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Replaced ChromaDB with Qdrant and upgraded the model to Gemini 2.0 Flash

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Sri Charan Thoutam 2025-02-12 00:57:44 +05:30
parent 5b73cc74ab
commit 2691e174b6
2 changed files with 5 additions and 5 deletions
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2
ai_agent_tutorials/ai_blog_search/README.md
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@ -23,7 +23,7 @@ https://github.com/user-attachments/assets/cee07380-d3dc-45f4-ad26-7d944ba9c32b
- **Database**: [Qdrant](https://qdrant.tech/) - **Database**: [Qdrant](https://qdrant.tech/)
- **Models**: - **Models**:
- Embeddings: [Google Gemini API (embedding-001)](https://ai.google.dev/gemini-api/docs/embeddings) - Embeddings: [Google Gemini API (embedding-001)](https://ai.google.dev/gemini-api/docs/embeddings)
- Chat: [Google Gemini API (gemini-1.5-pro)](https://ai.google.dev/gemini-api/docs/models/gemini#gemini-1.5-pro) - Chat: [Google Gemini API (gemini-2.0-flash)](https://ai.google.dev/gemini-api/docs/models/gemini#gemini-2.0-flash)
- **Blogs Loader**: [Langchain WebBaseLoader](https://python.langchain.com/docs/integrations/document_loaders/web_base/) - **Blogs Loader**: [Langchain WebBaseLoader](https://python.langchain.com/docs/integrations/document_loaders/web_base/)
- **Document Splitter**: [RecursiveCharacterTextSplitter](https://python.langchain.com/v0.1/docs/modules/data_connection/document_transformers/recursive_text_splitter/) - **Document Splitter**: [RecursiveCharacterTextSplitter](https://python.langchain.com/v0.1/docs/modules/data_connection/document_transformers/recursive_text_splitter/)
- **User Interface (UI)**: [Streamlit](https://docs.streamlit.io/) - **User Interface (UI)**: [Streamlit](https://docs.streamlit.io/)

8
ai_agent_tutorials/ai_blog_search/app.py
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@ -111,7 +111,7 @@ def grade_documents(state) -> Literal["generate", "rewrite"]:
binary_score: str = Field(description="Relevance score 'yes' or 'no'") binary_score: str = Field(description="Relevance score 'yes' or 'no'")
# LLM # LLM
model = ChatGoogleGenerativeAI(api_key=st.session_state.gemini_api_key, temperature=0, model="gemini-1.5-pro", streaming=True) model = ChatGoogleGenerativeAI(api_key=st.session_state.gemini_api_key, temperature=0, model="gemini-2.0-flash", streaming=True)
# LLM with tool and validation # LLM with tool and validation
llm_with_tool = model.with_structured_output(grade) llm_with_tool = model.with_structured_output(grade)
@ -163,7 +163,7 @@ def agent(state, tools):
""" """
print("---CALL AGENT---") print("---CALL AGENT---")
messages = state["messages"] messages = state["messages"]
model = ChatGoogleGenerativeAI(api_key=st.session_state.gemini_api_key, temperature=0, streaming=True, model="gemini-1.5-pro") model = ChatGoogleGenerativeAI(api_key=st.session_state.gemini_api_key, temperature=0, streaming=True, model="gemini-2.0-flash")
model = model.bind_tools(tools) model = model.bind_tools(tools)
response = model.invoke(messages) response = model.invoke(messages)
@ -199,7 +199,7 @@ def rewrite(state):
] ]
# Grader # Grader
model = ChatGoogleGenerativeAI(api_key=st.session_state.gemini_api_key, temperature=0, model="gemini-1.5-pro", streaming=True) model = ChatGoogleGenerativeAI(api_key=st.session_state.gemini_api_key, temperature=0, model="gemini-2.0-flash", streaming=True)
response = model.invoke(msg) response = model.invoke(msg)
return {"messages": [response]} return {"messages": [response]}
@ -225,7 +225,7 @@ def generate(state):
prompt_template = hub.pull("rlm/rag-prompt") prompt_template = hub.pull("rlm/rag-prompt")
# Initialize a Generator (i.e. Chat Model) # Initialize a Generator (i.e. Chat Model)
chat_model = ChatGoogleGenerativeAI(api_key=st.session_state.gemini_api_key, model="gemini-1.5-pro", temperature=0, streaming=True) chat_model = ChatGoogleGenerativeAI(api_key=st.session_state.gemini_api_key, model="gemini-2.0-flash", temperature=0, streaming=True)
# Initialize a Output Parser # Initialize a Output Parser
output_parser = StrOutputParser() output_parser = StrOutputParser()