id stringlengths 14 16 | text stringlengths 31 3.14k | source stringlengths 58 124 |
|---|---|---|
7afad359b4bb-18 | field retriever: BaseRetriever [Required]#
Index to connect to.
classmethod from_llm(llm: langchain.schema.BaseLanguageModel, retriever: langchain.schema.BaseRetriever, condense_question_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['chat_history', 'question'], output_parser=None, ... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-19 | field graph: NetworkxEntityGraph [Required]#
field qa_chain: LLMChain [Required]# | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-20 | classmethod from_llm(llm: langchain.llms.base.BaseLLM, qa_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['context', 'question'], output_parser=None, partial_variables={}, template="Use the following knowledge triplets to answer the question at the end. If you don't know the answer, ... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-21 | interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I'm working with Sam.\nOutput: Langchain, Sam\nEND OF EXAMPLE\n\nBegin!\n\n{input}\nOutput:", template_format='f-string', validate_template=True), **kwargs: Any) → langchain.chains.graph_qa.base.GraphQAChain[source]# | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-22 | Initialize from LLM.
pydantic model langchain.chains.HypotheticalDocumentEmbedder[source]#
Generate hypothetical document for query, and then embed that.
Based on https://arxiv.org/abs/2212.10496
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field base_embeddings: Embeddings [Required]#
field... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-23 | Example
from langchain import LLMBashChain, OpenAI
llm_bash = LLMBashChain(llm=OpenAI())
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field llm: langchain.schema.BaseLanguageModel [Required]#
LLM wrapper to use.
field prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_v... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-24 | Chain to run queries against LLMs.
Example
from langchain import LLMChain, OpenAI, PromptTemplate
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
input_variables=["adjective"], template=prompt_template
)
llm = LLMChain(llm=OpenAI(), prompt=prompt)
Validators
set_callback_manager » callback_m... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-25 | Call apply and then parse the results.
async apredict(**kwargs: Any) → str[source]#
Format prompt with kwargs and pass to LLM.
Parameters
**kwargs – Keys to pass to prompt template.
Returns
Completion from LLM.
Example
completion = llm.predict(adjective="funny")
async apredict_and_parse(**kwargs: Any) → Union[str, List... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-26 | Example
completion = llm.predict(adjective="funny")
predict_and_parse(**kwargs: Any) → Union[str, List[str], Dict[str, str]][source]#
Call predict and then parse the results.
prep_prompts(input_list: List[Dict[str, Any]]) → Tuple[List[langchain.schema.PromptValue], Optional[List[str]]][source]#
Prepare prompts from inp... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-27 | field create_draft_answer_prompt: langchain.prompts.prompt.PromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='{question}\n\n', template_format='f-string', validate_template=True)#
field list_assertions_prompt: langchain.prompts.prompt.PromptTemplate = Promp... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-28 | LLM wrapper to use.
field revised_answer_prompt: langchain.prompts.prompt.PromptTemplate = PromptTemplate(input_variables=['checked_assertions', 'question'], output_parser=None, partial_variables={}, template="{checked_assertions}\n\nQuestion: In light of the above assertions and checks, how would you answer the questi... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-29 | LLM wrapper to use.
field prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer th... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-30 | set_verbose » verbose
validate_environment » all fields
field llm_chain: LLMChain [Required]#
field requests_wrapper: TextRequestsWrapper [Optional]#
field text_length: int = 8000#
pydantic model langchain.chains.LLMSummarizationCheckerChain[source]#
Chain for question-answering with self-verification.
Example
from lan... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-31 | set_verbose » verbose
field are_all_true_prompt: langchain.prompts.prompt.PromptTemplate = PromptTemplate(input_variables=['checked_assertions'], output_parser=None, partial_variables={}, template='Below are some assertions that have been fact checked and are labeled as true or false.\n\nIf all of the assertions are tr... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-32 | field check_assertions_prompt: langchain.prompts.prompt.PromptTemplate = PromptTemplate(input_variables=['assertions'], output_parser=None, partial_variables={}, template='You are an expert fact checker. You have been hired by a major news organization to fact check a very important story.\n\nHere is a bullet point lis... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-33 | LLM wrapper to use.
field max_checks: int = 2#
Maximum number of times to check the assertions. Default to double-checking.
field revised_summary_prompt: langchain.prompts.prompt.PromptTemplate = PromptTemplate(input_variables=['checked_assertions', 'summary'], output_parser=None, partial_variables={}, template='Below ... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-34 | Text splitter to use.
classmethod from_params(llm: langchain.llms.base.BaseLLM, prompt: langchain.prompts.base.BasePromptTemplate, text_splitter: langchain.text_splitter.TextSplitter) → langchain.chains.mapreduce.MapReduceChain[source]#
Construct a map-reduce chain that uses the chain for map and reduce.
pydantic model... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-35 | set_callback_manager » callback_manager
set_verbose » verbose
field api_operation: APIOperation [Required]#
field api_request_chain: LLMChain [Required]#
field api_response_chain: Optional[LLMChain] = None#
field param_mapping: _ParamMapping [Required]#
field requests: Requests [Optional]#
field return_intermediate_ste... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-36 | Create an OpenAPIEndpointChain from an operation and a spec.
classmethod from_url_and_method(spec_url: str, path: str, method: str, llm: langchain.llms.base.BaseLLM, requests: Optional[langchain.requests.Requests] = None, return_intermediate_steps: bool = False, **kwargs: Any) → OpenAPIEndpointChain[source]#
Create an ... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-37 | Load PAL from colored object prompt.
classmethod from_math_prompt(llm: langchain.schema.BaseLanguageModel, **kwargs: Any) → langchain.chains.pal.base.PALChain[source]#
Load PAL from math prompt.
pydantic model langchain.chains.QAGenerationChain[source]#
Validators
set_callback_manager » callback_manager
set_verbose » v... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-38 | Chain for question-answering against an index.
Example
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain.faiss import FAISS
from langchain.vectorstores.base import VectorStoreRetriever
retriever = VectorStoreRetriever(vectorstore=FAISS(...))
retrievalQA = RetrievalQA.from_llm(llm... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-39 | Validators
set_callback_manager » callback_manager
set_verbose » verbose
field database: SQLDatabase [Required]#
SQL Database to connect to.
field llm: BaseLanguageModel [Required]#
LLM wrapper to use.
field prompt: Optional[BasePromptTemplate] = None#
Prompt to use to translate natural language to SQL.
field return_di... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-40 | classmethod from_llm(llm: langchain.schema.BaseLanguageModel, database: langchain.sql_database.SQLDatabase, query_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['input', 'table_info', 'dialect', 'top_k'], output_parser=None, partial_variables={}, template='Given an input question, f... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-41 | listed below.\n\n{table_info}\n\nQuestion: {input}', template_format='f-string', validate_template=True), decider_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['query', 'table_names'], output_parser=CommaSeparatedListOutputParser(), partial_variables={}, template='Given the below i... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-42 | Load the necessary chains.
pydantic model langchain.chains.SequentialChain[source]#
Chain where the outputs of one chain feed directly into next.
Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_chains » all fields
field chains: List[langchain.chains.base.Chain] [Required]#
field input_... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-43 | pydantic model langchain.chains.VectorDBQA[source]#
Chain for question-answering against a vector database.
Validators
raise_deprecation » all fields
set_callback_manager » callback_manager
set_verbose » verbose
validate_search_type » all fields
field k: int = 4#
Number of documents to query for.
field search_kwargs: D... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
7afad359b4bb-44 | Vector Database to connect to.
langchain.chains.load_chain(path: Union[str, pathlib.Path], **kwargs: Any) → langchain.chains.base.Chain[source]#
Unified method for loading a chain from LangChainHub or local fs.
previous
SQL Chain example
next
Agents
By Harrison Chase
© Copyright 2023, Harrison Chase.
... | /content/https://python.langchain.com/en/latest/reference/modules/chains.html |
fb5ad5a08443-0 | .rst
.pdf
Agents
Agents#
Interface for agents.
pydantic model langchain.agents.Agent[source]#
Class responsible for calling the language model and deciding the action.
This is driven by an LLMChain. The prompt in the LLMChain MUST include
a variable called “agent_scratchpad” where the agent can put its
intermediary wor... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-1 | Create a prompt for this class.
classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, **kwargs: Any... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-2 | **kwargs – User inputs.
Returns
Action specifying what tool to use.
return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → langchain.schema.AgentFinish[source]#
Return response when agent has been stopped due to max iterations.
tool_run_l... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-3 | field tools: Sequence[BaseTool] [Required]#
classmethod from_agent_and_tools(agent: Union[langchain.agents.agent.BaseSingleActionAgent, langchain.agents.agent.BaseMultiActionAgent], tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, **kwargs:... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-4 | CONVERSATIONAL_REACT_DESCRIPTION = 'conversational-react-description'#
REACT_DOCSTORE = 'react-docstore'#
SELF_ASK_WITH_SEARCH = 'self-ask-with-search'#
ZERO_SHOT_REACT_DESCRIPTION = 'zero-shot-react-description'#
pydantic model langchain.agents.BaseMultiActionAgent[source]#
Base Agent class.
abstract async aplan(inter... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-5 | along with observations
**kwargs – User inputs.
Returns
Actions specifying what tool to use.
return_stopped_response(early_stopping_method: str, intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → langchain.schema.AgentFinish[source]#
Return response when agent has been stopped due to m... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-6 | Return dictionary representation of agent.
classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, **kwargs: Any) → langchain.agents.agent.BaseSingleActionAgent[source]#
get_a... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-7 | Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path=”path/agent.yaml”)
tool_run_logging_kwargs() → Dict[source]#
property return_values: List[str]#
Return values of the agent.
pydantic model langchain.agents.ConversationalAgent[source]#
An agent designed to hold a conversation in... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-8 | classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], prefix: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of ... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-9 | format_instructions: str = 'To use a tool, please use the following format:\n\n```\nThought: Do I need to use a tool? Yes\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```\n\nWhen you have a response to say to the Human, or if... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-10 | Create prompt in the style of the zero shot agent.
Parameters
tools – List of tools the agent will have access to, used to format the
prompt.
prefix – String to put before the list of tools.
suffix – String to put after the list of tools.
ai_prefix – String to use before AI output.
human_prefix – String to use before h... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-11 | classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, prefix: str = 'Assistant is a large language ... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-12 | has access to the following tools:', suffix: str = 'Begin!\n\nPrevious conversation history:\n{chat_history}\n\nNew input: {input}\n{agent_scratchpad}', format_instructions: str = 'To use a tool, please use the following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the action to take, should be one of... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-13 | Construct an agent from an LLM and tools.
property llm_prefix: str#
Prefix to append the llm call with.
property observation_prefix: str#
Prefix to append the observation with.
pydantic model langchain.agents.ConversationalChatAgent[source]#
An agent designed to hold a conversation in addition to using tools.
field out... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-14 | classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], system_message: str = 'Assistant is a large language model trained by OpenAI.\n\nAssistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide r... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-15 | 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}}}}", input_variables: Optional[List[str]] = None, output_parser: Optional[langchain.schema.BaseOutputParser] = None) → langchain.prompts.base.Base... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-16 | Create a prompt for this class. | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-17 | classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.agent.AgentOutputParser] = None, system_message: str = 'Assistant is a large l... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-18 | about a particular topic, Assistant is here to assist.', human_message: 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-------------... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-19 | Construct an agent from an LLM and tools.
property llm_prefix: str#
Prefix to append the llm call with.
property observation_prefix: str#
Prefix to append the observation with.
pydantic model langchain.agents.LLMSingleActionAgent[source]#
field llm_chain: langchain.chains.llm.LLMChain [Required]#
field output_parser: l... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-20 | tool_run_logging_kwargs() → Dict[source]#
pydantic model langchain.agents.MRKLChain[source]#
Chain that implements the MRKL system.
Example
from langchain import OpenAI, MRKLChain
from langchain.chains.mrkl.base import ChainConfig
llm = OpenAI(temperature=0)
prompt = PromptTemplate(...)
chains = [...]
mrkl = MRKLChain.... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-21 | User friendly way to initialize the MRKL chain.
This is intended to be an easy way to get up and running with the
MRKL chain.
Parameters
llm – The LLM to use as the agent LLM.
chains – The chains the MRKL system has access to.
**kwargs – parameters to be passed to initialization.
Returns
An initialized MRKL chain.
Exam... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-22 | field callback_manager: BaseCallbackManager [Optional]#
field early_stopping_method: str = 'force'#
field max_execution_time: Optional[float] = None#
field max_iterations: Optional[int] = 15#
field memory: Optional[BaseMemory] = None#
field return_intermediate_steps: bool = False#
field tools: Sequence[BaseTool] [Requi... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-23 | validate_tools » all fields
field agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] [Required]#
field callback_manager: BaseCallbackManager [Optional]#
field early_stopping_method: str = 'force'#
field max_execution_time: Optional[float] = None#
field max_iterations: Optional[int] = 15#
field memory: Optional[B... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-24 | classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], prefix: str = 'Answer the following questions as best you can. You have access to the following tools:', suffix: str = 'Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}', format_instructions: str = 'Use the following format:\n\nQuestion: t... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-25 | Returns
A PromptTemplate with the template assembled from the pieces here.
classmethod from_llm_and_tools(llm: langchain.schema.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.agents.age... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-26 | Prefix to append the llm call with.
property observation_prefix: str#
Prefix to append the observation with.
langchain.agents.create_csv_agent(llm: langchain.llms.base.BaseLLM, path: str, pandas_kwargs: Optional[dict] = None, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]#
Create csv agent by loading to ... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-27 | langchain.agents.create_json_agent(llm: langchain.llms.base.BaseLLM, 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 ... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-28 | 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 ca... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-29 | None, verbose: bool = False, **kwargs: Any) → langchain.agents.agent.AgentExecutor[source]# | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-30 | Construct a json agent from an LLM and tools. | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-31 | langchain.agents.create_openapi_agent(llm: langchain.llms.base.BaseLLM, 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 ... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-32 | 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, sho... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-33 | Construct a json agent from an LLM and tools.
langchain.agents.create_pandas_dataframe_agent(llm: langchain.llms.base.BaseLLM, df: Any, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = '\nYou are working with a pandas dataframe in Python. The name of the dataframe is `df`.\... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-34 | langchain.agents.create_pbi_agent(llm: langchain.llms.base.BaseLLM, 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... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-35 | get a new query from the question to query tool.\n\nIf the question does not seem related to the dataset, just return "I don\'t know" as the answer.\n', suffix: str = 'Begin!\n\nQuestion: {input}\nThought: I should first ask which tables I have, then how each table is defined and then ask the question to query tool to ... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-36 | Construct a pbi agent from an LLM and tools. | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-37 | 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, ... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-38 | Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.\n\nOverall, Assistant is a powerful system that can help with a wide range of tasks and... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-39 | input_variables: Optional[List[str]] = None, memory: Optional[langchain.memory.chat_memory.BaseChatMemory] = None, top_k: int = 10, verbose: bool = False, agent_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Dict[str, Any]) → langchain.agents.agent.AgentExecutor[source]# | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-40 | 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. | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-41 | langchain.agents.create_sql_agent(llm: langchain.llms.base.BaseLLM, 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... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-42 | {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... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-43 | Construct a sql agent from an LLM and tools.
langchain.agents.create_vectorstore_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are an agent desig... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-44 | Construct a vectorstore agent from an LLM and tools.
langchain.agents.create_vectorstore_router_agent(llm: langchain.llms.base.BaseLLM, toolkit: langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreRouterToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'Y... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-45 | Get a list of all possible tool names.
langchain.agents.initialize_agent(tools: Sequence[langchain.tools.base.BaseTool], llm: langchain.schema.BaseLanguageModel, agent: Optional[langchain.agents.agent_types.AgentType] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, agent_path: O... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-46 | Unified method for loading a agent from LangChainHub or local fs.
langchain.agents.load_tools(tool_names: List[str], llm: Optional[langchain.llms.base.BaseLLM] = None, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, **kwargs: Any) → List[langchain.tools.base.BaseTool][source]#
Load tool... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
fb5ad5a08443-47 | 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 20... | /content/https://python.langchain.com/en/latest/reference/modules/agents.html |
a17e6783cd11-0 | .rst
.pdf
Document Loaders
Document Loaders#
All different types of document loaders.
class langchain.document_loaders.AZLyricsLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None)[source]#
Loader that loads AZLyrics webpages.
load() → List[langchain.schema.Document][source]#
Load webpage.
web... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-1 | Loading logic for loading documents from Azure Blob Storage.
load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.AzureBlobStorageFileLoader(conn_str: str, container: str, blob_name: str)[source]#
Loading logic for loading documents from Azure Blob Storage.
load() → List[la... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-2 | Load data into document objects.
class langchain.document_loaders.BiliBiliLoader(video_urls: List[str])[source]#
Loader that loads bilibili transcripts.
load() → List[langchain.schema.Document][source]#
Load from bilibili url.
class langchain.document_loaders.BlackboardLoader(blackboard_course_url: str, bbrouter: str, ... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-3 | download(path: str) → None[source]#
Download a file from a url.
Parameters
path – Path to the file.
folder_path: str#
load() → List[langchain.schema.Document][source]#
Load data into document objects.
Returns
List of documents.
load_all_recursively: bool#
parse_filename(url: str) → str[source]#
Parse the filename from ... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-4 | Load data into document objects.
class langchain.document_loaders.CSVLoader(file_path: str, source_column: Optional[str] = None, csv_args: Optional[Dict] = None, encoding: Optional[str] = None)[source]#
Loads a CSV file into a list of documents.
Each document represents one row of the CSV file. Every row is converted i... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-5 | Load from file path.
class langchain.document_loaders.CollegeConfidentialLoader(web_path: Union[str, List[str]], header_template: Optional[dict] = None)[source]#
Loader that loads College Confidential webpages.
load() → List[langchain.schema.Document][source]#
Load webpage.
web_paths: List[str]#
class langchain.documen... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-6 | SVG, Word and Excel.
Hint: space_key and page_id can both be found in the URL of a page in Confluence
- https://yoursite.atlassian.com/wiki/spaces/<space_key>/pages/<page_id>
Example
from langchain.document_loaders import ConfluenceLoader
loader = ConfluenceLoader(
url="https://yoursite.atlassian.com/wiki",
use... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-7 | ImportError – Required dependencies not installed.
load(space_key: Optional[str] = None, page_ids: Optional[List[str]] = None, label: Optional[str] = None, cql: Optional[str] = None, include_attachments: bool = False, include_comments: bool = False, limit: Optional[int] = 50, max_pages: Optional[int] = 1000) → List[lan... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-8 | Paginate the various methods to retrieve groups of pages.
Unfortunately, due to page size, sometimes the Confluence API
doesn’t match the limit value. If limit is >100 confluence
seems to cap the response to 100. Also, due to the Atlassian Python
package, we don’t get the “next” values from the “_links” key because
th... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-9 | process_xls(link: str) → str[source]#
static validate_init_args(url: Optional[str] = None, api_key: Optional[str] = None, username: Optional[str] = None, oauth2: Optional[dict] = None) → Optional[List][source]#
Validates proper combinations of init arguments
class langchain.document_loaders.DataFrameLoader(data_frame: ... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-10 | Extract text from Diffbot on all the URLs and return Document instances
class langchain.document_loaders.DirectoryLoader(path: str, glob: str = '**/[!.]*', silent_errors: bool = False, load_hidden: bool = False, loader_cls: typing.Union[typing.Type[langchain.document_loaders.unstructured.UnstructuredFileLoader], typing... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-11 | Load all chat messages.
class langchain.document_loaders.DuckDBLoader(query: str, database: str = ':memory:', read_only: bool = False, config: Optional[Dict[str, str]] = None, page_content_columns: Optional[List[str]] = None, metadata_columns: Optional[List[str]] = None)[source]#
Loads a query result from DuckDB into a... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-12 | load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.GCSFileLoader(project_name: str, bucket: str, blob: str)[source]#
Loading logic for loading documents from GCS.
load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.GitLoader(... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-13 | load() → List[langchain.schema.Document][source]#
Fetch text from one single GitBook page.
web_paths: List[str]#
class langchain.document_loaders.GoogleApiClient(credentials_path: pathlib.Path = PosixPath('/home/docs/.credentials/credentials.json'), service_account_path: pathlib.Path = PosixPath('/home/docs/.credential... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-14 | token_path: pathlib.Path = PosixPath('/home/docs/.credentials/token.json')#
classmethod validate_channel_or_videoIds_is_set(values: Dict[str, Any]) → Dict[str, Any][source]#
Validate that either folder_id or document_ids is set, but not both.
class langchain.document_loaders.GoogleApiYoutubeLoader(google_api_client: la... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-15 | channel_name = "CodeAesthetic"
)
load.load()
add_video_info: bool = True#
captions_language: str = 'en'#
channel_name: Optional[str] = None#
continue_on_failure: bool = False#
google_api_client: langchain.document_loaders.youtube.GoogleApiClient#
load() → List[langchain.schema.Document][source]#
Load documents.
classme... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-16 | field token_path: pathlib.Path = PosixPath('/home/docs/.credentials/token.json')#
load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.GutenbergLoader(file_path: str)[source]#
Loader that uses urllib to load .txt web files.
load() → List[langchain.schema.Document][source]#
... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-17 | Load items from an HN page.
web_paths: List[str]#
class langchain.document_loaders.HuggingFaceDatasetLoader(path: str, page_content_column: str = 'text', name: Optional[str] = None, data_dir: Optional[str] = None, data_files: Optional[Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]]] = None, cache_dir... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-18 | Load data into document objects.
load_device(url_override: Optional[str] = None, include_guides: bool = True) → List[langchain.schema.Document][source]#
load_guide(url_override: Optional[str] = None) → List[langchain.schema.Document][source]#
load_questions_and_answers(url_override: Optional[str] = None) → List[langcha... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-19 | Load from a list of image files
class langchain.document_loaders.NotebookLoader(path: str, include_outputs: bool = False, max_output_length: int = 10, remove_newline: bool = False, traceback: bool = False)[source]#
Loader that loads .ipynb notebook files.
load() → List[langchain.schema.Document][source]#
Load documents... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-20 | Loader that loads Obsidian files from disk.
FRONT_MATTER_REGEX = re.compile('^---\\n(.*?)\\n---\\n', re.MULTILINE|re.DOTALL)#
load() → List[langchain.schema.Document][source]#
Load documents.
class langchain.document_loaders.OnlinePDFLoader(file_path: str)[source]#
Loader that loads online PDFs.
file_path: str#
load() ... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-21 | alias of langchain.document_loaders.pdf.PyPDFLoader
class langchain.document_loaders.PlaywrightURLLoader(urls: List[str], continue_on_failure: bool = True, headless: bool = True, remove_selectors: Optional[List[str]] = None)[source]#
Loader that uses Playwright and to load a page and unstructured to load the html.
This... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-22 | Load given path as pages.
class langchain.document_loaders.PythonLoader(file_path: str)[source]#
Load Python files, respecting any non-default encoding if specified.
class langchain.document_loaders.ReadTheDocsLoader(path: str, encoding: Optional[str] = None, errors: Optional[str] = None, **kwargs: Optional[Any])[sourc... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-23 | Load using pysrt file.
class langchain.document_loaders.SeleniumURLLoader(urls: List[str], continue_on_failure: bool = True, browser: Literal['chrome', 'firefox'] = 'chrome', executable_path: Optional[str] = None, headless: bool = True)[source]#
Loader that uses Selenium and to load a page and unstructured to load the ... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
a17e6783cd11-24 | parse_sitemap(soup: Any) → List[dict][source]#
Parse sitemap xml and load into a list of dicts.
web_paths: List[str]#
class langchain.document_loaders.SlackDirectoryLoader(zip_path: str, workspace_url: Optional[str] = None)[source]#
Loader for loading documents from a Slack directory dump.
load() → List[langchain.schem... | /content/https://python.langchain.com/en/latest/reference/modules/document_loaders.html |
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