id
stringlengths 14
16
| text
stringlengths 45
2.73k
| source
stringlengths 49
114
|
|---|---|---|
cc47f48085d0-2 | embed_documents(texts: List[str]) β List[List[float]][source]#
Call out to Cohereβs embedding endpoint.
Parameters
texts β The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) β List[float][source]#
Call out to Cohereβs embedding endpoint.
Parameters
text β The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.FakeEmbeddings[source]#
embed_documents(texts: List[str]) β List[List[float]][source]#
Embed search docs.
embed_query(text: str) β List[float][source]#
Embed query text.
pydantic model langchain.embeddings.HuggingFaceEmbeddings[source]#
Wrapper around sentence_transformers embedding models.
To use, you should have the sentence_transformers python package installed.
Example
from langchain.embeddings import HuggingFaceEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'cpu'}
hf = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
field cache_folder: Optional[str] = None#
Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME enviroment variable.
field model_kwargs: Dict[str, Any] [Optional]#
Key word arguments to pass to the model.
field model_name: str = 'sentence-transformers/all-mpnet-base-v2'#
Model name to use.
embed_documents(texts: List[str]) β List[List[float]][source]#
Compute doc embeddings using a HuggingFace transformer model.
Parameters
texts β The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) β List[float][source]# | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
cc47f48085d0-3 | embed_query(text: str) β List[float][source]#
Compute query embeddings using a HuggingFace transformer model.
Parameters
text β The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.HuggingFaceHubEmbeddings[source]#
Wrapper around HuggingFaceHub embedding models.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Example
from langchain.embeddings import HuggingFaceHubEmbeddings
repo_id = "sentence-transformers/all-mpnet-base-v2"
hf = HuggingFaceHubEmbeddings(
repo_id=repo_id,
task="feature-extraction",
huggingfacehub_api_token="my-api-key",
)
field model_kwargs: Optional[dict] = None#
Key word arguments to pass to the model.
field repo_id: str = 'sentence-transformers/all-mpnet-base-v2'#
Model name to use.
field task: Optional[str] = 'feature-extraction'#
Task to call the model with.
embed_documents(texts: List[str]) β List[List[float]][source]#
Call out to HuggingFaceHubβs embedding endpoint for embedding search docs.
Parameters
texts β The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) β List[float][source]#
Call out to HuggingFaceHubβs embedding endpoint for embedding query text.
Parameters
text β The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.HuggingFaceInstructEmbeddings[source]#
Wrapper around sentence_transformers embedding models. | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
cc47f48085d0-4 | Wrapper around sentence_transformers embedding models.
To use, you should have the sentence_transformers
and InstructorEmbedding python package installed.
Example
from langchain.embeddings import HuggingFaceInstructEmbeddings
model_name = "hkunlp/instructor-large"
model_kwargs = {'device': 'cpu'}
hf = HuggingFaceInstructEmbeddings(
model_name=model_name, model_kwargs=model_kwargs
)
field cache_folder: Optional[str] = None#
Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME enviroment variable.
field embed_instruction: str = 'Represent the document for retrieval: '#
Instruction to use for embedding documents.
field model_kwargs: Dict[str, Any] [Optional]#
Key word arguments to pass to the model.
field model_name: str = 'hkunlp/instructor-large'#
Model name to use.
field query_instruction: str = 'Represent the question for retrieving supporting documents: '#
Instruction to use for embedding query.
embed_documents(texts: List[str]) β List[List[float]][source]#
Compute doc embeddings using a HuggingFace instruct model.
Parameters
texts β The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) β List[float][source]#
Compute query embeddings using a HuggingFace instruct model.
Parameters
text β The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.LlamaCppEmbeddings[source]#
Wrapper around llama.cpp embedding models.
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 | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
cc47f48085d0-5 | Example
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
field f16_kv: bool = False#
Use half-precision for key/value cache.
field logits_all: bool = False#
Return logits for all tokens, not just the last token.
field n_batch: Optional[int] = 8#
Number of tokens to process in parallel.
Should be a number between 1 and n_ctx.
field n_ctx: int = 512#
Token context window.
field n_parts: int = -1#
Number of parts to split the model into.
If -1, the number of parts is automatically determined.
field n_threads: Optional[int] = None#
Number of threads to use. If None, the number
of threads is automatically determined.
field seed: int = -1#
Seed. If -1, a random seed is used.
field use_mlock: bool = False#
Force system to keep model in RAM.
field vocab_only: bool = False#
Only load the vocabulary, no weights.
embed_documents(texts: List[str]) β List[List[float]][source]#
Embed a list of documents using the Llama model.
Parameters
texts β The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) β List[float][source]#
Embed a query using the Llama model.
Parameters
text β The text to embed.
Returns
Embeddings for the text.
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 | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
cc47f48085d0-6 | environment variable OPENAI_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Example
from langchain.embeddings import OpenAIEmbeddings
openai = OpenAIEmbeddings(openai_api_key="my-api-key")
In order to use the library with Microsoft Azure endpoints, you need to set
the OPENAI_API_TYPE, OPENAI_API_BASE, OPENAI_API_KEY and optionally and
API_VERSION.
The OPENAI_API_TYPE must be set to βazureβ and the others correspond to
the properties of your endpoint.
In addition, the deployment name must be passed as the model parameter.
Example
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(
deployment="your-embeddings-deployment-name",
model="your-embeddings-model-name"
)
text = "This is a test query."
query_result = embeddings.embed_query(text)
field chunk_size: int = 1000#
Maximum number of texts to embed in each batch
field max_retries: int = 6#
Maximum number of retries to make when generating.
embed_documents(texts: List[str], chunk_size: Optional[int] = 0) β List[List[float]][source]#
Call out to OpenAIβs embedding endpoint for embedding search docs.
Parameters
texts β The list of texts to embed.
chunk_size β 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) β List[float][source]# | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
cc47f48085d0-7 | embed_query(text: str) β List[float][source]#
Call out to OpenAIβs embedding endpoint for embedding query text.
Parameters
text β The text to embed.
Returns
Embedding for the text.
pydantic model langchain.embeddings.SagemakerEndpointEmbeddings[source]#
Wrapper around custom Sagemaker Inference Endpoints.
To use, you must supply the endpoint name from your deployed
Sagemaker model & the region where it is deployed.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
field content_handler: langchain.llms.sagemaker_endpoint.ContentHandlerBase [Required]#
The content handler class that provides an input and
output transform functions to handle formats between LLM
and the endpoint.
field credentials_profile_name: Optional[str] = None#
The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
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/latest/index.html> | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
cc47f48085d0-8 | field endpoint_name: str = ''#
The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region.
field model_kwargs: Optional[Dict] = None#
Key word arguments to pass to the model.
field region_name: str = ''#
The aws region where the Sagemaker model is deployed, eg. us-west-2.
embed_documents(texts: List[str], chunk_size: int = 64) β List[List[float]][source]#
Compute doc embeddings using a SageMaker Inference Endpoint.
Parameters
texts β The list of texts to embed.
chunk_size β The chunk size defines how many input texts will
be grouped together as request. If None, will use the
chunk size specified by the class.
Returns
List of embeddings, one for each text.
embed_query(text: str) β List[float][source]#
Compute query embeddings using a SageMaker inference endpoint.
Parameters
text β The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.SelfHostedEmbeddings[source]#
Runs custom embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another
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", instance_type="A100:1")
def get_pipeline():
model_id = "facebook/bart-large" | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
cc47f48085d0-9 | def get_pipeline():
model_id = "facebook/bart-large"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
return pipeline("feature-extraction", model=model, tokenizer=tokenizer)
embeddings = SelfHostedEmbeddings(
model_load_fn=get_pipeline,
hardware=gpu
model_reqs=["./", "torch", "transformers"],
)
Example passing in a pipeline path:from langchain.embeddings import SelfHostedHFEmbeddings
import runhouse as rh
from transformers import pipeline
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
pipeline = pipeline(model="bert-base-uncased", task="feature-extraction")
rh.blob(pickle.dumps(pipeline),
path="models/pipeline.pkl").save().to(gpu, path="models")
embeddings = SelfHostedHFEmbeddings.from_pipeline(
pipeline="models/pipeline.pkl",
hardware=gpu,
model_reqs=["./", "torch", "transformers"],
)
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
field inference_fn: Callable = <function _embed_documents>#
Inference function to extract the embeddings on the remote hardware.
field inference_kwargs: Any = None#
Any kwargs to pass to the modelβs inference function.
embed_documents(texts: List[str]) β List[List[float]][source]#
Compute doc embeddings using a HuggingFace transformer model.
Parameters
texts β The list of texts to embed.s
Returns
List of embeddings, one for each text.
embed_query(text: str) β List[float][source]#
Compute query embeddings using a HuggingFace transformer model.
Parameters
text β The text to embed.
Returns
Embeddings for the text. | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
cc47f48085d0-10 | Parameters
text β The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.SelfHostedHuggingFaceEmbeddings[source]#
Runs sentence_transformers embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another cloud
like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example
from langchain.embeddings import SelfHostedHuggingFaceEmbeddings
import runhouse as rh
model_name = "sentence-transformers/all-mpnet-base-v2"
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
hf = SelfHostedHuggingFaceEmbeddings(model_name=model_name, hardware=gpu)
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
field hardware: Any = None#
Remote hardware to send the inference function to.
field inference_fn: Callable = <function _embed_documents>#
Inference function to extract the embeddings.
field load_fn_kwargs: Optional[dict] = None#
Key word arguments to pass to the model load function.
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 the model.
pydantic model langchain.embeddings.SelfHostedHuggingFaceInstructEmbeddings[source]#
Runs InstructorEmbedding embedding models on self-hosted remote hardware. | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
cc47f48085d0-11 | Runs InstructorEmbedding embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another
cloud like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example
from langchain.embeddings import SelfHostedHuggingFaceInstructEmbeddings
import runhouse as rh
model_name = "hkunlp/instructor-large"
gpu = rh.cluster(name='rh-a10x', instance_type='A100:1')
hf = SelfHostedHuggingFaceInstructEmbeddings(
model_name=model_name, hardware=gpu)
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
field embed_instruction: str = 'Represent the document for retrieval: '#
Instruction to use for embedding documents.
field model_id: str = 'hkunlp/instructor-large'#
Model name to use.
field model_reqs: List[str] = ['./', 'InstructorEmbedding', 'torch']#
Requirements to install on hardware to inference the model.
field query_instruction: str = 'Represent the question for retrieving supporting documents: '#
Instruction to use for embedding query.
embed_documents(texts: List[str]) β List[List[float]][source]#
Compute doc embeddings using a HuggingFace instruct model.
Parameters
texts β The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) β List[float][source]#
Compute query embeddings using a HuggingFace instruct model.
Parameters
text β The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.TensorflowHubEmbeddings[source]# | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
cc47f48085d0-12 | pydantic model langchain.embeddings.TensorflowHubEmbeddings[source]#
Wrapper around tensorflow_hub embedding models.
To use, you should have the tensorflow_text python package installed.
Example
from langchain.embeddings import TensorflowHubEmbeddings
url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
tf = TensorflowHubEmbeddings(model_url=url)
field model_url: str = 'https://tfhub.dev/google/universal-sentence-encoder-multilingual/3'#
Model name to use.
embed_documents(texts: List[str]) β List[List[float]][source]#
Compute doc embeddings using a TensorflowHub embedding model.
Parameters
texts β The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) β List[float][source]#
Compute query embeddings using a TensorflowHub embedding model.
Parameters
text β The text to embed.
Returns
Embeddings for the text.
previous
Text Splitter
next
VectorStores
By Harrison Chase
Β© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/reference/modules/embeddings.html |
af268a022377-0 | .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]) β None[source]#
Add texts to in memory dictionary.
search(search: str) β Union[str, langchain.schema.Document][source]#
Search via direct lookup.
class langchain.docstore.Wikipedia[source]#
Wrapper around wikipedia API.
search(search: str) β Union[str, langchain.schema.Document][source]#
Try to search for wiki page.
If page exists, return the page summary, and a PageWithLookups object.
If page does not exist, return similar entries.
previous
SearxNG Search
next
Text Splitter
By Harrison Chase
Β© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/reference/modules/docstore.html |
fbeba0d8e856-0 | .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 parameter to the constructor.
Example
from langchain import SerpAPIWrapper
serpapi = SerpAPIWrapper()
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 arun(query: str) β str[source]#
Use aiohttp to run query through SerpAPI and parse result.
get_params(query: str) β Dict[str, str][source]#
Get parameters for SerpAPI.
results(query: str) β dict[source]#
Run query through SerpAPI and return the raw result.
run(query: str) β str[source]#
Run query through SerpAPI and parse result.
previous
Python REPL
next
SearxNG Search
By Harrison Chase
Β© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/reference/modules/serpapi.html |
31fa97c521c0-0 | .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 detailes on the installtion instructions here.
For the search API refer to https://docs.searxng.org/dev/search_api.html
Quick Start#
In order to use this utility you need to provide the searx host. This can be done
by passing the named parameter searx_host
or exporting the environment variable SEARX_HOST.
Note: this is the only required parameter.
Then create a searx search instance like this:
from langchain.utilities import SearxSearchWrapper
# when the host starts with `http` SSL is disabled and the connection
# is assumed to be on a private network
searx_host='http://self.hosted'
search = SearxSearchWrapper(searx_host=searx_host)
You can now use the search instance to query the searx API.
Searching#
Use the run() and
results() methods to query the searx API.
Other methods are are available for convenience.
SearxResults is a convenience wrapper around the raw json result.
Example usage of the run method to make a search:
s.run(query="what is the best search engine?")
Engine Parameters#
You can pass any accepted searx search API parameters to the
SearxSearchWrapper instance.
In the following example we are using the
engines and the language parameters:
# assuming the searx host is set as above or exported as an env variable | https://python.langchain.com/en/latest/reference/modules/searx_search.html |
31fa97c521c0-1 | # 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 = SearxSearchWrapper("langchain library", engines=['github'])
# can also be written as:
s = SearxSearchWrapper("langchain library !github")
# or even:
s = SearxSearchWrapper("langchain library !gh")
In some situations you might want to pass an extra string to the search query.
For example when the run() method is called by an agent. The search suffix can
also be used as a way to pass extra parameters to searx or the underlying search
engines.
# select the github engine and pass the search suffix
s = SearchWrapper("langchain library", query_suffix="!gh")
s = SearchWrapper("langchain library")
# select github the conventional google search syntax
s.run("large language models", query_suffix="site:github.com")
NOTE: A search suffix can be defined on both the instance and the method level.
The resulting query will be the concatenation of the two with the former taking
precedence.
See SearxNG Configured Engines and
SearxNG Search Syntax
for more details.
Notes
This wrapper is based on the SearxNG fork searxng/searxng which is
better maintained than the original Searx project and offers more features.
Public searxNG instances often use a rate limiter for API usage, so you might want to
use a self hosted instance and disable the rate limiter. | https://python.langchain.com/en/latest/reference/modules/searx_search.html |
31fa97c521c0-2 | 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 wrapper around search api results.
property answers: Any#
Helper accessor on the json result.
pydantic model langchain.utilities.searx_search.SearxSearchWrapper[source]#
Wrapper for Searx API.
To use you need to provide the searx host by passing the named parameter
searx_host or exporting the environment variable SEARX_HOST.
In some situations you might want to disable SSL verification, for example
if you are running searx locally. You can do this by passing the named parameter
unsecure. You can also pass the host url scheme as http to disable SSL.
Example
from langchain.utilities import SearxSearchWrapper
searx = SearxSearchWrapper(searx_host="http://localhost:8888")
Example with SSL disabled:from langchain.utilities import SearxSearchWrapper
# note the unsecure parameter is not needed if you pass the url scheme as
# http
searx = SearxSearchWrapper(searx_host="http://localhost:8888",
unsecure=True)
Validators
disable_ssl_warnings Β» unsecure
validate_params Β» all fields
field aiosession: Optional[Any] = None#
field categories: Optional[List[str]] = []#
field engines: Optional[List[str]] = []#
field headers: Optional[dict] = None#
field k: int = 10#
field params: dict [Optional]#
field query_suffix: Optional[str] = ''# | https://python.langchain.com/en/latest/reference/modules/searx_search.html |
31fa97c521c0-3 | 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) β List[Dict][source]#
Asynchronously query with json results.
Uses aiohttp. See results for more info.
async arun(query: str, engines: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) β str[source]#
Asynchronously version of run.
results(query: str, num_results: int, engines: Optional[List[str]] = None, categories: Optional[List[str]] = None, query_suffix: Optional[str] = '', **kwargs: Any) β List[Dict][source]#
Run query through Searx API and returns the results with metadata.
Parameters
query β The query to search for.
query_suffix β Extra suffix appended to the query.
num_results β Limit the number of results to return.
engines β List of engines to use for the query.
categories β List of categories to use for the query.
**kwargs β extra parameters to pass to the searx API.
Returns
{snippet: The description of the result.
title: The title of the result.
link: The link to the result.
engines: The engines used for the result.
category: Searx category of the result.
}
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) β str[source]# | https://python.langchain.com/en/latest/reference/modules/searx_search.html |
31fa97c521c0-4 | Run query through Searx API and parse results.
You can pass any other params to the searx query API.
Parameters
query β The query to search for.
query_suffix β Extra suffix appended to the query.
engines β List of engines to use for the query.
categories β List of categories to use for the query.
**kwargs β extra parameters to pass to the searx API.
Returns
The result of the query.
Return type
str
Raises
ValueError β If an error occured with the query.
Example
This will make a query to the qwant engine:
from langchain.utilities import SearxSearchWrapper
searx = SearxSearchWrapper(searx_host="http://my.searx.host")
searx.run("what is the weather in France ?", engine="qwant")
# the same result can be achieved using the `!` syntax of searx
# to select the engine using `query_suffix`
searx.run("what is the weather in France ?", query_suffix="!qwant")
previous
SerpAPI
next
Docstore
Contents
Quick Start
Searching
Engine Parameters
Search Tips
By Harrison Chase
Β© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/reference/modules/searx_search.html |
a494bf45d4c4-0 | .rst
.pdf
Example Selector
Example Selector#
Logic for selecting examples to include in prompts.
pydantic model langchain.prompts.example_selector.LengthBasedExampleSelector[source]#
Select examples based on length.
Validators
calculate_example_text_lengths Β» example_text_lengths
field example_prompt: langchain.prompts.prompt.PromptTemplate [Required]#
Prompt template used to format the examples.
field examples: List[dict] [Required]#
A list of the examples that the prompt template expects.
field get_text_length: Callable[[str], int] = <function _get_length_based>#
Function to measure prompt length. Defaults to word count.
field max_length: int = 2048#
Max length for the prompt, beyond which examples are cut.
add_example(example: Dict[str, str]) β None[source]#
Add new example to list.
select_examples(input_variables: Dict[str, str]) β List[dict][source]#
Select which examples to use based on the input lengths.
pydantic model langchain.prompts.example_selector.MaxMarginalRelevanceExampleSelector[source]#
ExampleSelector that selects examples based on Max Marginal Relevance.
This was shown to improve performance in this paper:
https://arxiv.org/pdf/2211.13892.pdf
field fetch_k: int = 20#
Number of examples to fetch to rerank.
classmethod from_examples(examples: List[dict], embeddings: langchain.embeddings.base.Embeddings, vectorstore_cls: Type[langchain.vectorstores.base.VectorStore], k: int = 4, input_keys: Optional[List[str]] = None, fetch_k: int = 20, **vectorstore_cls_kwargs: Any) β langchain.prompts.example_selector.semantic_similarity.MaxMarginalRelevanceExampleSelector[source]#
Create k-shot example selector using example list and embeddings. | https://python.langchain.com/en/latest/reference/modules/example_selector.html |
a494bf45d4c4-1 | Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Parameters
examples β List of examples to use in the prompt.
embeddings β An iniialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls β A vector store DB interface class, e.g. FAISS.
k β Number of examples to select
input_keys β If provided, the search is based on the input variables
instead of all variables.
vectorstore_cls_kwargs β optional kwargs containing url for vector store
Returns
The ExampleSelector instantiated, backed by a vector store.
select_examples(input_variables: Dict[str, str]) β List[dict][source]#
Select which examples to use based on semantic similarity.
pydantic model langchain.prompts.example_selector.SemanticSimilarityExampleSelector[source]#
Example selector that selects examples based on SemanticSimilarity.
field example_keys: Optional[List[str]] = None#
Optional keys to filter examples to.
field input_keys: Optional[List[str]] = None#
Optional keys to filter input to. If provided, the search is based on
the input variables instead of all variables.
field k: int = 4#
Number of examples to select.
field vectorstore: langchain.vectorstores.base.VectorStore [Required]#
VectorStore than contains information about examples.
add_example(example: Dict[str, str]) β str[source]#
Add new example to vectorstore.
classmethod from_examples(examples: List[dict], embeddings: langchain.embeddings.base.Embeddings, vectorstore_cls: Type[langchain.vectorstores.base.VectorStore], k: int = 4, input_keys: Optional[List[str]] = None, **vectorstore_cls_kwargs: Any) β langchain.prompts.example_selector.semantic_similarity.SemanticSimilarityExampleSelector[source]# | https://python.langchain.com/en/latest/reference/modules/example_selector.html |
a494bf45d4c4-2 | Create k-shot example selector using example list and embeddings.
Reshuffles examples dynamically based on query similarity.
Parameters
examples β List of examples to use in the prompt.
embeddings β An initialized embedding API interface, e.g. OpenAIEmbeddings().
vectorstore_cls β A vector store DB interface class, e.g. FAISS.
k β Number of examples to select
input_keys β If provided, the search is based on the input variables
instead of all variables.
vectorstore_cls_kwargs β optional kwargs containing url for vector store
Returns
The ExampleSelector instantiated, backed by a vector store.
select_examples(input_variables: Dict[str, str]) β List[dict][source]#
Select which examples to use based on semantic similarity.
previous
PromptTemplates
next
Chat Prompt Template
By Harrison Chase
Β© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/reference/modules/example_selector.html |
01c61c5f8653-0 | .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) β List[str][source]#
Split incoming text and return chunks.
class langchain.text_splitter.LatexTextSplitter(**kwargs: Any)[source]#
Attempts to split the text along Latex-formatted layout elements.
class langchain.text_splitter.MarkdownTextSplitter(**kwargs: Any)[source]#
Attempts to split the text along Markdown-formatted headings.
class langchain.text_splitter.NLTKTextSplitter(separator: str = '\n\n', **kwargs: Any)[source]#
Implementation of splitting text that looks at sentences using NLTK.
split_text(text: str) β List[str][source]#
Split incoming text and return chunks.
class langchain.text_splitter.PythonCodeTextSplitter(**kwargs: Any)[source]#
Attempts to split the text along Python syntax.
class langchain.text_splitter.RecursiveCharacterTextSplitter(separators: Optional[List[str]] = None, **kwargs: Any)[source]#
Implementation of splitting text that looks at characters.
Recursively tries to split by different characters to find one
that works.
split_text(text: str) β List[str][source]#
Split incoming text and return chunks.
class langchain.text_splitter.SpacyTextSplitter(separator: str = '\n\n', pipeline: str = 'en_core_web_sm', **kwargs: Any)[source]#
Implementation of splitting text that looks at sentences using Spacy.
split_text(text: str) β List[str][source]#
Split incoming text and return chunks. | https://python.langchain.com/en/latest/reference/modules/text_splitter.html |
01c61c5f8653-1 | Split incoming text and return chunks.
class langchain.text_splitter.TextSplitter(chunk_size: int = 4000, chunk_overlap: int = 200, length_function: typing.Callable[[str], int] = <built-in function len>)[source]#
Interface for splitting text into chunks.
async atransform_documents(documents: Sequence[langchain.schema.Document], **kwargs: Any) β Sequence[langchain.schema.Document][source]#
Asynchronously transform a sequence of documents by splitting them.
create_documents(texts: List[str], metadatas: Optional[List[dict]] = None) β List[langchain.schema.Document][source]#
Create documents from a list of texts.
classmethod from_huggingface_tokenizer(tokenizer: Any, **kwargs: Any) β langchain.text_splitter.TextSplitter[source]#
Text splitter that uses HuggingFace tokenizer to count length.
classmethod from_tiktoken_encoder(encoding_name: str = 'gpt2', model_name: Optional[str] = None, allowed_special: Union[Literal['all'], AbstractSet[str]] = {}, disallowed_special: Union[Literal['all'], Collection[str]] = 'all', **kwargs: Any) β langchain.text_splitter.TextSplitter[source]#
Text splitter that uses tiktoken encoder to count length.
split_documents(documents: List[langchain.schema.Document]) β List[langchain.schema.Document][source]#
Split documents.
abstract split_text(text: str) β List[str][source]#
Split text into multiple components.
transform_documents(documents: Sequence[langchain.schema.Document], **kwargs: Any) β Sequence[langchain.schema.Document][source]#
Transform sequence of documents by splitting them. | https://python.langchain.com/en/latest/reference/modules/text_splitter.html |
01c61c5f8653-2 | Transform sequence of documents by splitting them.
class langchain.text_splitter.TokenTextSplitter(encoding_name: str = 'gpt2', model_name: Optional[str] = None, allowed_special: Union[Literal['all'], AbstractSet[str]] = {}, disallowed_special: Union[Literal['all'], Collection[str]] = 'all', **kwargs: Any)[source]#
Implementation of splitting text that looks at tokens.
split_text(text: str) β List[str][source]#
Split incoming text and return chunks.
previous
Docstore
next
Embeddings
By Harrison Chase
Β© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/reference/modules/text_splitter.html |
88eb8a8384fe-0 | .rst
.pdf
PromptTemplates
PromptTemplates#
Prompt template classes.
pydantic model langchain.prompts.BaseChatPromptTemplate[source]#
format(**kwargs: Any) β str[source]#
Format the prompt with the inputs.
Parameters
kwargs β Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
abstract format_messages(**kwargs: Any) β List[langchain.schema.BaseMessage][source]#
Format kwargs into a list of messages.
format_prompt(**kwargs: Any) β langchain.schema.PromptValue[source]#
Create Chat Messages.
pydantic model langchain.prompts.BasePromptTemplate[source]#
Base class for all prompt templates, returning a prompt.
field input_variables: List[str] [Required]#
A list of the names of the variables the prompt template expects.
field output_parser: Optional[langchain.schema.BaseOutputParser] = None#
How to parse the output of calling an LLM on this formatted prompt.
dict(**kwargs: Any) β Dict[source]#
Return dictionary representation of prompt.
abstract format(**kwargs: Any) β str[source]#
Format the prompt with the inputs.
Parameters
kwargs β Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
abstract format_prompt(**kwargs: Any) β langchain.schema.PromptValue[source]#
Create Chat Messages.
partial(**kwargs: Union[str, Callable[[], str]]) β langchain.prompts.base.BasePromptTemplate[source]#
Return a partial of the prompt template.
save(file_path: Union[pathlib.Path, str]) β None[source]#
Save the prompt.
Parameters
file_path β Path to directory to save prompt to.
Example:
.. code-block:: python | https://python.langchain.com/en/latest/reference/modules/prompt.html |
88eb8a8384fe-1 | file_path β Path to directory to save prompt to.
Example:
.. code-block:: python
prompt.save(file_path=βpath/prompt.yamlβ)
pydantic model langchain.prompts.ChatPromptTemplate[source]#
format(**kwargs: Any) β str[source]#
Format the prompt with the inputs.
Parameters
kwargs β Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
format_messages(**kwargs: Any) β List[langchain.schema.BaseMessage][source]#
Format kwargs into a list of messages.
partial(**kwargs: Union[str, Callable[[], str]]) β langchain.prompts.base.BasePromptTemplate[source]#
Return a partial of the prompt template.
save(file_path: Union[pathlib.Path, str]) β None[source]#
Save the prompt.
Parameters
file_path β Path to directory to save prompt to.
Example:
.. code-block:: python
prompt.save(file_path=βpath/prompt.yamlβ)
pydantic model langchain.prompts.FewShotPromptTemplate[source]#
Prompt template that contains few shot examples.
field example_prompt: langchain.prompts.prompt.PromptTemplate [Required]#
PromptTemplate used to format an individual example.
field example_selector: Optional[langchain.prompts.example_selector.base.BaseExampleSelector] = None#
ExampleSelector to choose the examples to format into the prompt.
Either this or examples should be provided.
field example_separator: str = '\n\n'#
String separator used to join the prefix, the examples, and suffix.
field examples: Optional[List[dict]] = None#
Examples to format into the prompt.
Either this or example_selector should be provided.
field input_variables: List[str] [Required]#
A list of the names of the variables the prompt template expects. | https://python.langchain.com/en/latest/reference/modules/prompt.html |
88eb8a8384fe-2 | A list of the names of the variables the prompt template expects.
field prefix: str = ''#
A prompt template string to put before the examples.
field suffix: str [Required]#
A prompt template string to put after the examples.
field template_format: str = 'f-string'#
The format of the prompt template. Options are: βf-stringβ, βjinja2β.
field validate_template: bool = True#
Whether or not to try validating the template.
dict(**kwargs: Any) β Dict[source]#
Return a dictionary of the prompt.
format(**kwargs: Any) β str[source]#
Format the prompt with the inputs.
Parameters
kwargs β Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
pydantic model langchain.prompts.FewShotPromptWithTemplates[source]#
Prompt template that contains few shot examples.
field example_prompt: langchain.prompts.prompt.PromptTemplate [Required]#
PromptTemplate used to format an individual example.
field example_selector: Optional[langchain.prompts.example_selector.base.BaseExampleSelector] = None#
ExampleSelector to choose the examples to format into the prompt.
Either this or examples should be provided.
field example_separator: str = '\n\n'#
String separator used to join the prefix, the examples, and suffix.
field examples: Optional[List[dict]] = None#
Examples to format into the prompt.
Either this or example_selector should be provided.
field input_variables: List[str] [Required]#
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]# | https://python.langchain.com/en/latest/reference/modules/prompt.html |
88eb8a8384fe-3 | field suffix: langchain.prompts.base.StringPromptTemplate [Required]#
A PromptTemplate to put after the examples.
field template_format: str = 'f-string'#
The format of the prompt template. Options are: βf-stringβ, βjinja2β.
field validate_template: bool = True#
Whether or not to try validating the template.
dict(**kwargs: Any) β Dict[source]#
Return a dictionary of the prompt.
format(**kwargs: Any) β str[source]#
Format the prompt with the inputs.
Parameters
kwargs β Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
pydantic model langchain.prompts.MessagesPlaceholder[source]#
Prompt template that assumes variable is already list of messages.
format_messages(**kwargs: Any) β List[langchain.schema.BaseMessage][source]#
To a BaseMessage.
property input_variables: List[str]#
Input variables for this prompt template.
langchain.prompts.Prompt#
alias of langchain.prompts.prompt.PromptTemplate
pydantic model langchain.prompts.PromptTemplate[source]#
Schema to represent a prompt for an LLM.
Example
from langchain import PromptTemplate
prompt = PromptTemplate(input_variables=["foo"], template="Say {foo}")
field input_variables: List[str] [Required]#
A list of the names of the variables the prompt template expects.
field template: str [Required]#
The prompt template.
field template_format: str = 'f-string'#
The format of the prompt template. Options are: βf-stringβ, βjinja2β.
field validate_template: bool = True#
Whether or not to try validating the template.
format(**kwargs: Any) β str[source]#
Format the prompt with the inputs.
Parameters | https://python.langchain.com/en/latest/reference/modules/prompt.html |
88eb8a8384fe-4 | Format the prompt with the inputs.
Parameters
kwargs β Any arguments to be passed to the prompt template.
Returns
A formatted string.
Example:
prompt.format(variable1="foo")
classmethod from_examples(examples: List[str], suffix: str, input_variables: List[str], example_separator: str = '\n\n', prefix: str = '', **kwargs: Any) β langchain.prompts.prompt.PromptTemplate[source]#
Take examples in list format with prefix and suffix to create a prompt.
Intended to be used as a way to dynamically create a prompt from examples.
Parameters
examples β List of examples to use in the prompt.
suffix β String to go after the list of examples. Should generally
set up the userβs input.
input_variables β A list of variable names the final prompt template
will expect.
example_separator β The separator to use in between examples. Defaults
to two new line characters.
prefix β String that should go before any examples. Generally includes
examples. Default to an empty string.
Returns
The final prompt generated.
classmethod from_file(template_file: Union[str, pathlib.Path], input_variables: List[str], **kwargs: Any) β langchain.prompts.prompt.PromptTemplate[source]#
Load a prompt from a file.
Parameters
template_file β The path to the file containing the prompt template.
input_variables β A list of variable names the final prompt template
will expect.
Returns
The prompt loaded from the file.
classmethod from_template(template: str, **kwargs: Any) β langchain.prompts.prompt.PromptTemplate[source]#
Load a prompt template from a template.
pydantic model langchain.prompts.StringPromptTemplate[source]#
String prompt should expose the format method, returning a prompt.
format_prompt(**kwargs: Any) β langchain.schema.PromptValue[source]#
Create Chat Messages. | https://python.langchain.com/en/latest/reference/modules/prompt.html |
88eb8a8384fe-5 | Create Chat Messages.
langchain.prompts.load_prompt(path: Union[str, pathlib.Path]) β langchain.prompts.base.BasePromptTemplate[source]#
Unified method for loading a prompt from LangChainHub or local fs.
previous
Prompts
next
Example Selector
By Harrison Chase
Β© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/reference/modules/prompt.html |
6833f1fca458-0 | .rst
.pdf
LLMs
LLMs#
Wrappers on top of large language models APIs.
pydantic model langchain.llms.AI21[source]#
Wrapper around AI21 large language models.
To use, you should have the environment variable AI21_API_KEY
set with your API key.
Example
from langchain.llms import AI21
ai21 = AI21(model="j2-jumbo-instruct")
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field base_url: Optional[str] = None#
Base url to use, if None decides based on model name.
field countPenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)#
Penalizes repeated tokens according to count.
field frequencyPenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)#
Penalizes repeated tokens according to frequency.
field logitBias: Optional[Dict[str, float]] = None#
Adjust the probability of specific tokens being generated.
field maxTokens: int = 256#
The maximum number of tokens to generate in the completion.
field minTokens: int = 0#
The minimum number of tokens to generate in the completion.
field model: str = 'j2-jumbo-instruct'#
Model name to use.
field numResults: int = 1#
How many completions to generate for each prompt. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-1 | field numResults: int = 1#
How many completions to generate for each prompt.
field presencePenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)#
Penalizes repeated tokens.
field temperature: float = 0.7#
What sampling temperature to use.
field topP: float = 1.0#
Total probability mass of tokens to consider at each step.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-2 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-3 | encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.AlephAlpha[source]#
Wrapper around Aleph Alpha large language models.
To use, you should have the aleph_alpha_client python package installed, and the
environment variable ALEPH_ALPHA_API_KEY set with your API key, or pass
it as a named parameter to the constructor.
Parameters are explained more in depth here:
Aleph-Alpha/aleph-alpha-client
Example
from langchain.llms import AlephAlpha
alpeh_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key")
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field aleph_alpha_api_key: Optional[str] = None#
API key for Aleph Alpha API.
field best_of: Optional[int] = None#
returns the one with the βbest ofβ results
(highest log probability per token)
field completion_bias_exclusion_first_token_only: bool = False#
Only consider the first token for the completion_bias_exclusion.
field contextual_control_threshold: Optional[float] = None#
If set to None, attention control parameters only apply to those tokens that have
explicitly been set in the request.
If set to a non-None value, control parameters are also applied to similar tokens. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-4 | If set to a non-None value, control parameters are also applied to similar tokens.
field control_log_additive: Optional[bool] = True#
True: apply control by adding the log(control_factor) to attention scores.
False: (attention_scores - - attention_scores.min(-1)) * control_factor
field echo: bool = False#
Echo the prompt in the completion.
field frequency_penalty: float = 0.0#
Penalizes repeated tokens according to frequency.
field log_probs: Optional[int] = None#
Number of top log probabilities to be returned for each generated token.
field logit_bias: Optional[Dict[int, float]] = None#
The logit bias allows to influence the likelihood of generating tokens.
field maximum_tokens: int = 64#
The maximum number of tokens to be generated.
field minimum_tokens: Optional[int] = 0#
Generate at least this number of tokens.
field model: Optional[str] = 'luminous-base'#
Model name to use.
field n: int = 1#
How many completions to generate for each prompt.
field penalty_bias: Optional[str] = None#
Penalty bias for the completion.
field penalty_exceptions: Optional[List[str]] = None#
List of strings that may be generated without penalty,
regardless of other penalty settings
field penalty_exceptions_include_stop_sequences: Optional[bool] = None#
Should stop_sequences be included in penalty_exceptions.
field presence_penalty: float = 0.0#
Penalizes repeated tokens.
field raw_completion: bool = False#
Force the raw completion of the model to be returned.
field repetition_penalties_include_completion: bool = True#
Flag deciding whether presence penalty or frequency penalty
are updated from the completion.
field repetition_penalties_include_prompt: Optional[bool] = False# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-5 | field repetition_penalties_include_prompt: Optional[bool] = False#
Flag deciding whether presence penalty or frequency penalty are
updated from the prompt.
field stop_sequences: Optional[List[str]] = None#
Stop sequences to use.
field temperature: float = 0.0#
A non-negative float that tunes the degree of randomness in generation.
field tokens: Optional[bool] = False#
return tokens of completion.
field top_k: int = 0#
Number of most likely tokens to consider at each step.
field top_p: float = 0.0#
Total probability mass of tokens to consider at each step.
field use_multiplicative_presence_penalty: Optional[bool] = False#
Flag deciding whether presence penalty is applied
multiplicatively (True) or additively (False).
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-6 | Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-7 | Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Anthropic[source]#
Wrapper around Anthropicβs large language models.
To use, you should have the anthropic python package installed, and the
environment variable ANTHROPIC_API_KEY set with your API key, or pass
it as a named parameter to the constructor.
Example
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field max_tokens_to_sample: int = 256#
Denotes the number of tokens to predict per generation.
field model: str = 'claude-v1'#
Model name to use.
field streaming: bool = False#
Whether to stream the results. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-8 | Model name to use.
field streaming: bool = False#
Whether to stream the results.
field temperature: Optional[float] = None#
A non-negative float that tunes the degree of randomness in generation.
field top_k: Optional[int] = None#
Number of most likely tokens to consider at each step.
field top_p: Optional[float] = None#
Total probability mass of tokens to consider at each step.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-9 | exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-10 | Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
stream(prompt: str, stop: Optional[List[str]] = None) β Generator[source]#
Call Anthropic completion_stream and return the resulting generator.
BETA: this is a beta feature while we figure out the right abstraction.
Once that happens, this interface could change.
Parameters
prompt β The prompt to pass into the model.
stop β Optional list of stop words to use when generating.
Returns
A generator representing the stream of tokens from Anthropic.
Example
prompt = "Write a poem about a stream."
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
generator = anthropic.stream(prompt)
for token in generator:
yield token
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.AzureOpenAI[source]#
Wrapper around Azure-specific OpenAI large language models.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example
from langchain.llms import AzureOpenAI
openai = AzureOpenAI(model_name="text-davinci-003")
Validators
build_extra Β» all fields
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field allowed_special: Union[Literal['all'], AbstractSet[str]] = {}#
Set of special tokens that are allowedγ
field batch_size: int = 20# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-11 | Set of special tokens that are allowedγ
field batch_size: int = 20#
Batch size to use when passing multiple documents to generate.
field best_of: int = 1#
Generates best_of completions server-side and returns the βbestβ.
field deployment_name: str = ''#
Deployment name to use.
field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'#
Set of special tokens that are not allowedγ
field frequency_penalty: float = 0#
Penalizes repeated tokens according to frequency.
field logit_bias: Optional[Dict[str, float]] [Optional]#
Adjust the probability of specific tokens being generated.
field max_retries: int = 6#
Maximum number of retries to make when generating.
field max_tokens: int = 256#
The maximum number of tokens to generate in the completion.
-1 returns as many tokens as possible given the prompt and
the models maximal context size.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not explicitly specified.
field model_name: str = 'text-davinci-003'#
Model name to use.
field n: int = 1#
How many completions to generate for each prompt.
field presence_penalty: float = 0#
Penalizes repeated tokens.
field request_timeout: Optional[Union[float, Tuple[float, float]]] = None#
Timeout for requests to OpenAI completion API. Default is 600 seconds.
field streaming: bool = False#
Whether to stream the results or not.
field temperature: float = 0.7#
What sampling temperature to use.
field top_p: float = 1#
Total probability mass of tokens to consider at each step.
field verbose: bool [Optional]# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-12 | Total probability mass of tokens to consider at each step.
field verbose: bool [Optional]#
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-13 | deep β set to True to make a deep copy of the model
Returns
new model instance
create_llm_result(choices: Any, prompts: List[str], token_usage: Dict[str, int]) β langchain.schema.LLMResult#
Create the LLMResult from the choices and prompts.
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Calculate num tokens with tiktoken package.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) β List[List[str]]#
Get the sub prompts for llm call.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict(). | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-14 | Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
max_tokens_for_prompt(prompt: str) β int#
Calculate the maximum number of tokens possible to generate for a prompt.
Parameters
prompt β The prompt to pass into the model.
Returns
The maximum number of tokens to generate for a prompt.
Example
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
modelname_to_contextsize(modelname: str) β int#
Calculate the maximum number of tokens possible to generate for a model.
Parameters
modelname β The modelname we want to know the context size for.
Returns
The maximum context size
Example
max_tokens = openai.modelname_to_contextsize("text-davinci-003")
prep_streaming_params(stop: Optional[List[str]] = None) β Dict[str, Any]#
Prepare the params for streaming.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
stream(prompt: str, stop: Optional[List[str]] = None) β Generator#
Call OpenAI with streaming flag and return the resulting generator.
BETA: this is a beta feature while we figure out the right abstraction.
Once that happens, this interface could change.
Parameters
prompt β The prompts to pass into the model.
stop β Optional list of stop words to use when generating.
Returns
A generator representing the stream of tokens from OpenAI.
Example
generator = openai.stream("Tell me a joke.")
for token in generator:
yield token | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-15 | for token in generator:
yield token
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Banana[source]#
Wrapper around Banana large language models.
To use, you should have the banana-dev python package installed,
and the environment variable BANANA_API_KEY set with your API key.
Any parameters that are valid to be passed to the call can be passed
in, even if not explicitly saved on this class.
Example
Validators
build_extra Β» all fields
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field model_key: str = ''#
model endpoint to use
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not
explicitly specified.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-16 | Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-17 | Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.CerebriumAI[source]#
Wrapper around CerebriumAI large language models.
To use, you should have the cerebrium python package installed, and the
environment variable CEREBRIUMAI_API_KEY set with your API key.
Any parameters that are valid to be passed to the call can be passed
in, even if not explicitly saved on this class.
Example
Validators
build_extra Β» all fields
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field endpoint_url: str = ''#
model endpoint to use
field model_kwargs: Dict[str, Any] [Optional]# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-18 | model endpoint to use
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not
explicitly specified.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-19 | deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-20 | classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Cohere[source]#
Wrapper around Cohere large language models.
To use, you should have the cohere python package installed, and the
environment variable COHERE_API_KEY set with your API key, or pass
it as a named parameter to the constructor.
Example
from langchain.llms import Cohere
cohere = Cohere(model="gptd-instruct-tft", cohere_api_key="my-api-key")
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field frequency_penalty: float = 0.0#
Penalizes repeated tokens according to frequency. Between 0 and 1.
field k: int = 0#
Number of most likely tokens to consider at each step.
field max_tokens: int = 256#
Denotes the number of tokens to predict per generation.
field model: Optional[str] = None#
Model name to use.
field p: int = 1#
Total probability mass of tokens to consider at each step.
field presence_penalty: float = 0.0#
Penalizes repeated tokens. Between 0 and 1.
field temperature: float = 0.75#
A non-negative float that tunes the degree of randomness in generation.
field truncate: Optional[str] = None#
Specify how the client handles inputs longer than the maximum token
length: Truncate from START, END or NONE
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-21 | Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-22 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.DeepInfra[source]#
Wrapper around DeepInfra deployed models.
To use, you should have the requests python package installed, and the | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-23 | To use, you should have the requests python package installed, and the
environment variable DEEPINFRA_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Only supports text-generation and text2text-generation for now.
Example
from langchain.llms import DeepInfra
di = DeepInfra(model_id="google/flan-t5-xl",
deepinfra_api_token="my-api-key")
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-24 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-25 | encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.ForefrontAI[source]#
Wrapper around ForefrontAI large language models.
To use, you should have the environment variable FOREFRONTAI_API_KEY
set with your API key.
Example
from langchain.llms import ForefrontAI
forefrontai = ForefrontAI(endpoint_url="")
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field base_url: Optional[str] = None#
Base url to use, if None decides based on model name.
field endpoint_url: str = ''#
Model name to use.
field length: int = 256#
The maximum number of tokens to generate in the completion.
field repetition_penalty: int = 1#
Penalizes repeated tokens according to frequency.
field temperature: float = 0.7#
What sampling temperature to use.
field top_k: int = 40#
The number of highest probability vocabulary tokens to
keep for top-k-filtering.
field top_p: float = 1.0#
Total probability mass of tokens to consider at each step.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-26 | Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-27 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.GPT4All[source]#
Wrapper around GPT4All language models.
To use, you should have the pyllamacpp python package installed, the | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-28 | To use, you should have the pyllamacpp python package installed, the
pre-trained model file, and the modelβs config information.
Example
from langchain.llms import GPT4All
model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
# Simplest invocation
response = model("Once upon a time, ")
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field echo: Optional[bool] = False#
Whether to echo the prompt.
field embedding: bool = False#
Use embedding mode only.
field f16_kv: bool = False#
Use half-precision for key/value cache.
field logits_all: bool = False#
Return logits for all tokens, not just the last token.
field model: str [Required]#
Path to the pre-trained GPT4All model file.
field n_batch: int = 1#
Batch size for prompt processing.
field n_ctx: int = 512#
Token context window.
field n_parts: int = -1#
Number of parts to split the model into.
If -1, the number of parts is automatically determined.
field n_predict: Optional[int] = 256#
The maximum number of tokens to generate.
field n_threads: Optional[int] = 4#
Number of threads to use.
field repeat_last_n: Optional[int] = 64#
Last n tokens to penalize
field repeat_penalty: Optional[float] = 1.3#
The penalty to apply to repeated tokens.
field seed: int = 0#
Seed. If -1, a random seed is used.
field stop: Optional[List[str]] = []#
A list of strings to stop generation when encountered. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-29 | A list of strings to stop generation when encountered.
field streaming: bool = False#
Whether to stream the results or not.
field temp: Optional[float] = 0.8#
The temperature to use for sampling.
field top_k: Optional[int] = 40#
The top-k value to use for sampling.
field top_p: Optional[float] = 0.95#
The top-p value to use for sampling.
field use_mlock: bool = False#
Force system to keep model in RAM.
field vocab_only: bool = False#
Only load the vocabulary, no weights.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-30 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-31 | encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.GooseAI[source]#
Wrapper around OpenAI large language models.
To use, you should have the openai python package installed, and the
environment variable GOOSEAI_API_KEY set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example
Validators
build_extra Β» all fields
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field frequency_penalty: float = 0#
Penalizes repeated tokens according to frequency.
field logit_bias: Optional[Dict[str, float]] [Optional]#
Adjust the probability of specific tokens being generated.
field max_tokens: int = 256#
The maximum number of tokens to generate in the completion.
-1 returns as many tokens as possible given the prompt and
the models maximal context size.
field min_tokens: int = 1#
The minimum number of tokens to generate in the completion.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not explicitly specified.
field model_name: str = 'gpt-neo-20b'#
Model name to use
field n: int = 1# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-32 | Model name to use
field n: int = 1#
How many completions to generate for each prompt.
field presence_penalty: float = 0#
Penalizes repeated tokens.
field temperature: float = 0.7#
What sampling temperature to use
field top_p: float = 1#
Total probability mass of tokens to consider at each step.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-33 | exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-34 | Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.HuggingFaceEndpoint[source]#
Wrapper around HuggingFaceHub Inference Endpoints.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Only supports text-generation and text2text-generation for now.
Example
from langchain.llms import HuggingFaceEndpoint
endpoint_url = (
"https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud"
)
hf = HuggingFaceEndpoint(
endpoint_url=endpoint_url,
huggingfacehub_api_token="my-api-key"
)
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field endpoint_url: str = ''#
Endpoint URL to use.
field model_kwargs: Optional[dict] = None#
Key word arguments to pass to the model.
field task: Optional[str] = None#
Task to call the model with. Should be a task that returns generated_text.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-35 | Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-36 | Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.HuggingFaceHub[source]#
Wrapper around HuggingFaceHub models.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Only supports text-generation and text2text-generation for now.
Example | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-37 | Only supports text-generation and text2text-generation for now.
Example
from langchain.llms import HuggingFaceHub
hf = HuggingFaceHub(repo_id="gpt2", huggingfacehub_api_token="my-api-key")
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field model_kwargs: Optional[dict] = None#
Key word arguments to pass to the model.
field repo_id: str = 'gpt2'#
Model name to use.
field task: Optional[str] = None#
Task to call the model with. Should be a task that returns generated_text.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-38 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-39 | encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.HuggingFacePipeline[source]#
Wrapper around HuggingFace Pipeline API.
To use, you should have the transformers python package installed.
Only supports text-generation and text2text-generation for now.
Example using from_model_id:from langchain.llms import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
model_id="gpt2", task="text-generation"
)
Example passing pipeline in directly:from langchain.llms import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
hf = HuggingFacePipeline(pipeline=pipe)
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
field model_id: str = 'gpt2'#
Model name to use.
field model_kwargs: Optional[dict] = None#
Key word arguments to pass to the model.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-40 | Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-41 | dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
classmethod from_model_id(model_id: str, task: str, device: int = - 1, model_kwargs: Optional[dict] = None, **kwargs: Any) β langchain.llms.base.LLM[source]#
Construct the pipeline object from model_id and task.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-42 | Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.LlamaCpp[source]#
Wrapper around the llama.cpp model.
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.llms import LlamaCppEmbeddings
llm = LlamaCppEmbeddings(model_path="/path/to/llama/model")
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field echo: Optional[bool] = False#
Whether to echo the prompt.
field f16_kv: bool = False#
Use half-precision for key/value cache.
field last_n_tokens_size: Optional[int] = 64#
The number of tokens to look back when applying the repeat_penalty.
field logits_all: bool = False#
Return logits for all tokens, not just the last token.
field logprobs: Optional[int] = None#
The number of logprobs to return. If None, no logprobs are returned.
field max_tokens: Optional[int] = 256#
The maximum number of tokens to generate.
field model_path: str [Required]#
The path to the Llama model file.
field n_batch: Optional[int] = 8#
Number of tokens to process in parallel.
Should be a number between 1 and n_ctx.
field n_ctx: int = 512# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-43 | field n_ctx: int = 512#
Token context window.
field n_parts: int = -1#
Number of parts to split the model into.
If -1, the number of parts is automatically determined.
field n_threads: Optional[int] = None#
Number of threads to use.
If None, the number of threads is automatically determined.
field repeat_penalty: Optional[float] = 1.1#
The penalty to apply to repeated tokens.
field seed: int = -1#
Seed. If -1, a random seed is used.
field stop: Optional[List[str]] = []#
A list of strings to stop generation when encountered.
field suffix: Optional[str] = None#
A suffix to append to the generated text. If None, no suffix is appended.
field temperature: Optional[float] = 0.8#
The temperature to use for sampling.
field top_k: Optional[int] = 40#
The top-k value to use for sampling.
field top_p: Optional[float] = 0.95#
The top-p value to use for sampling.
field use_mlock: bool = False#
Force system to keep model in RAM.
field vocab_only: bool = False#
Only load the vocabulary, no weights.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-44 | Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-45 | get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Modal[source]#
Wrapper around Modal large language models.
To use, you should have the modal-client python package installed.
Any parameters that are valid to be passed to the call can be passed
in, even if not explicitly saved on this class.
Example
Validators
build_extra Β» all fields
set_callback_manager Β» callback_manager
set_verbose Β» verbose
field endpoint_url: str = ''#
model endpoint to use
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not
explicitly specified. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-46 | Holds any model parameters valid for create call not
explicitly specified.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-47 | dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-48 | Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.NLPCloud[source]#
Wrapper around NLPCloud large language models.
To use, you should have the nlpcloud python package installed, and the
environment variable NLPCLOUD_API_KEY set with your API key.
Example
from langchain.llms import NLPCloud
nlpcloud = NLPCloud(model="gpt-neox-20b")
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field bad_words: List[str] = []#
List of tokens not allowed to be generated.
field do_sample: bool = True#
Whether to use sampling (True) or greedy decoding.
field early_stopping: bool = False#
Whether to stop beam search at num_beams sentences.
field length_no_input: bool = True#
Whether min_length and max_length should include the length of the input.
field length_penalty: float = 1.0#
Exponential penalty to the length.
field max_length: int = 256#
The maximum number of tokens to generate in the completion.
field min_length: int = 1#
The minimum number of tokens to generate in the completion.
field model_name: str = 'finetuned-gpt-neox-20b'#
Model name to use.
field num_beams: int = 1#
Number of beams for beam search.
field num_return_sequences: int = 1#
How many completions to generate for each prompt.
field remove_end_sequence: bool = True#
Whether or not to remove the end sequence token.
field remove_input: bool = True#
Remove input text from API response
field repetition_penalty: float = 1.0# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-49 | Remove input text from API response
field repetition_penalty: float = 1.0#
Penalizes repeated tokens. 1.0 means no penalty.
field temperature: float = 0.7#
What sampling temperature to use.
field top_k: int = 50#
The number of highest probability tokens to keep for top-k filtering.
field top_p: int = 1#
Total probability mass of tokens to consider at each step.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-50 | Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-51 | save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.OpenAI[source]#
Wrapper around OpenAI large language models.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example
from langchain.llms import OpenAI
openai = OpenAI(model_name="text-davinci-003")
Validators
build_extra Β» all fields
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field verbose: bool [Optional]#
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-52 | Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
create_llm_result(choices: Any, prompts: List[str], token_usage: Dict[str, int]) β langchain.schema.LLMResult#
Create the LLMResult from the choices and prompts.
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Calculate num tokens with tiktoken package.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-53 | get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) β List[List[str]]#
Get the sub prompts for llm call.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
max_tokens_for_prompt(prompt: str) β int#
Calculate the maximum number of tokens possible to generate for a prompt.
Parameters
prompt β The prompt to pass into the model.
Returns
The maximum number of tokens to generate for a prompt.
Example
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
modelname_to_contextsize(modelname: str) β int#
Calculate the maximum number of tokens possible to generate for a model.
Parameters
modelname β The modelname we want to know the context size for.
Returns
The maximum context size
Example
max_tokens = openai.modelname_to_contextsize("text-davinci-003")
prep_streaming_params(stop: Optional[List[str]] = None) β Dict[str, Any]#
Prepare the params for streaming. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-54 | Prepare the params for streaming.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
stream(prompt: str, stop: Optional[List[str]] = None) β Generator#
Call OpenAI with streaming flag and return the resulting generator.
BETA: this is a beta feature while we figure out the right abstraction.
Once that happens, this interface could change.
Parameters
prompt β The prompts to pass into the model.
stop β Optional list of stop words to use when generating.
Returns
A generator representing the stream of tokens from OpenAI.
Example
generator = openai.stream("Tell me a joke.")
for token in generator:
yield token
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.OpenAIChat[source]#
Wrapper around OpenAI Chat large language models.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example
from langchain.llms import OpenAIChat
openaichat = OpenAIChat(model_name="gpt-3.5-turbo")
Validators
build_extra Β» all fields
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field allowed_special: Union[Literal['all'], AbstractSet[str]] = {}#
Set of special tokens that are allowedγ | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-55 | Set of special tokens that are allowedγ
field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'#
Set of special tokens that are not allowedγ
field max_retries: int = 6#
Maximum number of retries to make when generating.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not explicitly specified.
field model_name: str = 'gpt-3.5-turbo'#
Model name to use.
field prefix_messages: List [Optional]#
Series of messages for Chat input.
field streaming: bool = False#
Whether to stream the results or not.
field verbose: bool [Optional]#
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-56 | Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int[source]#
Calculate num tokens with tiktoken package.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-57 | Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Petals[source]#
Wrapper around Petals Bloom models.
To use, you should have the petals python package installed, and the
environment variable HUGGINGFACE_API_KEY set with your API key.
Any parameters that are valid to be passed to the call can be passed
in, even if not explicitly saved on this class.
Example
Validators
build_extra Β» all fields
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field client: Any = None#
The client to use for the API calls.
field do_sample: bool = True#
Whether or not to use sampling; use greedy decoding otherwise. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-58 | Whether or not to use sampling; use greedy decoding otherwise.
field max_length: Optional[int] = None#
The maximum length of the sequence to be generated.
field max_new_tokens: int = 256#
The maximum number of new tokens to generate in the completion.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call
not explicitly specified.
field model_name: str = 'bigscience/bloom-petals'#
The model to use.
field temperature: float = 0.7#
What sampling temperature to use
field tokenizer: Any = None#
The tokenizer to use for the API calls.
field top_k: Optional[int] = None#
The number of highest probability vocabulary tokens
to keep for top-k-filtering.
field top_p: float = 0.9#
The cumulative probability for top-p sampling.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-59 | Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-60 | Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.PromptLayerOpenAI[source]#
Wrapper around OpenAI large language models.
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 OpenAI LLM can also
be passed here. The PromptLayerOpenAI LLM adds two optional
:param pl_tags: List of strings to tag the request with.
:param return_pl_id: If True, the PromptLayer request ID will be
returned in the generation_info field of the
Generation object.
Example | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-61 | returned in the generation_info field of the
Generation object.
Example
from langchain.llms import PromptLayerOpenAI
openai = PromptLayerOpenAI(model_name="text-davinci-003")
Validators
build_extra Β» all fields
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-62 | update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
create_llm_result(choices: Any, prompts: List[str], token_usage: Dict[str, int]) β langchain.schema.LLMResult#
Create the LLMResult from the choices and prompts.
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Calculate num tokens with tiktoken package.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) β List[List[str]]#
Get the sub prompts for llm call.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-63 | Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
max_tokens_for_prompt(prompt: str) β int#
Calculate the maximum number of tokens possible to generate for a prompt.
Parameters
prompt β The prompt to pass into the model.
Returns
The maximum number of tokens to generate for a prompt.
Example
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
modelname_to_contextsize(modelname: str) β int#
Calculate the maximum number of tokens possible to generate for a model.
Parameters
modelname β The modelname we want to know the context size for.
Returns
The maximum context size
Example
max_tokens = openai.modelname_to_contextsize("text-davinci-003")
prep_streaming_params(stop: Optional[List[str]] = None) β Dict[str, Any]#
Prepare the params for streaming.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
stream(prompt: str, stop: Optional[List[str]] = None) β Generator#
Call OpenAI with streaming flag and return the resulting generator.
BETA: this is a beta feature while we figure out the right abstraction.
Once that happens, this interface could change.
Parameters
prompt β The prompts to pass into the model.
stop β Optional list of stop words to use when generating.
Returns
A generator representing the stream of tokens from OpenAI.
Example
generator = openai.stream("Tell me a joke.")
for token in generator:
yield token | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-64 | for token in generator:
yield token
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.PromptLayerOpenAIChat[source]#
Wrapper around OpenAI large language models.
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 OpenAIChat LLM can also
be passed here. The PromptLayerOpenAIChat adds two optional
:param pl_tags: List of strings to tag the request with.
:param return_pl_id: If True, the PromptLayer request ID will be
returned in the generation_info field of the
Generation object.
Example
from langchain.llms import PromptLayerOpenAIChat
openaichat = PromptLayerOpenAIChat(model_name="gpt-3.5-turbo")
Validators
build_extra Β» all fields
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field allowed_special: Union[Literal['all'], AbstractSet[str]] = {}#
Set of special tokens that are allowedγ
field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'#
Set of special tokens that are not allowedγ
field max_retries: int = 6#
Maximum number of retries to make when generating.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not explicitly specified.
field model_name: str = 'gpt-3.5-turbo'#
Model name to use.
field prefix_messages: List [Optional]# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-65 | Model name to use.
field prefix_messages: List [Optional]#
Series of messages for Chat input.
field streaming: bool = False#
Whether to stream the results or not.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-66 | deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Calculate num tokens with tiktoken package.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-67 | classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.RWKV[source]#
Wrapper around RWKV language models.
To use, you should have the rwkv python package installed, the
pre-trained model file, and the modelβs config information.
Example
from langchain.llms import RWKV
model = RWKV(model="./models/rwkv-3b-fp16.bin", strategy="cpu fp32")
# Simplest invocation
response = model("Once upon a time, ")
Validators
set_callback_manager Β» callback_manager
set_verbose Β» verbose
validate_environment Β» all fields
field CHUNK_LEN: int = 256#
Batch size for prompt processing.
field max_tokens_per_generation: int = 256#
Maximum number of tokens to generate.
field model: str [Required]#
Path to the pre-trained RWKV model file.
field penalty_alpha_frequency: float = 0.4#
Positive values penalize new tokens based on their existing frequency
in the text so far, decreasing the modelβs likelihood to repeat the same
line verbatim..
field penalty_alpha_presence: float = 0.4#
Positive values penalize new tokens based on whether they appear
in the text so far, increasing the modelβs likelihood to talk about
new topics..
field rwkv_verbose: bool = True#
Print debug information.
field strategy: str = 'cpu fp32'#
Token context window.
field temperature: float = 1.0#
The temperature to use for sampling.
field tokens_path: str [Required]#
Path to the RWKV tokens file.
field top_p: float = 0.5#
The top-p value to use for sampling. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-68 | The top-p value to use for sampling.
__call__(prompt: str, stop: Optional[List[str]] = None) β str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = βallowβ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-69 | dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) β unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns. | https://python.langchain.com/en/latest/reference/modules/llms.html |