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Co-authored-by: jacoblee93 <jacoblee93@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
158 lines
3.7 KiB
Plaintext
158 lines
3.7 KiB
Plaintext
```python
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from langchain.memory import ConversationBufferMemory
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```
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```python
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memory = ConversationBufferMemory()
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memory.save_context({"input": "hi"}, {"output": "whats up"})
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```
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```python
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memory.load_memory_variables({})
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```
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<CodeOutputBlock lang="python">
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```
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{'history': 'Human: hi\nAI: whats up'}
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```
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</CodeOutputBlock>
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We can also get the history as a list of messages (this is useful if you are using this with a chat model).
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```python
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memory = ConversationBufferMemory(return_messages=True)
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memory.save_context({"input": "hi"}, {"output": "whats up"})
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```
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```python
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memory.load_memory_variables({})
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```
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<CodeOutputBlock lang="python">
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```
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{'history': [HumanMessage(content='hi', additional_kwargs={}),
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AIMessage(content='whats up', additional_kwargs={})]}
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```
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</CodeOutputBlock>
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## Using in a chain
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Finally, let's take a look at using this in a chain (setting `verbose=True` so we can see the prompt).
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```python
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from langchain.llms import OpenAI
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from langchain.chains import ConversationChain
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llm = OpenAI(temperature=0)
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conversation = ConversationChain(
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llm=llm,
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verbose=True,
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memory=ConversationBufferMemory()
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)
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```
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```python
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conversation.predict(input="Hi there!")
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new ConversationChain chain...
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Prompt after formatting:
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The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
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Current conversation:
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Human: Hi there!
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AI:
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> Finished chain.
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" Hi there! It's nice to meet you. How can I help you today?"
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```
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</CodeOutputBlock>
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```python
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conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new ConversationChain chain...
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Prompt after formatting:
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The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
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Current conversation:
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Human: Hi there!
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AI: Hi there! It's nice to meet you. How can I help you today?
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Human: I'm doing well! Just having a conversation with an AI.
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AI:
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> Finished chain.
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" That's great! It's always nice to have a conversation with someone new. What would you like to talk about?"
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```
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</CodeOutputBlock>
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```python
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conversation.predict(input="Tell me about yourself.")
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```
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<CodeOutputBlock lang="python">
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```
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> Entering new ConversationChain chain...
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Prompt after formatting:
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The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
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Current conversation:
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Human: Hi there!
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AI: Hi there! It's nice to meet you. How can I help you today?
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Human: I'm doing well! Just having a conversation with an AI.
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AI: That's great! It's always nice to have a conversation with someone new. What would you like to talk about?
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Human: Tell me about yourself.
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AI:
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> Finished chain.
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" Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers."
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```
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</CodeOutputBlock>
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And that's it for the getting started! There are plenty of different types of memory, check out our examples to see them all
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