id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
5ff2273f5859-37 | are some assertions that have been fact checked and are labeled as true or false. If the answer is false, a suggestion is given for a correction.\n\nChecked Assertions:\n"""\n{checked_assertions}\n"""\n\nOriginal Summary:\n"""\n{summary}\n"""\n\nUsing these checked assertions, rewrite the original summary to be complet... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-38 | The sky is blue: True\n- Water is wet: True\n- The sun is a star: True\n"""\nResult: True\n\n===\n\nChecked Assertions: """\n- The sky is blue - True\n- Water is made of lava- False\n- The sun is a star - True\n"""\nResult: False\n\n===\n\nChecked Assertions:"""\n{checked_assertions}\n"""\nResult:', template_format='f-... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-39 | pydantic model langchain.chains.MapReduceChain[source]#
Map-reduce chain.
Validators
raise_deprecation » all fields
set_verbose » verbose
field combine_documents_chain: BaseCombineDocumentsChain [Required]#
Chain to use to combine documents.
field text_splitter: TextSplitter [Required]#
Text splitter to use.
classmetho... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-40 | Chain interacts with an OpenAPI endpoint using natural language.
Validators
raise_deprecation » all fields
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]#... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-41 | set_verbose » verbose
field get_answer_expr: str = 'print(solution())'#
field llm: Optional[BaseLanguageModel] = None#
[Deprecated]
field llm_chain: LLMChain [Required]# | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-42 | field prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?\n\n# solution in Python:\n\n\ndef solution():\n """Olivia has $23. She bought five bagels for $... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-43 | end of wednesday?"""\n golf_balls_initial = 58\n golf_balls_lost_tuesday = 23\n golf_balls_lost_wednesday = 2\n golf_balls_left = golf_balls_initial - golf_balls_lost_tuesday - golf_balls_lost_wednesday\n result = golf_balls_left\n return result\n\n\n\n\n\nQ: There were nine computers in the server ro... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-44 | toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?\n\n# solution in Python:\n\n\ndef solution():\n """Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?"""\n toys_initial = 5\n mom_toys = 2\n dad_toys = ... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-45 | = 12\n denny_lollipops = jason_lollipops_initial - jason_lollipops_after\n result = denny_lollipops\n return result\n\n\n\n\n\nQ: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?\n\n# solution in Python:\n\n\ndef solution():\n """Leah had 32 chocolate... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-46 | and 2 more cars arrive, how many cars are in the parking lot?"""\n cars_initial = 3\n cars_arrived = 2\n total_cars = cars_initial + cars_arrived\n result = total_cars\n return result\n\n\n\n\n\nQ: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, th... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-47 | [Deprecated]
field python_globals: Optional[Dict[str, Any]] = None#
field python_locals: Optional[Dict[str, Any]] = None#
field return_intermediate_steps: bool = False#
field stop: str = '\n\n'#
classmethod from_colored_object_prompt(llm: langchain.base_language.BaseLanguageModel, **kwargs: Any) → langchain.chains.pal.... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-48 | 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... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-49 | [Deprecated] LLM wrapper to use.
field llm_chain: LLMChain [Required]#
field prompt: Optional[BasePromptTemplate] = None#
[Deprecated] Prompt to use to translate natural language to SQL.
field query_checker_prompt: Optional[BasePromptTemplate] = None#
The prompt template that should be used by the query checker
field r... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-50 | classmethod from_llm(llm: langchain.base_language.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 ques... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-51 | SQL Query to run\nSQLResult: Result of the SQLQuery\nAnswer: Final answer here\n\nOnly use the following tables:\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'], ... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-52 | Load the necessary chains.
pydantic model langchain.chains.SequentialChain[source]#
Chain where the outputs of one chain feed directly into next.
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_chains » all fields
field chains: List[langchain.chains.base.Chain] [Required]#
field input_variables... | https://python.langchain.com/en/latest/reference/modules/chains.html |
5ff2273f5859-53 | field vectorstore: VectorStore [Required]#
Vector Database to connect to.
pydantic model langchain.chains.VectorDBQAWithSourcesChain[source]#
Question-answering with sources over a vector database.
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_naming » all fields
field k: int = 4#
Number of r... | https://python.langchain.com/en/latest/reference/modules/chains.html |
66d0f9f81445-0 | .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... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-1 | Construct an agent from an LLM and tools.
get_allowed_tools() → Optional[List[str]][source]#
get_full_inputs(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], **kwargs: Any) → Dict[str, Any][source]#
Create the full inputs for the LLMChain from intermediate steps.
plan(intermediate_steps: List[Tuple[l... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-2 | field early_stopping_method: str = 'force'#
field handle_parsing_errors: Union[bool, str, Callable[[OutputParserException], str]] = False#
field max_execution_time: Optional[float] = None#
field max_iterations: Optional[int] = 15#
field return_intermediate_steps: bool = False#
field tools: Sequence[BaseTool] [Required]... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-3 | SELF_ASK_WITH_SEARCH = 'self-ask-with-search'#
STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION = 'structured-chat-zero-shot-react-description'#
ZERO_SHOT_REACT_DESCRIPTION = 'zero-shot-react-description'#
pydantic model langchain.agents.BaseMultiActionAgent[source]#
Base Agent class.
abstract async aplan(intermediate_steps... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-4 | Return response when agent has been stopped due to max iterations.
save(file_path: Union[pathlib.Path, str]) → None[source]#
Save the agent.
Parameters
file_path – Path to file to save the agent to.
Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path=”path/agent.yaml”)
tool_run_l... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-5 | get_allowed_tools() → Optional[List[str]][source]#
abstract plan(intermediate_steps: List[Tuple[langchain.schema.AgentAction, str]], callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → Union[langchain.schema.AgentAction, l... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-6 | 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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-7 | powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n\nTOOLS:\n------\n\nAssistant has access to the follo... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-8 | 'AI', human_prefix: str = 'Human', input_variables: Optional[List[str]] = None) → langchain.prompts.prompt.PromptTemplate[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-9 | 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... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-10 | classmethod from_llm_and_tools(llm: langchain.base_language.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 la... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-11 | receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-12 | the format:\n\n```\nThought: Do I need to use a tool? No\n{ai_prefix}: [your response here]\n```', ai_prefix: str = 'AI', human_prefix: str = 'Human', input_variables: Optional[List[str]] = None, **kwargs: Any) → langchain.agents.agent.Agent[source]# | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-13 | 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... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-14 | 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... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-15 | classmethod from_llm_and_tools(llm: langchain.base_language.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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-16 | it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n\nOverall, Assistant is a powerful system that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific questi... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-17 | 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... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-18 | 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.from_chains(llm=llm, prompt=prompt)
Valida... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-19 | action_description="useful for doing math"
)
]
mrkl = MRKLChain.from_chains(llm, chains)
pydantic model langchain.agents.ReActChain[source]#
Chain that implements the ReAct paper.
Example
from langchain import ReActChain, OpenAI
react = ReAct(llm=OpenAI())
Validators
raise_deprecation » all fields
set_verbose » ver... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-20 | field output_parser: langchain.agents.agent.AgentOutputParser [Optional]#
classmethod create_prompt(tools: Sequence[langchain.tools.base.BaseTool], prefix: str = 'Respond to the human as helpfully and accurately as possible. You have access to the following tools:', suffix: str = 'Begin! Reminder to ALWAYS respond with... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-21 | classmethod from_llm_and_tools(llm: langchain.base_language.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 = 'Respond to the human as... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-22 | 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.Tool[source]#
Tool that takes in function or coroutine directly.
field coroutine: Optional[Callable[[...], Awai... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-23 | field output_parser: langchain.agents.agent.AgentOutputParser [Optional]#
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_scratchp... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-24 | Returns
A PromptTemplate with the template assembled from the pieces here.
classmethod from_llm_and_tools(llm: langchain.base_language.BaseLanguageModel, tools: Sequence[langchain.tools.base.BaseTool], callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, output_parser: Optional[langchain.age... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-25 | langchain.agents.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... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-26 | 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 y... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-27 | '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, sh... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-28 | Construct a json agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-29 | langchain.agents.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 reque... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-30 | 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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-31 | 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/agents.html |
66d0f9f81445-32 | Construct a json agent from an LLM and tools.
langchain.agents.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[Lis... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-33 | langchain.agents.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, pref... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-34 | 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 h... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-35 | 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 |
66d0f9f81445-36 | Construct a pbi agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-37 | 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, ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-38 | 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--... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-39 | 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 = '\... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-40 | 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... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-41 | 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 databas... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-42 | 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 |
66d0f9f81445-43 | Construct a sql agent from an LLM and tools. | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-44 | 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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-45 | 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 databas... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-46 | 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 |
66d0f9f81445-47 | 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 ... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-48 | 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... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-49 | 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 tool... | https://python.langchain.com/en/latest/reference/modules/agents.html |
66d0f9f81445-50 | Tools
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 28, 2023. | https://python.langchain.com/en/latest/reference/modules/agents.html |
41a82885e453-0 | .rst
.pdf
Example Selector
Example Selector#
Logic for selecting examples to include in prompts.
pydantic model langchain.prompts.example_selector.LengthBasedExampleSelector[source]#
Select examples based on length.
Validators
calculate_example_text_lengths » example_text_lengths
field example_prompt: langchain.prompts... | https://python.langchain.com/en/latest/reference/modules/example_selector.html |
41a82885e453-1 | Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Parameters
examples – List of examples to use in the prompt.
embeddings – An iniialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls – A vector store DB interface class, e.g.... | https://python.langchain.com/en/latest/reference/modules/example_selector.html |
41a82885e453-2 | Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Parameters
examples – List of examples to use in the prompt.
embeddings – An initialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls – A vector store DB interface class, e.g... | https://python.langchain.com/en/latest/reference/modules/example_selector.html |
1232521a494f-0 | .rst
.pdf
PromptTemplates
PromptTemplates#
Prompt template classes.
pydantic model langchain.prompts.BaseChatPromptTemplate[source]#
format(**kwargs: Any) → str[source]#
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
1232521a494f-1 | file_path – Path to directory to save prompt to.
Example:
.. code-block:: python
prompt.save(file_path=”path/prompt.yaml”)
pydantic model langchain.prompts.ChatPromptTemplate[source]#
format(**kwargs: Any) → str[source]#
Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt tem... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
1232521a494f-2 | A list of the names of the variables the prompt template expects.
field prefix: str = ''#
A prompt template string to put before the examples.
field suffix: str [Required]#
A prompt template string to put after the examples.
field template_format: str = 'f-string'#
The format of the prompt template. Options are: ‘f-str... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
1232521a494f-3 | field suffix: langchain.prompts.base.StringPromptTemplate [Required]#
A PromptTemplate to put after the examples.
field template_format: str = 'f-string'#
The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.
field validate_template: bool = True#
Whether or not to try validating the template.
dict(**kwa... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
1232521a494f-4 | Format the prompt with the inputs.
Parameters
kwargs – Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
classmethod from_examples(examples: List[str], suffix: str, input_variables: List[str], example_separator: str = '\n\n', prefix: str = '', **kwarg... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
1232521a494f-5 | Create Chat Messages.
langchain.prompts.load_prompt(path: Union[str, pathlib.Path]) → langchain.prompts.base.BasePromptTemplate[source]#
Unified method for loading a prompt from LangChainHub or local fs.
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Prompts
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Example Selector
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last ... | https://python.langchain.com/en/latest/reference/modules/prompts.html |
fca5983fa07d-0 | .rst
.pdf
Vector Stores
Vector Stores#
Wrappers on top of vector stores.
class langchain.vectorstores.AnalyticDB(connection_string: str, embedding_function: langchain.embeddings.base.Embeddings, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, pre_delete_collection: bool = False, logger: ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-1 | Return connection string from database parameters.
create_collection() → None[source]#
create_tables_if_not_exists() → None[source]#
delete_collection() → None[source]#
drop_tables() → None[source]#
classmethod from_documents(documents: List[langchain.schema.Document], embedding: langchain.embeddings.base.Embeddings, c... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-2 | k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[langchain.schema.Docum... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-3 | Example
from langchain import Annoy
db = Annoy(embedding_function, index, docstore, index_to_docstore_id)
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings t... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-4 | text_embedding_pairs = list(zip(texts, text_embeddings))
db = Annoy.from_embeddings(text_embedding_pairs, embeddings)
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, metric: str = 'angular', trees: int = 100, n_jobs: int = - 1, **kwargs: ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-5 | and index_to_docstore_id from.
embeddings – Embeddings to use when generating queries.
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs selected using the maximal marginal relevance.
Maximal marginal ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-6 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
process_index_results(idxs: List[int], dists: List[float]) → List[Tuple[langchain.schema.Document, float]][source]#
Turns annoy results into a list of documents and scores.
Parameters
idxs – List of indices of the documents in the index.... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-7 | to n_trees * n if not provided
Returns
List of Documents most similar to the embedding.
similarity_search_by_vector(embedding: List[float], k: int = 4, search_k: int = - 1, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-8 | Returns
List of Documents most similar to the query and score for each
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, search_k: int = - 1) → List[Tuple[langchain.schema.Document, float]][source]#
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – ... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-9 | ids (Optional[List[str]]) – An optional list of ids.
refresh (bool) – Whether or not to refresh indices with the updated data.
Default True.
Returns
List of IDs of the added texts.
Return type
List[str]
create_index(**kwargs: Any) → Any[source]#
Creates an index in your project.
See
https://docs.nomic.ai/atlas_api.html... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-10 | index_kwargs (Optional[dict]) – Dict of kwargs for index creation.
See https://docs.nomic.ai/atlas_api.html
Returns
Nomic’s neural database and finest rhizomatic instrument
Return type
AtlasDB
classmethod from_texts(texts: List[str], embedding: Optional[langchain.embeddings.base.Embeddings] = None, metadatas: Optional[... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-11 | Returns
Nomic’s neural database and finest rhizomatic instrument
Return type
AtlasDB
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Run similarity search with AtlasDB
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults t... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-12 | Return type
List[str]
delete_collection() → None[source]#
Delete the collection.
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, ids: Optional[List[str]] = None, collection_name: str = 'langchain', persist_directory: Optional[str] = None, client_settings: Optional[chromadb.... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-13 | Otherwise, the data will be ephemeral in-memory.
Parameters
texts (List[str]) – List of texts to add to the collection.
collection_name (str) – Name of the collection to create.
persist_directory (Optional[str]) – Directory to persist the collection.
embedding (Optional[Embeddings]) – Embedding function. Defaults to No... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-14 | filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → L... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-15 | filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of documents most similar to the query text.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[langchain.schema.Document][source]... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-16 | document (Document) – Document to update.
class langchain.vectorstores.DeepLake(dataset_path: str = './deeplake/', token: Optional[str] = None, embedding_function: Optional[langchain.embeddings.base.Embeddings] = None, read_only: Optional[bool] = False, ingestion_batch_size: int = 1024, num_workers: int = 0, verbose: b... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-17 | Returns
List of IDs of the added texts.
Return type
List[str]
delete(ids: Any[List[str], None] = None, filter: Any[Dict[str, str], None] = None, delete_all: Any[bool, None] = None) → bool[source]#
Delete the entities in the dataset
Parameters
ids (Optional[List[str]], optional) – The document_ids to delete.
Defaults to... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-18 | in either the environment
Local file system path of the form ./path/to/dataset or~/path/to/dataset or path/to/dataset.
In-memory path of the form mem://path/to/dataset which doesn’tsave the dataset, but keeps it in memory instead.
Should be used only for testing as it does not persist.
documents (List[Document]) – List... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-19 | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
:param embedding: Embedding to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
:param fetch_k: Number of Documents to fetc... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-20 | Returns
List of Documents most similar to the query vector.
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-21 | You can install it with pip install “langchain[docarray]”.
classmethod from_params(embedding: langchain.embeddings.base.Embeddings, work_dir: str, n_dim: int, dist_metric: Literal['cosine', 'ip', 'l2'] = 'cosine', max_elements: int = 1024, index: bool = True, ef_construction: int = 200, ef: int = 10, M: int = 16, allow... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
fca5983fa07d-22 | num_threads (int) – Sets the number of cpu threads to use. Defaults to 1.
**kwargs – Other keyword arguments to be passed to the get_doc_cls method.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, work_dir: Optional[str] = None, n_dim: Op... | https://python.langchain.com/en/latest/reference/modules/vectorstores.html |
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