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34
docs/extras/guides/expression_language/interface.ipynb
View File

@ -62,7 +62,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 3,
"id": "d1850a1f",
"metadata": {},
"outputs": [],
@ -72,7 +72,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 4,
"id": "56d0669f",
"metadata": {},
"outputs": [],
@ -170,6 +170,36 @@
"chain.batch([{\"topic\": \"bears\"}, {\"topic\": \"cats\"}])"
]
},
{
"cell_type": "markdown",
"id": "2434ab15",
"metadata": {},
"source": [
"You can set the number of concurrent requests by using the `max_concurrency` parameter"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a08522f6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[AIMessage(content=\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False),\n",
" AIMessage(content=\"Why don't cats play poker in the wild?\\n\\nToo many cheetahs!\", additional_kwargs={}, example=False)]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.batch([{\"topic\": \"bears\"}, {\"topic\": \"cats\"}], config={\"max_concurrency\": 5})"
]
},
{
"cell_type": "markdown",
"id": "b960cbfe",

216
docs/extras/guides/privacy/presidio_data_anonymization.ipynb
View File

@ -28,12 +28,12 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install necessary packages\n",
"# ! pip install langchain langchain-experimental openai\n",
"# ! pip install langchain langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker\n",
"# ! python -m spacy download en_core_web_lg"
]
},
@ -47,16 +47,16 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'My name is Marie Santos, call me at 313-666-7440 or email me at real.slim.shady@gmail.com'"
"'My name is Mrs. Rachel Chen DDS, call me at 849-829-7628x073 or email me at christopherfrey@example.org'"
]
},
"execution_count": 2,
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@ -64,6 +64,92 @@
"source": [
"from langchain_experimental.data_anonymizer import PresidioAnonymizer\n",
"\n",
"anonymizer = PresidioAnonymizer()\n",
"\n",
"anonymizer.anonymize(\n",
" \"My name is Slim Shady, call me at 313-666-7440 or email me at real.slim.shady@gmail.com\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using with LangChain Expression Language\n",
"\n",
"With LCEL we can easily chain together anonymization with the rest of our application."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Set env var OPENAI_API_KEY or load from a .env file:\n",
"# import dotenv\n",
"\n",
"# dotenv.load_dotenv()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='You can find our super secret data at https://www.ross.com/', additional_kwargs={}, example=False)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.prompts.prompt import PromptTemplate\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.schema.runnable import RunnablePassthrough\n",
"\n",
"template = \"\"\"According to this text, where can you find our super secret data?\n",
"\n",
"{anonymized_text}\n",
"\n",
"Answer:\"\"\"\n",
"prompt = PromptTemplate.from_template(template)\n",
"llm = ChatOpenAI()\n",
"\n",
"chain = {\"anonymized_text\": anonymizer.anonymize} | prompt | llm\n",
"chain.invoke(\"You can find our super secret data at https://supersecretdata.com\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Customization\n",
"We can specify ``analyzed_fields`` to only anonymize particular types of data."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'My name is Gabrielle Edwards, call me at 313-666-7440 or email me at real.slim.shady@gmail.com'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anonymizer = PresidioAnonymizer(analyzed_fields=[\"PERSON\"])\n",
"\n",
"anonymizer.anonymize(\n",
@ -75,7 +161,6 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"\\\n",
"As can be observed, the name was correctly identified and replaced with another. The `analyzed_fields` attribute is responsible for what values are to be detected and substituted. We can add *PHONE_NUMBER* to the list:"
]
},
@ -331,125 +416,6 @@
"anonymizer.anonymize(\"My polish phone number is 666555444\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\\\n",
"Finally, it is worth showing how to implement anonymizer as a chain. Since anonymization is based on string operations, we can use `TransformChain` for this:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'text': 'You can find our super secret data at https://supersecretdata.com',\n",
" 'anonymized_text': 'You can find our super secret data at https://www.fox.org/'}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.chains.transform import TransformChain\n",
"\n",
"anonymizer = PresidioAnonymizer()\n",
"\n",
"\n",
"def anonymize_func(inputs: dict) -> dict:\n",
" text = inputs[\"text\"]\n",
" return {\"anonymized_text\": anonymizer.anonymize(text)}\n",
"\n",
"\n",
"anonymize_chain = TransformChain(\n",
" input_variables=[\"text\"],\n",
" output_variables=[\"anonymized_text\"],\n",
" transform=anonymize_func,\n",
")\n",
"\n",
"anonymize_chain(\"You can find our super secret data at https://supersecretdata.com\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\\\n",
"Later, you can, for example, use such anonymization as part of chain sequence. We will use `LangChain Expression Language` ([learn more here](https://python.langchain.com/docs/guides/expression_language/)) for composing these chains together, as shown below:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ! pip install openai\n",
"\n",
"# Set env var OPENAI_API_KEY or load from a .env file:\n",
"import dotenv\n",
"\n",
"dotenv.load_dotenv()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'anonymized_text': StringPromptValue(text='According to this text, where can you find our super secret data?\\n\\nYou can find our super secret data at https://evans-summers.info/\\n\\nAnswer:'),\n",
" 'text': ' https://evans-summers.info/'}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from operator import itemgetter\n",
"from langchain.prompts.prompt import PromptTemplate\n",
"from langchain.chains.llm import LLMChain\n",
"from langchain.llms.openai import OpenAI\n",
"\n",
"template = \"\"\"According to this text, where can you find our super secret data?\n",
"\n",
"{anonymized_text}\n",
"\n",
"Answer:\"\"\"\n",
"prompt = PromptTemplate(input_variables=[\"anonymized_text\"], template=template)\n",
"llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)\n",
"\n",
"\n",
"chain = (\n",
" anonymize_chain\n",
" | {\"anonymized_text\": itemgetter(\"anonymized_text\")}\n",
" | prompt\n",
" | llm_chain\n",
")\n",
"chain.invoke(\"You can find our super secret data at https://supersecretdata.com\")"
]
},
{
"cell_type": "markdown",
"metadata": {},

36
docs/extras/integrations/llms/promptguard.ipynb → docs/extras/integrations/llms/opaqueprompts.ipynb
View File

@ -4,12 +4,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# PromptGuard\n",
"# OpaquePrompts\n",
"\n",
"[PromptGuard](https://promptguard.readthedocs.io/en/latest/) is a service that enables applications to leverage the power of language models without compromising user privacy. Designed for composability and ease of integration into existing applications and services, PromptGuard is consumable via a simple Python library as well as through LangChain. Perhaps more importantly, PromptGuard leverages the power of [confidential computing](https://en.wikipedia.org/wiki/Confidential_computing) to ensure that even the PromptGuard service itself cannot access the data it is protecting.\n",
"[OpaquePrompts](https://opaqueprompts.readthedocs.io/en/latest/) is a service that enables applications to leverage the power of language models without compromising user privacy. Designed for composability and ease of integration into existing applications and services, OpaquePrompts is consumable via a simple Python library as well as through LangChain. Perhaps more importantly, OpaquePrompts leverages the power of [confidential computing](https://en.wikipedia.org/wiki/Confidential_computing) to ensure that even the OpaquePrompts service itself cannot access the data it is protecting.\n",
" \n",
"\n",
"This notebook goes over how to use LangChain to interact with `PromptGuard`."
"This notebook goes over how to use LangChain to interact with `OpaquePrompts`."
]
},
{
@ -18,15 +18,15 @@
"metadata": {},
"outputs": [],
"source": [
"# install the promptguard and langchain packages\n",
"! pip install promptguard langchain"
"# install the opaqueprompts and langchain packages\n",
"! pip install opaqueprompts langchain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Accessing the PromptGuard API requires an API key, which you can get by creating an account on [the PromptGuard website](https://promptguard.opaque.co/). Once you have an account, you can find your API key on [the API Keys page](https://promptguard.opaque.co/api-keys)."
"Accessing the OpaquePrompts API requires an API key, which you can get by creating an account on [the OpaquePrompts website](https://opaqueprompts.opaque.co/). Once you have an account, you can find your API key on [the API Keys page](https:opaqueprompts.opaque.co/api-keys)."
]
},
{
@ -39,7 +39,7 @@
"\n",
"# Set API keys\n",
"\n",
"os.environ['PROMPTGUARD_API_KEY'] = \"<PROMPTGUARD_API_KEY>\"\n",
"os.environ['OPAQUEPROMPTS_API_KEY'] = \"<OPAQUEPROMPTS_API_KEY>\"\n",
"os.environ['OPENAI_API_KEY'] = \"<OPENAI_API_KEY>\""
]
},
@ -47,9 +47,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use PromptGuard LLM Wrapper\n",
"# Use OpaquePrompts LLM Wrapper\n",
"\n",
"Applying promptguard to your application could be as simple as wrapping your LLM using the PromptGuard class by replace `llm=OpenAI()` with `llm=PromptGuard(base_llm=OpenAI())`."
"Applying OpaquePrompts to your application could be as simple as wrapping your LLM using the OpaquePrompts class by replace `llm=OpenAI()` with `llm=OpaquePrompts(base_llm=OpenAI())`."
]
},
{
@ -64,7 +64,7 @@
"from langchain.llms import OpenAI\n",
"from langchain.memory import ConversationBufferWindowMemory\n",
"\n",
"from langchain.llms import PromptGuard\n",
"from langchain.llms import OpaquePrompts\n",
"\n",
"langchain.verbose = True\n",
"langchain.debug = True\n",
@ -106,7 +106,7 @@
"\n",
"chain = LLMChain(\n",
" prompt=PromptTemplate.from_template(prompt_template),\n",
" llm=PromptGuard(base_llm=OpenAI()),\n",
" llm=OpaquePrompts(base_llm=OpenAI()),\n",
" memory=ConversationBufferWindowMemory(k=2),\n",
" verbose=True,\n",
")\n",
@ -132,10 +132,10 @@
"During our recent meeting on February 23, 2023, at 10:30 AM, John Doe provided me with his personal details. His email is johndoe@example.com and his contact number is 650-456-7890. He lives in New York City, USA, and belongs to the American nationality with Christian beliefs and a leaning towards the Democratic party. He mentioned that he recently made a transaction using his credit card 4111 1111 1111 1111 and transferred bitcoins to the wallet address 1A1zP1eP5QGefi2DMPTfTL5SLmv7DivfNa. While discussing his European travels, he noted down his IBAN as GB29 NWBK 6016 1331 9268 19. Additionally, he provided his website as https://johndoeportfolio.com. John also discussed some of his US-specific details. He said his bank account number is 1234567890123456 and his drivers license is Y12345678. His ITIN is 987-65-4321, and he recently renewed his passport, the number for which is 123456789. He emphasized not to share his SSN, which is 669-45-6789. Furthermore, he mentioned that he accesses his work files remotely through the IP 192.168.1.1 and has a medical license number MED-123456.\n",
"```\n",
"\n",
"PromptGuard will automatically detect the sensitive data and replace it with a placeholder. \n",
"OpaquePrompts will automatically detect the sensitive data and replace it with a placeholder. \n",
"\n",
"```\n",
"# Context after PromptGuard\n",
"# Context after OpaquePrompts\n",
"\n",
"During our recent meeting on DATE_TIME_3, at DATE_TIME_2, PERSON_3 provided me with his personal details. His email is EMAIL_ADDRESS_1 and his contact number is PHONE_NUMBER_1. He lives in LOCATION_3, LOCATION_2, and belongs to the NRP_3 nationality with NRP_2 beliefs and a leaning towards the Democratic party. He mentioned that he recently made a transaction using his credit card CREDIT_CARD_1 and transferred bitcoins to the wallet address CRYPTO_1. While discussing his NRP_1 travels, he noted down his IBAN as IBAN_CODE_1. Additionally, he provided his website as URL_1. PERSON_2 also discussed some of his LOCATION_1-specific details. He said his bank account number is US_BANK_NUMBER_1 and his drivers license is US_DRIVER_LICENSE_2. His ITIN is US_ITIN_1, and he recently renewed his passport, the number for which is DATE_TIME_1. He emphasized not to share his SSN, which is US_SSN_1. Furthermore, he mentioned that he accesses his work files remotely through the IP IP_ADDRESS_1 and has a medical license number MED-US_DRIVER_LICENSE_1.\n",
"```\n",
@ -151,7 +151,7 @@
"Response is desanitized by replacing the placeholder with the original sensitive data.\n",
"\n",
"```\n",
"# desanitized LLM response from PromptGuard\n",
"# desanitized LLM response from OpaquePrompts\n",
"\n",
"Hey John, just wanted to remind you to do a password reset for your website https://johndoeportfolio.com through your email johndoe@example.com. It's important to stay secure online, so don't forget to do it!\n",
"```"
@ -161,7 +161,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use PromptGuard in LangChain expression\n",
"# Use OpaquePrompts in LangChain expression\n",
"\n",
"There are functions that can be used with LangChain expression as well if a drop-in replacement doesn't offer the flexibility you need. "
]
@ -172,7 +172,7 @@
"metadata": {},
"outputs": [],
"source": [
"import langchain.utilities.promptguard as pgf\n",
"import langchain.utilities.opaqueprompts as op\n",
"from langchain.schema.runnable import RunnableMap\n",
"from langchain.schema.output_parser import StrOutputParser\n",
"\n",
@ -180,7 +180,7 @@
"prompt=PromptTemplate.from_template(prompt_template), \n",
"llm = OpenAI()\n",
"pg_chain = (\n",
" pgf.sanitize\n",
" op.sanitize\n",
" | RunnableMap(\n",
" {\n",
" \"response\": (lambda x: x[\"sanitized_input\"])\n",
@ -190,7 +190,7 @@
" \"secure_context\": lambda x: x[\"secure_context\"],\n",
" }\n",
" )\n",
" | (lambda x: pgf.desanitize(x[\"response\"], x[\"secure_context\"]))\n",
" | (lambda x: op.desanitize(x[\"response\"], x[\"secure_context\"]))\n",
")\n",
"\n",
"pg_chain.invoke({\"question\": \"Write a text message to remind John to do password reset for his website through his email to stay secure.\", \"history\": \"\"})"

2
docs/extras/integrations/providers/cassandra.mdx
View File

@ -21,7 +21,7 @@ pip install cassio
See a [usage example](/docs/integrations/vectorstores/cassandra).
```python
from langchain.memory import CassandraChatMessageHistory
from langchain.vectorstores import Cassandra
```

12
docs/snippets/modules/chains/foundational/sequential_chains.mdx
View File

@ -8,12 +8,12 @@ from langchain.prompts import PromptTemplate
```python
# This is an LLMChain to write a synopsis given a title of a play.
llm = OpenAI(temperature=.7)
template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
synopsis_template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.
Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)
synopsis_prompt_template = PromptTemplate(input_variables=["title"], template=synopsis_template)
synopsis_chain = LLMChain(llm=llm, prompt=synopsis_prompt_template)
```
@ -95,13 +95,13 @@ Of particular importance is how we name the input/output variable names. In the
```python
# This is an LLMChain to write a synopsis given a title of a play and the era it is set in.
llm = OpenAI(temperature=.7)
template = """You are a playwright. Given the title of play and the era it is set in, it is your job to write a synopsis for that title.
synopsis_template = """You are a playwright. Given the title of play and the era it is set in, it is your job to write a synopsis for that title.
Title: {title}
Era: {era}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title", "era"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, output_key="synopsis")
synopsis_prompt_template = PromptTemplate(input_variables=["title", "era"], template=synopsis_template)
synopsis_chain = LLMChain(llm=llm, prompt=synopsis_prompt_template, output_key="synopsis")
```

1
libs/langchain/Makefile
View File

@ -76,6 +76,7 @@ lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/langchain --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint lint_diff:
./scripts/check_pydantic.sh .
poetry run ruff .
poetry run black $(PYTHON_FILES) --check
poetry run mypy $(PYTHON_FILES)

1
libs/langchain/langchain/callbacks/tracers/schemas.py
View File

@ -120,6 +120,7 @@ class Run(BaseRunV2):
ChainRun.update_forward_refs()
ToolRun.update_forward_refs()
Run.update_forward_refs()
__all__ = [
"BaseRun",

46
libs/langchain/langchain/chains/question_answering/refine_prompts.py
View File

@ -11,7 +11,7 @@ from langchain.prompts.prompt import PromptTemplate
DEFAULT_REFINE_PROMPT_TMPL = (
"The original question is as follows: {question}\n"
"We have provided an existing answer: {existing_answer}\n"
"We have the opportunity to refine the existing answer"
"We have the opportunity to refine the existing answer "
"(only if needed) with some more context below.\n"
"------------\n"
"{context_str}\n"
@ -20,12 +20,10 @@ DEFAULT_REFINE_PROMPT_TMPL = (
"answer the question. "
"If the context isn't useful, return the original answer."
)
DEFAULT_REFINE_PROMPT = PromptTemplate(
input_variables=["question", "existing_answer", "context_str"],
template=DEFAULT_REFINE_PROMPT_TMPL,
)
DEFAULT_REFINE_PROMPT = PromptTemplate.from_template(DEFAULT_REFINE_PROMPT_TMPL)
refine_template = (
"We have the opportunity to refine the existing answer"
"We have the opportunity to refine the existing answer "
"(only if needed) with some more context below.\n"
"------------\n"
"{context_str}\n"
@ -34,12 +32,9 @@ refine_template = (
"answer the question. "
"If the context isn't useful, return the original answer."
)
messages = [
HumanMessagePromptTemplate.from_template("{question}"),
AIMessagePromptTemplate.from_template("{existing_answer}"),
HumanMessagePromptTemplate.from_template(refine_template),
]
CHAT_REFINE_PROMPT = ChatPromptTemplate.from_messages(messages)
CHAT_REFINE_PROMPT = ChatPromptTemplate.from_messages(
[("human", "{question}"), ("ai", "{existing_answer}"), ("human", "refine_template")]
)
REFINE_PROMPT_SELECTOR = ConditionalPromptSelector(
default_prompt=DEFAULT_REFINE_PROMPT,
conditionals=[(is_chat_model, CHAT_REFINE_PROMPT)],
@ -48,28 +43,25 @@ REFINE_PROMPT_SELECTOR = ConditionalPromptSelector(
DEFAULT_TEXT_QA_PROMPT_TMPL = (
"Context information is below. \n"
"---------------------\n"
"{context_str}"
"\n---------------------\n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the context information and not prior knowledge, "
"answer the question: {question}\n"
)
DEFAULT_TEXT_QA_PROMPT = PromptTemplate(
input_variables=["context_str", "question"], template=DEFAULT_TEXT_QA_PROMPT_TMPL
)
DEFAULT_TEXT_QA_PROMPT = PromptTemplate.from_template(DEFAULT_TEXT_QA_PROMPT_TMPL)
chat_qa_prompt_template = (
"Context information is below. \n"
"---------------------\n"
"{context_str}"
"\n---------------------\n"
"Context information is below.\n"
"------------\n"
"{context_str}\n"
"------------\n"
"Given the context information and not prior knowledge, "
"answer any questions"
)
messages = [
SystemMessagePromptTemplate.from_template(chat_qa_prompt_template),
HumanMessagePromptTemplate.from_template("{question}"),
]
CHAT_QUESTION_PROMPT = ChatPromptTemplate.from_messages(messages)
CHAT_QUESTION_PROMPT = ChatPromptTemplate.from_messages(
[("system", chat_qa_prompt_template), ("human", "{question}")]
)
QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(
default_prompt=DEFAULT_TEXT_QA_PROMPT,
conditionals=[(is_chat_model, CHAT_QUESTION_PROMPT)],

29
libs/langchain/langchain/chains/summarize/refine_prompts.py
View File

@ -1,21 +1,16 @@
# flake8: noqa
from langchain.prompts import PromptTemplate
REFINE_PROMPT_TMPL = (
"Your job is to produce a final summary\n"
"We have provided an existing summary up to a certain point: {existing_answer}\n"
"We have the opportunity to refine the existing summary"
"(only if needed) with some more context below.\n"
"------------\n"
"{text}\n"
"------------\n"
"Given the new context, refine the original summary\n"
"If the context isn't useful, return the original summary."
)
REFINE_PROMPT = PromptTemplate(
input_variables=["existing_answer", "text"],
template=REFINE_PROMPT_TMPL,
)
REFINE_PROMPT_TMPL = """\
Your job is to produce a final summary.
We have provided an existing summary up to a certain point: {existing_answer}
We have the opportunity to refine the existing summary (only if needed) with some more context below.
------------
{text}
------------
Given the new context, refine the original summary.
If the context isn't useful, return the original summary.\
""" # noqa: E501
REFINE_PROMPT = PromptTemplate.from_template(REFINE_PROMPT_TMPL)
prompt_template = """Write a concise summary of the following:
@ -25,4 +20,4 @@ prompt_template = """Write a concise summary of the following:
CONCISE SUMMARY:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
PROMPT = PromptTemplate.from_template(prompt_template)

4
libs/langchain/langchain/indexes/_api.py
View File

@ -332,9 +332,9 @@ def index(
uids_to_delete = record_manager.list_keys(before=index_start_dt)
if uids_to_delete:
# Then delete from vector store.
vector_store.delete(uids_to_delete)
# First delete from record store.
vector_store.delete(uids_to_delete)
# Then delete from record manager.
record_manager.delete_keys(uids_to_delete)
num_deleted = len(uids_to_delete)

60
libs/langchain/langchain/indexes/_sql_record_manager.py
View File

@ -14,10 +14,12 @@ allow it to work with a variety of SQL as a backend.
* Keys can be deleted.
"""
import contextlib
import decimal
import uuid
from typing import Any, Dict, Generator, List, Optional, Sequence
from typing import Any, Dict, Generator, List, Optional, Sequence, Union
from sqlalchemy import (
URL,
Column,
Engine,
Float,
@ -28,7 +30,6 @@ from sqlalchemy import (
create_engine,
text,
)
from sqlalchemy.dialects.sqlite import insert
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import Session, sessionmaker
@ -77,7 +78,7 @@ class SQLRecordManager(RecordManager):
namespace: str,
*,
engine: Optional[Engine] = None,
db_url: Optional[str] = None,
db_url: Union[None, str, URL] = None,
engine_kwargs: Optional[Dict[str, Any]] = None,
) -> None:
"""Initialize the SQLRecordManager.
@ -114,6 +115,7 @@ class SQLRecordManager(RecordManager):
raise AssertionError("Something went wrong with configuration of engine.")
self.engine = _engine
self.dialect = _engine.dialect.name
self.session_factory = sessionmaker(bind=self.engine)
def create_schema(self) -> None:
@ -145,8 +147,16 @@ class SQLRecordManager(RecordManager):
# 2440587.5 - constant represents the Julian day number for January 1, 1970
# 86400.0 - constant represents the number of seconds
# in a day (24 hours * 60 minutes * 60 seconds)
query = text("SELECT (julianday('now') - 2440587.5) * 86400.0;")
if self.dialect == "sqlite":
query = text("SELECT (julianday('now') - 2440587.5) * 86400.0;")
elif self.dialect == "postgresql":
query = text("SELECT EXTRACT (EPOCH FROM CURRENT_TIMESTAMP);")
else:
raise NotImplementedError(f"Not implemented for dialect {self.dialect}")
dt = session.execute(query).scalar()
if isinstance(dt, decimal.Decimal):
dt = float(dt)
if not isinstance(dt, float):
raise AssertionError(f"Unexpected type for datetime: {type(dt)}")
return dt
@ -192,17 +202,37 @@ class SQLRecordManager(RecordManager):
]
with self._make_session() as session:
# Note: uses SQLite insert to make on_conflict_do_update work.
# This code needs to be generalized a bit to work with more dialects.
insert_stmt = insert(UpsertionRecord).values(records_to_upsert)
stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined]
[UpsertionRecord.key, UpsertionRecord.namespace],
set_=dict(
# attr-defined type ignore
updated_at=insert_stmt.excluded.updated_at, # type: ignore
group_id=insert_stmt.excluded.group_id, # type: ignore
),
)
if self.dialect == "sqlite":
from sqlalchemy.dialects.sqlite import insert as sqlite_insert
# Note: uses SQLite insert to make on_conflict_do_update work.
# This code needs to be generalized a bit to work with more dialects.
insert_stmt = sqlite_insert(UpsertionRecord).values(records_to_upsert)
stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined]
[UpsertionRecord.key, UpsertionRecord.namespace],
set_=dict(
# attr-defined type ignore
updated_at=insert_stmt.excluded.updated_at, # type: ignore
group_id=insert_stmt.excluded.group_id, # type: ignore
),
)
elif self.dialect == "postgresql":
from sqlalchemy.dialects.postgresql import insert as pg_insert
# Note: uses SQLite insert to make on_conflict_do_update work.
# This code needs to be generalized a bit to work with more dialects.
insert_stmt = pg_insert(UpsertionRecord).values(records_to_upsert)
stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined]
"uix_key_namespace", # Name of constraint
set_=dict(
# attr-defined type ignore
updated_at=insert_stmt.excluded.updated_at, # type: ignore
group_id=insert_stmt.excluded.group_id, # type: ignore
),
)
else:
raise NotImplementedError(f"Unsupported dialect {self.dialect}")
session.execute(stmt)
session.commit()

6
libs/langchain/langchain/llms/__init__.py
View File

@ -62,6 +62,7 @@ from langchain.llms.mosaicml import MosaicML
from langchain.llms.nlpcloud import NLPCloud
from langchain.llms.octoai_endpoint import OctoAIEndpoint
from langchain.llms.ollama import Ollama
from langchain.llms.opaqueprompts import OpaquePrompts
from langchain.llms.openai import AzureOpenAI, OpenAI, OpenAIChat
from langchain.llms.openllm import OpenLLM
from langchain.llms.openlm import OpenLM
@ -69,7 +70,6 @@ from langchain.llms.petals import Petals
from langchain.llms.pipelineai import PipelineAI
from langchain.llms.predibase import Predibase
from langchain.llms.predictionguard import PredictionGuard
from langchain.llms.promptguard import PromptGuard
from langchain.llms.promptlayer_openai import PromptLayerOpenAI, PromptLayerOpenAIChat
from langchain.llms.replicate import Replicate
from langchain.llms.rwkv import RWKV
@ -142,7 +142,7 @@ __all__ = [
"PredictionGuard",
"PromptLayerOpenAI",
"PromptLayerOpenAIChat",
"PromptGuard",
"OpaquePrompts",
"RWKV",
"Replicate",
"SagemakerEndpoint",
@ -207,7 +207,7 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
"petals": Petals,
"pipelineai": PipelineAI,
"predibase": Predibase,
"promptguard": PromptGuard,
"opaqueprompts": OpaquePrompts,
"replicate": Replicate,
"rwkv": RWKV,
"sagemaker_endpoint": SagemakerEndpoint,

46
libs/langchain/langchain/llms/promptguard.py → libs/langchain/langchain/llms/opaqueprompts.py
View File

@ -10,23 +10,23 @@ from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
class PromptGuard(LLM):
"""An LLM wrapper that uses PromptGuard to sanitize prompts.
class OpaquePrompts(LLM):
"""An LLM wrapper that uses OpaquePrompts to sanitize prompts.
Wraps another LLM and sanitizes prompts before passing it to the LLM, then
de-sanitizes the response.
To use, you should have the ``promptguard`` python package installed,
and the environment variable ``PROMPTGUARD_API_KEY`` set with
To use, you should have the ``opaqueprompts`` python package installed,
and the environment variable ``OPAQUEPROMPTS_API_KEY`` set with
your API key, or pass it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.llms import PromptGuard
from langchain.llms import OpaquePrompts
from langchain.chat_models import ChatOpenAI
prompt_guard_llm = PromptGuard(base_llm=ChatOpenAI())
op_llm = OpaquePrompts(base_llm=ChatOpenAI())
"""
base_llm: BaseLanguageModel
@ -39,29 +39,29 @@ class PromptGuard(LLM):
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validates that the PromptGuard API key and the Python package exist."""
"""Validates that the OpaquePrompts API key and the Python package exist."""
try:
import promptguard as pg
import opaqueprompts as op
except ImportError:
raise ImportError(
"Could not import the `promptguard` Python package, "
"please install it with `pip install promptguard`."
"Could not import the `opaqueprompts` Python package, "
"please install it with `pip install opaqueprompts`."
)
if pg.__package__ is None:
if op.__package__ is None:
raise ValueError(
"Could not properly import `promptguard`, "
"promptguard.__package__ is None."
"Could not properly import `opaqueprompts`, "
"opaqueprompts.__package__ is None."
)
api_key = get_from_dict_or_env(
values, "promptguard_api_key", "PROMPTGUARD_API_KEY", default=""
values, "opaqueprompts_api_key", "OPAQUEPROMPTS_API_KEY", default=""
)
if not api_key:
raise ValueError(
"Could not find PROMPTGUARD_API_KEY in the environment. "
"Please set it to your PromptGuard API key."
"You can get it by creating an account on the PromptGuard website: "
"https://promptguard.opaque.co/ ."
"Could not find OPAQUEPROMPTS_API_KEY in the environment. "
"Please set it to your OpaquePrompts API key."
"You can get it by creating an account on the OpaquePrompts website: "
"https://opaqueprompts.opaque.co/ ."
)
return values
@ -83,14 +83,14 @@ class PromptGuard(LLM):
Example:
.. code-block:: python
response = prompt_guard_llm("Tell me a joke.")
response = op_llm("Tell me a joke.")
"""
import promptguard as pg
import opaqueprompts as op
_run_manager = run_manager or CallbackManagerForLLMRun.get_noop_manager()
# sanitize the prompt by replacing the sensitive information with a placeholder
sanitize_response: pg.SanitizeResponse = pg.sanitize([prompt])
sanitize_response: op.SanitizeResponse = op.sanitize([prompt])
sanitized_prompt_value_str = sanitize_response.sanitized_texts[0]
# TODO: Add in callbacks once child runs for LLMs are supported by LangSmith.
@ -101,7 +101,7 @@ class PromptGuard(LLM):
)
# desanitize the response by restoring the original sensitive information
desanitize_response: pg.DesanitizeResponse = pg.desanitize(
desanitize_response: op.DesanitizeResponse = op.desanitize(
llm_response,
secure_context=sanitize_response.secure_context,
)
@ -113,4 +113,4 @@ class PromptGuard(LLM):
This is an override of the base class method.
"""
return "promptguard"
return "opaqueprompts"

2
libs/langchain/langchain/output_parsers/json.py
View File

@ -13,7 +13,7 @@ def _replace_new_line(match: re.Match[str]) -> str:
value = re.sub(r"\n", r"\\n", value)
value = re.sub(r"\r", r"\\r", value)
value = re.sub(r"\t", r"\\t", value)
value = re.sub('"', r"\"", value)
value = re.sub(r'(?<!\\)"', r"\"", value)
return match.group(1) + value + match.group(3)

2
libs/langchain/langchain/retrievers/web_research.py
View File

@ -150,7 +150,7 @@ class WebResearchRetriever(BaseRetriever):
return query.strip()
def search_tool(self, query: str, num_search_results: int = 1) -> List[dict]:
"""Returns num_serch_results pages per Google search."""
"""Returns num_search_results pages per Google search."""
query_clean = self.clean_search_query(query)
result = self.search.results(query_clean, num_search_results)
return result

18
libs/langchain/langchain/tools/base.py
View File

@ -273,7 +273,11 @@ class ChildTool(BaseTool):
Add run_manager: Optional[AsyncCallbackManagerForToolRun] = None
to child implementations to enable tracing,
"""
raise NotImplementedError()
return await asyncio.get_running_loop().run_in_executor(
None,
partial(self._run, **kwargs),
*args,
)
def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
# For backwards compatibility, if run_input is a string,
@ -522,7 +526,10 @@ class Tool(BaseTool):
if new_argument_supported
else await self.coroutine(*args, **kwargs)
)
raise NotImplementedError("Tool does not support async")
else:
return await asyncio.get_running_loop().run_in_executor(
None, partial(self._run, run_manager=run_manager, **kwargs), *args
)
# TODO: this is for backwards compatibility, remove in future
def __init__(
@ -634,7 +641,12 @@ class StructuredTool(BaseTool):
if new_argument_supported
else await self.coroutine(*args, **kwargs)
)
raise NotImplementedError("Tool does not support async")
return await asyncio.get_running_loop().run_in_executor(
None,
self._run,
partial(self._run, run_manager=run_manager, **kwargs),
*args,
)
@classmethod
def from_function(

2
libs/langchain/langchain/utilities/__init__.py
View File

@ -4,6 +4,7 @@ Other LangChain classes use **Utilities** to interact with third-part systems
and packages.
"""
from langchain.utilities.alpha_vantage import AlphaVantageAPIWrapper
from langchain.utilities.apify import ApifyWrapper
from langchain.utilities.arxiv import ArxivAPIWrapper
from langchain.utilities.awslambda import LambdaWrapper
from langchain.utilities.bash import BashProcess
@ -38,6 +39,7 @@ from langchain.utilities.zapier import ZapierNLAWrapper
__all__ = [
"AlphaVantageAPIWrapper",
"ApifyWrapper",
"ArxivAPIWrapper",
"BashProcess",
"BibtexparserWrapper",

194
libs/langchain/langchain/utilities/apify.py Normal file
View File

@ -0,0 +1,194 @@
from typing import Any, Callable, Dict, Optional
from langchain.document_loaders import ApifyDatasetLoader
from langchain.document_loaders.base import Document
from langchain.pydantic_v1 import BaseModel, root_validator
from langchain.utils import get_from_dict_or_env
class ApifyWrapper(BaseModel):
"""Wrapper around Apify.
To use, you should have the ``apify-client`` python package installed,
and the environment variable ``APIFY_API_TOKEN`` set with your API key, or pass
`apify_api_token` as a named parameter to the constructor.
"""
apify_client: Any
apify_client_async: Any
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate environment.
Validate that an Apify API token is set and the apify-client
Python package exists in the current environment.
"""
apify_api_token = get_from_dict_or_env(
values, "apify_api_token", "APIFY_API_TOKEN"
)
try:
from apify_client import ApifyClient, ApifyClientAsync
values["apify_client"] = ApifyClient(apify_api_token)
values["apify_client_async"] = ApifyClientAsync(apify_api_token)
except ImportError:
raise ValueError(
"Could not import apify-client Python package. "
"Please install it with `pip install apify-client`."
)
return values
def call_actor(
self,
actor_id: str,
run_input: Dict,
dataset_mapping_function: Callable[[Dict], Document],
*,
build: Optional[str] = None,
memory_mbytes: Optional[int] = None,
timeout_secs: Optional[int] = None,
) -> ApifyDatasetLoader:
"""Run an Actor on the Apify platform and wait for results to be ready.
Args:
actor_id (str): The ID or name of the Actor on the Apify platform.
run_input (Dict): The input object of the Actor that you're trying to run.
dataset_mapping_function (Callable): A function that takes a single
dictionary (an Apify dataset item) and converts it to an
instance of the Document class.
build (str, optional): Optionally specifies the actor build to run.
It can be either a build tag or build number.
memory_mbytes (int, optional): Optional memory limit for the run,
in megabytes.
timeout_secs (int, optional): Optional timeout for the run, in seconds.
Returns:
ApifyDatasetLoader: A loader that will fetch the records from the
Actor run's default dataset.
"""
actor_call = self.apify_client.actor(actor_id).call(
run_input=run_input,
build=build,
memory_mbytes=memory_mbytes,
timeout_secs=timeout_secs,
)
return ApifyDatasetLoader(
dataset_id=actor_call["defaultDatasetId"],
dataset_mapping_function=dataset_mapping_function,
)
async def acall_actor(
self,
actor_id: str,
run_input: Dict,
dataset_mapping_function: Callable[[Dict], Document],
*,
build: Optional[str] = None,
memory_mbytes: Optional[int] = None,
timeout_secs: Optional[int] = None,
) -> ApifyDatasetLoader:
"""Run an Actor on the Apify platform and wait for results to be ready.
Args:
actor_id (str): The ID or name of the Actor on the Apify platform.
run_input (Dict): The input object of the Actor that you're trying to run.
dataset_mapping_function (Callable): A function that takes a single
dictionary (an Apify dataset item) and converts it to
an instance of the Document class.
build (str, optional): Optionally specifies the actor build to run.
It can be either a build tag or build number.
memory_mbytes (int, optional): Optional memory limit for the run,
in megabytes.
timeout_secs (int, optional): Optional timeout for the run, in seconds.
Returns:
ApifyDatasetLoader: A loader that will fetch the records from the
Actor run's default dataset.
"""
actor_call = await self.apify_client_async.actor(actor_id).call(
run_input=run_input,
build=build,
memory_mbytes=memory_mbytes,
timeout_secs=timeout_secs,
)
return ApifyDatasetLoader(
dataset_id=actor_call["defaultDatasetId"],
dataset_mapping_function=dataset_mapping_function,
)
def call_actor_task(
self,
task_id: str,
task_input: Dict,
dataset_mapping_function: Callable[[Dict], Document],
*,
build: Optional[str] = None,
memory_mbytes: Optional[int] = None,
timeout_secs: Optional[int] = None,
) -> ApifyDatasetLoader:
"""Run a saved Actor task on Apify and wait for results to be ready.
Args:
task_id (str): The ID or name of the task on the Apify platform.
task_input (Dict): The input object of the task that you're trying to run.
Overrides the task's saved input.
dataset_mapping_function (Callable): A function that takes a single
dictionary (an Apify dataset item) and converts it to an
instance of the Document class.
build (str, optional): Optionally specifies the actor build to run.
It can be either a build tag or build number.
memory_mbytes (int, optional): Optional memory limit for the run,
in megabytes.
timeout_secs (int, optional): Optional timeout for the run, in seconds.
Returns:
ApifyDatasetLoader: A loader that will fetch the records from the
task run's default dataset.
"""
task_call = self.apify_client.task(task_id).call(
task_input=task_input,
build=build,
memory_mbytes=memory_mbytes,
timeout_secs=timeout_secs,
)
return ApifyDatasetLoader(
dataset_id=task_call["defaultDatasetId"],
dataset_mapping_function=dataset_mapping_function,
)
async def acall_actor_task(
self,
task_id: str,
task_input: Dict,
dataset_mapping_function: Callable[[Dict], Document],
*,
build: Optional[str] = None,
memory_mbytes: Optional[int] = None,
timeout_secs: Optional[int] = None,
) -> ApifyDatasetLoader:
"""Run a saved Actor task on Apify and wait for results to be ready.
Args:
task_id (str): The ID or name of the task on the Apify platform.
task_input (Dict): The input object of the task that you're trying to run.
Overrides the task's saved input.
dataset_mapping_function (Callable): A function that takes a single
dictionary (an Apify dataset item) and converts it to an
instance of the Document class.
build (str, optional): Optionally specifies the actor build to run.
It can be either a build tag or build number.
memory_mbytes (int, optional): Optional memory limit for the run,
in megabytes.
timeout_secs (int, optional): Optional timeout for the run, in seconds.
Returns:
ApifyDatasetLoader: A loader that will fetch the records from the
task run's default dataset.
"""
task_call = await self.apify_client_async.task(task_id).call(
task_input=task_input,
build=build,
memory_mbytes=memory_mbytes,
timeout_secs=timeout_secs,
)
return ApifyDatasetLoader(
dataset_id=task_call["defaultDatasetId"],
dataset_mapping_function=dataset_mapping_function,
)

18
libs/langchain/langchain/utilities/promptguard.py → libs/langchain/langchain/utilities/opaqueprompts.py
View File

@ -31,16 +31,16 @@ def sanitize(
The `secure_context` needs to be passed to the `desanitize` function.
"""
try:
import promptguard as pg
import opaqueprompts as op
except ImportError:
raise ImportError(
"Could not import the `promptguard` Python package, "
"please install it with `pip install promptguard`."
"Could not import the `opaqueprompts` Python package, "
"please install it with `pip install opaqueprompts`."
)
if isinstance(input, str):
# the input could be a string, so we sanitize the string
sanitize_response: pg.SanitizeResponse = pg.sanitize([input])
sanitize_response: op.SanitizeResponse = op.sanitize([input])
return {
"sanitized_input": sanitize_response.sanitized_texts[0],
"secure_context": sanitize_response.secure_context,
@ -55,7 +55,7 @@ def sanitize(
values.append(input[key])
# sanitize the values
sanitize_values_response: pg.SanitizeResponse = pg.sanitize(values)
sanitize_values_response: op.SanitizeResponse = op.sanitize(values)
# reconstruct the dict with the sanitized values
sanitized_input_values = sanitize_values_response.sanitized_texts
@ -85,13 +85,13 @@ def desanitize(sanitized_text: str, secure_context: bytes) -> str:
De-sanitized text.
"""
try:
import promptguard as pg
import opaqueprompts as op
except ImportError:
raise ImportError(
"Could not import the `promptguard` Python package, "
"please install it with `pip install promptguard`."
"Could not import the `opaqueprompts` Python package, "
"please install it with `pip install opaqueprompts`."
)
desanitize_response: pg.DesanitizeResponse = pg.desanitize(
desanitize_response: op.DesanitizeResponse = op.desanitize(
sanitized_text, secure_context
)
return desanitize_response.desanitized_text

2
libs/langchain/pyproject.toml
View File

@ -1,6 +1,6 @@
[tool.poetry]
name = "langchain"
version = "0.0.277"
version = "0.0.278"
description = "Building applications with LLMs through composability"
authors = []
license = "MIT"

27
libs/langchain/scripts/check_pydantic.sh Executable file
View File

@ -0,0 +1,27 @@
#!/bin/bash
#
# This script searches for lines starting with "import pydantic" or "from pydantic"
# in tracked files within a Git repository.
#
# Usage: ./scripts/check_pydantic.sh /path/to/repository
# Check if a path argument is provided
if [ $# -ne 1 ]; then
echo "Usage: $0 /path/to/repository"
exit 1
fi
repository_path="$1"
# Search for lines matching the pattern within the specified repository
result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic')
# Check if any matching lines were found
if [ -n "$result" ]; then
echo "ERROR: The following lines need to be updated:"
echo "$result"
echo "Please replace the code with an import from langchain.pydantic_v1."
echo "For example, replace 'from pydantic import BaseModel'"
echo "with 'from langchain.pydantic_v1 import BaseModel'"
exit 1
fi

14
libs/langchain/tests/integration_tests/llms/test_promptguard.py → libs/langchain/tests/integration_tests/llms/test_opaqueprompts.py
View File

@ -1,7 +1,7 @@
import langchain.utilities.promptguard as pgf
import langchain.utilities.opaqueprompts as op
from langchain import LLMChain, PromptTemplate
from langchain.llms import OpenAI
from langchain.llms.promptguard import PromptGuard
from langchain.llms.opaqueprompts import OpaquePrompts
from langchain.memory import ConversationBufferWindowMemory
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnableMap
@ -42,10 +42,10 @@ Question: ```{question}```
"""
def test_promptguard() -> None:
def test_opaqueprompts() -> None:
chain = LLMChain(
prompt=PromptTemplate.from_template(prompt_template),
llm=PromptGuard(llm=OpenAI()),
llm=OpaquePrompts(llm=OpenAI()),
memory=ConversationBufferWindowMemory(k=2),
)
@ -58,11 +58,11 @@ def test_promptguard() -> None:
assert isinstance(output, str)
def test_promptguard_functions() -> None:
def test_opaqueprompts_functions() -> None:
prompt = (PromptTemplate.from_template(prompt_template),)
llm = OpenAI()
pg_chain = (
pgf.sanitize
op.sanitize
| RunnableMap(
{
"response": (lambda x: x["sanitized_input"]) # type: ignore
@ -72,7 +72,7 @@ def test_promptguard_functions() -> None:
"secure_context": lambda x: x["secure_context"],
}
)
| (lambda x: pgf.desanitize(x["response"], x["secure_context"]))
| (lambda x: op.desanitize(x["response"], x["secure_context"]))
)
pg_chain.invoke(

52
libs/langchain/tests/unit_tests/output_parsers/test_json.py
View File

@ -67,6 +67,34 @@ JSON_WITH_MARKDOWN_CODE_BLOCK_AND_NEWLINES = """```json
}
```"""
JSON_WITH_UNESCAPED_QUOTES_IN_NESTED_JSON = """```json
{
"action": "Final Answer",
"action_input": "{"foo": "bar", "bar": "foo"}"
}
```"""
JSON_WITH_ESCAPED_QUOTES_IN_NESTED_JSON = """```json
{
"action": "Final Answer",
"action_input": "{\"foo\": \"bar\", \"bar\": \"foo\"}"
}
```"""
JSON_WITH_PYTHON_DICT = """```json
{
"action": "Final Answer",
"action_input": {"foo": "bar", "bar": "foo"}
}
```"""
JSON_WITH_ESCAPED_DOUBLE_QUOTES_IN_NESTED_JSON = """```json
{
"action": "Final Answer",
"action_input": "{\\"foo\\": \\"bar\\", \\"bar\\": \\"foo\\"}"
}
```"""
NO_TICKS = """{
"foo": "bar"
}"""
@ -131,3 +159,27 @@ def test_parse_json_with_code_blocks() -> None:
"action": "Final Answer",
"action_input": '```bar\n<div id="1" class="value">\n\ttext\n</div>```',
}
TEST_CASES_ESCAPED_QUOTES = [
JSON_WITH_UNESCAPED_QUOTES_IN_NESTED_JSON,
JSON_WITH_ESCAPED_QUOTES_IN_NESTED_JSON,
JSON_WITH_ESCAPED_DOUBLE_QUOTES_IN_NESTED_JSON,
]
@pytest.mark.parametrize("json_string", TEST_CASES_ESCAPED_QUOTES)
def test_parse_nested_json_with_escaped_quotes(json_string: str) -> None:
parsed = parse_json_markdown(json_string)
assert parsed == {
"action": "Final Answer",
"action_input": '{"foo": "bar", "bar": "foo"}',
}
def test_parse_json_with_python_dict() -> None:
parsed = parse_json_markdown(JSON_WITH_PYTHON_DICT)
assert parsed == {
"action": "Final Answer",
"action_input": {"foo": "bar", "bar": "foo"},
}