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30
docs/snippets/modules/chains/how_to/debugging.mdx
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Setting `verbose` to `True` will print out some internal states of the `Chain` object while it is being ran.
```python
conversation = ConversationChain(
llm=chat,
memory=ConversationBufferMemory(),
verbose=True
)
conversation.run("What is ChatGPT?")
```
<CodeOutputBlock lang="python">
```
> Entering new ConversationChain chain...
Prompt after formatting:
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.
Current conversation:
Human: What is ChatGPT?
AI:
> Finished chain.
'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics. It is often used for chatbots, virtual assistants, and other conversational AI applications.'
```
</CodeOutputBlock>

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docs/snippets/modules/chains/how_to/memory.mdx
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```python
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
conversation = ConversationChain(
llm=chat,
memory=ConversationBufferMemory()
)
conversation.run("Answer briefly. What are the first 3 colors of a rainbow?")
# -> The first three colors of a rainbow are red, orange, and yellow.
conversation.run("And the next 4?")
# -> The next four colors of a rainbow are green, blue, indigo, and violet.
```
<CodeOutputBlock lang="python">
```
'The next four colors of a rainbow are green, blue, indigo, and violet.'
```
</CodeOutputBlock>
Essentially, `BaseMemory` defines an interface of how `langchain` stores memory. It allows reading of stored data through `load_memory_variables` method and storing new data through `save_context` method. You can learn more about it in the [Memory](/docs/modules/memory/) section.