docs: use standard openai params (#20160)

Part of #20085

cookbook/autogpt/marathon_times.ipynb

@@ -59,7 +59,7 @@
    },
    "outputs": [],
    "source": [
-    "llm = ChatOpenAI(model_name=\"gpt-4\", temperature=1.0)"
+    "llm = ChatOpenAI(model=\"gpt-4\", temperature=1.0)"
    ]
   },
   {

cookbook/elasticsearch_db_qa.ipynb

@@ -84,7 +84,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = ChatOpenAI(model_name=\"gpt-4\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4\", temperature=0)\n",
     "chain = ElasticsearchDatabaseChain.from_llm(llm=llm, database=db, verbose=True)"
    ]
   },

cookbook/langgraph_crag.ipynb

@@ -229,7 +229,7 @@
     "    prompt = hub.pull(\"rlm/rag-prompt\")\n",
     "\n",
     "    # LLM\n",
-    "    llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0, streaming=True)\n",
+    "    llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0, streaming=True)\n",
     "\n",
     "    # Post-processing\n",
     "    def format_docs(docs):\n",

cookbook/langgraph_self_rag.ipynb

@@ -236,7 +236,7 @@
     "    prompt = hub.pull(\"rlm/rag-prompt\")\n",
     "\n",
     "    # LLM\n",
-    "    llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "    llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "\n",
     "    # Post-processing\n",
     "    def format_docs(docs):\n",

cookbook/press_releases.ipynb

@@ -84,7 +84,7 @@
     "from langchain.retrievers import KayAiRetriever\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
     "retriever = KayAiRetriever.create(\n",
     "    dataset_id=\"company\", data_types=[\"PressRelease\"], num_contexts=6\n",
     ")\n",

cookbook/retrieval_in_sql.ipynb

@@ -274,7 +274,7 @@
     "db = SQLDatabase.from_uri(\n",
     "    CONNECTION_STRING\n",
     ")  # We reconnect to db so the new columns are loaded as well.\n",
-    "llm = ChatOpenAI(model_name=\"gpt-4\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-4\", temperature=0)\n",
     "\n",
     "sql_query_chain = (\n",
     "    RunnablePassthrough.assign(schema=get_schema)\n",

cookbook/twitter-the-algorithm-analysis-deeplake.ipynb

@@ -3811,7 +3811,7 @@
     "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo-0613\")  # switch to 'gpt-4'\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo-0613\")  # switch to 'gpt-4'\n",
     "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
    ]
   },

cookbook/two_agent_debate_tools.ipynb

@@ -424,7 +424,7 @@
     "    DialogueAgentWithTools(\n",
     "        name=name,\n",
     "        system_message=SystemMessage(content=system_message),\n",
-    "        model=ChatOpenAI(model_name=\"gpt-4\", temperature=0.2),\n",
+    "        model=ChatOpenAI(model=\"gpt-4\", temperature=0.2),\n",
     "        tool_names=tools,\n",
     "        top_k_results=2,\n",
     "    )\n",

cookbook/wikibase_agent.ipynb

@@ -601,7 +601,7 @@
    "source": [
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model_name=\"gpt-4\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-4\", temperature=0)"
    ]
   },
   {

docs/docs/get_started/quickstart.mdx

@@ -94,12 +94,12 @@ from langchain_openai import ChatOpenAI
 llm = ChatOpenAI()
 ```
 
-If you'd prefer not to set an environment variable you can pass the key in directly via the `openai_api_key` named parameter when initiating the OpenAI LLM class:
+If you'd prefer not to set an environment variable you can pass the key in directly via the `api_key` named parameter when initiating the OpenAI LLM class:
 
 ```python
 from langchain_openai import ChatOpenAI
 
-llm = ChatOpenAI(openai_api_key="...")
+llm = ChatOpenAI(api_key="...")
 ```
 
   </TabItem>
@@ -509,7 +509,7 @@ from langchain.agents import AgentExecutor
 # Get the prompt to use - you can modify this!
 prompt = hub.pull("hwchase17/openai-functions-agent")
 
-# You need to set OPENAI_API_KEY environment variable or pass it as argument `openai_api_key`.
+# You need to set OPENAI_API_KEY environment variable or pass it as argument `api_key`.
 llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
 agent = create_openai_functions_agent(llm, tools, prompt)
 agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

docs/docs/guides/development/debugging.md

@@ -27,7 +27,7 @@ Let's suppose we have a simple agent, and want to visualize the actions it takes
 from langchain.agents import AgentType, initialize_agent, load_tools
 from langchain_openai import ChatOpenAI
 
-llm = ChatOpenAI(model_name="gpt-4", temperature=0)
+llm = ChatOpenAI(model="gpt-4", temperature=0)
 tools = load_tools(["ddg-search", "llm-math"], llm=llm)
 agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
 ```

docs/docs/guides/productionization/fallbacks.ipynb

@@ -204,7 +204,7 @@
     "    ]\n",
     ")\n",
     "# Here we're going to use a bad model name to easily create a chain that will error\n",
-    "chat_model = ChatOpenAI(model_name=\"gpt-fake\")\n",
+    "chat_model = ChatOpenAI(model=\"gpt-fake\")\n",
     "bad_chain = chat_prompt | chat_model | StrOutputParser()"
    ]
   },

docs/docs/integrations/document_loaders/youtube_audio.ipynb

@@ -218,7 +218,7 @@
    "source": [
     "# Build a QA chain\n",
     "qa_chain = RetrievalQA.from_chain_type(\n",
-    "    llm=ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0),\n",
+    "    llm=ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0),\n",
     "    chain_type=\"stuff\",\n",
     "    retriever=vectordb.as_retriever(),\n",
     ")"

docs/docs/integrations/providers/log10.mdx

@@ -30,7 +30,7 @@ messages = [
     HumanMessage(content="Ping?"),
 ]
 
-llm = ChatOpenAI(model_name="gpt-3.5-turbo", callbacks=[log10_callback])
+llm = ChatOpenAI(model="gpt-3.5-turbo", callbacks=[log10_callback])
 ```
 
 [Log10 + Langchain + Logs docs](https://github.com/log10-io/log10/blob/main/logging.md#langchain-logger)
@@ -55,7 +55,7 @@ messages = [
     HumanMessage(content="Ping?"),
 ]
 
-llm = ChatOpenAI(model_name="gpt-3.5-turbo", callbacks=[log10_callback], temperature=0.5, tags=["test"])
+llm = ChatOpenAI(model="gpt-3.5-turbo", callbacks=[log10_callback], temperature=0.5, tags=["test"])
 completion = llm.predict_messages(messages, tags=["foobar"])
 print(completion)
 

docs/docs/integrations/retrievers/arxiv.ipynb

@@ -203,7 +203,7 @@
     "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\")  # switch to 'gpt-4'\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo\")  # switch to 'gpt-4'\n",
     "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
    ]
   },

docs/docs/integrations/retrievers/kay.ipynb

@@ -153,7 +153,7 @@
     "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
     "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
    ]
   },

docs/docs/integrations/retrievers/outline.ipynb

@@ -140,7 +140,7 @@
     "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
     "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
    ]
   },

docs/docs/integrations/retrievers/sec_filings.ipynb

@@ -81,7 +81,7 @@
     "from langchain_community.retrievers import KayAiRetriever\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
     "retriever = KayAiRetriever.create(\n",
     "    dataset_id=\"company\", data_types=[\"10-K\", \"10-Q\"], num_contexts=6\n",
     ")\n",

docs/docs/integrations/retrievers/wikipedia.ipynb

@@ -202,7 +202,7 @@
     "from langchain.chains import ConversationalRetrievalChain\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\")  # switch to 'gpt-4'\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo\")  # switch to 'gpt-4'\n",
     "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
    ]
   },

docs/docs/integrations/vectorstores/jaguar.ipynb

@@ -144,7 +144,7 @@
     "prompt = ChatPromptTemplate.from_template(template)\n",
     "\n",
     "\"\"\" Obtain a Large Language Model \"\"\"\n",
-    "LLM = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "LLM = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "\n",
     "\"\"\" Create a chain for the RAG flow \"\"\"\n",
     "rag_chain = (\n",

docs/docs/integrations/vectorstores/momento_vector_index.ipynb

@@ -394,7 +394,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "qa_chain = RetrievalQA.from_chain_type(llm, retriever=vector_db.as_retriever())"
    ]
   },

docs/docs/integrations/vectorstores/sap_hanavector.ipynb

@@ -437,7 +437,7 @@
    "source": [
     "from langchain.chains import ConversationalRetrievalChain\n",
     "\n",
-    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
     "memory = ConversationBufferMemory(\n",
     "    memory_key=\"chat_history\", output_key=\"answer\", return_messages=True\n",
     ")\n",

docs/docs/integrations/vectorstores/weaviate.ipynb

@@ -589,7 +589,7 @@
    "source": [
     "from langchain_community.chat_models import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "llm.predict(\"What did the president say about Justice Breyer\")"
    ]
   },
@@ -824,7 +824,7 @@
    "source": [
     "from langchain_community.chat_models import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)"
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)"
    ]
   },
   {

docs/docs/modules/data_connection/text_embedding/index.mdx

@@ -35,12 +35,12 @@ Accessing the API requires an API key, which you can get by creating an account
 export OPENAI_API_KEY="..."
 ```
 
-If you'd prefer not to set an environment variable you can pass the key in directly via the `openai_api_key` named parameter when initiating the OpenAI LLM class:
+If you'd prefer not to set an environment variable you can pass the key in directly via the `api_key` named parameter when initiating the OpenAI LLM class:
 
 ```python
 from langchain_openai import OpenAIEmbeddings
 
-embeddings_model = OpenAIEmbeddings(openai_api_key="...")
+embeddings_model = OpenAIEmbeddings(api_key="...")
 ```
 
 Otherwise you can initialize without any params:

docs/docs/modules/model_io/chat/quick_start.ipynb

@@ -52,7 +52,7 @@
    "source": [
     "```{=mdx}\n",
     "<ChatModelTabs\n",
-    "  openaiParams={`model=\"gpt-3.5-turbo-0125\", openai_api_key=\"...\"`}\n",
+    "  openaiParams={`model=\"gpt-3.5-turbo-0125\", api_key=\"...\"`}\n",
     "  anthropicParams={`model=\"claude-3-sonnet-20240229\", anthropic_api_key=\"...\"`}\n",
     "  fireworksParams={`model=\"accounts/fireworks/models/mixtral-8x7b-instruct\", fireworks_api_key=\"...\"`}\n",
     "  mistralParams={`model=\"mistral-large-latest\", mistral_api_key=\"...\"`}\n",

docs/docs/modules/model_io/chat/token_usage_tracking.ipynb

@@ -30,7 +30,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "llm = ChatOpenAI(model_name=\"gpt-4\")"
+    "llm = ChatOpenAI(model=\"gpt-4\")"
    ]
   },
   {

docs/docs/modules/model_io/index.mdx

@@ -63,11 +63,11 @@ llm = OpenAI()
 chat_model = ChatOpenAI(model="gpt-3.5-turbo-0125")
 ```
 
-If you'd prefer not to set an environment variable you can pass the key in directly via the `openai_api_key` named parameter when initiating the OpenAI LLM class:
+If you'd prefer not to set an environment variable you can pass the key in directly via the `api_key` named parameter when initiating the OpenAI LLM class:
 
 ```python
 from langchain_openai import ChatOpenAI
-llm = ChatOpenAI(openai_api_key="...")
+llm = ChatOpenAI(api_key="...")
 ```
 
 Both `llm` and `chat_model` are objects that represent configuration for a particular model. 

docs/docs/modules/model_io/llms/quick_start.ipynb

@@ -40,7 +40,7 @@
     "export OPENAI_API_KEY=\"...\"\n",
     "```\n",
     "\n",
-    "If you'd prefer not to set an environment variable you can pass the key in directly via the `openai_api_key` named parameter when initiating the OpenAI LLM class:\n",
+    "If you'd prefer not to set an environment variable you can pass the key in directly via the `api_key` named parameter when initiating the OpenAI LLM class:\n",
     "\n"
    ]
   },
@@ -53,7 +53,7 @@
    "source": [
     "from langchain_openai import OpenAI\n",
     "\n",
-    "llm = OpenAI(openai_api_key=\"...\")"
+    "llm = OpenAI(api_key=\"...\")"
    ]
   },
   {

docs/docs/modules/model_io/quick_start.mdx

@@ -38,11 +38,11 @@ llm = OpenAI()
 chat_model = ChatOpenAI(model="gpt-3.5-turbo-0125")
 ```
 
-If you'd prefer not to set an environment variable you can pass the key in directly via the `openai_api_key` named parameter when initiating the OpenAI LLM class:
+If you'd prefer not to set an environment variable you can pass the key in directly via the `api_key` named parameter when initiating the OpenAI LLM class:
 
 ```python
 from langchain_openai import ChatOpenAI
-llm = ChatOpenAI(openai_api_key="...")
+llm = ChatOpenAI(api_key="...")
 ```
 
   </TabItem>

docs/docs/use_cases/code_understanding.ipynb

@@ -237,7 +237,7 @@
     "from langchain_core.prompts import ChatPromptTemplate\n",
     "from langchain_openai import ChatOpenAI\n",
     "\n",
-    "llm = ChatOpenAI(model_name=\"gpt-4\")\n",
+    "llm = ChatOpenAI(model=\"gpt-4\")\n",
     "\n",
     "# First we need a prompt that we can pass into an LLM to generate this search query\n",
     "\n",

docs/docs/use_cases/data_generation.ipynb

@@ -269,7 +269,7 @@
    "outputs": [],
    "source": [
     "# LLM\n",
-    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0.7)\n",
+    "model = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0.7)\n",
     "chain = create_data_generation_chain(model)"
    ]
   },

docs/docs/use_cases/question_answering/chat_history.ipynb

@@ -151,7 +151,7 @@
     "# Retrieve and generate using the relevant snippets of the blog.\n",
     "retriever = vectorstore.as_retriever()\n",
     "prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "\n",
     "\n",
     "def format_docs(docs):\n",
@@ -417,7 +417,7 @@
     "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
     "\n",
-    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "\n",
     "\n",
     "### Construct retriever ###\n",

docs/docs/use_cases/question_answering/sources.ipynb

@@ -143,7 +143,7 @@
     "# Retrieve and generate using the relevant snippets of the blog.\n",
     "retriever = vectorstore.as_retriever()\n",
     "prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "\n",
     "\n",
     "def format_docs(docs):\n",

docs/docs/use_cases/question_answering/streaming.ipynb

@@ -143,7 +143,7 @@
     "# Retrieve and generate using the relevant snippets of the blog.\n",
     "retriever = vectorstore.as_retriever()\n",
     "prompt = hub.pull(\"rlm/rag-prompt\")\n",
-    "llm = ChatOpenAI(model_name=\"gpt-3.5-turbo\", temperature=0)\n",
+    "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
     "\n",
     "\n",
     "def format_docs(docs):\n",

libs/community/langchain_community/chat_models/openai.py

@@ -160,7 +160,7 @@ class ChatOpenAI(BaseChatModel):
         .. code-block:: python
 
             from langchain_community.chat_models import ChatOpenAI
-            openai = ChatOpenAI(model_name="gpt-3.5-turbo")
+            openai = ChatOpenAI(model="gpt-3.5-turbo")
     """
 
     @property

libs/community/langchain_community/chat_models/promptlayer_openai.py

@@ -33,7 +33,7 @@ class PromptLayerChatOpenAI(ChatOpenAI):
         .. code-block:: python
 
             from langchain_community.chat_models import PromptLayerChatOpenAI
-            openai = PromptLayerChatOpenAI(model_name="gpt-3.5-turbo")
+            openai = PromptLayerChatOpenAI(model="gpt-3.5-turbo")
     """
 
     pl_tags: Optional[List[str]]

libs/langchain/langchain/chains/combine_documents/stuff.py

@@ -60,7 +60,7 @@ def create_stuff_documents_chain(
             prompt = ChatPromptTemplate.from_messages(
                 [("system", "What are everyone's favorite colors:\\n\\n{context}")]
             )
-            llm = ChatOpenAI(model_name="gpt-3.5-turbo")
+            llm = ChatOpenAI(model="gpt-3.5-turbo")
             chain = create_stuff_documents_chain(llm, prompt)
 
             docs = [

libs/partners/openai/langchain_openai/chat_models/base.py

@@ -240,7 +240,7 @@ class ChatOpenAI(BaseChatModel):
 
             from langchain_openai import ChatOpenAI
 
-            model = ChatOpenAI(model_name="gpt-3.5-turbo")
+            model = ChatOpenAI(model="gpt-3.5-turbo")
     """
 
     @property

templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py

@@ -20,8 +20,8 @@ corrector_schema = [
 cypher_validation = CypherQueryCorrector(corrector_schema)
 
 # LLMs
-cypher_llm = ChatOpenAI(model_name="gpt-4", temperature=0.0)
-qa_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0)
+cypher_llm = ChatOpenAI(model="gpt-4", temperature=0.0)
+qa_llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.0)
 
 
 # Extract entities from text

templates/neo4j-cypher-memory/neo4j_cypher_memory/chain.py

@@ -20,8 +20,8 @@ corrector_schema = [
 cypher_validation = CypherQueryCorrector(corrector_schema)
 
 # LLMs
-cypher_llm = ChatOpenAI(model_name="gpt-4", temperature=0.0)
-qa_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0)
+cypher_llm = ChatOpenAI(model="gpt-4", temperature=0.0)
+qa_llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.0)
 
 
 def convert_messages(input: List[Dict[str, Any]]) -> ChatMessageHistory:

templates/neo4j-cypher/neo4j_cypher/chain.py

@@ -17,8 +17,8 @@ corrector_schema = [
 cypher_validation = CypherQueryCorrector(corrector_schema)
 
 # LLMs
-cypher_llm = ChatOpenAI(model_name="gpt-4", temperature=0.0)
-qa_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0)
+cypher_llm = ChatOpenAI(model="gpt-4", temperature=0.0)
+qa_llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.0)
 
 # Generate Cypher statement based on natural language input
 cypher_template = """Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question:

templates/python-lint/python_lint/agent_executor.py

@@ -203,13 +203,13 @@ class Instruction(BaseModel):
 
 
 agent_executor = (
-    get_agent_executor(ChatOpenAI(model_name="gpt-4-1106-preview", temperature=0.0))
+    get_agent_executor(ChatOpenAI(model="gpt-4-1106-preview", temperature=0.0))
     .configurable_alternatives(
         ConfigurableField("model_name"),
         default_key="gpt4turbo",
-        gpt4=get_agent_executor(ChatOpenAI(model_name="gpt-4", temperature=0.0)),
+        gpt4=get_agent_executor(ChatOpenAI(model="gpt-4", temperature=0.0)),
         gpt35t=get_agent_executor(
-            ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0),
+            ChatOpenAI(model="gpt-3.5-turbo", temperature=0.0),
         ),
     )
     .with_types(input_type=Instruction, output_type=str)

templates/rag-jaguardb/rag_jaguardb/chain.py

@@ -47,7 +47,7 @@ prompt = ChatPromptTemplate.from_template(template)
 
 
 # RAG
-model = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
+model = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
 chain = (
     RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
     | prompt

templates/rag-redis/rag_redis/chain.py

@@ -54,7 +54,7 @@ prompt = ChatPromptTemplate.from_template(template)
 
 
 # RAG Chain
-model = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
+model = ChatOpenAI(model="gpt-3.5-turbo-16k")
 chain = (
     RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
     | prompt