adjusmtents

files/oci_genai_llm_context.py

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+from langchain_community.chat_models.oci_generative_ai import ChatOCIGenAI
+from langchain_core.prompts import PromptTemplate
+from langchain.schema.output_parser import StrOutputParser
+from langchain_community.embeddings import OCIGenAIEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.schema.runnable import RunnableMap
+from langchain_community.document_loaders import PyPDFLoader, UnstructuredPowerPointLoader, UnstructuredPDFLoader, PyMuPDFLoader
+from langchain_core.documents import Document
+from tqdm import tqdm
+import os
+import pickle
+import re
+from langchain_core.runnables import RunnableLambda
+
+INDEX_PATH = "./faiss_index"
+PROCESSED_DOCS_FILE = os.path.join(INDEX_PATH, "processed_docs.pkl")
+
+chapter_separator_regex = r"^(#{1,6} .+|\*\*.+\*\*)$"
+
+def split_llm_output_into_chapters(llm_text):
+    """
+    Splits the LLM output text into chapters, assuming the LLM separates chapters using markdown-style headings like '# Title'
+    """
+    chapters = []
+    current_chapter = []
+    lines = llm_text.splitlines()
+
+    for line in lines:
+        if re.match(chapter_separator_regex, line):
+            if current_chapter:
+                chapters.append("\n".join(current_chapter).strip())
+            current_chapter = [line]
+        else:
+            current_chapter.append(line)
+
+    if current_chapter:
+        chapters.append("\n".join(current_chapter).strip())
+
+    return chapters
+
+def semantic_chunking(text):
+    llm = ChatOCIGenAI(
+        model_id="meta.llama-3.1-405b-instruct",
+        service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
+        compartment_id="ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
+        auth_profile="DEFAULT",
+    )
+
+    prompt = f"""
+    You received the following text extracted via OCR:
+
+    {text}
+
+    Your task:
+    1. Identify headings (short uppercase or bold lines, no period at the end)
+    2. Separate paragraphs by heading
+    3. Indicate columns with [COLUMN 1], [COLUMN 2] if present
+    4. Indicate tables with [TABLE] in markdown format
+    """
+
+    response = llm.invoke(prompt)
+    return response
+
+def read_pdfs(pdf_path):
+    if "-ocr" in pdf_path:
+        doc_pages = PyMuPDFLoader(str(pdf_path)).load()
+    else:
+        doc_pages = UnstructuredPDFLoader(str(pdf_path)).load()
+    full_text = "\n".join([page.page_content for page in doc_pages])
+    return full_text
+
+def smart_split_text(text, max_chunk_size=10_000):
+    chunks = []
+    start = 0
+    text_length = len(text)
+
+    while start < text_length:
+        end = min(start + max_chunk_size, text_length)
+
+        # Try to find the last sentence end before the limit (., ?, !, \n\n)
+        split_point = max(
+            text.rfind('.', start, end),
+            text.rfind('!', start, end),
+            text.rfind('?', start, end),
+            text.rfind('\n\n', start, end)
+        )
+
+        # If not found, make a hard cut
+        if split_point == -1 or split_point <= start:
+            split_point = end
+        else:
+            split_point += 1  # Include the ending character
+
+        chunk = text[start:split_point].strip()
+        if chunk:
+            chunks.append(chunk)
+
+        start = split_point
+
+    return chunks
+
+def load_previously_indexed_docs():
+    if os.path.exists(PROCESSED_DOCS_FILE):
+        with open(PROCESSED_DOCS_FILE, "rb") as f:
+            return pickle.load(f)
+    return set()
+
+def save_indexed_docs(docs):
+    with open(PROCESSED_DOCS_FILE, "wb") as f:
+        pickle.dump(docs, f)
+
+def append_text_to_file(file_path, text):
+    """
+    Appends text to the end of a file.
+    If the file doesn't exist, it will be created.
+
+    Args:
+        file_path (str): Path to the file where the text will be saved.
+        text (str): Text to append.
+    """
+    with open(file_path, "a", encoding="utf-8") as f:
+        f.write(text + "\n")
+
+def chat():
+    llm = ChatOCIGenAI(
+        model_id="meta.llama-3.1-405b-instruct",
+        service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
+        compartment_id="ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
+        auth_profile="DEFAULT",  # Replace with your profile name,
+        model_kwargs={"temperature": 0.7, "top_p": 0.75, "max_tokens": 4000},
+    )
+
+    embeddings = OCIGenAIEmbeddings(
+        model_id="cohere.embed-multilingual-v3.0",
+        service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
+        compartment_id="ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
+        auth_profile="DEFAULT",  # Replace with your profile name,
+    )
+
+    pdf_paths = [
+        './Manuals/using-integrations-oracle-integration-3.pdf',
+        './Manuals/SOASE.pdf',
+        './Manuals/SOASUITEHL7.pdf'
+    ]
+
+    already_indexed_docs = load_previously_indexed_docs()
+    updated_docs = set()
+
+    # Try loading existing FAISS index
+    try:
+        vectorstore = FAISS.load_local(INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
+        print("✔️ FAISS index loaded.")
+    except Exception:
+        print("⚠️ FAISS index not found, creating a new one.")
+        vectorstore = None
+
+    new_chunks = []
+
+    pages = []
+    for pdf_path in tqdm(pdf_paths, desc=f"📄 Processing PDFs"):
+        print(f" {os.path.basename(pdf_path)}")
+        if pdf_path in already_indexed_docs:
+            print(f"✅ Document already indexed: {pdf_path}")
+            continue
+        full_text = read_pdfs(pdf_path=pdf_path)
+
+        # Split the text into ~10 KB chunks (~10,000 characters)
+        text_chunks = smart_split_text(full_text, max_chunk_size=10_000)
+        overflow_buffer = ""  # Remainder from the previous chapter, if any
+
+        for chunk in tqdm(text_chunks, desc=f"📄 Processing text chunks", dynamic_ncols=True, leave=False):
+            # Join with leftover from previous chunk
+            current_text = overflow_buffer + chunk
+
+            # Send text to LLM for semantic splitting
+            treated_text = semantic_chunking(current_text)
+
+            if hasattr(treated_text, "content"):
+                chapters = split_llm_output_into_chapters(treated_text.content)
+
+                # Check if the last chapter seems incomplete
+                last_chapter = chapters[-1] if chapters else ""
+
+                # Simple criteria: if text ends without punctuation (like . ! ?) or is too short
+                if last_chapter and not last_chapter.strip().endswith((".", "!", "?", "\n\n")):
+                    print("📌 Last chapter seems incomplete, saving for the next cycle")
+                    overflow_buffer = last_chapter
+                    chapters = chapters[:-1]  # Don't index the last incomplete chapter yet
+                else:
+                    overflow_buffer = ""  # Nothing left over
+
+                # Save complete chapters as document chunks
+                for chapter_text in chapters:
+                    doc = Document(page_content=chapter_text, metadata={"source": pdf_path})
+                    new_chunks.append(doc)
+                    print(f"✅ New chapter indexed:\n{chapter_text}...\n")
+
+            else:
+                print(f"[ERROR] semantic_chunking returned unexpected type: {type(treated_text)}")
+
+        updated_docs.add(str(pdf_path))
+
+    # If there are new documents, index them
+    if new_chunks:
+        if vectorstore:
+            vectorstore.add_documents(new_chunks)
+        else:
+            vectorstore = FAISS.from_documents(new_chunks, embedding=embeddings)
+
+        vectorstore.save_local(INDEX_PATH)
+        save_indexed_docs(already_indexed_docs.union(updated_docs))
+        print(f"💾 {len(new_chunks)} chunks added to FAISS index.")
+    else:
+        print("📁 No new documents to index.")
+
+    retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 50, "fetch_k": 100})
+
+    template = """ 
+        Document context:
+        {context}
+        
+        Question:
+        {input}
+        
+        Interpretation rules:
+        Rule 1: SOA SUITE documents: `SOASUITE.pdf` and `SOASUITEHL7.pdf`
+        Rule 2: Oracle Integration (known as OIC) document: `using-integrations-oracle-integration-3.pdf`
+        Rule 3: If the query is not a comparison between SOA SUITE and Oracle Integration (OIC), only consider documents relevant to the product.
+        Rule 4: If the question is a comparison between SOA SUITE and OIC, consider all documents and compare between them.
+        Mention at the beginning which tool is being addressed: {input}
+    """
+    prompt = PromptTemplate.from_template(template)
+
+    def get_context(x):
+        query = x.get("input") if isinstance(x, dict) else x
+        return retriever.invoke(query)
+
+    chain = (
+            RunnableMap({
+                "context": RunnableLambda(get_context),
+                "input": lambda x: x.get("input") if isinstance(x, dict) else x
+            })
+            | prompt
+            | llm
+            | StrOutputParser()
+    )
+
+    print("READY")
+
+    while True:
+        query = input()
+        if query == "quit":
+            break
+        response = chain.invoke(query)
+        print(type(response))  # <class 'str'>
+        print(response)
+
+chat()

files/oci_genai_llm_context_fast.py

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+from langchain_community.chat_models.oci_generative_ai import ChatOCIGenAI
+from langchain_core.prompts import PromptTemplate
+from langchain.schema.output_parser import StrOutputParser
+from langchain_community.embeddings import OCIGenAIEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.schema.runnable import RunnableMap
+from langchain_community.document_loaders import PyPDFLoader, UnstructuredPowerPointLoader, UnstructuredPDFLoader, PyMuPDFLoader
+from langchain_core.documents import Document
+from langchain_core.runnables import RunnableLambda
+from tqdm import tqdm
+import os
+import pickle
+
+INDEX_PATH = "./faiss_index"
+PROCESSED_DOCS_FILE = os.path.join(INDEX_PATH, "processed_docs.pkl")
+
+def read_pdfs(pdf_path):
+    if "-ocr" in pdf_path:
+        doc_pages = PyMuPDFLoader(str(pdf_path)).load()
+    else:
+        doc_pages = UnstructuredPDFLoader(str(pdf_path)).load()
+    full_text = "\n".join([page.page_content for page in doc_pages])
+    return full_text
+
+def smart_split_text(text, max_chunk_size=2000):
+    chunks = []
+    start = 0
+    text_length = len(text)
+
+    while start < text_length:
+        end = min(start + max_chunk_size, text_length)
+        split_point = max(
+            text.rfind('.', start, end),
+            text.rfind('!', start, end),
+            text.rfind('?', start, end),
+            text.rfind('\n\n', start, end)
+        )
+
+        if split_point == -1 or split_point <= start:
+            split_point = end
+        else:
+            split_point += 1
+
+        chunk = text[start:split_point].strip()
+        if chunk:
+            chunks.append(chunk)
+
+        start = split_point
+
+    return chunks
+
+def load_previously_indexed_docs():
+    if os.path.exists(PROCESSED_DOCS_FILE):
+        with open(PROCESSED_DOCS_FILE, "rb") as f:
+            return pickle.load(f)
+    return set()
+
+def save_indexed_docs(docs):
+    with open(PROCESSED_DOCS_FILE, "wb") as f:
+        pickle.dump(docs, f)
+
+def chat():
+    llm = ChatOCIGenAI(
+        model_id="meta.llama-3.1-405b-instruct",
+        service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
+        compartment_id="ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
+        auth_profile="DEFAULT",
+        model_kwargs={"temperature": 0.7, "top_p": 0.75, "max_tokens": 4000},
+    )
+
+    embeddings = OCIGenAIEmbeddings(
+        model_id="cohere.embed-multilingual-v3.0",
+        service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
+        compartment_id="ocid1.compartment.oc1..aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
+        auth_profile="DEFAULT",
+    )
+
+    pdf_paths = [
+        './Manuals/using-integrations-oracle-integration-3.pdf',
+        './Manuals/SOASE.pdf',
+        './Manuals/SOASUITEHL7.pdf'
+    ]
+
+    already_indexed_docs = load_previously_indexed_docs()
+    updated_docs = set()
+
+    try:
+        vectorstore = FAISS.load_local(INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
+        print("✔️ FAISS index loaded.")
+    except Exception:
+        print("⚠️ FAISS index not found, creating a new one.")
+        vectorstore = None
+
+    new_chunks = []
+
+    for pdf_path in tqdm(pdf_paths, desc="📄 Processing PDFs"):
+        print(f" {os.path.basename(pdf_path)}")
+        if pdf_path in already_indexed_docs:
+            print(f"✅ Already indexed: {pdf_path}")
+            continue
+
+        full_text = read_pdfs(pdf_path=pdf_path)
+        text_chunks = smart_split_text(full_text, max_chunk_size=2000)
+
+        for chunk_text in tqdm(text_chunks, desc=f"📄 Splitting text", dynamic_ncols=True, leave=False):
+            doc = Document(page_content=chunk_text, metadata={"source": pdf_path})
+            new_chunks.append(doc)
+            print(f"✅ Indexed chunk with {len(chunk_text)} chars.")
+
+        updated_docs.add(str(pdf_path))
+
+    if new_chunks:
+        if vectorstore:
+            vectorstore.add_documents(new_chunks)
+        else:
+            vectorstore = FAISS.from_documents(new_chunks, embedding=embeddings)
+
+        vectorstore.save_local(INDEX_PATH)
+        save_indexed_docs(already_indexed_docs.union(updated_docs))
+        print(f"💾 {len(new_chunks)} chunks saved to FAISS index.")
+    else:
+        print("📁 No new documents to index.")
+
+    retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 50, "fetch_k": 100})
+
+    template = """ 
+        Document context:
+        {context}
+        
+        Question:
+        {input}
+        
+        Interpretation rules:
+        Rule 1: SOA SUITE documents: `SOASUITE.pdf` and `SOASUITEHL7.pdf`
+        Rule 2: Oracle Integration (OIC) document: `using-integrations-oracle-integration-3.pdf`
+        Rule 3: If not a comparison between SOA SUITE and OIC, only consider documents relevant to the product.
+        Rule 4: If the question compares SOA SUITE and OIC, compare both.
+        Mention at the beginning which tool is being addressed: {input}
+    """
+    prompt = PromptTemplate.from_template(template)
+
+    def get_context(x):
+        query = x.get("input") if isinstance(x, dict) else x
+        return retriever.invoke(query)
+
+    chain = (
+            RunnableMap({
+                "context": RunnableLambda(get_context),
+                "input": lambda x: x.get("input") if isinstance(x, dict) else x
+            })
+            | prompt
+            | llm
+            | StrOutputParser()
+    )
+
+    print("READY")
+    while True:
+        query = input()
+        if query == "quit":
+            break
+        response = chain.invoke(query)
+        print(response)
+
+chat()

files/requirements.txt

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+langchain==0.2.0
+langchain-community==0.0.30
+langchain-core==0.2.0
+tqdm
+faiss-cpu
+unstructured[pdf,ppt]==0.13.2
+PyMuPDF==1.24.1
+PyPDF2==3.0.1
+ocrmypdf==14.1.0  # opcional, se quiser OCR fallback
+pypandoc  # necessário para alguns loaders .pptx
+pillow
+python-docx
+chardet
+lxml
+oci
+oci-cli