Rename LLMonitor Callback Handler

.github/workflows/_integration_test.yml

@@ -37,20 +37,13 @@ jobs:
         shell: bash
         run: poetry install --with test,test_integration
 
-      - name: 'Authenticate to Google Cloud'
-        id: 'auth'
-        uses: google-github-actions/auth@v2
-        with:
-          credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
-
       - name: Run integration tests
         shell: bash
         env:
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         run: |
           make integration_tests
 

.github/workflows/_release.yml

@@ -1,5 +1,5 @@
 name: release
-run-name: Release ${{ inputs.working-directory }} by @${{ github.actor }}
+
 on:
   workflow_call:
     inputs:
@@ -149,20 +149,13 @@ jobs:
         run: make tests
         working-directory: ${{ inputs.working-directory }}
 
-      - name: 'Authenticate to Google Cloud'
-        id: 'auth'
-        uses: google-github-actions/auth@v2
-        with:
-          credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
-
       - name: Run integration tests
         if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
         env:
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
-          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 

cookbook/LLaMA2_sql_chat.ipynb

@@ -149,7 +149,7 @@
    ],
    "source": [
     "# Prompt\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
     "\n",
     "# Update the template based on the type of SQL Database like MySQL, Microsoft SQL Server and so on\n",
     "template = \"\"\"Based on the table schema below, write a SQL query that would answer the user's question:\n",
@@ -278,7 +278,7 @@
    "source": [
     "# Prompt\n",
     "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "\n",
     "template = \"\"\"Given an input question, convert it to a SQL query. No pre-amble. Based on the table schema below, write a SQL query that would answer the user's question:\n",
     "{schema}\n",

cookbook/Multi_modal_RAG.ipynb

@@ -198,9 +198,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "\n",
     "# Generate summaries of text elements\n",
@@ -355,9 +355,9 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
+    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.documents import Document\n",
-    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "\n",
     "def create_multi_vector_retriever(\n",

cookbook/Semi_Structured_RAG.ipynb

@@ -235,9 +235,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_openai import ChatOpenAI"
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_core.output_parsers import StrOutputParser"
    ]
   },
   {
@@ -320,9 +320,9 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
+    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.documents import Document\n",
-    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",

cookbook/Semi_structured_and_multi_modal_RAG.ipynb

@@ -211,9 +211,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_openai import ChatOpenAI"
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_core.output_parsers import StrOutputParser"
    ]
   },
   {
@@ -375,9 +375,9 @@
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
     "from langchain.storage import InMemoryStore\n",
+    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import Chroma\n",
     "from langchain_core.documents import Document\n",
-    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",

cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb

@@ -209,9 +209,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain_community.chat_models import ChatOllama\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate"
+    "from langchain_core.output_parsers import StrOutputParser"
    ]
   },
   {

cookbook/advanced_rag_eval.ipynb

@@ -132,8 +132,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import Chroma\n",
-    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "baseline = Chroma.from_texts(\n",
     "    texts=all_splits_pypdf_texts,\n",
@@ -160,9 +160,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
-    "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "# Prompt\n",
     "prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text for retrieval. \\\n",

cookbook/agent_vectorstore.ipynb

@@ -29,8 +29,9 @@
    "source": [
     "from langchain.chains import RetrievalQA\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain_community.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain_community.llms import OpenAI\n",
     "from langchain_community.vectorstores import Chroma\n",
-    "from langchain_openai import OpenAI, OpenAIEmbeddings\n",
     "\n",
     "llm = OpenAI(temperature=0)"
    ]
@@ -160,7 +161,7 @@
    "source": [
     "# Import things that are needed generically\n",
     "from langchain.agents import AgentType, Tool, initialize_agent\n",
-    "from langchain_openai import OpenAI"
+    "from langchain_community.llms import OpenAI"
    ]
   },
   {

cookbook/analyze_document.ipynb

@@ -29,7 +29,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import AnalyzeDocumentChain\n",
-    "from langchain_openai import ChatOpenAI\n",
+    "from langchain_community.chat_models import ChatOpenAI\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)"
    ]

cookbook/autogpt/autogpt.ipynb

@@ -62,8 +62,8 @@
    "outputs": [],
    "source": [
     "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain_community.vectorstores import FAISS\n",
-    "from langchain_openai import OpenAIEmbeddings"
+    "from langchain_community.embeddings import OpenAIEmbeddings\n",
+    "from langchain_community.vectorstores import FAISS"
    ]
   },
   {
@@ -100,8 +100,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_experimental.autonomous_agents import AutoGPT\n",
-    "from langchain_openai import ChatOpenAI"
+    "from langchain_community.chat_models import ChatOpenAI\n",
+    "from langchain_experimental.autonomous_agents import AutoGPT"
    ]
   },
   {

cookbook/autogpt/marathon_times.ipynb

@@ -41,8 +41,8 @@
     "import pandas as pd\n",
     "from langchain.docstore.document import Document\n",
     "from langchain_community.agent_toolkits.pandas.base import create_pandas_dataframe_agent\n",
+    "from langchain_community.chat_models import ChatOpenAI\n",
     "from langchain_experimental.autonomous_agents import AutoGPT\n",
-    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "# Needed synce jupyter runs an async eventloop\n",
     "nest_asyncio.apply()"
@@ -311,8 +311,8 @@
     "# Memory\n",
     "import faiss\n",
     "from langchain.docstore import InMemoryDocstore\n",
+    "from langchain_community.embeddings import OpenAIEmbeddings\n",
     "from langchain_community.vectorstores import FAISS\n",
-    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "embeddings_model = OpenAIEmbeddings()\n",
     "embedding_size = 1536\n",

cookbook/baby_agi.ipynb

@@ -31,8 +31,9 @@
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from langchain_experimental.autonomous_agents import BabyAGI\n",
-    "from langchain_openai import OpenAI, OpenAIEmbeddings"
+    "from langchain_community.embeddings import OpenAIEmbeddings\n",
+    "from langchain_community.llms import OpenAI\n",
+    "from langchain_experimental.autonomous_agents import BabyAGI"
    ]
   },
   {

cookbook/baby_agi_with_agent.ipynb

@@ -29,8 +29,9 @@
     "\n",
     "from langchain.chains import LLMChain\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_experimental.autonomous_agents import BabyAGI\n",
-    "from langchain_openai import OpenAI, OpenAIEmbeddings"
+    "from langchain_community.embeddings import OpenAIEmbeddings\n",
+    "from langchain_community.llms import OpenAI\n",
+    "from langchain_experimental.autonomous_agents import BabyAGI"
    ]
   },
   {
@@ -107,8 +108,8 @@
    "source": [
     "from langchain.agents import AgentExecutor, Tool, ZeroShotAgent\n",
     "from langchain.chains import LLMChain\n",
+    "from langchain_community.llms import OpenAI\n",
     "from langchain_community.utilities import SerpAPIWrapper\n",
-    "from langchain_openai import OpenAI\n",
     "\n",
     "todo_prompt = PromptTemplate.from_template(\n",
     "    \"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}\"\n",

cookbook/camel_role_playing.ipynb

@@ -46,7 +46,7 @@
     "    HumanMessage,\n",
     "    SystemMessage,\n",
     ")\n",
-    "from langchain_openai import ChatOpenAI"
+    "from langchain_community.chat_models import ChatOpenAI"
    ]
   },
   {

cookbook/causal_program_aided_language_model.ipynb

@@ -47,9 +47,9 @@
    "outputs": [],
    "source": [
     "from IPython.display import SVG\n",
+    "from langchain_community.llms import OpenAI\n",
     "from langchain_experimental.cpal.base import CPALChain\n",
     "from langchain_experimental.pal_chain import PALChain\n",
-    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0, max_tokens=512)\n",
     "cpal_chain = CPALChain.from_univariate_prompt(llm=llm, verbose=True)\n",

cookbook/code-analysis-deeplake.ipynb

@@ -657,7 +657,7 @@
     }
    ],
    "source": [
-    "from langchain_openai import OpenAIEmbeddings\n",
+    "from langchain_community.embeddings.openai import OpenAIEmbeddings\n",
     "\n",
     "embeddings = OpenAIEmbeddings()\n",
     "embeddings"
@@ -834,7 +834,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import ConversationalRetrievalChain\n",
-    "from langchain_openai import ChatOpenAI\n",
+    "from langchain_community.chat_models import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(\n",
     "    model_name=\"gpt-3.5-turbo-0613\"\n",

cookbook/custom_agent_with_plugin_retrieval.ipynb

@@ -44,8 +44,8 @@
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
     "from langchain_community.agent_toolkits import NLAToolkit\n",
-    "from langchain_community.tools.plugin import AIPlugin\n",
-    "from langchain_openai import OpenAI"
+    "from langchain_community.llms import OpenAI\n",
+    "from langchain_community.tools.plugin import AIPlugin"
    ]
   },
   {
@@ -115,8 +115,8 @@
    "outputs": [],
    "source": [
     "from langchain.schema import Document\n",
-    "from langchain_community.vectorstores import FAISS\n",
-    "from langchain_openai import OpenAIEmbeddings"
+    "from langchain_community.embeddings import OpenAIEmbeddings\n",
+    "from langchain_community.vectorstores import FAISS"
    ]
   },
   {

cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb

@@ -69,8 +69,8 @@
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
     "from langchain_community.agent_toolkits import NLAToolkit\n",
-    "from langchain_community.tools.plugin import AIPlugin\n",
-    "from langchain_openai import OpenAI"
+    "from langchain_community.llms import OpenAI\n",
+    "from langchain_community.tools.plugin import AIPlugin"
    ]
   },
   {
@@ -139,8 +139,8 @@
    "outputs": [],
    "source": [
     "from langchain.schema import Document\n",
-    "from langchain_community.vectorstores import FAISS\n",
-    "from langchain_openai import OpenAIEmbeddings"
+    "from langchain_community.embeddings import OpenAIEmbeddings\n",
+    "from langchain_community.vectorstores import FAISS"
    ]
   },
   {

cookbook/custom_agent_with_tool_retrieval.ipynb

@@ -41,8 +41,8 @@
     "from langchain.chains import LLMChain\n",
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
-    "from langchain_community.utilities import SerpAPIWrapper\n",
-    "from langchain_openai import OpenAI"
+    "from langchain_community.llms import OpenAI\n",
+    "from langchain_community.utilities import SerpAPIWrapper"
    ]
   },
   {
@@ -104,8 +104,8 @@
    "outputs": [],
    "source": [
     "from langchain.schema import Document\n",
-    "from langchain_community.vectorstores import FAISS\n",
-    "from langchain_openai import OpenAIEmbeddings"
+    "from langchain_community.embeddings import OpenAIEmbeddings\n",
+    "from langchain_community.vectorstores import FAISS"
    ]
   },
   {

cookbook/databricks_sql_db.ipynb

@@ -93,7 +93,7 @@
    "outputs": [],
    "source": [
     "# Creating a OpenAI Chat LLM wrapper\n",
-    "from langchain_openai import ChatOpenAI\n",
+    "from langchain_community.chat_models import ChatOpenAI\n",
     "\n",
     "llm = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
    ]