docs: remove __call__

cookbook/Multi_modal_RAG_google.ipynb

@@ -256,7 +256,7 @@
     "    \"\"\"Make image summary\"\"\"\n",
     "    model = ChatVertexAI(model_name=\"gemini-pro-vision\", max_output_tokens=1024)\n",
     "\n",
-    "    msg = model(\n",
+    "    msg = model.invoke(\n",
     "        [\n",
     "            HumanMessage(\n",
     "                content=[\n",

cookbook/forward_looking_retrieval_augmented_generation.ipynb

@@ -362,7 +362,7 @@
    ],
    "source": [
     "llm = OpenAI()\n",
-    "llm(query)"
+    "llm.invoke(query)"
    ]
   },
   {

docs/docs/integrations/callbacks/labelstudio.ipynb

@@ -194,7 +194,7 @@
     "llm = OpenAI(\n",
     "    temperature=0, callbacks=[LabelStudioCallbackHandler(project_name=\"My Project\")]\n",
     ")\n",
-    "print(llm(\"Tell me a joke\"))"
+    "print(llm.invoke(\"Tell me a joke\"))"
    ]
   },
   {
@@ -270,7 +270,7 @@
     "        )\n",
     "    ]\n",
     ")\n",
-    "llm_results = chat_llm(\n",
+    "llm_results = chat_llm.invoke(\n",
     "    [\n",
     "        SystemMessage(content=\"Always use a lot of emojis\"),\n",
     "        HumanMessage(content=\"Tell me a joke\"),\n",

docs/docs/integrations/callbacks/llmonitor.md

@@ -107,7 +107,7 @@ User tracking allows you to identify your users, track their cost, conversations
 from langchain_community.callbacks.llmonitor_callback import LLMonitorCallbackHandler, identify
 
 with identify("user-123"):
-    llm("Tell me a joke")
+    llm.invoke("Tell me a joke")
 
 with identify("user-456", user_props={"email": "[email protected]"}):
     agen.run("Who is Leo DiCaprio's girlfriend?")

docs/docs/integrations/callbacks/promptlayer.ipynb

@@ -103,7 +103,7 @@
     "    temperature=0,\n",
     "    callbacks=[PromptLayerCallbackHandler(pl_tags=[\"chatopenai\"])],\n",
     ")\n",
-    "llm_results = chat_llm(\n",
+    "llm_results = chat_llm.invoke(\n",
     "    [\n",
     "        HumanMessage(content=\"What comes after 1,2,3 ?\"),\n",
     "        HumanMessage(content=\"Tell me another joke?\"),\n",
@@ -129,10 +129,11 @@
     "from langchain_community.llms import GPT4All\n",
     "\n",
     "model = GPT4All(model=\"./models/gpt4all-model.bin\", n_ctx=512, n_threads=8)\n",
+    "callbacks = [PromptLayerCallbackHandler(pl_tags=[\"langchain\", \"gpt4all\"])]\n",
     "\n",
-    "response = model(\n",
+    "response = model.invoke(\n",
     "    \"Once upon a time, \",\n",
-    "    callbacks=[PromptLayerCallbackHandler(pl_tags=[\"langchain\", \"gpt4all\"])],\n",
+    "    config={\"callbacks\": callbacks},\n",
     ")"
    ]
   },
@@ -181,7 +182,7 @@
     ")\n",
     "\n",
     "example_prompt = promptlayer.prompts.get(\"example\", version=1, langchain=True)\n",
-    "openai_llm(example_prompt.format(product=\"toasters\"))"
+    "openai_llm.invoke(example_prompt.format(product=\"toasters\"))"
    ]
   },
   {

docs/docs/integrations/callbacks/trubrics.ipynb

@@ -315,7 +315,7 @@
     }
    ],
    "source": [
-    "chat_res = chat_llm(\n",
+    "chat_res = chat_llm.invoke(\n",
     "    [\n",
     "        SystemMessage(content=\"Every answer of yours must be about OpenAI.\"),\n",
     "        HumanMessage(content=\"Tell me a joke\"),\n",

docs/docs/integrations/chat/alibaba_cloud_pai_eas.ipynb

@@ -72,7 +72,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "output = chat([HumanMessage(content=\"write a funny joke\")])\n",
+    "output = chat.invoke([HumanMessage(content=\"write a funny joke\")])\n",
     "print(\"output:\", output)"
    ]
   },
@@ -90,7 +90,7 @@
    "outputs": [],
    "source": [
     "kwargs = {\"temperature\": 0.8, \"top_p\": 0.8, \"top_k\": 5}\n",
-    "output = chat([HumanMessage(content=\"write a funny joke\")], **kwargs)\n",
+    "output = chat.invoke([HumanMessage(content=\"write a funny joke\")], **kwargs)\n",
     "print(\"output:\", output)"
    ]
   },

docs/docs/integrations/chat/llama_edge.ipynb

@@ -62,7 +62,7 @@
     "messages = [system_message, user_message]\n",
     "\n",
     "# chat with wasm-chat service\n",
-    "response = chat(messages)\n",
+    "response = chat.invoke(messages)\n",
     "\n",
     "print(f\"[Bot] {response.content}\")"
    ]

docs/docs/integrations/chat/zhipuai.ipynb

@@ -119,7 +119,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "response = chat(messages)\n",
+    "response = chat.invoke(messages)\n",
     "print(response.content)  # Displays the AI-generated poem"
    ]
   },

docs/docs/integrations/llms/anyscale.ipynb

@@ -147,7 +147,7 @@
     "\n",
     "@ray.remote(num_cpus=0.1)\n",
     "def send_query(llm, prompt):\n",
-    "    resp = llm(prompt)\n",
+    "    resp = llm.invoke(prompt)\n",
     "    return resp\n",
     "\n",
     "\n",

docs/docs/integrations/llms/aphrodite.ipynb

@@ -96,7 +96,7 @@
     ")\n",
     "\n",
     "print(\n",
-    "    llm(\n",
+    "    llm.invoke(\n",
     "        '<|system|>Enter RP mode. You are Ayumu \"Osaka\" Kasuga.<|user|>Hey Osaka. Tell me about yourself.<|model|>'\n",
     "    )\n",
     ")"

docs/docs/integrations/llms/baichuan.ipynb

@@ -45,7 +45,7 @@
     "# Load the model\n",
     "llm = BaichuanLLM()\n",
     "\n",
-    "res = llm(\"What's your name?\")\n",
+    "res = llm.invoke(\"What's your name?\")\n",
     "print(res)"
    ]
   },

docs/docs/integrations/llms/baidu_qianfan_endpoint.ipynb

@@ -80,7 +80,7 @@
     "os.environ[\"QIANFAN_SK\"] = \"your_sk\"\n",
     "\n",
     "llm = QianfanLLMEndpoint(streaming=True)\n",
-    "res = llm(\"hi\")\n",
+    "res = llm.invoke(\"hi\")\n",
     "print(res)"
    ]
   },
@@ -185,7 +185,7 @@
     "    model=\"ERNIE-Bot-turbo\",\n",
     "    endpoint=\"eb-instant\",\n",
     ")\n",
-    "res = llm(\"hi\")"
+    "res = llm.invoke(\"hi\")"
    ]
   },
   {

docs/docs/integrations/llms/bittensor.ipynb

@@ -62,7 +62,7 @@
     "    } \"\"\"\n",
     "\n",
     "multi_response_llm = NIBittensorLLM(top_responses=10)\n",
-    "multi_resp = multi_response_llm(\"What is Neural Network Feeding Mechanism?\")\n",
+    "multi_resp = multi_response_llm.invoke(\"What is Neural Network Feeding Mechanism?\")\n",
     "json_multi_resp = json.loads(multi_resp)\n",
     "pprint(json_multi_resp)"
    ]

docs/docs/integrations/llms/ctransformers.ipynb

@@ -62,7 +62,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(llm(\"AI is going to\"))"
+    "print(llm.invoke(\"AI is going to\"))"
    ]
   },
   {
@@ -85,7 +85,7 @@
     "    model=\"marella/gpt-2-ggml\", callbacks=[StreamingStdOutCallbackHandler()]\n",
     ")\n",
     "\n",
-    "response = llm(\"AI is going to\")"
+    "response = llm.invoke(\"AI is going to\")"
    ]
   },
   {

docs/docs/integrations/llms/ctranslate2.ipynb

@@ -97,7 +97,7 @@
    ],
    "source": [
     "print(\n",
-    "    llm(\n",
+    "    llm.invoke(\n",
     "        \"He presented me with plausible evidence for the existence of unicorns: \",\n",
     "        max_length=256,\n",
     "        sampling_topk=50,\n",

docs/docs/integrations/llms/deepsparse.ipynb

@@ -32,7 +32,7 @@
     "    model=\"zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none\"\n",
     ")\n",
     "\n",
-    "print(llm(\"def fib():\"))"
+    "print(llm.invoke(\"def fib():\"))"
    ]
   },
   {

docs/docs/integrations/llms/edenai.ipynb

@@ -203,7 +203,7 @@
     "User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?\n",
     "Assistant:\n",
     "\"\"\"\n",
-    "print(llm(prompt))"
+    "print(llm.invoke(prompt))"
    ]
   },
   {

docs/docs/integrations/llms/google_vertex_ai_palm.ipynb

@@ -359,7 +359,7 @@
     "}\n",
     "message = HumanMessage(content=[text_message, image_message])\n",
     "\n",
-    "output = llm([message])\n",
+    "output = llm.invoke([message])\n",
     "print(output.content)"
    ]
   },
@@ -432,7 +432,7 @@
     "}\n",
     "message = HumanMessage(content=[text_message, image_message])\n",
     "\n",
-    "output = llm([message])\n",
+    "output = llm.invoke([message])\n",
     "print(output.content)"
    ]
   },
@@ -457,7 +457,7 @@
    "outputs": [],
    "source": [
     "message2 = HumanMessage(content=\"And where the image is taken?\")\n",
-    "output2 = llm([message, output, message2])\n",
+    "output2 = llm.invoke([message, output, message2])\n",
     "print(output2.content)"
    ]
   },
@@ -486,7 +486,7 @@
     "}\n",
     "message = HumanMessage(content=[text_message, image_message])\n",
     "\n",
-    "output = llm([message])\n",
+    "output = llm.invoke([message])\n",
     "print(output.content)"
    ]
   },

docs/docs/integrations/llms/koboldai.ipynb

@@ -57,7 +57,9 @@
    },
    "outputs": [],
    "source": [
-    "response = llm(\"### Instruction:\\nWhat is the first book of the bible?\\n### Response:\")"
+    "response = llm.invoke(\n",
+    "    \"### Instruction:\\nWhat is the first book of the bible?\\n### Response:\"\n",
+    ")"
    ]
   }
  ],

docs/docs/integrations/llms/konko.ipynb

@@ -90,7 +90,7 @@
     "llm = Konko(model=\"mistralai/mistral-7b-v0.1\", temperature=0.1, max_tokens=128)\n",
     "\n",
     "input_ = \"\"\"You are a helpful assistant. Explain Big Bang Theory briefly.\"\"\"\n",
-    "print(llm(input_))"
+    "print(llm.invoke(input_))"
    ]
   },
   {

docs/docs/integrations/llms/llm_caching.ipynb

@@ -1020,7 +1020,7 @@
    "source": [
     "%%time\n",
     "\n",
-    "print(llm(\"Why is the Moon always showing the same side?\"))"
+    "print(llm.invoke(\"Why is the Moon always showing the same side?\"))"
    ]
   },
   {
@@ -1044,7 +1044,7 @@
    "source": [
     "%%time\n",
     "\n",
-    "print(llm(\"Why is the Moon always showing the same side?\"))"
+    "print(llm.invoke(\"Why is the Moon always showing the same side?\"))"
    ]
   },
   {
@@ -1109,7 +1109,7 @@
    "source": [
     "%%time\n",
     "\n",
-    "print(llm(\"Why is the Moon always showing the same side?\"))"
+    "print(llm.invoke(\"Why is the Moon always showing the same side?\"))"
    ]
   },
   {
@@ -1133,7 +1133,7 @@
    "source": [
     "%%time\n",
     "\n",
-    "print(llm(\"How come we always see one face of the moon?\"))"
+    "print(llm.invoke(\"How come we always see one face of the moon?\"))"
    ]
   },
   {
@@ -1238,7 +1238,7 @@
    "source": [
     "%%time\n",
     "\n",
-    "print(llm(\"Is a true fakery the same as a fake truth?\"))"
+    "print(llm.invoke(\"Is a true fakery the same as a fake truth?\"))"
    ]
   },
   {
@@ -1262,7 +1262,7 @@
    "source": [
     "%%time\n",
     "\n",
-    "print(llm(\"Is a true fakery the same as a fake truth?\"))"
+    "print(llm.invoke(\"Is a true fakery the same as a fake truth?\"))"
    ]
   },
   {
@@ -1327,7 +1327,7 @@
    "source": [
     "%%time\n",
     "\n",
-    "print(llm(\"Are there truths that are false?\"))"
+    "print(llm.invoke(\"Are there truths that are false?\"))"
    ]
   },
   {
@@ -1351,7 +1351,7 @@
    "source": [
     "%%time\n",
     "\n",
-    "print(llm(\"Is is possible that something false can be also true?\"))"
+    "print(llm.invoke(\"Is is possible that something false can be also true?\"))"
    ]
   },
   {

docs/docs/integrations/llms/predibase.ipynb

@@ -94,7 +94,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "response = model(\"Can you recommend me a nice dry wine?\")\n",
+    "response = model.invoke(\"Can you recommend me a nice dry wine?\")\n",
     "print(response)"
    ]
   },
@@ -262,7 +262,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# response = model(\"Can you help categorize the following emails into positive, negative, and neutral?\")"
+    "# response = model.invoke(\"Can you help categorize the following emails into positive, negative, and neutral?\")"
    ]
   }
  ],

docs/docs/integrations/llms/replicate.ipynb

@@ -323,7 +323,7 @@
     "User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?\n",
     "Assistant:\n",
     "\"\"\"\n",
-    "_ = llm(prompt)"
+    "_ = llm.invoke(prompt)"
    ]
   },
   {
@@ -376,13 +376,13 @@
     "Assistant:\n",
     "\"\"\"\n",
     "start_time = time.perf_counter()\n",
-    "raw_output = llm(prompt)  # raw output, no stop\n",
+    "raw_output = llm.invoke(prompt)  # raw output, no stop\n",
     "end_time = time.perf_counter()\n",
     "print(f\"Raw output:\\n {raw_output}\")\n",
     "print(f\"Raw output runtime: {end_time - start_time} seconds\")\n",
     "\n",
     "start_time = time.perf_counter()\n",
-    "stopped_output = llm(prompt, stop=[\"\\n\\n\"])  # stop on double newlines\n",
+    "stopped_output = llm.invoke(prompt, stop=[\"\\n\\n\"])  # stop on double newlines\n",
     "end_time = time.perf_counter()\n",
     "print(f\"Stopped output:\\n {stopped_output}\")\n",
     "print(f\"Stopped output runtime: {end_time - start_time} seconds\")"