Create main.py

main.py

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+import os
+import uuid
+import sys
+import psycopg2
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from pinecone import Pinecone, ServerlessSpec
+import time
+from openai import OpenAI
+import fitz
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, UploadFile, File
+from pydantic import BaseModel
+import hashlib
+import cohere
+import asyncio  # Import asyncio for asynchronous operations
+
+load_dotenv()
+
+TOGETHER_API_KEY = os.getenv("TOGETHER_API")
+COHERE_API = os.getenv("COHERE_API")
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+HELICON_API_KEY = os.getenv("HELICON_API_KEY")
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
+
+app = FastAPI()
+
+SysPromptDefault = "You are now in the role of an expert AI."
+SummaryTextPrompt = "You are an assistant tasked with summarizing TEXT for retrieval. These summaries will be embedded and used to retrieve the raw text elements. Give a concise summary of the TEXT that is well optimized for retrieval."
+GenerationPrompt = "You are in the role of an expert AI whose task is to give ANSWER to the user's QUESTION based on the provided CONTEXT. Fully rely on CONTEXT; you can't also use your own intelligence too. The summary should be less than 300 words for each QUESTION. You must respond in markdown format; don't use big headings."
+
+
+class QuestionInput(BaseModel):
+    query: str
+
+
+# Global in-memory storage (consider using a proper database or caching mechanism for production)
+file_store = {}
+
+
+def pinecone_server():
+    pc = Pinecone(api_key=PINECONE_API_KEY)
+    index_name = 'law-compliance'
+    if index_name not in pc.list_indexes().names():
+        pc.create_index(
+            index_name,
+            dimension=1024,
+            metric='cosine',
+            spec=ServerlessSpec(
+                cloud='aws',
+                region='us-east-1'
+            )
+        )
+        time.sleep(1)
+    index = pc.Index(index_name)
+    index.describe_index_stats()
+    return index
+
+
+def extract_text_from_pdf(pdf_path):
+    doc = fitz.open(pdf_path)
+    texts = []
+
+    for page_number in range(len(doc)):
+        page = doc.load_page(page_number)
+        text = page.get_text()
+        texts.append(text)
+
+    doc.close()
+
+    return texts
+
+
+def split(texts):
+    text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(chunk_size=384, chunk_overlap=10)
+    text = "\n".join(texts)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+def response(message, model="llama3-8b-8192", SysPrompt=SysPromptDefault, temperature=0.2):
+    client = OpenAI(
+        api_key=GROQ_API_KEY,
+        base_url="https://gateway.hconeai.com/openai/v1",
+        default_headers={
+            "Helicone-Auth": f"Bearer {HELICON_API_KEY}",
+            "Helicone-Target-Url": "https://api.groq.com"
+        }
+    )
+
+    messages = [{"role": "system", "content": SysPrompt}, {"role": "user", "content": message}]
+    response = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        temperature=temperature,
+    )
+    return response.choices[0].message.content
+
+
+def generate_text_summaries(texts, summarize_texts):
+    text_summaries = []
+    if texts and summarize_texts:
+        message = f"TEXT:\n\n{texts}"
+        model = "llama3-8b-8192"
+        text_summaries = response(message=message, model=model, SysPrompt=SummaryTextPrompt, temperature=0)
+    elif texts:
+        text_summaries = texts
+
+    return text_summaries
+
+
+def get_digest(pdf_content):
+    h = hashlib.sha256()
+    h.update(pdf_content)  # Hash the binary content of the PDF
+    return h.hexdigest()
+
+
+def fetch_vectorstore_from_db(file_id):
+    conn = psycopg2.connect(
+        dbname="postgres",
+        user="postgres.kstfnkkxavowoutfytoq",
+        password="nI20th0in3@",
+        host="aws-0-us-east-1.pooler.supabase.com",
+        port="5432"
+    )
+    cur = conn.cursor()
+    create_table_query = '''
+        CREATE TABLE IF NOT EXISTS law_research_pro (
+            file_id VARCHAR(255) PRIMARY KEY,
+            file_name VARCHAR(255),
+            name_space VARCHAR(255)
+        );
+    '''
+    cur.execute(create_table_query)
+    conn.commit()
+    fetch_query = '''
+    SELECT name_space
+    FROM law_research_pro 
+    WHERE file_id = %s;
+    '''
+    cur.execute(fetch_query, (file_id,))
+    result = cur.fetchone()
+    cur.close()
+    conn.close()
+    if result:
+        return result[0]
+    return None
+
+
+def get_next_namespace():
+    conn = psycopg2.connect(
+        dbname="postgres",
+        user="postgres.kstfnkkxavowoutfytoq",
+        password="nI20th0in3@",
+        host="aws-0-us-east-1.pooler.supabase.com",
+        port="5432"
+    )
+    cur = conn.cursor()
+    cur.execute("SELECT COUNT(*) FROM law_research_pro")
+    count = cur.fetchone()[0]
+    next_namespace = f"pdf-{count + 1}"
+    cur.close()
+    conn.close()
+    return next_namespace
+
+
+def insert_data(file_id, file_name, name_space):
+    conn = psycopg2.connect(
+        dbname="postgres",
+        user="postgres.kstfnkkxavowoutfytoq",
+        password="nI20th0in3@",
+        host="aws-0-us-east-1.pooler.supabase.com",
+        port="5432"
+    )
+    cur = conn.cursor()
+    create_table_query = '''
+    CREATE TABLE IF NOT EXISTS law_research_pro (
+        file_id VARCHAR(255) PRIMARY KEY,
+        file_name VARCHAR(255),
+        name_space VARCHAR(255)
+    );
+    '''
+    cur.execute(create_table_query)
+    conn.commit()
+    insert_query = '''
+    INSERT INTO law_research_pro (file_id, file_name, name_space)
+    VALUES (%s, %s, %s)
+    ON CONFLICT (file_id) DO NOTHING;
+    '''
+    cur.execute(insert_query, (file_id, file_name, name_space))
+    conn.commit()
+    cur.close()
+    conn.close()
+
+
+def create_documents(chunks, summaries):
+    documents = []
+    retrieve_contents = []
+
+    for e, s in zip(chunks, summaries):
+        i = str(uuid.uuid4())
+        doc = {
+            'page_content': s,
+            'metadata': {
+                'id': i,
+                'type': 'text',
+                'original_content': e
+            }
+        }
+        retrieve_contents.append((i, e))
+        documents.append(doc)
+
+    return documents, retrieve_contents
+
+
+async def embed_and_upsert(documents, cohere_api_key, name_space):
+    cohere_client = cohere.Client(cohere_api_key)
+    summaries = [doc['page_content'] for doc in documents]
+    pinecone_index = pinecone_server()
+    embeddings = await cohere_client.embed(
+        texts=summaries,
+        input_type='search_document',
+        model="embed-english-v3.0"
+    ).embeddings
+
+    pinecone_data = []
+    for doc, embedding in zip(documents, embeddings):
+        pinecone_data.append({
+            'id': doc['metadata']['id'],
+            'values': embedding,
+            'metadata': doc['metadata']
+        })
+
+    pinecone_index.upsert(vectors=pinecone_data, namespace=name_space)
+
+
+async def embedding_creation(pdf_content, COHERE_API, name_space):
+    texts = extract_text_from_pdf(pdf_content)
+    chunks = split(texts)
+    text_summaries = generate_text_summaries(chunks, summarize_texts=False)
+    documents, retrieve_contents = create_documents(chunks, text_summaries)
+    await embed_and_upsert(documents, COHERE_API, name_space)
+    print("Embeddings created and upserted successfully into Pinecone.")
+
+
+def embed(question):
+    cohere_client = cohere.Client(COHERE_API)
+    embeddings = cohere_client.embed(
+        texts=[question],
+        model="embed-english-v3.0",
+        input_type='search_query'
+    ).embeddings
+    return embeddings
+
+
+def process_rerank_response(rerank_response, docs):
+    rerank_docs = []
+    for item in rerank_response.results:
+        index = item.index
+        if 0 <= index < len(docs):
+            rerank_docs.append(docs[index])
+        else:
+            print(f"Warning: Index {index} is out of range for documents list.")
+    return rerank_docs
+
+
+async def get_name_space(question, pdf_content, file_name):
+    file_id = get_digest(pdf_content)
+    existing_namespace = fetch_vectorstore_from_db(file_id)
+
+    if existing_namespace:
+        print("Document already exists. Using existing namespace.")
+        name_space = existing_namespace
+    else:
+        print("Document is new. Creating embeddings and new namespace.")
+        name_space = get_next_namespace()
+        await embedding_creation(pdf_content, COHERE_API, name_space)
+        insert_data(file_id, file_name, name_space)
+        await asyncio.sleep(10)  # Use asyncio.sleep instead of time.sleep
+
+    return name_space
+
+
+async def get_docs(question, pdf_content, file_name):
+    index = pinecone_server()
+    co = cohere.Client(COHERE_API)
+    xq = embed(question)[0]
+    name_space = await get_name_space(question, pdf_content, file_name)
+    print(name_space)
+    res = index.query(namespace=name_space, vector=xq, top_k=5, include_metadata=True)
+    print(res)
+    docs = [x["metadata"]['original_content'] for x in res["matches"]]
+
+    if not docs:
+        print("No matching documents found.")
+        return []
+
+    results = co.rerank(query=question, documents=docs, top_n=3, model='rerank-english-v3.0')
+    reranked_docs = process_rerank_response(results, docs)
+    return reranked_docs
+
+
+async def answer(question, pdf_content, file_name):
+    docs = await get_docs(question, pdf_content, file_name)
+    if not docs:
+        return "No relevant documents found for the given question."
+
+    context = "\n\n".join(docs)
+    message = f"CONTEXT:\n\n{context}\n\nQUESTION :\n\n{question}\n\nANSWER: \n"
+    model = "llama3-8b-8192"
+    output = response(message=message, model=model, SysPrompt=GenerationPrompt, temperature=0)
+    return output
+
+
+@app.post("/ask-question")
+async def ask_question(input: QuestionInput, file: UploadFile = File(...)):
+    if not file:
+        raise HTTPException(status_code=400, detail="PDF file not provided")
+    file_content = await file.read()
+    filename = file.filename
+
+    # Store the file content in the global store
+    file_id = get_digest(file_content)
+    file_store[file_id] = {
+        "pdf_content": file_content,
+        "filename": filename
+    }
+    answer_output = await answer(input.query, file_content, filename)
+
+    return {"answer": answer_output}