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

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

embedchain/embedder/clarifai.py

@@ -0,0 +1,52 @@
+import os
+from typing import Optional, Union
+
+from embedchain.config import BaseEmbedderConfig
+from embedchain.embedder.base import BaseEmbedder
+
+from chromadb import EmbeddingFunction, Embeddings
+
+
+class ClarifaiEmbeddingFunction(EmbeddingFunction):
+    def __init__(self, config: BaseEmbedderConfig) -> None:
+        super().__init__()
+        try:
+            from clarifai.client.model import Model
+            from clarifai.client.input import Inputs
+        except ModuleNotFoundError:
+            raise ModuleNotFoundError(
+                "The required dependencies for ClarifaiEmbeddingFunction are not installed."
+                'Please install with `pip install --upgrade "embedchain[clarifai]"`'
+            ) from None
+        self.config = config
+        self.api_key = config.api_key or os.getenv("CLARIFAI_PAT")
+        self.model = config.model
+        self.model_obj = Model(url=self.model, pat=self.api_key)
+        self.input_obj = Inputs(pat=self.api_key)
+
+    def __call__(self, input: Union[str, list[str]]) -> Embeddings:
+        if isinstance(input, str):
+            input = [input]
+
+        batch_size = 32
+        embeddings = []
+        try:
+            for i in range(0, len(input), batch_size):
+                batch = input[i : i + batch_size]
+                input_batch = [
+                    self.input_obj.get_text_input(input_id=str(id), raw_text=inp) for id, inp in enumerate(batch)
+                ]
+                response = self.model_obj.predict(input_batch)
+                embeddings.extend([list(output.data.embeddings[0].vector) for output in response.outputs])
+        except Exception as e:
+            print(f"Predict failed, exception: {e}")
+
+        return embeddings
+
+
+class ClarifaiEmbedder(BaseEmbedder):
+    def __init__(self, config: Optional[BaseEmbedderConfig] = None):
+        super().__init__(config)
+
+        embedding_func = ClarifaiEmbeddingFunction(config=self.config)
+        self.set_embedding_fn(embedding_fn=embedding_func)