Merge branch 'main' into dependabot/pip/ai/gen-ai-agents/travel_agent/protobuf-6.31.1

Author: anshoracle

ai/README.md

@@ -1,37 +1,28 @@
 # AI Services
 
-Oracle Cloud Infrastructure (OCI) AI Services and Generative AI Services are a collection of services with prebuilt machine learning and Generative AI models that make it easier for developers to apply AI to applications and business operations. The models can be custom-trained (or fine-tuned) for more accurate business results. Teams within an organization can reuse the models, datasets, and data labels across services. OCI AI Services makes it possible for developers to easily add machine learning to apps without slowing down application development.
+Oracle Cloud Infrastructure (OCI) AI Services, Generative AI Services and Generative AI Agents are a collection of services with prebuilt machine learning and Generative AI models that make it easy for developers to apply AI to applications and business processes. The models can be custom-trained (or fine-tuned) for more accurate business results. Teams within an organization can reuse the models, datasets, and data labels across services. OCI AI makes it possible for developers to easily add AI to applications without slowing down application development.
 
-Reviewed: 17.10.2024
+Reviewed: 03.06.2025
 
-# Team Publications
-- [Cloud Coaching - Low Code embraces Oracle Cloud Infrastructure AI services](https://www.youtube.com/watch?v=0oHixpA9JDc)
-    - Learn how to create applications that read, modify, and classify documents with a couple of clicks using our low code development platform and some of the OCI AI services offering.
-
-# Table of Contents
- 
-- [AI Services](#ai-services)
-- [Team Publications](#team-publications)
-- [Table of Contents](#table-of-contents)
-- [Useful Links](#useful-links)
-- [Reusable Assets](#reusable-assets)
-- [License](#license)
 
 # Useful Links
 
+## Examples and hands-on workshops
 - [AI Solutions Hub](https://www.oracle.com/artificial-intelligence/solutions/)
+- [Oracle LiveLabs](https://apexapps.oracle.com/pls/apex/r/dbpm/livelabs/home)
+
+## Discover Oracle AI
 - [Oracle AI Services on Oracle.com](https://www.oracle.com/artificial-intelligence/ai-services/)
 - [Oracle Generative AI on Oracle.com](https://www.oracle.com/artificial-intelligence/generative-ai/generative-ai-service/)
+- [Oracle AI Strategy and Platform webinar](https://go.oracle.com/LP=138234?elqCampaignId=489428&src1=:so:ch:or:dg::::&SC=:so:ch:or:dg::::&pcode=WWMK230822P00010)
 - [Oracle’s Generative AI strategy](https://blogs.oracle.com/ai-and-datascience/post/generative-ai-strategy)
-- [OCI AI Foundations Certification](https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2024/140164)
-- [OCI Generative AI Professional](https://mylearn.oracle.com/ou/learning-path/become-an-oci-generative-ai-professional/136227)
 - [AI use cases - 10 examples](https://www.oracle.com/a/ocom/docs/gated/ai-use-cases-ebook.pdf)
 - [Availability of AI Services across OCI datacenters](https://www.oracle.com/uk/cloud/public-cloud-regions/service-availability/#commercial)
-- [Oracle LiveLabs](https://apexapps.oracle.com/pls/apex/r/dbpm/livelabs/home)
 
-# Reusable Assets
-- [Smarter Applications with AI Services](https://go.oracle.com/LP=138234?elqCampaignId=489428&src1=:so:ch:or:dg::::&SC=:so:ch:or:dg::::&pcode=WWMK230822P00010)
-- [Oracle AI Strategy and Platform webinar](https://go.oracle.com/LP=138234?elqCampaignId=489428&src1=:so:ch:or:dg::::&SC=:so:ch:or:dg::::&pcode=WWMK230822P00010)
+## Learning paths and certifications
+- [OCI AI Foundations Certification](https://mylearn.oracle.com/ou/learning-path/become-an-oci-ai-foundations-associate-2024/140164)
+- [OCI Generative AI Professional](https://mylearn.oracle.com/ou/learning-path/become-an-oci-generative-ai-professional/136227)
+- [Become a Digital Assistant developer](https://mylearn.oracle.com/ou/learning-path/become-a-digital-assistant-developer-2025/147740)
 
 # License
 

ai/gen-ai-agents/custom_rag_agent/config.py

@@ -31,22 +31,24 @@
 
 # embeddings
 EMBED_MODEL_ID = "cohere.embed-multilingual-v3.0"
+# EMBED_MODEL_ID = "cohere.embed-multilingual-image-v3.0"
 
 # LLM
 # this is the default model
 LLM_MODEL_ID = "meta.llama-3.3-70b-instruct"
 TEMPERATURE = 0.1
-MAX_TOKENS = 1024
+MAX_TOKENS = 2048
 
 # for the UI
 LANGUAGE_LIST = ["same as the question", "en", "fr", "it", "es"]
-MODEL_LIST = ["meta.llama-3.3-70b-instruct", "cohere.command-r-plus-08-2024"]
+# replaced command-r with command-a
+MODEL_LIST = ["meta.llama-3.3-70b-instruct", "cohere.command-a-03-2025"]
 
 ENABLE_USER_FEEDBACK = True
 
 # semantic search
 TOP_K = 6
-COLLECTION_LIST = ["BOOKS", "CNAF"]
+COLLECTION_LIST = ["DEV_COACHING", "BOOKS", "CNAF"]
 
 # OCI general
 COMPARTMENT_ID = "ocid1.compartment.oc1..aaaaaaaaushuwb2evpuf7rcpl4r7ugmqoe7ekmaiik3ra3m7gec3d234eknq"
@@ -69,3 +71,8 @@
 # for loading
 CHUNK_SIZE = 2000
 CHUNK_OVERLAP = 100
+
+# for MCP server
+TRANSPORT = "streamable-http"
+HOST = "0.0.0.0"
+PORT = 9000

ai/gen-ai-agents/custom_rag_agent/mcp_semantic_search.py

@@ -0,0 +1,122 @@
+"""
+Semantic Search exposed as an MCP tool
+
+Author: L. Saetta
+License: MIT
+"""
+
+from typing import Annotated
+from pydantic import Field
+import oracledb
+from fastmcp import FastMCP
+from langchain_community.vectorstores.utils import DistanceStrategy
+from langchain_community.embeddings import OCIGenAIEmbeddings
+from langchain_community.vectorstores.oraclevs import OracleVS
+from utils import get_console_logger
+
+from config import DEBUG
+from config import AUTH, EMBED_MODEL_ID, SERVICE_ENDPOINT, COMPARTMENT_ID
+from config import TRANSPORT, HOST, PORT
+from config_private import CONNECT_ARGS
+
+logger = get_console_logger()
+
+mcp = FastMCP("Demo Semantic Search as MCP server")
+
+
+#
+# Helper functions
+#
+def get_connection():
+    """
+    get a connection to the DB
+    """
+    return oracledb.connect(**CONNECT_ARGS)
+
+
+def get_embedding_model():
+    """
+    Create the Embedding Model
+    """
+    embed_model = OCIGenAIEmbeddings(
+        auth_type=AUTH,
+        model_id=EMBED_MODEL_ID,
+        service_endpoint=SERVICE_ENDPOINT,
+        compartment_id=COMPARTMENT_ID,
+    )
+    return embed_model
+
+
+@mcp.tool
+def semantic_search(
+    query: Annotated[
+        str, Field(description="The search query to find relevant documents.")
+    ],
+    top_k: Annotated[int, Field(description="TOP_K parameter for search")] = 5,
+    collection_name: Annotated[
+        str, Field(description="The name of DB table")
+    ] = "BOOKS",
+) -> dict:
+    """
+    Perform a semantic search based on the provided query.
+    Args:
+        query (str): The search query.
+        top_k (int): The number of top results to return.
+    Returns:
+        dict: a dictionary containing the relevant documents.
+    """
+    try:
+        # must be the same embedding model used during load in the Vector Store
+        embed_model = get_embedding_model()
+
+        # get a connection to the DB and init VS
+        with get_connection() as conn:
+            v_store = OracleVS(
+                client=conn,
+                table_name=collection_name,
+                distance_strategy=DistanceStrategy.COSINE,
+                embedding_function=embed_model,
+            )
+
+            relevant_docs = v_store.similarity_search(query=query, k=top_k)
+
+            if DEBUG:
+                logger.info("Result from similarity search:")
+                logger.info(relevant_docs)
+
+    except Exception as e:
+        logger.error("Error in vector_store.invoke: %s", e)
+        error = str(e)
+        return {"error": error}
+
+    result = {"relevant_docs": relevant_docs}
+
+    return result
+
+@mcp.tool
+def get_collections() -> list:
+    """
+    Get the list of collections (DB tables) available in the Oracle Vector Store.
+    Returns:
+        list: A list of collection names.
+    """
+    with get_connection() as conn:
+        cursor = conn.cursor()
+        
+        cursor.execute(
+            """SELECT DISTINCT utc.table_name
+            FROM user_tab_columns utc
+            WHERE utc.data_type = 'VECTOR'
+            ORDER BY 1 ASC"""
+        )
+        collections = [row[0] for row in cursor.fetchall()]
+        return collections
+    
+if __name__ == "__main__":
+    mcp.run(
+        transport=TRANSPORT,
+        # Bind to all interfaces
+        host=HOST,
+        port=PORT,
+        log_level="INFO",
+    )

ai/generative-ai-service/oci_langflow/LICENSE.txt

@@ -0,0 +1,35 @@
+Copyright (c) 2024 Oracle and/or its affiliates.
+
+The Universal Permissive License (UPL), Version 1.0
+
+Subject to the condition set forth below, permission is hereby granted to any
+person obtaining a copy of this software, associated documentation and/or data
+(collectively the "Software"), free of charge and under any and all copyright
+rights in the Software, and any and all patent rights owned or freely
+licensable by each licensor hereunder covering either (i) the unmodified
+Software as contributed to or provided by such licensor, or (ii) the Larger
+Works (as defined below), to deal in both
+
+(a) the Software, and
+(b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
+one is included with the Software (each a "Larger Work" to which the Software
+is contributed by such licensors),
+
+without restriction, including without limitation the rights to copy, create
+derivative works of, display, perform, and distribute the Software and make,
+use, sell, offer for sale, import, export, have made, and have sold the
+Software and the Larger Work(s), and to sublicense the foregoing rights on
+either these or other terms.
+
+This license is subject to the following condition:
+The above copyright notice and either this complete permission notice or at
+a minimum a reference to the UPL must be included in all copies or
+substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

ai/generative-ai-service/oci_langflow/README.md

@@ -1,16 +1,21 @@
 # Integration of OCI Generative AI in Langflow
 
-This repository contains all the code for a prototype of the integration of OCI Generative AI in Langflow
+[![License: UPL](https://img.shields.io/badge/license-UPL-green)](https://img.shields.io/badge/license-UPL-green) [![Quality gate](https://sonarcloud.io/api/project_badges/quality_gate?project=oracle-devrel_test)](https://sonarcloud.io/dashboard?id=oracle-devrel_test)
 
-Reviewed: 23.01.2025
- 
-# **Link to code**
-   [Code](https://github.com/luigisaetta/oci_langflow/tree/main)
+## Introduction
+This repository contains the code for a prototype of the integration of OCI Generative AI in Langflow
 
-# License
- 
+Reviewed: 25.06.2025
+
+## Security
+Please consult the [security guide](./SECURITY.md) for our responsible security
+vulnerability disclosure process.
+
+## License
 Copyright (c) 2024 Oracle and/or its affiliates.
- 
+
 Licensed under the Universal Permissive License (UPL), Version 1.0.
- 
-See [LICENSE](https://github.com/oracle-devrel/technology-engineering/blob/main/LICENSE) for more details.
+
+See [LICENSE](LICENSE.txt) for more details.
+
+ORACLE AND ITS AFFILIATES DO NOT PROVIDE ANY WARRANTY WHATSOEVER, EXPRESS OR IMPLIED, FOR ANY SOFTWARE, MATERIAL OR CONTENT OF ANY KIND CONTAINED OR PRODUCED WITHIN THIS REPOSITORY, AND IN PARTICULAR SPECIFICALLY DISCLAIM ANY AND ALL IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, AND FITNESS FOR A PARTICULAR PURPOSE.  FURTHERMORE, ORACLE AND ITS AFFILIATES DO NOT REPRESENT THAT ANY CUSTOMARY SECURITY REVIEW HAS BEEN PERFORMED WITH RESPECT TO ANY SOFTWARE, MATERIAL OR CONTENT CONTAINED OR PRODUCED WITHIN THIS REPOSITORY. IN ADDITION, AND WITHOUT LIMITING THE FOREGOING, THIRD PARTIES MAY HAVE POSTED SOFTWARE, MATERIAL OR CONTENT TO THIS REPOSITORY WITHOUT ANY REVIEW. USE AT YOUR OWN RISK. 

ai/generative-ai-service/oci_langflow/custom_oci/embeddings/oci_embeddings_components.py

@@ -0,0 +1,82 @@
+"""
+Custom integration with Langflow and OCI Embeddings Model
+
+Author: L. Saetta (Oracle)
+
+"""
+
+from langchain_community.embeddings import OCIGenAIEmbeddings
+
+from langflow.base.models.model import LCModelComponent
+from langflow.io import DropdownInput, StrInput, Output, SecretStrInput
+from langflow.field_typing import Embeddings
+
+class OCIEmbeddingsComponent(LCModelComponent):
+    """
+    This class integrates the OCI Embeddings Model with Langflow.
+
+    Notes:
+        * Security: for now API_KEY, set your key-pair in $HOME/.oci
+
+    """
+
+    display_name = "OCI Cohere Embeddings"
+    description = "Generate Embeddings using OCI Cohere models."
+
+    inputs = [
+        # example of dropdown
+        DropdownInput(
+            name="auth_type",
+            display_name="auth_type",
+            info="The type of auth_type to use for the chat model",
+            advanced=True,
+            options=[
+                "API_KEY",
+                "RESOURCE_PRINCIPAL",
+            ],
+            value="API_KEY",
+        ),
+        DropdownInput(
+            name="model",
+            display_name="Model",
+            advanced=True,
+            options=[
+                "cohere.embed-english-v3.0",
+                "cohere.embed-multilingual-v3.0",
+            ],
+            value="cohere.embed-english-v3.0",
+        ),
+        StrInput(
+            name="service_endpoint",
+            display_name="Service Endpoint",
+            info="OCI Service Endpoint URL",
+            required=True,
+        ),
+        SecretStrInput(
+            name="compartment_id",
+            display_name="Compartment ID",
+            info="OCI Compartment OCID",
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
+    ]
+
+    def build_model(self) -> Embeddings:
+        """
+        build the embeddings model
+        """
+        return self.build_embeddings()
+
+    def build_embeddings(self) -> Embeddings:
+        """
+        build the embeddings model
+        """
+        # default truncate strategy is END
+        return OCIGenAIEmbeddings(
+            auth_type=self.auth_type,
+            model_id=self.model,
+            service_endpoint=self.service_endpoint,
+            compartment_id=self.compartment_id,
+        )