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add SpiceClient #2

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merged 3 commits into from
Mar 8, 2024
Merged

add SpiceClient #2

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ae09aa6
add SpiceClient
biobootloader Mar 8, 2024
a53ceff
Update spice.py
biobootloader Mar 8, 2024
4ae3272
fixes
biobootloader Mar 8, 2024
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11 changes: 8 additions & 3 deletions run.py
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Original file line number Diff line number Diff line change
@@ -1,14 +1,19 @@
from spice import call_llm
from spice import SpiceClient

# model = "gpt-4-0125-preview"
model = "claude-3-opus-20240229"

system_message = "You are a helpful assistant."
messages = [
{"role": "user", "content": "list 5 random words"},
]

for t in call_llm(system_message, messages, stream=True):
client = SpiceClient(model=model)

for t in client.call_llm(system_message, messages, stream=True):
print(t, end="")

print("\n####################\n")

response = call_llm(system_message, messages, stream=False)
response = client.call_llm(system_message, messages, stream=False)
print(response)
104 changes: 56 additions & 48 deletions spice.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,58 +4,66 @@
from dotenv import load_dotenv
from openai import OpenAI

load_dotenv()
_openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
_anthropic_client = Anthropic(
api_key=os.environ.get("ANTHROPIC_API_KEY"),
)
_openai_model = "gpt-4-0125-preview"
_anthropic_model = "claude-3-opus-20240229"

_use_anthropic = True


def call_llm(system_message, messages, stream=False):
if _use_anthropic:
chat_completion_or_stream = _anthropic_client.messages.create(
max_tokens=1024,
system=system_message,
messages=messages,
model=_anthropic_model,
temperature=0.3,
stream=stream,
)
else:
_messages = [
{
"role": "system",
"content": system_message,
}
] + messages
chat_completion_or_stream = _openai_client.chat.completions.create(
messages=_messages,
model=_openai_model,
temperature=0.3,
stream=stream,
)

if stream:
return _stream_generator(chat_completion_or_stream)
else:
if _use_anthropic:
response = chat_completion_or_stream.content[0].text
class SpiceClient:
def __init__(self, model):
if model == "gpt-4-0125-preview":
self._provider = "openai"
elif model == "claude-3-opus-20240229":
self._provider = "anthropic"
else:
response = chat_completion_or_stream.choices[0].message.content
return response
raise ValueError(f"Unknown model {model}")
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Consider adding support for more models or a default model to avoid raising an exception for unknown models. This would make the client more flexible.


self.model = model

load_dotenv()
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It's a good practice to call load_dotenv() at the entry point of the application to ensure environment variables are loaded once and available throughout the application. Consider moving this call outside of the class.

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good call

if self._provider == "openai":
self._client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
elif self._provider == "anthropic":
self._client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))

def _stream_generator(stream):
for chunk in stream:
if _use_anthropic:
content = ""
if chunk.type == "content_block_delta":
content = chunk.delta.text
def call_llm(self, system_message, messages, stream=False):
if self._provider == "anthropic":
chat_completion_or_stream = self._client.messages.create(
max_tokens=1024,
system=system_message,
messages=messages,
model=self.model,
temperature=0.3,
stream=stream,
)
else:
content = chunk.choices[0].delta.content
if content is not None:
yield content
_messages = [
{
"role": "system",
"content": system_message,
}
] + messages
chat_completion_or_stream = self._client.chat.completions.create(
messages=_messages,
model=self.model,
temperature=0.3,
stream=stream,
)

if stream:
return self._stream_generator(chat_completion_or_stream)
else:
if self._provider == "anthropic":
response = chat_completion_or_stream.content[0].text
else:
response = chat_completion_or_stream.choices[0].message.content
return response

def _stream_generator(self, stream):
for chunk in stream:
if self._provider == "anthropic":
content = ""
if chunk.type == "content_block_delta":
content = chunk.delta.text
else:
content = chunk.choices[0].delta.content
if content is not None:
yield content
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The check for content is not None might be redundant since you're initializing content to an empty string and only changing it if certain conditions are met. Consider removing this check.

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