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docs providers update (#18336)
Formatted pages into a consistent form. Added descriptions and links when needed.
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Leonid Ganeline
GitHub
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# Activeloop Deep Lake
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This page covers how to use the Deep Lake ecosystem within LangChain.
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>[Activeloop Deep Lake](https://docs.activeloop.ai/) is a data lake for Deep Learning applications, allowing you to use it
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> as a vector store.
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## Why Deep Lake?
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- More than just a (multi-modal) vector store. You can later use the dataset to fine-tune your own LLM models.
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- Not only stores embeddings, but also the original data with automatic version control.
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- Truly serverless. Doesn't require another service and can be used with major cloud providers (AWS S3, GCS, etc.)
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- Truly serverless. Doesn't require another service and can be used with major cloud providers (`AWS S3`, `GCS`, etc.)
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Activeloop Deep Lake supports SelfQuery Retrieval:
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`Activeloop Deep Lake` supports `SelfQuery Retrieval`:
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[Activeloop Deep Lake Self Query Retrieval](/docs/integrations/retrievers/self_query/activeloop_deeplake_self_query)
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## More Resources
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1. [Ultimate Guide to LangChain & Deep Lake: Build ChatGPT to Answer Questions on Your Financial Data](https://www.activeloop.ai/resources/ultimate-guide-to-lang-chain-deep-lake-build-chat-gpt-to-answer-questions-on-your-financial-data/)
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2. [Twitter the-algorithm codebase analysis with Deep Lake](https://github.com/langchain-ai/langchain/blob/master/cookbook/twitter-the-algorithm-analysis-deeplake.ipynb)
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3. Here is [whitepaper](https://www.deeplake.ai/whitepaper) and [academic paper](https://arxiv.org/pdf/2209.10785.pdf) for Deep Lake
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4. Here is a set of additional resources available for review: [Deep Lake](https://github.com/activeloopai/deeplake), [Get started](https://docs.activeloop.ai/getting-started) and [Tutorials](https://docs.activeloop.ai/hub-tutorials)
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## Installation and Setup
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- Install the Python package with `pip install deeplake`
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## Wrappers
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Install the Python package:
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### VectorStore
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```bash
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pip install deeplake
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```
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There exists a wrapper around Deep Lake, a data lake for Deep Learning applications, allowing you to use it as a vector store (for now), whether for semantic search or example selection.
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To import this vectorstore:
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## VectorStore
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```python
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from langchain_community.vectorstores import DeepLake
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```
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For a more detailed walkthrough of the Deep Lake wrapper, see [this notebook](/docs/integrations/vectorstores/activeloop_deeplake)
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See a [usage example](/docs/integrations/vectorstores/activeloop_deeplake).
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# AI21 Labs
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This page covers how to use the AI21 ecosystem within LangChain.
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It is broken into two parts: installation and setup, and then references to specific AI21 wrappers.
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>[AI21 Labs](https://www.ai21.com/about) is a company specializing in Natural
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> Language Processing (NLP), which develops AI systems
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> that can understand and generate natural language.
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This page covers how to use the `AI21` ecosystem within `LangChain`.
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## Installation and Setup
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- Get an AI21 api key and set it as an environment variable (`AI21_API_KEY`)
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- Install the Python package:
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## Wrappers
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```bash
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pip install langchain-ai21
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```
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### LLM
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## LLMs
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See a [usage example](/docs/integrations/llms/ai21).
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There exists an AI21 LLM wrapper, which you can access with
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```python
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from langchain_community.llms import AI21
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```
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## Chat models
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See a [usage example](/docs/integrations/chat/ai21).
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```python
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from langchain_ai21 import ChatAI21
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```
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## Embedding models
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See a [usage example](/docs/integrations/text_embedding/ai21).
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```python
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from langchain_ai21 import AI21Embeddings
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```
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# AnalyticDB
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>[AnalyticDB for PostgreSQL](https://www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview)
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> is a massively parallel processing (MPP) data warehousing service
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> from [Alibaba Cloud](https://www.alibabacloud.com/)
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>that is designed to analyze large volumes of data online.
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>`AnalyticDB for PostgreSQL` is developed based on the open-source `Greenplum Database`
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> project and is enhanced with in-depth extensions by `Alibaba Cloud`. AnalyticDB
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> for PostgreSQL is compatible with the ANSI SQL 2003 syntax and the PostgreSQL and
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> Oracle database ecosystems. AnalyticDB for PostgreSQL also supports row store and
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> column store. AnalyticDB for PostgreSQL processes petabytes of data offline at a
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> high performance level and supports highly concurrent.
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This page covers how to use the AnalyticDB ecosystem within LangChain.
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### VectorStore
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## Installation and Setup
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There exists a wrapper around AnalyticDB, allowing you to use it as a vectorstore,
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whether for semantic search or example selection.
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You need to install the `sqlalchemy` python package.
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```bash
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pip install sqlalchemy
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```
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## VectorStore
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See a [usage example](/docs/integrations/vectorstores/analyticdb).
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To import this vectorstore:
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```python
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from langchain_community.vectorstores import AnalyticDB
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```
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For a more detailed walkthrough of the AnalyticDB wrapper, see [this notebook](/docs/integrations/vectorstores/analyticdb)
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# Annoy
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> [Annoy](https://github.com/spotify/annoy) (`Approximate Nearest Neighbors Oh Yeah`) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.
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## Installation and Setup
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> [Annoy](https://github.com/spotify/annoy) (`Approximate Nearest Neighbors Oh Yeah`)
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> is a C++ library with Python bindings to search for points in space that are
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> close to a given query point. It also creates large read-only file-based data
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> structures that are mapped into memory so that many processes may share the same data.
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## Installation and Setup
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```bash
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pip install annoy
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>[Apache Doris](https://doris.apache.org/) is a modern data warehouse for real-time analytics.
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It delivers lightning-fast analytics on real-time data at scale.
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>Usually `Apache Doris` is categorized into OLAP, and it has showed excellent performance in [ClickBench — a Benchmark For Analytical DBMS](https://benchmark.clickhouse.com/). Since it has a super-fast vectorized execution engine, it could also be used as a fast vectordb.
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>Usually `Apache Doris` is categorized into OLAP, and it has showed excellent performance
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> in [ClickBench — a Benchmark For Analytical DBMS](https://benchmark.clickhouse.com/).
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> Since it has a super-fast vectorized execution engine, it could also be used as a fast vectordb.
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## Installation and Setup
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```bash
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pip install pymysql
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```
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# Apify
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This page covers how to use [Apify](https://apify.com) within LangChain.
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## Overview
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Apify is a cloud platform for web scraping and data extraction,
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which provides an [ecosystem](https://apify.com/store) of more than a thousand
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ready-made apps called *Actors* for various scraping, crawling, and extraction use cases.
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>[Apify](https://apify.com) is a cloud platform for web scraping and data extraction,
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>which provides an [ecosystem](https://apify.com/store) of more than a thousand
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>ready-made apps called *Actors* for various scraping, crawling, and extraction use cases.
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[](https://apify.com/store)
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This integration enables you run Actors on the Apify platform and load their results into LangChain to feed your vector
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This integration enables you run Actors on the `Apify` platform and load their results into LangChain to feed your vector
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indexes with documents and data from the web, e.g. to generate answers from websites with documentation,
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blogs, or knowledge bases.
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@ -22,9 +19,7 @@ blogs, or knowledge bases.
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an environment variable (`APIFY_API_TOKEN`) or pass it to the `ApifyWrapper` as `apify_api_token` in the constructor.
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## Wrappers
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### Utility
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## Utility
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You can use the `ApifyWrapper` to run Actors on the Apify platform.
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For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/apify).
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### Loader
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## Document loader
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You can also use our `ApifyDatasetLoader` to get data from Apify dataset.
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# ArangoDB
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>[ArangoDB](https://github.com/arangodb/arangodb) is a scalable graph database system to drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud – anywhere.
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>[ArangoDB](https://github.com/arangodb/arangodb) is a scalable graph database system to
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> drive value from connected data, faster. Native graphs, an integrated search engine, and JSON support, via a single query language. ArangoDB runs on-prem, in the cloud – anywhere.
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## Dependencies
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## Installation and Setup
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Install the [ArangoDB Python Driver](https://github.com/ArangoDB-Community/python-arango) package with
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```bash
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pip install python-arango
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```
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## Graph QA Chain
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Connect your ArangoDB Database with a chat model to get insights on your data.
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Connect your `ArangoDB` Database with a chat model to get insights on your data.
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See the notebook example [here](/docs/use_cases/graph/graph_arangodb_qa).
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"[Arthur](https://arthur.ai) is a model monitoring and observability platform.\n",
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">[Arthur](https://arthur.ai) is a model monitoring and observability platform.\n",
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"\n",
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"The following guide shows how to run a registered chat LLM with the Arthur callback handler to automatically log model inferences to Arthur.\n",
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"\n",
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"If you do not have a model currently onboarded to Arthur, visit our [onboarding guide for generative text models](https://docs.arthur.ai/user-guide/walkthroughs/model-onboarding/generative_text_onboarding.html). For more information about how to use the Arthur SDK, visit our [docs](https://docs.arthur.ai/)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "y8ku6X96sebl"
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},
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"outputs": [],
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"source": [
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"from langchain.callbacks import ArthurCallbackHandler\n",
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
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"from langchain_core.messages import HumanMessage\n",
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"from langchain_openai import ChatOpenAI"
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"If you do not have a model currently onboarded to Arthur, visit our [onboarding guide for generative text models](https://docs.arthur.ai/user-guide/walkthroughs/model-onboarding/generative_text_onboarding.html). For more information about how to use the `Arthur SDK`, visit our [docs](https://docs.arthur.ai/)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Installation and Setup\n",
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"\n",
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"Place Arthur credentials here"
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]
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},
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"arthur_model_id = \"your-arthur-model-id-here\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Callback handler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "y8ku6X96sebl"
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},
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"outputs": [],
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"source": [
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"from langchain.callbacks import ArthurCallbackHandler\n",
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
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"from langchain_core.messages import HumanMessage\n",
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"from langchain_openai import ChatOpenAI"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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"nbformat_minor": 4
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}
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