Feat/ibm memory (crewAIInc#1549)

* Everything looks like its working. Waiting for lorenze review.

* Update docs as well.

* clean up for PR

docs/concepts/memory.mdx

@@ -254,6 +254,31 @@ my_crew = Crew(
 )
 ```
 
+### Using Watson embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+# Note: Ensure you have installed and imported `ibm_watsonx_ai` for Watson embeddings to work.
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "watson",
+        "config": {
+            "model": "<model_name>",
+            "api_url": "<api_url>",
+            "api_key": "<YOUR_API_KEY>",
+            "project_id": "<YOUR_PROJECT_ID>",
+        }
+    }
+)
+```
+
 ### Resetting Memory
 
 ```shell

src/crewai/memory/contextual/contextual_memory.py

@@ -34,6 +34,7 @@ def _fetch_stm_context(self, query) -> str:
         formatted_results = "\n".join(
             [f"- {result['context']}" for result in stm_results]
         )
+        print("formatted_results stm", formatted_results)
         return f"Recent Insights:\n{formatted_results}" if stm_results else ""
 
     def _fetch_ltm_context(self, task) -> Optional[str]:
@@ -53,6 +54,8 @@ def _fetch_ltm_context(self, task) -> Optional[str]:
         formatted_results = list(dict.fromkeys(formatted_results))
         formatted_results = "\n".join([f"- {result}" for result in formatted_results])  # type: ignore # Incompatible types in assignment (expression has type "str", variable has type "list[str]")
 
+        print("formatted_results ltm", formatted_results)
+
         return f"Historical Data:\n{formatted_results}" if ltm_results else ""
 
     def _fetch_entity_context(self, query) -> str:
@@ -64,4 +67,5 @@ def _fetch_entity_context(self, query) -> str:
         formatted_results = "\n".join(
             [f"- {result['context']}" for result in em_results]  # type: ignore #  Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
         )
+        print("formatted_results em", formatted_results)
         return f"Entities:\n{formatted_results}" if em_results else ""

src/crewai/memory/storage/rag_storage.py

@@ -4,13 +4,13 @@
 import os
 import shutil
 import uuid
-from typing import Any, Dict, List, Optional
-from crewai.memory.storage.base_rag_storage import BaseRAGStorage
-from crewai.utilities.paths import db_storage_path
+from typing import Any, Dict, List, Optional, cast
+
+from chromadb import Documents, EmbeddingFunction, Embeddings
 from chromadb.api import ClientAPI
 from chromadb.api.types import validate_embedding_function
-from chromadb import Documents, EmbeddingFunction, Embeddings
-from typing import cast
+from crewai.memory.storage.base_rag_storage import BaseRAGStorage
+from crewai.utilities.paths import db_storage_path
 
 
 @contextlib.contextmanager
@@ -21,9 +21,11 @@ def suppress_logging(
     logger = logging.getLogger(logger_name)
     original_level = logger.getEffectiveLevel()
     logger.setLevel(level)
-    with contextlib.redirect_stdout(io.StringIO()), contextlib.redirect_stderr(
-        io.StringIO()
-    ), contextlib.suppress(UserWarning):
+    with (
+        contextlib.redirect_stdout(io.StringIO()),
+        contextlib.redirect_stderr(io.StringIO()),
+        contextlib.suppress(UserWarning),
+    ):
         yield
     logger.setLevel(original_level)
 
@@ -113,12 +115,52 @@ def __call__(self, input: Documents) -> Embeddings:
                 self.embedder_config = embedding_functions.HuggingFaceEmbeddingServer(
                     url=config.get("api_url"),
                 )
+            elif provider == "watson":
+                try:
+                    import ibm_watsonx_ai.foundation_models as watson_models
+                    from ibm_watsonx_ai import Credentials
+                    from ibm_watsonx_ai.metanames import (
+                        EmbedTextParamsMetaNames as EmbedParams,
+                    )
+                except ImportError as e:
+                    raise ImportError(
+                        "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
+                    ) from e
+
+                class WatsonEmbeddingFunction(EmbeddingFunction):
+                    def __call__(self, input: Documents) -> Embeddings:
+                        if isinstance(input, str):
+                            input = [input]
+
+                        embed_params = {
+                            EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
+                            EmbedParams.RETURN_OPTIONS: {"input_text": True},
+                        }
+
+                        embedding = watson_models.Embeddings(
+                            model_id=config.get("model"),
+                            params=embed_params,
+                            credentials=Credentials(
+                                api_key=config.get("api_key"), url=config.get("api_url")
+                            ),
+                            project_id=config.get("project_id"),
+                        )
+
+                        try:
+                            embeddings = embedding.embed_documents(input)
+                            return cast(Embeddings, embeddings)
+
+                        except Exception as e:
+                            print("Error during Watson embedding:", e)
+                            raise e
+
+                self.embedder_config = WatsonEmbeddingFunction()
             else:
                 raise Exception(
-                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface]"
+                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface, watson]"
                 )
         else:
-            validate_embedding_function(self.embedder_config)  # type: ignore # used for validating embedder_config if defined a embedding function/class
+            validate_embedding_function(self.embedder_config)
             self.embedder_config = self.embedder_config
 
     def _initialize_app(self):