Download SpringAI project as zip file (#37)

* Build SpringAI template and provide downloaded zip
* Documentation

.gitignore

@@ -33,4 +33,9 @@ sonar-project.properties
 __pycache__/
 *.py[cod]
 *$py.class
-env.sh
+spring_ai/src/main/resources/data/sandbox_settings.json
+spring_ai/target/**
+spring_ai/create_user.sql
+spring_ai/drop.sql
+start.sh
+spring_ai/env.sh

app/src/modules/st_common.py

@@ -6,6 +6,11 @@
 
 import json
 import copy
+import io
+import zipfile
+import tempfile
+import shutil
+import os
 
 # Streamlit
 import streamlit as st
@@ -18,8 +23,13 @@
 import modules.help as custom_help
 from modules.chatbot import ChatCmd
 
-logger = logging_config.logging.getLogger("modules.st_common")
 
+from langchain_huggingface import HuggingFaceEndpointEmbeddings
+from langchain_openai import OpenAIEmbeddings
+from langchain_ollama import OllamaEmbeddings
+from langchain_cohere import CohereEmbeddings
+
+logger = logging_config.logging.getLogger("modules.st_common")
 
 def clear_initialized():
     """Reset the initialization of the ChatBot"""
@@ -90,6 +100,7 @@ def reset_rag():
 
     # Set the RAG Prompt
     set_prompt()
+    state.show_download_spring_ai = False
 
 
 def initialize_rag():
@@ -238,6 +249,223 @@ def initialize_chatbot(ll_model):
     return cmd
 
 
+def get_yaml_obaas(session_state_json,provider):
+
+    ########## OBAAS VERSION ##########
+    # eureka:
+    #    instance:
+    #       hostname: ${spring.application.name}
+    #       preferIpAddress: true
+    #    client:
+    #      service-url:
+    #        defaultZone: ${eureka.service-url}
+    #    fetch-registry: true
+    #    register-with-eureka: true
+    #    enabled: true
+    vector_table,_= utilities.get_vs_table(session_state_json["rag_params"]["model"],session_state_json["rag_params"]["chunk_size"],session_state_json["rag_params"]["chunk_overlap"],session_state_json["rag_params"]["distance_metric"])
+    instr_context=session_state_json["lm_instr_config"][session_state_json["lm_instr_prompt"]]["prompt"]
+
+    yaml_base=f"""
+server:
+  servlet:
+    context-path: /v1
+spring:
+  datasource:
+    url: ${{spring.datasource.url}}
+    username: ${{spring.datasource.username}}
+    password: ${{spring.datasource.password}}
+  ai:
+    vectorstore:
+      oracle:
+        distance-type: {session_state_json["rag_params"]["distance_metric"]}
+        remove-existing-vector-store-table: True
+        initialize-schema: True
+        index-type: None
+    """
+
+    yaml_base_aims=f"""
+aims:
+  context_instr: \"{instr_context}\"
+  vectortable:
+    name: {vector_table}
+  rag_params: 
+    search_type: Similarity
+    top_k: {session_state_json.get("rag_params", {}).get("top_k",4)}  
+"""
+    openai_yaml=f"""
+    openai:
+      base-url: \"{session_state_json["ll_model_config"][session_state_json["ll_model"]]["url"]}\"
+      api-key: 
+      chat:
+        options:
+          temperature: {session_state_json["ll_model_config"][session_state_json["ll_model"]]["temperature"][0]}
+          frequencyPenalty: {session_state_json["ll_model_config"][session_state_json["ll_model"]]["frequency_penalty"][0]}
+          presencePenalty: {session_state_json["ll_model_config"][session_state_json["ll_model"]]["presence_penalty"][0]}
+          maxTokens: {session_state_json["ll_model_config"][session_state_json["ll_model"]]["max_tokens"][0]}
+          topP: {session_state_json["ll_model_config"][session_state_json["ll_model"]]["top_p"][0]}
+          model: {session_state_json["ll_model"]}
+      embedding:
+        options:
+          model: {session_state_json["rag_params"]["model"]}
+    """
+
+    ollama_yaml=f"""
+    ollama:
+      base-url: "http://ollama.ollama.svc.cluster.local:11434"
+      chat:
+        options:
+          top-p: {session_state_json["ll_model_config"][session_state_json["ll_model"]]["top_p"][0]}
+          presence-penalty: {session_state_json["ll_model_config"][session_state_json["ll_model"]]["presence_penalty"][0]}
+          frequency-penalty: {session_state_json["ll_model_config"][session_state_json["ll_model"]]["frequency_penalty"][0]}
+          temperature: {session_state_json["ll_model_config"][session_state_json["ll_model"]]["temperature"][0]}
+          num-predict: {session_state_json["ll_model_config"][session_state_json["ll_model"]]["max_tokens"][0]}
+          model: \"{session_state_json["ll_model"]}\"
+      embedding:
+        options: 
+          model: \"{session_state_json["rag_params"]["model"]}\"
+    """
+
+    if provider=="ollama":
+        yaml = yaml_base + ollama_yaml + yaml_base_aims
+    else:
+        yaml = yaml_base + openai_yaml + yaml_base_aims
+
+    return yaml
+
+def get_yaml_env(session_state_json,provider):
+
+    OLLAMA_MODEL="llama3.1"
+
+    instr_context=session_state_json["lm_instr_config"][session_state_json["lm_instr_prompt"]]["prompt"]
+    vector_table,_= utilities.get_vs_table(session_state_json["rag_params"]["model"],session_state_json["rag_params"]["chunk_size"],session_state_json["rag_params"]["chunk_overlap"],session_state_json["rag_params"]["distance_metric"])
+
+    logger.info("ll_model selected: %s",session_state_json["ll_model"])
+    logger.info(session_state_json["ll_model"] != OLLAMA_MODEL)
+
+    if session_state_json["ll_model"] != OLLAMA_MODEL:
+        env_vars_LLM= f"""
+    export OPENAI_CHAT_MODEL={session_state_json["ll_model"]}
+    export OPENAI_EMBEDDING_MODEL={session_state_json["rag_params"]["model"]}
+    export OPENAI_URL=\"{session_state_json["ll_model_config"][session_state_json["ll_model"]]["url"]}\"
+    export OP_TEMPERATURE={session_state_json["ll_model_config"][session_state_json["ll_model"]]["temperature"][0]}
+    export OP_FREQUENCY_PENALTY={session_state_json["ll_model_config"][session_state_json["ll_model"]]["frequency_penalty"][0]}
+    export OP_PRESENCE_PENALTY={session_state_json["ll_model_config"][session_state_json["ll_model"]]["presence_penalty"][0]}
+    export OP_MAX_TOKENS={session_state_json["ll_model_config"][session_state_json["ll_model"]]["max_tokens"][0]}
+    export OP_TOP_P={session_state_json["ll_model_config"][session_state_json["ll_model"]]["top_p"][0]}
+    export OL_TEMPERATURE=
+    export OL_FREQUENCY_PENALTY=
+    export OL_PRESENCE_PENALTY=
+    export OL_MAX_TOKENS=
+    export OL_TOP_P=
+    export OLLAMA_CHAT_MODEL=\"\"
+    export OLLAMA_EMBEDDING_MODEL=\"\"
+        """
+
+    else:
+        env_vars_LLM= f"""
+    export OPENAI_CHAT_MODEL=\"\"
+    export OPENAI_EMBEDDING_MODEL=\"\"
+    export OPENAI_URL=\"\"
+    export OLLAMA_CHAT_MODEL=\"{session_state_json["ll_model"]}\"
+    export OLLAMA_EMBEDDING_MODEL=\"{session_state_json["rag_params"]["model"]}\"
+    export OL_TEMPERATURE={session_state_json["ll_model_config"][session_state_json["ll_model"]]["temperature"][0]}
+    export OL_FREQUENCY_PENALTY={session_state_json["ll_model_config"][session_state_json["ll_model"]]["frequency_penalty"][0]}
+    export OL_PRESENCE_PENALTY={session_state_json["ll_model_config"][session_state_json["ll_model"]]["presence_penalty"][0]}
+    export OL_MAX_TOKENS={session_state_json["ll_model_config"][session_state_json["ll_model"]]["max_tokens"][0]}
+    export OL_TOP_P={session_state_json["ll_model_config"][session_state_json["ll_model"]]["top_p"][0]}
+    export OP_TEMPERATURE=
+    export OP_FREQUENCY_PENALTY=
+    export OP_PRESENCE_PENALTY=
+    export OP_MAX_TOKENS=
+    export OP_TOP_P=
+    """
+
+    env_vars= f"""
+    export SPRING_AI_OPENAI_API_KEY=$OPENAI_API_KEY
+    export DB_DSN=\"jdbc:oracle:thin:@{session_state_json["db_config"]["dsn"]}\"
+    export DB_USERNAME=\"{session_state_json["db_config"]["user"]}\"
+    export DB_PASSWORD=\"{session_state_json["db_config"]["password"]}\"
+    export DISTANCE_TYPE={session_state_json["rag_params"]["distance_metric"]}
+    export OLLAMA_BASE_URL=\"{session_state_json["ll_model_config"][OLLAMA_MODEL]["url"]}\"
+    export CONTEXT_INSTR=\"{instr_context}\"
+    export TOP_K={session_state_json.get("rag_params", {}).get("top_k",4)}  
+
+
+
+    export VECTOR_STORE={vector_table}
+    export PROVIDER={provider}
+    mvn spring-boot:run -P {provider}
+    """
+    logger.info(env_vars_LLM+env_vars)
+
+    return env_vars_LLM+env_vars
+
+
+def create_zip(state_dict_filt, provider):
+    # Source directory that you want to copy
+    toCopy=["mvnw","mvnw.cmd","pom.xml","README.md"]
+    source_dir_root = '../../spring_ai'
+    source_dir = '../../spring_ai/src'
+
+    logger.info(f"Local dir : {os.getcwd()}")
+    # Using TemporaryDirectory
+    with tempfile.TemporaryDirectory() as temp_dir:
+        destination_dir= os.path.join(temp_dir, "spring_ai")
+
+        shutil.copytree(source_dir, os.path.join(temp_dir, "spring_ai/src"))
+        for item in toCopy:
+            shutil.copy(os.path.join(source_dir_root,item), os.path.join(temp_dir, "spring_ai"))
+        logger.info(f"Temporary directory created and copied: {temp_dir}")
+
+        zip_buffer = io.BytesIO()
+
+        with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
+            for foldername, subfolders, filenames in os.walk(destination_dir):
+                for filename in filenames:
+                    file_path = os.path.join(foldername, filename)
+
+                    arcname = os.path.relpath(file_path, destination_dir)  # Make the path relative
+                    zip_file.write(file_path, arcname)
+            env_content = get_yaml_env(state_dict_filt, provider)
+            obaas_yaml = get_yaml_obaas(state_dict_filt,provider)
+            zip_file.writestr('env.sh', env_content)    
+            zip_file.writestr('src/main/resources/application-obaas.yml', obaas_yaml)     
+        zip_buffer.seek(0)  
+    return zip_buffer
+
+# Check if the conf is full ollama or openai, currently supported for springai export
+def check_hybrid_conf(session_state_json):
+
+    embedding_models = meta.embedding_models()
+    chat_models = meta.ll_models()
+
+    embModel =  embedding_models.get(session_state_json["rag_params"].get("model"))
+    chatModel = chat_models.get(session_state_json["ll_model"])
+    logger.info("Model: %s",session_state_json["ll_model"])
+    logger.info("Embedding Model embModel: %s",embModel)
+    logger.info("Chat Model: %s",chatModel)
+    if (chatModel is not None) and  (embModel is not None):
+
+        logger.info("Embeddings to string: %s", str(embModel["api"]))
+
+        if ("OpenAI" in str(embModel["api"])) and (chatModel["api"]== "OpenAI"):
+            logger.info("RETURN openai")
+            logger.info("chatModel[api]: %s",chatModel["api"])
+            logger.info("Embedding Model embModel[api]: %s",embModel["api"])
+            return "openai"
+        else:
+            if ("Ollama" in str(embModel["api"])) and (chatModel["api"]== "ChatOllama"):
+                logger.info("RETURN ollama")
+                logger.info("chatModel[api]: %s",chatModel["api"])
+                logger.info("Embedding Model embModel[api]: %s",embModel["api"])
+                return "ollama"
+            else:
+                return "hybrid"
+    else:
+        return "hybrid"
+
+
+
 ###################################
 # Language Model Sidebar
 ###################################
@@ -582,10 +810,25 @@ def empty_key(obj):
     state_dict_filt = empty_key(state_dict_filt)
     session_state_json = json.dumps(state_dict_filt, indent=4)
     # Only allow exporting settings if tools/admin is enabled
+
+
     if not state.disable_tools and not state.disable_admin:
         st.sidebar.download_button(
             label="Download Settings",
             data=session_state_json,
             file_name="sandbox_settings.json",
             use_container_width=True,
         )
+
+
+    state.provider= check_hybrid_conf(state_dict_filt)
+    logger.info("Provider type: %s", state.provider)
+
+    if not state.disable_tools and not state.disable_admin and state.provider != "hybrid":
+        st.sidebar.download_button(
+            label="Download SpringAI",
+            data=create_zip(state_dict_filt, state.provider),  # Generate zip on the fly
+            file_name="spring_ai.zip",  # Zip file name
+            mime="application/zip",  # Mime type for zip file
+            use_container_width=True,
+        )