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import weaviate
import langchain
import gradio as gr
from langchain.embeddings import CohereEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.prompts.prompt import PromptTemplate
from langchain.document_loaders import UnstructuredFileLoader
from langchain.vectorstores import Weaviate
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
import os
import urllib.request
import ssl
import mimetypes
from dotenv import load_dotenv
import cohere
# Load environment variables
load_dotenv()
openai_api_key = os.getenv('OPENAI')
cohere_api_key = os.getenv('COHERE')
weaviate_api_key = os.getenv('WEAVIATE')
weaviate_url = os.getenv('WEAVIATE_URL')
# Define your prompt templates
prompt_template = """
your preferred texts.
{context}
{chat_history}
Human: {human_input}
Chatbot:
"""
summary_prompt_template = """
Current summary:
{summary}
new lines of conversation:
{new_lines}
New summary:
"""
# Initialize chat history
chat_history = ChatMessageHistory.construct()
# Create prompt templates
summary_prompt = PromptTemplate(input_variables=["summary", "new_lines"], template=summary_prompt_template)
load_qa_chain_prompt = PromptTemplate(input_variables=["chat_history", "human_input", "context"], template=prompt_template)
# Initialize memory
memory = ConversationSummaryBufferMemory(
llm="your llm",
memory_key="chat_history",
input_key="human_input",
max_token=5000,
prompt=summary_prompt,
moving_summary_buffer="summary",
chat_memory=chat_history
)
# Load QA chain with memory
qa_chain = load_qa_chain(llm="your llm", chain_type="stuff", memory=memory, prompt=load_qa_chain_prompt)
# Weaviate connection
auth_config = weaviate.auth.AuthApiKey(api_key=weaviate_api_key)
client = weaviate.Client(url=weaviate_url, auth_client_secret=auth_config,
additional_headers={"X-Cohere-Api-Key": cohere_api_key})
# Initialize vectorstore
vectorstore = Weaviate(client, index_name="HereChat", text_key="text")
vectorstore._query_attrs = ["text", "title", "url", "views", "lang", "_additional {distance}"]
vectorstore.embedding = CohereEmbeddings(model="embed-multilingual-v2.0", cohere_api_key=cohere_api_key)
# Initialize Cohere client
co = cohere.Client(api_key=cohere_api_key)
def embed_pdf(file, collection_name):
# Save the uploaded file
filename = file.name
file_path = os.path.join('./', filename)
with open(file_path, 'wb') as f:
f.write(file.read())
# Checking filetype for document parsing
mime_type = mimetypes.guess_type(file_path)[0]
loader = UnstructuredFileLoader(file_path)
docs = loader.load()
# Generate embeddings and store documents in Weaviate
embeddings = CohereEmbeddings(model="embed-multilingual-v2.0", cohere_api_key=cohere_api_key)
for doc in docs:
embedding = embeddings.embed([doc['text']])
weaviate_document = {
"text": doc['text'],
"embedding": embedding
}
client.data_object.create(data_object=weaviate_document, class_name=collection_name)
os.remove(file_path)
return {"message": f"Documents embedded in Weaviate collection '{collection_name}'"}
def update_chat_history(user_message, ai_message):
chat_history.add_user_message(user_message)
chat_history.add_ai_message(ai_message)
# Update memory if needed
if len(chat_history) > memory.max_token:
memory.create_summary()
def retrieve_info(query):
update_chat_history(query, "")
llm = OpenAI(temperature=0, openai_api_key=openai_api_key)
qa = RetrievalQA.from_chain_type(llm, retriever=vectorstore.as_retriever())
# Retrieve initial results
initial_results = qa({"query": query})
# Assuming initial_results are in the desired format, extract the top documents
top_docs = initial_results[:25] # Adjust this if your result format is different
# Rerank the top results
reranked_results = co.rerank(query=query, documents=top_docs, top_n=3, model='rerank-english-v2.0')
# Format the reranked results
formatted_results = []
for idx, r in enumerate(reranked_results):
formatted_result = {
"Document Rank": idx + 1,
"Document Index": r.index,
"Document": r.document['text'],
"Relevance Score": f"{r.relevance_score:.2f}"
}
formatted_results.append(formatted_result)
return {"results": formatted_results}
# Format the reranked results and append to user prompt
user_prompt = f"User: {query}\n"
for idx, r in enumerate(reranked_results):
user_prompt += f"Document {idx + 1}: {r.document['text']}\nRelevance Score: {r.relevance_score:.2f}\n\n"
# Final API call to OpenAI
final_response = client.chat.completions.create(
model="gpt-4-1106-preview",
messages=[
{
"role": "system",
"content": "You are a redditor. Assess, rephrase, and explain the following. Provide long answers. Use the same words and language you receive."
},
{
"role": "user",
"content": user_prompt
}
],
temperature=1.63,
max_tokens=2240,
top_p=1,
frequency_penalty=1.73,
presence_penalty=1.76
)
return final_response.choices[0].text
def combined_interface(query, file, collection_name):
if query:
return retrieve_info(query)
elif file is not None and collection_name:
return embed_pdf(file, collection_name)
else:
return "Please enter a query or upload a PDF file."
iface = gr.Interface(
fn=combined_interface,
inputs=[
gr.Textbox(label="Query"),
gr.File(label="PDF File"),
gr.Textbox(label="Collection Name")
],
outputs="text"
)
iface.launch() |