Add an AI message to the store
add_user_message(message: str) → None[source]#
Add a user message to the store
append(message: langchain.schema.BaseMessage) → None[source]#
Append the message to the record in MongoDB
clear() → None[source]#
Clear session memory from MongoDB
property messages: List[langchain.schema.BaseMessage]#
Retrieve the messages from MongoDB
class langchain.memory.PostgresChatMessageHistory(session_id: str, connection_string: str = 'postgresql://postgres:mypassword@localhost/chat_history', table_name: str = 'message_store')[source]#
add_ai_message(message: str) → None[source]#
Add an AI message to the store
add_user_message(message: str) → None[source]#
Add a user message to the store
append(message: langchain.schema.BaseMessage) → None[source]#
Append the message to the record in PostgreSQL
clear() → None[source]#
Clear session memory from PostgreSQL
property messages: List[langchain.schema.BaseMessage]#
Retrieve the messages from PostgreSQL
pydantic model langchain.memory.ReadOnlySharedMemory[source]#
A memory wrapper that is read-only and cannot be changed.
field memory: langchain.schema.BaseMemory [Required]#
clear() → None[source]#
Nothing to clear, got a memory like a vault.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]#
Load memory variables from memory.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Nothing should be saved or changed | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-18 | Nothing should be saved or changed
property memory_variables: List[str]#
Return memory variables.
class langchain.memory.RedisChatMessageHistory(session_id: str, url: str = 'redis://localhost:6379/0', key_prefix: str = 'message_store:', ttl: Optional[int] = None)[source]#
add_ai_message(message: str) → None[source]#
Add an AI message to the store
add_user_message(message: str) → None[source]#
Add a user message to the store
append(message: langchain.schema.BaseMessage) → None[source]#
Append the message to the record in Redis
clear() → None[source]#
Clear session memory from Redis
property key: str#
Construct the record key to use
property messages: List[langchain.schema.BaseMessage]#
Retrieve the messages from Redis
class langchain.memory.RedisEntityStore(session_id: str = 'default', url: str = 'redis://localhost:6379/0', key_prefix: str = 'memory_store', ttl: Optional[int] = 86400, recall_ttl: Optional[int] = 259200, *args: Any, **kwargs: Any)[source]#
Redis-backed Entity store. Entities get a TTL of 1 day by default, and
that TTL is extended by 3 days every time the entity is read back.
clear() → None[source]#
Delete all entities from store.
delete(key: str) → None[source]#
Delete entity value from store.
exists(key: str) → bool[source]#
Check if entity exists in store.
property full_key_prefix: str#
get(key: str, default: Optional[str] = None) → Optional[str][source]#
Get entity value from store.
key_prefix: str = 'memory_store'#
recall_ttl: Optional[int] = 259200# | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-19 | recall_ttl: Optional[int] = 259200#
redis_client: Any#
session_id: str = 'default'#
set(key: str, value: Optional[str]) → None[source]#
Set entity value in store.
ttl: Optional[int] = 86400#
pydantic model langchain.memory.SimpleMemory[source]#
Simple memory for storing context or other bits of information that shouldn’t
ever change between prompts.
field memories: Dict[str, Any] = {}#
clear() → None[source]#
Nothing to clear, got a memory like a vault.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]#
Return key-value pairs given the text input to the chain.
If None, return all memories
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Nothing should be saved or changed, my memory is set in stone.
property memory_variables: List[str]#
Input keys this memory class will load dynamically.
pydantic model langchain.memory.VectorStoreRetrieverMemory[source]#
Class for a VectorStore-backed memory object.
field input_key: Optional[str] = None#
Key name to index the inputs to load_memory_variables.
field memory_key: str = 'history'#
Key name to locate the memories in the result of load_memory_variables.
field retriever: langchain.vectorstores.base.VectorStoreRetriever [Required]#
VectorStoreRetriever object to connect to.
field return_docs: bool = False#
Whether or not to return the result of querying the database directly.
clear() → None[source]#
Nothing to clear.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Union[List[langchain.schema.Document], str]][source]#
Return history buffer. | https://python.langchain.com/en/latest/reference/modules/memory.html |
6a4e22dd1a6a-20 | Return history buffer.
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) → None[source]#
Save context from this conversation to buffer.
property memory_variables: List[str]#
The list of keys emitted from the load_memory_variables method.
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Document Transformers
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Agents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/reference/modules/memory.html |
12951033217e-0 | .rst
.pdf
Tools
Tools#
Core toolkit implementations.
pydantic model langchain.tools.AIPluginTool[source]#
field api_spec: str [Required]#
field args_schema: Type[AIPluginToolSchema] = <class 'langchain.tools.plugin.AIPluginToolSchema'>#
Pydantic model class to validate and parse the tool’s input arguments.
field plugin: AIPlugin [Required]#
classmethod from_plugin_url(url: str) → langchain.tools.plugin.AIPluginTool[source]#
pydantic model langchain.tools.APIOperation[source]#
A model for a single API operation.
field base_url: str [Required]#
The base URL of the operation.
field description: Optional[str] = None#
The description of the operation.
field method: langchain.tools.openapi.utils.openapi_utils.HTTPVerb [Required]#
The HTTP method of the operation.
field operation_id: str [Required]#
The unique identifier of the operation.
field path: str [Required]#
The path of the operation.
field properties: Sequence[langchain.tools.openapi.utils.api_models.APIProperty] [Required]#
field request_body: Optional[langchain.tools.openapi.utils.api_models.APIRequestBody] = None#
The request body of the operation.
classmethod from_openapi_spec(spec: langchain.tools.openapi.utils.openapi_utils.OpenAPISpec, path: str, method: str) → langchain.tools.openapi.utils.api_models.APIOperation[source]#
Create an APIOperation from an OpenAPI spec.
classmethod from_openapi_url(spec_url: str, path: str, method: str) → langchain.tools.openapi.utils.api_models.APIOperation[source]#
Create an APIOperation from an OpenAPI URL.
to_typescript() → str[source]#
Get typescript string representation of the operation. | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-1 | to_typescript() → str[source]#
Get typescript string representation of the operation.
static ts_type_from_python(type_: Union[str, Type, tuple, None, enum.Enum]) → str[source]#
property body_params: List[str]#
property path_params: List[str]#
property query_params: List[str]#
pydantic model langchain.tools.AzureCogsFormRecognizerTool[source]#
Tool that queries the Azure Cognitive Services Form Recognizer API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/quickstarts/get-started-sdks-rest-api?view=form-recog-3.0.0&pivots=programming-language-python
pydantic model langchain.tools.AzureCogsImageAnalysisTool[source]#
Tool that queries the Azure Cognitive Services Image Analysis API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/quickstarts-sdk/image-analysis-client-library-40
pydantic model langchain.tools.AzureCogsSpeech2TextTool[source]#
Tool that queries the Azure Cognitive Services Speech2Text API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/get-started-speech-to-text?pivots=programming-language-python
pydantic model langchain.tools.AzureCogsText2SpeechTool[source]#
Tool that queries the Azure Cognitive Services Text2Speech API.
In order to set this up, follow instructions at:
https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/get-started-text-to-speech?pivots=programming-language-python
pydantic model langchain.tools.BaseTool[source]#
Interface LangChain tools must implement. | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-2 | pydantic model langchain.tools.BaseTool[source]#
Interface LangChain tools must implement.
field args_schema: Optional[Type[pydantic.main.BaseModel]] = None#
Pydantic model class to validate and parse the tool’s input arguments.
field callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None#
Deprecated. Please use callbacks instead.
field callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None#
Callbacks to be called during tool execution.
field description: str [Required]#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str [Required]#
The unique name of the tool that clearly communicates its purpose.
field return_direct: bool = False#
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
field verbose: bool = False#
Whether to log the tool’s progress.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → Any[source]#
Run the tool asynchronously.
run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → Any[source]#
Run the tool. | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-3 | Run the tool.
property args: dict#
property is_single_input: bool#
Whether the tool only accepts a single input.
pydantic model langchain.tools.BingSearchResults[source]#
Tool that has capability to query the Bing Search API and get back json.
field api_wrapper: langchain.utilities.bing_search.BingSearchAPIWrapper [Required]#
field num_results: int = 4#
pydantic model langchain.tools.BingSearchRun[source]#
Tool that adds the capability to query the Bing search API.
field api_wrapper: langchain.utilities.bing_search.BingSearchAPIWrapper [Required]#
pydantic model langchain.tools.ClickTool[source]#
field args_schema: Type[BaseModel] = <class 'langchain.tools.playwright.click.ClickToolInput'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Click on an element with the given CSS selector'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'click_element'#
The unique name of the tool that clearly communicates its purpose.
field playwright_strict: bool = False#
Whether to employ Playwright’s strict mode when clicking on elements.
field playwright_timeout: float = 1000#
Timeout (in ms) for Playwright to wait for element to be ready.
field visible_only: bool = True#
Whether to consider only visible elements.
pydantic model langchain.tools.CopyFileTool[source]#
field args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.copy.FileCopyInput'>#
Pydantic model class to validate and parse the tool’s input arguments. | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-4 | Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Create a copy of a file in a specified location'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'copy_file'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.CurrentWebPageTool[source]#
field args_schema: Type[BaseModel] = <class 'pydantic.main.BaseModel'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Returns the URL of the current page'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'current_webpage'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.DeleteFileTool[source]#
field args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.delete.FileDeleteInput'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Delete a file'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'file_delete'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.DuckDuckGoSearchResults[source]#
Tool that queries the Duck Duck Go Search API and get back json.
field api_wrapper: langchain.utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper [Optional]#
field num_results: int = 4# | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-5 | field num_results: int = 4#
pydantic model langchain.tools.DuckDuckGoSearchRun[source]#
Tool that adds the capability to query the DuckDuckGo search API.
field api_wrapper: langchain.utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper [Optional]#
pydantic model langchain.tools.ExtractHyperlinksTool[source]#
Extract all hyperlinks on the page.
field args_schema: Type[BaseModel] = <class 'langchain.tools.playwright.extract_hyperlinks.ExtractHyperlinksToolInput'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Extract all hyperlinks on the current webpage'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'extract_hyperlinks'#
The unique name of the tool that clearly communicates its purpose.
static scrape_page(page: Any, html_content: str, absolute_urls: bool) → str[source]#
pydantic model langchain.tools.ExtractTextTool[source]#
field args_schema: Type[BaseModel] = <class 'pydantic.main.BaseModel'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Extract all the text on the current webpage'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'extract_text'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.FileSearchTool[source]#
field args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.file_search.FileSearchInput'># | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-6 | Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Recursively search for files in a subdirectory that match the regex pattern'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'file_search'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.GetElementsTool[source]#
field args_schema: Type[BaseModel] = <class 'langchain.tools.playwright.get_elements.GetElementsToolInput'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Retrieve elements in the current web page matching the given CSS selector'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'get_elements'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.GmailCreateDraft[source]#
field args_schema: Type[langchain.tools.gmail.create_draft.CreateDraftSchema] = <class 'langchain.tools.gmail.create_draft.CreateDraftSchema'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Use this tool to create a draft email with the provided message fields.'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'create_gmail_draft'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.GmailGetMessage[source]# | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-7 | pydantic model langchain.tools.GmailGetMessage[source]#
field args_schema: Type[langchain.tools.gmail.get_message.SearchArgsSchema] = <class 'langchain.tools.gmail.get_message.SearchArgsSchema'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Use this tool to fetch an email by message ID. Returns the thread ID, snipet, body, subject, and sender.'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'get_gmail_message'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.GmailGetThread[source]#
field args_schema: Type[langchain.tools.gmail.get_thread.GetThreadSchema] = <class 'langchain.tools.gmail.get_thread.GetThreadSchema'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Use this tool to search for email messages. The input must be a valid Gmail query. The output is a JSON list of messages.'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'get_gmail_thread'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.GmailSearch[source]#
field args_schema: Type[langchain.tools.gmail.search.SearchArgsSchema] = <class 'langchain.tools.gmail.search.SearchArgsSchema'>#
Pydantic model class to validate and parse the tool’s input arguments. | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-8 | Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Use this tool to search for email messages or threads. The input must be a valid Gmail query. The output is a JSON list of the requested resource.'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'search_gmail'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.GmailSendMessage[source]#
field description: str = 'Use this tool to send email messages. The input is the message, recipents'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'send_gmail_message'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.GooglePlacesTool[source]#
Tool that adds the capability to query the Google places API.
field api_wrapper: langchain.utilities.google_places_api.GooglePlacesAPIWrapper [Optional]#
pydantic model langchain.tools.GoogleSearchResults[source]#
Tool that has capability to query the Google Search API and get back json.
field api_wrapper: langchain.utilities.google_search.GoogleSearchAPIWrapper [Required]#
field num_results: int = 4#
pydantic model langchain.tools.GoogleSearchRun[source]#
Tool that adds the capability to query the Google search API.
field api_wrapper: langchain.utilities.google_search.GoogleSearchAPIWrapper [Required]#
pydantic model langchain.tools.GoogleSerperResults[source]#
Tool that has capability to query the Serper.dev Google Search API
and get back json. | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-9 | Tool that has capability to query the Serper.dev Google Search API
and get back json.
field api_wrapper: langchain.utilities.google_serper.GoogleSerperAPIWrapper [Optional]#
pydantic model langchain.tools.GoogleSerperRun[source]#
Tool that adds the capability to query the Serper.dev Google search API.
field api_wrapper: langchain.utilities.google_serper.GoogleSerperAPIWrapper [Required]#
pydantic model langchain.tools.HumanInputRun[source]#
Tool that adds the capability to ask user for input.
field input_func: Callable [Optional]#
field prompt_func: Callable[[str], None] [Optional]#
pydantic model langchain.tools.IFTTTWebhook[source]#
IFTTT Webhook.
Parameters
name – name of the tool
description – description of the tool
url – url to hit with the json event.
field url: str [Required]#
pydantic model langchain.tools.InfoPowerBITool[source]#
Tool for getting metadata about a PowerBI Dataset.
field powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]#
pydantic model langchain.tools.ListDirectoryTool[source]#
field args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.list_dir.DirectoryListingInput'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'List files and directories in a specified folder'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'list_directory'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.ListPowerBITool[source]#
Tool for getting tables names. | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-10 | Tool for getting tables names.
field powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]#
pydantic model langchain.tools.MetaphorSearchResults[source]#
Tool that has capability to query the Metaphor Search API and get back json.
field api_wrapper: langchain.utilities.metaphor_search.MetaphorSearchAPIWrapper [Required]#
pydantic model langchain.tools.MoveFileTool[source]#
field args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.move.FileMoveInput'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Move or rename a file from one location to another'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'move_file'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.NavigateBackTool[source]#
Navigate back to the previous page in the browser history.
field args_schema: Type[BaseModel] = <class 'pydantic.main.BaseModel'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Navigate back to the previous page in the browser history'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'previous_webpage'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.NavigateTool[source]#
field args_schema: Type[BaseModel] = <class 'langchain.tools.playwright.navigate.NavigateToolInput'># | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-11 | Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Navigate a browser to the specified URL'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'navigate_browser'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.OpenAPISpec[source]#
OpenAPI Model that removes misformatted parts of the spec.
classmethod from_file(path: Union[str, pathlib.Path]) → langchain.tools.openapi.utils.openapi_utils.OpenAPISpec[source]#
Get an OpenAPI spec from a file path.
classmethod from_spec_dict(spec_dict: dict) → langchain.tools.openapi.utils.openapi_utils.OpenAPISpec[source]#
Get an OpenAPI spec from a dict.
classmethod from_text(text: str) → langchain.tools.openapi.utils.openapi_utils.OpenAPISpec[source]#
Get an OpenAPI spec from a text.
classmethod from_url(url: str) → langchain.tools.openapi.utils.openapi_utils.OpenAPISpec[source]#
Get an OpenAPI spec from a URL.
static get_cleaned_operation_id(operation: openapi_schema_pydantic.v3.v3_1_0.operation.Operation, path: str, method: str) → str[source]#
Get a cleaned operation id from an operation id.
get_methods_for_path(path: str) → List[str][source]#
Return a list of valid methods for the specified path.
get_operation(path: str, method: str) → openapi_schema_pydantic.v3.v3_1_0.operation.Operation[source]#
Get the operation object for a given path and HTTP method. | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-12 | Get the operation object for a given path and HTTP method.
get_parameters_for_operation(operation: openapi_schema_pydantic.v3.v3_1_0.operation.Operation) → List[openapi_schema_pydantic.v3.v3_1_0.parameter.Parameter][source]#
Get the components for a given operation.
get_referenced_schema(ref: openapi_schema_pydantic.v3.v3_1_0.reference.Reference) → openapi_schema_pydantic.v3.v3_1_0.schema.Schema[source]#
Get a schema (or nested reference) or err.
get_request_body_for_operation(operation: openapi_schema_pydantic.v3.v3_1_0.operation.Operation) → Optional[openapi_schema_pydantic.v3.v3_1_0.request_body.RequestBody][source]#
Get the request body for a given operation.
classmethod parse_obj(obj: dict) → langchain.tools.openapi.utils.openapi_utils.OpenAPISpec[source]#
property base_url: str#
Get the base url.
pydantic model langchain.tools.OpenWeatherMapQueryRun[source]#
Tool that adds the capability to query using the OpenWeatherMap API.
field api_wrapper: langchain.utilities.openweathermap.OpenWeatherMapAPIWrapper [Optional]#
pydantic model langchain.tools.QueryPowerBITool[source]#
Tool for querying a Power BI Dataset.
Validators
raise_deprecation » all fields
validate_llm_chain_input_variables » llm_chain | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-13 | Validators
raise_deprecation » all fields
validate_llm_chain_input_variables » llm_chain
field examples: Optional[str] = '\nQuestion: How many rows are in the table <table>?\nDAX: EVALUATE ROW("Number of rows", COUNTROWS(<table>))\n----\nQuestion: How many rows are in the table <table> where <column> is not empty?\nDAX: EVALUATE ROW("Number of rows", COUNTROWS(FILTER(<table>, <table>[<column>] <> "")))\n----\nQuestion: What was the average of <column> in <table>?\nDAX: EVALUATE ROW("Average", AVERAGE(<table>[<column>]))\n----\n'#
field llm_chain: langchain.chains.llm.LLMChain [Required]#
field max_iterations: int = 5#
field powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]#
field session_cache: Dict[str, Any] [Optional]# | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-14 | field template: Optional[str] = '\nAnswer the question below with a DAX query that can be sent to Power BI. DAX queries have a simple syntax comprised of just one required keyword, EVALUATE, and several optional keywords: ORDER BY, START AT, DEFINE, MEASURE, VAR, TABLE, and COLUMN. Each keyword defines a statement used for the duration of the query. Any time < or > are used in the text below it means that those values need to be replaced by table, columns or other things. If the question is not something you can answer with a DAX query, reply with "I cannot answer this" and the question will be escalated to a human.\n\nSome DAX functions return a table instead of a scalar, and must be wrapped in a function that evaluates the table and returns a scalar; unless the table is a single column, single row table, then it is treated as a scalar value. Most DAX functions require one or more arguments, which can include tables, columns, expressions, and values. However, some functions, | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-15 | columns, expressions, and values. However, some functions, such as PI, do not require any arguments, but always require parentheses to indicate the null argument. For example, you must always type PI(), not PI. You can also nest functions within other functions. \n\nSome commonly used functions are:\nEVALUATE <table> - At the most basic level, a DAX query is an EVALUATE statement containing a table expression. At least one EVALUATE statement is required, however, a query can contain any number of EVALUATE statements.\nEVALUATE <table> ORDER BY <expression> ASC or DESC - The optional ORDER BY keyword defines one or more expressions used to sort query results. Any expression that can be evaluated for each row of the result is valid.\nEVALUATE <table> ORDER BY <expression> ASC or DESC START AT <value> or <parameter> - The optional START AT keyword is used inside an ORDER BY clause. It defines the value at which the query results begin.\nDEFINE MEASURE | VAR; EVALUATE <table> - The optional DEFINE keyword | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-16 | VAR; EVALUATE <table> - The optional DEFINE keyword introduces one or more calculated entity definitions that exist only for the duration of the query. Definitions precede the EVALUATE statement and are valid for all EVALUATE statements in the query. Definitions can be variables, measures, tables1, and columns1. Definitions can reference other definitions that appear before or after the current definition. At least one definition is required if the DEFINE keyword is included in a query.\nMEASURE <table name>[<measure name>] = <scalar expression> - Introduces a measure definition in a DEFINE statement of a DAX query.\nVAR <name> = <expression> - Stores the result of an expression as a named variable, which can then be passed as an argument to other measure expressions. Once resultant values have been calculated for a variable expression, those values do not change, even if the variable is referenced in another expression.\n\nFILTER(<table>,<filter>) - Returns a table that represents a subset of another table or expression, where <filter> is a Boolean expression that is to be | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-17 | <filter> is a Boolean expression that is to be evaluated for each row of the table. For example, [Amount] > 0 or [Region] = "France"\nROW(<name>, <expression>) - Returns a table with a single row containing values that result from the expressions given to each column.\nDISTINCT(<column>) - Returns a one-column table that contains the distinct values from the specified column. In other words, duplicate values are removed and only unique values are returned. This function cannot be used to Return values into a cell or column on a worksheet; rather, you nest the DISTINCT function within a formula, to get a list of distinct values that can be passed to another function and then counted, summed, or used for other operations.\nDISTINCT(<table>) - Returns a table by removing duplicate rows from another table or expression.\n\nAggregation functions, names with a A in it, handle booleans and empty strings in appropriate ways, while the same function without A only uses the numeric values in a column. Functions names with an X in it can include a | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-18 | Functions names with an X in it can include a expression as an argument, this will be evaluated for each row in the table and the result will be used in the regular function calculation, these are the functions:\nCOUNT(<column>), COUNTA(<column>), COUNTX(<table>,<expression>), COUNTAX(<table>,<expression>), COUNTROWS([<table>]), COUNTBLANK(<column>), DISTINCTCOUNT(<column>), DISTINCTCOUNTNOBLANK (<column>) - these are all variantions of count functions.\nAVERAGE(<column>), AVERAGEA(<column>), AVERAGEX(<table>,<expression>) - these are all variantions of average functions.\nMAX(<column>), MAXA(<column>), MAXX(<table>,<expression>) - these are all variantions of max functions.\nMIN(<column>), MINA(<column>), MINX(<table>,<expression>) - these are all variantions of min functions.\nPRODUCT(<column>), PRODUCTX(<table>,<expression>) - these are all variantions of product functions.\nSUM(<column>), SUMX(<table>,<expression>) - these are all variantions of sum functions.\n\nDate and time functions:\nDATE(year, month, day) - Returns a date value that represents the specified year, | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-19 | Returns a date value that represents the specified year, month, and day.\nDATEDIFF(date1, date2, <interval>) - Returns the difference between two date values, in the specified interval, that can be SECOND, MINUTE, HOUR, DAY, WEEK, MONTH, QUARTER, YEAR.\nDATEVALUE(<date_text>) - Returns a date value that represents the specified date.\nYEAR(<date>), QUARTER(<date>), MONTH(<date>), DAY(<date>), HOUR(<date>), MINUTE(<date>), SECOND(<date>) - Returns the part of the date for the specified date.\n\nFinally, make sure to escape double quotes with a single backslash, and make sure that only table names have single quotes around them, while names of measures or the values of columns that you want to compare against are in escaped double quotes. Newlines are not necessary and can be skipped. The queries are serialized as json and so will have to fit be compliant with json syntax. Sometimes you will get a question, a DAX query and a error, in that case you need to rewrite the DAX query to get | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-20 | case you need to rewrite the DAX query to get the correct answer.\n\nThe following tables exist: {tables}\n\nand the schema\'s for some are given here:\n{schemas}\n\nExamples:\n{examples}\n\nQuestion: {tool_input}\nDAX: \n'# | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-21 | pydantic model langchain.tools.ReadFileTool[source]#
field args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.read.ReadFileInput'>#
Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Read file from disk'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'read_file'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.SceneXplainTool[source]#
Tool that adds the capability to explain images.
field api_wrapper: langchain.utilities.scenexplain.SceneXplainAPIWrapper [Optional]#
pydantic model langchain.tools.ShellTool[source]#
Tool to run shell commands.
field args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.shell.tool.ShellInput'>#
Schema for input arguments.
field description: str = 'Run shell commands on this Linux machine.'#
Description of tool.
field name: str = 'terminal'#
Name of tool.
field process: langchain.utilities.bash.BashProcess [Optional]#
Bash process to run commands.
pydantic model langchain.tools.SteamshipImageGenerationTool[source]#
field model_name: ModelName [Required]#
field return_urls: Optional[bool] = False#
field size: Optional[str] = '512x512'#
field steamship: Steamship [Required]#
pydantic model langchain.tools.StructuredTool[source]#
Tool that can operate on any number of inputs.
field args_schema: Type[pydantic.main.BaseModel] [Required]#
The input arguments’ schema.
The tool schema. | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-22 | The input arguments’ schema.
The tool schema.
field coroutine: Optional[Callable[[...], Awaitable[Any]]] = None#
The asynchronous version of the function.
field description: str = ''#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field func: Callable[[...], Any] [Required]#
The function to run when the tool is called.
classmethod from_function(func: Callable, name: Optional[str] = None, description: Optional[str] = None, return_direct: bool = False, args_schema: Optional[Type[pydantic.main.BaseModel]] = None, infer_schema: bool = True, **kwargs: Any) → langchain.tools.base.StructuredTool[source]#
property args: dict#
The tool’s input arguments.
pydantic model langchain.tools.Tool[source]#
Tool that takes in function or coroutine directly.
field args_schema: Optional[Type[pydantic.main.BaseModel]] = None#
Pydantic model class to validate and parse the tool’s input arguments.
field callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None#
Deprecated. Please use callbacks instead.
field callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None#
Callbacks to be called during tool execution.
field coroutine: Optional[Callable[[...], Awaitable[str]]] = None#
The asynchronous version of the function.
field description: str = ''#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field func: Callable[[...], str] [Required]#
The function to run when the tool is called. | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-23 | The function to run when the tool is called.
field name: str [Required]#
The unique name of the tool that clearly communicates its purpose.
field return_direct: bool = False#
Whether to return the tool’s output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
field verbose: bool = False#
Whether to log the tool’s progress.
classmethod from_function(func: Callable, name: str, description: str, return_direct: bool = False, args_schema: Optional[Type[pydantic.main.BaseModel]] = None, **kwargs: Any) → langchain.tools.base.Tool[source]#
Initialize tool from a function.
property args: dict#
The tool’s input arguments.
pydantic model langchain.tools.VectorStoreQATool[source]#
Tool for the VectorDBQA chain. To be initialized with name and chain.
static get_description(name: str, description: str) → str[source]#
pydantic model langchain.tools.VectorStoreQAWithSourcesTool[source]#
Tool for the VectorDBQAWithSources chain.
static get_description(name: str, description: str) → str[source]#
pydantic model langchain.tools.WikipediaQueryRun[source]#
Tool that adds the capability to search using the Wikipedia API.
field api_wrapper: langchain.utilities.wikipedia.WikipediaAPIWrapper [Required]#
pydantic model langchain.tools.WolframAlphaQueryRun[source]#
Tool that adds the capability to query using the Wolfram Alpha SDK.
field api_wrapper: langchain.utilities.wolfram_alpha.WolframAlphaAPIWrapper [Required]#
pydantic model langchain.tools.WriteFileTool[source]#
field args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.write.WriteFileInput'># | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-24 | Pydantic model class to validate and parse the tool’s input arguments.
field description: str = 'Write file to disk'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
field name: str = 'write_file'#
The unique name of the tool that clearly communicates its purpose.
pydantic model langchain.tools.YouTubeSearchTool[source]#
pydantic model langchain.tools.ZapierNLAListActions[source]#
Returns a list of all exposed (enabled) actions associated withcurrent user (associated with the set api_key). Change your exposed
actions here: https://nla.zapier.com/demo/start/
The return list can be empty if no actions exposed. Else will contain
a list of action objects:
[{“id”: str,
“description”: str,
“params”: Dict[str, str]
}]
params will always contain an instructions key, the only required
param. All others optional and if provided will override any AI guesses
(see “understanding the AI guessing flow” here:
https://nla.zapier.com/api/v1/docs)
Parameters
None –
field api_wrapper: langchain.utilities.zapier.ZapierNLAWrapper [Optional]#
pydantic model langchain.tools.ZapierNLARunAction[source]#
Executes an action that is identified by action_id, must be exposed(enabled) by the current user (associated with the set api_key). Change
your exposed actions here: https://nla.zapier.com/demo/start/
The return JSON is guaranteed to be less than ~500 words (350
tokens) making it safe to inject into the prompt of another LLM
call.
Parameters
action_id – a specific action ID (from list actions) of the action to execute | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-25 | Parameters
action_id – a specific action ID (from list actions) of the action to execute
(the set api_key must be associated with the action owner)
instructions – a natural language instruction string for using the action
(eg. “get the latest email from Mike Knoop” for “Gmail: find email” action)
params – a dict, optional. Any params provided will override AI guesses
from instructions (see “understanding the AI guessing flow” here:
https://nla.zapier.com/api/v1/docs)
field action_id: str [Required]#
field api_wrapper: langchain.utilities.zapier.ZapierNLAWrapper [Optional]#
field base_prompt: str = 'A wrapper around Zapier NLA actions. The input to this tool is a natural language instruction, for example "get the latest email from my bank" or "send a slack message to the #general channel". Each tool will have params associated with it that are specified as a list. You MUST take into account the params when creating the instruction. For example, if the params are [\'Message_Text\', \'Channel\'], your instruction should be something like \'send a slack message to the #general channel with the text hello world\'. Another example: if the params are [\'Calendar\', \'Search_Term\'], your instruction should be something like \'find the meeting in my personal calendar at 3pm\'. Do not make up params, they will be explicitly specified in the tool description. If you do not have enough information to fill in the params, just say \'not enough information provided in the instruction, missing <param>\'. If you get a none or null response, STOP EXECUTION, do not try to another tool!This tool specifically used for: {zapier_description}, and has params: {params}'#
field params: Optional[dict] = None# | https://python.langchain.com/en/latest/reference/modules/tools.html |
12951033217e-26 | field params: Optional[dict] = None#
field params_schema: Dict[str, str] [Optional]#
field zapier_description: str [Required]#
langchain.tools.tool(*args: Union[str, Callable], return_direct: bool = False, args_schema: Optional[Type[pydantic.main.BaseModel]] = None, infer_schema: bool = True) → Callable[source]#
Make tools out of functions, can be used with or without arguments.
Parameters
*args – The arguments to the tool.
return_direct – Whether to return directly from the tool rather
than continuing the agent loop.
args_schema – optional argument schema for user to specify
infer_schema – Whether to infer the schema of the arguments from
the function’s signature. This also makes the resultant tool
accept a dictionary input to its run() function.
Requires:
Function must be of type (str) -> str
Function must have a docstring
Examples
@tool
def search_api(query: str) -> str:
# Searches the API for the query.
return
@tool("search", return_direct=True)
def search_api(query: str) -> str:
# Searches the API for the query.
return
previous
Agents
next
Agent Toolkits
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/reference/modules/tools.html |
658971287a4f-0 | .rst
.pdf
SerpAPI
SerpAPI#
For backwards compatiblity.
pydantic model langchain.serpapi.SerpAPIWrapper[source]#
Wrapper around SerpAPI.
To use, you should have the google-search-results python package installed,
and the environment variable SERPAPI_API_KEY set with your API key, or pass
serpapi_api_key as a named parameter to the constructor.
Example
from langchain import SerpAPIWrapper
serpapi = SerpAPIWrapper()
field aiosession: Optional[aiohttp.client.ClientSession] = None#
field params: dict = {'engine': 'google', 'gl': 'us', 'google_domain': 'google.com', 'hl': 'en'}#
field serpapi_api_key: Optional[str] = None#
async aresults(query: str) → dict[source]#
Use aiohttp to run query through SerpAPI and return the results async.
async arun(query: str, **kwargs: Any) → str[source]#
Run query through SerpAPI and parse result async.
get_params(query: str) → Dict[str, str][source]#
Get parameters for SerpAPI.
results(query: str) → dict[source]#
Run query through SerpAPI and return the raw result.
run(query: str, **kwargs: Any) → str[source]#
Run query through SerpAPI and parse result.
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/reference/modules/serpapi.html |
2213e45f3c60-0 | .rst
.pdf
Agent Toolkits
Agent Toolkits#
Agent toolkits.
pydantic model langchain.agents.agent_toolkits.AzureCognitiveServicesToolkit[source]#
Toolkit for Azure Cognitive Services.
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.FileManagementToolkit[source]#
Toolkit for interacting with a Local Files.
field root_dir: Optional[str] = None#
If specified, all file operations are made relative to root_dir.
field selected_tools: Optional[List[str]] = None#
If provided, only provide the selected tools. Defaults to all.
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.GmailToolkit[source]#
Toolkit for interacting with Gmail.
field api_resource: Resource [Optional]#
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.JiraToolkit[source]#
Jira Toolkit.
field tools: List[langchain.tools.base.BaseTool] = []#
classmethod from_jira_api_wrapper(jira_api_wrapper: langchain.utilities.jira.JiraAPIWrapper) → langchain.agents.agent_toolkits.jira.toolkit.JiraToolkit[source]#
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.JsonToolkit[source]#
Toolkit for interacting with a JSON spec.
field spec: langchain.tools.json.tool.JsonSpec [Required]#
get_tools() → List[langchain.tools.base.BaseTool][source]# | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-1 | get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.NLAToolkit[source]#
Natural Language API Toolkit Definition.
field nla_tools: Sequence[langchain.agents.agent_toolkits.nla.tool.NLATool] [Required]#
List of API Endpoint Tools.
classmethod from_llm_and_ai_plugin(llm: langchain.base_language.BaseLanguageModel, ai_plugin: langchain.tools.plugin.AIPlugin, requests: Optional[langchain.requests.Requests] = None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit[source]#
Instantiate the toolkit from an OpenAPI Spec URL
classmethod from_llm_and_ai_plugin_url(llm: langchain.base_language.BaseLanguageModel, ai_plugin_url: str, requests: Optional[langchain.requests.Requests] = None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit[source]#
Instantiate the toolkit from an OpenAPI Spec URL
classmethod from_llm_and_spec(llm: langchain.base_language.BaseLanguageModel, spec: langchain.tools.openapi.utils.openapi_utils.OpenAPISpec, requests: Optional[langchain.requests.Requests] = None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit[source]#
Instantiate the toolkit by creating tools for each operation.
classmethod from_llm_and_url(llm: langchain.base_language.BaseLanguageModel, open_api_url: str, requests: Optional[langchain.requests.Requests] = None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent_toolkits.nla.toolkit.NLAToolkit[source]# | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-2 | Instantiate the toolkit from an OpenAPI Spec URL
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools for all the API operations.
pydantic model langchain.agents.agent_toolkits.OpenAPIToolkit[source]#
Toolkit for interacting with a OpenAPI api.
field json_agent: langchain.agents.agent.AgentExecutor [Required]#
field requests_wrapper: langchain.requests.TextRequestsWrapper [Required]#
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, json_spec: langchain.tools.json.tool.JsonSpec, requests_wrapper: langchain.requests.TextRequestsWrapper, **kwargs: Any) → langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit[source]#
Create json agent from llm, then initialize.
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.PlayWrightBrowserToolkit[source]#
Toolkit for web browser tools.
field async_browser: Optional['AsyncBrowser'] = None#
field sync_browser: Optional['SyncBrowser'] = None#
classmethod from_browser(sync_browser: Optional[SyncBrowser] = None, async_browser: Optional[AsyncBrowser] = None) → PlayWrightBrowserToolkit[source]#
Instantiate the toolkit.
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.PowerBIToolkit[source]#
Toolkit for interacting with PowerBI dataset.
field callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None#
field examples: Optional[str] = None#
field llm: langchain.base_language.BaseLanguageModel [Required]#
field max_iterations: int = 5# | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-3 | field max_iterations: int = 5#
field powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]#
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.SQLDatabaseToolkit[source]#
Toolkit for interacting with SQL databases.
field db: langchain.sql_database.SQLDatabase [Required]#
field llm: langchain.base_language.BaseLanguageModel [Required]#
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
property dialect: str#
Return string representation of dialect to use.
pydantic model langchain.agents.agent_toolkits.SparkSQLToolkit[source]#
Toolkit for interacting with Spark SQL.
field db: langchain.utilities.spark_sql.SparkSQL [Required]#
field llm: langchain.base_language.BaseLanguageModel [Required]#
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.VectorStoreInfo[source]#
Information about a vectorstore.
field description: str [Required]#
field name: str [Required]#
field vectorstore: langchain.vectorstores.base.VectorStore [Required]#
pydantic model langchain.agents.agent_toolkits.VectorStoreRouterToolkit[source]#
Toolkit for routing between vectorstores.
field llm: langchain.base_language.BaseLanguageModel [Optional]#
field vectorstores: List[langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo] [Required]#
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit. | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-4 | Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.VectorStoreToolkit[source]#
Toolkit for interacting with a vector store.
field llm: langchain.base_language.BaseLanguageModel [Optional]#
field vectorstore_info: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreInfo [Required]#
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.ZapierToolkit[source]#
Zapier Toolkit.
field tools: List[langchain.tools.base.BaseTool] = []#
classmethod from_zapier_nla_wrapper(zapier_nla_wrapper: langchain.utilities.zapier.ZapierNLAWrapper) → langchain.agents.agent_toolkits.zapier.toolkit.ZapierToolkit[source]#
Create a toolkit from a ZapierNLAWrapper.
get_tools() → List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
langchain.agents.agent_toolkits.create_csv_agent(llm: langchain.base_language.BaseLanguageModel, path: Union[str, List[str]], pandas_kwargs: Optional[dict] = None, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
Create csv agent by loading to a dataframe and using pandas agent. | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-5 | langchain.agents.agent_toolkits.create_json_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.json.toolkit.JsonToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with JSON.\nYour goal is to return a final answer by interacting with the JSON.\nYou have access to the following tools which help you learn more about the JSON you are interacting with.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nDo not make up any information that is not contained in the JSON.\nYour input to the tools should be in the form of `data["key"][0]` where `data` is the JSON blob you are interacting with, and the syntax used is Python. \nYou should only use keys that you know for a fact exist. You must validate that a key exists by seeing it previously when calling `json_spec_list_keys`. \nIf you have not seen a key in one of those responses, you cannot use it.\nYou should only add one key | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-6 | you cannot use it.\nYou should only add one key at a time to the path. You cannot add multiple keys at once.\nIf you encounter a "KeyError", go back to the previous key, look at the available keys, and try again.\n\nIf the question does not seem to be related to the JSON, just return "I don\'t know" as the answer.\nAlways begin your interaction with the `json_spec_list_keys` tool with input "data" to see what keys exist in the JSON.\n\nNote that sometimes the value at a given path is large. In this case, you will get an error "Value is a large dictionary, should explore its keys directly".\nIn this case, you should ALWAYS follow up by using the `json_spec_list_keys` tool to see what keys exist at that path.\nDo not simply refer the user to the JSON or a section of the JSON, as this is not a valid answer. Keep digging until you find the answer and explicitly return it.\n', suffix: str = 'Begin!"\n\nQuestion: {input}\nThought: | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-7 | str = 'Begin!"\n\nQuestion: {input}\nThought: I should look at the keys that exist in data to see what I have access to\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None, verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-8 | Construct a json agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-9 | langchain.agents.agent_toolkits.create_openapi_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = "You are an agent designed to answer questions by making web requests to an API given the openapi spec.\n\nIf the question does not seem related to the API, return I don't know. Do not make up an answer.\nOnly use information provided by the tools to construct your response.\n\nFirst, find the base URL needed to make the request.\n\nSecond, find the relevant paths needed to answer the question. Take note that, sometimes, you might need to make more than one request to more than one path to answer the question.\n\nThird, find the required parameters needed to make the request. For GET requests, these are usually URL parameters and for POST requests, these are request body parameters.\n\nFourth, make the requests needed to answer the question. Ensure that you are sending the correct parameters to the request by checking which parameters are required. For parameters with a fixed | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-10 | checking which parameters are required. For parameters with a fixed set of values, please use the spec to look at which values are allowed.\n\nUse the exact parameter names as listed in the spec, do not make up any names or abbreviate the names of parameters.\nIf you get a not found error, ensure that you are using a path that actually exists in the spec.\n", suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should explore the spec to find the base url for the API.\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None, max_iterations: Optional[int] = 15, max_execution_time: | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-11 | None, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = False, return_intermediate_steps: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-12 | Construct a json agent from an LLM and tools.
langchain.agents.agent_toolkits.create_pandas_dataframe_agent(llm: langchain.base_language.BaseLanguageModel, df: Any, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: Optional[str] = None, suffix: Optional[str] = None, input_variables: Optional[List[str]] = None, verbose: bool = False, return_intermediate_steps: bool = False, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', agent_executor_kwargs: Optional[Dict[str, Any]] = None, include_df_in_prompt: Optional[bool] = True, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]#
Construct a pandas agent from an LLM and dataframe. | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-13 | langchain.agents.agent_toolkits.create_pbi_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to help users interact with a PowerBI Dataset.\n\nAgent has access to a tool that can write a query based on the question and then run those against PowerBI, Microsofts business intelligence tool. The questions from the users should be interpreted as related to the dataset that is available and not general questions about the world. If the question does not seem related to the dataset, just return "This does not appear to be part of this dataset." as the answer.\n\nGiven an input question, ask to run the questions against the dataset, then look at the results and return the answer, the answer should be a complete sentence that answers the question, if multiple rows are asked find a way to write that in a easily readible format for a human, also make sure | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-14 | easily readible format for a human, also make sure to represent numbers in readable ways, like 1M instead of 1000000. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I can first ask which tables I have, then how each table is defined and then ask the query tool the question I need, and finally create a nice sentence that answers the question.\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', examples: Optional[str] = None, input_variables: Optional[List[str]] = None, top_k: | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-15 | None, input_variables: Optional[List[str]] = None, top_k: int = 10, verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-16 | Construct a pbi agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-17 | langchain.agents.agent_toolkits.create_pbi_chat_agent(llm: langchain.chat_models.base.BaseChatModel, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, prefix: str = 'Assistant is a large language model built to help users interact with a PowerBI Dataset.\n\nAssistant has access to a tool that can write a query based on the question and then run those against PowerBI, Microsofts business intelligence tool. The questions from the users should be interpreted as related to the dataset that is available and not general questions about the world. If the question does not seem related to the dataset, just return "This does not appear to be part of this dataset." as the answer.\n\nGiven an input question, ask to run the questions against the dataset, then look at the results and return the answer, the answer should be a complete sentence that answers the question, if multiple rows are asked find a way to write that | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-18 | multiple rows are asked find a way to write that in a easily readible format for a human, also make sure to represent numbers in readable ways, like 1M instead of 1000000. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\n', suffix: str = "TOOLS\n------\nAssistant can ask the user to use tools to look up information that may be helpful in answering the users original question. The tools the human can use are:\n\n{{tools}}\n\n{format_instructions}\n\nUSER'S INPUT\n--------------------\nHere is the user's input (remember to respond with a markdown code snippet of a json blob with a single action, and NOTHING else):\n\n{{{{input}}}}\n", examples: Optional[str] = None, input_variables: Optional[List[str]] = None, memory: Optional[langchain.memory.chat_memory.BaseChatMemory] = None, top_k: int = 10, verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-19 | Construct a pbi agent from an Chat LLM and tools.
If you supply only a toolkit and no powerbi dataset, the same LLM is used for both.
langchain.agents.agent_toolkits.create_python_agent(llm: langchain.base_language.BaseLanguageModel, tool: langchain.tools.python.tool.PythonREPLTool, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, verbose: bool = False, prefix: str = 'You are an agent designed to write and execute python code to answer questions.\nYou have access to a python REPL, which you can use to execute python code.\nIf you get an error, debug your code and try again.\nOnly use the output of your code to answer the question. \nYou might know the answer without running any code, but you should still run the code to get the answer.\nIf it does not seem like you can write code to answer the question, just return "I don\'t know" as the answer.\n', agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]#
Construct a python agent from an LLM and tool. | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-20 | Construct a python agent from an LLM and tool.
langchain.agents.agent_toolkits.create_spark_dataframe_agent(llm: langchain.llms.base.BaseLLM, df: Any, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = '\nYou are working with a spark dataframe in Python. The name of the dataframe is `df`.\nYou should use the tools below to answer the question posed of you:', suffix: str = '\nThis is the result of `print(df.first())`:\n{df}\n\nBegin!\nQuestion: {input}\n{agent_scratchpad}', input_variables: Optional[List[str]] = None, verbose: bool = False, return_intermediate_steps: bool = False, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]#
Construct a spark agent from an LLM and dataframe. | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-21 | langchain.agents.agent_toolkits.create_spark_sql_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.spark_sql.toolkit.SparkSQLToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with Spark SQL.\nGiven an input question, create a syntactically correct Spark SQL query to run, then look at the results of the query and return the answer.\nUnless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\nYou can order the results by a relevant column to return the most interesting examples in the database.\nNever query for all the columns from a specific table, only ask for the relevant columns given the question.\nYou have access to tools for interacting with the database.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nYou MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.\n\nDO | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-22 | a query, rewrite the query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return "I don\'t know" as the answer.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should look at the tables in the database to see what I can query.\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None, top_k: int = 10, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-23 | early_stopping_method: str = 'force', verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-24 | Construct a sql agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-25 | langchain.agents.agent_toolkits.create_sql_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to interact with a SQL database.\nGiven an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.\nUnless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\nYou can order the results by a relevant column to return the most interesting examples in the database.\nNever query for all the columns from a specific table, only ask for the relevant columns given the question.\nYou have access to tools for interacting with the database.\nOnly use the below tools. Only use the information returned by the below tools to construct your final answer.\nYou MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.\n\nDO NOT | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-26 | query, rewrite the query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return "I don\'t know" as the answer.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should look at the tables in the database to see what I can query.\n{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables: Optional[List[str]] = None, top_k: int = 10, max_iterations: Optional[int] = 15, max_execution_time: Optional[float] = None, early_stopping_method: str = 'force', verbose: bool = | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-27 | early_stopping_method: str = 'force', verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
2213e45f3c60-28 | Construct a sql agent from an LLM and tools.
langchain.agents.agent_toolkits.create_vectorstore_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to answer questions about sets of documents.\nYou have access to tools for interacting with the documents, and the inputs to the tools are questions.\nSometimes, you will be asked to provide sources for your questions, in which case you should use the appropriate tool to do so.\nIf the question does not seem relevant to any of the tools provided, just return "I don\'t know" as the answer.\n', verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]#
Construct a vectorstore agent from an LLM and tools.
langchain.agents.agent_toolkits.create_vectorstore_router_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreRouterToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent designed to answer questions.\nYou have access to tools for interacting with different sources, and the inputs to the tools are questions.\nYour main task is to decide which of the tools is relevant for answering question at hand.\nFor complex questions, you can break the question down into sub questions and use tools to answers the sub questions.\n', verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | https://python.langchain.com/en/latest/reference/modules/agent_toolkits.html |
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