add support for ksp rag from hw1

hw1/app.py

@@ -18,6 +18,15 @@ load_dotenv()
 def format_docs(docs):
     return "\n\n".join(doc.page_content for doc in docs)
 
+def get_rag_chain():
+    return (
+        {"context": retriever | format_docs, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+
+
 vectorstore = Chroma(
     embedding_function=OpenAIEmbeddings(),
     persist_directory="./rag_data/.chromadb"
@@ -26,23 +35,17 @@ prompt = hub.pull("rlm/rag-prompt")
 retriever = vectorstore.as_retriever()
 llm = ChatOpenAI(model="gpt-4")
 
-rag_chain = (
-    {"context": retriever | format_docs, "question": RunnablePassthrough()}
-    | prompt
-    | llm
-    | StrOutputParser()
-)
-
-print("Welcome to the Kerbal Space Program RAG application. I will try to assist you with any questions ")
 document_data_sources = set()
 for doc_metadata in retriever.vectorstore.get()['metadatas']:
     document_data_sources.add(doc_metadata['sourceURL']) 
 
-while True:
-    line = input("llm>> ")
-    if line:
-        result = rag_chain.invoke(line)
-        print(result)
-    else:
-        break
+if __name__ == "__main__" :
+    print("Welcome to the Kerbal Space Program RAG application. I will try to assist you with any questions ")
+    while True:
+        line = input("llm>> ")
+        if line:
+            result = get_rag_chain().invoke(line)
+            print(result)
+        else:
+            break
 

hw2/app.py

@@ -7,7 +7,7 @@ from langchain_community.tools.google_jobs import GoogleJobsQueryRun
 from langchain_community.utilities.google_jobs import GoogleJobsAPIWrapper
 
 from dotenv import load_dotenv
-from tools import lookup_ip, lookup_name
+from tools import lookup_ip, lookup_name, search_ksp
 from langsmith import Client
 
 """
@@ -25,7 +25,7 @@ load_dotenv()
 llm = ChatOpenAI(model_name="gpt-4-turbo", temperature=0)
 
 tools = load_tools(["serpapi", "terminal", "dalle-image-generator", "google-jobs"], allow_dangerous_tools=True, llm=llm)
-tools.extend([lookup_name, lookup_ip])
+tools.extend([lookup_name, lookup_ip, search_ksp])
 
 base_prompt = hub.pull("langchain-ai/react-agent-template")
 prompt = base_prompt.partial(instructions="Answer the user's request utilizing at most 8 tool calls")

hw2/tools.py

@@ -2,6 +2,10 @@ from langchain_core.pydantic_v1 import BaseModel, Field, root_validator
 from langchain.tools import tool
 import dns.resolver, dns.reversename
 import validators
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from hw1.app import get_rag_chain
 
 """
 Tools to be run by my custom agent.
@@ -25,6 +29,13 @@ class LookupIPInput(BaseModel):
             return values
         raise ValueError("Malformed IP address")
     
+class KSPTool(BaseModel):
+    query: str = Field(description="should be a kerbal space program (ksp) related query")
+
+@tool("kerbal_space_program_ksp_information", args_schema=KSPTool, return_direct=False)
+def search_ksp(query:str) -> str:
+    """Given a query about kerbal space program (ksp), it will send the query to the KSP rag applciation"""
+    return get_rag_chain().invoke(query)
 
 @tool("lookup_name",args_schema=LookupNameInput, return_direct=False)
 def lookup_name(hostname):