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44 | 44 | "metadata": {}, |
45 | 45 | "outputs": [], |
46 | 46 | "source": [ |
47 | | - "%pip install -q langchain-openai langgraph-checkpoint langgraph-checkpoint-redis \"langchain-community>=0.2.11\" tavily-python langchain-redis pydantic ulid \"git+https://github.com/redis/[email protected]\"" |
| 47 | + "%pip install -q langchain-openai langgraph-checkpoint langgraph-checkpoint-redis \"langchain-community>=0.2.11\" tavily-python langchain-redis pydantic ulid" |
48 | 48 | ] |
49 | 49 | }, |
50 | 50 | { |
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390 | 390 | "from redisvl.query import VectorRangeQuery\n", |
391 | 391 | "from redisvl.query.filter import Tag\n", |
392 | 392 | "from redisvl.utils.vectorize.text.openai import OpenAITextVectorizer\n", |
393 | | - "from redisvl.extensions.cache.embeddings import EmbeddingsCache\n", |
394 | 393 | "\n", |
395 | 394 | "\n", |
396 | 395 | "logger = logging.getLogger(__name__)\n", |
397 | 396 | "\n", |
398 | 397 | "# If we have any memories that aren't associated with a user, we'll use this ID.\n", |
399 | 398 | "SYSTEM_USER_ID = \"system\"\n", |
400 | 399 | "\n", |
401 | | - "openai_embed = OpenAITextVectorizer(\n", |
402 | | - " model=\"text-embedding-ada-002\",\n", |
403 | | - " cache=EmbeddingsCache(\n", |
404 | | - " name=\"embedcache\",\n", |
405 | | - " ttl=600,\n", |
406 | | - " redis_url=REDIS_URL,\n", |
407 | | - " )\n", |
408 | | - ")\n", |
| 400 | + "openai_embed = OpenAITextVectorizer(model=\"text-embedding-ada-002\")\n", |
409 | 401 | "\n", |
410 | 402 | "# Change this to MemoryStrategy.TOOLS to use function-calling to store and\n", |
411 | 403 | "# retrieve memories.\n", |
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