id stringlengths 14 15 | text stringlengths 101 5.26k | source stringlengths 57 120 |
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64456b903bc9-1 | field entity_cache: List[str] = []#
field entity_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a conversation between an AI and a human. Extract all... | https://langchain.readthedocs.io/en/latest/reference/modules/memory.html |
64456b903bc9-2 | information about knowledge triples in the conversation.
field ai_prefix: str = 'AI'#
field entity_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template='You are an AI assistant reading the transcript of a c... | https://langchain.readthedocs.io/en/latest/reference/modules/memory.html |
64456b903bc9-3 | field kg: langchain.graphs.networkx_graph.NetworkxEntityGraph [Optional]#
field knowledge_extraction_prompt: langchain.prompts.base.BasePromptTemplate = PromptTemplate(input_variables=['history', 'input'], output_parser=None, partial_variables={}, template="You are a networked intelligence helping a human track knowled... | https://langchain.readthedocs.io/en/latest/reference/modules/memory.html |
64456b903bc9-4 | clear() β None[source]#
Clear memory contents.
load_memory_variables(inputs: Dict[str, Any]) β Dict[str, Any][source]#
Return history buffer.
prune() β None[source]#
Prune buffer if it exceeds max token limit
save_context(inputs: Dict[str, Any], outputs: Dict[str, str]) β None[source]#
Save context from this conversati... | https://langchain.readthedocs.io/en/latest/reference/modules/memory.html |
64456b903bc9-5 | set(key: str, value: Optional[str]) β None[source]#
Set entity value in store.
class langchain.memory.MomentoChatMessageHistory(session_id: str, cache_client: momento.CacheClient, cache_name: str, *, key_prefix: str = 'message_store:', ttl: Optional[timedelta] = None, ensure_cache_exists: bool = True)[source]#
Chat mes... | https://langchain.readthedocs.io/en/latest/reference/modules/memory.html |
64456b903bc9-6 | exists(key: str) β bool[source]#
Check if entity exists in store.
get(key: str, default: Optional[str] = None) β Optional[str][source]#
Get entity value from store.
set(key: str, value: Optional[str]) β None[source]#
Set entity value in store.
property full_key_prefix: str#
pydantic model langchain.memory.SQLiteEntityS... | https://langchain.readthedocs.io/en/latest/reference/modules/memory.html |
d82e18b5bc14-0 | .rst
.pdf
Agent Toolkits
Agent Toolkits#
Agent toolkits.
pydantic model langchain.agents.agent_toolkits.AzureCognitiveServicesToolkit[source]#
Toolkit for Azure Cognitive Services.
get_tools() β List[langchain.tools.base.BaseTool][source]#
Get the tools in the toolkit.
pydantic model langchain.agents.agent_toolkits.Fil... | https://langchain.readthedocs.io/en/latest/reference/modules/agent_toolkits.html |
d82e18b5bc14-1 | Toolkit for web browser tools.
field async_browser: Optional['AsyncBrowser'] = None#
field sync_browser: Optional['SyncBrowser'] = None#
classmethod from_browser(sync_browser: Optional[SyncBrowser] = None, async_browser: Optional[AsyncBrowser] = None) β PlayWrightBrowserToolkit[source]#
Instantiate the toolkit.
get_too... | https://langchain.readthedocs.io/en/latest/reference/modules/agent_toolkits.html |
d82e18b5bc14-2 | Create csv agent by loading to a dataframe and using pandas agent.
langchain.agents.agent_toolkits.create_json_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.json.toolkit.JsonToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix:... | https://langchain.readthedocs.io/en/latest/reference/modules/agent_toolkits.html |
d82e18b5bc14-3 | Construct a json agent from an LLM and tools.
langchain.agents.agent_toolkits.create_openapi_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = "You ... | https://langchain.readthedocs.io/en/latest/reference/modules/agent_toolkits.html |
d82e18b5bc14-4 | Construct a pandas agent from an LLM and dataframe.
langchain.agents.agent_toolkits.create_pbi_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: ... | https://langchain.readthedocs.io/en/latest/reference/modules/agent_toolkits.html |
d82e18b5bc14-5 | Construct a pbi agent from an LLM and tools.
langchain.agents.agent_toolkits.create_pbi_chat_agent(llm: langchain.chat_models.base.BaseChatModel, toolkit: Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit], powerbi: Optional[langchain.utilities.powerbi.PowerBIDataset] = None, callback_manager: Opt... | https://langchain.readthedocs.io/en/latest/reference/modules/agent_toolkits.html |
d82e18b5bc14-6 | Construct a spark agent from an LLM and dataframe.
langchain.agents.agent_toolkits.create_spark_sql_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.spark_sql.toolkit.SparkSQLToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: s... | https://langchain.readthedocs.io/en/latest/reference/modules/agent_toolkits.html |
d82e18b5bc14-7 | Construct a sql agent from an LLM and tools.
langchain.agents.agent_toolkits.create_sql_agent(llm: langchain.base_language.BaseLanguageModel, toolkit: langchain.agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit, callback_manager: Optional[langchain.callbacks.base.BaseCallbackManager] = None, prefix: str = 'You are a... | https://langchain.readthedocs.io/en/latest/reference/modules/agent_toolkits.html |
e3a914c29c6c-0 | .rst
.pdf
Document Transformers
Document Transformers#
Transform documents
pydantic model langchain.document_transformers.EmbeddingsRedundantFilter[source]#
Filter that drops redundant documents by comparing their embeddings.
field embeddings: langchain.embeddings.base.Embeddings [Required]#
Embeddings to use for embed... | https://langchain.readthedocs.io/en/latest/reference/modules/document_transformers.html |
35c54f5cf747-0 | .rst
.pdf
Tools
Tools#
Core toolkit implementations.
pydantic model langchain.tools.AIPluginTool[source]#
field api_spec: str [Required]#
field args_schema: Type[AIPluginToolSchema] = <class 'langchain.tools.plugin.AIPluginToolSchema'>#
Pydantic model class to validate and parse the toolβs input arguments.
field plugin... | https://langchain.readthedocs.io/en/latest/reference/modules/tools.html |
35c54f5cf747-1 | Whether to return the toolβs output directly. Setting this to True means
that after the tool is called, the AgentExecutor will stop looping.
field verbose: bool = False#
Whether to log the toolβs progress.
async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', col... | https://langchain.readthedocs.io/en/latest/reference/modules/tools.html |
35c54f5cf747-2 | pydantic model langchain.tools.DuckDuckGoSearchResults[source]#
Tool that queries the Duck Duck Go Search API and get back json.
field api_wrapper: langchain.utilities.duckduckgo_search.DuckDuckGoSearchAPIWrapper [Optional]#
field num_results: int = 4#
pydantic model langchain.tools.DuckDuckGoSearchRun[source]#
Tool th... | https://langchain.readthedocs.io/en/latest/reference/modules/tools.html |
35c54f5cf747-3 | pydantic model langchain.tools.GmailGetThread[source]#
field args_schema: Type[langchain.tools.gmail.get_thread.GetThreadSchema] = <class 'langchain.tools.gmail.get_thread.GetThreadSchema'>#
Pydantic model class to validate and parse the toolβs input arguments.
field description: str = 'Use this tool to search for emai... | https://langchain.readthedocs.io/en/latest/reference/modules/tools.html |
35c54f5cf747-4 | pydantic model langchain.tools.ListPowerBITool[source]#
Tool for getting tables names.
field powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]#
pydantic model langchain.tools.MetaphorSearchResults[source]#
Tool that has capability to query the Metaphor Search API and get back json.
field api_wrapper: langc... | https://langchain.readthedocs.io/en/latest/reference/modules/tools.html |
35c54f5cf747-5 | pydantic model langchain.tools.OpenWeatherMapQueryRun[source]#
Tool that adds the capability to query using the OpenWeatherMap API.
field api_wrapper: langchain.utilities.openweathermap.OpenWeatherMapAPIWrapper [Optional]#
pydantic model langchain.tools.PubmedQueryRun[source]#
Tool that adds the capability to search us... | https://langchain.readthedocs.io/en/latest/reference/modules/tools.html |
35c54f5cf747-6 | field template: Optional[str] = '\nAnswer the question below with a DAX query that can be sent to Power BI. DAX queries have a simple syntax comprised of just one required keyword, EVALUATE, and several optional keywords: ORDER BY, START AT, DEFINE, MEASURE, VAR, TABLE, and COLUMN. Each keyword defines a statement used... | https://langchain.readthedocs.io/en/latest/reference/modules/tools.html |
35c54f5cf747-7 | AVERAGEX(<table>,<expression>) - these are all variantions of average functions.\nMAX(<column>), MAXA(<column>), MAXX(<table>,<expression>) - these are all variantions of max functions.\nMIN(<column>), MINA(<column>), MINX(<table>,<expression>) - these are all variantions of min functions.\nPRODUCT(<column>), PRODUCTX(... | https://langchain.readthedocs.io/en/latest/reference/modules/tools.html |
35c54f5cf747-8 | pydantic model langchain.tools.ReadFileTool[source]#
field args_schema: Type[pydantic.main.BaseModel] = <class 'langchain.tools.file_management.read.ReadFileInput'>#
Pydantic model class to validate and parse the toolβs input arguments.
field description: str = 'Read file from disk'#
Used to tell the model how/when/why... | https://langchain.readthedocs.io/en/latest/reference/modules/tools.html |
35c54f5cf747-9 | pydantic model langchain.tools.VectorStoreQATool[source]#
Tool for the VectorDBQA chain. To be initialized with name and chain.
static get_description(name: str, description: str) β str[source]#
pydantic model langchain.tools.VectorStoreQAWithSourcesTool[source]#
Tool for the VectorDBQAWithSources chain.
static get_des... | https://langchain.readthedocs.io/en/latest/reference/modules/tools.html |
35c54f5cf747-10 | field params: Optional[dict] = None#
field params_schema: Dict[str, str] [Optional]#
field zapier_description: str [Required]#
langchain.tools.tool(*args: Union[str, Callable], return_direct: bool = False, args_schema: Optional[Type[pydantic.main.BaseModel]] = None, infer_schema: bool = True) β Callable[source]#
Make t... | https://langchain.readthedocs.io/en/latest/reference/modules/tools.html |
02eaaead57f5-0 | .rst
.pdf
Models
Contents
Model Types
Models#
Note
Conceptual Guide
This section of the documentation deals with different types of models that are used in LangChain.
On this page we will go over the model types at a high level,
but we have individual pages for each model type.
The pages contain more detailed βhow-to... | https://langchain.readthedocs.io/en/latest/modules/models.html |
aeea8047b05a-0 | .rst
.pdf
Prompts
Prompts#
Note
Conceptual Guide
The new way of programming models is through prompts.
A prompt refers to the input to the model.
This input is often constructed from multiple components.
A PromptTemplate is responsible for the construction of this input.
LangChain provides several classes and functions... | https://langchain.readthedocs.io/en/latest/modules/prompts.html |
7b04887d7def-0 | .rst
.pdf
Indexes
Contents
Index Types
Indexes#
Note
Conceptual Guide
Indexes refer to ways to structure documents so that LLMs can best interact with them.
The most common way that indexes are used in chains is in a βretrievalβ step.
This step refers to taking a userβs query and returning the most relevant documents... | https://langchain.readthedocs.io/en/latest/modules/indexes.html |
4b37dba65969-0 | .rst
.pdf
Chains
Chains#
Note
Conceptual Guide
Using an LLM in isolation is fine for some simple applications,
but more complex applications require chaining LLMs - either with each other or with other experts.
LangChain provides a standard interface for Chains, as well as several common implementations of chains.
Gett... | https://langchain.readthedocs.io/en/latest/modules/chains.html |
38481b646b42-0 | .rst
.pdf
Agents
Contents
Action Agents
Plan-and-Execute Agents
Agents#
Note
Conceptual Guide
Some applications require not just a predetermined chain of calls to LLMs/other tools,
but potentially an unknown chain that depends on the userβs input.
In these types of chains, there is an agent which has access to a suit... | https://langchain.readthedocs.io/en/latest/modules/agents.html |
4b048f9cead6-0 | .rst
.pdf
Memory
Memory#
Note
Conceptual Guide
By default, Chains and Agents are stateless,
meaning that they treat each incoming query independently (as are the underlying LLMs and chat models).
In some applications (chatbots being a GREAT example) it is highly important
to remember previous interactions, both at a sh... | https://langchain.readthedocs.io/en/latest/modules/memory.html |
f22f920cac97-0 | .rst
.pdf
LLMs
LLMs#
Note
Conceptual Guide
Large Language Models (LLMs) are a core component of LangChain.
LangChain is not a provider of LLMs, but rather provides a standard interface through which
you can interact with a variety of LLMs.
The following sections of documentation are provided:
Getting Started: An overvi... | https://langchain.readthedocs.io/en/latest/modules/models/llms.html |
394e80e1d743-0 | .rst
.pdf
Text Embedding Models
Text Embedding Models#
Note
Conceptual Guide
This documentation goes over how to use the Embedding class in LangChain.
The Embedding class is a class designed for interfacing with embeddings. There are lots of Embedding providers (OpenAI, Cohere, Hugging Face, etc) - this class is design... | https://langchain.readthedocs.io/en/latest/modules/models/text_embedding.html |
a1ec9c95141e-0 | .ipynb
.pdf
Getting Started
Contents
Language Models
text -> text interface
messages -> message interface
Getting Started#
One of the core value props of LangChain is that it provides a standard interface to models. This allows you to swap easily between models. At a high level, there are two main types of models:
La... | https://langchain.readthedocs.io/en/latest/modules/models/getting_started.html |
9d86444f89bc-0 | .rst
.pdf
Chat Models
Chat Models#
Note
Conceptual Guide
Chat models are a variation on language models.
While chat models use language models under the hood, the interface they expose is a bit different.
Rather than expose a βtext in, text outβ API, they expose an interface where βchat messagesβ are the inputs and out... | https://langchain.readthedocs.io/en/latest/modules/models/chat.html |
f97316815651-0 | .ipynb
.pdf
Getting Started
Contents
PromptTemplates
LLMChain
Streaming
Getting Started#
This notebook covers how to get started with chat models. The interface is based around messages rather than raw text.
from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate, LLMChain
from langchain.pro... | https://langchain.readthedocs.io/en/latest/modules/models/chat/getting_started.html |
f97316815651-1 | "J'adore la programmation."
Streaming#
Streaming is supported for ChatOpenAI through callback handling.
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
chat = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)
resp = chat([HumanMessage(content="Write ... | https://langchain.readthedocs.io/en/latest/modules/models/chat/getting_started.html |
030cc8cbd4d1-0 | .rst
.pdf
How-To Guides
How-To Guides#
The examples here all address certain βhow-toβ guides for working with chat models.
How to use few shot examples
How to stream responses
previous
Getting Started
next
How to use few shot examples
By Harrison Chase
Β© Copyright 2023, Harrison Chase.
Last updated ... | https://langchain.readthedocs.io/en/latest/modules/models/chat/how_to_guides.html |
6bc58d8ac987-0 | .rst
.pdf
Integrations
Integrations#
The examples here all highlight how to integrate with different chat models.
Anthropic
Azure
Google Vertex AI PaLM
OpenAI
PromptLayer ChatOpenAI
previous
How to stream responses
next
Anthropic
By Harrison Chase
Β© Copyright 2023, Harrison Chase.
Last updated on Ju... | https://langchain.readthedocs.io/en/latest/modules/models/chat/integrations.html |
23e07c25024d-0 | .ipynb
.pdf
PromptLayer ChatOpenAI
Contents
Install PromptLayer
Imports
Set the Environment API Key
Use the PromptLayerOpenAI LLM like normal
Using PromptLayer Track
PromptLayer ChatOpenAI#
PromptLayer
is a devtool that allows you to track, manage, and share your GPT prompt engineering.
It acts as a middleware betwee... | https://langchain.readthedocs.io/en/latest/modules/models/chat/integrations/promptlayer_chatopenai.html |
9b25e26baf98-0 | .ipynb
.pdf
Google Vertex AI PaLM
Google Vertex AI PaLM#
Vertex AI is a machine learning (ML)
platform that lets you train and deploy ML models and AI applications.
Vertex AI combines data engineering, data science, and ML engineering workflows, enabling your teams to
collaborate using a common toolset.
Note: This is s... | https://langchain.readthedocs.io/en/latest/modules/models/chat/integrations/google_vertex_ai_palm.html |
68bc3168b86a-0 | .ipynb
.pdf
Anthropic
Contents
ChatAnthropic also supports async and streaming functionality:
Anthropic#
Anthropic is an American artificial intelligence (AI) startup and
public-benefit corporation, founded by former members of OpenAI. Anthropic specializes in developing general AI
systems and language models, with a... | https://langchain.readthedocs.io/en/latest/modules/models/chat/integrations/anthropic.html |
9d0d21a46ae0-0 | .ipynb
.pdf
OpenAI
OpenAI#
This notebook covers how to get started with OpenAI chat models.
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema impo... | https://langchain.readthedocs.io/en/latest/modules/models/chat/integrations/openai.html |
d57d7dc8daab-0 | .ipynb
.pdf
Azure
Azure#
This notebook goes over how to connect to an Azure hosted OpenAI endpoint
from langchain.chat_models import AzureChatOpenAI
from langchain.schema import HumanMessage
BASE_URL = "https://${TODO}.openai.azure.com"
API_KEY = "..."
DEPLOYMENT_NAME = "chat"
model = AzureChatOpenAI(
openai_api_ba... | https://langchain.readthedocs.io/en/latest/modules/models/chat/integrations/azure_chat_openai.html |
a36603026f21-0 | .ipynb
.pdf
How to stream responses
How to stream responses#
This notebook goes over how to use streaming with a chat model.
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
HumanMessage,
)
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
chat = ChatOpenAI(s... | https://langchain.readthedocs.io/en/latest/modules/models/chat/examples/streaming.html |
7def20fc6c66-0 | .ipynb
.pdf
How to use few shot examples
Contents
Alternating Human/AI messages
System Messages
How to use few shot examples#
This notebook covers how to use few shot examples in chat models.
There does not appear to be solid consensus on how best to do few shot prompting. As a result, we are not solidifying any abst... | https://langchain.readthedocs.io/en/latest/modules/models/chat/examples/few_shot_examples.html |
6fb7e9b90c68-0 | .ipynb
.pdf
Getting Started
Getting Started#
This notebook goes over how to use the LLM class in LangChain.
The LLM class is a class designed for interfacing with LLMs. There are lots of LLM providers (OpenAI, Cohere, Hugging Face, etc) - this class is designed to provide a standard interface for all of them. In this p... | https://langchain.readthedocs.io/en/latest/modules/models/llms/getting_started.html |
4b8f8b8e2431-0 | .rst
.pdf
Generic Functionality
Generic Functionality#
The examples here all address certain βhow-toβ guides for working with LLMs.
How to use the async API for LLMs
How to write a custom LLM wrapper
How (and why) to use the fake LLM
How (and why) to use the human input LLM
How to cache LLM calls
How to serialize LLM c... | https://langchain.readthedocs.io/en/latest/modules/models/llms/how_to_guides.html |
edf776916a32-0 | .rst
.pdf
Integrations
Integrations#
The examples here are all βhow-toβ guides for how to integrate with various LLM providers.
AI21
Aleph Alpha
Anyscale
Aviary
Azure OpenAI
Banana
Beam
Bedrock
CerebriumAI
Cohere
C Transformers
Databricks
DeepInfra
ForefrontAI
Google Cloud Platform Vertex AI PaLM
GooseAI
GPT4All
Huggin... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations.html |
cef8273f7d60-0 | .ipynb
.pdf
ForefrontAI
Contents
Imports
Set the Environment API Key
Create the ForefrontAI instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
ForefrontAI#
The Forefront platform gives you the ability to fine-tune and use open source large language models.
This notebook goes over how to use Lang... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/forefrontai_example.html |
3789748e1823-0 | .ipynb
.pdf
Azure OpenAI
Contents
API configuration
Deployments
Azure OpenAI#
This notebook goes over how to use Langchain with Azure OpenAI.
The Azure OpenAI API is compatible with OpenAIβs API. The openai Python package makes it easy to use both OpenAI and Azure OpenAI. You can call Azure OpenAI the same way you ... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/azure_openai_example.html |
55825e7c57cb-0 | .ipynb
.pdf
Anyscale
Anyscale#
Anyscale is a fully-managed Ray platform, on which you can build, deploy, and manage scalable AI and Python applications
This example goes over how to use LangChain to interact with Anyscale service
import os
os.environ["ANYSCALE_SERVICE_URL"] = ANYSCALE_SERVICE_URL
os.environ["ANYSCALE_S... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/anyscale.html |
6824c50dd26d-0 | .ipynb
.pdf
Bedrock
Contents
Using in a conversation chain
Bedrock#
Amazon Bedrock is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case
%pip install boto3
from langchain.ll... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/bedrock.html |
0c8cdeb5635e-0 | .ipynb
.pdf
Runhouse
Runhouse#
The Runhouse allows remote compute and data across environments and users. See the Runhouse docs.
This example goes over how to use LangChain and Runhouse to interact with models hosted on your own GPU, or on-demand GPUs on AWS, GCP, AWS, or Lambda.
Note: Code uses SelfHosted name instead... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/runhouse.html |
0c8cdeb5635e-1 | llm = SelfHostedPipeline.from_pipeline(pipeline="models/pipeline.pkl", hardware=gpu)
previous
Replicate
next
SageMaker Endpoint
By Harrison Chase
Β© Copyright 2023, Harrison Chase.
Last updated on Jun 08, 2023. | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/runhouse.html |
db70a1dc79f4-0 | .ipynb
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AI21
AI21#
AI21 Studio provides API access to Jurassic-2 large language models.
This example goes over how to use LangChain to interact with AI21 models.
# install the package:
!pip install ai21
# get AI21_API_KEY. Use https://studio.ai21.com/account/account
from getpass import getpass
AI21_API_KEY = getpa... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/ai21.html |
03570e9198c0-0 | .ipynb
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OpenLM
Contents
Setup
Using LangChain with OpenLM
OpenLM#
OpenLM is a zero-dependency OpenAI-compatible LLM provider that can call different inference endpoints directly via HTTP.
It implements the OpenAI Completion class so that it can be used as a drop-in replacement for the OpenAI API. This changeset u... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/openlm.html |
05d1989954ed-0 | .ipynb
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Manifest
Contents
Compare HF Models
Manifest#
This notebook goes over how to use Manifest and LangChain.
For more detailed information on manifest, and how to use it with local hugginface models like in this example, see https://github.com/HazyResearch/manifest
Another example of using Manifest with Langc... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/manifest.html |
be1cc2036820-0 | .ipynb
.pdf
GooseAI
Contents
Install openai
Imports
Set the Environment API Key
Create the GooseAI instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
GooseAI#
GooseAI is a fully managed NLP-as-a-Service, delivered via API. GooseAI provides access to these models.
This notebook goes over how to u... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/gooseai_example.html |
01573e1dcd4a-0 | .ipynb
.pdf
NLP Cloud
NLP Cloud#
The NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, grammar and spelling correction, keywords and keyphrases extraction, chatbot, product description and ad generation, intent classification, text g... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/nlpcloud.html |
dbba96b7ddd9-0 | .ipynb
.pdf
Beam
Beam#
Beam makes it easy to run code on GPUs, deploy scalable web APIs,
schedule cron jobs, and run massively parallel workloads β without managing any infrastructure.
Calls the Beam API wrapper to deploy and make subsequent calls to an instance of the gpt2 LLM in a cloud deployment. Requires installat... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/beam.html |
b8a5e880a727-0 | .ipynb
.pdf
Hugging Face Hub
Contents
Examples
StableLM, by Stability AI
Dolly, by Databricks
Camel, by Writer
Hugging Face Hub#
The Hugging Face Hub is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily col... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/huggingface_hub.html |
bb805bf9082e-0 | .ipynb
.pdf
SageMaker Endpoint
Contents
Set up
Example
SageMaker Endpoint#
Amazon SageMaker is a system that can build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.
This notebooks goes over how to use an LLM hosted on a SageMaker endpoint.
!p... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/sagemaker.html |
6c1927bdabca-0 | .ipynb
.pdf
Google Cloud Platform Vertex AI PaLM
Google Cloud Platform Vertex AI PaLM#
Note: This is seperate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there.
PaLM API on Vertex AI is a Preview offering, su... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/google_vertex_ai_palm.html |
c1d86a971c0c-0 | .ipynb
.pdf
GPT4All
Contents
Specify Model
GPT4All#
GitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.
This example goes over how to use LangChain to interact with GPT4All models.
%pip install gpt4all > /dev/null
... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/gpt4all.html |
5f31a564acc1-0 | .ipynb
.pdf
Huggingface TextGen Inference
Huggingface TextGen Inference#
Text Generation Inference is a Rust, Python and gRPC server for text generation inference. Used in production at HuggingFace to power LLMs api-inference widgets.
This notebooks goes over how to use a self hosted LLM using Text Generation Inference... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/huggingface_textgen_inference.html |
8b5dfee89727-0 | .ipynb
.pdf
Llama-cpp
Contents
Installation
CPU only installation
Installation with OpenBLAS / cuBLAS / CLBlast
Usage
CPU
GPU
Llama-cpp#
llama-cpp is a Python binding for llama.cpp.
It supports several LLMs.
This notebook goes over how to run llama-cpp within LangChain.
Installation#
There is a banch of options how t... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/llamacpp.html |
8b5dfee89727-1 | n_batch - how many tokens are processed in parallel.
Setting these parameters correctly will dramatically improve the evaluation speed (see wrapper code for more details).
n_gpu_layers = 40 # Change this value based on your model and your GPU VRAM pool.
n_batch = 512 # Should be between 1 and n_ctx, consider the amount... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/llamacpp.html |
526d94c3c4a5-0 | .ipynb
.pdf
CerebriumAI
Contents
Install cerebrium
Imports
Set the Environment API Key
Create the CerebriumAI instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
CerebriumAI#
Cerebrium is an AWS Sagemaker alternative. It also provides API access to several LLM models.
This notebook goes over how ... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/cerebriumai_example.html |
ba134dac5dc0-0 | .ipynb
.pdf
DeepInfra
Contents
Imports
Set the Environment API Key
Create the DeepInfra instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
DeepInfra#
DeepInfra provides several LLMs.
This notebook goes over how to use Langchain with DeepInfra.
Imports#
import os
from langchain.llms import DeepIn... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/deepinfra_example.html |
142d2e6d9403-0 | .ipynb
.pdf
OpenAI
OpenAI#
OpenAI offers a spectrum of models with different levels of power suitable for different tasks.
This example goes over how to use LangChain to interact with OpenAI models
# get a token: https://platform.openai.com/account/api-keys
from getpass import getpass
OPENAI_API_KEY = getpass()
Β·Β·Β·Β·Β·Β·... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/openai.html |
a0c475e9c443-0 | .ipynb
.pdf
Petals
Contents
Install petals
Imports
Set the Environment API Key
Create the Petals instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
Petals#
Petals runs 100B+ language models at home, BitTorrent-style.
This notebook goes over how to use Langchain with Petals.
Install petals#
The p... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/petals_example.html |
573c61cdf0b6-0 | .ipynb
.pdf
Aviary
Aviary#
Aviary is an open source tooklit for evaluating and deploying production open source LLMs.
This example goes over how to use LangChain to interact with Aviary. You can try Aviary out https://aviary.anyscale.com.
You can find out more about Aviary at https://github.com/ray-project/aviary.
One ... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/aviary.html |
c18caeb8fbe7-0 | .ipynb
.pdf
ReLLM
Contents
Hugging Face Baseline
RELLM LLM Wrapper
ReLLM#
ReLLM is a library that wraps local Hugging Face pipeline models for structured decoding.
It works by generating tokens one at a time. At each step, it masks tokens that donβt conform to the provided partial regular expression.
Warning - this m... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/rellm_experimental.html |
27647d5369bb-0 | .ipynb
.pdf
PipelineAI
Contents
Install pipeline-ai
Imports
Set the Environment API Key
Create the PipelineAI instance
Create a Prompt Template
Initiate the LLMChain
Run the LLMChain
PipelineAI#
PipelineAI allows you to run your ML models at scale in the cloud. It also provides API access to several LLM models.
This ... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/pipelineai_example.html |
a1fc71f33da1-0 | .ipynb
.pdf
PromptLayer OpenAI
Contents
Install PromptLayer
Imports
Set the Environment API Key
Use the PromptLayerOpenAI LLM like normal
Using PromptLayer Track
PromptLayer OpenAI#
PromptLayer is the first platform that allows you to track, manage, and share your GPT prompt engineering. PromptLayer acts a middleware... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/promptlayer_openai.html |
4332467bf60c-0 | .ipynb
.pdf
Databricks
Contents
Wrapping a serving endpoint
Wrapping a cluster driver proxy app
Databricks#
The Databricks Lakehouse Platform unifies data, analytics, and AI on one platform.
This example notebook shows how to wrap Databricks endpoints as LLMs in LangChain.
It supports two endpoint types:
Serving endp... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/databricks.html |
4332467bf60c-1 | model = "databricks/dolly-v2-3b"
tokenizer = AutoTokenizer.from_pretrained(model, padding_side="left")
dolly = pipeline(model=model, tokenizer=tokenizer, trust_remote_code=True, device_map="auto")
device = dolly.device
class CheckStop(StoppingCriteria):
def __init__(self, stop=None):
super().__init__()
... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/databricks.html |
a89dda4b28f9-0 | .ipynb
.pdf
Prediction Guard
Contents
Prediction Guard
Control the output structure/ type of LLMs
Chaining
Prediction Guard#
Prediction Guard gives a quick and easy access to state-of-the-art open and closed access LLMs, without needing to spend days and weeks figuring out all of the implementation details, managing... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/predictionguard.html |
202ea09dce66-0 | .ipynb
.pdf
Jsonformer
Contents
HuggingFace Baseline
JSONFormer LLM Wrapper
Jsonformer#
Jsonformer is a library that wraps local HuggingFace pipeline models for structured decoding of a subset of the JSON Schema.
It works by filling in the structure tokens and then sampling the content tokens from the model.
Warning ... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/jsonformer_experimental.html |
202ea09dce66-1 | json_former = JsonFormer(json_schema=decoder_schema, pipeline=hf_model)
results = json_former.predict(prompt, stop=["Observation:", "Human:"])
print(results)
{"action": "ask_star_coder", "action_input": {"query": "What's the difference between an iterator and an iter", "temperature": 0.0, "max_new_tokens": 50.0}}
Voila... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/jsonformer_experimental.html |
8e17057eb757-0 | .ipynb
.pdf
StochasticAI
StochasticAI#
Stochastic Acceleration Platform aims to simplify the life cycle of a Deep Learning model. From uploading and versioning the model, through training, compression and acceleration to putting it into production.
This example goes over how to use LangChain to interact with Stochastic... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/stochasticai.html |
5090d5c6c019-0 | .ipynb
.pdf
C Transformers
C Transformers#
The C Transformers library provides Python bindings for GGML models.
This example goes over how to use LangChain to interact with C Transformers models.
Install
%pip install ctransformers
Load Model
from langchain.llms import CTransformers
llm = CTransformers(model='marella/gp... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/ctransformers.html |
3be102031509-0 | .ipynb
.pdf
Banana
Banana#
Banana is focused on building the machine learning infrastructure.
This example goes over how to use LangChain to interact with Banana models
# Install the package https://docs.banana.dev/banana-docs/core-concepts/sdks/python
!pip install banana-dev
# get new tokens: https://app.banana.dev/
... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/banana.html |
b4380670ce9e-0 | .ipynb
.pdf
Writer
Writer#
Writer is a platform to generate different language content.
This example goes over how to use LangChain to interact with Writer models.
You have to get the WRITER_API_KEY here.
from getpass import getpass
WRITER_API_KEY = getpass()
import os
os.environ["WRITER_API_KEY"] = WRITER_API_KEY
from... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/writer.html |
f0f0e4d0eefe-0 | .ipynb
.pdf
Hugging Face Pipeline
Contents
Load the model
Integrate the model in an LLMChain
Hugging Face Pipeline#
Hugging Face models can be run locally through the HuggingFacePipeline class.
The Hugging Face Model Hub hosts over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly av... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/huggingface_pipelines.html |
99359df7ce00-0 | .ipynb
.pdf
Aleph Alpha
Aleph Alpha#
The Luminous series is a family of large language models.
This example goes over how to use LangChain to interact with Aleph Alpha models
# Install the package
!pip install aleph-alpha-client
# create a new token: https://docs.aleph-alpha.com/docs/account/#create-a-new-token
from ge... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/aleph_alpha.html |
3a596b67fd81-0 | .ipynb
.pdf
MosaicML
MosaicML#
MosaicML offers a managed inference service. You can either use a variety of open source models, or deploy your own.
This example goes over how to use LangChain to interact with MosaicML Inference for text completion.
# sign up for an account: https://forms.mosaicml.com/demo?utm_source=la... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/mosaicml.html |
3a54aa28c60c-0 | .ipynb
.pdf
Replicate
Contents
Setup
Calling a model
Chaining Calls
Replicate#
Replicate runs machine learning models in the cloud. We have a library of open-source models that you can run with a few lines of code. If youβre building your own machine learning models, Replicate makes it easy to deploy them at scale.
T... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/replicate.html |
3a54aa28c60c-1 | Second prompt to get the logo for company description
second_prompt = PromptTemplate(
input_variables=["company_name"],
template="Write a description of a logo for this company: {company_name}",
)
chain_two = LLMChain(llm=dolly_llm, prompt=second_prompt)
Third prompt, letβs create the image based on the descrip... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/replicate.html |
fcabce51c202-0 | .ipynb
.pdf
Cohere
Cohere#
Cohere is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.
This example goes over how to use LangChain to interact with Cohere models.
# Install the package
!pip install cohere
# get a new token: https://dashboard.cohe... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/cohere.html |
bf714b54d5fb-0 | .ipynb
.pdf
Modal
Modal#
The Modal Python Library provides convenient, on-demand access to serverless cloud compute from Python scripts on your local computer.
The Modal itself does not provide any LLMs but only the infrastructure.
This example goes over how to use LangChain to interact with Modal.
Here is another exam... | https://langchain.readthedocs.io/en/latest/modules/models/llms/integrations/modal.html |
e98a40784bdd-0 | .ipynb
.pdf
How (and why) to use the fake LLM
How (and why) to use the fake LLM#
We expose a fake LLM class that can be used for testing. This allows you to mock out calls to the LLM and simulate what would happen if the LLM responded in a certain way.
In this notebook we go over how to use this.
We start this with usi... | https://langchain.readthedocs.io/en/latest/modules/models/llms/examples/fake_llm.html |
ccc5f662796f-0 | .ipynb
.pdf
How to write a custom LLM wrapper
How to write a custom LLM wrapper#
This notebook goes over how to create a custom LLM wrapper, in case you want to use your own LLM or a different wrapper than one that is supported in LangChain.
There is only one required thing that a custom LLM needs to implement:
A _call... | https://langchain.readthedocs.io/en/latest/modules/models/llms/examples/custom_llm.html |
54e11334c9f2-0 | .ipynb
.pdf
How to cache LLM calls
Contents
In Memory Cache
SQLite Cache
Redis Cache
Standard Cache
Semantic Cache
GPTCache
Momento Cache
SQLAlchemy Cache
Custom SQLAlchemy Schemas
Optional Caching
Optional Caching in Chains
How to cache LLM calls#
This notebook covers how to cache results of individual LLM calls.
im... | https://langchain.readthedocs.io/en/latest/modules/models/llms/examples/llm_caching.html |
54e11334c9f2-1 | Wall time: 262 ms
"\n\nWhy don't scientists trust atoms?\nBecause they make up everything."
GPTCache#
We can use GPTCache for exact match caching OR to cache results based on semantic similarity
Letβs first start with an example of exact match
from gptcache import Cache
from gptcache.manager.factory import manager_fact... | https://langchain.readthedocs.io/en/latest/modules/models/llms/examples/llm_caching.html |
54e11334c9f2-2 | # When run in the same region as the cache, latencies are single digit ms
llm("Tell me a joke")
CPU times: user 3.16 ms, sys: 2.98 ms, total: 6.14 ms
Wall time: 57.9 ms
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
SQLAlchemy Cache#
# You can use SQLAlchemyCache to cache with any SQL database s... | https://langchain.readthedocs.io/en/latest/modules/models/llms/examples/llm_caching.html |
54e11334c9f2-3 | Wall time: 5.09 s
'\n\nPresident Biden is discussing the American Rescue Plan and the Bipartisan Infrastructure Law, which will create jobs and help Americans. He also talks about his vision for America, which includes investing in education and infrastructure. In response to Russian aggression in Ukraine, the United S... | https://langchain.readthedocs.io/en/latest/modules/models/llms/examples/llm_caching.html |
05a8334ed79d-0 | .ipynb
.pdf
How to track token usage
How to track token usage#
This notebook goes over how to track your token usage for specific calls. It is currently only implemented for the OpenAI API.
Letβs first look at an extremely simple example of tracking token usage for a single LLM call.
from langchain.llms import OpenAI
f... | https://langchain.readthedocs.io/en/latest/modules/models/llms/examples/token_usage_tracking.html |
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