forked from Archives/langchain
Harrison/zep mem (#6388)
Co-authored-by: Daniel Chalef <131175+danielchalef@users.noreply.github.com>
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"metadata": {},
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"source": [
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"# Zep\n",
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"## Retriever Example for [Zep](https://docs.getzep.com/) - A long-term memory store for LLM applications.\n",
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"\n",
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">[Zep](https://docs.getzep.com/) - A long-term memory store for LLM applications.\n",
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"### More on Zep:\n",
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"\n",
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"More on `Zep`:\n",
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"\n",
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"`Zep` stores, summarizes, embeds, indexes, and enriches conversational AI chat histories, and exposes them via simple, low-latency APIs.\n",
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"Zep stores, summarizes, embeds, indexes, and enriches conversational AI chat histories, and exposes them via simple, low-latency APIs.\n",
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"\n",
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"Key Features:\n",
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"\n",
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"- Long-term memory persistence, with access to historical messages irrespective of your summarization strategy.\n",
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"- Auto-summarization of memory messages based on a configurable message window. A series of summaries are stored, providing flexibility for future summarization strategies.\n",
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"- Vector search over memories, with messages automatically embedded on creation.\n",
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"- Auto-token counting of memories and summaries, allowing finer-grained control over prompt assembly.\n",
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"- **Fast!** Zep’s async extractors operate independently of the your chat loop, ensuring a snappy user experience.\n",
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"- **Long-term memory persistence**, with access to historical messages irrespective of your summarization strategy.\n",
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"- **Auto-summarization** of memory messages based on a configurable message window. A series of summaries are stored, providing flexibility for future summarization strategies.\n",
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"- **Hybrid search** over memories and metadata, with messages automatically embedded on creation.\n",
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"- **Entity Extractor** that automatically extracts named entities from messages and stores them in the message metadata.\n",
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"- **Auto-token counting** of memories and summaries, allowing finer-grained control over prompt assembly.\n",
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"- Python and JavaScript SDKs.\n",
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"\n",
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"Zep's Go Extractor model is easily extensible, with a simple, clean interface available to build new enrichment functionality, such as summarizers, entity extractors, embedders, and more.\n",
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"\n",
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"`Zep` project: [https://github.com/getzep/zep](https://github.com/getzep/zep)\n"
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"Zep project: [https://github.com/getzep/zep](https://github.com/getzep/zep)\n",
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"Docs: [https://docs.getzep.com/](https://docs.getzep.com/)\n"
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]
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},
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{
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"source": [
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"# Zep Memory\n",
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"\n",
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"## REACT Agent Chat Message History Example\n",
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"## REACT Agent Chat Message History with Zep - A long-term memory store for LLM applications.\n",
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"\n",
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"This notebook demonstrates how to use the [Zep Long-term Memory Store](https://getzep.github.io/) as memory for your chatbot.\n",
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"This notebook demonstrates how to use the [Zep Long-term Memory Store](https://docs.getzep.com/) as memory for your chatbot.\n",
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"\n",
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"We'll demonstrate:\n",
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"\n",
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@ -18,20 +18,22 @@
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"3. Viewing the enriched messages.\n",
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"4. Vector search over the conversation history.\n",
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"\n",
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"More on Zep:\n",
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"### More on Zep:\n",
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"\n",
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"Zep stores, summarizes, embeds, indexes, and enriches conversational AI chat histories, and exposes them via simple, low-latency APIs.\n",
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"\n",
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"Key Features:\n",
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"\n",
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"- Long-term memory persistence, with access to historical messages irrespective of your summarization strategy.\n",
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"- Auto-summarization of memory messages based on a configurable message window. A series of summaries are stored, providing flexibility for future summarization strategies.\n",
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"- Vector search over memories, with messages automatically embedded on creation.\n",
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"- Auto-token counting of memories and summaries, allowing finer-grained control over prompt assembly.\n",
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"- **Fast!** Zep’s async extractors operate independently of the your chat loop, ensuring a snappy user experience.\n",
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"- **Long-term memory persistence**, with access to historical messages irrespective of your summarization strategy.\n",
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"- **Auto-summarization** of memory messages based on a configurable message window. A series of summaries are stored, providing flexibility for future summarization strategies.\n",
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"- **Hybrid search** over memories and metadata, with messages automatically embedded on creation.\n",
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"- **Entity Extractor** that automatically extracts named entities from messages and stores them in the message metadata.\n",
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"- **Auto-token counting** of memories and summaries, allowing finer-grained control over prompt assembly.\n",
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"- Python and JavaScript SDKs.\n",
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"\n",
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"Zep project: [https://github.com/getzep/zep](https://github.com/getzep/zep)\n",
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"Docs: [https://getzep.github.io](https://getzep.github.io/)\n"
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"Docs: [https://docs.getzep.com/](https://docs.getzep.com/)\n"
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]
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},
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{
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