- `result` (str): The generated response from the language model.
## Conclusion
The `LLM` class provides a convenient way to initialize and run different language models using either OpenAI's API or Hugging Face models. By providing the necessary credentials and a prompt, you can obtain the generated response from the language model.
The `LLM` class provides a convenient way to initialize and run different language models using either OpenAI's API or Hugging Face models. By providing the necessary credentials and a prompt, you can obtain the generated response from the language model.
# `GooglePalm` class:
### Example 1: Using Dictionaries as Messages
```python
from google_palm import GooglePalm
# Initialize the GooglePalm instance
gp = GooglePalm(
client=your_client,
model_name="models/chat-bison-001",
temperature=0.7,
top_p=0.9,
top_k=10,
n=5
)
# Create some messages
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Who won the world series in 2020?"},
]
# Generate a response
response = gp.generate(messages)
# Print the generated response
print(response)
```
### Example 2: Using BaseMessage and Its Subclasses as Messages
```python
from google_palm import GooglePalm
from langchain.schema.messages import SystemMessage, HumanMessage
# Initialize the GooglePalm instance
gp = GooglePalm(
client=your_client,
model_name="models/chat-bison-001",
temperature=0.7,
top_p=0.9,
top_k=10,
n=5
)
# Create some messages
messages = [
SystemMessage(content="You are a helpful assistant."),
HumanMessage(content="Who won the world series in 2020?"),
]
# Generate a response
response = gp.generate(messages)
# Print the generated response
print(response)
```
### Example 3: Using GooglePalm with Asynchronous Function
```python
import asyncio
from google_palm import GooglePalm
from langchain.schema.messages import SystemMessage, HumanMessage
# Initialize the GooglePalm instance
gp = GooglePalm(
client=your_client,
model_name="models/chat-bison-001",
temperature=0.7,
top_p=0.9,
top_k=10,
n=5
)
# Create some messages
messages = [
SystemMessage(content="You are a helpful assistant."),
HumanMessage(content="Who won the world series in 2020?"),
]
# Define an asynchronous function
async def generate_response():
response = await gp._agenerate(messages)
print(response)
# Run the asynchronous function
asyncio.run(generate_response())
```
Remember to replace `your_client` with an actual instance of your client. Also, ensure the `model_name` is the correct name of the model that you want to use.
The `temperature`, `top_p`, `top_k`, and `n` parameters control the randomness and diversity of the generated responses. You can adjust these parameters based on your application's requirements.
Kye
2 years ago