Wikipedia Tool. Final README updates

Author: screwgoth

first/README.md

@@ -1,11 +1,26 @@
 # First LangChain tutorial
 This is the first LangChain tutorial hands-on.
 It's a Youtube script generator using OpenAI LLM.
+It covers the following topics:
+* Streamlist for Web App development
+* LLMChain
+* PromptTemplate
+* SimpleSequentialChain
+* SequentialChain
+* ConversationalMemoryBuffer
+* WikipediaAPIWrapper
+
 
 ### Pre-requisites
+
     $ pip install openai langchain streamlit wikipedia chromadb tiktoken
 
+* Install the above Python packages
+* Replace the API key in the file `apikey.py` with your own OpenAI API
 ### Running
     $ streamlit run app.py
 
-Reference : https://www.youtube.com/watch?v=MlK6SIjcjE8
+## Reference
+* Video tutorial: https://www.youtube.com/watch?v=MlK6SIjcjE8
+* Langchain Docs: https://python.langchain.com/en/latest/index.html
+* Streamlist: https://streamlit.io/

first/app.py

@@ -6,6 +6,7 @@
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain, SequentialChain
 from langchain.memory import ConversationBufferMemory
+from langchain.utilities import WikipediaAPIWrapper
 
 os.environ['OPENAI_API_KEY'] = apikey
 
@@ -19,28 +20,40 @@
 )
 
 script_template = PromptTemplate(
-    input_variables = ['title'],
-    template='Write me a Youtube Video script based on this title, TITLE {title}'
+    input_variables = ['title', 'wikipedia_research'],
+    template='Write me a Youtube Video script based on the title, TITLE {title} while leveraging its Wikipedia researc: {wikipedia_research}'
 )
 
 # Memory
-memory = ConversationBufferMemory(input_key='topic', memory_key='chat_history')
+title_memory = ConversationBufferMemory(input_key='topic', memory_key='chat_history')
+script_memory = ConversationBufferMemory(input_key='title', memory_key='chat_history')
 
 # LLMs
 llm = OpenAI(temperature=0.9)
-title_chain = LLMChain(llm=llm, prompt=title_template, verbose=True, output_key='title', memory=memory)
-script_chain = LLMChain(llm=llm, prompt=script_template, verbose=True, output_key='script', memory=memory)
+title_chain = LLMChain(llm=llm, prompt=title_template, verbose=True, output_key='title', memory=title_memory)
+script_chain = LLMChain(llm=llm, prompt=script_template, verbose=True, output_key='script', memory=script_memory)
 
-sequential_chain = SequentialChain(chains=[title_chain, script_chain],
-                                   input_variables=['topic'],
-                                   output_variables=['title', 'script'],
-                                   verbose=True)
+# Tools
+wiki = WikipediaAPIWrapper()
 
-if prompt:
-    #response = title_chain.run(topic=prompt)
-	response = sequential_chain({'topic':prompt})
-	st.write(response['title'])
-	st.write(response['script'])
+# sequential_chain = SequentialChain(chains=[title_chain, script_chain],
+#                                    input_variables=['topic'],
+#                                    output_variables=['title', 'script'],
+#                                    verbose=True)
 
-	with st.expander('Message History:'):
-		st.info(memory.buffer)
+if prompt:
+	title = title_chain.run(prompt)
+	wiki_research = wiki.run(prompt)
+	script = script_chain.run(title=title, wikipedia_research=wiki_research)
+	
+	st.write(title)
+	st.write(script)
+
+	with st.expander('Title History:'):
+		st.info(title_memory.buffer)
+	
+	with st.expander('Script History:'):
+		st.info(script_memory.buffer)
+	
+	with st.expander('Wikipedia Research:'):
+		st.info(wiki_research)