doc: fix heading levels (#690)

Only one H1 for the title is allowed Signed-off-by: David B. Kinder <[email protected]>
opea-project · Sep 14, 2024 · f8f8854 · f8f8854
1 parent 3c5fc80
commit f8f8854
Show file tree

Hide file tree

Showing 2 changed files with 114 additions and 114 deletions.
diff --git a/comps/dataprep/vdms/README.md b/comps/dataprep/vdms/README.md
@@ -6,9 +6,9 @@ For dataprep microservice, we currently provide one framework: `Langchain`.
 
 We organized the folders in the same way, so you can use either framework for dataprep microservice with the following constructions.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 Install Single-process version (for 1-10 files processing)
 
@@ -25,11 +25,11 @@ pip install -r requirements.txt
 cd langchain_ray; pip install -r requirements_ray.txt
 ``` -->
 
-## 1.2 Start VDMS Server
+### 1.2 Start VDMS Server
 
-Please refer to this [readme](../../vectorstores/vdms/README.md).
+Refer to this [readme](../../vectorstores/vdms/README.md).
 
-## 1.3 Setup Environment Variables
+### 1.3 Setup Environment Variables
 
 ```bash
 export http_proxy=${your_http_proxy}
@@ -40,7 +40,7 @@ export COLLECTION_NAME=${your_collection_name}
 export PYTHONPATH=${path_to_comps}
 ```
 
-## 1.4 Start Document Preparation Microservice for VDMS with Python Script
+### 1.4 Start Document Preparation Microservice for VDMS with Python Script
 
 Start document preparation microservice for VDMS with below command.
 
@@ -56,13 +56,13 @@ python prepare_doc_vdms.py
 python prepare_doc_redis_on_ray.py
 ``` -->
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
-## 2.1 Start VDMS Server
+### 2.1 Start VDMS Server
 
-Please refer to this [readme](../../vectorstores/vdms/README.md).
+Refer to this [readme](../../vectorstores/vdms/README.md).
 
-## 2.2 Setup Environment Variables
+### 2.2 Setup Environment Variables
 
 ```bash
 export http_proxy=${your_http_proxy}
@@ -76,24 +76,24 @@ export DISTANCE_STRATEGY="L2"
 export PYTHONPATH=${path_to_comps}
 ```
 
-## 2.3 Build Docker Image
+### 2.3 Build Docker Image
 
 - Build docker image with langchain
 
-Start single-process version (for 1-10 files processing)
+  Start single-process version (for 1-10 files processing)
 
-```bash
-cd ../../../
-docker build -t opea/dataprep-vdms:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/vdms/langchain/Dockerfile .
-```
+  ```bash
+  cd ../../../
+  docker build -t opea/dataprep-vdms:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/vdms/langchain/Dockerfile .
+  ```
 
 <!-- - option 2: Start multi-process version (for >10 files processing)
 
 ```bash
 cd ../../../../
 docker build -t opea/dataprep-on-ray-vdms:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/vdms/langchain_ray/Dockerfile . -->
 
-## 2.4 Run Docker with CLI
+### 2.4 Run Docker with CLI
 
 Start single-process version (for 1-10 files processing)
 
@@ -113,75 +113,75 @@ docker run -d --name="dataprep-vdms-server" -p 6007:6007 --runtime=runc --ipc=ho
 -e TIMEOUT_SECONDS=600 opea/dataprep-on-ray-vdms:latest
 ``` -->
 
-# 🚀3. Status Microservice
+## 🚀3. Status Microservice
 
 ```bash
 docker container logs -f dataprep-vdms-server
 ```
 
-# 🚀4. Consume Microservice
+## 🚀4. Consume Microservice
 
 Once document preparation microservice for VDMS is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database.
 
 Make sure the file path after `files=@` is correct.
 
 - Single file upload
 
-```bash
-curl -X POST \
-    -H "Content-Type: multipart/form-data" \
-    -F "files=@./file1.txt" \
-    http://localhost:6007/v1/dataprep
-```
+  ```bash
+  curl -X POST \
+       -H "Content-Type: multipart/form-data" \
+       -F "files=@./file1.txt" \
+       http://localhost:6007/v1/dataprep
+  ```
 
-You can specify chunk_size and chunk_size by the following commands.
+  You can specify `chunk_size` and `chunk_overlap` by the following commands.
 
-```bash
-curl -X POST \
-    -H "Content-Type: multipart/form-data" \
-    -F "files=@./LLAMA2_page6.pdf" \
-    -F "chunk_size=1500" \
-    -F "chunk_overlap=100" \
-    http://localhost:6007/v1/dataprep
-```
+  ```bash
+  curl -X POST \
+       -H "Content-Type: multipart/form-data" \
+       -F "files=@./LLAMA2_page6.pdf" \
+       -F "chunk_size=1500" \
+       -F "chunk_overlap=100" \
+       http://localhost:6007/v1/dataprep
+  ```
 
 - Multiple file upload
 
-```bash
-curl -X POST \
-    -H "Content-Type: multipart/form-data" \
-    -F "files=@./file1.txt" \
-    -F "files=@./file2.txt" \
-    -F "files=@./file3.txt" \
-    http://localhost:6007/v1/dataprep
-```
-
-- Links upload (not supported for llama_index now)
-
-```bash
-curl -X POST \
-    -F 'link_list=["https://www.ces.tech/"]' \
-    http://localhost:6007/v1/dataprep
-```
-
-or
-
-```python
-import requests
-import json
-
-proxies = {"http": ""}
-url = "http://localhost:6007/v1/dataprep"
-urls = [
-    "https://towardsdatascience.com/no-gpu-no-party-fine-tune-bert-for-sentiment-analysis-with-vertex-ai-custom-jobs-d8fc410e908b?source=rss----7f60cf5620c9---4"
-]
-payload = {"link_list": json.dumps(urls)}
-
-try:
-    resp = requests.post(url=url, data=payload, proxies=proxies)
-    print(resp.text)
-    resp.raise_for_status()  # Raise an exception for unsuccessful HTTP status codes
-    print("Request successful!")
-except requests.exceptions.RequestException as e:
-    print("An error occurred:", e)
-```
+  ```bash
+  curl -X POST \
+       -H "Content-Type: multipart/form-data" \
+       -F "files=@./file1.txt" \
+       -F "files=@./file2.txt" \
+       -F "files=@./file3.txt" \
+       http://localhost:6007/v1/dataprep
+  ```
+
+- Links upload (not supported for `llama_index` now)
+
+  ```bash
+  curl -X POST \
+       -F 'link_list=["https://www.ces.tech/"]' \
+       http://localhost:6007/v1/dataprep
+  ```
+
+  or
+
+  ```python
+  import requests
+  import json
+
+  proxies = {"http": ""}
+  url = "http://localhost:6007/v1/dataprep"
+  urls = [
+      "https://towardsdatascience.com/no-gpu-no-party-fine-tune-bert-for-sentiment-analysis-with-vertex-ai-custom-jobs-d8fc410e908b?source=rss----7f60cf5620c9---4"
+  ]
+  payload = {"link_list": json.dumps(urls)}
+
+  try:
+      resp = requests.post(url=url, data=payload, proxies=proxies)
+      print(resp.text)
+      resp.raise_for_status()  # Raise an exception for unsuccessful HTTP status codes
+      print("Request successful!")
+  except requests.exceptions.RequestException as e:
+      print("An error occurred:", e)
+  ```
diff --git a/comps/dataprep/vdms/multimodal_langchain/README.md b/comps/dataprep/vdms/multimodal_langchain/README.md
@@ -2,25 +2,25 @@
 
 For dataprep microservice, we currently provide one framework: `Langchain`.
 
-# 🚀1. Start Microservice with Python (Option 1)
+## 🚀1. Start Microservice with Python (Option 1)
 
-## 1.1 Install Requirements
+### 1.1 Install Requirements
 
 - option 1: Install Single-process version (for 1-10 files processing)
 
-```bash
-apt-get update
-apt-get install -y default-jre tesseract-ocr libtesseract-dev poppler-utils
-pip install -r requirements.txt
-```
+  ```bash
+  apt-get update
+  apt-get install -y default-jre tesseract-ocr libtesseract-dev poppler-utils
+  pip install -r requirements.txt
+  ```
 
-## 1.2 Start VDMS Server
+### 1.2 Start VDMS Server
 
 ```bash
 docker run -d --name="vdms-vector-db" -p 55555:55555 intellabs/vdms:latest
 ```
 
-## 1.3 Setup Environment Variables
+### 1.3 Setup Environment Variables
 
 ```bash
 export http_proxy=${your_http_proxy}
@@ -33,23 +33,23 @@ export your_hf_api_token="{your_hf_token}"
 export PYTHONPATH=${path_to_comps}
 ```
 
-## 1.4 Start Data Preparation Microservice for VDMS with Python Script
+### 1.4 Start Data Preparation Microservice for VDMS with Python Script
 
 Start document preparation microservice for VDMS with below command.
 
 ```bash
 python ingest_videos.py
 ```
 
-# 🚀2. Start Microservice with Docker (Option 2)
+## 🚀2. Start Microservice with Docker (Option 2)
 
-## 2.1 Start VDMS Server
+### 2.1 Start VDMS Server
 
 ```bash
 docker run -d --name="vdms-vector-db" -p 55555:55555 intellabs/vdms:latest
 ```
 
-## 2.1 Setup Environment Variables
+### 2.1 Setup Environment Variables
 
 ```bash
 export http_proxy=${your_http_proxy}
@@ -61,64 +61,64 @@ export INDEX_NAME="rag-vdms"
 export your_hf_api_token="{your_hf_token}"
 ```
 
-## 2.3 Build Docker Image
+### 2.3 Build Docker Image
 
 - Build docker image
 
-```bash
-cd ../../../
- docker build -t opea/dataprep-vdms:latest --network host --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/vdms/multimodal_langchain/Dockerfile .
+  ```bash
+  cd ../../../
+  docker build -t opea/dataprep-vdms:latest --network host --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/vdms/multimodal_langchain/Dockerfile .
 
-```
+  ```
 
-## 2.4 Run Docker Compose
+### 2.4 Run Docker Compose
 
 ```bash
 docker compose -f comps/dataprep/vdms/multimodal_langchain/docker-compose-dataprep-vdms.yaml up -d
 ```
 
-# 🚀3. Status Microservice
+## 🚀3. Status Microservice
 
 ```bash
 docker container logs -f dataprep-vdms-server
 ```
 
-# 🚀4. Consume Microservice
+## 🚀4. Consume Microservice
 
 Once data preparation microservice for VDMS is started, user can use below command to invoke the microservice to convert the videos to embedding and save to the database.
 
 Make sure the file path after `files=@` is correct.
 
 - Single file upload
 
-```bash
-curl -X POST \
-    -H "Content-Type: multipart/form-data" \
-    -F "files=@./file1.mp4" \
-    http://localhost:6007/v1/dataprep
-```
+  ```bash
+  curl -X POST \
+       -H "Content-Type: multipart/form-data" \
+       -F "files=@./file1.mp4" \
+       http://localhost:6007/v1/dataprep
+  ```
 
 - Multiple file upload
 
-```bash
-curl -X POST \
-    -H "Content-Type: multipart/form-data" \
-    -F "files=@./file1.mp4" \
-    -F "files=@./file2.mp4" \
-    -F "files=@./file3.mp4" \
-    http://localhost:6007/v1/dataprep
-```
+  ```bash
+  curl -X POST \
+       -H "Content-Type: multipart/form-data" \
+       -F "files=@./file1.mp4" \
+       -F "files=@./file2.mp4" \
+       -F "files=@./file3.mp4" \
+       http://localhost:6007/v1/dataprep
+  ```
 
 - List of uploaded files
 
-```bash
-curl -X GET http://localhost:6007/v1/dataprep/get_videos
-```
+  ```bash
+  curl -X GET http://localhost:6007/v1/dataprep/get_videos
+  ```
 
 - Download uploaded files
 
-Please use the file name from the list
+  Use the file name from the list
 
-```bash
-curl -X GET http://localhost:6007/v1/dataprep/get_file/${filename}
-```
+  ```bash
+  curl -X GET http://localhost:6007/v1/dataprep/get_file/${filename}
+  ```