added the retriever from conversation tool

innovation_pathfinder_ai/structured_tools/structured_tools.py

@@ -156,4 +156,29 @@ def embed_arvix_paper(paper_id:str) -> None:
         collection_name=collection_name,
         pdf_file_location=full_path,
     )
-    
+    
+@tool
+def conversational_search(query:str) -> str:
+    """Search from past conversations  for docmunets and relevent chunks"""
+    # Since we have more than one collections we should change the name of this tool
+    client = chromadb.PersistentClient(
+    # path=persist_directory,
+    )
+    
+    collection_name=os.getenv("CONVERSATION_COLLECTION_NAME")
+    #store using envar
+    
+    embedding_function = SentenceTransformerEmbeddings(
+        model_name="all-MiniLM-L6-v2",
+        )
+    
+    vector_db = Chroma(
+    client=client, # client for Chroma
+    collection_name=collection_name,
+    embedding_function=embedding_function,
+    )
+    
+    retriever = vector_db.as_retriever()
+    docs = retriever.get_relevant_documents(query)
+    
+    return docs.__str__()