Merge branch 'main' into CI/test_update

comps/asr/whisper/Dockerfile

@@ -27,10 +27,9 @@ RUN pip install --no-cache-dir --upgrade pip && \
     else \
         pip install --no-cache-dir -r /home/user/comps/asr/requirements.txt ; \
     fi
-    pip list
 
 ENV PYTHONPATH=$PYTHONPATH:/home/user
 
 WORKDIR /home/user/comps/asr/whisper
 
-ENTRYPOINT ["python", "whisper_server.py", "--device", "cpu"]
+ENTRYPOINT ["python", "whisper_server.py", "--device", "cpu"]

comps/dataprep/redis/README.md

@@ -38,9 +38,6 @@ Please refer to this [readme](../../vectorstores/langchain/redis/README.md).
 ```bash
 export REDIS_URL="redis://${your_ip}:6379"
 export INDEX_NAME=${your_index_name}
-export LANGCHAIN_TRACING_V2=true
-export LANGCHAIN_API_KEY=${your_langchain_api_key}
-export LANGCHAIN_PROJECT="opea/gen-ai-comps:dataprep"
 export PYTHONPATH=${path_to_comps}
 ```
 
@@ -97,13 +94,9 @@ Please refer to this [readme](../../vectorstores/langchain/redis/README.md).
 ```bash
 export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
 export TEI_ENDPOINT="http://${your_ip}:6006"
-export REDIS_HOST=${your_ip}
-export REDIS_PORT=6379
 export REDIS_URL="redis://${your_ip}:6379"
 export INDEX_NAME=${your_index_name}
-export LANGCHAIN_TRACING_V2=true
-export LANGCHAIN_API_KEY=${your_langchain_api_key}
-export LANGCHAIN_PROJECT="opea/dataprep"
+export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
 ```
 
 ## 2.3 Build Docker Image
@@ -136,13 +129,13 @@ docker build -t opea/dataprep-on-ray-redis:latest --build-arg https_proxy=$https
 - option 1: Start single-process version (for 1-10 files processing)
 
 ```bash
-docker run -d --name="dataprep-redis-server" -p 6007:6007 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT opea/dataprep-redis:latest
+docker run -d --name="dataprep-redis-server" -p 6007:6007 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN opea/dataprep-redis:latest
 ```
 
 - option 2: Start multi-process version (for >10 files processing)
 
 ```bash
-docker run -d --name="dataprep-redis-server" -p 6007:6007 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT -e TIMEOUT_SECONDS=600 opea/dataprep-on-ray-redis:latest
+docker run -d --name="dataprep-redis-server" -p 6007:6007 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN -e TIMEOUT_SECONDS=600 opea/dataprep-on-ray-redis:latest
 ```
 
 ## 2.5 Run with Docker Compose (Option B - deprecated, will move to genAIExample in future)

comps/dataprep/redis/langchain/config.py

@@ -62,3 +62,5 @@ def format_redis_conn_from_env():
 KEY_INDEX_NAME = os.getenv("KEY_INDEX_NAME", "file-keys")
 
 TIMEOUT_SECONDS = int(os.getenv("TIMEOUT_SECONDS", 600))
+
+SEARCH_BATCH_SIZE = int(os.getenv("SEARCH_BATCH_SIZE", 10))

comps/dataprep/redis/langchain/docker/docker-compose-dataprep-redis.yaml

@@ -27,8 +27,6 @@ services:
     container_name: dataprep-redis-server
     ports:
       - "6007:6007"
-      - "6008:6008"
-      - "6009:6009"
     ipc: host
     environment:
       no_proxy: ${no_proxy}
@@ -39,7 +37,7 @@ services:
       REDIS_URL: ${REDIS_URL}
       INDEX_NAME: ${INDEX_NAME}
       TEI_ENDPOINT: ${TEI_ENDPOINT}
-      LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
+      HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
     restart: unless-stopped
 
 networks:

comps/dataprep/redis/langchain/prepare_doc_redis.py

@@ -10,13 +10,13 @@
 
 # from pyspark import SparkConf, SparkContext
 import redis
-from config import EMBED_MODEL, INDEX_NAME, KEY_INDEX_NAME, REDIS_URL
+from config import EMBED_MODEL, INDEX_NAME, KEY_INDEX_NAME, REDIS_URL, SEARCH_BATCH_SIZE
 from fastapi import Body, File, Form, HTTPException, UploadFile
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
 from langchain_community.vectorstores import Redis
+from langchain_huggingface import HuggingFaceEndpointEmbeddings
 from langchain_text_splitters import HTMLHeaderTextSplitter
-from langsmith import traceable
 from redis.commands.search.field import TextField
 from redis.commands.search.indexDefinition import IndexDefinition, IndexType
 
@@ -25,7 +25,7 @@
     create_upload_folder,
     document_loader,
     encode_filename,
-    get_file_structure,
+    format_search_results,
     get_separators,
     get_tables_result,
     parse_html,
@@ -76,7 +76,7 @@ def search_by_id(client, doc_id):
     print(f"[ search by id ] searching docs of {doc_id}")
     try:
         results = client.load_document(doc_id)
-        print(f"[ search by id ] search success of {doc_id}")
+        print(f"[ search by id ] search success of {doc_id}: {results}")
         return results
     except Exception as e:
         print(f"[ search by id ] fail to search docs of {doc_id}: {e}")
@@ -109,7 +109,7 @@ def ingest_chunks_to_redis(file_name: str, chunks: List):
     # Create vectorstore
     if tei_embedding_endpoint:
         # create embeddings using TEI endpoint service
-        embedder = HuggingFaceHubEmbeddings(model=tei_embedding_endpoint)
+        embedder = HuggingFaceEndpointEmbeddings(model=tei_embedding_endpoint)
     else:
         # create embeddings using local embedding model
         embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL)
@@ -139,8 +139,12 @@ def ingest_chunks_to_redis(file_name: str, chunks: List):
     client = r.ft(KEY_INDEX_NAME)
     if not check_index_existance(client):
         assert create_index(client)
-    assert store_by_id(client, key=file_name, value="#".join(file_ids))
 
+    try:
+        assert store_by_id(client, key=file_name, value="#".join(file_ids))
+    except Exception as e:
+        print(f"[ ingest chunks ] {e}. Fail to store chunks of file {file_name}.")
+        raise HTTPException(status_code=500, detail=f"Fail to store chunks of file {file_name}.")
     return True
 
 
@@ -177,7 +181,6 @@ def ingest_data_to_redis(doc_path: DocPath):
 
 
 @register_microservice(name="opea_service@prepare_doc_redis", endpoint="/v1/dataprep", host="0.0.0.0", port=6007)
-@traceable(run_type="tool")
 async def ingest_documents(
     files: Optional[Union[UploadFile, List[UploadFile]]] = File(None),
     link_list: Optional[str] = Form(None),
@@ -189,12 +192,30 @@ async def ingest_documents(
     print(f"files:{files}")
     print(f"link_list:{link_list}")
 
+    r = redis.Redis(connection_pool=redis_pool)
+    client = r.ft(KEY_INDEX_NAME)
+
     if files:
         if not isinstance(files, list):
             files = [files]
         uploaded_files = []
+
         for file in files:
             encode_file = encode_filename(file.filename)
+            doc_id = "file:" + encode_file
+
+            # check whether the file already exists
+            key_ids = None
+            try:
+                key_ids = search_by_id(client, doc_id).key_ids
+                print(f"[ upload file ] File {file.filename} already exists.")
+            except Exception as e:
+                print(f"[ upload file ] File {file.filename} does not exist.")
+            if key_ids:
+                raise HTTPException(
+                    status_code=400, detail=f"Uploaded file {file.filename} already exists. Please change file name."
+                )
+
             save_path = upload_folder + encode_file
             await save_content_to_local_disk(save_path, file)
             ingest_data_to_redis(
@@ -234,101 +255,172 @@ async def ingest_documents(
         return {"status": 200, "message": "Data preparation succeeded"}
 
     if link_list:
-        try:
-            link_list = json.loads(link_list)  # Parse JSON string to list
-            if not isinstance(link_list, list):
-                raise HTTPException(status_code=400, detail="link_list should be a list.")
-            for link in link_list:
-                encoded_link = encode_filename(link)
-                save_path = upload_folder + encoded_link + ".txt"
-                content = parse_html([link])[0][0]
-                await save_content_to_local_disk(save_path, content)
-                ingest_data_to_redis(
-                    DocPath(
-                        path=save_path,
-                        chunk_size=chunk_size,
-                        chunk_overlap=chunk_overlap,
-                        process_table=process_table,
-                        table_strategy=table_strategy,
-                    )
+        link_list = json.loads(link_list)  # Parse JSON string to list
+        if not isinstance(link_list, list):
+            raise HTTPException(status_code=400, detail=f"Link_list {link_list} should be a list.")
+        for link in link_list:
+            encoded_link = encode_filename(link)
+            doc_id = "file:" + encoded_link + ".txt"
+
+            # check whether the link file already exists
+            key_ids = None
+            try:
+                key_ids = search_by_id(client, doc_id).key_ids
+                print(f"[ upload file ] Link {link} already exists.")
+            except Exception as e:
+                print(f"[ upload file ] Link {link} does not exist. Keep storing.")
+            if key_ids:
+                raise HTTPException(
+                    status_code=400, detail=f"Uploaded link {link} already exists. Please change another link."
+                )
+
+            save_path = upload_folder + encoded_link + ".txt"
+            content = parse_html([link])[0][0]
+            await save_content_to_local_disk(save_path, content)
+            ingest_data_to_redis(
+                DocPath(
+                    path=save_path,
+                    chunk_size=chunk_size,
+                    chunk_overlap=chunk_overlap,
+                    process_table=process_table,
+                    table_strategy=table_strategy,
                 )
-            print(f"Successfully saved link list {link_list}")
-            return {"status": 200, "message": "Data preparation succeeded"}
-        except json.JSONDecodeError:
-            raise HTTPException(status_code=400, detail="Invalid JSON format for link_list.")
+            )
+        print(f"Successfully saved link list {link_list}")
+        return {"status": 200, "message": "Data preparation succeeded"}
 
     raise HTTPException(status_code=400, detail="Must provide either a file or a string list.")
 
 
 @register_microservice(
     name="opea_service@prepare_doc_redis", endpoint="/v1/dataprep/get_file", host="0.0.0.0", port=6007
 )
-@traceable(run_type="tool")
 async def rag_get_file_structure():
     print("[ dataprep - get file ] start to get file structure")
 
-    if not Path(upload_folder).exists():
-        print("No file uploaded, return empty list.")
-        return []
-
-    file_content = get_file_structure(upload_folder)
-    return file_content
+    # define redis client
+    r = redis.Redis(connection_pool=redis_pool)
+    offset = 0
+    file_list = []
+    while True:
+        response = r.execute_command("FT.SEARCH", KEY_INDEX_NAME, "*", "LIMIT", offset, offset + SEARCH_BATCH_SIZE)
+        # no doc retrieved
+        if len(response) < 2:
+            break
+        file_list = format_search_results(response, file_list)
+        offset += SEARCH_BATCH_SIZE
+        # last batch
+        if (len(response) - 1) // 2 < SEARCH_BATCH_SIZE:
+            break
+    return file_list
 
 
 @register_microservice(
     name="opea_service@prepare_doc_redis", endpoint="/v1/dataprep/delete_file", host="0.0.0.0", port=6007
 )
-@traceable(run_type="tool")
 async def delete_single_file(file_path: str = Body(..., embed=True)):
     """Delete file according to `file_path`.
 
     `file_path`:
         - specific file path (e.g. /path/to/file.txt)
-        - folder path (e.g. /path/to/folder)
         - "all": delete all files uploaded
     """
+
+    # define redis client
+    r = redis.Redis(connection_pool=redis_pool)
+    client = r.ft(KEY_INDEX_NAME)
+    client2 = r.ft(INDEX_NAME)
+
     # delete all uploaded files
     if file_path == "all":
         print("[dataprep - del] delete all files")
-        remove_folder_with_ignore(upload_folder)
-        assert drop_index(index_name=INDEX_NAME)
-        assert drop_index(index_name=KEY_INDEX_NAME)
+
+        # drop index KEY_INDEX_NAME
+        if check_index_existance(client):
+            try:
+                assert drop_index(index_name=KEY_INDEX_NAME)
+            except Exception as e:
+                print(f"[dataprep - del] {e}. Fail to drop index {KEY_INDEX_NAME}.")
+                raise HTTPException(status_code=500, detail=f"Fail to drop index {KEY_INDEX_NAME}.")
+        else:
+            print(f"[dataprep - del] Index {KEY_INDEX_NAME} does not exits.")
+
+        # drop index INDEX_NAME
+        if check_index_existance(client2):
+            try:
+                assert drop_index(index_name=INDEX_NAME)
+            except Exception as e:
+                print(f"[dataprep - del] {e}. Fail to drop index {INDEX_NAME}.")
+                raise HTTPException(status_code=500, detail=f"Fail to drop index {INDEX_NAME}.")
+        else:
+            print(f"[dataprep - del] Index {INDEX_NAME} does not exits.")
+
+        # delete files on local disk
+        try:
+            remove_folder_with_ignore(upload_folder)
+        except Exception as e:
+            print(f"[dataprep - del] {e}. Fail to delete {upload_folder}.")
+            raise HTTPException(status_code=500, detail=f"Fail to delete {upload_folder}.")
+
         print("[dataprep - del] successfully delete all files.")
         create_upload_folder(upload_folder)
         return {"status": True}
 
     delete_path = Path(upload_folder + "/" + encode_filename(file_path))
     print(f"[dataprep - del] delete_path: {delete_path}")
 
-    # partially delete files/folders
+    # partially delete files
     if delete_path.exists():
-        r = redis.Redis(connection_pool=redis_pool)
-        client = r.ft(KEY_INDEX_NAME)
-        client2 = r.ft(INDEX_NAME)
         doc_id = "file:" + encode_filename(file_path)
-        objs = search_by_id(client, doc_id).key_ids
-        file_ids = objs.split("#")
+
+        # determine whether this file exists in db KEY_INDEX_NAME
+        try:
+            key_ids = search_by_id(client, doc_id).key_ids
+        except Exception as e:
+            print(f"[dataprep - del] {e}, File {file_path} does not exists.")
+            raise HTTPException(
+                status_code=404, detail=f"File not found in db {KEY_INDEX_NAME}. Please check file_path."
+            )
+        file_ids = key_ids.split("#")
 
         # delete file
         if delete_path.is_file():
+            # delete file keys id in db KEY_INDEX_NAME
             try:
-                for file_id in file_ids:
-                    assert delete_by_id(client2, file_id)
                 assert delete_by_id(client, doc_id)
-                delete_path.unlink()
             except Exception as e:
-                print(f"[dataprep - del] fail to delete file {delete_path}: {e}")
-                return {"status": False}
+                print(f"[dataprep - del] {e}. File {file_path} delete failed for db {KEY_INDEX_NAME}.")
+                raise HTTPException(status_code=500, detail=f"File {file_path} delete failed.")
+
+            # delete file content in db INDEX_NAME
+            for file_id in file_ids:
+                # determine whether this file exists in db INDEX_NAME
+                try:
+                    content = search_by_id(client2, file_id).content
+                except Exception as e:
+                    print(f"[dataprep - del] {e}. File {file_path} does not exists.")
+                    raise HTTPException(
+                        status_code=404, detail=f"File not found in db {INDEX_NAME}. Please check file_path."
+                    )
+
+                # delete file content
+                try:
+                    assert delete_by_id(client2, file_id)
+                except Exception as e:
+                    print(f"[dataprep - del] {e}. File {file_path} delete failed for db {INDEX_NAME}")
+                    raise HTTPException(status_code=500, detail=f"File {file_path} delete failed.")
+
+            # delete file on local disk
+            delete_path.unlink()
+
+            return {"status": True}
+
         # delete folder
         else:
-            try:
-                shutil.rmtree(delete_path)
-            except Exception as e:
-                print(f"[dataprep - del] fail to delete folder {delete_path}: {e}")
-                return {"status": False}
-        return {"status": True}
+            print(f"[dataprep - del] Delete folder {file_path} is not supported for now.")
+            raise HTTPException(status_code=404, detail=f"Delete folder {file_path} is not supported for now.")
     else:
-        raise HTTPException(status_code=404, detail="File/folder not found. Please check del_path.")
+        raise HTTPException(status_code=404, detail=f"File {file_path} not found. Please check file_path.")
 
 
 if __name__ == "__main__":