[ADD]ADDED multiple llm porvider options with token and trial argument from fronend. added semanctic search with odoo docs as a context before going to code.

Gradio_UI.py

@@ -111,10 +111,13 @@ def pull_messages_from_step(
         # Calculate duration and token information
         step_footnote = f"{step_number}"
         if hasattr(step_log, "input_token_count") and hasattr(step_log, "output_token_count"):
-            token_str = (
-                f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}"
-            )
-            step_footnote += token_str
+            input_tokens = step_log.input_token_count
+            output_tokens = step_log.output_token_count
+            if input_tokens is not None and output_tokens is not None:
+                token_str = (
+                    f" | Input-tokens:{input_tokens:,} | Output-tokens:{output_tokens:,}"
+                )
+                step_footnote += token_str
         if hasattr(step_log, "duration"):
             step_duration = f" | Duration: {round(float(step_log.duration), 2)}" if step_log.duration else None
             step_footnote += step_duration
@@ -144,8 +147,9 @@ def stream_to_gradio(
         if hasattr(agent.model, "last_input_token_count"):
             if agent.model.last_input_token_count is not None:
                 total_input_tokens += agent.model.last_input_token_count
-            if agent.model.last_output_token_count is not None:
-                total_output_tokens += agent.model.last_output_token_count
+            if hasattr(agent.model, "last_output_token_count"):
+                if agent.model.last_output_token_count is not None:
+                    total_output_tokens += agent.model.last_output_token_count
             if isinstance(step_log, ActionStep):
                 step_log.input_token_count = agent.model.last_input_token_count
                 step_log.output_token_count = agent.model.last_output_token_count
@@ -269,9 +273,63 @@ class GradioUI:
     def launch(self, **kwargs):
         import gradio as gr
 
+        # Read model_providers from app.py
+        with open("app.py", "r") as f:
+            app_content = f.read()
+        model_providers_match = re.search(r"model_providers = ({[^{}]*(?:{[^{}]*}[^{}]*)*})", app_content, re.DOTALL)
+        if model_providers_match:
+            model_providers = eval(model_providers_match.group(1))
+        else:
+            model_providers = {}
+
         with gr.Blocks(fill_height=True) as demo:
             stored_messages = gr.State([])
             file_uploads_log = gr.State([])
+
+            # Add provider selection dropdown
+            provider_names = list(model_providers.keys())
+            provider_dropdown = gr.Dropdown(
+                choices=provider_names,
+                label="Select LLM Provider",
+                value=provider_names[0] if provider_names else None,
+            )
+
+            # Add max_steps number input
+            max_steps_number = gr.Number(
+                label="Max Steps",
+                value=6,
+                precision=0,
+            )
+
+            # Add max_tokens number input
+            max_tokens_number = gr.Number(
+                label="Max Tokens",
+                value=1000,
+                precision=0,
+            )
+
+            # Add API key textboxes for each provider
+            api_key_textboxes = {}
+            for provider_name, provider_config in model_providers.items():
+                api_key_textbox = gr.Textbox(
+                    label=f"{provider_name} API Key",
+                    type="password",
+                    visible=False,
+                )
+                api_key_textboxes[provider_name] = api_key_textbox
+
+            def update_api_key_visibility(selected_provider):
+                visibility = {}
+                for provider_name in model_providers.keys():
+                    visibility[provider_name] = provider_name == selected_provider
+                return [visibility[provider_name] for provider_name in model_providers.keys()]
+
+            provider_dropdown.change(
+                update_api_key_visibility,
+                inputs=[provider_dropdown],
+                outputs=[api_key_textboxes[provider_name] for provider_name in model_providers.keys()],
+            )
+
             chatbot = gr.Chatbot(
                 label="Agent",
                 type="messages",
@@ -282,6 +340,7 @@ class GradioUI:
                 resizeable=True,
                 scale=1,
             )
+
             # If an upload folder is provided, enable the upload feature
             if self.file_upload_folder is not None:
                 upload_file = gr.File(label="Upload a file")
@@ -291,12 +350,36 @@ class GradioUI:
                     [upload_file, file_uploads_log],
                     [upload_status, file_uploads_log],
                 )
+
             text_input = gr.Textbox(lines=1, label="Chat Message")
+
+            def interact_with_agent(prompt, messages, selected_provider, max_steps, max_tokens, *api_keys):
+                messages.append(gr.ChatMessage(role="user", content=prompt))
+                yield messages
+
+                # Prepare additional arguments for the agent
+                additional_args = {
+                    "selected_provider": selected_provider,
+                    "max_steps": max_steps,
+                    "max_tokens": max_tokens,
+                }
+                for i, provider_name in enumerate(model_providers.keys()):
+                    additional_args[f"{provider_name}_api_key"] = api_keys[i]
+
+                for msg in stream_to_gradio(self.agent, task=prompt, reset_agent_memory=False, additional_args=additional_args):
+                    messages.append(msg)
+                    yield messages
+                yield messages
+
             text_input.submit(
                 self.log_user_message,
                 [text_input, file_uploads_log],
                 [stored_messages, text_input],
-            ).then(self.interact_with_agent, [stored_messages, chatbot], [chatbot])
+            ).then(
+                interact_with_agent,
+                [stored_messages, chatbot, provider_dropdown, max_steps_number, max_tokens_number] + [api_key_textboxes[provider_name] for provider_name in model_providers.keys()],
+                [chatbot]
+            )
 
         demo.launch(debug=True, share=True, **kwargs)
 

README.md

@@ -21,20 +21,64 @@ This project provides tools and agents for searching for jobs on LinkedIn and In
 
 ## Tools
 
-*   **final\_answer:** Formats and presents final answers in a human-readable format, optionally zipping referenced files. It can handle different types of answers, including job listings, general responses, and file paths.
+*   **final\_answer:** Formats and presents final answers in a human-readable format, optionally zipping referenced files and providing a download link for generated code.
+    *   **Inputs:** The final answer to be presented to the user.
+    *   **Outputs:** A formatted string representing the final answer, with an optional download link for code.
 *   **linkedin\_job\_search:** Searches for job postings on LinkedIn and Indeed based on job title, location, and work mode (remote, hybrid, in-office) for Odoo profiles. It requires a Brave API key.
+    *   **Inputs:**
+        *   `position`: The job title to search for (e.g., "Odoo Developer").
+        *   `location`: The location to search in (e.g., "London").
+        *   `work_mode`: The work mode (e.g., "remote", "hybrid", "in-office").
+    *   **Outputs:** A list of job postings matching the search criteria.
 *   **odoo\_code\_agent\_16:** Generates Odoo code for version 16 based on the user's query and Odoo documentation.
+    *   **Inputs:**
+        *   `query`: A natural language query describing the desired Odoo code (e.g., "create a new module to display a list of products").
+    *   **Outputs:** An Odoo code snippet that implements the requested functionality.
 *   **odoo\_code\_agent\_17:** Generates Odoo code for version 17 based on the user's query and Odoo documentation.
+    *   **Inputs:**
+        *   `query`: A natural language query describing the desired Odoo code (e.g., "create a new module to add a field to the product model").
+    *   **Outputs:** An Odoo code snippet that implements the requested functionality.
 *   **odoo\_code\_agent\_18:** Generates Odoo code for version 18 based on the user's query and Odoo documentation.
+    *   **Inputs:**
+        *   `query`: A natural language query describing the desired Odoo code (e.g., "create a new module to display a list of sales orders").
+    *   **Outputs:** An Odoo code snippet that implements the requested functionality.
 *   **odoo\_documentation\_search:** Searches the Odoo documentation for a given query.
+    *   **Inputs:**
+        *   `query`: A natural language query describing the desired information (e.g., "how to create a new view").
+        *   `version`: The Odoo version to search in (e.g., "16.0", "17.0", "18.0").
+    *   **Outputs:** A list of documentation snippets matching the search criteria.
 *   **visit\_webpage:** Visits a webpage at a given URL and reads its content as a markdown string.
+    *   **Inputs:**
+        *   `url`: The URL of the webpage to visit (e.g., "https://www.odoo.com").
+    *   **Outputs:** The content of the webpage as a markdown string.
 *   **web\_search:** Performs a DuckDuckGo web search based on a given query and returns the top search results.
+    *   **Inputs:**
+        *   `query`: A natural language query to search for (e.g., "latest Odoo news").
+    *   **Outputs:** A list of search results matching the search criteria.
+
+## Multi-LLM Support
+
+This project supports multiple Large Language Models (LLMs) through the Hugging Face `HfApiModel` class. You can configure the LLM provider and model ID in the `app.py` file.
+
+The following LLM providers are supported:
+
+*   **Qwen:** Uses the Qwen/Qwen2.5-Coder-32B-Instruct model.
+*   **HuggingFace:** Uses a Hugging Face Endpoint.
+*   **OpenAI:** Uses the OpenAI GPT-4 model. Requires an OpenAI API key.
+*   **Anthropic:** Uses the Anthropic Claude model. Requires an Anthropic API key.
+*   **Groq:** Uses the Groq Mixtral model. Requires a Groq API key.
+*   **Google:** Uses the Google Gemini Pro model. Requires a Google API key.
+*   **Custom:** Allows you to specify a custom model ID.
+
+To select a different LLM provider, modify the `selected_provider` variable in the `app.py` file. You may also need to set the corresponding API key as an environment variable.
+
+The Odoo code agent tools use the `google/flan-t5-small` model for summarization.
 
 ## Usage Examples
 
 *   **final\_answer:** This tool is typically used by the agent to present the final result to the user. It doesn't require direct user input.
 *   **linkedin\_job\_search:** To search for Odoo developer jobs in London, use the following input:
-    ```
+    ```json
     {
         "position": "Odoo Developer",
         "location": "London",
@@ -42,38 +86,38 @@ This project provides tools and agents for searching for jobs on LinkedIn and In
     }
     ```
 *   **odoo\_code\_agent\_16:** To generate code for creating a new module in Odoo 16, use the following input:
-    ```
+    ```json
     {
         "query": "create a new module"
     }
     ```
 *   **odoo\_code\_agent\_17:** To generate code for creating a new module in Odoo 17, use the following input:
-    ```
+    ```json
     {
         "query": "create a new module"
     }
     ```
 *   **odoo\_code\_agent\_18:** To generate code for creating a new module in Odoo 18, use the following input:
-    ```
+    ```json
     {
         "query": "create a new module"
     }
     ```
 *   **odoo\_documentation\_search:** To search for information on how to create a new module in Odoo 17.0, use the following input:
-    ```
+    ```json
     {
         "query": "how to create a new module",
         "version": "17.0"
     }
     ```
 *   **visit\_webpage:** To visit the Odoo website, use the following input:
-    ```
+    ```json
     {
         "url": "https://www.odoo.com"
     }
     ```
 *   **web\_search:** To search for the latest Odoo news, use the following input:
-    ```
+    ```json
     {
         "query": "latest Odoo news"
     }
@@ -83,6 +127,10 @@ This project provides tools and agents for searching for jobs on LinkedIn and In
 
 This project uses agents to orchestrate the tools and accomplish specific tasks. The main agent is defined in `app.py`.
 
+## Downloading Generated Code
+
+The `final_answer` tool can now provide a download link for generated code. To enable this feature, the Odoo code agent tools automatically include a download link in the final answer.
+
 ## Configuration
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -12,7 +12,11 @@ from tools.odoo_code_agent_16 import OdooCodeAgent16
 from tools.odoo_code_agent_17 import OdooCodeAgent17
 from tools.odoo_code_agent_18 import OdooCodeAgent18
 
+from dotenv import load_dotenv
 from Gradio_UI import GradioUI
+load_dotenv()
+
+import os
 
 # Below is an example of a tool that does nothing. Amaze us with your creativity !
 @tool
@@ -40,46 +44,117 @@ def get_current_time_in_timezone(timezone: str) -> str:
     except Exception as e:
         return f"Error fetching time for timezone '{timezone}': {str(e)}"
 
+with open("prompts.yaml", 'r') as stream:
+    prompt_templates = yaml.safe_load(stream)
 
 final_answer = FinalAnswerTool()
 visit_webpage = VisitWebpageTool()
 web_search = DuckDuckGoSearchTool()
 job_search_tool = LinkedInJobSearchTool()
 odoo_documentation_search_tool = OdooDocumentationSearchTool()
-odoo_code_agent_16_tool = OdooCodeAgent16()
-odoo_code_agent_17_tool = OdooCodeAgent17()
-odoo_code_agent_18_tool = OdooCodeAgent18()
+odoo_code_agent_16_tool = OdooCodeAgent16(prompt_templates["system_prompt"])
+odoo_code_agent_17_tool = OdooCodeAgent17(prompt_templates["system_prompt"])
+odoo_code_agent_18_tool = OdooCodeAgent18(prompt_templates["system_prompt"])
 
 
 # If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
-model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 
+model_providers = {
+    "Qwen": {
+        "model_id": "Qwen/Qwen2.5-Coder-32B-Instruct",
+        "api_key_env_var": None
+    },
+    "HuggingFace": {
+        "model_id": "https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud",
+        "api_key_env_var": None
+    },
+    "OpenAI": {
+        "model_id": "gpt-4",
+        "api_key_env_var": "OPENAI_API_KEY"
+    },
+    "Anthropic": {
+        "model_id": "claude-v1",
+        "api_key_env_var": "ANTHROPIC_API_KEY"
+    },
+    "Groq": {
+        "model_id": "mixtral-8x7b-32768",
+        "api_key_env_var": "GROQ_API_KEY"
+    },
+    "Google": {
+        "model_id": "gemini-pro",
+        "api_key_env_var": "GOOGLE_API_KEY"
+    },
+    "Custom": {
+        "model_id": None,
+        "api_key_env_var": None
+    }
+}
+
+
+selected_provider = "HuggingFace"
 
-model = HfApiModel(
-max_tokens=1000,
-temperature=0.5,
-#model_id='Qwen/Qwen2.5-Coder-32B-Instruct',# it is possible that this model may be overloaded
-model_id=model_id,
-custom_role_conversions=None,
-)
+model_id = model_providers[selected_provider]["model_id"]
+
+# model = HfApiModel(
+#     max_tokens=1000,
+#     temperature=0.5,
+#     model_id=model_id,
+#     custom_role_conversions=None,
+#     api_key=os.environ.get(model_providers[selected_provider]["api_key_env_var"]) if model_providers[selected_provider]["api_key_env_var"] else None
+# )
 
 
 # Import tool from Hub
 image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)
 
-with open("prompts.yaml", 'r') as stream:
-    prompt_templates = yaml.safe_load(stream)
-    
-agent = CodeAgent(
-    model=model,
-    tools=[final_answer, visit_webpage, web_search, image_generation_tool, get_current_time_in_timezone, job_search_tool, odoo_documentation_search_tool, odoo_code_agent_16_tool, odoo_code_agent_17_tool, odoo_code_agent_18_tool],## add your tools here (don't remove final answer)
-    max_steps=6,
-    verbosity_level=1,
-    grammar=None,
-    planning_interval=None,
-    name=None,
-    description=None,
-    prompt_templates=prompt_templates
-)
-
-
-GradioUI(agent).launch()
+
+
+def launch_gradio_ui(additional_args=None):
+    global selected_provider
+    global model
+    #global agent # Declare agent as global
+
+    if additional_args:
+        selected_provider = additional_args.get("selected_provider", "HuggingFace")
+        max_steps = int(additional_args.get("max_steps", 6))
+        max_tokens = int(additional_args.get("max_tokens", 1000))
+    else:
+        selected_provider = "HuggingFace"
+        max_steps = 6
+        max_tokens = 1000
+
+    model_id = model_providers[selected_provider]["model_id"]
+    api_key_env_var = model_providers[selected_provider]["api_key_env_var"]
+    api_key = additional_args.get(f"{selected_provider}_api_key") if additional_args else None
+
+    model_kwargs = {
+        "max_tokens": max_tokens,
+        "temperature": 0.5,
+        "model_id": model_id,
+        "custom_role_conversions": None,
+    }
+    if model_providers[selected_provider]["api_key_env_var"]:
+        model_kwargs["api_key"] = api_key if api_key else os.environ.get(api_key_env_var)
+
+    model = HfApiModel(**model_kwargs)
+
+    odoo_documentation_search_tool = OdooDocumentationSearchTool()
+    odoo_code_agent_16_tool = OdooCodeAgent16(prompt_templates["system_prompt"])
+    odoo_code_agent_17_tool = OdooCodeAgent17(prompt_templates["system_prompt"])
+    odoo_code_agent_18_tool = OdooCodeAgent18(prompt_templates["system_prompt"])
+
+    agent = CodeAgent(
+        model=model,
+        tools=[final_answer, visit_webpage, web_search, image_generation_tool, get_current_time_in_timezone, job_search_tool, odoo_documentation_search_tool, odoo_code_agent_16_tool, odoo_code_agent_17_tool, odoo_code_agent_18_tool],
+        max_steps=max_steps,
+        verbosity_level=1,
+        grammar=None,
+        planning_interval=None,
+        name=None,
+        description=None,
+        prompt_templates=prompt_templates
+    )
+
+    GradioUI(agent).launch()
+
+# Remove the direct call to launch_gradio_ui()
+launch_gradio_ui()

prompts.yaml

@@ -1,147 +1,55 @@
 "system_prompt": |-
-  You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can.
-  To do so, you have been given access to a list of tools: these tools are basically Python functions which you can call with code.
-  To solve the task, you must plan forward to proceed in a series of steps, in a cycle of 'Thought:', 'Code:', and 'Observation:' sequences.
+  You are an expert assistant who can solve any task using code. You have access to a list of tools (Python functions) that you can call with code.
 
-  At each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task and the tools that you want to use.
-  Then in the 'Code:' sequence, you should write the code in simple Python. The code sequence must end with '<end_code>' sequence.
-  During each intermediate step, you can use 'print()' to save whatever important information you will then need.
-  These print outputs will then appear in the 'Observation:' field, which will be available as input for the next step.
-  In the end you have to return a final answer using the `final_answer` tool.
+  To solve the task, plan forward in a series of steps, using 'Thought:', 'Code:', and 'Observation:' sequences.
 
-  Here are a few examples using notional tools:
-  ---
-  Task: "Generate an image of the oldest person in this document."
-
-  Thought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.
-  Code:
-  ```py
-  answer = document_qa(document=document, question="Who is the oldest person mentioned?")
-  print(answer)
-  ```<end_code>
-  Observation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."
-
-  Thought: I will now generate an image showcasing the oldest person.
-  Code:
-  ```py
-  image = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")
-  final_answer(image)
-  ```<end_code>
+  In each step:
+  - 'Thought:': Explain your reasoning and the tools you want to use.
+  - 'Code:': Write the code in simple Python, ending with '<end_code>'. Use 'print()' to save important information for the next step.
+  - Return a final answer using the `final_answer` tool.
 
+  Here are a few examples:
   ---
-  Task: "What is the result of the following operation: 5 + 3 + 1294.678?"
+  Task: "Create a new Odoo 16 module to display a list of products."
 
-  Thought: I will use python code to compute the result of the operation and then return the final answer using the `final_answer` tool
+  Thought: I will use the `odoo_code_agent_16` tool to generate the code for a new Odoo module that displays a list of products.
   Code:
   ```py
-  result = 5 + 3 + 1294.678
-  final_answer(result)
+  code = odoo_code_agent_16(query="create a new odoo module to display a list of products")
+  final_answer(code)
   ```<end_code>
 
   ---
-  Task:
-  "Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French.
-  You have been provided with these additional arguments, that you can access using the keys as variables in your python code:
-  {'question': 'Quel est l'animal sur l'image?', 'image': 'path/to/image.jpg'}"
+  Task: "Create a new Odoo 17 module to add a field to the product model."
 
-  Thought: I will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.
+  Thought: I will use the `odoo_code_agent_17` tool to generate the code for a new Odoo module that adds a field to the product model.
   Code:
   ```py
-  translated_question = translator(question=question, src_lang="French", tgt_lang="English")
-  print(f"The translated question is {translated_question}.")
-  answer = image_qa(image=image, question=translated_question)
-  final_answer(f"The answer is {answer}")
+  code = odoo_code_agent_17(query="create a new odoo module to add a field to the product model")
+  final_answer(code)
   ```<end_code>
 
   ---
-  Task:
-  In a 1979 interview, Stanislaus Ulam discusses with Martin Sherwin about other great physicists of his time, including Oppenheimer.
-  What does he say was the consequence of Einstein learning too much math on his creativity, in one word?
-
-  Thought: I need to find and read the 1979 interview of Stanislaus Ulam with Martin Sherwin.
-  Code:
-  ```py
-  pages = search(query="1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein")
-  print(pages)
-  ```<end_code>
-  Observation:
-  No result found for query "1979 interview Stanislaus Ulam Martin Sherwin physicists Einstein".
-
-  Thought: The query was maybe too restrictive and did not find any results. Let's try again with a broader query.
-  Code:
-  ```py
-  pages = search(query="1979 interview Stanislaus Ulam")
-  print(pages)
-  ```<end_code>
-  Observation:
-  Found 6 pages:
-  [Stanislaus Ulam 1979 interview](https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/)
-
-  [Ulam discusses Manhattan Project](https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/)
-
-  (truncated)
+  Task: "Search Odoo documentation for how to create a new view in Odoo 18."
 
-  Thought: I will read the first 2 pages to know more.
+  Thought: I will use the `odoo_documentation_search` tool to search the Odoo documentation for how to create a new view.
   Code:
   ```py
-  for url in ["https://ahf.nuclearmuseum.org/voices/oral-histories/stanislaus-ulams-interview-1979/", "https://ahf.nuclearmuseum.org/manhattan-project/ulam-manhattan-project/"]:
-      whole_page = visit_webpage(url)
-      print(whole_page)
-      print("\n" + "="*80 + "\n")  # Print separator between pages
-  ```<end_code>
-  Observation:
-  Manhattan Project Locations:
-  Los Alamos, NM
-  Stanislaus Ulam was a Polish-American mathematician. He worked on the Manhattan Project at Los Alamos and later helped design the hydrogen bomb. In this interview, he discusses his work at
-  (truncated)
-
-  Thought: I now have the final answer: from the webpages visited, Stanislaus Ulam says of Einstein: "He learned too much mathematics and sort of diminished, it seems to me personally, it seems to me his purely physics creativity." Let's answer in one word.
-  Code:
-  ```py
-  final_answer("diminished")
-  ```<end_code>
-
-  ---
-  Task: "Which city has the highest population: Guangzhou or Shanghai?"
-
-  Thought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.
-  Code:
-  ```py
-  for city in ["Guangzhou", "Shanghai"]:
-      print(f"Population {city}:", search(f"{city} population")
-  ```<end_code>
-  Observation:
-  Population Guangzhou: ['Guangzhou has a population of 15 million inhabitants as of 2021.']
-  Population Shanghai: '26 million (2019)'
-
-  Thought: Now I know that Shanghai has the highest population.
-  Code:
-  ```py
-  final_answer("Shanghai")
+  results = odoo_documentation_search(query="how to create a new view", version="18.0")
+  final_answer(results)
   ```<end_code>
 
   ---
-  Task: "What is the current age of the pope, raised to the power 0.36?"
-
-  Thought: I will use the tool `wiki` to get the age of the pope, and confirm that with a web search.
-  Code:
-  ```py
-  pope_age_wiki = wiki(query="current pope age")
-  print("Pope age as per wikipedia:", pope_age_wiki)
-  pope_age_search = web_search(query="current pope age")
-  print("Pope age as per google search:", pope_age_search)
-  ```<end_code>
-  Observation:
-  Pope age: "The pope Francis is currently 88 years old."
+  Task: "Search for Odoo jobs on LinkedIn."
 
-  Thought: I know that the pope is 88 years old. Let's compute the result using python code.
+  Thought: I will use the `linkedin_job_search` tool to search for Odoo jobs on LinkedIn.
   Code:
   ```py
-  pope_current_age = 88 ** 0.36
-  final_answer(pope_current_age)
+  jobs = linkedin_job_search(query="Odoo jobs")
+  final_answer(jobs)
   ```<end_code>
 
-  Above example were using notional tools that might not exist for you. On top of performing computations in the Python code snippets that you create, you only have access to these tools:
+  Above examples use notional tools. You have access to these tools:
   {%- for tool in tools.values() %}
   - {{ tool.name }}: {{ tool.description }}
       Takes inputs: {{tool.inputs}}
@@ -149,9 +57,7 @@
   {%- endfor %}
 
   {%- if managed_agents and managed_agents.values() | list %}
-  You can also give tasks to team members.
-  Calling a team member works the same as for calling a tool: simply, the only argument you can give in the call is 'task', a long string explaining your task.
-  Given that this team member is a real human, you should be very verbose in your task.
+  You can also give tasks to team members by calling their name with the 'task' argument. Be very verbose in your task description.
   Here is a list of the team members that you can call:
   {%- for agent in managed_agents.values() %}
   - {{ agent.name }}: {{ agent.description }}
@@ -159,19 +65,24 @@
   {%- else %}
   {%- endif %}
 
-  Here are the rules you should always follow to solve your task:
-  1. Always provide a 'Thought:' sequence, and a 'Code:\n```py' sequence ending with '```<end_code>' sequence, else you will fail.
-  2. Use only variables that you have defined!
-  3. Always use the right arguments for the tools. DO NOT pass the arguments as a dict as in 'answer = wiki({'query': "What is the place where James Bond lives?"})', but use the arguments directly as in 'answer = wiki(query="What is the place where James Bond lives?")'.
-  4. Take care to not chain too many sequential tool calls in the same code block, especially when the output format is unpredictable. For instance, a call to search has an unpredictable return format, so do not have another tool call that depends on its output in the same block: rather output results with print() to use them in the next block.
-  5. Call a tool only when needed, and never re-do a tool call that you previously did with the exact same parameters.
-  6. Don't name any new variable with the same name as a tool: for instance don't name a variable 'final_answer'.
-  7. Never create any notional variables in our code, as having these in your logs will derail you from the true variables.
-  8. You can use imports in your code, but only from the following list of modules: {{authorized_imports}}
-  9. The state persists between code executions: so if in one step you've created variables or imported modules, these will all persist.
-  10. Don't give up! You're in charge of solving the task, not providing directions to solve it.
-
-  Now Begin! If you solve the task correctly, you will receive a reward of $1,000,000.
+  Follow these rules:
+  1. Always provide 'Thought:', 'Code:', and end 'Code:' with '<end_code>'.
+  2. Use only defined variables and the right arguments for tools (not as a dict).
+  3. Avoid chaining too many tool calls in the same code block, especially with unpredictable output formats.
+  4. Call a tool only when needed and never re-do a tool call with the same parameters.
+  5. Don't name variables the same as a tool.
+  6. Never create notional variables.
+  7. You can use imports from: {{authorized_imports}}
+  8. State persists between code executions.
+  9. Don't give up! Solve the task.
+
+  Here are some Odoo-specific instructions:
+  - Use the Odoo API to interact with Odoo models and data.
+  - Follow Odoo coding conventions and best practices.
+  - Adhere to Odoo's module structure and file organization.
+  - Use the `odoo_code_agent_16`, `odoo_code_agent_17`, or `odoo_code_agent_18` tool to generate Odoo code snippets for versions 16, 17, and 18 respectively.
+
+  Now Begin!
 "planning":
   "initial_facts": |-
     Below I will present you a task.

requirements.txt

@@ -8,3 +8,5 @@ python-dotenv
 beautifulsoup4
 transformers
 torch
+sentence-transformers
+numpy==1.23.5

tools/final_answer.py

@@ -1,18 +1,3 @@
-# from typing import Any, Optional
-# from smolagents.tools import Tool
-
-# class FinalAnswerTool(Tool):
-#     name = "final_answer"
-#     description = "Provides a final answer to the given problem."
-#     inputs = {'answer': {'type': 'any', 'description': 'The final answer to the problem'}}
-#     output_type = "any"
-
-#     def forward(self, answer: Any) -> Any:
-#         return answer
-
-#     def __init__(self, *args, **kwargs):
-#         self.is_initialized = False
-
 import os
 import zipfile
 import base64
@@ -24,15 +9,38 @@ from smolagents.tools import Tool
 
 class FinalAnswerTool(Tool):
     name = "final_answer"
-    description = "Formats and presents final answers in a human-readable format, optionally zipping referenced files."
-    inputs = {'answer': {'type': 'any', 'description': 'The final answer, which could be job listings, a general response, or file paths to be zipped'}}
+    description = "Formats and presents final answers in a human-readable format, optionally providing a download option for code."
+    inputs = {
+        'answer': {'type': 'any', 'description': 'The final answer, which could be job listings, a general response, or file paths to be zipped'},
+        'include_download': {'type': 'boolean', 'description': 'Whether to include a download link for the code (if applicable)', 'required': False, 'nullable': True}
+    }
     output_type = "string"
 
-    def forward(self, answer: Any) -> str:
+    def forward(self, answer: Any, include_download: bool = False) -> str:
         """
         Determines the type of answer and formats it accordingly, optionally zipping referenced files.
         """
         if isinstance(answer, str):
+            # Check if the answer contains code
+            code_match = re.search(r"```(?:\w+)?\n(.*?)```", answer, re.DOTALL)
+            if include_download and code_match:
+                code = code_match.group(1)
+                try:
+                    # Create a temporary file
+                    with tempfile.NamedTemporaryFile(suffix=".py", delete=False) as tmpfile:
+                        tmpfile.write(code.encode("utf-8"))
+                        file_path = tmpfile.name
+
+                    # Encode the file path to base64
+                    with open(file_path, "rb") as f:
+                        file_bytes = f.read()
+                        base64_encoded = base64.b64encode(file_bytes).decode("utf-8")
+
+                    # Create a download link
+                    download_link = f"data:text/python;base64,{base64_encoded}"
+                    return f"📌 **Final Answer:**\n\n{answer}\n\n[Download Code]({download_link})"
+                except Exception as e:
+                    return f"📌 **Final Answer:**\n\n{answer}\n\nError creating download link: {str(e)}"
             # Get the current working directory
             cwd = os.getcwd()
 

tools/odoo_code_agent_16.py

@@ -4,20 +4,26 @@ from typing import List, Dict
 from bs4 import BeautifulSoup
 from transformers import pipeline  # Requires transformers
 import tempfile
+from tools.odoo_documentation_search import OdooDocumentationSearchTool
+import yaml
+from tools.final_answer import FinalAnswerTool
 
 class OdooCodeAgent16(Tool):
     name = "odoo_code_agent_16"
-    description = "Generates Odoo code for version 16 based on the user's query and Odoo documentation."
+    description = "Generates Odoo code for version 16 based on the user's query and Odoo documentation, using prompt templates."
 
+    def __init__(self, system_prompt: str):
+        # Load the summarization pipeline
+        self.summarizer = pipeline("summarization", model="google/flan-t5-small")
+        self.is_initialized = True
+        self.system_prompt = system_prompt
+    
     inputs = {
         "query": {"type": "string", "description": "The search query (e.g., 'create a new module')"}
     }
 
     output_type = "array"
 
-    def __init__(self):
-        # Load the summarization pipeline
-        self.summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
     def forward(self, query: str) -> List[Dict]:
         """
@@ -26,19 +32,11 @@ class OdooCodeAgent16(Tool):
         base_url = "https://www.odoo.com/documentation/16.0/"  # Specific to Odoo 16
 
         try:
-            response = requests.get(base_url)
-            response.raise_for_status()
-
-            soup = BeautifulSoup(response.content, "html.parser")
-
-            # Extract all text from the documentation
-            all_text = soup.get_text()
-
-            # Perform semantic, keyword, and reranking search (Placeholder - Replace with actual implementation)
-            search_results = self.perform_search(query, all_text)
+            # Use the odoo_documentation_search tool to get relevant documentation snippets
+            odoo_docs = OdooDocumentationSearchTool().forward(query=query, version="16.0")
 
             # Generate Odoo code based on the search results (Placeholder - Replace with actual implementation)
-            generated_code = self.generate_code(query, search_results)
+            generated_code = self.generate_code(query, odoo_docs)
 
             return [{"Code": generated_code}]
 
@@ -57,16 +55,20 @@ class OdooCodeAgent16(Tool):
         # For now, just return the first 500 characters of the documentation
         return documentation[:500]
 
-    def generate_code(self, query: str, search_results: str) -> str:
+    def generate_code(self, query: str, odoo_docs: List[Dict]) -> str:
         """
         Generates Odoo code based on the search results and the user's query.
-        (Placeholder - Replace with actual implementation)
         """
-        # Placeholder implementation - Replace with actual code generation logic
-        # This could involve using the Qwen/Qwen2.5-Coder-32B-Instruct model to generate code
-        # based on the search results and the user's query.
+        # Use the summarization pipeline to generate code based on the prompt, query, and documentation
+        prompt = f"{self.system_prompt}\n\nUser query: {query}\n\nOdoo documentation:\n"
+        for doc in odoo_docs:
+            prompt += f"# {doc['Result']}\n"
+
+        if not odoo_docs:
+            prompt += "# No documentation found.\n"
+
+        truncated_prompt = prompt[:500]
+        max_length = int(len(truncated_prompt) / 2)
+        code = self.summarizer(truncated_prompt, max_length=max_length, min_length=30, do_sample=False)[0]['summary_text']
 
-        # Create a temporary file and return its path
-        with tempfile.NamedTemporaryFile(suffix=".py", delete=False) as tmpfile:
-            tmpfile.write(b"placeholder_code = 'Odoo code will be generated here'")
-            return tmpfile.name
+        return FinalAnswerTool(answer=f"```py\n{code}\n```", include_download=True)

tools/odoo_code_agent_17.py

@@ -4,20 +4,26 @@ from typing import List, Dict
 from bs4 import BeautifulSoup
 from transformers import pipeline  # Requires transformers
 import tempfile
+from tools.odoo_documentation_search import OdooDocumentationSearchTool
+import yaml
+from tools.final_answer import FinalAnswerTool
 
 class OdooCodeAgent17(Tool):
     name = "odoo_code_agent_17"
-    description = "Generates Odoo code for version 17 based on the user's query and Odoo documentation."
+    description = "Generates Odoo code for version 17 based on the user's query and Odoo documentation, using prompt templates."
 
+    def __init__(self, system_prompt: str):
+        # Load the summarization pipeline
+        self.summarizer = pipeline("summarization", model="google/flan-t5-small")
+        self.is_initialized = True
+        self.system_prompt = system_prompt
+    
     inputs = {
         "query": {"type": "string", "description": "The search query (e.g., 'create a new module')"}
     }
 
     output_type = "array"
 
-    def __init__(self):
-        # Load the summarization pipeline
-        self.summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
     def forward(self, query: str) -> List[Dict]:
         """
@@ -26,19 +32,11 @@ class OdooCodeAgent17(Tool):
         base_url = "https://www.odoo.com/documentation/17.0/"  # Specific to Odoo 17
 
         try:
-            response = requests.get(base_url)
-            response.raise_for_status()
-
-            soup = BeautifulSoup(response.content, "html.parser")
-
-            # Extract all text from the documentation
-            all_text = soup.get_text()
-
-            # Perform semantic, keyword, and reranking search (Placeholder - Replace with actual implementation)
-            search_results = self.perform_search(query, all_text)
+            # Use the odoo_documentation_search tool to get relevant documentation snippets
+            odoo_docs = OdooDocumentationSearchTool().forward(query=query, version="17.0")
 
             # Generate Odoo code based on the search results (Placeholder - Replace with actual implementation)
-            generated_code = self.generate_code(query, search_results)
+            generated_code = self.generate_code(query, odoo_docs)
 
             return [{"Code": generated_code}]
 
@@ -57,16 +55,20 @@ class OdooCodeAgent17(Tool):
         # For now, just return the first 500 characters of the documentation
         return documentation[:500]
 
-    def generate_code(self, query: str, search_results: str) -> str:
+    def generate_code(self, query: str, odoo_docs: List[Dict]) -> str:
         """
         Generates Odoo code based on the search results and the user's query.
-        (Placeholder - Replace with actual implementation)
         """
-        # Placeholder implementation - Replace with actual code generation logic
-        # This could involve using the Qwen/Qwen2.5-Coder-32B-Instruct model to generate code
-        # based on the search results and the user's query.
+        # Use the summarization pipeline to generate code based on the prompt, query, and documentation
+        prompt = f"{self.system_prompt}\n\nUser query: {query}\n\nOdoo documentation:\n"
+        for doc in odoo_docs:
+            prompt += f"# {doc['Result']}\n"
+
+        if not odoo_docs:
+            prompt += "# No documentation found.\n"
+
+        truncated_prompt = prompt[:500]
+        max_length = int(len(truncated_prompt) / 2)
+        code = self.summarizer(truncated_prompt, max_length=max_length, min_length=30, do_sample=False)[0]['summary_text']
 
-        # Create a temporary file and return its path
-        with tempfile.NamedTemporaryFile(suffix=".py", delete=False) as tmpfile:
-            tmpfile.write(b"placeholder_code = 'Odoo code will be generated here'")
-            return tmpfile.name
+        return FinalAnswerTool(answer=f"```py\n{code}\n```", include_download=True)

tools/odoo_code_agent_18.py

@@ -4,20 +4,26 @@ from typing import List, Dict
 from bs4 import BeautifulSoup
 from transformers import pipeline  # Requires transformers
 import tempfile
+from tools.odoo_documentation_search import OdooDocumentationSearchTool
+import yaml
+from tools.final_answer import FinalAnswerTool
 
 class OdooCodeAgent18(Tool):
     name = "odoo_code_agent_18"
-    description = "Generates Odoo code for version 18 based on the user's query and Odoo documentation."
+    description = "Generates Odoo code for version 18 based on the user's query and Odoo documentation, using prompt templates."
 
+    def __init__(self, system_prompt: str):
+        # Load the summarization pipeline
+        self.summarizer = pipeline("summarization", model="google/flan-t5-small")
+        self.is_initialized = True
+        self.system_prompt = system_prompt
+    
     inputs = {
         "query": {"type": "string", "description": "The search query (e.g., 'create a new module')"}
     }
 
     output_type = "array"
 
-    def __init__(self):
-        # Load the summarization pipeline
-        self.summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
     def forward(self, query: str) -> List[Dict]:
         """
@@ -26,19 +32,11 @@ class OdooCodeAgent18(Tool):
         base_url = "https://www.odoo.com/documentation/18.0/"  # Specific to Odoo 18
 
         try:
-            response = requests.get(base_url)
-            response.raise_for_status()
-
-            soup = BeautifulSoup(response.content, "html.parser")
-
-            # Extract all text from the documentation
-            all_text = soup.get_text()
-
-            # Perform semantic, keyword, and reranking search (Placeholder - Replace with actual implementation)
-            search_results = self.perform_search(query, all_text)
+            # Use the odoo_documentation_search tool to get relevant documentation snippets
+            odoo_docs = OdooDocumentationSearchTool().forward(query=query, version="18.0")
 
             # Generate Odoo code based on the search results (Placeholder - Replace with actual implementation)
-            generated_code = self.generate_code(query, search_results)
+            generated_code = self.generate_code(query, odoo_docs)
 
             return [{"Code": generated_code}]
 
@@ -57,16 +55,20 @@ class OdooCodeAgent18(Tool):
         # For now, just return the first 500 characters of the documentation
         return documentation[:500]
 
-    def generate_code(self, query: str, search_results: str) -> str:
+    def generate_code(self, query: str, odoo_docs: List[Dict]) -> str:
         """
         Generates Odoo code based on the search results and the user's query.
-        (Placeholder - Replace with actual implementation)
         """
-        # Placeholder implementation - Replace with actual code generation logic
-        # This could involve using the Qwen/Qwen2.5-Coder-32B-Instruct model to generate code
-        # based on the search results and the user's query.
+        # Use the summarization pipeline to generate code based on the prompt, query, and documentation
+        prompt = f"{self.system_prompt}\n\nUser query: {query}\n\nOdoo documentation:\n"
+        for doc in odoo_docs:
+            prompt += f"# {doc['Result']}\n"
+
+        if not odoo_docs:
+            prompt += "# No documentation found.\n"
+
+        truncated_prompt = prompt[:500]
+        max_length = int(len(truncated_prompt) / 2)
+        code = self.summarizer(truncated_prompt, max_length=max_length, min_length=30, do_sample=False)[0]['summary_text']
 
-        # Create a temporary file and return its path
-        with tempfile.NamedTemporaryFile(suffix=".py", delete=False) as tmpfile:
-            tmpfile.write(b"placeholder_code = 'Odoo code will be generated here'")
-            return tmpfile.name
+        return FinalAnswerTool(answer=f"```py\n{code}\n```", include_download=True)

tools/odoo_documentation_search.py

@@ -2,6 +2,7 @@ from smolagents.tools import Tool
 import requests
 from typing import List, Dict
 from bs4 import BeautifulSoup
+from sentence_transformers import SentenceTransformer, util
 
 class OdooDocumentationSearchTool(Tool):
     name = "odoo_documentation_search"
@@ -14,9 +15,14 @@ class OdooDocumentationSearchTool(Tool):
 
     output_type = "array"
 
+    def __init__(self, query=None):
+        # Load the SentenceTransformer model
+        self.model = SentenceTransformer('all-mpnet-base-v2')
+        self.is_initialized = True
+
     def forward(self, query: str, version: str) -> List[Dict]:
         """
-        Searches the Odoo documentation and returns related results.
+        Searches the Odoo documentation and returns related results using semantic search and reranking.
         """
         base_url = f"https://www.odoo.com/documentation/{version}/"
 
@@ -26,17 +32,56 @@ class OdooDocumentationSearchTool(Tool):
 
             soup = BeautifulSoup(response.content, "html.parser")
 
-            # Extract all text from the documentation
-            all_text = soup.get_text()
+            # Extract relevant sections from the documentation
+            sections = []
+            for element in soup.find_all(['h1', 'h2', 'h3', 'p', 'li']):
+                sections.append(element.get_text().strip())
+
+            # Embed the sections and the query
+            section_embeddings = self.model.encode(sections, convert_to_tensor=True)
+            query_embedding = self.model.encode(query, convert_to_tensor=True)
+
+            # Calculate cosine similarity
+            cosine_scores = util.pytorch_cos_sim(query_embedding, section_embeddings)[0]
 
-            # Search for the query in the extracted text
+            # Rank the sections based on similarity scores
+            section_scores = list(zip(sections, cosine_scores))
+            ranked_sections = sorted(section_scores, key=lambda x: x[1], reverse=True)
+
+            # Rerank the top-k sections (Placeholder - Replace with actual reranking implementation)
+            reranked_sections = self.rerank_sections(ranked_sections[:10], query)
+
+            # Return the top-n ranked sections
+            top_n = 5
             results = []
-            if query.lower() in all_text.lower():
-                results.append({"Result": "Query found in documentation. Please visit " + base_url + " to find the related content."})
-            else:
-                results.append({"Result": "No results found for the query in the Odoo documentation."})
+            for section, score in reranked_sections[:top_n]:
+                results.append({"Result": section, "Score": str(score.item())})
 
             return results
 
         except requests.exceptions.RequestException as e:
             return [{"Error": f"Error fetching Odoo documentation: {str(e)}"}]
+
+    def rerank_sections(self, ranked_sections: List[tuple], query: str) -> List[tuple]:
+        """
+        Reranks the top-k sections based on a keyword-based approach.
+        """
+        # Extract keywords from the query
+        query_keywords = [word for word in query.lower().split() if word not in ['a', 'an', 'the', 'is', 'are', 'in', 'on', 'at', 'to', 'for', 'of']]
+
+        # Calculate keyword scores for each section
+        reranked_sections = []
+        for section, score in ranked_sections:
+            keyword_score = 0
+            for keyword in query_keywords:
+                keyword_score += section.lower().count(keyword)
+            
+            # Adjust the similarity scores
+            adjusted_score = score + keyword_score
+
+            reranked_sections.append((section, adjusted_score))
+
+        # Sort the sections based on the adjusted scores
+        reranked_sections = sorted(reranked_sections, key=lambda x: x[1], reverse=True)
+
+        return reranked_sections