id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 49 115 |
|---|---|---|
70fdaeb2ac0b-8 | property headers: Dict[str, str]#
Get the token.
property request_url: str#
Get the request url.
property table_info: str#
Information about all tables in the database.
pydantic model langchain.utilities.PythonREPL[source]#
Simulates a standalone Python REPL.
field globals: Optional[Dict] [Optional] (alias '_globals')#... | https://python.langchain.com/en/latest/reference/modules/utilities.html |
70fdaeb2ac0b-9 | field headers: Optional[dict] = None#
field k: int = 10#
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) → L... | https://python.langchain.com/en/latest/reference/modules/utilities.html |
70fdaeb2ac0b-10 | }
Return type
Dict with the following keys
run(query: str, engines: Optional[List[str]] = None, categories: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) → str[source]#
Run query through Searx API and parse results.
You can pass any other params to the searx query API.
Parameters
query – ... | https://python.langchain.com/en/latest/reference/modules/utilities.html |
70fdaeb2ac0b-11 | field aiosession: Optional[aiohttp.client.ClientSession] = None#
field params: dict = {'engine': 'google', 'gl': 'us', 'google_domain': 'google.com', 'hl': 'en'}#
field serpapi_api_key: Optional[str] = None#
async aresults(query: str) → dict[source]#
Use aiohttp to run query through SerpAPI and return the results async... | https://python.langchain.com/en/latest/reference/modules/utilities.html |
70fdaeb2ac0b-12 | POST to the URL and return the text asynchronously.
async aput(url: str, data: Dict[str, Any], **kwargs: Any) → str[source]#
PUT the URL and return the text asynchronously.
delete(url: str, **kwargs: Any) → str[source]#
DELETE the URL and return the text.
get(url: str, **kwargs: Any) → str[source]#
GET the URL and retu... | https://python.langchain.com/en/latest/reference/modules/utilities.html |
70fdaeb2ac0b-13 | Save your APP ID into WOLFRAM_ALPHA_APPID env variable
pip install wolframalpha
field wolfram_alpha_appid: Optional[str] = None#
run(query: str) → str[source]#
Run query through WolframAlpha and parse result.
previous
Agent Toolkits
next
Experimental Modules
By Harrison Chase
© Copyright 2023, Harrison Chase... | https://python.langchain.com/en/latest/reference/modules/utilities.html |
5dae1c72a85d-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... | https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
5dae1c72a85d-1 | field retry_chain: langchain.chains.llm.LLMChain [Required]#
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, parser: langchain.schema.BaseOutputParser[langchain.output_parsers.fix.T], prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['completion', 'error', 'instruc... | https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
5dae1c72a85d-2 | and parses it into some structure.
Parameters
text – output of language model
Returns
structured output
pydantic model langchain.output_parsers.RegexDictParser[source]#
Class to parse the output into a dictionary.
field no_update_value: Optional[str] = None#
field output_key_to_format: Dict[str, str] [Required]#
field ... | https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
5dae1c72a85d-3 | field retry_chain: langchain.chains.llm.LLMChain [Required]#
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, parser: langchain.schema.BaseOutputParser[langchain.output_parsers.retry.T], prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['completion', 'prompt'], outp... | https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
5dae1c72a85d-4 | that was raised to another language and telling it that the completion
did not work, and raised the given error. Differs from RetryOutputParser
in that this implementation provides the error that was raised back to the
LLM, which in theory should give it more information on how to fix it.
field parser: langchain.schema... | https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
5dae1c72a85d-5 | The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – output of language model
prompt – prompt value
Returns
structured output
pydantic model langchain.output_parsers.StructuredOutputParser[sourc... | https://python.langchain.com/en/latest/reference/modules/output_parsers.html |
0d4c737e5152-0 | .rst
.pdf
Docstore
Docstore#
Wrappers on top of docstores.
class langchain.docstore.InMemoryDocstore(_dict: Dict[str, langchain.schema.Document])[source]#
Simple in memory docstore in the form of a dict.
add(texts: Dict[str, langchain.schema.Document]) → None[source]#
Add texts to in memory dictionary.
search(search: s... | https://python.langchain.com/en/latest/reference/modules/docstore.html |
bb5236069ce5-0 | .rst
.pdf
SerpAPI
SerpAPI#
For backwards compatiblity.
pydantic model langchain.serpapi.SerpAPIWrapper[source]#
Wrapper around SerpAPI.
To use, you should have the google-search-results python package installed,
and the environment variable SERPAPI_API_KEY set with your API key, or pass
serpapi_api_key as a named param... | https://python.langchain.com/en/latest/reference/modules/serpapi.html |
f527a9930db1-0 | .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 ... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
f527a9930db1-1 | Get the next task.
property input_keys: List[str]#
Input keys this chain expects.
property output_keys: List[str]#
Output keys this chain expects.
prioritize_tasks(this_task_id: int, objective: str) → List[Dict][source]#
Prioritize tasks.
class langchain.experimental.AutoGPT(ai_name: str, memory: langchain.vectorstores... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
f527a9930db1-2 | React to a given observation.
generate_reaction(observation: str) → Tuple[bool, str][source]#
React to a given observation.
get_full_header(force_refresh: bool = False) → str[source]#
Return a full header of the agent’s status, summary, and current time.
get_summary(force_refresh: bool = False) → str[source]#
Return a ... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
f527a9930db1-3 | field traits: str = 'N/A'#
Permanent traits to ascribe to the character.
class langchain.experimental.GenerativeAgentMemory(*, llm: langchain.base_language.BaseLanguageModel, memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever, verbose: bool = False, reflection_threshold: Opt... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
f527a9930db1-4 | Return key-value pairs given the text input to the chain.
field memory_retriever: langchain.retrievers.time_weighted_retriever.TimeWeightedVectorStoreRetriever [Required]#
The retriever to fetch related memories.
property memory_variables: List[str]#
Input keys this memory class will load dynamically.
pause_to_reflect(... | https://python.langchain.com/en/latest/reference/modules/experimental.html |
dc10ed6d93e1-0 | .rst
.pdf
Chat Models
Chat Models#
pydantic model langchain.chat_models.AzureChatOpenAI[source]#
Wrapper around Azure OpenAI Chat Completion API. To use this class you
must have a deployed model on Azure OpenAI. Use deployment_name in the
constructor to refer to the “Model deployment name” in the Azure portal.
In addit... | https://python.langchain.com/en/latest/reference/modules/chat_models.html |
dc10ed6d93e1-1 | field verbose: bool [Optional]#
Whether to print out response text.
pydantic model langchain.chat_models.ChatGooglePalm[source]#
Wrapper around Google’s PaLM Chat API.
To use you must have the google.generativeai Python package installed and
either:
The GOOGLE_API_KEY` environment varaible set with your API key, or
Pas... | https://python.langchain.com/en/latest/reference/modules/chat_models.html |
dc10ed6d93e1-2 | Example
from langchain.chat_models import ChatOpenAI
openai = ChatOpenAI(model_name="gpt-3.5-turbo")
field max_retries: int = 6#
Maximum number of retries to make when generating.
field max_tokens: Optional[int] = None#
Maximum number of tokens to generate.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model... | https://python.langchain.com/en/latest/reference/modules/chat_models.html |
dc10ed6d93e1-3 | Wrapper around OpenAI Chat large language models and PromptLayer.
To use, you should have the openai and promptlayer python
package installed, and the environment variable OPENAI_API_KEY
and PROMPTLAYER_API_KEY set with your openAI API key and
promptlayer key respectively.
All parameters that can be passed to the OpenA... | https://python.langchain.com/en/latest/reference/modules/chat_models.html |
521c8aa93adc-0 | .rst
.pdf
Embeddings
Embeddings#
Wrappers around embedding modules.
pydantic model langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding[source]#
Wrapper for Aleph Alpha’s Asymmetric Embeddings
AA provides you with an endpoint to embed a document and a query.
The models were optimized to make the embeddings of doc... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
521c8aa93adc-1 | Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to Aleph Alpha’s asymmetric, query embedding endpoint
:param text: The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.AlephAlphaSymmet... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
521c8aa93adc-2 | embed_documents(texts: List[str]) → List[List[float]][source]#
Call out to Cohere’s embedding endpoint.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to Cohere’s embedding endpoint.
Parameters
text – The text to embed... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
521c8aa93adc-3 | Compute doc embeddings using a HuggingFace transformer model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace transformer model.
Parameters
text – The text to embed.
Returns
Embedding... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
521c8aa93adc-4 | Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.HuggingFaceInstructEmbeddings[source]#
Wrapper around sentence_transformers embedding models.
To use, you should have the sentence_transformers
and InstructorEmbedding python packages installed.
Example
from langch... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
521c8aa93adc-5 | To use, you should have the llama-cpp-python library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out: abetlen/llama-cpp-python
Example
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
field f16_kv: bool ... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
521c8aa93adc-6 | pydantic model langchain.embeddings.OpenAIEmbeddings[source]#
Wrapper around OpenAI embedding models.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Example
from langchain.embeddings impor... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
521c8aa93adc-7 | Call out to OpenAI’s embedding endpoint for embedding search docs.
Parameters
texts – The list of texts to embed.
chunk_size – The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to O... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
521c8aa93adc-8 | credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
field endpoint_kwargs: Optional[Dict] = None#
Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
521c8aa93adc-9 | cloud like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example using a model load function:from langchain.embeddings import SelfHostedEmbeddings
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", ... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
521c8aa93adc-10 | field inference_kwargs: Any = None#
Any kwargs to pass to the model’s inference function.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a HuggingFace transformer model.
Parameters
texts – The list of texts to embed.s
Returns
List of embeddings, one for each text.
embed_quer... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
521c8aa93adc-11 | field model_id: str = 'sentence-transformers/all-mpnet-base-v2'#
Model name to use.
field model_load_fn: Callable = <function load_embedding_model>#
Function to load the model remotely on the server.
field model_reqs: List[str] = ['./', 'sentence_transformers', 'torch']#
Requirements to install on hardware to inference... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
521c8aa93adc-12 | embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a HuggingFace instruct model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace instruct mode... | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
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