Add pdf bot

docker-compose.yml

@@ -70,6 +70,7 @@ services:
           path: .
           ignore:
             - bot.py
+            - pdf_bot.py
 
 
   bot:
@@ -102,9 +103,44 @@ services:
           path: .
           ignore:
             - loader.py
+            - pdf_bot.py
 
     ports:
       - 8501:8501
 
+
+  pdf_bot:
+    build:
+      dockerfile: pdf_bot.Dockerfile
+    environment:
+      - NEO4J_URI=${NEO4J_URI-neo4j://database:7687}
+      - NEO4J_PASSWORD=${NEO4J_PASSWORD-password}
+      - NEO4J_USERNAME=${NEO4J_USERNAME-neo4j}
+      - OPENAI_API_KEY=${OPENAI_API_KEY}
+      - OLLAMA_BASE_URL=${OLLAMA_BASE_URL-http://host.docker.internal:11434}
+      - LLM=${LLM-llama2}
+      - EMBEDDING_MODEL=${EMBEDDING_MODEL-sentence_transformer}
+      - LANGCHAIN_ENDPOINT=${LANGCHAIN_ENDPOINT-"https://api.smith.langchain.com"}
+      - LANGCHAIN_TRACING_V2=${LANGCHAIN_TRACING_V2-false}
+      - LANGCHAIN_PROJECT=${LANGCHAIN_PROJECT}
+      - LANGCHAIN_API_KEY=${LANGCHAIN_API_KEY}
+    networks:
+      - net
+    depends_on:
+      database:
+        condition: service_healthy
+      pull-model:
+        condition: service_completed_successfully
+    x-develop:
+      watch:
+        - action: rebuild
+          path: .
+          ignore:
+            - loader.py
+            - bot.py
+
+    ports:
+      - 8503:8503
+
 networks:
   net:

pdf_bot.Dockerfile

@@ -0,0 +1,23 @@
+FROM langchain/langchain
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    software-properties-common \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+
+RUN pip install --upgrade -r requirements.txt
+
+COPY pdf_bot.py .
+COPY utils.py .
+COPY chains.py .
+
+EXPOSE 8503
+
+HEALTHCHECK CMD curl --fail http://localhost:8503/_stcore/health
+
+ENTRYPOINT ["streamlit", "run", "pdf_bot.py", "--server.port=8503", "--server.address=0.0.0.0"]

pdf_bot.py

@@ -0,0 +1,93 @@
+import os
+
+import streamlit as st
+from langchain.chains import RetrievalQA
+from PyPDF2 import PdfReader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.vectorstores.neo4j_vector import Neo4jVector
+from streamlit.logger import get_logger
+from chains import (
+    load_embedding_model,
+    load_llm,
+)
+
+# load api key lib
+from dotenv import load_dotenv
+
+load_dotenv(".env")
+
+
+url = os.getenv("NEO4J_URI")
+username = os.getenv("NEO4J_USERNAME")
+password = os.getenv("NEO4J_PASSWORD")
+ollama_base_url = os.getenv("OLLAMA_BASE_URL")
+embedding_model_name = os.getenv("EMBEDDING_MODEL")
+llm_name = os.getenv("LLM")
+# Remapping for Langchain Neo4j integration
+os.environ["NEO4J_URL"] = url
+
+logger = get_logger(__name__)
+
+
+embeddings, dimension = load_embedding_model(
+    embedding_model_name, config={ollama_base_url: ollama_base_url}, logger=logger
+)
+
+
+class StreamHandler(BaseCallbackHandler):
+    def __init__(self, container, initial_text=""):
+        self.container = container
+        self.text = initial_text
+
+    def on_llm_new_token(self, token: str, **kwargs) -> None:
+        self.text += token
+        self.container.markdown(self.text)
+
+
+llm = load_llm(llm_name, logger=logger, config={"ollama_base_url": ollama_base_url})
+
+
+def main():
+    st.header("📄Chat with your pdf file")
+
+    # upload a your pdf file
+    pdf = st.file_uploader("Upload your PDF", type="pdf")
+
+    if pdf is not None:
+        pdf_reader = PdfReader(pdf)
+
+        text = ""
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+
+        # langchain_textspliter
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1000, chunk_overlap=200, length_function=len
+        )
+
+        chunks = text_splitter.split_text(text=text)
+
+        # Store the chunks part in db (vector)
+        vectorstore = Neo4jVector.from_texts(
+            chunks,
+            url=url,
+            username=username,
+            password=password,
+            embedding=embeddings,
+            pre_delete_collection=True,  # Delete existing PDF data
+        )
+        qa = RetrievalQA.from_chain_type(
+            llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever()
+        )
+
+        # Accept user questions/query
+        query = st.text_input("Ask questions about related your upload pdf file")
+
+        if query:
+            stream_handler = StreamHandler(st.empty())
+            qa.run(query, callbacks=[stream_handler])
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -6,3 +6,4 @@ neo4j
 streamlit
 sentence_transformers
 Pillow
+PyPDF2