cohere-embedder-phi-2214 (#1586)

## Description

New Cohere Embedder Class and a cookbook example 

## Type of change

Please check the options that are relevant:

- [ ] Bug fix (non-breaking change which fixes an issue)
- [x] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to not work as expected)
- [ ] Model update
- [ ] Infrastructure change

## Checklist

- [x] My code follows Phidata's style guidelines and best practices
- [x] I have performed a self-review of my code
- [x] I have added docstrings and comments for complex logic
- [x] My changes generate no new warnings or errors
- [x] I have added cookbook examples for my new addition (if needed)
- [x] I have updated requirements.txt/pyproject.toml (if needed)
- [x] I have verified my changes in a clean environment

---------

Co-authored-by: Dirk Brand <[email protected]>

cookbook/embedders/cohere_embedder.py

@@ -0,0 +1,18 @@
+from phi.agent import AgentKnowledge
+from phi.vectordb.pgvector import PgVector
+from phi.embedder.cohere import CohereEmbedder
+
+embeddings = CohereEmbedder().get_embedding("The quick brown fox jumps over the lazy dog.")
+# Print the embeddings and their dimensions
+print(f"Embeddings: {embeddings[:5]}")
+print(f"Dimensions: {len(embeddings)}")
+
+# Example usage:
+knowledge_base = AgentKnowledge(
+    vector_db=PgVector(
+        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
+        table_name="cohere_embeddings",
+        embedder=CohereEmbedder(),
+    ),
+    num_documents=2,
+)

phi/embedder/cohere.py

@@ -0,0 +1,70 @@
+from typing import Optional, Dict, List, Tuple, Any, Union
+
+from phi.embedder.base import Embedder
+from phi.utils.log import logger
+
+try:
+    from cohere import Client as CohereClient
+    from cohere.types.embed_response import EmbeddingsFloatsEmbedResponse, EmbeddingsByTypeEmbedResponse
+except ImportError:
+    raise ImportError("`cohere` not installed. Please install using `pip install cohere`.")
+
+
+class CohereEmbedder(Embedder):
+    model: str = "embed-english-v3.0"
+    input_type: str = "search_query"
+    embedding_types: Optional[List[str]] = None
+    api_key: Optional[str] = None
+    request_params: Optional[Dict[str, Any]] = None
+    client_params: Optional[Dict[str, Any]] = None
+    cohere_client: Optional[CohereClient] = None
+
+    @property
+    def client(self) -> CohereClient:
+        if self.cohere_client:
+            return self.cohere_client
+        client_params: Dict[str, Any] = {}
+        if self.api_key:
+            client_params["api_key"] = self.api_key
+        return CohereClient(**client_params)
+
+    def response(self, text: str) -> Union[EmbeddingsFloatsEmbedResponse, EmbeddingsByTypeEmbedResponse]:
+        request_params: Dict[str, Any] = {}
+
+        if self.model:
+            request_params["model"] = self.model
+        if self.input_type:
+            request_params["input_type"] = self.input_type
+        if self.embedding_types:
+            request_params["embedding_types"] = self.embedding_types
+        if self.request_params:
+            request_params.update(self.request_params)
+        return self.client.embed(texts=[text], **request_params)
+
+    def get_embedding(self, text: str) -> List[float]:
+        response: Union[EmbeddingsFloatsEmbedResponse, EmbeddingsByTypeEmbedResponse] = self.response(text=text)
+        try:
+            if isinstance(response, EmbeddingsFloatsEmbedResponse):
+                return response.embeddings[0]
+            elif isinstance(response, EmbeddingsByTypeEmbedResponse):
+                return response.embeddings.float_[0] if response.embeddings.float_ else []
+            else:
+                logger.warning("No embeddings found")
+                return []
+        except Exception as e:
+            logger.warning(e)
+            return []
+
+    def get_embedding_and_usage(self, text: str) -> Tuple[List[float], Optional[Dict[str, Any]]]:
+        response: Union[EmbeddingsFloatsEmbedResponse, EmbeddingsByTypeEmbedResponse] = self.response(text=text)
+
+        embedding: List[float] = []
+        if isinstance(response, EmbeddingsFloatsEmbedResponse):
+            embedding = response.embeddings[0]
+        elif isinstance(response, EmbeddingsByTypeEmbedResponse):
+            embedding = response.embeddings.float_[0] if response.embeddings.float_ else []
+
+        usage = response.meta.billed_units if response.meta else None
+        if usage:
+            return embedding, usage.model_dump()
+        return embedding, None

phi/embedder/openai.py

@@ -39,7 +39,7 @@ def client(self) -> OpenAIClient:
             _client_params.update(self.client_params)
         return OpenAIClient(**_client_params)
 
-    def _response(self, text: str) -> CreateEmbeddingResponse:
+    def response(self, text: str) -> CreateEmbeddingResponse:
         _request_params: Dict[str, Any] = {
             "input": text,
             "model": self.model,
@@ -54,15 +54,15 @@ def _response(self, text: str) -> CreateEmbeddingResponse:
         return self.client.embeddings.create(**_request_params)
 
     def get_embedding(self, text: str) -> List[float]:
-        response: CreateEmbeddingResponse = self._response(text=text)
+        response: CreateEmbeddingResponse = self.response(text=text)
         try:
             return response.data[0].embedding
         except Exception as e:
             logger.warning(e)
             return []
 
     def get_embedding_and_usage(self, text: str) -> Tuple[List[float], Optional[Dict]]:
-        response: CreateEmbeddingResponse = self._response(text=text)
+        response: CreateEmbeddingResponse = self.response(text=text)
 
         embedding = response.data[0].embedding
         usage = response.usage