feat: add demo code

.gitignore

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+.vscode

main.py

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+import gradio as gr
+
+from langchain.chains import (
+    ConversationalRetrievalChain,
+    LLMChain,
+    MapReduceDocumentsChain,
+    ReduceDocumentsChain,
+    StuffDocumentsChain,
+)
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import PromptTemplate
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain_community.chat_models import ChatOpenAI
+from langchain_community.document_loaders import WebBaseLoader
+
+
+def wait_for_summarization(url):
+    return [(None, f"Please wait while I summarize the contents of {url}...")]
+
+
+def load_page(url, api_key, history):
+    global docs, summary, llm
+    loader = WebBaseLoader(url)
+    docs = loader.load()
+    llm = ChatOpenAI(
+        model_name="gpt-3.5-turbo-1106", temperature=0, openai_api_key=api_key
+    )
+    map_template = """The following is a set of snippets from a web page:
+{docs}
+Based on this list of snippets, please identify the main themes
+Helpful Answer:"""
+    map_prompt = PromptTemplate.from_template(map_template)
+    map_chain = LLMChain(llm=llm, prompt=map_prompt)
+
+    # Reduce
+
+    reduce_template = """The following is set of summaries of a web page:
+{docs}
+Take these and distill it into a final, consolidated summary of the main themes.
+Helpful Answer:"""
+    reduce_prompt = PromptTemplate.from_template(reduce_template)
+    reduce_chain = LLMChain(llm=llm, prompt=reduce_prompt)
+
+    # Takes a list of documents, combines them into a single string, and passes this to an LLMChain
+    combine_documents_chain = StuffDocumentsChain(
+        llm_chain=reduce_chain, document_variable_name="docs"
+    )
+
+    # Combines and iteratively reduces the mapped documents
+    reduce_documents_chain = ReduceDocumentsChain(
+        # This is final chain that is called.
+        combine_documents_chain=combine_documents_chain,
+        # If documents exceed context for `StuffDocumentsChain`
+        collapse_documents_chain=combine_documents_chain,
+        # The maximum number of tokens to group documents into.
+        token_max=4000,
+    )
+    # Combining documents by mapping a chain over them, then combining results
+    map_reduce_chain = MapReduceDocumentsChain(
+        # Map chain
+        llm_chain=map_chain,
+        # Reduce chain
+        reduce_documents_chain=reduce_documents_chain,
+        # The variable name in the llm_chain to put the documents in
+        document_variable_name="docs",
+        # Return the results of the map steps in the output
+        return_intermediate_steps=False,
+    )
+
+    text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+        chunk_size=1000, chunk_overlap=0
+    )
+    split_docs = text_splitter.split_documents(docs)
+
+    summary = map_reduce_chain.run(split_docs)
+    return history + [(None, summary)]
+
+
+def prepare_chat(api_key, history):
+    global docs, summary, llm, qa
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=128)
+    documents = text_splitter.split_documents(docs)
+    embeddings = OpenAIEmbeddings(openai_api_key=api_key)
+    vectorstore = Chroma.from_documents(documents, embeddings)
+    retriever = vectorstore.as_retriever(
+        search_type="similarity", search_kwargs={"k": 6}
+    )
+    qa_prompt_template = (
+        """As an AI assistant you help in answering questions about the contents of a web page.
+The summary of the current web page is this:
+
+"""
+        + summary
+        + """
+
+Also, consider this additional context that may be relevant for the user's question:
+
+{context}
+
+Please answer following question: {question}"""
+    )
+
+    qa_prompt = PromptTemplate(
+        template=qa_prompt_template, input_variables=["context", "question"]
+    )
+
+    memory = ConversationBufferMemory(
+        memory_key="chat_history", return_messages=True, output_key="answer"
+    )
+    qa = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        memory=memory,
+        retriever=retriever,
+        combine_docs_chain_kwargs={"prompt": qa_prompt},
+    )
+    return history + [(None, "You can now ask me specific questions about the page.")]
+
+
+def chatbot_function(message, history):
+    global qa
+    return "", history + [(message, qa.run(message))]
+
+
+def build_demo():
+    with gr.Blocks(theme=gr.themes.Default()) as demo:
+        with gr.Row() as config_row:
+            with gr.Column():
+                api_key_box = gr.Textbox(
+                    show_label=False,
+                    placeholder="OpenAI API Key",
+                    container=False,
+                    autofocus=True,
+                )
+                url_box = gr.Textbox(
+                    show_label=False,
+                    placeholder="URL",
+                    container=False,
+                )
+                load_btn = gr.Button(value="Load", variant="primary")
+        with gr.Row(visible=False) as chat_row:
+            with gr.Column():
+                with gr.Row():
+                    chatbot = gr.Chatbot(
+                        elem_id="chatbot",
+                        label="Web Chat",
+                        height=550,
+                    )
+                with gr.Row(visible=False) as inputs_row:
+                    with gr.Column(scale=8):
+                        text_box = gr.Textbox(
+                            show_label=False,
+                            placeholder="Enter text and press ENTER",
+                            autofocus=True,
+                            container=False,
+                        )
+                    with gr.Column(scale=1, min_width=50):
+                        submit_btn = gr.Button(
+                            value="Send",
+                            variant="primary",
+                        )
+
+        load_btn.click(
+            lambda: gr.update(visible=False),
+            outputs=[config_row],
+        ).then(
+            lambda: gr.update(visible=True),
+            outputs=[chat_row],
+        ).then(
+            wait_for_summarization,
+            inputs=[url_box],
+            outputs=[chatbot],
+        ).then(
+            load_page,
+            inputs=[url_box, api_key_box, chatbot],
+            outputs=[chatbot],
+        ).then(
+            prepare_chat,
+            inputs=[api_key_box, chatbot],
+            outputs=[chatbot],
+        ).then(
+            lambda: gr.update(visible=True),
+            outputs=[inputs_row],
+        )
+
+        text_box.submit(
+            chatbot_function,
+            [text_box, chatbot],
+            [text_box, chatbot],
+        )
+        submit_btn.click(
+            chatbot_function,
+            [text_box, chatbot],
+            [text_box, chatbot],
+        )
+
+    return demo
+
+
+if __name__ == "__main__":
+    demo = build_demo()
+    demo.launch()

requirements.txt

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+langchain==0.1.0
+langchain-community==0.0.12
+langchain-core==0.1.10
+langsmith==0.0.80
+openai==1.7.2
+chromadb==0.4.22
+tiktoken==0.5.2