Add api_key inputs

README.MD

@@ -1,61 +0,0 @@
----
-title: Ai Agents
-emoji: 📊
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.35.2
-app_file: server.py
-pinned: false
-license: apache-2.0
----
-
-# AI Agents
-A collection of patterns for experimenting with agents, llm pipelines, and ChainOfThoughtStrategy. 
-Heavily inspired by examples and code across the AI Open Source community but with some fearnworks patterns heavily interspersed. 
-
-## UI
-Front end for templates currently uses streamlit, however display for each agent example will be heavily decoupled from deployment avenue. 
-Strategies for jupyter, gradio, and react will be prioritized in general at this time. 
-
-## Modules
-
-### Reasoning 
-
-General problem solving requiring reasoned processing. Utilizing tree of thoughts, chain of thought, and reAct. Current prompts heavily inspired by
-work from https://github.com/mrspiggot
-
-### Knowledge Domains
-
-Example of knowledge domain retrieval. Given a question the router determines the best knowledge domain chain to use and then calls that chain to handle the response.
-
-Includes examples of queries that will return each of the four implemented domains. 
-
-### Generative Image Prompting Patterns
-
-Coming soon 
-
-### Generative Simulacra Pattern
-
-Coming soon
-
-### Story Telling / Dungeon Master Patterns
-
-Coming Soon 
-
-### Deployment
-
-Project is packaged with a dockerfile expose a gradio ui on localhost:7000. Please use the envtemplate file to create your .env for running the project
-
-To build & run
-
-```
-docker build . -t ai_agent:latest
-docker run -it -p 7000:7000 ai_agent:latest
-```
-
-To build, run, and, clean up image in one command :
-
-```
-docker build . -t ai_agent:latest && docker run --rm -it -p 7000:7000 --name ai_agent_container ai_agent:latest
-```

README.md

@@ -7,4 +7,56 @@ colorFrom: green
 colorTo: red
 pinned: true
 app_file: server.py
----
+app_port: 7000
+---
+
+
+# AI Agents
+A collection of patterns for experimenting with agents, llm pipelines, and ChainOfThoughtStrategy. 
+Heavily inspired by examples and code across the AI Open Source community but with some fearnworks patterns heavily interspersed. 
+
+## UI
+Front end for templates currently uses streamlit, however display for each agent example will be heavily decoupled from deployment avenue. 
+Strategies for jupyter, gradio, and react will be prioritized in general at this time. 
+
+## Modules
+
+### Reasoning 
+
+General problem solving requiring reasoned processing. Utilizing tree of thoughts, chain of thought, and reAct. Current prompts heavily inspired by
+work from https://github.com/mrspiggot
+
+### Knowledge Domains
+
+Example of knowledge domain retrieval. Given a question the router determines the best knowledge domain chain to use and then calls that chain to handle the response.
+
+Includes examples of queries that will return each of the four implemented domains. 
+
+### Generative Image Prompting Patterns
+
+Coming soon 
+
+### Generative Simulacra Pattern
+
+Coming soon
+
+### Story Telling / Dungeon Master Patterns
+
+Coming Soon 
+
+### Deployment
+
+Project is packaged with a dockerfile expose a gradio ui on localhost:7000. Please use the envtemplate file to create your .env for running the project
+
+To build & run
+
+```
+docker build . -t ai_agent:latest
+docker run -it -p 7000:7000 ai_agent:latest
+```
+
+To build, run, and, clean up image in one command :
+
+```
+docker build . -t ai_agent:latest && docker run --rm -it -p 7000:7000 --name ai_agent_container ai_agent:latest
+```

modules/knowledge_retrieval/component.py

@@ -3,16 +3,17 @@ import gradio as gr
 import os 
 openai_api_key = os.getenv("OPENAI_API_KEY")
 
-def determine_and_execute(question: str, temperature: float):
+def determine_and_execute(api_key, question: str, temperature: float):
     config = get_knowledge_domain_router_config(temperature=temperature)
     config.temperature = temperature
-    determiner = KnowledgeDomainRouter(api_key=openai_api_key, config=config, question=question, display=print)
+    determiner = KnowledgeDomainRouter(api_key=api_key, config=config, question=question, display=print)
     determine_output, execute_output = determiner.determine_and_execute(question=question)
     return determine_output, execute_output
 
 examples = [["""When is my grandmothers birthday?""", 0.6], ["What was my tax burden last year?", 0.6 ], ["What is the most recent magic the gathering card set released?", 0.6], ["What products are the most popular with my small business customers?", 0.6]]
 
 def create_knowledge_router_ui(cache_examples=False):  
+    api_key = gr.Textbox(label="You OpenAI API key", type="password")
     with gr.Row():
         question = gr.Textbox(label="Enter your question here:")
         temperature = gr.Slider(minimum=0, maximum=2, default=.7, label="Temperature")
@@ -21,6 +22,6 @@ def create_knowledge_router_ui(cache_examples=False):
         reasoning = gr.Textbox(label="Reasoning")
     
     generate_button = gr.Button(label="Generate")
-    generate_button.click(determine_and_execute, outputs=[reasoning_strategy, reasoning], inputs=[question, temperature])
+    generate_button.click(determine_and_execute, outputs=[reasoning_strategy, reasoning], inputs=[api_key, question, temperature])
     gr.Examples(examples=examples, fn=determine_and_execute, cache_examples=cache_examples, inputs=[question, temperature], outputs=[reasoning_strategy, reasoning])
 

modules/reasoning/component.py

@@ -4,10 +4,10 @@ import os
 openai_api_key = os.getenv("OPENAI_API_KEY")
 
 
-def determine_and_execute(question, temperature):
+def determine_and_execute(api_key,question, temperature):
     config = get_reasoning_router_config(temperature=temperature)
     config.temperature = temperature
-    determiner = ReasoningRouter(api_key=openai_api_key, config=config, question=question, display=print)
+    determiner = ReasoningRouter(api_key=api_key, config=config, question=question, display=print)
     determine_output, execute_output = determiner.determine_and_execute()
     return determine_output, execute_output
 
@@ -18,7 +18,8 @@ He turns the cup upside down, then walks to the garden.
 He puts the cup down in the garden, then walks to the garage.
 Where is the ball?""", 0.6], ["Given the task of building a house in the middle of a river, what are three strategies I could use to mitigate risk of flooding?", 0.6 ]]
 
-def create_reasoning_router_ui(cache_examples=False):  
+def create_reasoning_router_ui(cache_examples=False): 
+    api_key = gr.Textbox(label="You OpenAI API key", type="password")
     with gr.Row():
         question = gr.Textbox(label="Enter your question here:")
         temperature = gr.Slider(minimum=0, maximum=2, default=.7, label="Temperature")
@@ -27,6 +28,6 @@ def create_reasoning_router_ui(cache_examples=False):
         reasoning = gr.Textbox(label="Reasoning")
     
     generate_button = gr.Button(label="Generate")
-    generate_button.click(determine_and_execute, outputs=[reasoning_strategy, reasoning], inputs=[question, temperature])
-    gr.Examples(examples=examples, fn=determine_and_execute, cache_examples=cache_examples, inputs=[question, temperature], outputs=[reasoning_strategy, reasoning])
+    generate_button.click(determine_and_execute, outputs=[reasoning_strategy, reasoning], inputs=[api_key, question, temperature])
+    gr.Examples(examples=examples, fn=determine_and_execute, cache_examples=cache_examples, inputs=[api_key, question, temperature], outputs=[reasoning_strategy, reasoning])
 

server.py

@@ -17,9 +17,9 @@ def create_interface():
             create_reasoning_router_ui()
         with gr.Tab("Knowledge Domains"):
             create_knowledge_router_ui()
+        
     interface.queue()
     interface.launch(server_name="0.0.0.0", server_port=7000)
 
-
 if __name__ == "__main__":
     create_interface()