I am building a RAG with llamaindex and I need to connect multiple modules to get this wor

Please stand by @HK as I search the knowledge base. I normally take about 20 seconds or so. :wave:

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Based on the knowledge sources provided, you can build a system with multiple modules using LlamaIndex. Here's a general guide on how you can do it:

1. SQL Query Engine: You can use the SQLAutoVectorQueryEngine as mentioned in the source (). However, please be aware that executing arbitrary SQL queries can be a security risk. It is recommended to take precautions as needed, such as using restricted roles, read-only databases, sandboxing, etc.

1. Vector Search: You can use the VectorIndexAutoRetriever for vector search as shown in the source (). You can set this up with the RetrieverQueryEngine.

1. Keyword Search: The information provided does not include details about implementing a keyword search. You might need to refer to the LlamaIndex documentation or other resources for this.

1. Image Search (OpenAI ClIP): You can use the OpenAIMultiModal for image search as shown in the source (). You can set this up with the asl_index.as_query_engine.

Here's a code snippet from the sources that might help you get started:

```python
from llama_index.llms.openai import OpenAI
from llama_index.core.retrievers import VectorIndexAutoRetriever
from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.multi_modal_llms.openai import OpenAIMultiModal

vector_store_info = VectorStoreInfo(
content_info="articles about different cities",
metadata_info=[
MetadataInfo(
name="title", type="str", description="The name of the city"
),
],
)
vector_auto_retriever = VectorIndexAutoRetriever(
vector_index, vector_store_info=vector_store_info
)

retriever_query_engine = RetrieverQueryEngine.from_args(
vector_auto_retriever, llm=OpenAI(model="gpt-4")
)

For image search

multi_modal_llm = OpenAIMultiModal(...)
asl_index.as_query_engine(multi_modal_llm=openai_mm_llm, text_qa_template=qa_tmpl)
```

Please note that the above code is a simplified version and you might need to adjust it according to your specific needs. Also, the code for SQL query engine and keyword search is not provided in the sources. You might need to refer to the LlamaIndex documentation or other resources for this.
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