Building a Bot for General Queries and Platform-related Retrieval

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To build a bot with the capabilities you've described using LlamaIndex, you can combine several features and components. Here's a high-level approach to address your requirements:

1. General queries vs. platform-specific queries:
   You can use a router to determine whether a query is general or platform-specific. LlamaIndex provides a RouterQueryEngine that can help with this decision-making process.

1. Retrieval for platform-related queries:
   For platform-specific queries, you can use a vector store index with hybrid search and HyDE (Hypothetical Document Embeddings) for improved retrieval.

1. Streaming Chat:
   LlamaIndex supports streaming responses in chat engines.

1. Using Chat Engine:
   Instead of a query engine, you'll want to use a chat engine for maintaining conversation context.

1. HyDE and Hybrid RAG:
   You can combine HyDE and hybrid search in your retrieval process.

Here's a high-level outline of how you might implement this:

```python
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores import YourVectorStore # Replace with your chosen vector store
from llama_index.core.chat_engine import CondenseQuestionChatEngine
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.query_engine import TransformQueryEngine
from llama_index.core.indices.query.query_transform import HyDEQueryTransform
from llama_index.llms import YourLLM # Replace with your chosen LLM

Set up your vector store with hybrid search

vector_store = YourVectorStore(hybrid_search=True)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

Set up HyDE query transform

hyde = HyDEQueryTransform(llm=YourLLM())

Set up retriever with hybrid search

retriever = VectorIndexRetriever(
index=index,
similarity_top_k=5,
vector_store_query_mode="hybrid"
)

Set up query engine with HyDE

query_engine = RetrieverQueryEngine.from_args(retriever)
hyde_query_engine = TransformQueryEngine(query_engine, hyde)

Set up chat engine

chat_engine = CondenseQuestionChatEngine.from_defaults(
query_engine=hyde_query_engine,
streaming=True # Enable streaming
)

Use the chat engine

response = chat_engine.chat("Your query here")
`` This setup combines the various components you've requested. The CondenseQuestionChatEngine will maintain conversation context, while the underlying query engine uses hybrid search and HyDE for retrieval. For routing between general and platform-specific queries, you might need to implement a custom router or use LlamaIndex's routing capabilities. The exact implementation would depend on how you define "general" vs. "platform-specific" queries. Remember to replace YourVectorStore and YourLLM` with your chosen vector store and language model implementations.

For more detailed information on these components, you can refer to the following LlamaIndex documentation:

• Chat Engine Usage Pattern
• HyDE + Google Retriever
• Hybrid Search

Note that the exact implementation details may vary depending on your specific requirements and the vector store you choose to use.
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