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CodeGen/README.md

@@ -20,7 +20,6 @@ The workflow falls into the following architecture:
 
 The CodeGen example is implemented using the component-level microservices defined in [GenAIComps](https://github.com/opea-project/GenAIComps). The flow chart below shows the information flow between different microservices for this example.
 
-
 ```mermaid
 ---
 config:
@@ -155,11 +154,13 @@ cd GenAIExamples/CodeGen/docker_compose/intel/hpu/gaudi
 ```
 
 TGI service:
+
 ```bash
 docker compose --profile codegen-gaudi-tgi up -d
 ```
 
 vLLM service:
+
 ```bash
 docker compose --profile codegen-gaudi-vllm up -d
 ```
@@ -175,16 +176,17 @@ cd GenAIExamples/CodeGen/docker_compose/intel/cpu/xeon
 ```
 
 TGI service:
+
 ```bash
 docker compose --profile codegen-xeon-tgi up -d
 ```
 
 vLLM service:
+
 ```bash
 docker compose --profile codegen-xeon-vllm up -d
 ```
 
-
 Refer to the [Xeon Guide](./docker_compose/intel/cpu/xeon/README.md) for more instructions on building docker images from source.
 
 ### Deploy CodeGen on Kubernetes using Helm Chart

CodeGen/codegen.py

@@ -1,11 +1,11 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+import ast
 import asyncio
 import os
-import ast
 
-from comps import MegaServiceEndpoint, MicroService, ServiceOrchestrator, ServiceRoleType, ServiceType, CustomLogger
+from comps import CustomLogger, MegaServiceEndpoint, MicroService, ServiceOrchestrator, ServiceRoleType, ServiceType
 from comps.cores.mega.utils import handle_message
 from comps.cores.proto.api_protocol import (
     ChatCompletionRequest,
@@ -30,21 +30,21 @@
 TEI_EMBEDDING_HOST_IP = os.getenv("TEI_EMBEDDING_HOST_IP", "0.0.0.0")
 EMBEDDER_PORT = int(os.getenv("EMBEDDER_PORT", 6000))
 
-grader_prompt = """You are a grader assessing relevance of a retrieved document to a user question. \n                     
+grader_prompt = """You are a grader assessing relevance of a retrieved document to a user question. \n
 Here is the user question: {question} \n
 Here is the retrieved document: \n\n {document} \n\n
 
-If the document contains keywords related to the user question, grade it as relevant. 
-It does not need to be a stringent test. The goal is to filter out erroneous retrievals. 
+If the document contains keywords related to the user question, grade it as relevant.
+It does not need to be a stringent test. The goal is to filter out erroneous retrievals.
 Rules:
-- Do not return the question, the provided document or explanation. 
-- if this document is relevant to the question, return 'yes' otherwise return 'no'. 
+- Do not return the question, the provided document or explanation.
+- if this document is relevant to the question, return 'yes' otherwise return 'no'.
 - Do not include any other details in your response.
 """
 
+
 def align_inputs(self, inputs, cur_node, runtime_graph, llm_parameters_dict, **kwargs):
-    """
-    Aligns the inputs based on the service type of the current node.
+    """Aligns the inputs based on the service type of the current node.
 
     Parameters:
     - self: Reference to the current instance of the class.
@@ -57,25 +57,21 @@ def align_inputs(self, inputs, cur_node, runtime_graph, llm_parameters_dict, **k
     Returns:
     - inputs: The aligned inputs for the current node.
     """
-    
+
     # Check if the current service type is EMBEDDING
     if self.services[cur_node].service_type == ServiceType.EMBEDDING:
         # Store the input query for later use
         self.input_query = inputs["query"]
         # Set the input for the embedding service
         inputs["input"] = inputs["query"]
-                
+
     # Check if the current service type is RETRIEVER
     if self.services[cur_node].service_type == ServiceType.RETRIEVER:
         # Extract the embedding from the inputs
-        embedding = inputs['data'][0]['embedding']
+        embedding = inputs["data"][0]["embedding"]
         # Align the inputs for the retriever service
-        inputs = {
-            "index_name": llm_parameters_dict["index_name"],  
-            "text": self.input_query,
-            "embedding": embedding
-        }
-                
+        inputs = {"index_name": llm_parameters_dict["index_name"], "text": self.input_query, "embedding": embedding}
+
     return inputs
 
 
@@ -90,9 +86,7 @@ def __init__(self, host="0.0.0.0", port=8000):
         self.endpoint = str(MegaServiceEndpoint.CODE_GEN)
 
     def add_remote_service(self):
-        """
-        Adds remote microservices to the service orchestrators and defines the flow between them.
-        """
+        """Adds remote microservices to the service orchestrators and defines the flow between them."""
 
         # Define the embedding microservice
         embedding = MicroService(
@@ -137,8 +131,7 @@ def add_remote_service(self):
         self.megaservice_llm.add(llm)
 
     async def read_streaming_response(self, response: StreamingResponse):
-        """
-        Reads the streaming response from a StreamingResponse object.
+        """Reads the streaming response from a StreamingResponse object.
 
         Parameters:
         - self: Reference to the current instance of the class.
@@ -153,8 +146,7 @@ async def read_streaming_response(self, response: StreamingResponse):
         return body.decode("utf-8")  # Decode the accumulated byte string to a regular string
 
     async def handle_request(self, request: Request):
-        """
-        Handles the incoming request, processes it through the appropriate microservices,
+        """Handles the incoming request, processes it through the appropriate microservices,
         and returns the response.
 
         Parameters:
@@ -189,7 +181,7 @@ async def handle_request(self, request: Request):
             presence_penalty=chat_request.presence_penalty if chat_request.presence_penalty else 0.0,
             repetition_penalty=chat_request.repetition_penalty if chat_request.repetition_penalty else 1.03,
             stream=stream_opt,
-            index_name=chat_request.index_name
+            index_name=chat_request.index_name,
         )
 
         # Initialize the initial inputs with the generated prompt
@@ -237,18 +229,20 @@ async def handle_request(self, request: Request):
                                     if r["choices"][0]["text"] == "yes":
                                         # Append the document to the relevant_docs list
                                         relevant_docs.append(doc)
-                                    
+
                 # Update the initial inputs with the relevant documents
-                if len(relevant_docs)>0:
+                if len(relevant_docs) > 0:
                     logger.info(f"[ CodeGenService - handle_request ] {len(relevant_docs)} relevant document\s found.")
                     query = initial_inputs["query"]
                     initial_inputs = {}
                     initial_inputs["retrieved_docs"] = relevant_docs
                     initial_inputs["initial_query"] = query
-                    
+
                 else:
-                    logger.info("[ CodeGenService - handle_request ] Could not find any relevant documents. The query will be used as input to the LLM.")
-                    
+                    logger.info(
+                        "[ CodeGenService - handle_request ] Could not find any relevant documents. The query will be used as input to the LLM."
+                    )
+
             else:
                 # Use the combined retriever and LLM microservice
                 megaservice = self.megaservice_retriever_llm
@@ -301,4 +295,4 @@ def start(self):
 if __name__ == "__main__":
     chatqna = CodeGenService(port=MEGA_SERVICE_PORT)
     chatqna.add_remote_service()
-    chatqna.start()
+    chatqna.start()

CodeGen/docker_compose/intel/cpu/xeon/README.md

@@ -111,6 +111,7 @@ export https_proxy=${your_https_proxy}
 ```
 
 ## Start the Docker Containers for All Services
+
 Find the corresponding [compose.yaml](./docker_compose/intel/cpu/xeon/compose.yaml). User could start CodeGen based on TGI or vLLM service:
 
 ```bash
@@ -132,8 +133,8 @@ Then run the command `docker images`, you will have the following Docker images:
 - `opea/dataprep`
 - `opea/embedding`
 - `opea/llm-textgen`
-- `opea/retriever` 
-- `redis/redis-stack` 
+- `opea/retriever`
+- `redis/redis-stack`
 
 #### vLLM service:
 
@@ -150,8 +151,8 @@ Then run the command `docker images`, you will have the following Docker images:
 - `opea/dataprep`
 - `opea/embedding`
 - `opea/llm-textgen`
-- `opea/retriever` 
-- `redis/redis-stack` 
+- `opea/retriever`
+- `redis/redis-stack`
 - `opea/vllm`
 
 ### Validate the MicroServices and MegaService
@@ -177,16 +178,16 @@ Then run the command `docker images`, you will have the following Docker images:
 
 3. Dataprep Microservice
 
-    Make sure to replace the file name placeholders with your correct file name
+   Make sure to replace the file name placeholders with your correct file name
 
-      ```bash
-      curl http://${host_ip}:6007/v1/dataprep/ingest \
-     -X POST \
-     -H "Content-Type: multipart/form-data" \
-     -F "files=@./file1.pdf" \
-     -F "files=@./file2.txt" \ 
-     -F "index_name=my_API_document"
-      ```
+   ```bash
+   curl http://${host_ip}:6007/v1/dataprep/ingest \
+   -X POST \
+   -H "Content-Type: multipart/form-data" \
+   -F "files=@./file1.pdf" \
+   -F "files=@./file2.txt" \
+   -F "index_name=my_API_document"
+   ```
 
 4. MegaService
 
@@ -196,16 +197,16 @@ Then run the command `docker images`, you will have the following Docker images:
      -d '{"messages": "Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."}'
    ```
 
-    CodeGen service with RAG and Agents activated based on an index.
-   
-    ```bash
-    curl http://${host_ip}:7778/v1/codegen \
-      -H "Content-Type: application/json" \
-      -d '{"agents_flag": "True", "index_name": "my_API_document", "messages": "Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."}'
-    ```
-   
+   CodeGen service with RAG and Agents activated based on an index.
+
+   ```bash
+   curl http://${host_ip}:7778/v1/codegen \
+     -H "Content-Type: application/json" \
+     -d '{"agents_flag": "True", "index_name": "my_API_document", "messages": "Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."}'
+   ```
 
 ## 🚀 Launch the Gradio Based UI (Recommended)
+
 To access the Gradio frontend URL, follow the steps in [this README](../../../../ui/gradio/README.md)
 
 Code Generation Tab
@@ -222,7 +223,6 @@ Here is an example of running a query in the Gradio UI using an Index:
 
 ![project-screenshot](../../../../assets/img/codegen_gradio_ui_query.png)
 
-
 ## 🚀 Launch the Svelte Based UI (Optional)
 
 To access the frontend, open the following URL in your browser: `http://{host_ip}:5173`. By default, the UI runs on port 5173 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the `compose.yaml` file as shown below:

CodeGen/docker_compose/intel/cpu/xeon/compose.yaml

@@ -1,3 +1,6 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
 services:
 
   tgi-service:
@@ -132,7 +135,7 @@ services:
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}
       REDIS_URL: ${REDIS_URL}
-      REDIS_HOST: ${host_ip} 
+      REDIS_HOST: ${host_ip}
       INDEX_NAME: ${INDEX_NAME}
       HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
       LOGFLAG: true
@@ -197,4 +200,4 @@ services:
     restart: unless-stopped
 networks:
   default:
-    driver: bridge
+    driver: bridge

CodeGen/docker_compose/intel/hpu/gaudi/README.md

@@ -112,11 +112,13 @@ cd GenAIExamples/CodeGen/docker_compose/intel/hpu/gaudi
 ```
 
 TGI service:
+
 ```bash
 docker compose --profile codegen-gaudi-tgi up -d
 ```
 
 vLLM service:
+
 ```bash
 docker compose --profile codegen-gaudi-vllm up -d
 ```
@@ -146,16 +148,16 @@ Refer to the [Gaudi Guide](./docker_compose/intel/hpu/gaudi/README.md) to build
 
 3. Dataprep Microservice
 
-    Make sure to replace the file name placeholders with your correct file name
+   Make sure to replace the file name placeholders with your correct file name
 
-      ```bash
-      curl http://${host_ip}:6007/v1/dataprep/ingest \
-     -X POST \
-     -H "Content-Type: multipart/form-data" \
-     -F "files=@./file1.pdf" \
-     -F "files=@./file2.txt" \ 
-     -F "index_name=my_API_document"
-      ```
+   ```bash
+   curl http://${host_ip}:6007/v1/dataprep/ingest \
+   -X POST \
+   -H "Content-Type: multipart/form-data" \
+   -F "files=@./file1.pdf" \
+   -F "files=@./file2.txt" \
+   -F "index_name=my_API_document"
+   ```
 
 4. MegaService
 
@@ -165,15 +167,16 @@ Refer to the [Gaudi Guide](./docker_compose/intel/hpu/gaudi/README.md) to build
      -d '{"messages": "Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."}'
    ```
 
-    CodeGen service with RAG and Agents activated based on an index.
-   
-    ```bash
-    curl http://${host_ip}$:7778/v1/codegen \
-      -H "Content-Type: application/json" \
-      -d '{"agents_flag": "True", "index_name": "my_API_document", "messages": "Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."}'
-    ```
+   CodeGen service with RAG and Agents activated based on an index.
+
+   ```bash
+   curl http://${host_ip}$:7778/v1/codegen \
+     -H "Content-Type: application/json" \
+     -d '{"agents_flag": "True", "index_name": "my_API_document", "messages": "Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."}'
+   ```
 
 ## 🚀 Launch the Gradio Based UI (Recommended)
+
 To access the Gradio frontend URL, follow the steps in [this README](../../../../ui/gradio/README.md)
 
 Code Generation Tab
@@ -190,7 +193,6 @@ Here is an example of running a query in the Gradio UI using an Index:
 
 ![project-screenshot](../../../../assets/img/codegen_gradio_ui_query.png)
 
-
 ## 🚀 Launch the Svelte Based UI (Optional)
 
 To access the frontend, open the following URL in your browser: `http://{host_ip}:5173`. By default, the UI runs on port 5173 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the `compose.yaml` file as shown below:
@@ -300,4 +302,3 @@ For example:
 - Ask question and get answer
 
 ![qna](../../../../assets/img/codegen_qna.png)
-

CodeGen/docker_compose/intel/hpu/gaudi/compose.yaml

@@ -151,7 +151,7 @@ services:
       http_proxy: ${http_proxy}
       https_proxy: ${https_proxy}
       REDIS_URL: ${REDIS_URL}
-      REDIS_HOST: ${host_ip} 
+      REDIS_HOST: ${host_ip}
       INDEX_NAME: ${INDEX_NAME}
       HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
       LOGFLAG: true
@@ -216,4 +216,4 @@ services:
     restart: unless-stopped
 networks:
   default:
-    driver: bridge
+    driver: bridge