id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 59 127 |
|---|---|---|
1d1034b022d3-99 | (langchain.llms.OpenAI method)
(langchain.llms.OpenLM method)
(langchain.llms.PromptLayerOpenAI method)
streaming (langchain.chat_models.ChatOpenAI attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
(langchain.llm... | rtdocs_stable/api.python.langchain.com/en/stable/genindex.html |
1d1034b022d3-100 | table_names (langchain.utilities.PowerBIDataset attribute)
tags (langchain.llms.AI21 attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.Anyscale attribute)
(langchain.llms.Aviary attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.Banana attribute)
(langc... | rtdocs_stable/api.python.langchain.com/en/stable/genindex.html |
1d1034b022d3-101 | (langchain.llms.Replicate attribute)
(langchain.llms.RWKV attribute)
(langchain.llms.SagemakerEndpoint attribute)
(langchain.llms.SelfHostedHuggingFaceLLM attribute)
(langchain.llms.SelfHostedPipeline attribute)
(langchain.llms.StochasticAI attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute... | rtdocs_stable/api.python.langchain.com/en/stable/genindex.html |
1d1034b022d3-102 | (langchain.llms.RWKV attribute)
(langchain.llms.VertexAI attribute)
(langchain.llms.Writer attribute)
template (langchain.prompts.PromptTemplate attribute)
(langchain.tools.QueryPowerBITool attribute)
template_format (langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attri... | rtdocs_stable/api.python.langchain.com/en/stable/genindex.html |
1d1034b022d3-103 | TokenTextSplitter (class in langchain.text_splitter)
ToMarkdownLoader (class in langchain.document_loaders)
TomlLoader (class in langchain.document_loaders)
tool() (in module langchain.agents)
(in module langchain.tools)
tool_run_logging_kwargs() (langchain.agents.Agent method)
(langchain.agents.BaseMultiActionAgent me... | rtdocs_stable/api.python.langchain.com/en/stable/genindex.html |
1d1034b022d3-104 | (langchain.utilities.WikipediaAPIWrapper attribute)
top_n (langchain.retrievers.document_compressors.CohereRerank attribute)
top_p (langchain.chat_models.ChatGooglePalm attribute)
(langchain.llms.AlephAlpha attribute)
(langchain.llms.Anthropic attribute)
(langchain.llms.AzureOpenAI attribute)
(langchain.llms.ForefrontA... | rtdocs_stable/api.python.langchain.com/en/stable/genindex.html |
1d1034b022d3-105 | TwitterTweetLoader (class in langchain.document_loaders)
type (langchain.output_parsers.ResponseSchema attribute)
(langchain.utilities.GoogleSerperAPIWrapper attribute)
Typesense (class in langchain.vectorstores)
U
unsecure (langchain.utilities.searx_search.SearxSearchWrapper attribute)
(langchain.utilities.SearxSearch... | rtdocs_stable/api.python.langchain.com/en/stable/genindex.html |
1d1034b022d3-106 | (langchain.llms.Anthropic class method)
(langchain.llms.Anyscale class method)
(langchain.llms.Aviary class method)
(langchain.llms.AzureOpenAI class method)
(langchain.llms.Banana class method)
(langchain.llms.Baseten class method)
(langchain.llms.Beam class method)
(langchain.llms.Bedrock class method)
(langchain.llm... | rtdocs_stable/api.python.langchain.com/en/stable/genindex.html |
1d1034b022d3-107 | (langchain.llms.PromptLayerOpenAI class method)
(langchain.llms.PromptLayerOpenAIChat class method)
(langchain.llms.Replicate class method)
(langchain.llms.RWKV class method)
(langchain.llms.SagemakerEndpoint class method)
(langchain.llms.SelfHostedHuggingFaceLLM class method)
(langchain.llms.SelfHostedPipeline class m... | rtdocs_stable/api.python.langchain.com/en/stable/genindex.html |
1d1034b022d3-108 | validate_init_args() (langchain.document_loaders.ConfluenceLoader static method)
validate_template (langchain.prompts.FewShotPromptTemplate attribute)
(langchain.prompts.FewShotPromptWithTemplates attribute)
(langchain.prompts.PromptTemplate attribute)
Vectara (class in langchain.vectorstores)
vector_field (langchain.v... | rtdocs_stable/api.python.langchain.com/en/stable/genindex.html |
1d1034b022d3-109 | (langchain.llms.Cohere attribute)
(langchain.llms.CTransformers attribute)
(langchain.llms.Databricks attribute)
(langchain.llms.DeepInfra attribute)
(langchain.llms.FakeListLLM attribute)
(langchain.llms.ForefrontAI attribute)
(langchain.llms.GooglePalm attribute)
(langchain.llms.GooseAI attribute)
(langchain.llms.GPT... | rtdocs_stable/api.python.langchain.com/en/stable/genindex.html |
1d1034b022d3-110 | video_ids (langchain.document_loaders.GoogleApiYoutubeLoader attribute)
visible_only (langchain.tools.ClickTool attribute)
vocab_only (langchain.embeddings.LlamaCppEmbeddings attribute)
(langchain.llms.GPT4All attribute)
(langchain.llms.LlamaCpp attribute)
W
wait_for_processing() (langchain.document_loaders.MathpixPDFL... | rtdocs_stable/api.python.langchain.com/en/stable/genindex.html |
089816ffd331-0 | .md
.pdf
Dependents
Dependents#
Dependents stats for hwchase17/langchain
[update: 2023-06-05; only dependent repositories with Stars > 100]
Repository
Stars
openai/openai-cookbook
38024
LAION-AI/Open-Assistant
33609
microsoft/TaskMatrix
33136
hpcaitech/ColossalAI
30032
imartinez/privateGPT
28094
reworkd/AgentGPT
23430
... | rtdocs_stable/api.python.langchain.com/en/stable/dependents.html |
089816ffd331-1 | 3545
gkamradt/langchain-tutorials
3404
mmabrouk/chatgpt-wrapper
3303
postgresml/postgresml
3052
marqo-ai/marqo
3014
MineDojo/Voyager
2945
PrefectHQ/marvin
2761
project-baize/baize-chatbot
2673
hwchase17/chat-langchain
2589
whitead/paper-qa
2572
Azure-Samples/azure-search-openai-demo
2366
GerevAI/gerev
2330
OpenGVLab/In... | rtdocs_stable/api.python.langchain.com/en/stable/dependents.html |
089816ffd331-2 | thomas-yanxin/LangChain-ChatGLM-Webui
1182
ttengwang/Caption-Anything
1137
jina-ai/dev-gpt
1135
greshake/llm-security
1086
keephq/keep
1063
juncongmoo/chatllama
1037
richardyc/Chrome-GPT
1035
visual-openllm/visual-openllm
997
mmz-001/knowledge_gpt
995
jina-ai/langchain-serve
949
irgolic/AutoPR
936
microsoft/X-Decoder
9... | rtdocs_stable/api.python.langchain.com/en/stable/dependents.html |
089816ffd331-3 | 496
microsoft/PodcastCopilot
492
debanjum/khoj
485
akshata29/chatpdf
485
langchain-ai/langchain-aiplugin
462
jina-ai/agentchain
460
alexanderatallah/window.ai
457
yeagerai/yeagerai-agent
451
mckaywrigley/repo-chat
446
michaelthwan/searchGPT
446
mpaepper/content-chatbot
441
freddyaboulton/gradio-tools
439
ruoccofabrizio... | rtdocs_stable/api.python.langchain.com/en/stable/dependents.html |
089816ffd331-4 | 267
Anil-matcha/Website-to-Chatbot
266
Cheems-Seminar/grounded-segment-any-parts
260
sullivan-sean/chat-langchainjs
248
bborn/howdoi.ai
245
daveebbelaar/langchain-experiments
240
MagnivOrg/prompt-layer-library
237
ur-whitelab/exmol
234
conceptofmind/toolformer
234
recalign/RecAlign
226
OpenBMB/AgentVerse
220
alvarosevi... | rtdocs_stable/api.python.langchain.com/en/stable/dependents.html |
089816ffd331-5 | 148
chakkaradeep/pyCodeAGI
145
ccurme/yolopandas
145
shamspias/customizable-gpt-chatbot
144
realminchoi/babyagi-ui
143
PradipNichite/Youtube-Tutorials
140
gustavz/DataChad
140
Klingefjord/chatgpt-telegram
140
Jaseci-Labs/jaseci
139
handrew/browserpilot
137
jmpaz/promptlib
137
SamPink/dev-gpt
135
menloparklab/langchain-... | rtdocs_stable/api.python.langchain.com/en/stable/dependents.html |
089816ffd331-6 | 111
aurelio-labs/arxiv-bot
110
fixie-ai/fixie-examples
108
miaoshouai/miaoshouai-assistant
105
flurb18/AgentOoba
103
solana-labs/chatgpt-plugin
102
Significant-Gravitas/Auto-GPT-Benchmarks
102
kaarthik108/snowChat
100
Generated by github-dependents-info
github-dependents-info --repo hwchase17/langchain --markdownfile d... | rtdocs_stable/api.python.langchain.com/en/stable/dependents.html |
d62479a50a3b-0 | Search
Error
Please activate JavaScript to enable the search functionality.
Ctrl+K
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/search.html |
b1fe5618669c-0 | .rst
.pdf
Integrations
Contents
Integrations by Module
Dependencies
All Integrations
Integrations#
LangChain integrates with many LLMs, systems, and products.
Integrations by Module#
Integrations grouped by the core LangChain module they map to:
LLM Providers
Chat Model Providers
Text Embedding Model Providers
Docume... | rtdocs_stable/api.python.langchain.com/en/stable/integrations.html |
b1fe5618669c-1 | LanceDB
LangChain Decorators ✨
Quick start
Defining other parameters
Simplified streaming
Prompt declarations
Optional sections
Output parsers
Binding the prompt to an object
More examples:
Llama.cpp
MediaWikiDump
Metal
Microsoft OneDrive
Microsoft PowerPoint
Microsoft Word
Milvus
MLflow
Modal
Modern Treasury
Momento
M... | rtdocs_stable/api.python.langchain.com/en/stable/integrations.html |
1444749d7cc1-0 | .rst
.pdf
Welcome to LangChain
Contents
Getting Started
Modules
Use Cases
Reference Docs
Ecosystem
Additional Resources
Welcome to LangChain#
LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a l... | rtdocs_stable/api.python.langchain.com/en/stable/index.html |
1444749d7cc1-1 | Agents: An agent is a Chain in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes the action and observes the outcome until the high-level directive is complete.
Callbacks: Callbacks let you log and stream the intermediate steps of any chain, making it easy to observe,... | rtdocs_stable/api.python.langchain.com/en/stable/index.html |
1444749d7cc1-2 | Reference Docs#
Full documentation on all methods, classes, installation methods, and integration setups for LangChain.
LangChain Installation
Reference Documentation
Ecosystem#
LangChain integrates a lot of different LLMs, systems, and products.
From the other side, many systems and products depend on LangChain.
It cr... | rtdocs_stable/api.python.langchain.com/en/stable/index.html |
1444749d7cc1-3 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/index.html |
d5dfbdff3fec-0 | .rst
.pdf
Agents
Agents#
Reference guide for Agents and associated abstractions.
Agents
Tools
Agent Toolkits
previous
Memory
next
Agents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/reference/agents.html |
ed01a6709dc8-0 | .rst
.pdf
Models
Models#
LangChain provides interfaces and integrations for a number of different types of models.
LLMs
Chat Models
Embeddings
previous
API References
next
Chat Models
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/reference/models.html |
bf226b0336b2-0 | .md
.pdf
Installation
Contents
Official Releases
Installing from source
Installation#
Official Releases#
LangChain is available on PyPi, so to it is easily installable with:
pip install langchain
That will install the bare minimum requirements of LangChain.
A lot of the value of LangChain comes when integrating it wi... | rtdocs_stable/api.python.langchain.com/en/stable/reference/installation.html |
a6048f1f1ac5-0 | .rst
.pdf
Prompts
Prompts#
The reference guides here all relate to objects for working with Prompts.
PromptTemplates
Example Selector
Output Parsers
previous
How to serialize prompts
next
PromptTemplates
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/reference/prompts.html |
c936ed39e930-0 | .rst
.pdf
Indexes
Indexes#
Indexes refer to ways to structure documents so that LLMs can best interact with them.
LangChain has a number of modules that help you load, structure, store, and retrieve documents.
Docstore
Text Splitter
Document Loaders
Vector Stores
Retrievers
Document Compressors
Document Transformers
pr... | rtdocs_stable/api.python.langchain.com/en/stable/reference/indexes.html |
5df52031811b-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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-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]... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-3 | REACT_DOCSTORE = 'react-docstore'#
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.
abst... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-4 | **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 max iterations.
save(file... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-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 ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-7 | say to the Human, or if you do not need to use a tool, you MUST use 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) → langchain.prompts.prompt.PromptTemplate[source]# | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-8 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-9 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-10 | 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 you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\n{ai_prefix}: [your response ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-11 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-12 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-13 | 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 ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-14 | with a single action, and NOTHING else):\n\n{{{{input}}}}", input_variables: Optional[List[str]] = None, **kwargs: Any) → langchain.agents.agent.Agent[source]# | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-15 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-16 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-17 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-18 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-19 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-20 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-21 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-22 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-23 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-24 | 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: I sh... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-25 | Construct a json agent from an LLM and tools. | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-26 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-27 | 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', inpu... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-28 | Construct a json agent from an LLM and tools.
langchain.agents.create_pandas_dataframe_agent(llm: langchain.base_language.BaseLanguageModel, df: Any, agent_type: langchain.agents.agent_types.AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = Non... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-29 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-30 | 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', exam... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-31 | Construct a pbi agent from an LLM and tools. | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-32 | 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, ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-33 | (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_... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-34 | 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 = '\... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-35 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-36 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-37 | Construct a sql agent from an LLM and tools. | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-38 | langchain.agents.create_sql_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit, agent_type: langchain.agents.agent_types.AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-39 | 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: Optiona... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-40 | 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 ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-41 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-42 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
5df52031811b-43 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agents.html |
f7fd184413b7-0 | .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 ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/experimental.html |
f7fd184413b7-1 | Initialize the BabyAGI Controller.
get_next_task(result: str, task_description: str, objective: str) → List[Dict][source]#
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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/experimental.html |
f7fd184413b7-2 | 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.
generate_dialogue_response(observation: str, now: Optional[datetime.datetime] = None) → Tuple[bool, str][source]#
React to a given observation.
generate... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/experimental.html |
f7fd184413b7-3 | How frequently to re-generate the summary.
field traits: str = 'N/A'#
Permanent traits to ascribe to the character.
class langchain.experimental.GenerativeAgentMemory(*, lc_kwargs: Dict[str, Any] = None, llm: langchain.base_language.BaseLanguageModel, memory_retriever: langchain.retrievers.time_weighted_retriever.TimeW... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/experimental.html |
f7fd184413b7-4 | 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.bas... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/experimental.html |
ade01f84a537-0 | .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.Fil... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-1 | 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.
classme... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-2 | 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 ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-3 | 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: l... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-4 | 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]#
g... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-5 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-6 | 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}\nTh... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-7 | Construct a json agent from an LLM and tools. | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-8 | 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 m... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-9 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-10 | Construct a json agent from an LLM and tools.
langchain.agents.agent_toolkits.create_pandas_dataframe_agent(llm: langchain.base_language.BaseLanguageModel, df: Any, agent_type: langchain.agents.agent_types.AgentType = AgentType.ZERO_SHOT_REACT_DESCRIPTION, callback_manager: Optional[langchain.callbacks.base.BaseCallbac... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-11 | 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.BaseCallbackManage... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-12 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-13 | Construct a pbi agent from an LLM and tools. | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-14 | 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.BaseCallbackMa... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-15 | (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_... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-16 | 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, agent_type: langchain.agents.agent_t... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-17 | 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 dataf... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-18 | 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 Sp... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
ade01f84a537-19 | 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... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/agent_toolkits.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.