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Merge pull request #108 from langchain-ai/rlm/update_parser

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Minor parser modifications in CRAG example
This commit is contained in:
Lance Martin
2024-02-13 11:37:55 -08:00
committed by GitHub
parent f61dd21305 f399bb7512
commit c3942874eb
1 changed files with 28 additions and 71 deletions
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examples/rag/langgraph_crag_mistral.ipynb
+28 -71
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@@ -70,7 +70,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 13,
"id": "abc064ab-7de1-4d03-a987-cd3078438d61",
"metadata": {},
"outputs": [],
@@ -84,14 +84,14 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 12,
"id": "9f644869-436e-4bf6-a267-b2465c7b5aef",
"metadata": {},
"outputs": [],
"source": [
"# Flags for running locally\n",
"\n",
"run_local = \"Yes\"\n",
"run_local = \"No\"\n",
"local_llm = \"mistral:instruct\""
]
},
@@ -113,24 +113,10 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"id": "254ae533-79e0-42f4-b200-1ec9160e1d3d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bert_load_from_file: gguf version = 2\n",
"bert_load_from_file: gguf alignment = 32\n",
"bert_load_from_file: gguf data offset = 695552\n",
"bert_load_from_file: model name = BERT\n",
"bert_load_from_file: model architecture = bert\n",
"bert_load_from_file: model file type = 1\n",
"bert_load_from_file: bert tokenizer vocab = 30522\n"
]
}
],
"outputs": [],
"source": [
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain_community.document_loaders import WebBaseLoader\n",
@@ -201,7 +187,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 14,
"id": "10028794-2fbc-43f9-aa4c-7fe3abd69c1e",
"metadata": {},
"outputs": [],
@@ -238,7 +224,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 15,
"id": "447d1333-082d-479a-a6fa-0ac0df78bb9d",
"metadata": {},
"outputs": [],
@@ -248,14 +234,13 @@
"from typing import Annotated, Sequence, TypedDict\n",
"\n",
"from langchain import hub\n",
"from langchain.output_parsers import PydanticOutputParser\n",
"from langchain_core.output_parsers import JsonOutputParser\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain.schema import Document\n",
"from langchain_community.chat_models import ChatOllama\n",
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_mistralai.chat_models import ChatMistralAI\n",
"\n",
@@ -346,19 +331,6 @@
" mistral_api_key=mistral_api_key, temperature=0, model=\"mistral-medium\"\n",
" )\n",
"\n",
" # Data model\n",
" class grade(BaseModel):\n",
" \"\"\"Binary score for relevance check.\"\"\"\n",
"\n",
" score: str = Field(description=\"Relevance score 'yes' or 'no'\")\n",
"\n",
" # Set up a parser + inject instructions into the prompt template.\n",
" parser = PydanticOutputParser(pydantic_object=grade)\n",
"\n",
" from langchain_core.output_parsers import JsonOutputParser\n",
"\n",
" parser = JsonOutputParser(pydantic_object=grade)\n",
"\n",
" prompt = PromptTemplate(\n",
" template=\"\"\"You are a grader assessing relevance of a retrieved document to a user question. \\n \n",
" Here is the retrieved document: \\n\\n {context} \\n\\n\n",
@@ -366,12 +338,11 @@
" If the document contains keywords related to the user question, grade it as relevant. \\n\n",
" It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \\n\n",
" Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. \\n\n",
" Provide the binary score as a JSON with no premable or explaination and use these instructons to format the output: {format_instructions}\"\"\",\n",
" input_variables=[\"query\"],\n",
" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
" Provide the binary score as a JSON with a single key 'score' and no premable or explaination.\"\"\",\n",
" input_variables=[\"question\",\"context\"],\n",
" )\n",
"\n",
" chain = prompt | llm | parser\n",
" chain = prompt | llm | JsonOutputParser()\n",
"\n",
" # Score\n",
" filtered_docs = []\n",
@@ -381,7 +352,6 @@
" {\n",
" \"question\": question,\n",
" \"context\": d.page_content,\n",
" \"format_instructions\": parser.get_format_instructions(),\n",
" }\n",
" )\n",
" grade = score[\"score\"]\n",
@@ -519,7 +489,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 16,
"id": "0a63776c-f9cd-46ce-b8cf-95c066dc5b06",
"metadata": {},
"outputs": [],
@@ -563,12 +533,14 @@
"source": [
"## Run\n",
"\n",
"`Mistral API -` "
"`Mistral API -` \n",
"\n",
"Trace for below run: https://smith.langchain.com/public/0a5cbc97-a2f6-4697-856c-90a6302fd13e/r"
]
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 11,
"id": "3ab1d8df-a74e-4b48-a30b-e39bbfd5925a",
"metadata": {},
"outputs": [
@@ -599,14 +571,13 @@
"'\\n---\\n'\n",
"\"Node '__end__':\"\n",
"'\\n---\\n'\n",
"('Episodic memory stores specific events or experiences, making them unique to '\n",
" 'each individual. Semantic memory, on the other hand, involves general '\n",
" 'knowledge and facts that are not tied to personal experiences. Procedural '\n",
" 'memory is responsible for learning and remembering sequences of actions, '\n",
" \"such as riding a bike. These memory types contribute to an agent's learning \"\n",
" 'and decision-making processes by allowing it to recall past experiences '\n",
" '(episodic), understand and use information (semantic), and perform tasks '\n",
" '(procedural).')\n"
"('In agent-based systems, episodic memory can be likened to a long-term memory '\n",
" \"module that records agents' experiences in natural language, with retrieval \"\n",
" 'based on relevance, recency, and importance. Semantic memory is similar to '\n",
" 'an external vector store that provides agents with the ability to retain and '\n",
" 'recall information over extended periods. Procedural memory can be seen as '\n",
" 'the reflection mechanism that synthesizes memories into higher-level '\n",
" \"inferences, guiding the agent's future behavior.\")\n"
]
}
],
@@ -635,12 +606,14 @@
"id": "03ee2be9-2368-46ea-9edd-dc064a7c7c96",
"metadata": {},
"source": [
"`Locall (Ollama) -` "
"`Local (Ollama) -` \n",
"\n",
"Trace for blow run: https://smith.langchain.com/public/3b23a1d4-720a-4b26-8f34-70d2f20f8832/r"
]
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 11,
"id": "16ea2032-59c7-433d-aca4-2828a1239074",
"metadata": {},
"outputs": [
@@ -714,22 +687,6 @@
"pprint.pprint(value['keys']['generation'])"
]
},
{
"cell_type": "markdown",
"id": "0931b76a-3ea8-4f2f-9d27-242d48ec3fe3",
"metadata": {},
"source": [
"## LangSmith Traces\n",
"\n",
"`Mistral API -` \n",
"\n",
"https://smith.langchain.com/public/1c9ce3f2-76bb-4514-a107-076823e9849e/r\n",
"\n",
"`Locall (Ollama) -` \n",
"\n",
"https://smith.langchain.com/public/fd650c43-b0e9-48f4-8cb7-ca77c736d10d/r"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -755,7 +712,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
"version": "3.9.16"
}
},
"nbformat": 4,