found a way to collect sources from tools

mixtral_agent.py

@@ -26,7 +26,7 @@ OLLMA_BASE_URL = os.getenv("OLLMA_BASE_URL")
 # supports many more optional parameters. Hover on your `ChatOllama(...)`
 # class to view the latest available supported parameters
 llm = ChatOllama(
-    model="mistral",
+    model="mistral:instruct",
     base_url= OLLMA_BASE_URL
     )
 prompt = ChatPromptTemplate.from_template("Tell me a short joke about {topic}")
@@ -43,21 +43,86 @@ print(chain.invoke({"topic": "Space travel"}))
 
 retriever = ArxivRetriever(load_max_docs=2)
 
-tools = [
-    create_retriever_tool(
-    retriever,
-    "search arxiv's database for",
-    "Use this to recomend the user a paper to read Unless stated please choose the most recent models",
-    # "Searches and returns excerpts from the 2022 State of the Union.",
-    ),
-
-    Tool(
-        name="SerpAPI",
-        description="A low-cost Google Search API. Useful for when you need to answer questions about current events. Input should be a search query.",
-        func=SerpAPIWrapper().run,
-    )
-
-]
+# Import things that are needed generically
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain.tools import BaseTool, StructuredTool, tool
+
+
+global all_sources
+
+# @tool
+# def search(query: str) -> str:
+#     """Look up things online."""
+#     # return "LangChain"
+#     data = retriever.invoke(query)
+#     meta_data = [i.metadata for i in data]
+#     # meta_data += all_sources
+#     # all_sources += meta_data
+#     all_sources += meta_data
+#     # all_sources = []
+#     return meta_data
+
+from typing import List, Dict
+from datetime import datetime
+
+def format_info_list(info_list: List[Dict[str, str]]) -> str:
+    """
+    Format a list of dictionaries containing information into a single string.
+
+    Args:
+        info_list (List[Dict[str, str]]): A list of dictionaries containing information.
+
+    Returns:
+        str: A formatted string containing the information from the list.
+    """
+    formatted_strings = []
+    for info_dict in info_list:
+        formatted_string = "|"
+        for key, value in info_dict.items():
+            if isinstance(value, datetime.date):
+                value = value.strftime('%Y-%m-%d')
+            formatted_string += f"'{key}': '{value}', "
+        formatted_string = formatted_string.rstrip(', ') + "|"
+        formatted_strings.append(formatted_string)
+    return '\n'.join(formatted_strings)
+    
+@tool
+def search(query: str) -> str:
+    """Look up things online."""
+    # return "LangChain"
+    global all_sources
+    data = retriever.invoke(query)
+    meta_data = [i.metadata for i in data]
+    # meta_data += all_sources
+    # all_sources += meta_data
+    all_sources += meta_data
+    
+    # formatted_info = format_info(entry_id, published, title, authors)
+    
+    # formatted_info = format_info_list(all_sources)
+    
+    return meta_data.__str__()
+    
+    # all_sources = []
+    # return meta_data
+
+tools = [search]
+
+# tools = [
+#     create_retriever_tool(
+#     retriever,
+#     "search arxiv's database for",
+#     "Use this to recomend the user a paper to read Unless stated please choose the most recent models",
+#     # "Searches and returns excerpts from the 2022 State of the Union.",
+#     ),
+
+#     Tool(
+#         name="SerpAPI",
+#         description="A low-cost Google Search API. Useful for when you need to answer questions about current events. Input should be a search query.",
+#         func=SerpAPIWrapper().run,
+#     )
+
+# ]
 
 
 
@@ -106,12 +171,40 @@ agent_executor = AgentExecutor(
 #     }
 # )
 
+# class AgentSample:
+    
+    
+#     def __init__(self, agent_executor_object,*args, **kwargs):
+#         self.agent_executor_object = agent_executor_object
+#         self.meta_data = []
+        
+#     def sample_invokex
+        
+        
+    
+
+if __name__ == "__main__":
+    
+    # global variable for collecting sources
+    all_sources =  []    
+
+    input = agent_executor.invoke(
+        {
+            "input": "How to generate videos from images using state of the art macchine learning models; Using the axriv retriever  " +
+            "add the urls of the papers used in the final answer using the metadata from the retriever"
+            # f"Please prioritize the newest papers this is the current data {get_current_date()}"
+        }
+    )
+    
+    x = 0
+
+    input_1 = agent_executor.invoke(
+        {
+            "input": "I am looking for a text to 3d model; Using the axriv retriever  " +
+            "add the urls of the papers used in the final answer using the metadata from the retriever"
+            # f"Please prioritize the newest papers this is the current data {get_current_date()}"
+        }
+    )
 
-agent_executor.invoke(
-    {
-        "input": "How to generate videos from images using state of the art macchine learning models; Using the axriv retriever  " +
-        "add the urls of the papers used in the final answer using the metadata from the retriever"
-        # f"Please prioritize the newest papers this is the current data {get_current_date()}"
-    }
-)
+    x = 0