removed redundant file

langhchain_generate_components.py

@@ -1,152 +0,0 @@
-""" 
-#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"),
-# ]
-
-
-response_schemas = [
-    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"),
-    ResponseSchema(name="Feild Of Study", description="List the field of study this can be used for", type="list"),
-]
-
-output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
-memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-format_instructions = output_parser.get_format_instructions()
-
-
-# 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()
-# maker_format_instructions = maker_output_parser.get_format_instructions()
-
-# output_parser = StructuredOutputParser.from_response_schemas(maker_schema)
-
-format_instructions = 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. \
-        Also describe the purpose for a text-to-3d model to use for extra context\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])
-
-
-
-# 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
-    | 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
-