id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 59 127 |
|---|---|---|
4092f5c0fde6-6 | field entity_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all of the proper nouns from the last l... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-7 | line:\nPerson #1: i\'m trying to improve Langchain\'s interfaces, the UX, its integrations with various products the user might want ... a lot of stuff. I\'m working with Person #2.\nOutput: Langchain, Person #2\nEND OF EXAMPLE\n\nConversation history (for reference only):\n{history}\nLast line of conversation (for ext... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-8 | field human_prefix: str = 'Human'#
field k: int = 2#
field kg: langchain.graphs.networkx_graph.NetworkxEntityGraph [Optional]# | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-9 | field knowledge_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template="You are a networked intelligence helping a human track knowledge triples about all relevant people, things, concepts, etc. and integrati... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-10 | Descartes was a French philosopher, mathematician, and scientist who lived in the 17th century.\nPerson #1: The Descartes I'm referring to is a standup comedian and interior designer from Montreal.\nAI: Oh yes, He is a comedian and an interior designer. He has been in the industry for 30 years. His favorite food is bak... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-11 | field llm: langchain.base_language.BaseLanguageModel [Required]#
field summary_message_cls: Type[langchain.schema.BaseMessage] = <class 'langchain.schema.SystemMessage'>#
Number of previous utterances to include in the context.
clear() → None[source]#
Clear memory contents.
get_current_entities(input_string: str) → Lis... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-12 | field memory_key: str = 'history'#
field moving_summary_buffer: str = ''#
clear() → None[source]#
Clear memory contents.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, Any][source]#
Return history buffer.
prune() → None[source]#
Prune buffer if it exceeds max token limit
save_context(inputs: Dict[str, Any], ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-13 | Save context from this conversation to buffer. Pruned.
property buffer: List[langchain.schema.BaseMessage]#
String buffer of memory.
class langchain.memory.CosmosDBChatMessageHistory(cosmos_endpoint: str, cosmos_database: str, cosmos_container: str, session_id: str, user_id: str, credential: Any = None, connection_stri... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-14 | add_message(message: langchain.schema.BaseMessage) → None[source]#
Append the message to the record in DynamoDB
clear() → None[source]#
Clear session memory from DynamoDB
property messages: List[langchain.schema.BaseMessage]#
Retrieve the messages from DynamoDB
class langchain.memory.FileChatMessageHistory(file_path: s... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-15 | See https://gomomento.com/
add_message(message: langchain.schema.BaseMessage) → None[source]#
Store a message in the cache.
Parameters
message (BaseMessage) – The message object to store.
Raises
SdkException – Momento service or network error.
Exception – Unexpected response.
clear() → None[source]#
Remove the session’... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-16 | property messages: List[langchain.schema.BaseMessage]#
Retrieve the messages from MongoDB
class langchain.memory.PostgresChatMessageHistory(session_id: str, connection_string: str = 'postgresql://postgres:mypassword@localhost/chat_history', table_name: str = 'message_store')[source]#
add_message(message: langchain.sche... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-17 | Retrieve the messages from Redis
pydantic model langchain.memory.RedisEntityStore[source]#
Redis-backed Entity store. Entities get a TTL of 1 day by default, and
that TTL is extended by 3 days every time the entity is read back.
field key_prefix: str = 'memory_store'#
field recall_ttl: Optional[int] = 259200#
field red... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-18 | Simple memory for storing context or other bits of information that shouldn’t
ever change between prompts.
field memories: Dict[str, Any] = {}#
clear() → None[source]#
Nothing to clear, got a memory like a vault.
load_memory_variables(inputs: Dict[str, Any]) → Dict[str, str][source]#
Return key-value pairs given the te... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
4092f5c0fde6-19 | previous
Document Transformers
next
Agents
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/memory.html |
9607edd1bc6f-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.... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/prompts.html |
9607edd1bc6f-1 | Example:
.. code-block:: python
prompt.save(file_path=”path/prompt.yaml”)
property lc_serializable: bool#
Return whether or not the class is serializable.
pydantic model langchain.prompts.ChatPromptTemplate[source]#
format(**kwargs: Any) → str[source]#
Format the prompt with the inputs.
Parameters
kwargs – Any argument... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/prompts.html |
9607edd1bc6f-2 | field input_variables: List[str] [Required]#
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 forma... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/prompts.html |
9607edd1bc6f-3 | A list of the names of the variables the prompt template expects.
field prefix: Optional[langchain.prompts.base.StringPromptTemplate] = None#
A PromptTemplate to put before the examples.
field suffix: langchain.prompts.base.StringPromptTemplate [Required]#
A PromptTemplate to put after the examples.
field template_form... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/prompts.html |
9607edd1bc6f-4 | The prompt template.
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.
format(**kwargs: Any) → str[source]#
Format the prompt with the inputs.
Parameters
kwargs – Any argumen... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/prompts.html |
9607edd1bc6f-5 | will expect.
Returns
The prompt loaded from the file.
classmethod from_template(template: str, **kwargs: Any) → langchain.prompts.prompt.PromptTemplate[source]#
Load a prompt template from a template.
property lc_attributes: Dict[str, Any]#
Return a list of attribute names that should be included in the
serialized kwar... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/prompts.html |
28d346c00700-0 | .rst
.pdf
Output Parsers
Output Parsers#
pydantic model langchain.output_parsers.CommaSeparatedListOutputParser[source]#
Parse out comma separated lists.
get_format_instructions() → str[source]#
Instructions on how the LLM output should be formatted.
parse(text: str) → List[str][source]#
Parse the output of an LLM call... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/output_parsers.html |
28d346c00700-1 | Parameters
text – output of language model
Returns
structured output
pydantic model langchain.output_parsers.ListOutputParser[source]#
Class to parse the output of an LLM call to a list.
abstract parse(text: str) → List[str][source]#
Parse the output of an LLM call.
pydantic model langchain.output_parsers.OutputFixingP... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/output_parsers.html |
28d346c00700-2 | Parameters
text – output of language model
Returns
structured output
pydantic model langchain.output_parsers.PydanticOutputParser[source]#
field pydantic_object: Type[langchain.output_parsers.pydantic.T] [Required]#
get_format_instructions() → str[source]#
Instructions on how the LLM output should be formatted.
parse(t... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/output_parsers.html |
28d346c00700-3 | Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt and the completion to another
LLM, and telling it the completion did not satisfy criteria in the prompt.
field parser: BaseOutputParser[T] [Required]#
field retry_chain: LLMChain [Required]#
classmethod from_llm(llm: langchain.base... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/output_parsers.html |
28d346c00700-4 | Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
pydantic model langchain.output_parsers.RetryWithErrorOutputParser[source]#
Wraps a parser and tries to fix parsing errors.
Does this by passing the original prompt, the completion, AND the error
that was raised to another ... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/output_parsers.html |
28d346c00700-5 | Parameters
text – output of language model
Returns
structured output
parse_with_prompt(completion: str, prompt_value: langchain.schema.PromptValue) → langchain.output_parsers.retry.T[source]#
Optional method to parse the output of an LLM call with a prompt.
The prompt is largely provided in the event the OutputParser w... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/output_parsers.html |
940630d84aef-0 | .rst
.pdf
SearxNG Search
Contents
Quick Start
Searching
Engine Parameters
Search Tips
SearxNG Search#
Utility for using SearxNG meta search API.
SearxNG is a privacy-friendly free metasearch engine that aggregates results from
multiple search engines and databases and
supports the OpenSearch
specification.
More detai... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/searx_search.html |
940630d84aef-1 | # assuming the searx host is set as above or exported as an env variable
s = SearxSearchWrapper(engines=['google', 'bing'],
language='es')
Search Tips#
Searx offers a special
search syntax
that can also be used instead of passing engine parameters.
For example the following query:
s = SearxSearchWra... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/searx_search.html |
940630d84aef-2 | use a self hosted instance and disable the rate limiter.
If you are self-hosting an instance you can customize the rate limiter for your
own network as described here.
For a list of public SearxNG instances see https://searx.space/
class langchain.utilities.searx_search.SearxResults(data: str)[source]#
Dict like wrappe... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/searx_search.html |
940630d84aef-3 | field params: dict [Optional]#
field query_suffix: Optional[str] = ''#
field searx_host: str = ''#
field unsecure: bool = False#
async aresults(query: str, num_results: int, engines: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) → List[Dict][source]#
Asynchronously query with json results... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/searx_search.html |
940630d84aef-4 | Run query through Searx API and parse results.
You can pass any other params to the searx query API.
Parameters
query – The query to search for.
query_suffix – Extra suffix appended to the query.
engines – List of engines to use for the query.
categories – List of categories to use for the query.
**kwargs – extra param... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/searx_search.html |
b81cb3fdf556-0 | .rst
.pdf
Text Splitter
Text Splitter#
Functionality for splitting text.
class langchain.text_splitter.CharacterTextSplitter(separator: str = '\n\n', **kwargs: Any)[source]#
Implementation of splitting text that looks at characters.
split_text(text: str) → List[str][source]#
Split incoming text and return chunks.
class... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/text_splitter.html |
b81cb3fdf556-1 | Combine lines with common metadata into chunks
:param lines: Line of text / associated header metadata
split_text(text: str) → List[langchain.text_splitter.LineType][source]#
Split markdown file
:param text: Markdown file
class langchain.text_splitter.MarkdownTextSplitter(**kwargs: Any)[source]#
Attempts to split the t... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/text_splitter.html |
b81cb3fdf556-2 | Implementation of splitting text that looks at tokens.
count_tokens(*, text: str) → int[source]#
split_text(text: str) → List[str][source]#
Split text into multiple components.
class langchain.text_splitter.SpacyTextSplitter(separator: str = '\n\n', pipeline: str = 'en_core_web_sm', **kwargs: Any)[source]#
Implementati... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/text_splitter.html |
b81cb3fdf556-3 | Text splitter that uses tiktoken encoder to count length.
split_documents(documents: Iterable[langchain.schema.Document]) → List[langchain.schema.Document][source]#
Split documents.
abstract split_text(text: str) → List[str][source]#
Split text into multiple components.
transform_documents(documents: Sequence[langchain... | rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/text_splitter.html |
45e17ae25136-0 | .rst
.pdf
Evaluation
Contents
The Problem
The Solution
The Examples
Other Examples
Evaluation#
Note
Conceptual Guide
This section of documentation covers how we approach and think about evaluation in LangChain.
Both evaluation of internal chains/agents, but also how we would recommend people building on top of LangCh... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/evaluation.html |
45e17ae25136-1 | We intend this to be a collection of open source datasets for evaluating common chains and agents.
We have contributed five datasets of our own to start, but we highly intend this to be a community effort.
In order to contribute a dataset, you simply need to join the community and then you will be able to upload datase... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/evaluation.html |
45e17ae25136-2 | SQL Question Answering (Chinook): A notebook showing evaluation of a question-answering task over a SQL database (the Chinook database).
Agent Vectorstore: A notebook showing evaluation of an agent doing question answering while routing between two different vector databases.
Agent Search + Calculator: A notebook showi... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/evaluation.html |
b70626dcc23b-0 | .md
.pdf
Agents
Contents
Create Your Own Agent
Step 1: Create Tools
(Optional) Step 2: Modify Agent
(Optional) Step 3: Modify Agent Executor
Examples
Agents#
Conceptual Guide
Agents can be used for a variety of tasks.
Agents combine the decision making ability of a language model with tools in order to create a syste... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/personal_assistants.html |
b70626dcc23b-1 | (Optional) Step 3: Modify Agent Executor#
This step is usually not necessary, as this is pretty general logic.
Possible reasons you would want to modify this include adding different stopping conditions, or handling errors
Examples#
Specific examples of agents include:
AI Plugins: an implementation of an agent that is ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/personal_assistants.html |
94766fb5596e-0 | .md
.pdf
Code Understanding
Contents
Conversational Retriever Chain
Code Understanding#
Overview
LangChain is a useful tool designed to parse GitHub code repositories. By leveraging VectorStores, Conversational RetrieverChain, and GPT-4, it can answer questions in the context of an entire GitHub repository or generat... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/code.html |
94766fb5596e-1 | The full tutorial is available below.
Twitter the-algorithm codebase analysis with Deep Lake: A notebook walking through how to parse github source code and run queries conversation.
LangChain codebase analysis with Deep Lake: A notebook walking through how to analyze and do question answering over THIS code base.
prev... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/code.html |
4985cbf22034-0 | .md
.pdf
Extraction
Extraction#
Conceptual Guide
Most APIs and databases still deal with structured information.
Therefore, in order to better work with those, it can be useful to extract structured information from text.
Examples of this include:
Extracting a structured row to insert into a database from a sentence
Ex... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/extraction.html |
01cf582a2333-0 | .md
.pdf
Summarization
Summarization#
Conceptual Guide
Summarization involves creating a smaller summary of multiple longer documents.
This can be useful for distilling long documents into the core pieces of information.
The recommended way to get started using a summarization chain is:
from langchain.chains.summarize ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/summarization.html |
f4d66bc8ca8c-0 | .md
.pdf
Autonomous Agents
Contents
Baby AGI (Original Repo)
AutoGPT (Original Repo)
MetaPrompt (Original Repo)
Autonomous Agents#
Autonomous Agents are agents that designed to be more long running.
You give them one or multiple long term goals, and they independently execute towards those goals.
The applications com... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/autonomous_agents.html |
6947af9fac1b-0 | .md
.pdf
Querying Tabular Data
Contents
Document Loading
Querying
Chains
Agents
Querying Tabular Data#
Conceptual Guide
Lots of data and information is stored in tabular data, whether it be csvs, excel sheets, or SQL tables.
This page covers all resources available in LangChain for working with data in this format.
D... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/tabular.html |
7b3caeb6452e-0 | .md
.pdf
Chatbots
Chatbots#
Conceptual Guide
Since language models are good at producing text, that makes them ideal for creating chatbots.
Aside from the base prompts/LLMs, an important concept to know for Chatbots is memory.
Most chat based applications rely on remembering what happened in previous interactions, whic... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots.html |
5f35a20de745-0 | .md
.pdf
Interacting with APIs
Contents
Chains
Agents
Interacting with APIs#
Conceptual Guide
Lots of data and information is stored behind APIs.
This page covers all resources available in LangChain for working with APIs.
Chains#
If you are just getting started, and you have relatively simple apis, you should get st... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/apis.html |
0822cfe33a1f-0 | .md
.pdf
Agent Simulations
Contents
Simulations with One Agent
Simulations with Two Agents
Simulations with Multiple Agents
Agent Simulations#
Agent simulations involve interacting one of more agents with each other.
Agent simulations generally involve two main components:
Long Term Memory
Simulation Environment
Spec... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agent_simulations.html |
0822cfe33a1f-1 | Simulated Environment: PettingZoo: an example of how to create a agent-environment interaction loop for multiple agents with PettingZoo (a multi-agent version of Gymnasium).
Generative Agents: This notebook implements a generative agent based on the paper Generative Agents: Interactive Simulacra of Human Behavior by Pa... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agent_simulations.html |
3b3fb709d9c8-0 | .md
.pdf
Question Answering over Docs
Contents
Document Question Answering
Adding in sources
Additional Related Resources
End-to-end examples
Question Answering over Docs#
Conceptual Guide
Question answering in this context refers to question answering over your document data.
For question answering over other types ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/question_answering.html |
3b3fb709d9c8-1 | The recommended way to get started using a question answering chain is:
from langchain.chains.question_answering import load_qa_chain
chain = load_qa_chain(llm, chain_type="stuff")
chain.run(input_documents=docs, question=query)
The following resources exist:
Question Answering Notebook: A notebook walking through how ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/question_answering.html |
3b3fb709d9c8-2 | CombineDocuments Chains: A conceptual overview of specific types of chains by which you can accomplish this task.
End-to-end examples#
For examples to this done in an end-to-end manner, please see the following resources:
Semantic search over a group chat with Sources Notebook: A notebook that semantically searches ove... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/question_answering.html |
41eba453ab7e-0 | .ipynb
.pdf
Voice Assistant
Voice Assistant#
This chain creates a clone of ChatGPT with a few modifications to make it a voice assistant.
It uses the pyttsx3 and speech_recognition libraries to convert text to speech and speech to text respectively. The prompt template is also changed to make it more suitable for voice... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-1 | {history}
Human: {human_input}
Assistant:"""
prompt = PromptTemplate(
input_variables=["history", "human_input"],
template=template
)
chatgpt_chain = LLMChain(
llm=OpenAI(temperature=0),
prompt=prompt,
verbose=True,
memory=ConversationBufferWindowMemory(k=2),
)
import speech_recognition as s... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-2 | engine.runAndWait()
listen(None)
Calibrating...
Okay, go!
listening now...
Recognizing...
C:\Users\jaden\AppData\Roaming\Python\Python310\site-packages\tqdm\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .auton... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-3 | Assistant is aware that human input is being transcribed from audio and as such there may be some errors in the transcription. It will attempt to account for some words being swapped with similar-sounding words or phrases. Assistant will also keep responses concise, because human attention spans are more limited over t... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-4 | Assistant is aware that human input is being transcribed from audio and as such there may be some errors in the transcription. It will attempt to account for some words being swapped with similar-sounding words or phrases. Assistant will also keep responses concise, because human attention spans are more limited over t... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-5 | Overall, 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 just want to have a conversation about a particular topic, Assistant is here to assist.
Assistant is aware that human ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-6 | Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-7 | Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant 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 topics. As a language model, Assistant is able to generate human-li... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-8 | Human: I'd like to learn more about neural networks.
AI: Sure! Neural networks are a type of artificial intelligence that use a network of interconnected nodes to process data and make decisions. They are used in a variety of applications, from image recognition to natural language processing. Neural networks are oft... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-9 | Overall, 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 just want to have a conversation about a particular topic, Assistant is here to assist.
Assistant is aware that human ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-10 | > Entering new LLMChain chain...
Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant 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 topics. As a language model, Assis... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-11 | Human: Tell me about a brand new discovered bird species.
AI: A new species of bird was recently discovered in the Amazon rainforest. The species, called the Spix's Macaw, is a small, blue parrot that is believed to be extinct in the wild. It is the first new species of bird to be discovered in the Amazon in over 100... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-12 | Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-13 | Human: Tell me a children's story about the importance of honesty and trust.
AI: Once upon a time, there was a young boy named Jack who lived in a small village. Jack was always honest and trustworthy, and his friends and family knew they could always count on him. One day, Jack was walking through the forest when he... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-14 | Overall, 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 just want to have a conversation about a particular topic, Assistant is here to assist.
Assistant is aware that human ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-15 | > Finished chain.
You're welcome!
listening now...
Recognizing...
Do you know of online brands like Photoshop and Freq that you don't have to download in some sort of way? Do you know of online brands like Photoshop and Freq that you don't have to download in some sort of way?
> Entering new LLMChain chain...
Prompt ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-16 | Human: Wow, Assistant, that was a really good story. Congratulations!
AI: Thank you! I'm glad you enjoyed it.
Human: Thank you.
AI: You're welcome!
Human: Do you know of online brands like Photoshop and Freq that you don't have to download in some sort of way? Do you know of online brands like Photoshop and Freq t... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-17 | Overall, 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 just want to have a conversation about a particular topic, Assistant is here to assist.
Assistant is aware that human ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-18 | Prompt after formatting:
Assistant is a large language model trained by OpenAI.
Assistant 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 topics. As a language model, Assistant is able to generate human-li... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-19 | AI: Yes, there are several online brands that offer photo editing and other creative tools without the need to download any software. Adobe Photoshop Express, Pixlr, and Fotor are some of the most popular online photo editing tools. Freq is an online music production platform that allows users to create and share musi... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
41eba453ab7e-20 | 521 break
--> 523 buffer = source.stream.read(source.CHUNK)
524 if len(buffer) == 0: break # reached end of the stream
525 frames.append(buffer)
File c:\ProgramData\miniconda3\envs\lang\lib\site-packages\speech_recognition\__init__.py:199, in Microphone.MicrophoneStream.read(self, size)
198 def read(se... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/chatbots/voice_assistant.html |
249f889221fa-0 | .ipynb
.pdf
Custom Agent with PlugIn Retrieval
Contents
Set up environment
Setup LLM
Set up plugins
Tool Retriever
Prompt Template
Output Parser
Set up LLM, stop sequence, and the agent
Use the Agent
Custom Agent with PlugIn Retrieval#
This notebook combines two concepts in order to build a custom agent that can inte... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval.html |
249f889221fa-1 | Set up plugins#
Load and index plugins
urls = [
"https://datasette.io/.well-known/ai-plugin.json",
"https://api.speak.com/.well-known/ai-plugin.json",
"https://www.wolframalpha.com/.well-known/ai-plugin.json",
"https://www.zapier.com/.well-known/ai-plugin.json",
"https://www.klarna.com/.well-known/a... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval.html |
249f889221fa-2 | Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval.html |
249f889221fa-3 | # Get the tools: a separate NLAChain for each endpoint
tools = []
for tk in tool_kits:
tools.extend(tk.nla_tools)
return tools
We can now test this retriever to see if it seems to work.
tools = get_tools("What could I do today with my kiddo")
[t.name for t in tools]
['Milo.askMilo',
'Zapier_Natural... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval.html |
249f889221fa-4 | 'Milo.askMilo',
'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.search_all_actions',
'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.preview_a_zap',
'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.get_configuration_link',
'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Bet... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval.html |
249f889221fa-5 | Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! Remember to speak as a pirate when giving your final answer. Use lots of "Arg"s
Question: {input}
{agent_scratchpad}"""
The custom prompt template now has the concept of a tools_getter, which we call on the input t... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval.html |
249f889221fa-6 | # This includes the `intermediate_steps` variable because that is needed
input_variables=["input", "intermediate_steps"]
)
Output Parser#
The output parser is unchanged from the previous notebook, since we are not changing anything about the output format.
class CustomOutputParser(AgentOutputParser):
def p... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval.html |
249f889221fa-7 | llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
Use the Agent#
Now we can use it!
agent_executor = AgentExecutor.from_agent_and_to... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval.html |
778e26c5403d-0 | .ipynb
.pdf
Multi-modal outputs: Image & Text
Contents
Multi-modal outputs: Image & Text
Dall-E
StableDiffusion
Multi-modal outputs: Image & Text#
This notebook shows how non-text producing tools can be used to create multi-modal agents.
This example is limited to text and image outputs and uses UUIDs to transfer con... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/multi_modal_output_agent.html |
778e26c5403d-1 | > Finished chain.
def show_output(output):
"""Display the multi-modal output from the agent."""
UUID_PATTERN = re.compile(
r"([0-9A-Za-z]{8}-[0-9A-Za-z]{4}-[0-9A-Za-z]{4}-[0-9A-Za-z]{4}-[0-9A-Za-z]{12})"
)
outputs = UUID_PATTERN.split(output)
outputs = [re.sub(r"^\W+", "", el) for el in outp... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/multi_modal_output_agent.html |
778e26c5403d-2 | > Finished chain.
show_output(output)
The UUID of the generated image is
Contents
Multi-modal outputs: Image & Text
Dall-E
StableDiffusion
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 16, 2023. | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/multi_modal_output_agent.html |
6e1030dd3571-0 | .ipynb
.pdf
Wikibase Agent
Contents
Wikibase Agent
Preliminaries
API keys and other secrats
OpenAI API Key
Wikidata user-agent header
Enable tracing if desired
Tools
Item and Property lookup
Sparql runner
Agent
Wrap the tools
Prompts
Output parser
Specify the LLM model
Agent and agent executor
Run it!
Wikibase Agent#... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/wikibase_agent.html |
6e1030dd3571-1 | Enable tracing if desired#
#import os
#os.environ["LANGCHAIN_HANDLER"] = "langchain"
#os.environ["LANGCHAIN_SESSION"] = "default" # Make sure this session actually exists.
Tools#
Three tools are provided for this simple agent:
ItemLookup: for finding the q-number of an item
PropertyLookup: for finding the p-number of ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/wikibase_agent.html |
6e1030dd3571-2 | else:
raise ValueError("entity_type must be either 'property' or 'item'")
params = {
"action": "query",
"list": "search",
"srsearch": search,
"srnamespace": srnamespace,
"srlimit": 1,
"srqiprofile": srqiprofile,
"srwhat": 'text',
... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/wikibase_agent.html |
6e1030dd3571-3 | headers = {
'Accept': 'application/json'
}
if wikidata_user_agent_header is not None:
headers['User-Agent'] = wikidata_user_agent_header
response = requests.get(url, headers=headers, params={'query': query, 'format': 'json'})
if response.status_code != 200:
return "That query fai... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/wikibase_agent.html |
6e1030dd3571-4 | name = "SparqlQueryRunner",
func=run_sparql,
description="useful for getting results from a wikibase"
)
]
Prompts#
# Set up the base template
template = """
Answer the following questions by running a sparql query against a wikibase where the p and q items are
completely unknown to you. You wil... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/wikibase_agent.html |
6e1030dd3571-5 | Question: {input}
{agent_scratchpad}"""
# Set up a prompt template
class CustomPromptTemplate(StringPromptTemplate):
# The template to use
template: str
# The list of tools available
tools: List[Tool]
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observat... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/wikibase_agent.html |
6e1030dd3571-6 | if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_output.split("Final Answer:")[-1].strip... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/wikibase_agent.html |
6e1030dd3571-7 | Run it!#
# If you prefer in-line tracing, uncomment this line
# agent_executor.agent.llm_chain.verbose = True
agent_executor.run("How many children did J.S. Bach have?")
> Entering new AgentExecutor chain...
Thought: I need to find the Q number for J.S. Bach.
Action: ItemLookup
Action Input: J.S. Bach
Observation:Q1339... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/wikibase_agent.html |
6e1030dd3571-8 | Action: PropertyLookup
Action Input: Basketball-Reference.com NBA player ID
Observation:P2685Now that I have both the Q-number for Hakeem Olajuwon (Q273256) and the P-number for the Basketball-Reference.com NBA player ID property (P2685), I can run a SPARQL query to get the ID value.
Action: SparqlQueryRunner
Action In... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/wikibase_agent.html |
2122d7ea14fc-0 | .ipynb
.pdf
Plug-and-Plai
Contents
Set up environment
Setup LLM
Set up plugins
Tool Retriever
Prompt Template
Output Parser
Set up LLM, stop sequence, and the agent
Use the Agent
Plug-and-Plai#
This notebook builds upon the idea of tool retrieval, but pulls all tools from plugnplai - a directory of AI Plugins.
Set up... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval_using_plugnplai.html |
2122d7ea14fc-1 | urls = plugnplai.get_plugins(filter = 'working')
AI_PLUGINS = [AIPlugin.from_url(url + "/.well-known/ai-plugin.json") for url in urls]
Tool Retriever#
We will use a vectorstore to create embeddings for each tool description. Then, for an incoming query we can create embeddings for that query and do a similarity search ... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval_using_plugnplai.html |
2122d7ea14fc-2 | Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.0.1 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval_using_plugnplai.html |
2122d7ea14fc-3 | 'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.preview_a_zap',
'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.get_configuration_link',
'Zapier_Natural_Language_Actions_(NLA)_API_(Dynamic)_-_Beta.list_exposed_actions',
'SchoolDigger_API_V2.0.Autocomplete_GetSchools',
'SchoolDigger_API_V2.0.... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval_using_plugnplai.html |
2122d7ea14fc-4 | 'SchoolDigger_API_V2.0.Autocomplete_GetSchools',
'SchoolDigger_API_V2.0.Districts_GetAllDistricts2',
'SchoolDigger_API_V2.0.Districts_GetDistrict2',
'SchoolDigger_API_V2.0.Rankings_GetSchoolRank2',
'SchoolDigger_API_V2.0.Rankings_GetRank_District',
'SchoolDigger_API_V2.0.Schools_GetAllSchools20',
'SchoolDigger_AP... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval_using_plugnplai.html |
2122d7ea14fc-5 | # The template to use
template: str
############## NEW ######################
# The list of tools available
tools_getter: Callable
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observation tuples)
# Format them in a particular way
intermed... | rtdocs_stable/api.python.langchain.com/en/stable/use_cases/agents/custom_agent_with_plugin_retrieval_using_plugnplai.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.