id stringlengths 14 16 | text stringlengths 29 2.73k | source stringlengths 50 116 |
|---|---|---|
ae1a1d5c8490-104 | exclude – fields to exclude from new model, as with values this takes precedence over include
update – 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 – set to True to make a deep copy of the model
Returns
new model instance
dict(**kw... | https:///python.langchain.com/en/latest/reference/modules/llms.html |
ae1a1d5c8490-105 | 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]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Exa... | https:///python.langchain.com/en/latest/reference/modules/llms.html |
ae1a1d5c8490-106 | shorter candidates
field logprobs: bool = False#
Whether to return log probabilities.
field model_id: str = 'palmyra-base'#
Model name to use.
field random_seed: int = 0#
The model generates random results.
Changing the random seed alone will produce a different response
with similar characteristics. It is possible to ... | https:///python.langchain.com/en/latest/reference/modules/llms.html |
ae1a1d5c8490-107 | Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a li... | https:///python.langchain.com/en/latest/reference/modules/llms.html |
ae1a1d5c8490-108 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#
Take in a list of p... | https:///python.langchain.com/en/latest/reference/modules/llms.html |
ae1a1d5c8490-109 | previous
Writer
next
Chat Models
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on May 02, 2023. | https:///python.langchain.com/en/latest/reference/modules/llms.html |
c32ff36d4644-0 | .rst
.pdf
Embeddings
Embeddings#
Wrappers around embedding modules.
pydantic model langchain.embeddings.AlephAlphaAsymmetricSemanticEmbedding[source]#
Wrapper for Aleph Alpha’s Asymmetric Embeddings
AA provides you with an endpoint to embed a document and a query.
The models were optimized to make the embeddings of doc... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
c32ff36d4644-1 | Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to Aleph Alpha’s asymmetric, query embedding endpoint
:param text: The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.AlephAlphaSymmet... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
c32ff36d4644-2 | embed_documents(texts: List[str]) → List[List[float]][source]#
Call out to Cohere’s embedding endpoint.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to Cohere’s embedding endpoint.
Parameters
text – The text to embed... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
c32ff36d4644-3 | Compute doc embeddings using a HuggingFace transformer model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace transformer model.
Parameters
text – The text to embed.
Returns
Embedding... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
c32ff36d4644-4 | Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.HuggingFaceInstructEmbeddings[source]#
Wrapper around sentence_transformers embedding models.
To use, you should have the sentence_transformers
and InstructorEmbedding python packages installed.
Example
from langch... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
c32ff36d4644-5 | To use, you should have the llama-cpp-python library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out: abetlen/llama-cpp-python
Example
from langchain.embeddings import LlamaCppEmbeddings
llama = LlamaCppEmbeddings(model_path="/path/to/model.bin")
field f16_kv: bool ... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
c32ff36d4644-6 | pydantic model langchain.embeddings.OpenAIEmbeddings[source]#
Wrapper around OpenAI embedding models.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key or pass it
as a named parameter to the constructor.
Example
from langchain.embeddings impor... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
c32ff36d4644-7 | Call out to OpenAI’s embedding endpoint for embedding search docs.
Parameters
texts – The list of texts to embed.
chunk_size – The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Call out to O... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
c32ff36d4644-8 | credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
field endpoint_kwargs: Optional[Dict] = None#
Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
c32ff36d4644-9 | cloud like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example using a model load function:from langchain.embeddings import SelfHostedEmbeddings
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", ... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
c32ff36d4644-10 | field inference_kwargs: Any = None#
Any kwargs to pass to the model’s inference function.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a HuggingFace transformer model.
Parameters
texts – The list of texts to embed.s
Returns
List of embeddings, one for each text.
embed_quer... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
c32ff36d4644-11 | field model_id: str = 'sentence-transformers/all-mpnet-base-v2'#
Model name to use.
field model_load_fn: Callable = <function load_embedding_model>#
Function to load the model remotely on the server.
field model_reqs: List[str] = ['./', 'sentence_transformers', 'torch']#
Requirements to install on hardware to inference... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
c32ff36d4644-12 | embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a HuggingFace instruct model.
Parameters
texts – The list of texts to embed.
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace instruct mode... | https:///python.langchain.com/en/latest/reference/modules/embeddings.html |
ed1d262969d5-0 | .md
.pdf
Quickstart Guide
Contents
Installation
Environment Setup
Building a Language Model Application: LLMs
LLMs: Get predictions from a language model
Prompt Templates: Manage prompts for LLMs
Chains: Combine LLMs and prompts in multi-step workflows
Agents: Dynamically Call Chains Based on User Input
Memory: Add S... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
ed1d262969d5-1 | The most basic building block of LangChain is calling an LLM on some input.
Let’s walk through a simple example of how to do this.
For this purpose, let’s pretend we are building a service that generates a company name based on what the company makes.
In order to do this, we first need to import the LLM wrapper.
from l... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
ed1d262969d5-2 | template="What is a good name for a company that makes {product}?",
)
Let’s now see how this works! We can call the .format method to format it.
print(prompt.format(product="colorful socks"))
What is a good name for a company that makes colorful socks?
For more details, check out the getting started guide for prompts.
... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
ed1d262969d5-3 | There we go! There’s the first chain - an LLM Chain.
This is one of the simpler types of chains, but understanding how it works will set you up well for working with more complex chains.
For more details, check out the getting started guide for chains.
Agents: Dynamically Call Chains Based on User Input#
So far the cha... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
ed1d262969d5-4 | Now we can get started!
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import OpenAI
# First, let's load the language model we're going to use to control the agent.
llm = OpenAI(temperature=0)
# Next, let's load some tools... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
ed1d262969d5-5 | > Finished chain.
Memory: Add State to Chains and Agents#
So far, all the chains and agents we’ve gone through have been stateless. But often, you may want a chain or agent to have some concept of “memory” so that it may remember information about its previous interactions. The clearest and simple example of this is wh... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
ed1d262969d5-6 | print(output)
> Entering new chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
Huma... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
ed1d262969d5-7 | chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
You can also pass in multiple messages for OpenAI’s gpt-3.5-turbo and gpt-4 models.
messages = [
SystemMessage(content="You are a helpful assistant t... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
ed1d262969d5-8 | result.llm_output['token_usage']
# -> {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}
Chat Prompt Templates#
Similar to LLMs, you can make use of templating by using a MessagePromptTemplate. You can build a ChatPromptTemplate from one or more MessagePromptTemplates. You can use ChatPromptTemplate’s f... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
ed1d262969d5-9 | ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
chat = ChatOpenAI(temperature=0)
template = "You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
hu... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
ed1d262969d5-10 | # Now let's test it out!
agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
> Entering new AgentExecutor chain...
Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power.
Action:
{
"action": "Search",
"a... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
ed1d262969d5-11 | from langchain.prompts import (
ChatPromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate
)
from langchain.chains import ConversationChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
prompt = ChatPromptTempl... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
ed1d262969d5-12 | LLMs: Get predictions from a language model
Prompt Templates: Manage prompts for LLMs
Chains: Combine LLMs and prompts in multi-step workflows
Agents: Dynamically Call Chains Based on User Input
Memory: Add State to Chains and Agents
Building a Language Model Application: Chat Models
Get Message Completions from a Chat... | https:///python.langchain.com/en/latest/getting_started/getting_started.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.