optimize srv broker and executor logic (#630)

### What problem does this PR solve?

Optimize task broker and executor for reduce memory usage and deployment
complexity.

### Type of change
- [x] Performance Improvement
- [x] Refactoring

### Change Log
- Enhance redis utils for message queue(use stream)
- Modify task broker logic via message queue (1.get parse event from
message queue 2.use ThreadPoolExecutor async executor )
- Modify the table column name of document and task (process_duation ->
process_duration maybe just a spelling mistake)
- Reformat some code style(just what i see)
- Add requirement_dev.txt for developer
- Add redis container on docker compose

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Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>

rag/llm/embedding_model.py

@@ -95,8 +95,7 @@ class OpenAIEmbed(Base):
     def encode(self, texts: list, batch_size=32):
         res = self.client.embeddings.create(input=texts,
                                             model=self.model_name)
-        return np.array([d.embedding for d in res.data]
-                        ), res.usage.total_tokens
+        return np.array([d.embedding for d in res.data]), res.usage.total_tokens
 
     def encode_queries(self, text):
         res = self.client.embeddings.create(input=[text],