Merge branch 'main' into fn-datasafe-dbaudit-to-oci-logging-patch2

Author: oheimburger

.gitignore

@@ -3,6 +3,7 @@
 .AppleDouble
 .LSOverride
 .vscode
+.bak
 
 # Icon must end with two \r
 Icon

ai-and-app-modernisation/ai-services/generative-ai-service/README.md

@@ -1,16 +1,14 @@
 # Generative AI service
 
-Oracle’s AI strategy is to make artificial intelligence (AI) pervasive across its cloud
-applications and cloud infrastructure. We serve business users who want to
-improve business processes and outcomes through prebuilt AI capabilities, data
-scientists and developers who want complete control to build and deploy AI
-models of any kind, and independent software vendors (ISVs) who want the
-most performant and cost-effective platform to host their AI services.
+Oracle’s AI strategy is to make artificial intelligence (AI) pervasive across its cloud applications and cloud infrastructure. We serve business users who want to improve business processes and outcomes through prebuilt AI capabilities, data scientists and developers who want complete control to build and deploy AI models of any kind, and independent software vendors (ISVs) who want the most performant and cost-effective platform to host their AI services.
 
 Reviewed: 30.01.2024
 
 # Team Publications
 
+- [Enable a Low Code Modular LLM App Engine using Oracle Integration and OCI Generative AI](https://docs.oracle.com/en/solutions/oci-generative-ai-integration/index.html)
+    - This reference architecture lets you understand the necessary considerations and recommendations to enable an AI-based, modular and event-driven  LLM App Engine using a low-code approach with Oracle Integration as the LLM orchestrator, OCI Generative AI and other OCI services
+    - Build enterprise-grade, modular, scalable, secure & maintainable LLM Apps
 - [Oracle Generative AI webinar](https://go.oracle.com/LP=138234?elqCampaignId=489428&src1=:so:ch:or:dg::::&SC=:so:ch:or:dg::::&pcode=WWMK230822P00010)
     - Deep dive into Oracle Generative AI platform
 - [Creating a RAG (Retrieval-Augmented Generation) with Oracle Generative AI Service in just 21 lines of code](https://github.com/oracle-devrel/technology-engineering/tree/main/ai-and-app-modernisation/ai-services/generative-ai-service/rag-genai)
@@ -20,9 +18,9 @@ Reviewed: 30.01.2024
 - [Build a knowledge search solution by implementing Retrieval Augmented Generation with Generative AI](https://www.youtube.com/watch?v=lJptksR5omo&t=1530s)
 - [Gen AI-powered document search (Generative AI with custom UI)](https://youtu.be/vyJAZvYKY34)
 - [Enabling a WhatsApp Customer HelpMate using OCI Generative AI, AI-Language & Integration](https://youtu.be/ryo3wVB_69E?si=SxSuTVawoRcbhReU)
-    - Use OCI Generative AI (in pre-availability) for "Customer Service Quick Replies" Generation for Whatsapp Neutral Messages (customer questions, queries, etc.), sentence-level sentiment analysis from OCI AI Language to uncover overall sentiment and set service ticket severity for negative Whatsapp messages, automatically classify Customer Service tickets through OCI AI Language custom text classification and aspect-based sentiment analysis (ABSA) services
+    - Use OCI Generative AI (in pre-availability) for "Customer Service Quick Replies" Generation for Whatsapp Neutral Messages (customer questions, queries, etc.), sentence-level sentiment analysis from OCI AI-Language to uncover overall sentiment and set service ticket severity for negative Whatsapp messages, automatically classify Customer Service tickets through OCI AI-Language custom text classification and aspect-based sentiment analysis (ABSA) services
     - Learn how Oracle Integration Cloud and Oracle Cloud Infrastructure (OCI) Streaming allow real-time capture of WhatsApp messages.
-    - All this automation using OCI Generative AI and AI Language Services APIs orchestrated by Oracle Integration Cloud (using a no-code integration approach)
+    - All this automation using OCI Generative AI and AI-Language Services APIs orchestrated by Oracle Integration Cloud (using a no-code integration approach)
 - [Deploy 3rd party generative AI models to OCI](https://docs.oracle.com/en/solutions/deploy-generative-ai-to-oci/index.html#GUID-5567B1F2-4733-4D9C-B4BE-5B5429CA8C02)
     - The reference architecture shows generative AI models deployed to an OCI GPU instance for image and text generation.
 - [Running 3rd party LLMs on OCI](https://github.com/oracle-devrel/oci-generative-ai)
@@ -34,11 +32,14 @@ Reviewed: 30.01.2024
 
 # Useful Links
 
+- [Navigating the frontier: Key considerations for developing a generative AI integration strategy for the enterprise](https://blogs.oracle.com/ai-and-datascience/post/considerations-generative-ai-integration-strategy)
 - [The Future of Generative AI: What Enterprises Need to Know](https://blogs.oracle.com/ai-and-datascience/post/future-generative-ai-what-enterprises-need-to-know) 
 - [Announcing the general availability of OCI Generative AI](https://blogs.oracle.com/ai-and-datascience/post/ga-oci-generative-ai)
 - [Oracle Embeds Generative AI Across the Technology Stack to Enable Enterprise AI Adoption at Scale](https://www.oracle.com/news/announcement/oracle-announces-availability-oci-generative-ai-service-2024-01-23/)
 - [Implement Oracle Cloud Infrastructure Generative AI based on Cohere Large Language Model - Blog & Architecture](https://docs.oracle.com/en/solutions/oci-generative-ai-llm/index.html#GUID-76987F35-F646-40CF-9CF2-42724BA57191)
 - [Generative AI Chatbot using LLaMA-2, Qdrant, RAG, LangChain & Streamlit](https://blogs.oracle.com/ai-and-datascience/post/ai-chatbot-llama2-qdrant-rag-langchain-streamlit)
+- [Developing AI applications with OCI Generative AI and LangChain](https://blogs.oracle.com/ai-and-datascience/post/developing-ai-apps-oci-generative-ai-langchain)
+- [Guide to prompt engineering: Translating natural language to SQL with Llama 2](https://blogs.oracle.com/ai-and-datascience/post/prompt-engineering-natural-language-sql-llama2)
 - [Oracle and Generative AI](https://docs.oracle.com/en-us/iaas/Content/Resources/Assets/whitepapers/oracle-and-generative-ai.pdf)
 - [Oracle’s generative AI strategy - blog by Greg Pavlik](https://blogs.oracle.com/ai-and-datascience/post/generative-ai-strategy)
 - [Oracle to Deliver Powerful and Secure Generative AI Services for Business](https://www.oracle.com/news/announcement/oracle-to-deliver-powerful-and-secure-generative-ai-service-for-business-2023-06-13/)
@@ -49,8 +50,6 @@ Reviewed: 30.01.2024
 - [Generative AI on Oracle.com](https://www.oracle.com/artificial-intelligence/generative-ai/)
 - [Generative AI product documentation](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm)
 
-
-
 # License
 
 Copyright (c) 2024 Oracle and/or its affiliates.

ai-and-app-modernisation/ai-services/generative-ai-service/rag-genai/files/LangChainRAG.py

@@ -0,0 +1,21 @@
+from langchain_community.embeddings import OCIGenAIEmbeddings
+from langchain.chains import RetrievalQA
+from langchain_community.vectorstores import Qdrant
+from langchain_core.prompts import PromptTemplate
+from langchain_community.llms import OCIGenAI
+from langchain_community.document_loaders import UnstructuredURLLoader
+compartment_id = "ocid1.compartment.oc1..aaaaaaaa7ggqkd4ptkeb7ugk6ipsl3gqjofhkr6yacluwj4fitf2ufrdm65q"
+embeddings = OCIGenAIEmbeddings(model_id="cohere.embed-english-light-v3.0",service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",compartment_id= compartment_id,)
+testurls = ['https://docs.oracle.com/iaas/odsaz/odsa-rotate-wallet.html', 'https://docs.oracle.com/iaas/odsaz/odsa-change-password.html', 'https://docs.oracle.com/iaas/odsaz/odsa-database-actions.html']
+loader = UnstructuredURLLoader(urls=testurls)
+docs = loader.load()
+vectorstore = Qdrant.from_documents(docs,embeddings,location=":memory:",prefer_grpc=False,collection_name="test_db")
+retriever = vectorstore.as_retriever()
+rag_prompt_template = """Answer the question based only on the following context:
+{context}
+Question: {question}"""
+rag_prompt = PromptTemplate.from_template(rag_prompt_template)
+llm = OCIGenAI(model_id="cohere.command",service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",compartment_id= compartment_id,model_kwargs={"temperature": 0, "max_tokens": 300})
+rag = RetrievalQA.from_chain_type(llm=llm,retriever=retriever,chain_type_kwargs={"prompt": rag_prompt,},)
+data = rag.invoke("What is rotate a wallet")
+print(data['result'])

ai-and-app-modernisation/ai-services/generative-ai-service/rag-genai/files/README.md

@@ -3,30 +3,27 @@
 ## Introduction
 In this article, we'll explore how to create a Retrieval-Augmented Generation (RAG) model using Oracle Gen AI, llama index, Qdrant Vector Database, and SentenceTransformerEmbeddings. This 21-line code will allow you to scrape through web pages, use llama index for indexing, Oracle Generative AI Service for question generation, and Qdrant for vector indexing.
 
+Find below the code of building a RAG using llamaIndex with Oracle Generative AI Service.
+Also check the file LangChainRAG.py which allows you to create an application (implementing RAG) using Langchain and the file langChainRagWithUI.py which includes a UI build with Streamlit.
+
 <img src="./RagArchitecture.svg">
 </img>
 
-## Limited Availability
-
-Oracle Generative AI Service is in Limited Availability as of today when we are creating this repo.
-
-Customers can easily enter in the LA programs. To test these functionalities you need to enrol in the LA programs and install the proper versions of software libraries.
-
-Code and functionalities can change, as a result of changes and new features
-
 ## Prerequisites
 
 Before getting started, make sure you have the following installed:
 
 - Oracle Generative AI Service
-- llama index
-- qdrant client
+- Llama index
+- Langchain
+- Qdrant client
 - SentenceTransformerEmbeddings
 
 ## Setting up the Environment
 1. Install the required packages:
    ```bash
-   pip install oci==2.118.1+preview.1.1697 llama-index qdrant-client sentence-transformers
+   pip install -U langchain oci
+   pip install langchain llama-index qdrant-client sentence-transformers transformers
    ```
 
 ## Loading data
@@ -41,26 +38,26 @@ sitemap used : https://objectstorage.eu-frankfurt-1.oraclecloud.com/n/frpj5kvxry
 ## Entire code
 
    ```bash
-   from genai_langchain_integration.langchain_oci import OCIGenAI
 from llama_index import VectorStoreIndex
 from llama_index import ServiceContext
 from llama_index.vector_stores.qdrant import QdrantVectorStore
 from llama_index.storage.storage_context import StorageContext
 from qdrant_client import qdrant_client
-from langchain.embeddings import SentenceTransformerEmbeddings
+from langchain_community.embeddings import SentenceTransformerEmbeddings
 from llama_hub.web.sitemap import SitemapReader
+from langchain_community.llms import OCIGenAI
 loader = SitemapReader()
-documents = loader.load_data(sitemap_url='https://objectstorage.eu-frankfurt-1.oraclecloud.com/n/frpj5kvxryk1/b/thisIsThePlace/o/combined.xml')
+documents = loader.load_data(sitemap_url='https://objectstorage.eu-frankfurt-1.oraclecloud.com/n/frpj5kvxryk1/b/thisIsThePlace/o/latest.xml')
 client = qdrant_client.QdrantClient(location=":memory:")
 embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
-llm = OCIGenAI(model_id="cohere.command",service_endpoint="https://generativeai.aiservice.us-chicago-1.oci.oraclecloud.com",compartment_id = "ocid1.tenancy.oc1..aaaaaaaa5hwtrus75rauufcfvtnjnz3mc4xm2bzibbigva2bw4ne7ezkvzha",temperature=0.0)
+llm = OCIGenAI(model_id="cohere.command",service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",model_kwargs={"temperature": 0.0, "max_tokens": 300},compartment_id = "ocid1.compartment.oc1..aaaaaaaa7ggqkd4ptkeb7ugk6ipsl3gqjofhkr6yacluwj4fitf2ufrdm65q")
 system_prompt="As a support engineer, your role is to leverage the information in the context provided. Your task is to respond to queries based strictly on the information available in the provided context. Do not create new information under any circumstances. Refrain from repeating yourself. Extract your response solely from the context mentioned above. If the context does not contain relevant information for the question, respond with 'How can I assist you with questions related to the document?"
 service_context = ServiceContext.from_defaults(llm=llm, chunk_size=1000, chunk_overlap=100, embed_model=embeddings,system_prompt=system_prompt)
 vector_store = QdrantVectorStore(client=client, collection_name="ansh")
 storage_context = StorageContext.from_defaults(vector_store=vector_store)
 index = VectorStoreIndex.from_documents(documents, storage_context=storage_context, service_context=service_context)
 query_engine = index.as_query_engine()
-response = query_engine.query("can i use OCI document understanding for files in french ?")
+response = query_engine.query('What is activity auditing report?')
 print(response)
    ```
 

ai-and-app-modernisation/ai-services/generative-ai-service/rag-genai/files/langChainRagWithUI.py

@@ -0,0 +1,42 @@
+import streamlit as st
+from langchain_community.embeddings import OCIGenAIEmbeddings
+from langchain.chains import RetrievalQA
+from langchain_community.vectorstores import Qdrant
+from langchain_core.prompts import PromptTemplate
+from langchain_community.llms import OCIGenAI
+from langchain_community.document_loaders import UnstructuredURLLoader
+st.title("Oracle QA Chatbot")
+st.text_input("Ask a question:", key="question")  # Input field for questions
+# Data loading (outside any function)
+compartment_id = "ocid1.compartment.oc1..aaaaaaaa7ggqkd4ptkeb7ugk6ipsl3gqjofhkr6yacluwj4fitf2ufrdm65q"
+embeddings = OCIGenAIEmbeddings(model_id="cohere.embed-english-light-v3.0",service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",compartment_id=compartment_id,)
+testurls = ['https://docs.oracle.com/iaas/odsaz/odsa-rotate-wallet.html','https://docs.oracle.com/iaas/odsaz/odsa-change-password.html','https://docs.oracle.com/iaas/odsaz/odsa-database-actions.html',]
+# Cache the loaded documents (outside any function)
+@st.cache_data
+def load_documents():
+    docs = UnstructuredURLLoader(urls=testurls).load()
+    print("Loading data")
+    print(docs)
+    return docs  # Return the loaded documents
+docs = load_documents()
+vectorstore = Qdrant.from_documents(docs, embeddings, location=":memory:", prefer_grpc=False, collection_name="test_db")
+retriever = vectorstore.as_retriever()
+rag_prompt_template = """Answer the question based only on the following context:
+{context}
+Question: {question}"""
+rag_prompt = PromptTemplate.from_template(rag_prompt_template)
+llm = OCIGenAI(
+    model_id="cohere.command",
+    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
+    compartment_id=compartment_id,
+    model_kwargs={"temperature": 0, "max_tokens": 300},
+)
+rag = RetrievalQA.from_chain_type(llm=llm, retriever=retriever, chain_type_kwargs={"prompt": rag_prompt})
+# Answer generation when a question is asked
+if st.button("Get Answer"):
+    question = st.session_state.question
+    # Ensure correct access to cached documents
+    docs = load_documents()  # Call the cached function to retrieve documents
+    data = rag.invoke(question, context=docs)  # Pass documents as context
+    answer = data["result"]
+    st.write("Answer:", answer)

ai-and-app-modernisation/ai-services/generative-ai-service/speech-genai/LICENSE

@@ -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.