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@@ -15,7 +15,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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- "! pip install -U langchain_community tiktoken langchainhub chromadb langchain langgraph tavily-python sentence-transformers"
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+ "! pip install -U langchain-ollama langchain_community tiktoken langchainhub chromadb langchain langgraph tavily-python sentence-transformers"
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]
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},
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{
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@@ -158,27 +158,27 @@
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"metadata": {},
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"outputs": [],
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"source": [
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- "### Retrieval Grader \n",
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+ "### Retrieval Grader\n",
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"\n",
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"from langchain.prompts import PromptTemplate\n",
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- "from langchain_community.chat_models import ChatOllama\n",
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+ "from langchain_ollama import ChatOllama\n",
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"from langchain_core.output_parsers import JsonOutputParser\n",
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"\n",
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"# LLM\n",
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"llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
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"\n",
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"prompt = PromptTemplate(\n",
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- " template=\"\"\"You are a grader assessing relevance \n",
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- " of a retrieved document to a user question. If the document contains keywords related to the user question, \n",
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- " grade it as relevant. It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \n",
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- " \n",
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+ " template=\"\"\"You are a grader assessing relevance\n",
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+ " of a retrieved document to a user question. If the document contains keywords related to the user question,\n",
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+ " grade it as relevant. It does not need to be a stringent test. The goal is to filter out erroneous retrievals.\n",
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+ "\n",
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" Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\n",
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" Provide the binary score as a JSON with a single key 'score' and no premable or explanation.\n",
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- " \n",
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- " Here is the retrieved document: \n",
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+ "\n",
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+ " Here is the retrieved document:\n",
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" {document}\n",
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- " \n",
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- " Here is the user question: \n",
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+ "\n",
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+ " Here is the user question:\n",
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" {question}\n",
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" \"\"\",\n",
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" input_variables=[\"question\", \"document\"],\n",
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@@ -206,12 +206,12 @@
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"\n",
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"# Prompt\n",
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"prompt = PromptTemplate(\n",
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- " template=\"\"\"You are an assistant for question-answering tasks. \n",
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- " Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. \n",
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+ " template=\"\"\"You are an assistant for question-answering tasks.\n",
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+ " Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know.\n",
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" Use three sentences maximum and keep the answer concise:\n",
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- " Question: {question} \n",
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- " Context: {context} \n",
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- " Answer: \n",
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+ " Question: {question}\n",
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+ " Context: {context}\n",
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+ " Answer:\n",
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" \"\"\",\n",
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" input_variables=[\"question\", \"document\"],\n",
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")\n",
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@@ -239,22 +239,22 @@
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"metadata": {},
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"outputs": [],
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"source": [
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- "### Hallucination Grader \n",
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+ "### Hallucination Grader\n",
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"\n",
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"# LLM\n",
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"llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
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"\n",
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"# Prompt\n",
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"prompt = PromptTemplate(\n",
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- " template=\"\"\"You are a grader assessing whether \n",
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- " an answer is grounded in / supported by a set of facts. Give a binary score 'yes' or 'no' score to indicate \n",
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- " whether the answer is grounded in / supported by a set of facts. Provide the binary score as a JSON with a \n",
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+ " template=\"\"\"You are a grader assessing whether\n",
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+ " an answer is grounded in / supported by a set of facts. Give a binary score 'yes' or 'no' score to indicate\n",
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+ " whether the answer is grounded in / supported by a set of facts. Provide the binary score as a JSON with a\n",
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" single key 'score' and no preamble or explanation.\n",
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- " \n",
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+ "\n",
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" Here are the facts:\n",
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- " {documents} \n",
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+ " {documents}\n",
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"\n",
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- " Here is the answer: \n",
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+ " Here is the answer:\n",
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" {generation}\n",
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" \"\"\",\n",
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" input_variables=[\"generation\", \"documents\"],\n",
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@@ -271,19 +271,19 @@
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"metadata": {},
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"outputs": [],
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"source": [
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- "### Answer Grader \n",
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+ "### Answer Grader\n",
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"\n",
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"# LLM\n",
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"llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
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"\n",
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"# Prompt\n",
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"prompt = PromptTemplate(\n",
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- " template=\"\"\"You are a grader assessing whether an \n",
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- " answer is useful to resolve a question. Give a binary score 'yes' or 'no' to indicate whether the answer is \n",
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+ " template=\"\"\"You are a grader assessing whether an\n",
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+ " answer is useful to resolve a question. Give a binary score 'yes' or 'no' to indicate whether the answer is\n",
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" useful to resolve a question. Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\n",
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- " \n",
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+ "\n",
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" Here is the answer:\n",
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- " {generation} \n",
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+ " {generation}\n",
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"\n",
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" Here is the question: {question}\n",
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" \"\"\",\n",
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@@ -311,14 +311,14 @@
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"llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
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"\n",
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"prompt = PromptTemplate(\n",
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- " template=\"\"\"You are an expert at routing a \n",
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- " user question to a vectorstore or web search. Use the vectorstore for questions on LLM agents, \n",
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- " prompt engineering, and adversarial attacks. You do not need to be stringent with the keywords \n",
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- " in the question related to these topics. Otherwise, use web-search. Give a binary choice 'web_search' \n",
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- " or 'vectorstore' based on the question. Return the a JSON with a single key 'datasource' and \n",
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- " no premable or explanation. \n",
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- " \n",
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- " Question to route: \n",
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+ " template=\"\"\"You are an expert at routing a\n",
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+ " user question to a vectorstore or web search. Use the vectorstore for questions on LLM agents,\n",
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+ " prompt engineering, and adversarial attacks. You do not need to be stringent with the keywords\n",
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+ " in the question related to these topics. Otherwise, use web-search. Give a binary choice 'web_search'\n",
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+ " or 'vectorstore' based on the question. Return the a JSON with a single key 'datasource' and\n",
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+ " no premable or explanation.\n",
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+ "\n",
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+ " Question to route:\n",
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" {question}\"\"\",\n",
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" input_variables=[\"question\"],\n",
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")\n",
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@@ -371,7 +371,7 @@
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" question: question\n",
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" generation: LLM generation\n",
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" web_search: whether to add search\n",
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- " documents: list of documents \n",
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+ " documents: list of documents\n",
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" \"\"\"\n",
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" question : str\n",
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" generation : str\n",
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@@ -412,7 +412,7 @@
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" print(\"---GENERATE---\")\n",
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" question = state[\"question\"]\n",
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" documents = state[\"documents\"]\n",
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- " \n",
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+ "\n",
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" # RAG generation\n",
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" generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
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" return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
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@@ -432,7 +432,7 @@
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" print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
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" question = state[\"question\"]\n",
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" documents = state[\"documents\"]\n",
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- " \n",
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+ "\n",
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" # Score each doc\n",
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" filtered_docs = []\n",
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" web_search = \"No\"\n",
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@@ -451,7 +451,7 @@
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" web_search = \"Yes\"\n",
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" continue\n",
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" return {\"documents\": filtered_docs, \"question\": question, \"web_search\": web_search}\n",
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- " \n",
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+ "\n",
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"def web_search(state):\n",
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" \"\"\"\n",
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" Web search based based on the question\n",
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@@ -493,7 +493,7 @@
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" print(\"---ROUTE QUESTION---\")\n",
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" question = state[\"question\"]\n",
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" print(question)\n",
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- " source = question_router.invoke({\"question\": question}) \n",
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+ " source = question_router.invoke({\"question\": question})\n",
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" print(source)\n",
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" print(source['datasource'])\n",
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" if source['datasource'] == 'web_search':\n",
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Srinidhi Viswanathan