Mixedbread Cloud : Rag 파이프 라인을위한 통합 API

pip install mixedbread openai

import os
import time
from mixedbread import Mixedbread
from openai import OpenAI

# --- Configuration ---
# 1. Get your Mixedbread API Key
mxbai_api_key = os.getenv("MXBAI_API_KEY")

# 2. Get your OpenAI API Key
openai_api_key = os.getenv("OPENAI_API_KEY")

# 3. Define the path to the FOLDER containing your PDF files
pdf_folder_path = "/work/docs"

# 4. Vector Store Configuration
vector_store_name = "Abid Articles"

# 5. OpenAI Model Configuration
openai_model = "gpt-4.1-nano-2025-04-14"

# --- Initialize Clients ---
mxbai = Mixedbread(api_key=mxbai_api_key)
openai_client = OpenAI(api_key=openai_api_key)

import glob

pdf_files_to_upload = glob.glob(os.path.join(pdf_folder_path, "*.pdf")) # Find all .pdf files

print(f"Found {len(pdf_files_to_upload)} PDF files to upload:")
for pdf_path in pdf_files_to_upload:
    print(f"  - {os.path.basename(pdf_path)}")

uploaded_file_ids = []
print("\nUploading files...")
for pdf_path in pdf_files_to_upload:
    filename = os.path.basename(pdf_path)
    print(f"  Uploading {filename}...")
    with open(pdf_path, "rb") as f:
        upload_response = mxbai.files.create(file=f)
        file_id = upload_response.id
        uploaded_file_ids.append(file_id)
        print(f"    -> Uploaded successfully. File ID: {file_id}")

print(f"\nSuccessfully uploaded {len(uploaded_file_ids)} files.")

Found 4 PDF files to upload:
  - Building Agentic Application using Streamlit and Langchain.pdf
  - Deploying DeepSeek Janus Pro locally.pdf
  - Fine-Tuning GPT-4o.pdf
  - How to Reach $500k on Upwork.pdf

Uploading files...
  Uploading Building Agentic Application using Streamlit and Langchain.pdf...
    -> Uploaded successfully. File ID: 8a538aa9-3bde-4498-90db-dbfcf22b29e9
  Uploading Deploying DeepSeek Janus Pro locally.pdf...
    -> Uploaded successfully. File ID: 52c7dfed-1f9d-492c-9cf8-039cc64834fe
  Uploading Fine-Tuning GPT-4o.pdf...
    -> Uploaded successfully. File ID: 3eaa584f-918d-4671-9b9c-6c91d5ca0595
  Uploading How to Reach $500k on Upwork.pdf...
    -> Uploaded successfully. File ID: 0e47ba93-550a-4d4b-9da1-6880a748402b

Successfully uploaded 4 files.

vector_store_response = mxbai.vector_stores.create(
    name=vector_store_name,
    file_ids=uploaded_file_ids # Add all uploaded file IDs during creation
)
vector_store_id = vector_store_response.id

print("\nMonitoring file processing status (this may take some time)...")
all_files_processed = False
max_wait_time = 600 # Maximum seconds to wait (10 minutes, adjust as needed)
check_interval = 20 # Seconds between checks
start_time = time.time()
final_statuses = {}

while not all_files_processed and (time.time() - start_time)  0:
     print("\nWarning: Some files failed processing. RAG will proceed using only the successfully processed files.")
elif not all_files_processed:
     print(f"\nWarning: File processing did not complete for all files within the maximum wait time ({max_wait_time}s). RAG will proceed using only the successfully processed files.")

Monitoring file processing status (this may take some time)...
  Status Check (Elapsed: 0s): Completed: 0, Failed: 0, In Progress: 4, Pending: 0, Other: 0 / Total: 4
  Status Check (Elapsed: 21s): Completed: 0, Failed: 0, In Progress: 4, Pending: 0, Other: 0 / Total: 4
  Status Check (Elapsed: 42s): Completed: 4, Failed: 0, In Progress: 0, Pending: 0, Other: 0 / Total: 4

--- Processing Summary ---
Total files processed: 4
Successfully completed: 4
Failed or Cancelled: 0

user_query = "How to Deploy Deepseek Janus Pro?"

retrieved_context = ""

search_results = mxbai.vector_stores.search(
    vector_store_ids=[vector_store_id], # Search within our newly created store
    query=user_query,
    top_k=10 # Retrieve top 10 relevant chunks across all documents
)

if search_results.data:
    # Combine the content of the chunks into a single context string
    context_parts = []
    for i, chunk in enumerate(search_results.data):
        context_parts.append(f"Chunk {i+1} from '{chunk.filename}' (Score: {chunk.score:.4f}):\n{chunk.content}\n---")
    retrieved_context = "\n".join(context_parts)
else:
    retrieved_context = "No context was retrieved." 

prompt_template = f"""
You are an assistant answering questions based *only* on the provided context from multiple documents.
Do not use any prior knowledge. If the context does not contain the answer to the question, state that clearly.

Context from the documents:
---
{retrieved_context}
---

Question: {user_query}

Answer:
"""

response = openai_client.chat.completions.create(
    model=openai_model,
    messages=[
        {"role": "user", "content": prompt_template}
    ],
    temperature=0.2,
    max_tokens=500
)

final_answer = response.choices[0].message.content.strip()

print(final_answer)

To deploy DeepSeek Janus Pro locally, follow these steps:

1. Install Docker Desktop from  and set it up with default settings. On Windows, ensure WSL is installed if prompted.

2. Clone the Janus repository by running:
   ```
   git clone 
   ```
3. Navigate into the cloned directory:
   ```
   cd Janus
   ```
4. Build the Docker image using the provided Dockerfile:
   ```
   docker build -t janus .
   ```
5. Run the Docker container with the following command, which sets up port forwarding, GPU access, and persistent storage:
   ```
   docker run -it --rm -p 7860:7860 --gpus all --name janus_pro -e TRANSFORMERS_CACHE=/root/.cache/huggingface -v huggingface:/root/.cache/huggingface janus:latest
   ```
6. Wait for the container to download the model and start the Gradio application. Once running, access the app at 

7. The application has two sections: one for image understanding and one for image generation, allowing you to upload images, ask for descriptions or poems, and generate images based on prompts.

This process enables you to deploy DeepSeek Janus Pro locally on your machine.