better approach for LLMs, minor changes, new models and fixes

examples/async_bard.py

@@ -1,10 +1,11 @@
 import asyncio
 from lexica import AsyncClient
+from lexica.constants import languageModels
 
-async def main(prompt: str) -> dict:
+async def async_main(prompt: str) -> dict:
  client = AsyncClient()
- response = await client.bard(prompt)
+ response = await client.ChatCompletion(prompt,languageModels.bard)
  return response
 
 if __name__ == "__main__":
- print(asyncio.run(main("hello, who are you?")))
+ print(asyncio.run(async_main("hello, who are you?")))

examples/async_gpt.py

@@ -1,10 +1,11 @@
 import asyncio
 from lexica import AsyncClient
+from lexica.constants import languageModels
 
-async def main(prompt: str) -> dict:
+async def async_main(prompt: str) -> dict:
  client = AsyncClient()
- response = await client.gpt(prompt)
+ response = await client.ChatCompletion(prompt,languageModels.gpt)
  return response
 
 if __name__ == "__main__":
- print(asyncio.run(main("hello, who are you?")))
+ print(asyncio.run(async_main("hello, who are you?")))

examples/async_llama.py

@@ -0,0 +1,11 @@
+import asyncio
+from lexica import AsyncClient
+from lexica.constants import languageModels
+
+async def async_main(prompt: str) -> dict:
+ client = AsyncClient()
+ response = await client.ChatCompletion(prompt,languageModels.llama)
+ return response
+
+if __name__ == "__main__":
+ print(asyncio.run(async_main("hello, who are you?")))

examples/async_mistral.py

@@ -0,0 +1,11 @@
+import asyncio
+from lexica import AsyncClient
+from lexica.constants import languageModels
+
+async def async_main(prompt: str) -> dict:
+ client = AsyncClient()
+ response = await client.ChatCompletion(prompt,languageModels.mistral)
+ return response
+
+if __name__ == "__main__":
+ print(asyncio.run(async_main("hello, who are you?")))

examples/async_palm.py

@@ -1,11 +1,12 @@
 from lexica import AsyncClient
 import asyncio
+from lexica.constants import languageModels
 
 async def async_main(prompt: str) -> dict:
  client = AsyncClient()
- response = await client.palm(prompt)
+ response = await client.ChatCompletion(prompt,languageModels.palm)
  await client.close()
  return response
 
 if __name__ == "__main__":
- print(asyncio.run(async_main("hello world")))
+ print(asyncio.run(async_main("hello, who are you?")))

examples/bard.py

@@ -1,8 +1,9 @@
 from lexica import Client
+from lexica.constants import languageModels
 
 def main(prompt: str) -> dict:
  client = Client()
- response = client.bard(prompt)
+ response = client.ChatCompletion(prompt,languageModels.bard)
  return response
 
 if __name__ == "__main__":

examples/gpt.py

@@ -1,8 +1,9 @@
 from lexica import Client
+from lexica.constants import languageModels
 
 def main(prompt: str) -> dict:
  client = Client()
- response = client.gpt(prompt)
+ response = client.ChatCompletion(prompt,languageModels.gpt)
  return response
 
 if __name__ == "__main__":

examples/llama.py

@@ -0,0 +1,10 @@
+from lexica import Client
+from lexica.constants import languageModels
+
+def main(prompt: str) -> dict:
+ client = Client()
+ response = client.ChatCompletion(prompt,languageModels.llama)
+ return response
+
+if __name__ == "__main__":
+ print(main("hello, who are you?"))

examples/mistral.py

@@ -0,0 +1,10 @@
+from lexica import Client
+from lexica.constants import languageModels
+
+def main(prompt: str) -> dict:
+ client = Client()
+ response = client.ChatCompletion(prompt,languageModels.mistral)
+ return response
+
+if __name__ == "__main__":
+ print(main("hello, who are you?"))

examples/palm.py

@@ -1,8 +1,9 @@
 from lexica import Client
+from lexica.constants import languageModels
 
 def main(prompt: str) -> dict:
  client = Client()
- response = client.palm(prompt)
+ response = client.ChatCompletion(prompt,languageModels.palm)
  return response
 
 if __name__ == "__main__":

lexica/constants.py

@@ -3,3 +3,11 @@
 SESSION_HEADERS = {
  "Host": "lexica.qewertyy.me",
 }
+
+class languageModels(object):
+ bard = {"modelId":20,"name":"Bard"}
+ palm = {"modelId":0,"name":"PaLM"}
+ palm2 = {"modelId":1,"name":"PaLM 2"}
+ mistral = {"modelId":21,"name":"LLAMA 2"}
+ llama = {"modelId":18,"name":"LLAMA"}
+ gpt = {"modelId":5,"name":"ChatGPT"}

lexica/core.py

@@ -49,16 +49,17 @@ def getModels(self) -> dict:
  resp = self._request(url=f'{self.url}/models')
  return resp
 
- def palm(self, prompt: str) -> dict:
+ def ChatCompletion(self, prompt: str,model : dict = languageModels.palm ) -> dict:
  """ 
- Get an answer from PaLM 2 for the given prompt
+ Get an answer from LLMs' for the given prompt
  Example:
  >>> client = Client()
- >>> response = client.palm("Hello, Who are you?")
+ >>> response = client.ChatCompletion("Hello, Who are you?",0)
  >>> print(response)
 
  Args:
  prompt (str): Input text for the query.
+ model (dict): Model dict of the LLM defaults to palm.
 
  Returns:
  dict: Answer from the API in the following format:
@@ -69,40 +70,8 @@ def palm(self, prompt: str) -> dict:
  }
  """
  params = {
- "model_id": 0,
- "prompt": prompt
- }
- resp = self._request(
- url=f'{self.url}/models',
- method='POST',
- params=params,
- headers = {"content-type": "application/json"}
- )
- return resp
-
- def gpt(self, prompt: str,context: str=False) -> dict:
- """ 
- Get an answer from GPT-3.5-Turbo for the given prompt
- Example:
- >>> client = Client()
- >>> response = client.gpt("Hello, Who are you?")
- >>> print(response)
-
- Args:
- prompt (str): Input text for the query.
-
- Returns:
- dict: Answer from the API in the following format:
- {
- "message": str,
- "content": str,
- "code": int
- }
- """
- params = {
- "model_id": 5,
- "prompt": prompt
- ,"context": context if context else ''
+ "prompt": prompt,
+ "model_id": model.get('modelId',0),
  }
  resp = self._request(
  url=f'{self.url}/models',
@@ -198,35 +167,4 @@ def getImages(self,task_id:str,request_id:str) -> dict:
  json=payload,
  headers={"content-type": "application/json"}
  )
- return resp
-
- def bard(self, prompt: str,context: str=False) -> dict:
- """ 
- Get an answer from Bard AI by google for the given prompt
- Example:
- >>> client = Client()
- >>> response = client.bard("Hello, Who are you?")
- >>> print(response)
-
- Args:
- prompt (str): Input text for the query.
-
- Returns:
- dict: Answer from the API in the following format:
- {
- "message": str,
- "content": str,
- "code": int
- }
- """
- params = {
- "model_id": 20,
- "prompt": prompt
- }
- resp = self._request(
- url=f'{self.url}/models',
- method='POST',
- params=params,
- headers={"content-type": "application/json"}
- )
  return resp

lexica/core_async.py

@@ -58,48 +58,18 @@ async def __aenter__(self):
  async def close(self) -> None:
  """Close async session"""
  return await self.session.aclose()
-
- async def palm(self, prompt: str,model_id:int=0) -> dict:
- """ 
- Get an answer from PaLM 2 for the given prompt
- Example:
- >>> client = Client()
- >>> response = await client.palm("Hello, Who are you?")
- >>> print(response)
-
- Args:
- prompt (str): Input text for the query.
 
- Returns:
- dict: Answer from the API in the following format:
- {
- "message": str,
- "content": str,
- "code": int
- }
+ async def ChatCompletion(self, prompt: str,model : dict = languageModels.palm2) -> dict:
  """
- params = {
- "model_id": model_id,
- "prompt": prompt
- }
- resp = await self._request(
- url=f'{self.url}/models',
- method='POST',
- params=params,
- headers = {"content-type": "application/json"}
- )
- return resp
-
- async def gpt(self, prompt: str,context: str = False) -> dict:
- """ 
- Get an answer from GPT-3.5-Turbo for the given prompt
+ Get an answer from LLMs' for the given prompt
  Example:
  >>> client = Client()
- >>> response = await client.gpt("Hello, Who are you?")
+ >>> response = await client.ChatCompletion("Hello, Who are you?")
  >>> print(response)
 
  Args:
  prompt (str): Input text for the query.
+ model (dict): Model dict of the LLM defaults to palm.
 
  Returns:
  dict: Answer from the API in the following format:
@@ -110,40 +80,8 @@ async def gpt(self, prompt: str,context: str = False) -> dict:
  }
  """
  params = {
- "model_id": 5,
+ "model_id": model.get('modelId',0),
  "prompt": prompt,
- "context": context if context else ''
- }
- resp = await self._request(
- url=f'{self.url}/models',
- method='POST',
- params=params,
- headers = {"content-type": "application/json"}
- )
- return resp
-
- async def bard(self, prompt: str,context: str = False) -> dict:
- """ 
- Get an answer from Bard AI by google for the given prompt
- Example:
- >>> client = Client()
- >>> response = await client.bard("Hello, Who are you?")
- >>> print(response)
-
- Args:
- prompt (str): Input text for the query.
-
- Returns:
- dict: Answer from the API in the following format:
- {
- "message": str,
- "content": str,
- "code": int
- }
- """
- params = {
- "model_id": 20,
- "prompt": prompt
  }
  resp = await self._request(
  url=f'{self.url}/models',

setup.py

@@ -8,7 +8,7 @@ def get_long_description():
 
 setup(
  name="lexica-api",
- version="1.4.0",
+ version="1.4.3",
  author="Qewertyy",
  author_email="[email protected]",
  description="The python package for api.qewertyy.me",