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fix: update references from phidata to agno in routing agent implementation and documentation

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ShubhamSaboo
2025-06-23 20:52:03 -05:00
parent 5728fb86bc
commit 0c7527b4e1
3 changed files with 4 additions and 4 deletions
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4
rag_tutorials/rag_database_routing/README.md
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@@ -6,7 +6,7 @@ A Streamlit application that demonstrates an advanced implementation of RAG Agen
- **Document Upload**: Users can upload multiple PDF documents related to a particular company. These documents are processed and stored in one of the three databases: Product Information, Customer Support & FAQ, or Financial Information.
- **Natural Language Querying**: Users can ask questions in natural language. The system automatically routes the query to the most relevant database using a phidata agent as the router.
- **Natural Language Querying**: Users can ask questions in natural language. The system automatically routes the query to the most relevant database using a agno agent as the router.
- **RAG Orchestration**: Utilizes Langchain for orchestrating the retrieval augmented generation process, ensuring that the most relevant information is retrieved and presented to the user.
@@ -47,7 +47,7 @@ A Streamlit application that demonstrates an advanced implementation of RAG Agen
## Technologies Used
- **Langchain**: For RAG orchestration, ensuring efficient retrieval and generation of information.
- **Phidata Agent**: Used as the router agent to determine the most relevant database for a given query.
- **Agno Agent**: Used as the router agent to determine the most relevant database for a given query.
- **LangGraph Agent**: Acts as a fallback mechanism, utilizing DuckDuckGo for web research when necessary.
- **Streamlit**: Provides a user-friendly interface for document upload and querying.
- **Qdrant**: Used for managing the databases, storing and retrieving document embeddings efficiently.

2
rag_tutorials/rag_database_routing/rag_database_routing.py
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@@ -134,7 +134,7 @@ def process_document(file) -> List[Document]:
return []
def create_routing_agent() -> Agent:
"""Creates a routing agent using phidata framework"""
"""Creates a routing agent using agno framework"""
return Agent(
model=OpenAIChat(
id="gpt-4o",

2
rag_tutorials/rag_database_routing/requirements.txt
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@@ -5,7 +5,7 @@ qdrant-client==1.12.1
streamlit>=1.29.0
pypdf>=4.0.0
sentence-transformers>=2.2.2
phidata==2.7.3
agno
langchain-openai==0.2.14
langgraph==0.2.53
duckduckgo-search==6.4.1