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Update vectorstore_retriever_memory.mdx (#15275)
removed bad comments <!-- Thank you for contributing to LangChain! Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified. Replace this entire comment with: - **Description:** a description of the change, - **Issue:** the issue # it fixes if applicable, - **Dependencies:** any dependencies required for this change, - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out! Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally. See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/ If you're adding a new integration, please include: 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17. -->
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@ -52,8 +52,6 @@ memory.save_context({"input": "I don't the Celtics"}, {"output": "ok"}) #
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```python
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# Notice the first result returned is the memory pertaining to tax help, which the language model deems more semantically relevant
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# to a 1099 than the other documents, despite them both containing numbers.
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print(memory.load_memory_variables({"prompt": "what sport should i watch?"})["history"])
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```
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@ -88,7 +86,6 @@ PROMPT = PromptTemplate(
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conversation_with_summary = ConversationChain(
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llm=llm,
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prompt=PROMPT,
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# We set a very low max_token_limit for the purposes of testing.
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memory=memory,
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verbose=True
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)
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