langchain agent modification

langchain_local/tools/rag_tool.py

@@ -1,25 +1,26 @@
 import os
+import openai
 
 from langchain.pydantic_v1 import BaseModel, Field
-from langchain_local.tools import BaseTool, StructuredTool, tool
+from langchain_core.tools import tool
 from langchain_community.document_loaders import PyPDFLoader
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores.docarray import DocArrayInMemorySearch
 from langchain.chains import RetrievalQA
 
-from dotenv import load_dotenv, find_dotenv
+from dotenv import load_dotenv
+load_dotenv()
 
-_ = load_dotenv(find_dotenv())  # reading local files
-
-openai_api_key = os.getenv('OPENAI_API_KEY')
+openai.api_key = os.environ.get('OPENAI_API_KEY')
+api_key = os.getenv('OPENAI_API_KEY')
 
 
 class RagTool(BaseModel):
     query: str = Field(description = "This should be a search query")
 
 
-@tool("rag-tool-one", args_schema=RagTool, return_direct=True)
+@tool("rag-tool-one", args_schema=RagTool, return_direct=False)
 def rag_one(query: str) -> str:
     """A tool that retrieves contents that are semantically relevant to the input query from the provided document.
 
@@ -29,15 +30,15 @@ def rag_one(query: str) -> str:
     Returns:
         str: top k amount of retrieved content from the uploaded document. content that are semantically similar to the input query.
     """
-    pdf_file_path = _ #placehloder code from html, the path to pdf file
-    
+    pdf_file_path = #'/content/data/cover_docs.pdf'
+
     loader = PyPDFLoader(pdf_file_path)
     pages = loader.load_and_split()
 
     text_splitter = RecursiveCharacterTextSplitter()
     splits = text_splitter.split_documents(pages)
 
-    embedding = OpenAIEmbeddings(api_key = openai_api_key)
+    embedding = OpenAIEmbeddings(api_key = api_key)
 
     vectordb = DocArrayInMemorySearch.from_documents(splits, embedding)
 
@@ -51,7 +52,7 @@ def rag_one(query: str) -> str:
 
 """The second RAG tool"""
 
-@tool("rag-tool-two", args_schema=RagTool, return_direct=True)
+@tool("rag-tool-two", args_schema=RagTool, return_direct=False)
 def rag_two(query: str) -> str:
     """A tool that retrieves contents that are semantically relevant to the input query from the provided document.
 
@@ -61,19 +62,19 @@ def rag_two(query: str) -> str:
     Returns:
         str: top k amount of retrieved content from the uploaded document. content that are semantically similar to the input query.
     """
-    pdf_file_path = _ #placehloder code from html, the path to pdf file
-    
+    pdf_file_path = #'/content/data/ptdf_sop.pdf'
+
     loader = PyPDFLoader(pdf_file_path)
     pages = loader.load_and_split()
 
     text_splitter = RecursiveCharacterTextSplitter()
     splits = text_splitter.split_documents(pages)
 
-    embedding = OpenAIEmbeddings(api_key = openai_api_key)
+    embedding = OpenAIEmbeddings(api_key = api_key)
 
     vectordb = DocArrayInMemorySearch.from_documents(splits, embedding)
 
     results = vectordb.similarity_search(query, k = 4 )
     result_string = "\n\n".join(str(result) for result in results)
 
-    return result_string
+    return result_string

langchain_local/tools/serper_tool.py

@@ -1,18 +1,27 @@
 import os
 import pprint
 
-from langchain_core.tools import Tool
-from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
-from dotenv import load_dotenv, find_dotenv
 
-_ = load_dotenv(find_dotenv())
+from langchain_core.tools import tool
+from langchain.pydantic_v1 import BaseModel, Field
+from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
+from dotenv import load_dotenv
 
-serper_api_key = os.getenv('SERPER_API_KEY')
+load_dotenv()
+os.environ["SERPER_API_KEY"] = os.getenv('SERPER_API_KEY')
 
-search = GoogleSerperAPIWrapper(serper_api_key = serper_api_key)
+class SerperTool(BaseModel):
+  query: str = Field(description = "This should be a search query")
 
-serper_tool = Tool(
-    name = "Intermediate Answer",
-    func = search.run,
-    description="useful for when you need to ask with search"
-)
+@tool("serper_tool_main", args_schema=SerperTool, return_direct=False)
+def serper_tool(query:str) -> str:
+  """A useful for when you need to ask with search. Very useful when recent or specific information is needed from the web 
+  """
+  search = GoogleSerperAPIWrapper(k=4, type="search")
+  initial_result = search.results(query)
+  result = initial_result['organic']
+  results = ""
+  for r in result:
+    data = f"'Title':{r['title']}\n 'content':{r['snippet']}"
+    results += f"{data}\n\n"
+  return results