Vector Search over Chat History

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from zep_python import (
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    MemorySearchPayload,
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    ZepClient,
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)
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# This uniquely identifies the user's session
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session_id = "my_session_id"
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# Initialize the Zep client before running this code
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search_payload = MemorySearchPayload(
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    text="Is Lauren Olamina a character in a book?",
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    search_scope="summary", # This could be messages or summary
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    search_type="mmr", # remove this if you'd prefer not to rerank results
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    mmr_lambda=0.5, # tune diversity vs relevance
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)
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search_results = await client.memory.asearch_memory(session_id, search_payload)
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for search_result in search_results:
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    # Uncomment for message search
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    # print(search_result.messsage.dict())
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    print(search_result.summary.dict())

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{
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  "summary": {
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    "uuid": "b47b83da-16ae-49c8-bacb-f7d049f9df99",
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    "created_at": "2023-11-02T18:22:10.103867Z",
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    "content": "The human asks the AI to explain the book Parable of the Sower by Octavia Butler. The AI responds by explaining that Parable of the Sower is a science fiction novel by Octavia Butler. The book follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.",
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    "token_count": 66
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  },
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  "metadata": null,
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  "dist": 0.8440576791763306
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}

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import { MemorySearchPayload, ZepClient } from "@getzep/zep-js";
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// This uniquely identifies the user's session
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const sessionID = "my_session_id";
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const searchText = "Is Lauren Olamina a character in a book?";
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// Initialize the ZepClient before running this code
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// Create a new MemorySearchPayload with the search text, scope, type, and MMR lambda
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const searchPayload = new MemorySearchPayload({
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  text: searchText,
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  search_scope: "summary", // This could be messages or summary
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  search_type: "mmr", // remove this if you'd prefer not to rerank results
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  mmr_lambda: 0.5, // tune diversity vs relevance
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});
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// Perform the memory search with the session ID, search payload, and a limit of 3 results
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const searchResults = await client.memory.searchMemory(
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  sessionID,
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  searchPayload,
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  3
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);
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searchResults.forEach((searchResult) => {
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  console.debug(JSON.stringify(searchResult));
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});

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{
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  "summary": {
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    "uuid": "b47b83da-16ae-49c8-bacb-f7d049f9df99",
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    "created_at": "2023-11-02T18:22:10.103867Z",
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    "content": "The human asks the AI to explain the book Parable of the Sower by Octavia Butler. The AI responds by explaining that Parable of the Sower is a science fiction novel by Octavia Butler. The book follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.",
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    "token_count": 66
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  },
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  "metadata": null,
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  "dist": 0.8440576791763306
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}

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zep_client.search_memory(
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    session_id=session_id,
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    search_payload=MemorySearchPayload(
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        query="I enjoy reading science fiction.",
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        metadata={
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            "where": {"jsonpath": '$[*] ? (@.foo == "bar")'},
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        },
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    ),
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)

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{
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  "dist": 0.7170433826192629,
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  "message": {
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    "content": "I've read many books written by Octavia Butler.",
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    "created_at": "2023-06-03T22:00:43.034056Z",
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    "metadata": {
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      "foo": "bar",
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      "system": {
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        "entities": [
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          {
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            "Label": "PERSON",
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            "Matches": [
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              {
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                "End": 46,
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                "Start": 32,
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                "Text": "Octavia Butler"
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              }
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            ],
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            "Name": "Octavia Butler"
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          }
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        ]
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      }
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    },
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    "role": "human",
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    "token_count": 13,
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    "uuid": "8f3a06dd-0625-41da-a2af-b549f2056b3f"
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  },
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  "metadata": null,
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  "summary": null
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}

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const searchText = "I enjoy reading science fiction.";
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const searchPayload = new MemorySearchPayload({
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  metadata: {
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    where: { jsonpath: '$[*] ? (@.foo == "bar")' },
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  },
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  text: searchText,
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});
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const searchResults = await zepClient.searchMemory(sessionID, searchPayload);

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{
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  "dist": 0.7170433826192629,
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  "message": {
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    "content": "I've read many books written by Octavia Butler.",
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    "created_at": "2023-06-03T22:00:43.034056Z",
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    "metadata": {
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      "foo": "bar",
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      "system": {
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        "entities": [
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          {
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            "Label": "PERSON",
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            "Matches": [
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              {
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                "End": 46,
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                "Start": 32,
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                "Text": "Octavia Butler"
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              }
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            ],
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            "Name": "Octavia Butler"
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          }
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        ]
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      }
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    },
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    "role": "human",
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    "token_count": 13,
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    "uuid": "8f3a06dd-0625-41da-a2af-b549f2056b3f"
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  },
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  "metadata": null,
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  "summary": null
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}

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CREATE INDEX ON message_embedding USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);
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CREATE INDEX ON summary_embedding USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);