> ## Documentation Index > Fetch the complete documentation index at: https://veniceai-docs-revamp.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # تكامل LangChain > استخدم Venice AI مع LangChain للسلاسل والوكلاء وخطوط أنابيب RAG يعمل Venice AI بسلاسة مع [LangChain](https://python.langchain.com/) بفضل التوافق الكامل مع OpenAI SDK. ابنِ السلاسل والوكلاء وخطوط أنابيب RAG ببنية تحتية تركز على الخصوصية من Venice. ## الإعداد ```bash theme={"dark"} pip install langchain langchain-openai openai ``` ## نماذج الدردشة استخدم `ChatOpenAI` مع عنوان URL الأساسي لـ Venice: ```python theme={"dark"} from langchain_openai import ChatOpenAI llm = ChatOpenAI( model="venice-uncensored-1-2", api_key="your-venice-api-key", base_url="https://api.venice.ai/api/v1", temperature=0.7, ) response = llm.invoke("Explain privacy-preserving AI in 2 sentences.") print(response.content) ``` ## البث المباشر ```python theme={"dark"} for chunk in llm.stream("Write a haiku about decentralization."): print(chunk.content, end="", flush=True) ``` ## التضمينات ```python theme={"dark"} from langchain_openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings( model="text-embedding-bge-m3", api_key="your-venice-api-key", base_url="https://api.venice.ai/api/v1", check_embedding_ctx_length=False, # مطلوب لـ Venice ) vectors = embeddings.embed_documents([ "Venice AI provides private inference.", "No data is retained after processing.", ]) print(f"Embedding dimension: {len(vectors[0])}") ``` ## السلاسل ### سلسلة بسيطة مع قالب الموجّه ```python theme={"dark"} from langchain_core.prompts import ChatPromptTemplate prompt = ChatPromptTemplate.from_messages([ ("system", "You are a {role}. Answer concisely."), ("user", "{question}"), ]) chain = prompt | llm response = chain.invoke({"role": "privacy expert", "question": "Why does zero data retention matter?"}) print(response.content) ``` ### سلسلة متسلسلة ```python theme={"dark"} from langchain_core.output_parsers import StrOutputParser # السلسلة 1: توليد ملخص للموضوع summarizer = ChatPromptTemplate.from_messages([ ("user", "Summarize this topic in 3 bullet points: {topic}") ]) | llm | StrOutputParser() # السلسلة 2: توليد أسئلة من الملخص questioner = ChatPromptTemplate.from_messages([ ("user", "Based on this summary, generate 3 thought-provoking questions:\n{summary}") ]) | llm | StrOutputParser() # التكوين summary = summarizer.invoke({"topic": "decentralized AI inference"}) questions = questioner.invoke({"summary": summary}) print(questions) ``` ## خط أنابيب RAG ابنِ خط أنابيب توليد مدعوم بالاسترجاع مع Venice: ```python theme={"dark"} from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langchain_community.vectorstores import FAISS from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnablePassthrough from langchain_core.output_parsers import StrOutputParser # تهيئة نماذج Venice llm = ChatOpenAI( model="zai-org-glm-5-1", api_key="your-venice-api-key", base_url="https://api.venice.ai/api/v1", ) embeddings = OpenAIEmbeddings( model="text-embedding-bge-m3", api_key="your-venice-api-key", base_url="https://api.venice.ai/api/v1", check_embedding_ctx_length=False, ) # تحميل المستندات وتقسيمها documents = [ "Venice AI provides private, uncensored AI inference with zero data retention.", "The Venice API is OpenAI-compatible, supporting chat completions, images, audio, video, and embeddings.", "Venice supports function calling, structured outputs, web search, and reasoning models.", "Privacy levels include Private (zero retention) and Anonymized (third-party processed).", ] # إنشاء مخزن المتجهات vectorstore = FAISS.from_texts(documents, embeddings) retriever = vectorstore.as_retriever(search_kwargs={"k": 2}) # موجّه RAG rag_prompt = ChatPromptTemplate.from_messages([ ("system", "Answer the question based only on the following context:\n\n{context}"), ("user", "{question}"), ]) # سلسلة RAG def format_docs(docs): return "\n\n".join(doc.page_content for doc in docs) rag_chain = ( {"context": retriever | format_docs, "question": RunnablePassthrough()} | rag_prompt | llm | StrOutputParser() ) answer = rag_chain.invoke("What privacy levels does Venice offer?") print(answer) ``` ## استدعاء الدوال مع الوكلاء ```python theme={"dark"} from langchain_core.tools import tool from langchain.agents import create_tool_calling_agent, AgentExecutor from langchain_core.prompts import ChatPromptTemplate # استخدم نموذجاً قادراً على استدعاء الدوال llm = ChatOpenAI( model="zai-org-glm-5-1", api_key="your-venice-api-key", base_url="https://api.venice.ai/api/v1", ) @tool def get_venice_model_price(model_id: str) -> str: """Get the pricing for a Venice AI model.""" prices = { "venice-uncensored-1-2": "Input: $0.20/1M, Output: $0.90/1M", "zai-org-glm-5-1": "Input: $1.75/1M, Output: $5.50/1M", "qwen3-5-9b": "Input: $0.10/1M, Output: $0.15/1M", } return prices.get(model_id, f"Model {model_id} not found in price list.") prompt = ChatPromptTemplate.from_messages([ ("system", "You help users find the right Venice AI model. Use tools when needed."), ("placeholder", "{chat_history}"), ("user", "{input}"), ("placeholder", "{agent_scratchpad}"), ]) agent = create_tool_calling_agent(llm, [get_venice_model_price], prompt) executor = AgentExecutor(agent=agent, tools=[get_venice_model_price], verbose=True) result = executor.invoke({"input": "What's the cheapest Venice text model?", "chat_history": []}) print(result["output"]) ``` ## المخرجات المنظمة ```python theme={"dark"} from pydantic import BaseModel, Field class MovieReview(BaseModel): title: str = Field(description="Movie title") rating: float = Field(description="Rating out of 10") summary: str = Field(description="One-sentence summary") structured_llm = llm.with_structured_output(MovieReview) review = structured_llm.invoke("Review the movie Inception") print(f"{review.title}: {review.rating}/10 — {review.summary}") ``` ## تكامل البحث على الويب استخدم بحث الويب المدمج في Venice عبر `venice_parameters`: ```python theme={"dark"} from langchain_openai import ChatOpenAI llm_with_search = ChatOpenAI( model="venice-uncensored", api_key="your-venice-api-key", base_url="https://api.venice.ai/api/v1", extra_body={ "venice_parameters": { "enable_web_search": "auto" } } ) response = llm_with_search.invoke("What are the latest developments in AI this week?") print(response.content) ``` أو مرره لكل طلب: ```python theme={"dark"} response = llm.invoke( "What are the latest developments in AI this week?", extra_body={"venice_parameters": {"enable_web_search": "auto"}} ) ``` ## النماذج الموصى بها لـ LangChain | حالة الاستخدام | النموذج | السبب | | ------------------ | -------------------------------- | ---------------------------- | | السلاسل العامة | `venice-uncensored` | سريع، رخيص، غير خاضع للرقابة | | التفكير المعقد | `zai-org-glm-5-1` | أفضل نموذج رائد خاص | | استدعاء الدوال | `zai-org-glm-5-1` | استخدام موثوق للأدوات | | الرؤية + النص | `qwen3-vl-235b-a22b` | فهم رؤية متقدم | | توليد الكود | `qwen3-coder-480b-a35b-instruct` | محسن للكود | | التضمينات (RAG) | `text-embedding-bge-m3` | تضمينات خاصة | | اقتصادي / حجم عالٍ | `qwen3-5-9b` | \$0.10/1M للإدخال | تصفح جميع نماذج Venice مع التسعير والقدرات