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136 changes: 101 additions & 35 deletions lab-chains-in-langchain.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -59,15 +59,15 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"id": "974acf8e-8f88-42de-88f8-40a82cb58e8b",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"df = pd.read_csv('./lab/data/Data.csv')"
"df = pd.read_csv('./data/Data.csv')\n"
]
},
{
Expand Down Expand Up @@ -139,7 +139,7 @@
"1 Waterproof Phone Pouch I loved the waterproof sac, although the openi...\n",
"2 Luxury Air Mattress This mattress had a small hole in the top of i...\n",
"3 Pillows Insert This is the best throw pillow fillers on Amazo...\n",
"4 Milk Frother Handheld\\n  I loved this product. But they only seem to l..."
"4 Milk Frother Handheld\\n \u00a0I loved this product. But they only seem to l..."
]
},
"execution_count": 15,
Expand Down Expand Up @@ -190,8 +190,9 @@
"metadata": {},
"outputs": [],
"source": [
"#Replace None by your own value and justify\n",
"llm = ChatOpenAI(temperature=None)\n"
"# Using temperature=0.0 for deterministic, factual product descriptions.\n",
"# A low temperature avoids creative variance so the description stays focused on attributes.\n",
"llm = ChatOpenAI(temperature=0.0)\n"
]
},
{
Expand All @@ -201,9 +202,9 @@
"metadata": {},
"outputs": [],
"source": [
"prompt = ChatPromptTemplate.from_template( #Write a query that would take a variable to describe any product\n",
" \n",
")"
"prompt = ChatPromptTemplate.from_template(\n",
" \"Write a concise marketing description (2-3 sentences) for the following product: {product}\"\n",
")\n"
]
},
{
Expand All @@ -224,8 +225,8 @@
"metadata": {},
"outputs": [],
"source": [
"product = #Select a product type to be describe\n",
"chain.run(product)"
"product = df.Product[0] # pick the first product from the dataset\n",
"chain.run(product)\n"
]
},
{
Expand Down Expand Up @@ -255,13 +256,13 @@
"source": [
"llm = ChatOpenAI(temperature=0.9)\n",
"\n",
"# prompt template 1\n",
"# prompt template 1: suggest a company name that could make the given product\n",
"first_prompt = ChatPromptTemplate.from_template(\n",
" #Repeat the initial query or create a new query that would feed into the second prompt\n",
" \"What is a good, catchy company name for a business that makes {product}?\"\n",
")\n",
"\n",
"# Chain 1\n",
"chain_one = LLMChain(llm=llm, prompt=first_prompt)"
"chain_one = LLMChain(llm=llm, prompt=first_prompt)\n"
]
},
{
Expand All @@ -272,12 +273,12 @@
"outputs": [],
"source": [
"\n",
"# prompt template 2\n",
"# prompt template 2: take the company name from chain 1 and produce a short description\n",
"second_prompt = ChatPromptTemplate.from_template(\n",
" #Write the second prompt query that takes an input variable whose input will come from the previous prompt\"\n",
" \"Write a 20-word marketing description for the following company: {company_name}\"\n",
")\n",
"# chain 2\n",
"chain_two = LLMChain(llm=llm, prompt=second_prompt)"
"chain_two = LLMChain(llm=llm, prompt=second_prompt)\n"
]
},
{
Expand Down Expand Up @@ -310,6 +311,28 @@
"**Repeat the above twice for different products**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Repeat #1: different product\n",
"product_2 = df.Product[1]\n",
"overall_simple_chain.run(product_2)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Repeat #2: different product\n",
"product_3 = df.Product[2]\n",
"overall_simple_chain.run(product_3)\n"
]
},
{
"cell_type": "markdown",
"id": "7b5ce18c",
Expand Down Expand Up @@ -339,11 +362,11 @@
"\n",
"\n",
"first_prompt = ChatPromptTemplate.from_template(\n",
" #This prompt should translate a review\n",
" \"Translate the following review to English:\\n\\n{Review}\"\n",
")\n",
"\n",
"chain_one = LLMChain(llm=llm, prompt=first_prompt, \n",
" output_key=None #Give a name to your output\n",
"chain_one = LLMChain(llm=llm, prompt=first_prompt,\n",
" output_key=\"English_Review\"\n",
" )\n"
]
},
Expand All @@ -355,11 +378,11 @@
"outputs": [],
"source": [
"second_prompt = ChatPromptTemplate.from_template(\n",
" #Write a promplt to summarize a review\n",
" \"Summarize the following review in one short sentence:\\n\\n{English_Review}\"\n",
")\n",
"\n",
"chain_two = LLMChain(llm=llm, prompt=second_prompt, \n",
" output_key=None #give a name to this output\n",
"chain_two = LLMChain(llm=llm, prompt=second_prompt,\n",
" output_key=\"summary\"\n",
" )\n"
]
},
Expand All @@ -370,13 +393,13 @@
"metadata": {},
"outputs": [],
"source": [
"# prompt template 3: translate to english or other language\n",
"# prompt template 3: detect the language of the original review\n",
"third_prompt = ChatPromptTemplate.from_template(\n",
" None\n",
" \"What language is the following review written in? Respond with only the language name.\\n\\n{Review}\"\n",
")\n",
"# chain 3: input= Review and output= language\n",
"chain_three = LLMChain(llm=llm, prompt=third_prompt,\n",
" output_key=None\n",
" output_key=\"language\"\n",
" )\n"
]
},
Expand All @@ -388,12 +411,13 @@
"outputs": [],
"source": [
"\n",
"# prompt template 4: follow up message that take as inputs the two previous prompts' variables\n",
"# prompt template 4: follow up message that takes the summary and language as inputs\n",
"fourth_prompt = ChatPromptTemplate.from_template(\n",
" None\n",
" \"Write a polite, helpful follow-up reply to the customer in {language}. \"\n",
" \"Address the points raised in this summary:\\n\\n{summary}\"\n",
")\n",
"chain_four = LLMChain(llm=llm, prompt=fourth_prompt,\n",
" output_key=None\n",
" output_key=\"followup_message\"\n",
" )\n"
]
},
Expand All @@ -404,14 +428,14 @@
"metadata": {},
"outputs": [],
"source": [
"# overall_chain: input= Review \n",
"# and output= English_Review,summary, followup_message\n",
"# overall_chain: input= Review\n",
"# and output= English_Review, summary, followup_message\n",
"overall_chain = SequentialChain(\n",
" chains=[chain_one, chain_two, chain_three, chain_four],\n",
" input_variables=None,\n",
" output_variables=[None, None, None],\n",
" input_variables=[\"Review\"],\n",
" output_variables=[\"English_Review\", \"summary\", \"followup_message\"],\n",
" verbose=True\n",
")"
")\n"
]
},
{
Expand All @@ -422,7 +446,7 @@
"outputs": [],
"source": [
"review = df.Review[5]\n",
"overall_chain(review)"
"overall_chain({\"Review\": review})\n"
]
},
{
Expand All @@ -433,6 +457,28 @@
"**Repeat the above twice for different products or reviews**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Repeat #1: different review\n",
"review_2 = df.Review[1]\n",
"overall_chain({\"Review\": review_2})\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Repeat #2: different review\n",
"review_3 = df.Review[3]\n",
"overall_chain({\"Review\": review_3})\n"
]
},
{
"cell_type": "markdown",
"id": "3041ea4c",
Expand Down Expand Up @@ -702,6 +748,26 @@
"source": [
"**Repeat the above at least once for different inputs and chains executions - Be creative!**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Creative router query #1 -> should route to computer science (default chain since none defined explicitly)\n",
"chain.run(\"What is the time complexity of merge sort and why?\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Creative router query #2 -> should route to biology\n",
"chain.run(\"How does photosynthesis convert sunlight into chemical energy?\")\n"
]
}
],
"metadata": {
Expand All @@ -725,4 +791,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
}
}