dont care

app.py

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+# # we'll need a few dependencies before we can do this
+# #!pip install chromadb -q
+
+# from langchain.vectorstores import Chroma
+
+# persist_directory = "vector_db"
+
+# vectordb = Chroma.from_documents(documents=documents, embedding=embeddings, persist_directory=persist_directory) #### YOUR CODE HERE
+
+# """Now we can persist our Chroma vector store - and then show an example of how you would load that persisted vector store."""
+
+# vectordb.persist()
+# vectordb = None
+
+# """As you can see when you run the following cell - loaded the persisted vectore store is *much* quicker than reinstantiating it - and that is the benefit of `persist_directory`!"""
+
+# vectordb = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
+
+# """Now that we have our docs set-up - we're ready to progress to the next part of the LangChain applciation puzzle!
+
+# ### Tool Chain
+
+# Now we can leverage our `oc_retriever` as a tool in our LangChain application!
+
+# We'll be utilizing the BLOOMZ-1b7 model as our LLM today - so we can expect that our results will be less effective than if we used OpenAI's gpt-3.5-turbo, but the advantage is that no information will escape outside of our Colab environment.
+
+# First up, let's load our model!
+# """
+
+# from langchain import HuggingFacePipeline
+
+# llm = HuggingFacePipeline.from_model_id(
+#     model_id="bigscience/bloomz-1b7", ### YOUR CODE HERE
+#     task="text-generation", ### YOUR CODE HERE
+#     model_kwargs={"temperature" : 0, "max_length" : 500})
+
+# """Now let's set up our document vector store as a Retriever tool so we can leverage it in our chain!"""
+
+# doc_retriever = vectordb.as_retriever() ### YOUR CODE HERE
+
+# """### Final Chain
+
+# With that set-up, we're good to set-up our final RetrievalQA chain and leverage all the documents we have in our Vector DB!
+# """
+
+# from langchain.chains import RetrievalQA
+
+# shakespeare_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=doc_retriever) ### YOUR CODE HERE
+
+# """Let's test it out by itself!"""
+
+# #shakespeare_qa.run("Who was Hamlet's Mother?")
+
+# """### Conclusion
+
+# Here we have it! 
+
+# A system capable of querying over multiple documents - all without every needing to hit an external API!
+# """
+
+# def make_inference(query):
+#     docs = docsearch.get_relevant_documents(query)
+#     return(chain.run(input_documents=docs, question=query))
+
+# if __name__ == "__main__":
+#     # make a gradio interface
+#     import gradio as gr
+
+#     gr.Interface(
+#         make_inference,
+#         [
+#             gr.inputs.Textbox(lines=2, label="Query"),
+#         ],
+#         gr.outputs.Textbox(label="Response"),
+#         title="🗣️TalkToMyDoc📄",
+#         description="🗣️TalkToMyDoc📄 is a tool that allows you to ask questions about a document. In this case - Hitch Hitchhiker's Guide to the Galaxy.",
+#     ).launch()
+import gradio as gr
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()

requirements.txt

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+langchain
+openai
+tiktoken
+chromadb