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AWS Bedrock RAG template (#12450)

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Lance Martin
2023-10-27 13:15:54 -07:00
committed by GitHub
parent 5d40e36c75
commit 5c564e62e1
11 changed files with 2602 additions and 9 deletions
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99
templates/rag-chroma-private/rag_chroma_private.ipynb
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@@ -16,25 +16,112 @@
{
"cell_type": "code",
"execution_count": 1,
"id": "888494ca-0509-4070-b36f-600a042f352c",
"id": "78fb41d3-d2aa-40a6-b144-491f38a7cf88",
"metadata": {},
"outputs": [],
"source": [
"from langserve.client import RemoteRunnable\n",
"rag_app = RemoteRunnable('http://0.0.0.0:8001/rag_chroma_private/')\n",
"for item in rag_app.stream(\"How does agent memory work?\"):\n",
" print(item)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ce39d358-1934-4404-bd3e-3fd497974aff",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Based on the provided context, agent memory is a long-term memory module that records a comprehensive list of agents' experiences in natural language. Each element is an observation or event directly provided by the agent, and inter-agent communication can trigger new natural language statements. The agent memory is complemented by several key components, including LLM (large language model) as the agent's brain, planning, reflection, and memory mechanisms. The design of generative agents combines LLM with memory, planning, and reflection mechanisms to enable agents to behave conditioned on past experiences and interact with other agents. The agent learns to call external APIs for missing information, including current information, code execution capability, access to proprietary information sources, and more. In summary, the agent memory works by recording and storing observations and events in natural language, allowing the agent to retrieve and use this information to inform its behavior.\n"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "a554971a-e724-4c99-84d1-5d646ae4ac3e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6891d028-43ac-4a70-b2ad-6fbd3d937283",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"' Based on the given context, the answer to the question \"How does agent memory work?\" can be inferred as follows:\\n\\nAgent memory refers to the long-term memory module of an autonomous agent system, which records a comprehensive list of agents\\' experiences in natural language. Each element is an observation or event directly provided by the agent, and inter-agent communication can trigger new natural language statements. The retrieval model surfaces the context to inform the agent\\'s behavior according to relevance, recency, and importance.\\n\\nIn other words, the agent memory is a component of the autonomous agent system that stores and manages the agent\\'s experiences and observations in a long-term memory module, which is based on natural language processing and generation capabilities of a large language model (LLM). The memory is used to inform the agent\\'s behavior and decision-making, and it can be triggered by inter-agent communication.'"
"<generator object RemoteRunnable.stream at 0x1245d25f0>"
]
},
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langserve.client import RemoteRunnable\n",
"rag_app = RemoteRunnable('http://0.0.0.0:8001/rag_chroma_private/')\n",
"rag_app.invoke(\"How does agent memory work?\")"
"rag_app.stream(\"How does agent memory work?\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "888494ca-0509-4070-b36f-600a042f352c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Based on the provided context, agent memory is a long-term memory module that records a comprehensive list of agents' experiences in natural language. Each element is an observation, an event directly provided by the agent, and inter-agent communication can trigger new natural language statements. The memory module surfaces the context to inform the agent's behavior according to relevance, recency, and importance.\n"
]
}
],
"source": [
"\n",
"stream = \n",
"for i in stream:\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ff2169c9-dab2-41c4-8f38-1f8aebb16814",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting httpx_sse\n",
" Obtaining dependency information for httpx_sse from https://files.pythonhosted.org/packages/62/33/d35b4ccf8c1ac7266bd1d068c48f842d3c7392cca87e32751c79ee553d7a/httpx_sse-0.3.1-py3-none-any.whl.metadata\n",
" Using cached httpx_sse-0.3.1-py3-none-any.whl.metadata (8.6 kB)\n",
"Using cached httpx_sse-0.3.1-py3-none-any.whl (7.7 kB)\n",
"Installing collected packages: httpx_sse\n",
"Successfully installed httpx_sse-0.3.1\n"
]
}
],
"source": [
"! pip install httpx_sse"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d843f23-686a-4138-8a9d-087bb00b2e13",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {