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WIP: Harrison/base retriever (#1765)

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Harrison Chase
2023-03-24 07:46:49 -07:00
committed by GitHub
parent 4f364db9a9
commit 47d37db2d2
25 changed files with 509 additions and 339 deletions
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30
docs/modules/chat/examples/chat_vector_db.ipynb
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@@ -9,7 +9,7 @@
"\n",
"This notebook goes over how to set up a chat model to chat with a vector database.\n",
"\n",
"This notebook is very similar to the example of using an LLM in the ChatVectorDBChain. The only differences here are (1) using a ChatModel, and (2) passing in a ChatPromptTemplate (optimized for chat models)."
"This notebook is very similar to the example of using an LLM in the ConversationalRetrievalChain. The only differences here are (1) using a ChatModel, and (2) passing in a ChatPromptTemplate (optimized for chat models)."
]
},
{
@@ -24,7 +24,7 @@
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.chains import ChatVectorDBChain"
"from langchain.chains import ConversationalRetrievalChain"
]
},
{
@@ -157,7 +157,7 @@
"id": "3c96b118",
"metadata": {},
"source": [
"We now initialize the ChatVectorDBChain"
"We now initialize the ConversationalRetrievalChain"
]
},
{
@@ -169,7 +169,7 @@
},
"outputs": [],
"source": [
"qa = ChatVectorDBChain.from_llm(ChatOpenAI(temperature=0), vectorstore,qa_prompt=prompt)"
"qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0), vectorstore,qa_prompt=prompt)"
]
},
{
@@ -205,7 +205,7 @@
{
"data": {
"text/plain": [
"\"The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. He described her as one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and a consensus builder. He also mentioned that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
"\"The President nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. He described her as one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and a consensus builder. She has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
]
},
"execution_count": 9,
@@ -227,7 +227,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 10,
"id": "00b4cf00",
"metadata": {
"tags": []
@@ -241,7 +241,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 11,
"id": "f01828d1",
"metadata": {
"tags": []
@@ -250,10 +250,10 @@
{
"data": {
"text/plain": [
"'The context does not provide information about the predecessor of Ketanji Brown Jackson.'"
"\"The President mentioned Circuit Court of Appeals Judge Ketanji Brown Jackson as the nominee for the United States Supreme Court. He described her as one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. The President did not mention any specific sources of support for Judge Jackson, but he did note that advancing immigration reform is supported by everyone from labor unions to religious leaders to the U.S. Chamber of Commerce.\""
]
},
"execution_count": 13,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -267,14 +267,14 @@
"id": "2324cdc6-98bf-4708-b8cd-02a98b1e5b67",
"metadata": {},
"source": [
"## Chat Vector DB with streaming to `stdout`\n",
"## ConversationalRetrievalChain with streaming to `stdout`\n",
"\n",
"Output from the chain will be streamed to `stdout` token by token in this example."
]
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 12,
"id": "2efacec3-2690-4b05-8de3-a32fd2ac3911",
"metadata": {
"tags": []
@@ -285,7 +285,7 @@
"from langchain.llms import OpenAI\n",
"from langchain.callbacks.base import CallbackManager\n",
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
"from langchain.chains.chat_vector_db.prompts import CONDENSE_QUESTION_PROMPT\n",
"from langchain.chains.chat_index.prompts import CONDENSE_QUESTION_PROMPT\n",
"from langchain.chains.question_answering import load_qa_chain\n",
"\n",
"# Construct a ChatVectorDBChain with a streaming llm for combine docs\n",
@@ -296,12 +296,12 @@
"question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)\n",
"doc_chain = load_qa_chain(streaming_llm, chain_type=\"stuff\", prompt=prompt)\n",
"\n",
"qa = ChatVectorDBChain(vectorstore=vectorstore, combine_docs_chain=doc_chain, question_generator=question_generator)"
"qa = ConversationalRetrievalChain(retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 13,
"id": "fd6d43f4-7428-44a4-81bc-26fe88a98762",
"metadata": {
"tags": []
@@ -323,7 +323,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 14,
"id": "5ab38978-f3e8-4fa7-808c-c79dec48379a",
"metadata": {
"tags": []

18
docs/modules/chat/examples/vector_db_qa.ipynb
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@@ -5,16 +5,16 @@
"id": "07c1e3b9",
"metadata": {},
"source": [
"# Vector DB Question/Answering\n",
"# Retrieval Question/Answering\n",
"\n",
"This example showcases using a chat model to do question answering over a vector database.\n",
"\n",
"This notebook is very similar to the example of using an LLM in the ChatVectorDBChain. The only differences here are (1) using a ChatModel, and (2) passing in a ChatPromptTemplate (optimized for chat models)."
"This notebook is very similar to the example of using an LLM in the RetrievalQA. The only differences here are (1) using a ChatModel, and (2) passing in a ChatPromptTemplate (optimized for chat models)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 7,
"id": "82525493",
"metadata": {},
"outputs": [],
@@ -22,7 +22,7 @@
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.chains import VectorDBQA"
"from langchain.chains import RetrievalQA"
]
},
{
@@ -100,28 +100,28 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 8,
"id": "3018f865",
"metadata": {},
"outputs": [],
"source": [
"chain_type_kwargs = {\"prompt\": prompt}\n",
"qa = VectorDBQA.from_chain_type(llm=ChatOpenAI(), chain_type=\"stuff\", vectorstore=docsearch, chain_type_kwargs=chain_type_kwargs)"
"qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(), chain_type=\"stuff\", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 9,
"id": "032a47f8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"The President nominated Ketanji Brown Jackson as a Judge for the United States Supreme Court. He described her as one of the nation's top legal minds and a former top litigator in private practice, a former federal public defender, and a consensus builder.\""
"\"The president nominated Ketanji Brown Jackson to serve on the United States Supreme Court. He referred to her as one of our nation's top legal minds, a former federal public defender, a consensus builder, and from a family of public school educators and police officers. Since she's been nominated, she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
]
},
"execution_count": 7,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}

34
docs/modules/chat/examples/vector_db_qa_with_sources.ipynb
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@@ -5,11 +5,11 @@
"id": "efc5be67",
"metadata": {},
"source": [
"# VectorDB Question Answering with Sources\n",
"# Retrieval Question Answering with Sources\n",
"\n",
"This notebook goes over how to do question-answering with sources with a chat model over a vector database. It does this by using the `VectorDBQAWithSourcesChain`, which does the lookup of the documents from a vector database. \n",
"This notebook goes over how to do question-answering with sources with a chat model over a vector database. It does this by using the `RetrievalQAWithSourcesChain`, which does the lookup of the documents from a vector database. \n",
"\n",
"This notebook is very similar to the example of using an LLM in the ChatVectorDBChain. The only differences here are (1) using a ChatModel, and (2) passing in a ChatPromptTemplate (optimized for chat models)."
"This notebook is very similar to the example of using an LLM in the RetrievalQAWithSources. The only differences here are (1) using a ChatModel, and (2) passing in a ChatPromptTemplate (optimized for chat models)."
]
},
{
@@ -51,6 +51,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Running Chroma using direct local API.\n",
"Using DuckDB in-memory for database. Data will be transient.\n",
"Running Chroma using direct local API.\n",
"Using DuckDB in-memory for database. Data will be transient.\n"
]
@@ -62,12 +64,12 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 9,
"id": "8aa571ae",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import VectorDBQAWithSourcesChain"
"from langchain.chains import RetrievalQAWithSourcesChain"
]
},
{
@@ -101,7 +103,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 6,
"id": "ed00e906",
"metadata": {},
"outputs": [],
@@ -130,23 +132,23 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 10,
"id": "aa859d4c",
"metadata": {},
"outputs": [],
"source": [
"chain_type_kwargs = {\"prompt\": prompt}\n",
"chain = VectorDBQAWithSourcesChain.from_chain_type(\n",
"chain = RetrievalQAWithSourcesChain.from_chain_type(\n",
" ChatOpenAI(temperature=0), \n",
" chain_type=\"stuff\", \n",
" vectorstore=docsearch,\n",
" retriever=docsearch.as_retriever(),\n",
" chain_type_kwargs=chain_type_kwargs\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 11,
"id": "8ba36fa7",
"metadata": {},
"outputs": [
@@ -154,10 +156,10 @@
"data": {
"text/plain": [
"{'answer': 'The President honored Justice Stephen Breyer, an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court, for his dedicated service to the country. \\n',\n",
" 'sources': '30-pl'}"
" 'sources': '31-pl'}"
]
},
"execution_count": 19,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -165,6 +167,14 @@
"source": [
"chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8308fbf7",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {