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🦜🔗 LangChain 0.0.188

Getting Started

  • Quickstart Guide
  • Concepts
  • Tutorials

Modules

  • Models
    • Getting Started
    • LLMs
      • Getting Started
      • Generic Functionality
        • 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 classes
        • How to stream LLM and Chat Model responses
        • How to track token usage
      • Integrations
        • AI21
        • Aleph Alpha
        • Anyscale
        • Azure OpenAI
        • Banana
        • Beam integration for langchain
        • Amazon Bedrock
        • CerebriumAI
        • Cohere
        • C Transformers
        • Databricks
        • DeepInfra
        • ForefrontAI
        • Google Cloud Platform Vertex AI PaLM
        • GooseAI
        • GPT4All
        • Hugging Face Hub
        • Hugging Face Local Pipelines
        • Huggingface TextGen Inference
        • Structured Decoding with JSONFormer
        • Llama-cpp
        • Manifest
        • Modal
        • MosaicML
        • NLP Cloud
        • OpenAI
        • OpenLM
        • Petals
        • PipelineAI
        • Basic LLM usage
        • PromptLayer OpenAI
        • Structured Decoding with RELLM
        • Replicate
        • Runhouse
        • SageMakerEndpoint
        • StochasticAI
        • Writer
      • Reference
    • Chat Models
      • Getting Started
      • How-To Guides
        • How to use few shot examples
        • How to stream responses
      • Integrations
        • Anthropic
        • Azure
        • Google Cloud Platform Vertex AI PaLM
        • OpenAI
        • PromptLayer ChatOpenAI
    • Text Embedding Models
      • Aleph Alpha
      • AzureOpenAI
      • Bedrock Embeddings
      • Cohere
      • Elasticsearch
      • Fake Embeddings
      • Google Cloud Platform Vertex AI PaLM
      • Hugging Face Hub
      • InstructEmbeddings
      • Jina
      • Llama-cpp
      • MiniMax
      • ModelScope
      • MosaicML embeddings
      • OpenAI
      • SageMaker Endpoint Embeddings
      • Self Hosted Embeddings
      • Sentence Transformers Embeddings
      • TensorflowHub
  • Prompts
    • Getting Started
    • Prompt Templates
      • Getting Started
      • How-To Guides
        • Connecting to a Feature Store
        • How to create a custom prompt template
        • How to create a prompt template that uses few shot examples
        • How to work with partial Prompt Templates
        • How to serialize prompts
      • Reference
        • PromptTemplates
        • Example Selector
        • Output Parsers
    • Chat Prompt Template
    • Example Selectors
      • How to create a custom example selector
      • LengthBased ExampleSelector
      • Maximal Marginal Relevance ExampleSelector
      • NGram Overlap ExampleSelector
      • Similarity ExampleSelector
    • Output Parsers
      • Output Parsers
      • CommaSeparatedListOutputParser
      • Datetime
      • Enum Output Parser
      • OutputFixingParser
      • PydanticOutputParser
      • RetryOutputParser
      • Structured Output Parser
  • Memory
    • Getting Started
    • How-To Guides
      • ConversationBufferMemory
      • ConversationBufferWindowMemory
      • Entity Memory
      • Conversation Knowledge Graph Memory
      • ConversationSummaryMemory
      • ConversationSummaryBufferMemory
      • ConversationTokenBufferMemory
      • VectorStore-Backed Memory
      • How to add Memory to an LLMChain
      • How to add memory to a Multi-Input Chain
      • How to add Memory to an Agent
      • Adding Message Memory backed by a database to an Agent
      • Cassandra Chat Message History
      • How to customize conversational memory
      • How to create a custom Memory class
      • Dynamodb Chat Message History
      • Entity Memory with SQLite storage
      • Momento
      • Mongodb Chat Message History
      • Motörhead Memory
      • Motörhead Memory (Managed)
      • How to use multiple memory classes in the same chain
      • Postgres Chat Message History
      • Redis Chat Message History
      • Zep Memory
  • Indexes
    • Getting Started
    • Document Loaders
      • CoNLL-U
      • Copy Paste
      • CSV
      • Email
      • EPub
      • EverNote
      • Facebook Chat
      • File Directory
      • HTML
      • Images
      • Jupyter Notebook
      • JSON
      • Markdown
      • Microsoft PowerPoint
      • Microsoft Word
      • Open Document Format (ODT)
      • Pandas DataFrame
      • PDF
      • Sitemap
      • Subtitle
      • Telegram
      • TOML
      • Unstructured File
      • URL
      • WebBaseLoader
      • Weather
      • WhatsApp Chat
      • Arxiv
      • AZLyrics
      • BiliBili
      • College Confidential
      • Gutenberg
      • Hacker News
      • HuggingFace dataset
      • iFixit
      • IMSDb
      • MediaWikiDump
      • Wikipedia
      • YouTube transcripts
      • Airbyte JSON
      • Apify Dataset
      • AWS S3 Directory
      • AWS S3 File
      • Azure Blob Storage Container
      • Azure Blob Storage File
      • Blackboard
      • Blockchain
      • ChatGPT Data
      • Confluence
      • Diffbot
      • Docugami
      • DuckDB
      • Figma
      • GitBook
      • Git
      • Google BigQuery
      • Google Cloud Storage Directory
      • Google Cloud Storage File
      • Google Drive
      • Image captions
      • Iugu
      • Joplin
      • Microsoft OneDrive
      • Modern Treasury
      • Notion DB 2/2
      • Notion DB 1/2
      • Obsidian
      • Psychic
      • PySpark DataFrame Loader
      • ReadTheDocs Documentation
      • Reddit
      • Roam
      • Slack
      • Spreedly
      • Stripe
      • 2Markdown
      • Twitter
    • Text Splitters
      • Getting Started
      • Character
      • CodeTextSplitter
      • NLTK
      • Recursive Character
      • spaCy
      • Tiktoken
      • Hugging Face tokenizer
      • tiktoken (OpenAI) tokenizer
    • Vectorstores
      • Getting Started
      • AnalyticDB
      • Annoy
      • Atlas
      • Chroma
      • Deep Lake
      • DocArrayHnswSearch
      • DocArrayInMemorySearch
      • ElasticSearch
      • FAISS
      • LanceDB
      • MatchingEngine
      • Milvus
      • MongoDB Atlas Vector Search
      • MyScale
      • OpenSearch
      • PGVector
      • Pinecone
      • Qdrant
      • Redis
      • SKLearnVectorStore
      • Supabase (Postgres)
      • Tair
      • Typesense
      • Vectara
      • Weaviate
      • Zilliz
    • Retrievers
      • Arxiv
      • Azure Cognitive Search Retriever
      • ChatGPT Plugin
      • Self-querying with Chroma
      • Cohere Reranker
      • Contextual Compression
      • Databerry
      • ElasticSearch BM25
      • kNN
      • Metal
      • Pinecone Hybrid Search
      • Self-querying with Qdrant
      • Self-querying
      • SVM
      • TF-IDF
      • Time Weighted VectorStore
      • VectorStore
      • Vespa
      • Weaviate Hybrid Search
      • Self-querying with Weaviate
      • Wikipedia
      • Zep Memory
  • Chains
    • Getting Started
    • How-To Guides
      • Async API for Chain
      • Creating a custom Chain
      • Loading from LangChainHub
      • LLM Chain
      • Router Chains
      • Sequential Chains
      • Serialization
      • Transformation Chain
      • Analyze Document
      • Chat Over Documents with Chat History
      • Graph QA
      • Hypothetical Document Embeddings
      • Question Answering with Sources
      • Question Answering
      • Summarization
      • Retrieval Question/Answering
      • Retrieval Question Answering with Sources
      • Vector DB Text Generation
      • API Chains
      • Self-Critique Chain with Constitutional AI
      • FLARE
      • GraphCypherQAChain
      • BashChain
      • LLMCheckerChain
      • LLM Math
      • LLMRequestsChain
      • LLMSummarizationCheckerChain
      • Moderation
      • Router Chains: Selecting from multiple prompts with MultiPromptChain
      • Router Chains: Selecting from multiple prompts with MultiRetrievalQAChain
      • OpenAPI Chain
      • PAL
      • SQL Chain example
    • Reference
  • Agents
    • Getting Started
    • Tools
      • Getting Started
      • Defining Custom Tools
      • Multi-Input Tools
      • Tool Input Schema
      • Apify
      • ArXiv API Tool
      • AWS Lambda API
      • Shell Tool
      • Bing Search
      • Brave Search
      • ChatGPT Plugins
      • DuckDuckGo Search
      • File System Tools
      • Google Places
      • Google Search
      • Google Serper API
      • Gradio Tools
      • GraphQL tool
      • HuggingFace Tools
      • Human as a tool
      • IFTTT WebHooks
      • Metaphor Search
      • OpenWeatherMap API
      • Python REPL
      • Requests
      • SceneXplain
      • Search Tools
      • SearxNG Search API
      • SerpAPI
      • Twilio
      • Wikipedia
      • Wolfram Alpha
      • YouTubeSearchTool
      • Zapier Natural Language Actions API
    • Agents
      • Agent Types
      • Custom Agent
      • Custom LLM Agent
      • Custom LLM Agent (with a ChatModel)
      • Custom MRKL Agent
      • Custom MultiAction Agent
      • Custom Agent with Tool Retrieval
      • Conversation Agent (for Chat Models)
      • Conversation Agent
      • MRKL
      • MRKL Chat
      • ReAct
      • Self Ask With Search
      • Structured Tool Chat Agent
    • Toolkits
      • Azure Cognitive Services Toolkit
      • CSV Agent
      • Gmail Toolkit
      • Jira
      • JSON Agent
      • OpenAPI agents
      • Natural Language APIs
      • Pandas Dataframe Agent
      • PlayWright Browser Toolkit
      • PowerBI Dataset Agent
      • Python Agent
      • Spark Dataframe Agent
      • Spark SQL Agent
      • SQL Database Agent
      • Vectorstore Agent
    • Agent Executors
      • How to combine agents and vectorstores
      • How to use the async API for Agents
      • How to create ChatGPT Clone
      • Handle Parsing Errors
      • How to access intermediate steps
      • How to cap the max number of iterations
      • How to use a timeout for the agent
      • How to add SharedMemory to an Agent and its Tools
    • Plan and Execute
  • Callbacks

Use Cases

  • Autonomous Agents
  • Agent Simulations
  • Agents
  • Question Answering over Docs
  • Chatbots
  • Querying Tabular Data
  • Code Understanding
  • Interacting with APIs
  • Extraction
  • Summarization
  • Evaluation
    • Agent Benchmarking: Search + Calculator
    • Agent VectorDB Question Answering Benchmarking
    • Benchmarking Template
    • Data Augmented Question Answering
    • Generic Agent Evaluation
    • Using Hugging Face Datasets
    • LLM Math
    • Evaluating an OpenAPI Chain
    • Question Answering Benchmarking: Paul Graham Essay
    • Question Answering Benchmarking: State of the Union Address
    • QA Generation
    • Question Answering
    • SQL Question Answering Benchmarking: Chinook

Reference

  • Installation
  • API References
    • Models
      • LLMs
      • Chat Models
      • Embeddings
    • Prompts
      • PromptTemplates
      • Example Selector
      • Output Parsers
    • Indexes
      • Docstore
      • Text Splitter
      • Document Loaders
      • Vector Stores
      • Retrievers
      • Document Compressors
      • Document Transformers
    • Memory
    • Chains
    • Agents
      • Agents
      • Tools
      • Agent Toolkits
    • Utilities
    • Experimental Modules

Ecosystem

  • Integrations
    • Tracing Walkthrough
    • AI21 Labs
    • Aim
    • Airbyte
    • Aleph Alpha
    • AnalyticDB
    • Anyscale
    • Apify
    • Arxiv
    • AtlasDB
    • AWS S3 Directory
    • AZLyrics
    • Azure Blob Storage
    • Azure OpenAI
    • Banana
    • Beam
    • Amazon Bedrock
    • BiliBili
    • Blackboard
    • CerebriumAI
    • Chroma
    • ClearML
    • Cohere
    • College Confidential
    • Comet
    • Confluence
    • C Transformers
    • Databerry
    • Databricks
    • DeepInfra
    • Deep Lake
    • Diffbot
    • Discord
    • Docugami
    • DuckDB
    • EverNote
    • Facebook Chat
    • Figma
    • ForefrontAI
    • Git
    • GitBook
    • Google BigQuery
    • Google Cloud Storage
    • Google Drive
    • Google Search
    • Google Serper
    • GooseAI
    • GPT4All
    • Graphsignal
    • Gutenberg
    • Hacker News
    • Hazy Research
    • Helicone
    • Hugging Face
    • iFixit
    • IMSDb
    • Jina
    • LanceDB
    • Llama.cpp
    • MediaWikiDump
    • Metal
    • Microsoft OneDrive
    • Microsoft PowerPoint
    • Microsoft Word
    • Milvus
    • MLflow
    • Modal
    • Modern Treasury
    • Momento
    • MyScale
    • NLPCloud
    • Notion DB
    • Obsidian
    • OpenAI
    • OpenSearch
    • OpenWeatherMap
    • Petals
    • PGVector
    • Pinecone
    • PipelineAI
    • Prediction Guard
    • PromptLayer
    • Psychic
    • Qdrant
    • Rebuff
    • Reddit
    • Redis
    • Replicate
    • Runhouse
    • RWKV-4
    • SageMaker Endpoint
    • SearxNG Search API
    • SerpAPI
    • scikit-learn
    • StochasticAI
    • Tair
    • Unstructured
    • Vectara
    • Weights & Biases
    • Weaviate
    • WhyLabs
    • Wolfram Alpha
    • Writer
    • Yeager.ai
    • Zilliz
  • Dependents
  • Deployments

Additional Resources

  • LangChainHub
  • Gallery
  • Tracing
  • Model Comparison
  • Discord
  • YouTube
  • Production Support
  • .ipynb

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 example how to use LangChain to interact with Modal.

!pip install modal-client
# register and get a new token

!modal token new
[?25lLaunching login page in your browser window...
If this is not showing up, please copy this URL into your web browser manually:
mâ ™ Waiting for authentication in the web browser...
]8;id=417802;https://modal.com/token-flow/tf-ptEuGecm7T1T5YQe42kwM1\https://modal.com/token-flow/tf-ptEuGecm7T1T5YQe42kwM1]8;;\

â ™ Waiting for authentication in the web browser...
^C

Aborted.

Follow these instructions to deal with secrets.

from langchain.llms import Modal
from langchain import PromptTemplate, LLMChain
template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate(template=template, input_variables=["question"])
llm = Modal(endpoint_url="YOUR_ENDPOINT_URL")
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"

llm_chain.run(question)

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Manifest

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MosaicML

By Harrison Chase

© Copyright 2023, Harrison Chase.

Last updated on Jun 02, 2023.