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37 lines
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37 lines
2.0 KiB
Plaintext
# Activeloop Deep Lake
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This page covers how to use the Deep Lake ecosystem within LangChain.
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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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Activeloop Deep Lake supports SelfQuery Retrieval:
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[Activeloop Deep Lake Self Query Retrieval](/docs/extras/modules/data_connection/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](/docs/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake)
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4. [Code Understanding](/docs/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query)
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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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### VectorStore
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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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```python
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from langchain.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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