experimenting with outputting item components

langhchain_generate_components.py

@@ -0,0 +1,141 @@
+""" 
+#TODO: make a agent that uses HUMAMN as a tool to get:
+- Purpose of science experiment
+- What fields of study do they already know of
+
+#IDEA: Platform generate more indepth experiments by generaing a data set and generate / collect scienfic data
+
+### Chatbot
+the chatbot helps the BOUNTY_BOARD_CHAIN generate science experiments
+
+### EXPERIMENT and Provide feedback on experiments
+
+### Interrgration
+
+- I need to intergrate this code into the app. This includes creating an id for each post, and potentially and a comment section for each "Experiment"
+- I addition i need to generate a mostly pinecone retriever to geenrate scientific experiments from the "community vectore search"
+- potentially have prenium users store their private data, but i may not implement this during the hackathon
+"""
+
+# https://python.langchain.com/docs/modules/model_io/output_parsers/types/structured
+from langchain.output_parsers import ResponseSchema, StructuredOutputParser
+from langchain.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.memory import ConversationBufferMemory
+from langchain_core.runnables import RunnablePassthrough
+from langchain.retrievers import ArxivRetriever, pubmed
+from langchain_core.output_parsers import StrOutputParser
+from langchain.retrievers import ArxivRetriever
+from langchain.retrievers import PubMedRetriever
+from langchain.retrievers import WikipediaRetriever
+from operator import itemgetter
+# import dotenv
+import os
+from dotenv import load_dotenv
+
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+
+
+# The scheme for creating experiments
+experiment_schema = [
+    ResponseSchema(name="Material", description="list of materials need to perfrom the experiments please be specific", type="list"),
+]
+
+
+maker_schema = [
+    ResponseSchema(name="Material", description="The base components needed to create this items from scratch DIY This item must be exact and not an estimation", type="list"),
+]
+
+experiment_output_parser = StructuredOutputParser.from_response_schemas(experiment_schema)
+maker_output_parser = StructuredOutputParser.from_response_schemas(maker_schema)
+
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+format_instructions = experiment_output_parser.get_format_instructions()
+
+
+experiment_prompt = PromptTemplate(
+    template="You must generate well detailed science experiments.\n{format_instructions}\n{question}\n{context}",
+    input_variables=["question"],
+    partial_variables={"format_instructions": format_instructions},
+    memory = memory
+)
+
+maker_prompt = PromptTemplate(
+    template="You must generate a well detailed list of items for creating a given item from scratch.\n{format_instructions}\n{question}\n{context}",
+    input_variables=["question"],
+    partial_variables={"format_instructions": format_instructions},
+    memory = memory
+)
+
+
+def join_strings(*args: str) -> str:
+    """
+    Join an arbitrary number of strings into one string.
+    
+    Args:
+        *args: Variable number of strings to join.
+    
+    Returns:
+        str: Joined string.
+    """
+    return ''.join(args)
+
+def format_docs(docs):
+    return "\n\n".join([join_strings(d.page_content, d.metadata['Entry ID'],d.metadata['Title'], ) for d in docs])
+
+
+arxiv_retriever = ArxivRetriever(load_max_docs=2)
+
+# model = ChatOpenAI(temperature=0)
+model = ChatOpenAI(temperature=0,model="gpt-4")
+
+
+arxiv_retriever = ArxivRetriever(load_max_docs=2)
+
+pub_med_retriever = PubMedRetriever()
+
+wikipedia_retriever = WikipediaRetriever()
+
+arxiv_chain = (
+    {"context": arxiv_retriever, "question": RunnablePassthrough()}
+    | experiment_prompt
+    | model
+    | experiment_output_parser
+)
+
+pub_med_chain = (
+    {"context": pub_med_retriever, "question": RunnablePassthrough()}
+    | experiment_prompt
+    | model
+    | experiment_output_parser
+)
+
+wikipedia_chain = (
+    {"context": wikipedia_retriever, "question": RunnablePassthrough()}
+    | experiment_prompt
+    | model
+    | experiment_output_parser
+)
+
+maker_wikipedia_chain = (
+    {"context": wikipedia_retriever, "question": RunnablePassthrough()}
+    | maker_prompt
+    | model
+    | maker_output_parser
+)
+
+
+
+
+if __name__ == "__main__":
+
+
+    # query = "how to create electronoic on a cellulose subtstrate"
+    query = "A Microscope"
+
+    # output = wikipedia_chain.invoke(query)
+    output = maker_wikipedia_chain.invoke(query)
+    x=0
+