mirror of
https://github.com/hwchase17/langchain.git
synced 2025-09-05 13:06:03 +00:00
Extend DynamoDBChatMessageHistory to support composite keys (#9896)
- Description: Adds two optional parameters to the DynamoDBChatMessageHistory class to enable users to pass in a name for their PrimaryKey, or a Key object itself to enable the use of composite keys, a common DynamoDB paradigm. [AWS DynamoDB Key docs](https://aws.amazon.com/blogs/database/choosing-the-right-dynamodb-partition-key/) - Issue: N/A - Dependencies: N/A - Twitter handle: N/A --------- Co-authored-by: Josh White <josh@ctrlstack.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Josh White
GitHub
@@ -28,7 +28,7 @@
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@@ -71,7 +71,7 @@
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@@ -87,18 +87,15 @@
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"cell_type": "code",
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"data": {
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"text/plain": [
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"[HumanMessage(content='hi!', additional_kwargs={}, example=False),\n",
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" AIMessage(content='whats up?', additional_kwargs={}, example=False)]"
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]
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"text/plain": "[HumanMessage(content='hi!', additional_kwargs={}, example=False),\n AIMessage(content='whats up?', additional_kwargs={}, example=False),\n HumanMessage(content='hi!', additional_kwargs={}, example=False),\n AIMessage(content='whats up?', additional_kwargs={}, example=False)]"
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"execution_count": 3,
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"execution_count": 12,
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"output_type": "execute_result"
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 13,
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"id": "225713c8",
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@@ -133,6 +130,81 @@
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")"
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{
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"cell_type": "markdown",
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"source": [
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"## DynamoDBChatMessageHistory With Different Keys Composite Keys\n",
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"The default key for DynamoDBChatMessageHistory is ```{\"SessionId\": self.session_id}```, but you can modify this to match your table design.\n",
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"\n",
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"### Primary Key Name\n",
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"You may modify the primary key by passing in a primary_key_name value in the constructor, resulting in the following:\n",
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"```{self.primary_key_name: self.session_id}```\n",
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"\n",
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"### Composite Keys\n",
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"When using an existing DynamoDB table, you may need to modify the key structure from the default of to something including a Sort Key. To do this you may use the ```key``` parameter.\n",
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"\n",
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"Passing a value for key will override the primary_key parameter, and the resulting key structure will be the passed value.\n"
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],
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"metadata": {
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"collapsed": false
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"cell_type": "code",
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"execution_count": 14,
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"0\n"
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"data": {
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"text/plain": "[HumanMessage(content='hello, composite dynamodb table!', additional_kwargs={}, example=False)]"
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},
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"execution_count": 14,
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain.memory.chat_message_histories import DynamoDBChatMessageHistory\n",
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"\n",
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"composite_table = dynamodb.create_table(\n",
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" TableName=\"CompositeTable\",\n",
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" KeySchema=[{\"AttributeName\": \"PK\", \"KeyType\": \"HASH\"}, {\"AttributeName\": \"SK\", \"KeyType\": \"RANGE\"}],\n",
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" AttributeDefinitions=[{\"AttributeName\": \"PK\", \"AttributeType\": \"S\"}, {\"AttributeName\": \"SK\", \"AttributeType\": \"S\"}],\n",
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" BillingMode=\"PAY_PER_REQUEST\",\n",
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")\n",
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"\n",
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"# Wait until the table exists.\n",
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"composite_table.meta.client.get_waiter(\"table_exists\").wait(TableName=\"CompositeTable\")\n",
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"\n",
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"# Print out some data about the table.\n",
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"print(composite_table.item_count)\n",
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"\n",
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"my_key = {\n",
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" \"PK\": \"session_id::0\",\n",
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" \"SK\": \"langchain_history\",\n",
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"}\n",
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"\n",
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"composite_key_history = DynamoDBChatMessageHistory(\n",
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" table_name=\"CompositeTable\",\n",
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" session_id=\"0\",\n",
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" endpoint_url=\"http://localhost.localstack.cloud:4566\",\n",
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" key=my_key,\n",
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")\n",
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"\n",
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"composite_key_history.add_user_message(\"hello, composite dynamodb table!\")\n",
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"\n",
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"composite_key_history.messages"
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],
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"metadata": {
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"collapsed": false
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}
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"attachments": {},
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"cell_type": "markdown",
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"cell_type": "code",
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@@ -165,7 +237,7 @@
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"cell_type": "code",
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@@ -184,10 +256,24 @@
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 17,
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"id": "fce085c5",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"ename": "ValidationError",
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"evalue": "1 validation error for ChatOpenAI\n__root__\n Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. (type=value_error)",
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"output_type": "error",
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"traceback": [
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"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[0;31mValidationError\u001B[0m Traceback (most recent call last)",
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"Cell \u001B[0;32mIn[17], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m llm \u001B[38;5;241m=\u001B[39m \u001B[43mChatOpenAI\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtemperature\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m 2\u001B[0m agent_chain \u001B[38;5;241m=\u001B[39m initialize_agent(\n\u001B[1;32m 3\u001B[0m tools,\n\u001B[1;32m 4\u001B[0m llm,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 7\u001B[0m memory\u001B[38;5;241m=\u001B[39mmemory,\n\u001B[1;32m 8\u001B[0m )\n",
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"File \u001B[0;32m~/Documents/projects/langchain/libs/langchain/langchain/load/serializable.py:74\u001B[0m, in \u001B[0;36mSerializable.__init__\u001B[0;34m(self, **kwargs)\u001B[0m\n\u001B[1;32m 73\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__init__\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs: Any) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m---> 74\u001B[0m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[38;5;21;43m__init__\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 75\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lc_kwargs \u001B[38;5;241m=\u001B[39m kwargs\n",
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"File \u001B[0;32m~/Documents/projects/langchain/.venv/lib/python3.9/site-packages/pydantic/main.py:341\u001B[0m, in \u001B[0;36mpydantic.main.BaseModel.__init__\u001B[0;34m()\u001B[0m\n",
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"\u001B[0;31mValidationError\u001B[0m: 1 validation error for ChatOpenAI\n__root__\n Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. (type=value_error)"
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]
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}
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],
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"source": [
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"llm = ChatOpenAI(temperature=0)\n",
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"agent_chain = initialize_agent(\n",
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@@ -201,152 +287,42 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"id": "952a3103",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"Hello! How can I assist you today?\"\n",
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"}\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'Hello! How can I assist you today?'"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"agent_chain.run(input=\"Hello!\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"id": "54c4aaf4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m{\n",
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" \"action\": \"python_repl\",\n",
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" \"action_input\": \"import requests\\nfrom bs4 import BeautifulSoup\\n\\nurl = 'https://en.wikipedia.org/wiki/Twitter'\\nresponse = requests.get(url)\\nsoup = BeautifulSoup(response.content, 'html.parser')\\nowner = soup.find('th', text='Owner').find_next_sibling('td').text.strip()\\nprint(owner)\"\n",
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"}\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mX Corp. (2023–present)Twitter, Inc. (2006–2023)\n",
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"\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"X Corp. (2023–present)Twitter, Inc. (2006–2023)\"\n",
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"}\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'X Corp. (2023–present)Twitter, Inc. (2006–2023)'"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"agent_chain.run(input=\"Who owns Twitter?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": null,
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"id": "f9013118",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"Hello Bob! How can I assist you today?\"\n",
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"}\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'Hello Bob! How can I assist you today?'"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"agent_chain.run(input=\"My name is Bob.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": null,
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"id": "405e5315",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"Your name is Bob.\"\n",
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"}\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'Your name is Bob.'"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"agent_chain.run(input=\"Who am I?\")"
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"agent_chain.run(input=\"Who am I?\")\n"
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]
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}
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],
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