Create 2_Twitter_GPT_Search.py

pages/2_Twitter_GPT_Search.py

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+from langchain.embeddings import HuggingFaceEmbeddings,HuggingFaceInstructEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.chat_models.openai import ChatOpenAI
+from langchain import VectorDBQA
+import pandas as pd
+
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    AIMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+from langchain.schema import (
+    AIMessage,
+    HumanMessage,
+    SystemMessage
+)
+
+from datetime import datetime as dt
+
+st.set_page_config(page_title="Tweets Question/Answering with Langchain and OpenAI", page_icon="🔎")
+
+system_template="""Use the following pieces of context to answer the users question. 
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+ALWAYS return a "SOURCES" part in your answer.
+The "SOURCES" part should be a reference to the source of the document from which you got your answer.
+
+Example of your response should be:
+
+```
+The answer is foo
+SOURCES: xyz
+```
+
+Begin!
+----------------
+{context}"""
+messages = [
+    SystemMessagePromptTemplate.from_template(system_template),
+    HumanMessagePromptTemplate.from_template("{question}")
+]
+prompt = ChatPromptTemplate.from_messages(messages)
+
+current_time = dt.strftime(dt.today(),'%d_%m_%Y_%H_%M')
+
+st.markdown("## Financial Tweets GPT Search")
+
+twitter_link = """
+[![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi)
+"""
+
+st.markdown(twitter_link)
+
+bi_enc_dict = {'mpnet-base-v2':"sentence-transformers/all-mpnet-base-v2",
+              'instructor-base': 'hkunlp/instructor-base'}
+
+search_input = st.text_input(
+        label='Enter Your Search Query',value= "What are the most topical risks?", key='search')
+        
+sbert_model_name = st.sidebar.selectbox("Embedding Model", options=list(bi_enc_dict.keys()), key='sbox')
+
+with open('tweets.txt') as f:
+    tweets = f.read()
+
+def process_tweets(file,embed_model,query):
+    '''Process file with latest tweets'''
+
+    # Split tweets int chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    texts = text_splitter.split_text(file)
+
+    model = bi_enc_dict[embed_model]
+
+    if model == "hkunlp/instructor-large":
+        emb = HuggingFaceInstructEmbeddings(model_name=model,
+                                            query_instruction='Represent the Financial question for retrieving supporting documents: ',
+                                            embed_instruction='Represent the Financial document for retrieval: ')
+        
+    elif model == "sentence-transformers/all-mpnet-base-v2":
+        emb = HuggingFaceEmbeddings(model_name=model)
+
+    docsearch = FAISS.from_texts(texts, emb)
+
+    chain_type_kwargs = {"prompt": prompt}
+    chain = VectorDBQA.from_chain_type(
+    ChatOpenAI(temperature=0), 
+    chain_type="stuff", 
+    vectorstore=docsearch,
+    chain_type_kwargs=chain_type_kwargs
+    )
+
+    result = chain({"query": query}, return_only_outputs=True)
+
+    return result
+
+    
+