mirror of https://github.com/hwchase17/langchain
more query analysis docs (#18358)
Harrison Chase
4 months ago
committed by
GitHub
parent
f96dd57501
commit
bc768a12ed
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "df7d42b9-58a6-434c-a2d7-0b61142f6d3e",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 6\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f2195672-0cab-4967-ba8a-c6544635547d",
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"metadata": {},
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"source": [
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"# Construct Filters\n",
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"\n",
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"We may want to do query analysis to extract filters to pass into retrievers. One way we ask the LLM to represent these filters is as a Pydantic model. There is then the issue of converting that Pydantic model into a filter that can be passed into a retriever. \n",
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"\n",
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"This can be done manually, but LangChain also provides some \"Translators\" that are able to translate from a common syntax into filters specific to each retriever. Here, we will cover how to use those translators."
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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": 13,
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"id": "8ca446a0",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Optional\n",
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"\n",
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"from langchain.chains.query_constructor.ir import (\n",
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" Comparator,\n",
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" Comparison,\n",
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" Operation,\n",
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" Operator,\n",
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" StructuredQuery,\n",
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")\n",
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"from langchain.retrievers.self_query.chroma import ChromaTranslator\n",
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"from langchain.retrievers.self_query.elasticsearch import ElasticsearchTranslator\n",
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"from langchain_core.pydantic_v1 import BaseModel"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bc1302ff",
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"metadata": {},
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"source": [
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"In this example, `year` and `author` are both attributes to filter on."
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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": 11,
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"id": "64055006",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Search(BaseModel):\n",
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" query: str\n",
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" start_year: Optional[int]\n",
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" author: Optional[str]"
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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": 12,
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"id": "44eb6d98",
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"metadata": {},
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"outputs": [],
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"source": [
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"search_query = Search(query=\"RAG\", start_year=2022, author=\"LangChain\")"
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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": 15,
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"id": "e8ba6705",
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"metadata": {},
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"outputs": [],
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"source": [
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"def construct_comparisons(query: Search):\n",
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" comparisons = []\n",
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" if query.start_year is not None:\n",
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" comparisons.append(\n",
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" Comparison(\n",
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" comparator=Comparator.GT,\n",
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" attribute=\"start_year\",\n",
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" value=query.start_year,\n",
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" )\n",
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" )\n",
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" if query.author is not None:\n",
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" comparisons.append(\n",
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" Comparison(\n",
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" comparator=Comparator.EQ,\n",
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" attribute=\"author\",\n",
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" value=query.author,\n",
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" )\n",
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" )\n",
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" return comparisons"
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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": 16,
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"id": "6a79c9da",
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"metadata": {},
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"outputs": [],
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"source": [
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"comparisons = construct_comparisons(search_query)"
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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": 17,
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"id": "2d0e9689",
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"metadata": {},
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"outputs": [],
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"source": [
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"_filter = Operation(operator=Operator.AND, arguments=comparisons)"
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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": 18,
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"id": "e4c0b2ce",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'bool': {'must': [{'range': {'metadata.start_year': {'gt': 2022}}},\n",
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" {'term': {'metadata.author.keyword': 'LangChain'}}]}}"
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]
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},
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"execution_count": 18,
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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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"source": [
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"ElasticsearchTranslator().visit_operation(_filter)"
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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": 19,
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"id": "d75455ae",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'$and': [{'start_year': {'$gt': 2022}}, {'author': {'$eq': 'LangChain'}}]}"
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]
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},
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"execution_count": 19,
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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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"source": [
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"ChromaTranslator().visit_operation(_filter)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,585 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "df7d42b9-58a6-434c-a2d7-0b61142f6d3e",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 7\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f2195672-0cab-4967-ba8a-c6544635547d",
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"metadata": {},
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"source": [
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"# High Cardinality\n",
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"\n",
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"You may want to do query analysis to create a filter on a categorical column. One of the difficulties here is that you usually need to specify the EXACT categorical value. The issue is you need to make sure the LLM generates that categorical value exactly. This can be done relatively easy with prompting when there are only a few values that are valid. When there are a high number of valid values then it becomes more difficult, as those values may not fit in the LLM context, or (if they do) there may be too many for the LLM to properly attend to.\n",
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"\n",
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"In this notebook we take a look at how to approach this."
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]
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},
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{
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"cell_type": "markdown",
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"id": "a4079b57-4369-49c9-b2ad-c809b5408d7e",
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"metadata": {},
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"source": [
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"## Setup\n",
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"#### Install dependencies"
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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": 1,
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"id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
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"metadata": {},
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"outputs": [],
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"source": [
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"# %pip install -qU langchain langchain-community langchain-openai faker"
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]
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},
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{
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"cell_type": "markdown",
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"id": "79d66a45-a05c-4d22-b011-b1cdbdfc8f9c",
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"metadata": {},
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"source": [
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"#### Set environment variables\n",
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"\n",
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"We'll use OpenAI in this example:"
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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": 1,
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"id": "40e2979e-a818-4b96-ac25-039336f94319",
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
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"\n",
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"# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d8d47f4b",
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"metadata": {},
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"source": [
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"#### Set up data\n",
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"\n",
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"We will generate a bunch of fake names"
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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": 1,
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"id": "e5ba65c2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from faker import Faker\n",
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"\n",
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"fake = Faker()\n",
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"\n",
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"names = [fake.name() for _ in range(10000)]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "41133694",
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"metadata": {},
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"source": [
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"Let's look at some of the names"
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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": 2,
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"id": "c901ea97",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Hayley Gonzalez'"
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]
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},
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"execution_count": 2,
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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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"source": [
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"names[0]"
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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": 3,
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"id": "b0d42ae2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'Jesse Knight'"
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]
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},
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"execution_count": 3,
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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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"source": [
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"names[567]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1725883d",
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"metadata": {},
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"source": [
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"## Query Analysis\n",
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"\n",
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"We can now set up a baseline query analysis"
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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": 4,
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"id": "0ae69afc",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.pydantic_v1 import BaseModel, Field"
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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": 5,
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"id": "6c9485ce",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Search(BaseModel):\n",
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" query: str\n",
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" author: str"
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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": 6,
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"id": "aebd704a",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/harrisonchase/workplace/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.\n",
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" warn_beta(\n"
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]
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}
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],
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"from langchain_core.runnables import RunnablePassthrough\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"system = \"\"\"Generate a relevant search query for a library system\"\"\"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", system),\n",
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" (\"human\", \"{question}\"),\n",
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" ]\n",
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")\n",
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"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
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"structured_llm = llm.with_structured_output(Search)\n",
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"query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
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]
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},
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{
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"cell_type": "markdown",
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"id": "41709a2e",
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"metadata": {},
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"source": [
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"We can see that if we spell the name exactly correctly, it knows how to handle it"
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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": 33,
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"id": "cc0d344b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Search(query='books about aliens', author='Jesse Knight')"
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]
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},
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"execution_count": 33,
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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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"source": [
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"query_analyzer.invoke(\"what are books about aliens by Jesse Knight\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a1b57eab",
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"metadata": {},
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"source": [
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"The issue is that the values you want to filter on may NOT be spelled exactly correctly"
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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": 34,
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"id": "82b6b2ad",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Search(query='books about aliens', author='Jess Knight')"
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]
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},
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"execution_count": 34,
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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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"source": [
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"query_analyzer.invoke(\"what are books about aliens by jess knight\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0b60b7c2",
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"metadata": {},
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"source": [
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"### Add in all values\n",
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"\n",
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"One way around this is to add ALL possible values to the prompt. That will generally guide the query in the right direction"
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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": 35,
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"id": "98788a94",
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"metadata": {},
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"outputs": [],
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"source": [
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"system = \"\"\"Generate a relevant search query for a library system.\n",
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"\n",
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"`author` attribute MUST be one of:\n",
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"\n",
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"{authors}\n",
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"\n",
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"Do NOT hallucinate author name!\"\"\"\n",
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"base_prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", system),\n",
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" (\"human\", \"{question}\"),\n",
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" ]\n",
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")\n",
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"prompt = base_prompt.partial(authors=\", \".join(names))"
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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": 36,
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"id": "e65412f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_analyzer_all = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e639285a",
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"metadata": {},
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"source": [
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"However... if the list of categoricals is long enough, it may error!"
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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": 37,
|
||||
"id": "696b000f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Error code: 400 - {'error': {'message': \"This model's maximum context length is 16385 tokens. However, your messages resulted in 33885 tokens (33855 in the messages, 30 in the functions). Please reduce the length of the messages or functions.\", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"try:\n",
|
||||
" res = query_analyzer_all.invoke(\"what are books about aliens by jess knight\")\n",
|
||||
"except Exception as e:\n",
|
||||
" print(e)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1d5d7891",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can try to use a longer context window... but with so much information in there, it is not garunteed to pick it up reliably"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"id": "0f0d0757",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"llm_long = ChatOpenAI(model=\"gpt-4-turbo-preview\", temperature=0)\n",
|
||||
"structured_llm_long = llm_long.with_structured_output(Search)\n",
|
||||
"query_analyzer_all = {\"question\": RunnablePassthrough()} | prompt | structured_llm_long"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"id": "03e5b7b2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Search(query='aliens', author='Kevin Knight')"
|
||||
]
|
||||
},
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"query_analyzer_all.invoke(\"what are books about aliens by jess knight\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "73ecf52b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Find and all relevant values\n",
|
||||
"\n",
|
||||
"Instead, what we can do is create an index over the relevant values and then query that for the N most relevant values,"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"id": "32b19e07",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_community.vectorstores import Chroma\n",
|
||||
"from langchain_openai import OpenAIEmbeddings\n",
|
||||
"\n",
|
||||
"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
|
||||
"vectorstore = Chroma.from_texts(names, embeddings, collection_name=\"author_names\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"id": "774cb7b0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def select_names(question):\n",
|
||||
" _docs = vectorstore.similarity_search(question, k=10)\n",
|
||||
" _names = [d.page_content for d in _docs]\n",
|
||||
" return \", \".join(_names)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"id": "1173159c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"create_prompt = {\n",
|
||||
" \"question\": RunnablePassthrough(),\n",
|
||||
" \"authors\": select_names,\n",
|
||||
"} | base_prompt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"id": "0a892607",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query_analyzer_select = create_prompt | structured_llm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"id": "8195d7cd",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"ChatPromptValue(messages=[SystemMessage(content='Generate a relevant search query for a library system.\\n\\n`author` attribute MUST be one of:\\n\\nJesse Knight, Kelly Knight, Scott Knight, Richard Knight, Andrew Knight, Katherine Knight, Erica Knight, Ashley Knight, Becky Knight, Kevin Knight\\n\\nDo NOT hallucinate author name!'), HumanMessage(content='what are books by jess knight')])"
|
||||
]
|
||||
},
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"create_prompt.invoke(\"what are books by jess knight\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"id": "d3228b4e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Search(query='books about aliens', author='Jesse Knight')"
|
||||
]
|
||||
},
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"query_analyzer_select.invoke(\"what are books about aliens by jess knight\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "46ef88bb",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Replace after selection\n",
|
||||
"\n",
|
||||
"Another method is to let the LLM fill in whatever value, but then convert that value to a valid value.\n",
|
||||
"This can actually be done with the Pydantic class itself!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"id": "a2e8b434",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.pydantic_v1 import validator\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class Search(BaseModel):\n",
|
||||
" query: str\n",
|
||||
" author: str\n",
|
||||
"\n",
|
||||
" @validator(\"author\")\n",
|
||||
" def double(cls, v: str) -> str:\n",
|
||||
" return vectorstore.similarity_search(v, k=1)[0].page_content"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"id": "919c0601",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"system = \"\"\"Generate a relevant search query for a library system\"\"\"\n",
|
||||
"prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [\n",
|
||||
" (\"system\", system),\n",
|
||||
" (\"human\", \"{question}\"),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"corrective_structure_llm = llm.with_structured_output(Search)\n",
|
||||
"corrective_query_analyzer = (\n",
|
||||
" {\"question\": RunnablePassthrough()} | prompt | corrective_structure_llm\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"id": "6c4f3e9a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Search(query='books about aliens', author='Jesse Knight')"
|
||||
]
|
||||
},
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"corrective_query_analyzer.invoke(\"what are books about aliens by jes knight\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a309cb11",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# TODO: show trigram similarity"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,329 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"id": "df7d42b9-58a6-434c-a2d7-0b61142f6d3e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"---\n",
|
||||
"sidebar_position: 4\n",
|
||||
"---"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f2195672-0cab-4967-ba8a-c6544635547d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Handle Multiple Queries\n",
|
||||
"\n",
|
||||
"Sometimes, a query analysis technique may allow for multiple queries to be generated. In these cases, we need to remember to run all queries and then to combine the results. We will show a simple example (using mock data) of how to do that."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a4079b57-4369-49c9-b2ad-c809b5408d7e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"#### Install dependencies"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %pip install -qU langchain langchain-community langchain-openai chromadb"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "79d66a45-a05c-4d22-b011-b1cdbdfc8f9c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Set environment variables\n",
|
||||
"\n",
|
||||
"We'll use OpenAI in this example:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "40e2979e-a818-4b96-ac25-039336f94319",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
|
||||
"\n",
|
||||
"# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
|
||||
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
|
||||
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c20b48b8-16d7-4089-bc17-f2d240b3935a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create Index\n",
|
||||
"\n",
|
||||
"We will create a vectorstore over fake information."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "1f621694",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
|
||||
"from langchain_community.vectorstores import Chroma\n",
|
||||
"from langchain_openai import OpenAIEmbeddings\n",
|
||||
"\n",
|
||||
"texts = [\"Harrison worked at Kensho\", \"Ankush worked at Facebook\"]\n",
|
||||
"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
|
||||
"vectorstore = Chroma.from_texts(\n",
|
||||
" texts,\n",
|
||||
" embeddings,\n",
|
||||
")\n",
|
||||
"retriever = vectorstore.as_retriever(search_kwargs={\"k\": 1})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "57396e23-c192-4d97-846b-5eacea4d6b8d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Query analysis\n",
|
||||
"\n",
|
||||
"We will use function calling to structure the output. We will let it return multiple queries."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import List, Optional\n",
|
||||
"\n",
|
||||
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class Search(BaseModel):\n",
|
||||
" \"\"\"Search over a database of job records.\"\"\"\n",
|
||||
"\n",
|
||||
" queries: List[str] = Field(\n",
|
||||
" ...,\n",
|
||||
" description=\"Distinct queries to search for\",\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/Users/harrisonchase/workplace/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: The function `with_structured_output` is in beta. It is actively being worked on, so the API may change.\n",
|
||||
" warn_beta(\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"from langchain_core.runnables import RunnablePassthrough\n",
|
||||
"from langchain_openai import ChatOpenAI\n",
|
||||
"\n",
|
||||
"output_parser = PydanticToolsParser(tools=[Search])\n",
|
||||
"\n",
|
||||
"system = \"\"\"You have the ability to issue search queries to get information to help answer user information.\n",
|
||||
"\n",
|
||||
"If you need to look up two distinct pieces of information, you are allowed to do that!\"\"\"\n",
|
||||
"prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [\n",
|
||||
" (\"system\", system),\n",
|
||||
" (\"human\", \"{question}\"),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
|
||||
"structured_llm = llm.with_structured_output(Search)\n",
|
||||
"query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b9564078",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can see that this allows for creating multiple queries"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "bc1d3863",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Search(queries=['Harrison work location'])"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"query_analyzer.invoke(\"where did Harrison Work\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Search(queries=['Harrison work place', 'Ankush work place'])"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"query_analyzer.invoke(\"where did Harrison and ankush Work\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c7c65b2f-7881-45fc-a47b-a4eaaf48245f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Retrieval with query analysis\n",
|
||||
"\n",
|
||||
"So how would we include this in a chain? One thing that will make this a lot easier is if we call our retriever asyncronously - this will let us loop over the queries and not get blocked on the response time."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "1e047d87",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.runnables import chain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"id": "8dac7866",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@chain\n",
|
||||
"async def custom_chain(question):\n",
|
||||
" response = await query_analyzer.ainvoke(question)\n",
|
||||
" docs = []\n",
|
||||
" for query in response.queries:\n",
|
||||
" new_docs = await retriever.ainvoke(query)\n",
|
||||
" docs.extend(new_docs)\n",
|
||||
" # You probably want to think about reranking or deduplicating documents here\n",
|
||||
" # But that is a separate topic\n",
|
||||
" return docs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"id": "232ad8a7-7990-4066-9228-d35a555f7293",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Harrison worked at Kensho')]"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"await custom_chain.ainvoke(\"where did Harrison Work\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"id": "28e14ba5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Harrison worked at Kensho'),\n",
|
||||
" Document(page_content='Ankush worked at Facebook')]"
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"await custom_chain.ainvoke(\"where did Harrison and ankush Work\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "88de5a36",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,331 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"id": "df7d42b9-58a6-434c-a2d7-0b61142f6d3e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"---\n",
|
||||
"sidebar_position: 5\n",
|
||||
"---"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f2195672-0cab-4967-ba8a-c6544635547d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Handle Multiple Retrievers\n",
|
||||
"\n",
|
||||
"Sometimes, a query analysis technique may allow for selection of which retriever to use. To use this, you will need to add some logic to select the retriever to do. We will show a simple example (using mock data) of how to do that."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a4079b57-4369-49c9-b2ad-c809b5408d7e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"#### Install dependencies"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %pip install -qU langchain langchain-community langchain-openai chromadb"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "79d66a45-a05c-4d22-b011-b1cdbdfc8f9c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Set environment variables\n",
|
||||
"\n",
|
||||
"We'll use OpenAI in this example:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "40e2979e-a818-4b96-ac25-039336f94319",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
|
||||
"\n",
|
||||
"# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
|
||||
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
|
||||
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c20b48b8-16d7-4089-bc17-f2d240b3935a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create Index\n",
|
||||
"\n",
|
||||
"We will create a vectorstore over fake information."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "1f621694",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
|
||||
"from langchain_community.vectorstores import Chroma\n",
|
||||
"from langchain_openai import OpenAIEmbeddings\n",
|
||||
"\n",
|
||||
"texts = [\"Harrison worked at Kensho\"]\n",
|
||||
"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
|
||||
"vectorstore = Chroma.from_texts(texts, embeddings, collection_name=\"harrison\")\n",
|
||||
"retriever_harrison = vectorstore.as_retriever(search_kwargs={\"k\": 1})\n",
|
||||
"\n",
|
||||
"texts = [\"Ankush worked at Facebook\"]\n",
|
||||
"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
|
||||
"vectorstore = Chroma.from_texts(texts, embeddings, collection_name=\"ankush\")\n",
|
||||
"retriever_ankush = vectorstore.as_retriever(search_kwargs={\"k\": 1})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "57396e23-c192-4d97-846b-5eacea4d6b8d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Query analysis\n",
|
||||
"\n",
|
||||
"We will use function calling to structure the output. We will let it return multiple queries."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import List, Optional\n",
|
||||
"\n",
|
||||
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class Search(BaseModel):\n",
|
||||
" \"\"\"Search for information about a person.\"\"\"\n",
|
||||
"\n",
|
||||
" query: str = Field(\n",
|
||||
" ...,\n",
|
||||
" description=\"Query to look up\",\n",
|
||||
" )\n",
|
||||
" person: str = Field(\n",
|
||||
" ...,\n",
|
||||
" description=\"Person to look things up for. Should be `HARRISON` or `ANKUSH`.\",\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"from langchain_core.runnables import RunnablePassthrough\n",
|
||||
"from langchain_openai import ChatOpenAI\n",
|
||||
"\n",
|
||||
"output_parser = PydanticToolsParser(tools=[Search])\n",
|
||||
"\n",
|
||||
"system = \"\"\"You have the ability to issue search queries to get information to help answer user information.\"\"\"\n",
|
||||
"prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [\n",
|
||||
" (\"system\", system),\n",
|
||||
" (\"human\", \"{question}\"),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
|
||||
"structured_llm = llm.with_structured_output(Search)\n",
|
||||
"query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b9564078",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can see that this allows for routing between retrievers"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"id": "bc1d3863",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Search(query='workplace', person='HARRISON')"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"query_analyzer.invoke(\"where did Harrison Work\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Search(query='workplace', person='ANKUSH')"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"query_analyzer.invoke(\"where did ankush Work\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c7c65b2f-7881-45fc-a47b-a4eaaf48245f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Retrieval with query analysis\n",
|
||||
"\n",
|
||||
"So how would we include this in a chain? We just need some simple logic to select the retriever and pass in the search query"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"id": "1e047d87",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.runnables import chain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"id": "4ec0c7fe",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"retrievers = {\n",
|
||||
" \"HARRISON\": retriever_harrison,\n",
|
||||
" \"ANKUSH\": retriever_ankush,\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"id": "8dac7866",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@chain\n",
|
||||
"def custom_chain(question):\n",
|
||||
" response = query_analyzer.invoke(question)\n",
|
||||
" retriever = retrievers[response.person]\n",
|
||||
" return retriever.invoke(response.query)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"id": "232ad8a7-7990-4066-9228-d35a555f7293",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Harrison worked at Kensho')]"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"custom_chain.invoke(\"where did Harrison Work\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"id": "28e14ba5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Ankush worked at Facebook')]"
|
||||
]
|
||||
},
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"custom_chain.invoke(\"where did ankush Work\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "33338d4f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,328 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "raw",
|
||||
"id": "df7d42b9-58a6-434c-a2d7-0b61142f6d3e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"---\n",
|
||||
"sidebar_position: 3\n",
|
||||
"---"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f2195672-0cab-4967-ba8a-c6544635547d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Handle Cases Where No Queries are Generated\n",
|
||||
"\n",
|
||||
"Sometimes, a query analysis technique may allow for any number of queries to be generated - including no queries! In this case, our overall chain will need to inspect the result of the query analysis before deciding whether to call the retriever or not.\n",
|
||||
"\n",
|
||||
"We will use mock data for this example."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a4079b57-4369-49c9-b2ad-c809b5408d7e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"#### Install dependencies"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "e168ef5c-e54e-49a6-8552-5502854a6f01",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# %pip install -qU langchain langchain-community langchain-openai chromadb"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "79d66a45-a05c-4d22-b011-b1cdbdfc8f9c",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Set environment variables\n",
|
||||
"\n",
|
||||
"We'll use OpenAI in this example:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "40e2979e-a818-4b96-ac25-039336f94319",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import getpass\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
|
||||
"\n",
|
||||
"# Optional, uncomment to trace runs with LangSmith. Sign up here: https://smith.langchain.com.\n",
|
||||
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
|
||||
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c20b48b8-16d7-4089-bc17-f2d240b3935a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create Index\n",
|
||||
"\n",
|
||||
"We will create a vectorstore over fake information."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "1f621694",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
|
||||
"from langchain_community.vectorstores import Chroma\n",
|
||||
"from langchain_openai import OpenAIEmbeddings\n",
|
||||
"\n",
|
||||
"texts = [\"Harrison worked at Kensho\"]\n",
|
||||
"embeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n",
|
||||
"vectorstore = Chroma.from_texts(\n",
|
||||
" texts,\n",
|
||||
" embeddings,\n",
|
||||
")\n",
|
||||
"retriever = vectorstore.as_retriever()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "57396e23-c192-4d97-846b-5eacea4d6b8d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Query analysis\n",
|
||||
"\n",
|
||||
"We will use function calling to structure the output. However, we will configure the LLM such that is doesn't NEED to call the function representing a search query (should it decide not to). We will also then use a prompt to do query analysis that explicitly lays when it should and shouldn't make a search."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "0b51dd76-820d-41a4-98c8-893f6fe0d1ea",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import Optional\n",
|
||||
"\n",
|
||||
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class Search(BaseModel):\n",
|
||||
" \"\"\"Search over a database of job records.\"\"\"\n",
|
||||
"\n",
|
||||
" query: str = Field(\n",
|
||||
" ...,\n",
|
||||
" description=\"Similarity search query applied to job record.\",\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "783c03c3-8c72-4f88-9cf4-5829ce6745d6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.prompts import ChatPromptTemplate\n",
|
||||
"from langchain_core.runnables import RunnablePassthrough\n",
|
||||
"from langchain_openai import ChatOpenAI\n",
|
||||
"\n",
|
||||
"system = \"\"\"You have the ability to issue search queries to get information to help answer user information.\n",
|
||||
"\n",
|
||||
"You do not NEED to look things up. If you don't need to, then just respond normally.\"\"\"\n",
|
||||
"prompt = ChatPromptTemplate.from_messages(\n",
|
||||
" [\n",
|
||||
" (\"system\", system),\n",
|
||||
" (\"human\", \"{question}\"),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)\n",
|
||||
"structured_llm = llm.bind_tools([Search])\n",
|
||||
"query_analyzer = {\"question\": RunnablePassthrough()} | prompt | structured_llm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b9564078",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can see that by invoking this we get an message that sometimes - but not always - returns a tool call."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "bc1d3863",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_ZnoVX4j9Mn8wgChaORyd1cvq', 'function': {'arguments': '{\"query\":\"Harrison\"}', 'name': 'Search'}, 'type': 'function'}]})"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"query_analyzer.invoke(\"where did Harrison Work\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "af62af17-4f90-4dbd-a8b4-dfff51f1db95",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='Hello! How can I assist you today?')"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"query_analyzer.invoke(\"hi!\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c7c65b2f-7881-45fc-a47b-a4eaaf48245f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Retrieval with query analysis\n",
|
||||
"\n",
|
||||
"So how would we include this in a chain? Let's look at an example below."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "1e047d87",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_core.output_parsers.openai_tools import PydanticToolsParser\n",
|
||||
"from langchain_core.runnables import chain\n",
|
||||
"\n",
|
||||
"output_parser = PydanticToolsParser(tools=[Search])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "8dac7866",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@chain\n",
|
||||
"def custom_chain(question):\n",
|
||||
" response = query_analyzer.invoke(question)\n",
|
||||
" if \"tool_calls\" in response.additional_kwargs:\n",
|
||||
" query = output_parser.invoke(response)\n",
|
||||
" docs = retriever.invoke(query[0].query)\n",
|
||||
" # Could add more logic - like another LLM call - here\n",
|
||||
" return docs\n",
|
||||
" else:\n",
|
||||
" return response"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "232ad8a7-7990-4066-9228-d35a555f7293",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[Document(page_content='Harrison worked at Kensho')]"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"custom_chain.invoke(\"where did Harrison Work\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "28e14ba5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"AIMessage(content='Hello! How can I assist you today?')"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"custom_chain.invoke(\"hi!\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "33338d4f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in New Issue