Create app.py

app.py

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+from flask import Flask, request, jsonify
+import os
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.llms import HuggingFaceEndpoint
+from langchain.memory import ConversationBufferMemory
+from pathlib import Path
+import chromadb
+from unidecode import unidecode
+import re
+
+app = Flask(__name__)
+
+# Configuration variables
+PDF_PATH = "path/to/your/static.pdf"  # Replace with your static PDF path
+CHUNK_SIZE = 512
+CHUNK_OVERLAP = 24
+LLM_MODEL = "mistralai/Mistral-7B-Instruct-v0.2"
+TEMPERATURE = 0.1
+MAX_TOKENS = 512
+TOP_K = 20
+
+# Load PDF document and create doc splits
+def load_doc(pdf_path, chunk_size, chunk_overlap):
+    loader = PyPDFLoader(pdf_path)
+    pages = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+# Create vector database
+def create_db(splits, collection_name):
+    embedding = HuggingFaceEmbeddings()
+    new_client = chromadb.EphemeralClient()
+    vectordb = Chroma.from_documents(
+        documents=splits,
+        embedding=embedding,
+        client=new_client,
+        collection_name=collection_name,
+    )
+    return vectordb
+
+# Initialize langchain LLM chain
+def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
+    llm = HuggingFaceEndpoint(
+        repo_id=llm_model,
+        temperature=temperature,
+        max_new_tokens=max_tokens,
+        top_k=top_k,
+    )
+
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        output_key='answer',
+        return_messages=True
+    )
+    retriever = vector_db.as_retriever()
+    qa_chain = ConversationalRetrievalChain.from_llm(
+        llm,
+        retriever=retriever,
+        chain_type="stuff",
+        memory=memory,
+        return_source_documents=True,
+        verbose=False,
+    )
+    return qa_chain
+
+# Generate collection name for vector database
+def create_collection_name(filepath):
+    collection_name = Path(filepath).stem
+    collection_name = collection_name.replace(" ", "-")
+    collection_name = unidecode(collection_name)
+    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
+    collection_name = collection_name[:50]
+    if len(collection_name) < 3:
+        collection_name = collection_name + 'xyz'
+    if not collection_name[0].isalnum():
+        collection_name = 'A' + collection_name[1:]
+    if not collection_name[-1].isalnum():
+        collection_name = collection_name[:-1] + 'Z'
+    return collection_name
+
+# Initialize database and QA chain
+doc_splits = load_doc(PDF_PATH, CHUNK_SIZE, CHUNK_OVERLAP)
+collection_name = create_collection_name(PDF_PATH)
+vector_db = create_db(doc_splits, collection_name)
+qa_chain = initialize_llmchain(LLM_MODEL, TEMPERATURE, MAX_TOKENS, TOP_K, vector_db)
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    data = request.json
+    message = data.get('message', '')
+    history = data.get('history', [])
+
+    formatted_chat_history = []
+    for user_message, bot_message in history:
+        formatted_chat_history.append(f"User: {user_message}")
+        formatted_chat_history.append(f"Assistant: {bot_message}")
+
+    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
+    response_answer = response["answer"]
+    if response_answer.find("Helpful Answer:") != -1:
+        response_answer = response_answer.split("Helpful Answer:")[-1]
+    response_sources = response["source_documents"]
+
+    result = {
+        "answer": response_answer,
+        "sources": [
+            {"content": doc.page_content.strip(), "page": doc.metadata["page"] + 1}
+            for doc in response_sources
+        ]
+    }
+    return jsonify(result)
+
+if __name__ == '__main__':
+    app.run(debug=True, host='0.0.0.0', port=5000)