Natuurlijk! Hier is een uitgebreide gids voor het creëren van een LangChain-agent die automatische documentanalyse en samenvatting uitvoert voor documenten zoals rapporten, artikelen en juridische teksten. --- ### 1. Analysemethoden en samenvattingsstrategieën **Analysemethoden:** - **Tekstanalyse en Named Entity Recognition (NER):** Identificeer belangrijke entiteiten zoals personen, organisaties, datums, juridische termen. - **Thema- en topicmodellering:** Begrijp de hoofdonderwerpen van het document. - **Sentimentanalyse:** Niet altijd essentieel voor juridische documenten, maar kan nuttig voor rapporten of opinies. - **Relatie- en netwerkanalyse:** Ontdek verbanden tussen entiteiten. **Samenvattingsstrategieën:** - **Extractieve samenvatting:** Selecteer de meest relevante zinnen of paragrafen uit het document. - **Abstracterende samenvatting:** Creëer een nieuwe tekst die de kernpunten samenvat, met behulp van taalmodellen zoals GPT-4. - **Specifieke samenvatting voor juridische documenten:** Focus op belangrijke clausules, verplichtingen, rechten en plichten. --- ### 2. Prompt templates en ketens voor documentanalyse en samenvatting **Voorbeeld van een prompt voor juridische samenvatting:** ```python prompt_template = """ Je bent een juridische expert. Lees het onderstaande document en geef een korte samenvatting van de belangrijkste punten, inclusief belangrijke clausules en verplichtingen. Document: {document} Samenvatting: """ ``` **Voorbeeld van een analyse prompt (entiteiten en relaties):** ```python prompt_template = """ Je bent een juridische expert. Analyseer het volgende document en identificeer belangrijke entiteiten, zoals personen, organisaties, datums, en juridische termen. Geef ook een overzicht van de belangrijkste relaties tussen deze entiteiten. Document: {document} Analyse: """ ``` **Voorbeeld van een keten (chains):** ```python from langchain.chains import SequentialChain from langchain.prompts import PromptTemplate from langchain.llms import OpenAI # Stap 1: Extractie van belangrijke entiteiten entity_prompt = PromptTemplate(template=prompt_template, input_variables=["document"]) entity_chain = LLMChain(llm=OpenAI(), prompt=entity_prompt) # Stap 2: Samenvatting van het document summary_prompt = PromptTemplate( template=""" Je bent een juridische expert. Geef een korte samenvatting van de volgende tekst: {document} Samenvatting: """, input_variables=["document"] ) summary_chain = LLMChain(llm=OpenAI(), prompt=summary_prompt) # Sequential Chain overall_chain = SequentialChain( chains=[entity_chain, summary_chain], input_variables=["document"], output_variables=["analyse", "samenvatting"] ) ``` ### 3. Omgaan met verschillende documentformaten - **Tekstbestanden (txt, docx, pdf):** - Gebruik libraries zoals `python-docx` voor Word-documenten en `PyPDF2` of `pdfplumber` voor PDF's om tekst te extraheren. - ** gestructureerde gegevens:** Zorg dat de tekst goed wordt opgeschoond en genormaliseerd voor analyse. - **Oplossing voor grote documenten:** Splits grote documenten op in paragrafen of secties om de verwerking te vergemakkelijken. ### 4. Waarborgen van nauwkeurigheid en privacy - **Hoge nauwkeurigheid:** - Gebruik geavanceerde taalmodellen (zoals GPT-4) via API's met juiste parameters. - Voeg instructies toe in prompts die expliciet vragen om precisie en volledigheid. - Voer meerdere analyses uit en vergelijk resultaten voor consistentie. - **Bescherming van gevoelige informatie:** - Voer alle verwerking op beveiligde systemen uit. - Anonimiseer of maskeer gevoelige gegevens voordat je ze door het model stuurt. - Gebruik encryptie en toegangscontrole. - Beperk de output tot alleen de benodigde samenvatting of analyse. --- ### Samenvatting Voor jouw toepassing is het belangrijk om: - **Gebruik te maken van extractieve en abstracterende samenvattingstechnieken** met duidelijke prompts. - **Nadruk te leggen op juridische precisie** door instructies en context toe te voegen. - **Documenten correct te extraheren en voor te bereiden** op basis van hun formaat. - **Privacy en beveiliging te waarborgen** door goede datahandhabing en encryptie. --- Wil je dat ik een complete codevoorbeeld met specifieke implementaties voor jouw situatie opstel?

Of course. Here is a comprehensive guide to developing a LangChain agent for automated document analysis and summarization, tailored for high-accuracy and sensitive domains like legal texts. ### Core Components & Architecture Your agent will be built around several key LangChain modules: 1. **Document Loaders:** To handle different file formats. 2. **Text Splitters:** To break down large documents into manageable chunks for LLM context windows. 3. **LLM (Large Language Model):** The core engine for analysis and summarization. For high accuracy, use a powerful model like **OpenAI's GPT-4-turbo** or **Anthropic's Claude 3 (Sonnet or Opus)**. Open-source alternatives like **Llama 3 70B** or **Mistral Large** are also excellent but require more setup. 4. **Chains & Agents:** To sequence the steps logically (Sequential Chain) and optionally use tools for more complex tasks (Agent). 5. **Prompt Templates:** To ensure consistent, high-quality, and secure instructions for the LLM. 6. **Output Parsers:** To structure the LLM's response into consistent JSON or Pydantic objects. --- ### Analysis Methods & Summarization Strategies Given the sensitive nature of your documents, you cannot use a simple "summarize this" prompt. You need a structured, multi-step approach. #### 1. Analysis Methods: * **Extractive & Abstractive Hybrid:** Identify key sentences, clauses, or definitions (extractive) and then rewrite them into a coherent summary (abstractive). * **Structured Decomposition:** Break the document into its logical components for analysis. * **For Legal Contracts:** Identify Parties, Effective Date, Term, Key Obligations, Payment Terms, Termination Clauses, Limitation of Liability, Governing Law, Confidentiality clauses, and Unusual/Burdensome terms. * **For Court Rulings:** Identify Parties, Case Number, Judge, Core Issue, Relevant Laws Cited, Key Arguments, Final Judgment/Ruling, and Legal Precedent set. * **Query-Based Analysis:** Allow users to ask specific questions about the document (e.g., "What is the liability cap?" or "On what grounds did the judge dismiss the case?"). #### 2. Summarization Strategies: * **Multi-Stage Summarization:** This is critical for long documents. 1. **Chunk Summarization:** Split the document and create a summary for each chunk. 2. **Summary Consolidation:** Summarize the collection of chunk summaries into a final, coherent summary. This prevents loss of context from the beginning of a long document. * **Hierarchical Summarization:** * **Extreme Summary:** One-paragraph overview. * **Key Points:** Bulleted list of the most critical clauses or findings. * **Detailed Summary:** A multi-paragraph breakdown covering all major sections. --- ### Prompt Templates & Chains Here are examples of prompts designed for accuracy and security. #### Prompt Template 1: Clause Extraction & Analysis (for Contracts) ```python from langchain.prompts import PromptTemplate legal_analysis_template = """ You are a highly skilled legal AI assistant. Your task is to analyze the provided legal contract text and extract specific information. **IMPORTANT GUIDELINES:** 1. **Accuracy is Paramount:** Do not hallucinate information. If a piece of information is not explicitly stated in the text, state "Not explicitly stated." 2. **Protect Sensitive Information:** NEVER generate or guess at sensitive information like names, addresses, or exact financial figures unless they are provided in the text below. If they are provided, you may summarize their purpose (e.g., "A fixed payment of $X is due upon signing."). **Contract Text:** {document_text} **Please extract the following information in a structured JSON format:** - "parties_involved": [List the names of the parties] - "effective_date": The effective date of the contract - "key_obligations": A list of the top 5 most important obligations for each party - "termination_clauses": Describe the conditions under which this contract can be terminated - "confidentiality": Does the contract include a confidentiality clause? (True/False) and a one-sentence description. - "governing_law": Which state/country's laws govern this contract? """ LEGAL_ANALYSIS_PROMPT = PromptTemplate.from_template(legal_analysis_template) ``` #### Prompt Template 2: Multi-Stage Summarization ```python summarize_chunk_template = """ Write a concise and accurate summary of the following text section from a legal document. Focus on the key facts, rulings, or clauses presented in this specific section. TEXT SECTION: {chunk_text} CONCISE SUMMARY: """ CHUNK_SUMMARY_PROMPT = PromptTemplate.from_template(summarize_chunk_template) consolidate_summaries_template = """ You will be provided with a series of summaries from a single legal document: {document_type}. Synthesize these summaries into a final, comprehensive, and coherent summary. **Guidelines:** - Maintain all critical legal and factual information. - Structure the summary logically (e.g., Introduction, Key Findings, Conclusion). - Do not add any information not present in the provided summaries. - Protect sensitive information. SUMMARIES: {summaries} FINAL COMPREHENSIVE SUMMARY: """ CONSOLIDATE_PROMPT = PromptTemplate.from_template(consolidate_summaries_template) ``` #### Example Chain: Sequential Processing Chain This chain uses the "Map-Reduce" method, which is perfect for this task. ```python from langchain.chains import LLMChain, ReduceDocumentsChain, MapReduceDocumentsChain from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain_community.llms import OpenAI # or ChatOpenAI, ChatAnthropic # Initialize LLM (use a high-quality model) llm = OpenAI(temperature=0) # Temperature=0 for maximum determinism # Create the map chain for individual chunks map_chain = LLMChain(llm=llm, prompt=CHUNK_SUMMARY_PROMPT) # Create the reduce chain to combine summaries reduce_chain = LLMChain(llm=llm, prompt=CONSOLIDATE_PROMPT) # Takes a list of documents, combines them into a single string, and passes to LLM combine_documents_chain = StuffDocumentsChain( llm_chain=reduce_chain, document_variable_name="summaries" ) # Combines the map and reduce steps reduce_documents_chain = ReduceDocumentsChain( combine_documents_chain=combine_documents_chain, collapse_documents_chain=combine_documents_chain, # Only needed if collapsing is required token_max=4000, # Adjust based on your LLM's context window ) # The final map-reduce chain map_reduce_chain = MapReduceDocumentsChain( llm_chain=map_chain, reduce_documents_chain=reduce_documents_chain, document_variable_name="chunk_text", return_intermediate_steps=False, ) # Usage: # loaded_docs = loader.load() # split_docs = text_splitter.split_documents(loaded_docs) # final_result = map_reduce_chain.run(split_docs) ``` --- ### Guidelines for Handling Documents & Ensuring Accuracy #### 1. Document Format Handling: * **PDFs:** Use `PyPDFLoader` (for simple text) or `UnstructuredPDFLoader` (better for complex layouts and tables). For highest fidelity, consider OCR-based loaders like `UnstructuredOCRLoader` if documents are scanned. * **DOCX:** Use `UnstructuredWordDocumentLoader`. * **HTML/Web Pages:** Use `WebBaseLoader` or `AsyncHtmlLoader`. * **Pre-processing:** Always clean the extracted text to remove page numbers, headers/footers, and excessive whitespace that could confuse the LLM. #### 2. Ensuring High Accuracy: * **Chunking Strategy:** Use `RecursiveCharacterTextSplitter` with a small chunk overlap (e.g., 200 characters). This helps maintain context between chunks. * **Temperature Setting:** Always set `temperature=0` for analysis tasks to make the LLM's output more deterministic and factual. * **Grounding & Citations:** Implement a **Retrieval-Augmented Generation (RAG)** system. Store document chunks in a vector database (e.g., Chroma, Pinecone). When the LLM answers a question, first retrieve the most relevant chunks and provide them as context. This drastically reduces hallucination. * **Human-in-the-Loop (HITL):** For extremely high-stakes scenarios, design the system to flag low-confidence answers or critical clauses for human lawyer review before finalizing. #### 3. Protecting Sensitive Information (PII): * **Prompt Engineering:** As shown in the templates, explicitly instruct the LLM not to generate or guess sensitive data. * **Pre-processing Redaction:** **This is the most secure method.** Before the document reaches the LLM, use a dedicated PII detection library (e.g., `Microsoft Presidio`, `spaCy` with NER) to find and redact information like names, emails, credit card numbers, and replace them with placeholders like `[REDACTED_NAME]` or `[REDACTED_DATE]`. * **API Choice:** Do not use LLM APIs that log and train on user data by default. Use providers like OpenAI and Anthropic that offer **data privacy commitments** and ensure your requests are not used for model training. For maximum security, use a self-hosted open-source model (e.g., Llama 3) within your own infrastructure. * **Access Controls:** Ensure the entire application pipeline is secure, with encryption in transit and at rest, and strict access controls defining who can upload and analyze documents. By combining these methods—structured prompts, multi-stage chains, careful chunking, pre-processing redaction, and choosing the right APIs—you can build a powerful, accurate, and secure document analysis agent suitable for sensitive legal work.