LLM: Update to langchain-cratedb 0.0.0

amotl · amotl · commit cb99f35dc673 · 2024-12-16T01:19:11.000+01:00
After quite a bit of back and forth, and a slow genesis in general,
this subsystem is finally approaching departures to take off.

On the chrome/surface/interface, this update doesn't change much.
diff --git a/topic/machine-learning/llm-langchain/conversational_memory.ipynb b/topic/machine-learning/llm-langchain/conversational_memory.ipynb
@@ -59,14 +59,14 @@
    "execution_count": 2,
    "outputs": [],
    "source": [
-    "from langchain_community.chat_message_histories import CrateDBChatMessageHistory\n",
+    "from langchain_cratedb.chat_message_histories import CrateDBChatMessageHistory\n",
     "\n",
     "# Connect to a self-managed CrateDB instance.\n",
     "CONNECTION_STRING = \"crate://crate@localhost/?schema=notebook\"\n",
     "\n",
     "chat_message_history = CrateDBChatMessageHistory(\n",
     "\tsession_id=\"test_session\",\n",
-    "\tconnection_string=CONNECTION_STRING\n",
+    "\tconnection=CONNECTION_STRING\n",
     ")\n",
     "\n",
     "# Make sure to start with a blank canvas.\n",
@@ -216,7 +216,7 @@
     "\n",
     "\tchat_message_history = CrateDBChatMessageHistory(\n",
     "\t\tsession_id=\"test_session\",\n",
-    "\t\tconnection_string=CONNECTION_STRING,\n",
+    "\t\tconnection=CONNECTION_STRING,\n",
     "\t\tcustom_message_converter=CustomMessageConverter(\n",
     "\t\t\tauthor_email=\"test@example.com\"\n",
     "\t\t)\n",
@@ -286,7 +286,7 @@
     "import json\n",
     "import typing as t\n",
     "\n",
-    "from langchain_community.chat_message_histories.cratedb import CrateDBMessageConverter\n",
+    "from langchain_cratedb.chat_message_histories import CrateDBMessageConverter\n",
     "from langchain.schema import _message_to_dict\n",
     "\n",
     "\n",
@@ -314,7 +314,7 @@
     "\n",
     "\tchat_message_history = CrateDBChatMessageHistory(\n",
     "\t\tsession_id=\"test_session\",\n",
-    "\t\tconnection_string=CONNECTION_STRING,\n",
+    "\t\tconnection=CONNECTION_STRING,\n",
     "\t\tcustom_message_converter=CustomMessageConverterWithDifferentSessionIdColumn(),\n",
     "\t\tsession_id_field_name=\"custom_session_id\",\n",
     "\t)\n",
diff --git a/topic/machine-learning/llm-langchain/conversational_memory.py b/topic/machine-learning/llm-langchain/conversational_memory.py
@@ -16,7 +16,7 @@
 import os
 from pprint import pprint
 
-from langchain_community.chat_message_histories import CrateDBChatMessageHistory
+from langchain_cratedb.chat_message_histories import CrateDBChatMessageHistory
 
 
 CONNECTION_STRING = os.environ.get(
@@ -29,7 +29,7 @@ def main():
 
     chat_message_history = CrateDBChatMessageHistory(
         session_id="test_session",
-        connection_string=CONNECTION_STRING,
+        connection=CONNECTION_STRING,
     )
     chat_message_history.add_user_message("Hello")
     chat_message_history.add_ai_message("Hi")
diff --git a/topic/machine-learning/llm-langchain/cratedb-vectorstore-rag-openai-sql.ipynb b/topic/machine-learning/llm-langchain/cratedb-vectorstore-rag-openai-sql.ipynb
@@ -75,13 +75,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import openai\n",
     "import pandas as pd\n",
     "import sqlalchemy as sa\n",
     "\n",
     "from langchain_community.document_loaders import PyPDFLoader\n",
-    "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
-    "from langchain_openai import OpenAIEmbeddings"
+    "from langchain_text_splitters import RecursiveCharacterTextSplitter"
    ]
   },
   {
@@ -162,7 +160,7 @@
     "# environment variables.\n",
     "import os\n",
     "\n",
-    "CONNECTION_STRING = CrateDBVectorSearch.connection_string_from_db_params(\n",
+    "CONNECTION_STRING = CrateDBVectorStore.connection_string_from_db_params(\n",
     "    driver=os.environ.get(\"CRATEDB_DRIVER\", \"crate\"),\n",
     "    host=os.environ.get(\"CRATEDB_HOST\", \"localhost\"),\n",
     "    port=int(os.environ.get(\"CRATEDB_PORT\", \"4200\")),\n",
diff --git a/topic/machine-learning/llm-langchain/cratedb_rag_customer_support.ipynb b/topic/machine-learning/llm-langchain/cratedb_rag_customer_support.ipynb
@@ -65,20 +65,15 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.chains import RetrievalQA, ConversationalRetrievalChain\n",
-    "from langchain_openai import ChatOpenAI, OpenAI, OpenAIEmbeddings\n",
-    "import pandas as pd\n",
-    "import sqlalchemy as sa\n",
-    "from sqlalchemy import create_engine\n",
-    "from sqlalchemy import text\n",
-    "import crate\n",
     "import openai\n",
-    "import os\n",
     "import requests\n",
-    "from pueblo.util.environ import getenvpass\n",
+    "import pandas as pd\n",
+    "from langchain.chains import RetrievalQA, ConversationalRetrievalChain\n",
     "from langchain_community.document_loaders import CSVLoader\n",
-    "from langchain_community.vectorstores import Chroma\n",
-    "from langchain_text_splitters import RecursiveCharacterTextSplitter"
+    "from langchain_openai import OpenAIEmbeddings\n",
+    "from pueblo.util.environ import getenvpass\n",
+    "from sqlalchemy import create_engine\n",
+    "from sqlalchemy import text"
    ]
   },
   {
diff --git a/topic/machine-learning/llm-langchain/cratedb_rag_customer_support_langchain.ipynb b/topic/machine-learning/llm-langchain/cratedb_rag_customer_support_langchain.ipynb
@@ -107,7 +107,7 @@
     "from pueblo.util.environ import getenvpass\n",
     "from langchain_openai import OpenAIEmbeddings\n",
     "from langchain_community.document_loaders import CSVLoader\n",
-    "from langchain_community.vectorstores import CrateDBVectorSearch\n",
+    "from langchain_cratedb.vectorstores import CrateDBVectorStore\n",
     "\n",
     "warnings.filterwarnings('ignore')"
    ]
@@ -301,11 +301,11 @@
    "source": [
     "embeddings = OpenAIEmbeddings()\n",
     "\n",
-    "store = CrateDBVectorSearch.from_documents(\n",
+    "store = CrateDBVectorStore.from_documents(\n",
     "    embedding=embeddings,\n",
     "    documents=data,\n",
     "    collection_name=COLLECTION_NAME,\n",
-    "    connection_string=CONNECTION_STRING,\n",
+    "    connection=CONNECTION_STRING,\n",
     ")"
    ]
   },
@@ -519,11 +519,11 @@
     "\n",
     "COLLECTION_NAME = \"customer_data_jina\"\n",
     "\n",
-    "store = CrateDBVectorSearch.from_documents(\n",
+    "store = CrateDBVectorStore.from_documents(\n",
     "    embedding=embeddings,\n",
     "    documents=data,\n",
     "    collection_name=COLLECTION_NAME,\n",
-    "    connection_string=CONNECTION_STRING,\n",
+    "    connection=CONNECTION_STRING,\n",
     ")\n",
     "documents = return_documents(store, my_question)"
    ]
diff --git a/topic/machine-learning/llm-langchain/cratedb_rag_customer_support_vertexai.ipynb b/topic/machine-learning/llm-langchain/cratedb_rag_customer_support_vertexai.ipynb
@@ -97,28 +97,20 @@
    "outputs": [],
    "source": [
     "import os\n",
+    "import re\n",
     "\n",
-    "import openai\n",
     "import pandas as pd\n",
-    "import warnings\n",
     "import requests\n",
-    "import re\n",
-    "from typing import Dict, List, Optional, Tuple, Union\n",
-    "\n",
+    "import warnings\n",
     "\n",
-    "from pueblo.util.environ import getenvpass\n",
     "from google.cloud import aiplatform\n",
     "from vertexai.generative_models import (\n",
     "    GenerationConfig,\n",
-    "    GenerationResponse,\n",
     "    GenerativeModel,\n",
-    "    HarmBlockThreshold,\n",
-    "    HarmCategory,\n",
     ")\n",
     "from langchain_community.document_loaders import CSVLoader\n",
     "from langchain_community.embeddings import VertexAIEmbeddings\n",
-    "from langchain_community.llms import VertexAI\n",
-    "from langchain_community.vectorstores import CrateDBVectorSearch\n",
+    "from langchain_cratedb.vectorstores import CrateDBVectorStore\n",
     "\n",
     "warnings.filterwarnings('ignore')"
    ]
@@ -347,11 +339,11 @@
    "source": [
     "embeddings = VertexAIEmbeddings(model_name=\"textembedding-gecko@001\")\n",
     "\n",
-    "store = CrateDBVectorSearch.from_documents(\n",
+    "store = CrateDBVectorStore.from_documents(\n",
     "    embedding=embeddings,\n",
     "    documents=data,\n",
     "    collection_name=COLLECTION_NAME,\n",
-    "    connection_string=CONNECTION_STRING,\n",
+    "    connection=CONNECTION_STRING,\n",
     ")"
    ]
   },
diff --git a/topic/machine-learning/llm-langchain/document_loader.ipynb b/topic/machine-learning/llm-langchain/document_loader.ipynb
@@ -107,8 +107,8 @@
    "outputs": [],
    "source": [
     "import sqlalchemy as sa\n",
-    "from langchain_community.document_loaders import CrateDBLoader\n",
     "from langchain_community.utilities.sql_database import SQLDatabase\n",
+    "from langchain_cratedb.document_loaders import CrateDBLoader\n",
     "from pprint import pprint\n",
     "\n",
     "db = SQLDatabase(engine=sa.create_engine(CONNECTION_STRING))\n",
diff --git a/topic/machine-learning/llm-langchain/document_loader.py b/topic/machine-learning/llm-langchain/document_loader.py
@@ -29,8 +29,8 @@
 import requests
 import sqlalchemy as sa
 from cratedb_toolkit.util import DatabaseAdapter
-from langchain_community.document_loaders import CrateDBLoader
 from langchain_community.utilities.sql_database import SQLDatabase
+from langchain_cratedb.document_loaders import CrateDBLoader
 from pprint import pprint
 
 
diff --git a/topic/machine-learning/llm-langchain/requirements-dev.txt b/topic/machine-learning/llm-langchain/requirements-dev.txt
@@ -1,11 +1,11 @@
-# Real.
+# Production.
 cratedb-toolkit[io]
 pueblo[notebook,testing]
 
-# Development.
+# Staging.
 # cratedb-toolkit[io] @ git+https://github.com/crate-workbench/cratedb-toolkit.git@main
 # pueblo[notebook,testing] @ git+https://github.com/pyveci/pueblo.git@main
 
-# Workstation.
+# Development.
 #--editable=/Users/amo/dev/crate/ecosystem/cratedb-retentions[io]
 #--editable=/Users/amo/dev/pyveci/sources/pueblo[testing]
diff --git a/topic/machine-learning/llm-langchain/requirements.txt b/topic/machine-learning/llm-langchain/requirements.txt
@@ -1,22 +1,12 @@
-# Real.
 crash
-crate>=1.0.0.dev2
 google-cloud-aiplatform<2
+langchain-cratedb>=0.0.0.dev0
 langchain-google-vertexai<3
 langchain-openai<0.3
 langchain-text-splitters<0.4
 pueblo[cli,nlp]>=0.0.10
-pydantic>=2,<3
 pypdf<6
 python-dotenv<2
 requests<3
 requests-cache<2
-sqlalchemy==2.*
-sqlalchemy-cratedb>=0.40.0
 unstructured<0.17
-
-# Development.
-# cratedb-toolkit @ git+https://github.com/crate-workbench/cratedb-toolkit.git@main
-langchain @ git+https://github.com/crate-workbench/langchain.git@cratedb#subdirectory=libs/langchain
-langchain-community @ git+https://github.com/crate-workbench/langchain.git@cratedb#subdirectory=libs/community
-# pueblo[cli,fileio,nlp] @ git+https://github.com/pyveci/pueblo.git@main
diff --git a/topic/machine-learning/llm-langchain/vector_search.ipynb b/topic/machine-learning/llm-langchain/vector_search.ipynb
@@ -142,7 +142,7 @@
     "# environment variables.\n",
     "import os\n",
     "\n",
-    "CONNECTION_STRING = CrateDBVectorSearch.connection_string_from_db_params(\n",
+    "CONNECTION_STRING = CrateDBVectorStore.connection_string_from_db_params(\n",
     "    driver=os.environ.get(\"CRATEDB_DRIVER\", \"crate\"),\n",
     "    host=os.environ.get(\"CRATEDB_HOST\", \"localhost\"),\n",
     "    port=int(os.environ.get(\"CRATEDB_PORT\", \"4200\")),\n",
@@ -166,8 +166,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.vectorstores import CrateDBVectorSearch\n",
     "from langchain_core.documents import Document\n",
+    "from langchain_cratedb.vectorstores import CrateDBVectorStore\n",
     "from langchain_openai import OpenAIEmbeddings"
    ]
   },
@@ -223,11 +223,11 @@
    "source": [
     "embeddings = OpenAIEmbeddings()\n",
     "\n",
-    "store = CrateDBVectorSearch.from_documents(\n",
+    "store = CrateDBVectorStore.from_documents(\n",
     "    embedding=embeddings,\n",
     "    documents=docs,\n",
     "    collection_name=COLLECTION_NAME,\n",
-    "    connection_string=CONNECTION_STRING,\n",
+    "    connection=CONNECTION_STRING,\n",
     ")"
    ]
   },
@@ -334,10 +334,10 @@
    },
    "outputs": [],
    "source": [
-    "store = CrateDBVectorSearch(\n",
+    "store = CrateDBVectorStore(\n",
     "    collection_name=COLLECTION_NAME,\n",
-    "    connection_string=CONNECTION_STRING,\n",
-    "    embedding_function=embeddings,\n",
+    "    connection=CONNECTION_STRING,\n",
+    "    embeddings=embeddings,\n",
     ")"
    ]
   },
@@ -426,11 +426,11 @@
    },
    "outputs": [],
    "source": [
-    "store = CrateDBVectorSearch.from_documents(\n",
+    "store = CrateDBVectorStore.from_documents(\n",
     "    documents=docs,\n",
     "    embedding=embeddings,\n",
     "    collection_name=COLLECTION_NAME,\n",
-    "    connection_string=CONNECTION_STRING,\n",
+    "    connection=CONNECTION_STRING,\n",
     "    pre_delete_collection=True,\n",
     ")"
    ]
diff --git a/topic/machine-learning/llm-langchain/vector_search.py b/topic/machine-learning/llm-langchain/vector_search.py
@@ -20,7 +20,7 @@
     python vector_search.py
 """  # noqa: E501
 
-from langchain_community.vectorstores import CrateDBVectorSearch
+from langchain_cratedb.vectorstores import CrateDBVectorStore
 from langchain_openai import OpenAIEmbeddings
 
 import nltk
@@ -37,7 +37,7 @@ def main():
     documents = CachedWebResource(url).langchain_documents(chunk_size=1000, chunk_overlap=0)
 
     # Embed each chunk, and load them into the vector store.
-    db = CrateDBVectorSearch.from_documents(documents, OpenAIEmbeddings())
+    db = CrateDBVectorStore.from_documents(documents, OpenAIEmbeddings(), connection="crate://")
 
     # Invoke a query, and display the first result.
     query = "What did the president say about Ketanji Brown Jackson"
