Clarifai : Added Clarifai as LLM and embedding model provider. (#1311)

Co-authored-by: Deshraj Yadav <deshraj@gatech.edu>

embedchain/llm/clarifai.py

@@ -0,0 +1,47 @@
+import logging
+import os
+from typing import Optional
+
+from embedchain.config import BaseLlmConfig
+from embedchain.helpers.json_serializable import register_deserializable
+from embedchain.llm.base import BaseLlm
+
+
+@register_deserializable
+class ClarifaiLlm(BaseLlm):
+    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        super().__init__(config=config)
+        if not self.config.api_key and "CLARIFAI_PAT" not in os.environ:
+            raise ValueError("Please set the CLARIFAI_PAT environment variable.")
+
+    def get_llm_model_answer(self, prompt):
+        return self._get_answer(prompt=prompt, config=self.config)
+
+    @staticmethod
+    def _get_answer(prompt: str, config: BaseLlmConfig) -> str:
+        try:
+            from clarifai.client.model import Model
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(
+                "The required dependencies for Clarifai are not installed."
+                'Please install with `pip install --upgrade "embedchain[clarifai]"`'
+            ) from None
+
+        model_name = config.model
+        logging.info(f"Using clarifai LLM model: {model_name}")
+        api_key = config.api_key or os.getenv("CLARIFAI_PAT")
+        model = Model(url=model_name, pat=api_key)
+        params = config.model_kwargs
+
+        try:
+            (params := {}) if config.model_kwargs is None else config.model_kwargs
+            predict_response = model.predict_by_bytes(
+                bytes(prompt, "utf-8"),
+                input_type="text",
+                inference_params=params,
+            )
+            text = predict_response.outputs[0].data.text.raw
+            return text
+
+        except Exception as e:
+            logging.error(f"Predict failed, exception: {e}")