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PR_kwDOIPDwls5Uq_GY
| 7,178 |
Fix: Recognize `List` at `from_function`
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| false | null |
[] | null | 2 | 2023-07-05T07:22:22 | 2023-07-06T21:22:09 | 2023-07-06T21:22:09 |
CONTRIBUTOR
| null |
- Description: pydantic's `ModelField.type_` only exposes the native data type but not complex type hints like `List`. Thus, generating a Tool with `from_function` through function signature produces incorrect argument schemas (e.g., `str` instead of `List[str]`)
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: @hinthornw
- Twitter handle: `mapped`
All the unittest (with an additional one in this PR) passed, though I didn't try integration tests...
|
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I_kwDOIPDwls5qoPzH
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Issue: RetrievalQA response incomplete
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[] | null | 1 | 2023-07-05T07:19:18 | 2023-07-05T07:33:37 | 2023-07-05T07:33:37 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
## Problem descriptions
I've used `retrievalQA.from_chain_type()` with `refine` type to design a chatPDF.
But the response often **incomplete**, see the following result, the `Answer` is not complete which will let the `json.loads` not work.
Futhermore, I've used `get_openai_callback` to check if the token exceeds the limit.
Howerver, the callback show the total token is 3432 which didn't exceeds the limit.
## Questions
1. Why the response incomplete?
2. How can I let the response complete then I can do `json.loads`
## Code
```python
from datetime import datetime
from typing import List
import langchain
from langchain.callbacks import get_openai_callback
from langchain.chains import RetrievalQA
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.output_parsers import PydanticOutputParser
from langchain.prompts import PromptTemplate
from langchain.vectorstores import Chroma
from langchain.document_loaders import PyMuPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from pydantic import BaseModel
docs = PyMuPDFLoader('file.pdf').load()
splitter = RecursiveCharacterTextSplitter(docs, chunk_size=1000, chunk_overlap=300)
docs = splitter.split_documents(docs)
class Specification(BaseModel):
product_name: str
manufactured_date: datetime
size_inch: str
resolution: str
contrast: str
operation_temperature: str
power_supply: str
sunlight_readable: bool
antiglare: bool
low_power_consumption: bool
high_brightness: bool
wide_temperature: bool
fast_response: bool
screen_features: List[str]
parser = PydanticOutputParser(pydantic_object=Specification)
prompt_template = """
Use the following pieces of context to answer the question, if you don't know the answer, leave it blank('') don't try to make up an answer.
{context_str}
Question: {question}
{format_instructions}
"""
prompt = PromptTemplate(
template=prompt_template,
input_variables=['context_str', 'question'],
partial_variables={'format_instructions': parser.get_format_instructions()}
)
chain_type_kwargs = {
'question_prompt': prompt,
'verbose': True
}
llm = OpenAI(temperature=0)
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(
documents=docs,
embedding= embeddings
)
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type = 'refine',
retriever= db.as_retriever(),
chain_type_kwargs=chain_type_kwargs
)
query = f"""What's the display specifications?"""
with get_openai_callback() as cb:
res = qa_chain.run(query)
print(cb, '\n')
print(res)
```
## Process of Chain
```
[1m> Entering new chain...[0m
[1m> Entering new chain...[0m
Prompt after formatting:
[32;1m[1;3m
Use the following pieces of context to answer the question, if you don't know the answer, leave it blank('') don't try to make up an answer.
Dimensions (W × H × D)
Touch screen: 7.0" × 5.1" × 0.75" (178 × 130 × 19 mm)
Dock: 4.2" × 2.3" × 3.1" (106 × 57 × 78 mm)
Weight
Touch screen: 0.7 lbs. (0.32 kg)
Dock: 0.8 lbs (0.36 kg)
Planning the installation
• Make sure the dock can be located near a power outlet and a strong WiFi signal.
• For the most reliable connection, we recommend running Ethernet to the dock.
• Make sure the touch screen's WiFi or Ethernet connection is on the same network as your controller and that the signal is strong.
• Communication agent is required for intercom use.
• Charge the touch screen for at least six hours before use.
For more product information
Visit ctrl4.co/t3series
Control4® T3-7 7" Tabletop Touch Screen
DOC-00148-C
2015-10-09 MSC
Copyright ©2015, Control4 Corporation. All rights reserved. Control4, the Control4 logo, the 4-ball logo, 4Store,
4Sight, Control My Home, Everyday Easy, and Mockupancy are registered trademarks or trademarks of Control4
Question: What's the display specifications?
The output should be formatted as a JSON instance that conforms to the JSON schema below.
As an example, for the schema {"properties": {"foo": {"title": "Foo", "description": "a list of strings", "type": "array", "items": {"type": "string"}}}, "required": ["foo"]}}
the object {"foo": ["bar", "baz"]} is a well-formatted instance of the schema. The object {"properties": {"foo": ["bar", "baz"]}} is not well-formatted.
Here is the output schema:
{"properties": {"product_name": {"title": "Product Name", "type": "string"}, "manufactured_date": {"title": "Manufactured Date", "type": "string", "format": "date-time"}, "size_inch": {"title": "Size Inch", "type": "string"}, "resolution": {"title": "Resolution", "type": "string"}, "contrast": {"title": "Contrast", "type": "string"}, "operation_temperature": {"title": "Operation Temperature", "type": "string"}, "power_supply": {"title": "Power Supply", "type": "string"}, "sunlight_readable": {"title": "Sunlight Readable", "type": "boolean"}, "antiglare": {"title": "Antiglare", "type": "boolean"}, "low_power_consumption": {"title": "Low Power Consumption", "type": "boolean"}, "high_brightness": {"title": "High Brightness", "type": "boolean"}, "wide_temperature": {"title": "Wide Temperature", "type": "boolean"}, "fast_response": {"title": "Fast Response", "type": "boolean"}, "screen_features": {"title": "Screen Features", "type": "array", "items": {"type": "string"}}}, "required": ["product_name", "manufactured_date", "size_inch", "resolution", "contrast", "operation_temperature", "power_supply", "sunlight_readable", "antiglare", "low_power_consumption", "high_brightness", "wide_temperature", "fast_response", "screen_features"]}
[0m
[1m> Finished chain.[0m
[1m> Entering new chain...[0m
Prompt after formatting:
[32;1m[1;3mThe original question is as follows: What's the display specifications?
We have provided an existing answer:
Answer: {
"product_name": "Control4® T3-7 7\" Tabletop Touch Screen",
"manufactured_date": "2015-10-09",
"size_inch": "7.0\" × 5.1\" × 0.75\"",
"resolution": "N/A",
"contrast": "N/A",
"operation_temperature": "N/A",
"power_supply": "N/A",
"sunlight_readable": "N/A",
"antiglare": "N/A",
"low_power_consumption": "N/A",
"high_brightness": "N/A",
"wide_temperature": "N/A",
"fast_response": "N/A",
"screen_features": []
}
We have the opportunity to refine the existing answer(only if needed) with some more context below.
------------
Model numbers
C4-TT7-1-BL, C4-TT7-1-WH, C4-TT7-1-RD
Features
Screen
Resolution: 1280 × 800
Capacitive touch
Camera: 720p
Network
Ethernet or WiFi (802.11 g/n [2.4 GHz])
Notes:
(1) 802.11b is not supported for Video Intercom.
(2) Wireless-N is recommended for Video Intercom. The more devices that Video Intercom is broadcast to, the
more response time and images are degraded.
Battery
3100 mAh Li-ion
Power supply
PoE (IEEE802.3af)
100VAC ~ 240VAC, 50-60 Hz
International power supply adapters included
Dock connections
•
Ethernet
•
PoE
•
DC power
Mounting
Tabletop or portable
Environmental
Operating temperature
32 ~ 104˚F (0˚ ~ 40˚C)
Storage temperature
32 ~ 104˚F (0˚ ~ 40˚C)
Dimensions (W × H × D)
Touch screen: 7.0" × 5.1" × 0.75" (178 × 130 × 19 mm)
Dock: 4.2" × 2.3" × 3.1" (106 × 57 × 78 mm)
Weight
Touch screen: 0.7 lbs. (0.32 kg)
Dock: 0.8 lbs (0.36 kg)
Planning the installation
• Make sure the dock can be located near a power outlet and a strong WiFi signal.
------------
Given the new context, refine the original answer to better answer the question. If the context isn't useful, return the original answer.[0m
[1m> Finished chain.[0m
[1m> Entering new chain...[0m
Prompt after formatting:
[32;1m[1;3mThe original question is as follows: What's the display specifications?
We have provided an existing answer:
Answer: {
"product_name": "Control4® T3-7 7\" Tabletop Touch Screen",
"manufactured_date": "2015-10-09",
"model_numbers": ["C4-TT7-1-BL", "C4-TT7-1-WH", "C4-TT7-1-RD"],
"size_inch": "7.0\" × 5.1\" × 0.75\"",
"resolution": "1280 × 800",
"contrast": "N/A",
"operation_temperature": "32 ~ 104˚F (0˚ ~ 40˚C)",
"storage_temperature": "32 ~ 104˚F (0˚ ~ 40˚C)",
"power_supply": "PoE (IEEE802.3af) 100VAC ~ 240VAC, 50-60 Hz",
"sunlight_readable": "N/A",
"antiglare": "N/A",
"low_power_consumption": "N/A",
"high_bright
We have the opportunity to refine the existing answer(only if needed) with some more context below.
------------
Control4® T3-7 7" Tabletop Touch Screen
DOC-00148-C
2015-10-09 MSC
Copyright ©2015, Control4 Corporation. All rights reserved. Control4, the Control4 logo, the 4-ball logo, 4Store,
4Sight, Control My Home, Everyday Easy, and Mockupancy are registered trademarks or trademarks of Control4
Corporation in the United States and/or other countries. All other names and brands may be claimed as the
property of their respective owners. All specifications subject to change without notice.
------------
Given the new context, refine the original answer to better answer the question. If the context isn't useful, return the original answer.[0m
[1m> Finished chain.[0m
[1m> Entering new chain...[0m
Prompt after formatting:
[32;1m[1;3mThe original question is as follows: What's the display specifications?
We have provided an existing answer:
Answer: {
"product_name": "Control4® T3-7 7\" Tabletop Touch Screen",
"manufactured_date": "2015-10-09",
"model_numbers": ["C4-TT7-1-BL", "C4-TT7-1-WH", "C4-TT7-1-RD"],
"size_inch": "7.0\" × 5.1\" × 0.75\"",
"resolution": "1280 × 800",
"contrast": "N/A",
"operation_temperature": "32 ~ 104˚F (0˚ ~ 40˚C)",
"storage_temperature": "32 ~ 104˚F (0˚ ~ 40˚C)",
"power_supply": "PoE (IEEE802.3af) 100VAC ~ 240VAC, 50-60 Hz",
"sunlight_readable": "N/A",
"antiglare": "N/A",
"low_power_consumption": "N/A",
"high_bright
We have the opportunity to refine the existing answer(only if needed) with some more context below.
------------
Control4® T3 Series 7" Tabletop Touch Screen
The Control4® T3 Series Tabletop Touch Screen delivers always-on, dedicated, and mobile control over all the technology in your home
or business. Featuring a gorgeous new tablet design and stunning high-resolution graphics, this portable screen looks beautiful whether
on a kitchen countertop or in the theater on your lap. This model includes HD video intercom and crystal-clear audio intercom for
convenient communications from room to room or with visitors at the door.
• Available in a 7" model, the T3 Series Portable Touch Screen provides dedicated, elegant, and mobile control of your home.
• HD camera, combined with speakers and microphone, provides the best video intercom experience yet.
• Crisp picture with two and a half times the resolution of previous models.
• Extremely fast and responsive—up to 16 times faster than our previous touch screens.
------------
Given the new context, refine the original answer to better answer the question. If the context isn't useful, return the original answer.[0m
[1m> Finished chain.[0m
[1m> Finished chain.[0m
Tokens Used: 3432
Prompt Tokens: 2465
Completion Tokens: 967
Successful Requests: 4
Total Cost (USD): $0.06864
```
## Result
```
Tokens Used: 3432
Prompt Tokens: 2465
Completion Tokens: 967
Successful Requests: 4
Total Cost (USD): $0.06864
Answer: {
"product_name": "Control4® T3 Series 7\" Tabletop Touch Screen",
"manufactured_date": "2015-10-09",
"model_numbers": ["C4-TT7-1-BL", "C4-TT7-1-WH", "C4-TT7-1-RD"],
"size_inch": "7.0\" × 5.1\" × 0.75\"",
"resolution": "1280 × 800",
"contrast": "N/A",
"operation_temperature": "32 ~ 104˚F (0˚ ~ 40˚C)",
"storage_temperature": "32 ~ 104˚F (0˚ ~ 40˚C)",
"power_supply": "PoE (IEEE802.3af) 100VAC ~ 240VAC, 50-60 Hz",
"sunlight_readable": "N/A",
"antiglare": "N/A",
"low_power_consumption": "N/A",
"high_brightness
```
### Suggestion:
_No response_
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fix: rename the invalid function name of GoogleSerperResults Tool for OpenAIFunctionCall
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[] | null | 1 | 2023-07-05T06:57:23 | 2023-07-05T16:47:51 | 2023-07-05T16:47:50 |
CONTRIBUTOR
| null |
- Description: rename the invalid function name of GoogleSerperResults Tool for OpenAIFunctionCall
- Tag maintainer: @hinthornw
When I use the GoogleSerperResults in OpenAIFunctionCall agent, the following error occurs:
```shell
openai.error.InvalidRequestError: 'Google Serrper Results JSON' does not match '^[a-zA-Z0-9_-]{1,64}$' - 'functions.0.name'
```
So I rename the GoogleSerperResults's property "name" from "Google Serrper Results JSON" to "google_serrper_results_json" just like GoogleSerperRun's name: "google_serper", and it works.
I guess this should be reasonable.
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saving and loading embedding from Chroma
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[] | null | 8 | 2023-07-05T06:52:10 | 2023-11-29T16:09:05 | 2023-11-29T16:09:04 |
NONE
| null |
### Issue with current documentation:
```
# import
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders import TextLoader
# load the document and split it into chunks
loader = TextLoader("../../../state_of_the_union.txt")
documents = loader.load()
# split it into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
# create the open-source embedding function
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
# load it into Chroma
db = Chroma.from_documents(docs, embedding_function)
# query it
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
# print results
print(docs[0].page_content)
```
```
# save to disk
db2 = Chroma.from_documents(docs, embedding_function, persist_directory="./chroma_db")
db2.persist()
docs = db.similarity_search(query)
# load from disk
db3 = Chroma(persist_directory="./chroma_db")
docs = db.similarity_search(query)
print(docs[0].page_content)
```
### Idea or request for content:
In above code, I find it difficult to understand this paragraph:
```
# save to disk
db2 = Chroma.from_documents(docs, embedding_function, persist_directory="./chroma_db")
db2.persist()
docs = db.similarity_search(query)
# load from disk
db3 = Chroma(persist_directory="./chroma_db")
docs = db.similarity_search(query)
print(docs[0].page_content)
```
Although `db2 `and `db3 `do demonstrate the saving and loading of Chroma,
But Two pieces of code( `docs = db.similarity_search(query) `) have nothing to do with saving and loading,
and it still searches for answers from the `db`
Is this an error?
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PR_kwDOIPDwls5Uqh61
| 7,174 |
Fixed typos in MongoDB Atlas Vector Search documentation
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[] | null | 1 | 2023-07-05T05:41:36 | 2023-07-05T16:48:02 | 2023-07-05T16:48:01 |
CONTRIBUTOR
| null |
Fix for typos in MongoDB Atlas Vector Search documentation
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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PR_kwDOIPDwls5UqeTD
| 7,173 |
docstrings top level update
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COLLABORATOR
| null |
Updated docstrings so, that [API Reference](https://api.python.langchain.com/en/latest/api_reference.html) page has text in the second column (class/function/... description.
@baskaryan
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I_kwDOIPDwls5qnr1S
| 7,172 |
StreamlitCallbackHandler doesn't double escape dollar signs, so two dollar signs makes everything between an equation
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[] | null | 1 | 2023-07-05T05:23:14 | 2023-10-12T16:06:56 | 2023-10-12T16:06:55 |
NONE
| null |
### System Info
langchain = 0.0.220
python = 3.11.4
### Who can help?
@hwchase17 @agola11
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Open the chat with search langchain/streamlit app[here](https://llm-examples.streamlit.app/Chat_with_search) and go to the "Chat with search" page
2. Ask "current meta and tesla stock price?" in the chat
3. You should see in the response the formatting get messed up because it interprets two dollar signs as a latex equation. Normally I use a function to escape the dollar signs but you may want to do this on your callback.
### Expected behavior
Expect text instead of latex equation. I've attached an example I used with escapes.
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PR_kwDOIPDwls5UqU3k
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Update JSON parser to work w/ multiple JSON objects in markdown
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[] | null | 3 | 2023-07-05T04:38:10 | 2023-08-10T23:00:05 | 2023-08-10T23:00:05 |
COLLABORATOR
| null |
JSON output parser did not work on cases w/ multiple JSON objects.
Schema -
```
response_schemas = [
ResponseSchema(name="question", description="question derived from the text"),
ResponseSchema(name="answer", description="answer to the question derived from the text"),
ResponseSchema(name="index", description="approximate location in the text (charecter count) that the question is drawn from")
]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
```
Answer -
```
'\n```json\n{\n\t"question": "What is a black hole from a theoretical perspective?", \n\t"answer": "A black hole is defined theoretically as a region of space-time from which light can never escape, therefore it\'s black.",\n\t"index": "1230" \n}\n```\n\n```json\n{\n\t"question": "What is light?", \n\t"answer": "Light is the stuff that comes out of the sun, that stuff that goes into your eyes. Light is one of the stuff that disappears when the lights go off. This is stuff that appears when the lights come on.",\n\t"index": "1350"\n}\n```\n\n```json \n{\n\t"question": "What is the holographic principle?",\n\t"answer": "The holographic principle says that all the information that is in some volume of space-time can be stored on the boundary of that region.",\n\t"index": "11000"\n} \n```\n\n```json\n{\n\t"question": "What is a photon ring?",\n\t"answer": "A photon ring are the photons that orbit around a black hole.", \n\t'
```
Result we new see -
```
output_parser.parse(answer)
[{'question': 'What is a black hole from a theoretical perspective?',
'answer': "A black hole is defined theoretically as a region of space-time from which light can never escape, therefore it's black.",
'index': '1230'},
{'question': 'What is light?',
'answer': 'Light is the stuff that comes out of the sun, that stuff that goes into your eyes. Light is one of the stuff that disappears when the lights go off. This is stuff that appears when the lights come on.',
'index': '1350'},
{'question': 'What is the holographic principle?',
'answer': 'The holographic principle says that all the information that is in some volume of space-time can be stored on the boundary of that region.',
'index': '11000'}]
```
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| 1,788,710,081 |
PR_kwDOIPDwls5UqNI3
| 7,170 |
Implement delete interface of vector store on AnalyticDB
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CONTRIBUTOR
| null |
Hi, there
This pull request contains two commit:
**1. Implement delete interface with optional ids parameter on AnalyticDB.**
**2. Allow customization of database connection behavior by exposing engine_args parameter in interfaces.**
- This commit adds the `engine_args` parameter to the interfaces, allowing users to customize the behavior of the database connection. The `engine_args` parameter accepts a dictionary of additional arguments that will be passed to the create_engine function. Users can now modify various aspects of the database connection, such as connection pool size and recycle time. This enhancement provides more flexibility and control to users when interacting with the database through the exposed interfaces.
This commit is related to VectorStores @rlancemartin @eyurtsev
Thank you for your attention and consideration.
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PR_kwDOIPDwls5UqJTo
| 7,169 |
fix: Chroma filter symbols not supporting LIKE and CONTAIN
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[] | null | 2 | 2023-07-05T03:22:00 | 2023-10-16T17:24:37 | 2023-07-05T18:04:18 |
CONTRIBUTOR
| null |
Fixing issue with SelfQueryRetriever due to unsupported LIKE and CONTAIN comparators in Chroma's WHERE filter statements. This pull request introduces a redefined set of comparators in Chroma to address the problem and make it compatible with SelfQueryRetriever. For information on the comparators supported by Chroma's filter, please refer to https://docs.trychroma.com/usage-guide#using-where-filters.
<img width="495" alt="image" src="https://github.com/hwchase17/langchain/assets/22267652/34789191-0293-4f63-9bdf-ad1e1f2567c4">
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I_kwDOIPDwls5qnOny
| 7,168 |
RetrievalQA.from_chain_type‘s parameter retriever can not use ContextualCompressionRetriever
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[] | null | 7 | 2023-07-05T02:43:25 | 2023-10-16T09:45:05 | 2023-10-16T09:44:56 |
NONE
| null |
### System Info
Based on the official doc, I created two type of retriever:
1. `faiss_retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever()`serving as a`VectorStoreRetriever`(referenced from the API doc)
2. `compression_retriever = ContextualCompressionRetriever(base_compressor=relevant_filter, base_retriever=retriever)`functioning as a`ContextualCompressionRetriever` （also referenced from the API doc）
Then I ran the RetrievalQA to get relative content by Chain by code below:
```python
qa = RetrievalQA.from_chain_type(llm=OpenAI( verbose=True), chain_type="stuff", retriever=compression_retriever,return_source_documents=True, chain_type_kwargs=chain_type_kwargs)
# or
qa = RetrievalQA.from_chain_type(llm=OpenAI( verbose=True), chain_type="stuff", retriever=faiss_retriever,return_source_documents=True, chain_type_kwargs=chain_type_kwargs)
```
The result was that the qa with compression_retriever failed to return context for the prompt(return with empty array), whereas the qa with faiss_retriever successfully returned the context.
### Who can help?
@hwchase17 @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
loader = UnstructuredFileLoader("NER.txt")
document = loader.load()
separators = ["。", " "]
text_splitter = RecursiveCharacterTextSplitter(separators=separators, chunk_size=500, chunk_overlap=0)
texts = text_splitter.split_documents(document)
embeddings = OpenAIEmbeddings()
relevant_filter = EmbeddingsFilter(embeddings=embeddings, similarity_threshold=0.81)
retriever = FAISS.from_documents(texts, OpenAIEmbeddings()).as_retriever() # base retriever
compression_retriever = ContextualCompressionRetriever(base_compressor=relevant_filter, base_retriever=retriever) # document compression retriver
from langchain.prompts import PromptTemplate
prompt_template1 = """plase use context to answer question.
{context}
question: {question}
anwser:"""
PROMPT = PromptTemplate(
template=prompt_template1, input_variables=["context", "question"]
)
chain_type_kwargs = {"prompt": PROMPT,'verbose': True}
qa = RetrievalQA.from_chain_type(llm=OpenAI( verbose=True), chain_type="stuff", retriever=compression_retriever,return_source_documents=True, chain_type_kwargs=chain_type_kwargs)
query = "balabalabala" # replace it with question
result = qa({"query": query})
print(result)```
### Expected behavior
While using `ContextualCompressionRetriever` for `RetrievalQA` could output a not-null context., aka not [].
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PR_kwDOIPDwls5Up6xl
| 7,167 |
fix AmazonAPIGateway _identifying_params
|
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[] | null | 1 | 2023-07-05T01:30:37 | 2023-07-05T03:14:52 | 2023-07-05T03:14:51 |
CONTRIBUTOR
| null |
- correct `endpoint_name` to `api_url`
- add `headers`
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5qnAyl
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DOC: The Quickstart tutorial for Agents has an error
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[] | null | 3 | 2023-07-05T01:11:06 | 2023-10-12T16:07:06 | 2023-10-12T16:07:05 |
NONE
| null |
### Issue with current documentation:
https://python.langchain.com/docs/get_started/quickstart.html
On the above link, I have tried following along on google colab and ran into an issue as following:
Observation: Olivia Wilde started dating Harry Styles after ending her years-long engagement to Jason Sudeikis — see their relationship timeline.
Thought:I have found information about Olivia Wilde's boyfriend. Now I need to calculate his current age raised to the 0.23 power.
Action:
```
{
"action": "Calculator",
"action_input": "age ^ 0.23"
}
```
As you can see, instead of getting information about Harry Styles' age, it just puts in the string 'age' and raises value error.
This is quite weird seeing the tutorial for agents work well:
https://python.langchain.com/docs/modules/agents.html
What could be the issue?
### Idea or request for content:
_No response_
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PR_kwDOIPDwls5UpsXT
| 7,165 |
Support for SPARQL
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[] | null | 4 | 2023-07-04T23:18:13 | 2023-07-31T09:44:13 | 2023-07-05T17:00:17 |
CONTRIBUTOR
| null |
# [SPARQL](https://www.w3.org/TR/rdf-sparql-query/) for [LangChain](https://github.com/hwchase17/langchain)
## Description
LangChain support for knowledge graphs relying on W3C standards using RDFlib: SPARQL/ RDF(S)/ OWL with special focus on RDF \
* Works with local files, files from the web, and SPARQL endpoints
* Supports both SELECT and UPDATE queries
* Includes both a Jupyter notebook with an example and integration tests
## Contribution compared to related PRs and discussions
* [Wikibase agent](https://github.com/hwchase17/langchain/pull/2690) - uses SPARQL, but specifically for wikibase querying
* [Cypher qa](https://github.com/hwchase17/langchain/pull/5078) - graph DB question answering for Neo4J via Cypher
* [PR 6050](https://github.com/hwchase17/langchain/pull/6050) - tries something similar, but does not cover UPDATE queries and supports only RDF
* Discussions on [w3c mailing list](mailto:[email protected]) related to the combination of LLMs (specifically ChatGPT) and knowledge graphs
## Dependencies
* [RDFlib](https://github.com/RDFLib/rdflib)
## Tag maintainer
Graph database related to memory -> @hwchase17
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PR_kwDOIPDwls5UppGO
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LlamaCppEmbeddings not under langchain.llms
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[] | null | 1 | 2023-07-04T22:49:21 | 2023-07-04T23:32:41 | 2023-07-04T23:32:40 |
CONTRIBUTOR
| null |
Description: doc string suggests `from langchain.llms import LlamaCppEmbeddings` under `LlamaCpp()` class example but `LlamaCppEmbeddings` is not in `langchain.llms`
Issue: None open
Tag maintainer: @baskaryan
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I_kwDOIPDwls5qmswi
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OpenAIFunctionsAgent + ConversationalChatAgent?
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[] | null | 6 | 2023-07-04T22:28:54 | 2023-10-19T16:06:43 | 2023-10-19T16:06:42 |
CONTRIBUTOR
| null |
### Feature request
I'd like to make the user experience more conversational while supporting OpenAI functions. However, OpenAIFunctionsAgent implementation doesn't accept "memory" to make it more conversational. I'd like to have ReACT planning capability + functions as tools. I think the minimal implementation is to just add a memory to OpenAIFunctionsAgent.
### Motivation
While answering and executing tools is a great feature supported by OpenAIFunctionsAgent, more streamlined user experiences like chat are often desired as well.
### Your contribution
Happy to make a PR with a guideline if this is something desired in langchain.
|
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| 7,162 |
improve docstring of doc formatting
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[] | null | 1 | 2023-07-04T22:14:53 | 2023-07-04T23:31:30 | 2023-07-04T23:31:29 |
COLLABORATOR
| null |
so it shows up nice
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PR_kwDOIPDwls5UpjLZ
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Jinja2 validation changed to issue warnings rather than issuing exceptions.
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[] | null | 2 | 2023-07-04T21:53:13 | 2023-07-06T10:51:26 | 2023-07-05T18:04:29 |
CONTRIBUTOR
| null |
- Description: If their are missing or extra variables when validating Jinja 2 template then a warning is issued rather than raising an exception. This allows for better flexibility for the developer as described in #7044. Also changed the relevant test so pytest is checking for raised warnings rather than exceptions.
- Issue: #7044
- Tag maintainer: @hwchase17, @baskaryan
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add docstring for in memory class
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PR_kwDOIPDwls5UpgUq
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Replace stop clause with shorter, pythonic alternative
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[] | null | 1 | 2023-07-04T21:31:59 | 2023-07-05T17:03:23 | 2023-07-05T17:03:22 |
CONTRIBUTOR
| null |
Replace this comment with:
- Description: Replace `if var is not None:` with `if var:`, a concise and pythonic alternative
- Issue: N/A
- Dependencies: None
- Tag maintainer: Unsure
- Twitter handle: N/A
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I_kwDOIPDwls5qmA9t
| 7,157 |
SelfQuering Retrieval no support $contain operator
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[] | null | 2 | 2023-07-04T18:56:27 | 2023-10-12T16:07:11 | 2023-10-12T16:07:10 |
NONE
| null |
### System Info
I'm using the following on Windows:
Python 3.11.3
langchain 0.0.222
lark 1.1.5
With a Pinecone index:
Environment: us-east4-gcp
Metric: cosine
Pod Type: p1.x1
Dimensions: 1536
### Who can help?
@hwchase17 @angola
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.retrievers.self_query.base import SelfQueryRetriever
retriever = SelfQueryRetriever.from_llm(llm, vectorstore, document_content_description, metadata_description, verbose=True)
embeddings = OpenAIEmbeddings()
vectorstore= Pinecone.from_existing_index(index_name="index1",embedding=embeddings,namespace="metamovies")
# This example specifies a query and composite filter
relevantdocs=retriever.get_relevant_documents(
"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated")
### Expected behavior
a list of selected Documents
I get a runtime error:
query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.CONTAIN: 'contain'>, attribute='genre', value='animated')]) limit=None
HTTP response body: {"code":3,"message":"$contain is not a valid operator","details":[]}
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PR_kwDOIPDwls5Uo-_3
| 7,155 |
bump 223
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[] | null | 1 | 2023-07-04T18:52:54 | 2023-07-04T20:13:42 | 2023-07-04T20:13:41 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5UowKj
| 7,154 |
Added ability to add extra fields to AzureSearch VectorStore when ad…
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NONE
| null |
- Description: Added ability to add extra fields to AzureSearch VectorStore when adding documents,
- Issue: https://github.com/hwchase17/langchain/issues/6134,
- Dependencies: None,
- Tag maintainer: @rlancemartin, @eyurtsev,
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PR_kwDOIPDwls5UoZlp
| 7,152 |
Update landing page for "question answering over documents"
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COLLABORATOR
| null |
Improve documentation for a central use-case, qa / chat over documents.
This will be merged as an update to `index.mdx` [here](https://python.langchain.com/docs/use_cases/question_answering/).
Testing w/ local Docusaurus server:
```
From `docs` directory:
mkdir _dist
cp -r {docs_skeleton,snippets} _dist
cp -r extras/* _dist/docs_skeleton/docs
cd _dist/docs_skeleton
yarn install
yarn start
```
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PR_kwDOIPDwls5UoZTR
| 7,151 |
Add a callback handler for Context (https://getcontext.ai)
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CONTRIBUTOR
| null |
### Description
Adding a callback handler for Context. Context is a product analytics platform for AI chat experiences to help you understand how users are interacting with your product.
I've added the callback library + an example notebook showing its use.
### Dependencies
Requires the user to install the `context-python` library. The library is lazily-loaded when the callback is instantiated.
### Announcing the feature
We spoke with Harrison a few weeks ago about also doing a blog post announcing our integration, so will coordinate this with him. Our Twitter handle for the company is @getcontextai, and the founders are @_agamble and @HenrySG.
Tagging @agola11 for callbacks.
Thanks in advance!
|
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| 1,788,162,432 |
I_kwDOIPDwls5qlTGA
| 7,150 |
Make "embedding" an optional parameter in VectorStore interface
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[] | null | 2 | 2023-07-04T15:47:14 | 2023-10-12T16:07:16 | 2023-10-12T16:07:15 |
CONTRIBUTOR
| null |
### Feature request
Some vectorstores (e.g. Vectara) internally create their own embeddings. The request is to generalize the VectorStore base class to allow for embeddings to be optional.
### Motivation
Currently users have to send "None" or FakeEmbeddings instead, which creates additional work and is not needed.
### Your contribution
Happy to help with a PR (with guidance from the main project team)
|
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PR_kwDOIPDwls5UoUMQ
| 7,149 |
docs: update Jina ecosystem
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[] | null | 1 | 2023-07-04T15:37:48 | 2023-07-04T22:07:51 | 2023-07-04T22:07:50 |
CONTRIBUTOR
| null |
Documentation update for [Jina ecosystem](https://python.langchain.com/docs/ecosystem/integrations/jina) and `langchain-serve` in the deployments section to latest features.
@hwchase17
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5qlPlL
| 7,148 |
Issue: The API call keeps getting timed out
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[] | null | 12 | 2023-07-04T15:35:27 | 2023-10-12T07:52:09 | null |
NONE
| null |
### Issue you'd like to raise.
I keep getting this error for the past couple of days for gpt-3.5-turbo-16k:
Timeout: Request timed out: HTTPSConnectionPool(host='api.openai.com', port=443): Read timed out. (read timeout=120.0)
The OpenAI API seems to be working fine by itself. Can someone please tell me if they are facing the same issue? Or any suggestions on how to resolve this?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5UoRkz
| 7,147 |
cleaned Arthur tracking demo notebook
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[] | null | 1 | 2023-07-04T15:28:40 | 2023-07-04T22:15:26 | 2023-07-04T22:15:25 |
CONTRIBUTOR
| null |
Cleaned title and reduced clutter for integration demo notebook for the Arthur callback handler
|
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I_kwDOIPDwls5qky98
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GPT4All generate() TypeError 'n_ctx' since a commit on GPT4All's python binding changed arguments
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[] | null | 7 | 2023-07-04T14:22:38 | 2023-11-07T14:10:16 | 2023-11-07T14:10:07 |
CONTRIBUTOR
| null |
### System Info
- langchain-0.0.222 (and all before)
- Any GPT4All python package after [this commit](https://github.com/nomic-ai/gpt4all/commit/46a0762bd5a7e605e9bd63e4f435b482eff026f6#diff-cc3ea7dfbfc9837a4c42dae1089a1eda0ed175d17f2628cf16c13d3cd9da6e13R174) was merged. So latest: >= 1.0.1.
Note this issue is in langchain caused by GPT4All's change. We need to alter the _default_params() return values to exclude many keys that were removed from GPT4All's function kwargs.
### Who can help?
@hwchase17 @agola11 👋
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Install the latest gpt4all package: `pip install --upgrade gpt4all`
2. Use the GPT4All LLM and LLMChain as normal (llm = GPT4All(model="ggml.gpt4all.xyz.bin"); chain = LLMChain(llm, prompt=any_prompt))
3. Run the chain: chain("prompt")
4. TypeError: generate() got an unexpected keyword argument 'n_ctx'
### Expected behavior
Should not cause TypeError. It should not pass n_ctx from default parameters to GPT4All's `generate()`
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PR_kwDOIPDwls5Unwfs
| 7,144 |
added missing req chromadb tiktoken
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NONE
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<!-- Thank you for contributing to LangChain!
Replace this comment with:
added missing req chromadb tiktoken
@baskaryan
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PR_kwDOIPDwls5Unu2-
| 7,143 |
PowerBI updates
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[] | null | 1 | 2023-07-04T13:54:17 | 2023-07-06T13:48:12 | 2023-07-06T13:39:23 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
Several updates for the PowerBI tools:
- Handle 0 records returned by requesting redo with different filtering
- Handle too large results by optionally tokenizing the result and comparing against a max (change in signature, non-breaking)
- Implemented LLMChain with Chat for chat models for the tools.
- Updates to the main prompt including tables
- Update to Tool prompt with TOPN function
- Split the tool prompt to allow the LLMChain with ChatPromptTemplate
Smaller fixes for stability.
For visibility: @hinthornw
|
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PR_kwDOIPDwls5Unpe-
| 7,142 |
feat(SingleStoreVectorStore): change connection attributes in the database connection
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[] | null | 1 | 2023-07-04T13:39:34 | 2023-07-06T08:05:47 | 2023-07-05T00:12:56 |
CONTRIBUTOR
| null |
Minor change to the SingleStoreVectorStore:
Updated connection attributes names according to the SingleStoreDB recommendations
@rlancemartin, @eyurtsev
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I_kwDOIPDwls5qkLDV
| 7,141 |
Caching: allows to cache only the prompt
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[] | null | 11 | 2023-07-04T12:46:56 | 2024-01-08T08:00:05 | null |
NONE
| null |
### Feature request
Hi there 👋
Thanks a lot for the awesome library. The current implementation of `BaseCache` stores the prompt + the llm generated text as key.
This means that I am not really caching since I'll have to do a request to OpenAI to get the llm text
### Motivation
I'd like to cache a prompt
### Your contribution
I am willing to contribute but you need to explain me how :)
|
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I_kwDOIPDwls5qj3pi
| 7,139 |
H2O Wave callback integration
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[] | null | 4 | 2023-07-04T11:56:42 | 2023-10-06T07:09:12 | null |
NONE
| null |
### Feature request
I would love to add an [H2O Wave](https://wave.h2o.ai/) framework callback integration in a similar manner as done for [Streamlit](https://python.langchain.com/docs/modules/callbacks/integrations/streamlit). Wave has recently added a dedicated chatbot card, which seems like a perfect fit.
### Motivation
This would allow for bringing more diversity when it comes to Python UI frameworks + Langchain integration. Moreover, Wave is async by nature, so seems like [custom async callback](https://python.langchain.com/docs/modules/callbacks/how_to/async_callbacks) or maybe an [async generator](https://github.com/hwchase17/langchain/blob/master/langchain/callbacks/streaming_aiter.py) would do.
### Your contribution
I am willing to provide the PR with everything that is needed.
|
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PR_kwDOIPDwls5UnCkh
| 7,138 |
Remove duplicate lines
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[] | null | 1 | 2023-07-04T11:49:34 | 2023-07-05T00:13:28 | 2023-07-05T00:13:27 |
CONTRIBUTOR
| null |
I believe these two lines are unnecessary, the variable `function_call` is already defined.
|
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I_kwDOIPDwls5qjji7
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Returns the standone alone question while using ConversationalRetrievalChain
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[] | null | 4 | 2023-07-04T11:02:11 | 2023-10-06T19:11:14 | null |
NONE
| null |
### Feature request
I am using the ConversationalRetrievalChain to retrieve answers for questions while condensing the chat history to a standalone question. However, the standalone question would show in the streaming output.
I expect only to return the final answer. Is there any way to achieve it?
### Motivation
The immediate is not necessary for the answer.
### Your contribution
I am not sure.
|
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I_kwDOIPDwls5qi-jE
| 7,135 |
"ConversationBufferMemory" object has no field "buffer"
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[] | null | 4 | 2023-07-04T09:36:49 | 2023-10-12T16:07:22 | 2023-10-12T16:07:21 |
NONE
| null |
### System Info
0.0.220
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output')
self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt
### Expected behavior
no error output
|
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PR_kwDOIPDwls5UmC0P
| 7,134 |
Added documentation for add_texts function for Pinecone integration
|
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[] | null | 3 | 2023-07-04T08:59:39 | 2023-07-06T00:25:45 | 2023-07-05T17:11:38 |
CONTRIBUTOR
| null |
- Description: added some documentation to the Pinecone vector store docs page.
- Issue: #7126
- Dependencies: None
- Tag maintainer: @baskaryan
I can add more documentation on the Pinecone integration functions as I am going to go in great depth into this area. Just wanted to check with the maintainers is if this is all good.
|
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PR_kwDOIPDwls5Ul4RJ
| 7,133 |
Fix steamship import error
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[] | null | 3 | 2023-07-04T08:30:42 | 2023-07-05T18:04:39 | 2023-07-05T18:04:38 |
CONTRIBUTOR
| null |
Description: Fix steamship import error
When running multi_modal_output_agent:
field "steamship" not yet prepared so type is still a ForwardRef, you might need to call SteamshipImageGenerationTool.update_forward_refs().
Tag maintainer: @hinthornw
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PR_kwDOIPDwls5Ulwyc
| 7,132 |
Add HugeGraphQAChain to support gremlin generating chain
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[] | null | 2 | 2023-07-04T08:09:52 | 2023-07-04T16:21:22 | 2023-07-04T16:21:22 |
CONTRIBUTOR
| null |
[Apache HugeGraph](https://github.com/apache/incubator-hugegraph) is a convenient, efficient, and adaptable graph database, compatible with the Apache TinkerPop3 framework and the Gremlin query language.
In this PR, the HugeGraph and HugeGraphQAChain provide the same functionality as the existing integration with Neo4j and enables query generation and question answering over HugeGraph database. The difference is that the graph query language supported by HugeGraph is not cypher but another very popular graph query language [Gremlin](https://tinkerpop.apache.org/gremlin.html).
A notebook example and a simple test case have also been added.
Tag maintainer: @hwchase17 @dev2049 thanks
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I_kwDOIPDwls5qiYEu
| 7,131 |
Issue: Can not import the Langchain modules.
|
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[] | null | 8 | 2023-07-04T08:07:28 | 2023-09-21T20:14:46 | 2023-07-27T18:46:41 |
NONE
| null |
### Issue you'd like to raise.
I installed "langchain==0.0.27" on a linux machine but i am getting the folowing error when i try to import langchain in a script. Was running this with python 3.7.
`/home/s0s06c3/lang/lang_env/bin/python /home/s0s06c3/lang/hugging_lanchain.py
Traceback (most recent call last):
File "/home/s0s06c3/lang/hugging_lanchain.py", line 2, in <module>
from langchain import PromptTemplate, HuggingFaceHub, LLMChain
File "/home/s0s06c3/lang/lang_env/lib/python3.7/site-packages/langchain/__init__.py", line 8, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/home/s0s06c3/lang/lang_env/lib/python3.7/site-packages/langchain/agents/__init__.py", line 2, in <module>
from langchain.agents.agent import Agent
File "/home/s0s06c3/lang/lang_env/lib/python3.7/site-packages/langchain/agents/agent.py", line 10, in <module>
from langchain.chains.base import Chain
File "/home/s0s06c3/lang/lang_env/lib/python3.7/site-packages/langchain/chains/__init__.py", line 2, in <module>
from langchain.chains.conversation.base import ConversationChain
File "/home/s0s06c3/lang/lang_env/lib/python3.7/site-packages/langchain/chains/conversation/base.py", line 7, in <module>
from langchain.chains.conversation.memory import ConversationBufferMemory
File "/home/s0s06c3/lang/lang_env/lib/python3.7/site-packages/langchain/chains/conversation/memory.py", line 7, in <module>
from langchain.chains.conversation.prompt import SUMMARY_PROMPT
File "/home/s0s06c3/lang/lang_env/lib/python3.7/site-packages/langchain/chains/conversation/prompt.py", line 2, in <module>
from langchain.prompts.prompt import PromptTemplate
File "/home/s0s06c3/lang/lang_env/lib/python3.7/site-packages/langchain/prompts/__init__.py", line 2, in <module>
from langchain.prompts.base import BasePromptTemplate
File "/home/s0s06c3/lang/lang_env/lib/python3.7/site-packages/langchain/prompts/base.py", line 35, in <module>
class BasePromptTemplate(BaseModel, ABC):
File "/home/s0s06c3/lang/lang_env/lib/python3.7/site-packages/langchain/prompts/base.py", line 41, in BasePromptTemplate
@root_validator()
File "/home/s0s06c3/lang/lang_env/lib/python3.7/site-packages/pydantic/deprecated/class_validators.py", line 231, in root_validator
code='root-validator-pre-skip',
pydantic.errors.PydanticUserError: If you use `@root_validator` with pre=False (the default) you MUST specify `skip_on_failure=True`. Note that `@root_validator` is deprecated and should be replaced with `@model_validator`.`
I tried setting this up on my local machine as well but there also got the same issue.
### Suggestion:
_No response_
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[] | null | 4 | 2023-07-04T07:50:43 | 2023-10-12T16:07:27 | 2023-10-12T16:07:26 |
NONE
| null |
### System Info
Retrying langchain.embeddings.openai.embed_with_retry.<locals>._embed_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-text-embedding-ada-002 in organization org-gvlyS3A1UcZNvf8Qch6TJZe3 on tokens per min. Limit: 150000 / min. Current: 1 / min. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method..
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import PyPDFLoader
import os
loader = PyPDFLoader("3gpp_cn/29502-i30.pdf")
pages = loader.load_and_split()
embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(pages, embeddings)
db.save_local("numpy_faiss_index")
### Expected behavior
How to solve it?
|
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I_kwDOIPDwls5qiQP7
| 7,129 |
DOC: The LLM type given in the Caching section is incorrect. It should be changed from ChatOpenAI to ChatGPT
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[] | null | 3 | 2023-07-04T07:48:45 | 2023-10-16T16:06:34 | 2023-10-16T16:06:33 |
NONE
| null |
### Issue with current documentation:
During the use of ChatOpenAI, it was found that even if langchain.llm_cache=True is set, the answer is different each time for the same question, such as "What is LangChain".
Upon tracing the source code, it was discovered that ChatOpenAI inherited BaseChatModel, which does not support the caching logic.
If the type of LLM switched from ChatOpenAI to ChatGPT, caching will be effective. Therefore, using ChatOpenAI in the LangChain Document example is incorrect, and should be replaced with ChatGPT. The corresponding address is: https://python.langchain.com/docs/modules/model_io/models/chat/how_to/chat_model_caching.
### Idea or request for content:
It is suggested to modify the example and replace ChatOpenAI with ChatGPT.
llm = ChatGPT()
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PR_kwDOIPDwls5UloCe
| 7,128 |
Improve Bing Search example
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CONTRIBUTOR
| null |
# Description
Improve Bing Search example:
https://python.langchain.com/docs/modules/agents/tools/integrations/bing_search
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I_kwDOIPDwls5qh8rI
| 7,126 |
DOC: Pinecone documentation missing features
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[] | null | 0 | 2023-07-04T06:58:58 | 2023-07-05T19:18:03 | 2023-07-05T19:18:03 |
CONTRIBUTOR
| null |
### Issue with current documentation:
I have been working on a side project using Pinecone and discovered that there are a bunch of useful Pinecone features in Langchain that are not mentioned in Langchain's documentation. [https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone)
### Idea or request for content:
I suggest adding to the documentation to mention the other functions of Langchain's Pinecone integration. A sentence or two about the use cases of each function could also be helpful too. :+1:
P.S. I would not mind implementing this change, would be a first good issue :)
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PR_kwDOIPDwls5UlXhG
| 7,125 |
feat: implement python repl tool arun
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[] | null | 1 | 2023-07-04T06:57:37 | 2023-07-05T00:15:50 | 2023-07-05T00:15:49 |
CONTRIBUTOR
| null |
Description: implement python repl tool arun
Tag maintainer: @agola11
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PR_kwDOIPDwls5UlMaZ
| 7,124 |
Wfh/evals docs reorg draft
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PR_kwDOIPDwls5UlMBI
| 7,123 |
Add String Distance and Embedding Evaluators
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[] | null | 1 | 2023-07-04T06:19:46 | 2023-07-08T04:44:33 | 2023-07-08T04:44:32 |
COLLABORATOR
| null |
Add a string evaluator and pairwise string evaluator implementation for:
- Embedding distance
- String distance
Also updates docs:
<img width="1106" alt="image" src="https://github.com/hwchase17/langchain/assets/13333726/0a1adbbc-f72a-46c9-8280-943cc1a00502">
<img width="1035" alt="image" src="https://github.com/hwchase17/langchain/assets/13333726/f963283b-b1e9-4f66-8970-e954247253cc">
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| 1,787,226,553 |
PR_kwDOIPDwls5UlLy8
| 7,122 |
Add Agent Trajectory Interface
|
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[] | null | 1 | 2023-07-04T06:18:43 | 2023-07-06T20:33:35 | 2023-07-06T20:33:34 |
COLLABORATOR
| null | null |
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| 1,787,208,532 |
I_kwDOIPDwls5qhqNU
| 7,121 |
ConversationalBufferMemory is not working with my Data
|
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[] | null | 1 | 2023-07-04T06:02:36 | 2023-10-12T16:07:37 | 2023-10-12T16:07:36 |
NONE
| null |
### Issue you'd like to raise.
Here is my code where i use Faiss for vectorestore but it is not capable to giving a response from my pdf data where i am doing wrong please correct me.
def chat_bot_langchain():
from langchain.llms import OpenAI
index = faiss.read_index("faisss_audien_new.index")
print(index)
with open("faisss_audien_new.pkl", "rb") as f:
store = pickle.load(f)
store.index = index
retriever = store.as_retriever(search_kwargs=dict(k=3))
memory = ConversationBufferMemory(retriever=retriever)
llm = OpenAI(temperature=0, max_tokens=1500, model_name='gpt-3.5-turbo') # Can be any valid LLM
_DEFAULT_TEMPLATE = """
You are having a conversation with a human your name is Kira and your age is 25 year old. Please interact naturally
and avoid mentioning that you are an AI language model.\n
You also a human being. provides lots of specific details from its context.\n
Relevant pieces of previous conversation:
{history}
(You do not need to use these pieces of information if not relevant)
Current conversation:
User: {input}
Kira AI:
"""
PROMPT = PromptTemplate(
input_variables=["history", "input"], template=_DEFAULT_TEMPLATE
)
conversation_with_summary = ConversationChain(
llm=llm,
prompt=PROMPT,
memory=memory,
verbose=True
)
while True:
user_input = input("> ")
ai_response = conversation_with_summary.predict(input=user_input)
print("\nAssistant:\n", ai_response, "\n")
# conversation_with_summary.predict(input="")
chat_bot_langchain()
### Suggestion:
_No response_
|
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| 1,787,206,864 |
PR_kwDOIPDwls5UlHqU
| 7,120 |
support adding custom metadata to runs
|
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[] | null | 1 | 2023-07-04T06:00:47 | 2023-07-05T18:11:40 | 2023-07-05T18:11:39 |
COLLABORATOR
| null |
- [x] wire up tools
- [x] wire up retrievers
- [x] add integration test
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
|
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https://api.github.com/repos/langchain-ai/langchain/issues/7119
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| 1,787,076,435 |
I_kwDOIPDwls5qhJ9T
| 7,119 |
Issue: from_documents error
|
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[] | null | 2 | 2023-07-04T03:25:31 | 2023-10-12T16:07:42 | 2023-10-12T16:07:41 |
NONE
| null |
### Issue you'd like to raise.
Getting AttributeError when using chroma.from_documents
my code: db = chroma.from_documents(texts, embeddings, persist_directory=persist_directory,
client_settings=CHROMA_SETTINGS)
AttributeError: module 'langchain.vectorstores.chroma' has no attribute 'from_documents
### Suggestion:
Please help resolve this error
|
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| 1,787,025,353 |
PR_kwDOIPDwls5Ukhix
| 7,118 |
in ElasticKnnSearch added back create_index, add_texts, from_texts
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[] | null | 4 | 2023-07-04T02:10:58 | 2023-07-24T14:25:44 | 2023-07-24T14:25:42 |
CONTRIBUTOR
| null |
Fixes:
https://github.com/hwchase17/langchain/issues/7117
https://github.com/hwchase17/langchain/issues/5760
Adding back `create_index` , `add_texts`, `from_texts` to ElasticKnnSearch
`from_texts` matches standard `from_texts` methods as quick start up method
`knn_search` and `hybrid_result` return a list of [`Document()`, `score`,]
# Test `from_texts` for quick start
```
# create new index using from_text
from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch
from langchain.embeddings import ElasticsearchEmbeddings
model_id = "sentence-transformers__all-distilroberta-v1"
dims = 768
es_cloud_id = ""
es_user = ""
es_password = ""
test_index = "knn_test_index_305"
embeddings = ElasticsearchEmbeddings.from_credentials(
model_id,
#input_field=input_field,
es_cloud_id=es_cloud_id,
es_user=es_user,
es_password=es_password,
)
# add texts and create class instance
texts = ["This is a test document", "This is another test document"]
knnvectorsearch = ElasticKnnSearch.from_texts(
texts=texts,
embedding=embeddings,
index_name= test_index,
vector_query_field='vector',
query_field='text',
model_id=model_id,
dims=dims,
es_cloud_id=es_cloud_id,
es_user=es_user,
es_password=es_password
)
# Test `add_texts` method
texts2 = ["Hello, world!", "Machine learning is fun.", "I love Python."]
knnvectorsearch.add_texts(texts2)
query = "Hello"
knn_result = knnvectorsearch.knn_search(query = query, model_id= model_id, k=2)
hybrid_result = knnvectorsearch.knn_hybrid_search(query = query, model_id= model_id, k=2)
```
The mapping is as follows:
```
{
"knn_test_index_012": {
"mappings": {
"properties": {
"text": {
"type": "text"
},
"vector": {
"type": "dense_vector",
"dims": 768,
"index": true,
"similarity": "dot_product"
}
}
}
}
}
```
# Check response type
```
>>> hybrid_result
[(Document(page_content='Hello, world!', metadata={}), 0.94232327), (Document(page_content='I love Python.', metadata={}), 0.5321523)]
>>> hybrid_result[0]
(Document(page_content='Hello, world!', metadata={}), 0.94232327)
>>> hybrid_result[0][0]
Document(page_content='Hello, world!', metadata={})
>>> type(hybrid_result[0][0])
<class 'langchain.schema.document.Document'>
```
# Test with existing Index
```
from langchain.vectorstores.elastic_vector_search import ElasticKnnSearch
from langchain.embeddings import ElasticsearchEmbeddings
## Initialize ElasticsearchEmbeddings
model_id = "sentence-transformers__all-distilroberta-v1"
dims = 768
es_cloud_id =
es_user = ""
es_password = ""
test_index = "knn_test_index_012"
embeddings = ElasticsearchEmbeddings.from_credentials(
model_id,
es_cloud_id=es_cloud_id,
es_user=es_user,
es_password=es_password,
)
## Initialize ElasticKnnSearch
knn_search = ElasticKnnSearch(
es_cloud_id=es_cloud_id,
es_user=es_user,
es_password=es_password,
index_name= test_index,
embedding= embeddings
)
## Test adding vectors
### Test `add_texts` method when index created
texts = ["Hello, world!", "Machine learning is fun.", "I love Python."]
knn_search.add_texts(texts)
```
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I_kwDOIPDwls5qg6gF
| 7,117 |
ElasticKnnSearch not creating mapping correctly
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[] | null | 1 | 2023-07-04T01:52:06 | 2023-07-28T05:00:22 | 2023-07-28T05:00:22 |
CONTRIBUTOR
| null |
### System Info
`from_texts` in `ElasticKnnSearch` is not creating a new index.
`add_texts` is not creating the correct mapping.
There were class instances of both these methods at one point, but they [got removed accidentally](https://github.com/hwchase17/langchain/pull/5569/commits/98f5038b1a6a6ee6f3108f95b27408ca23901724#) during some commit.
I will add them back to the `ElasticKnnSearch` with the correct mapping and functions
### Who can help?
@jeffvea
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Running
```
# Test `add_texts` method
texts = ["Hello, world!", "Machine learning is fun.", "I love Python."]
knn_search.add_texts(texts)
```
Incorrectly creates an index with dense_vector type and index:false
Running
```
# Test `from_texts` method
new_texts = ["This is a new text.", "Elasticsearch is powerful.", "Python is fun."]
knn_search.from_texts(new_texts, dims=768)
```
throws an error about not having a keyword arg for embeddings
### Expected behavior
Correctly throw an exception when index has not been previously created.
```
# Test `add_texts` method
texts = ["Hello, world!", "Machine learning is fun.", "I love Python."]
knn_search.add_texts(texts)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/runner/langchain-1/langchain/vectorstores/elastic_vector_search.py", line 621, in add_texts
raise Exception(f"The index '{self.index_name}' does not exist. If you want to create a new index while encoding texts, call 'from_texts' instead.")
Exception: The index 'knn_test_index_012' does not exist. If you want to create a new index while encoding texts, call 'from_texts' instead.
```
Correctly create new index
```
# Test `from_texts` method
new_texts = ["This is a new text.", "Elasticsearch is powerful.", "Python is fun."]
knn_search.from_texts(new_texts, dims=768)
```
The mapping is as follows:
```
{
"knn_test_index_012": {
"mappings": {
"properties": {
"text": {
"type": "text"
},
"vector": {
"type": "dense_vector",
"dims": 768,
"index": true,
"similarity": "dot_product"
}
}
}
}
}
```
Correctly index texts after index has been created
```
knn_search.add_texts(texts)
```
|
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I_kwDOIPDwls5qg3ce
| 7,116 |
The agent run output parser cause error when run a simple quick start
|
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[] | null | 1 | 2023-07-04T01:32:55 | 2023-07-04T02:38:26 | 2023-07-04T02:38:26 |
NONE
| null |
### System Info
Langchain: 0.0.222
OS: Windows 11+WSL Ubuntu22
I run a simple test according to the agent document guide page:
https://python.langchain.com/docs/modules/agents/
I just changed the input a little bit, it throw an error about the output parser.
The code is below:
=========================
llm = langchain.chat_models.ChatOpenAI(model_name="gpt-3.5-turbo-16k-0613", temperature=0)
tools = langchain.agents.load_tools(
["serpapi", "llm-math"],
llm=llm
)
agentexecutor = langchain.agents.initialize_agent(
tools=tools,
llm=llm,
agent=langchain.agents.AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION
)
results = agentexecutor.run("What is the number that the age of Donald Trump's wife in year of 2020 to power to 3?")
print(results)
=========================
The error is below:
=========================
[chain/error] [1:RunTypeEnum.chain:AgentExecutor] [40.48s] Chain run errored with error:
"OutputParserException('Could not parse LLM output: I now know the final answer.')"
=========================
The full log file attached here.
[agent_bug20230704.log.txt](https://github.com/hwchase17/langchain/files/11943427/agent_bug20230704.log.txt)
### Who can help?
Agent
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The code is below:
=========================
llm = langchain.chat_models.ChatOpenAI(model_name="gpt-3.5-turbo-16k-0613", temperature=0)
tools = langchain.agents.load_tools(
["serpapi", "llm-math"],
llm=llm
)
agentexecutor = langchain.agents.initialize_agent(
tools=tools,
llm=llm,
agent=langchain.agents.AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION
)
results = agentexecutor.run("What is the number that the age of Donald Trump's wife in year of 2020 to power to 3?")
print(results)
=========================
The error is below:
=========================
[chain/error] [1:RunTypeEnum.chain:AgentExecutor] [40.48s] Chain run errored with error:
"OutputParserException('Could not parse LLM output: I now know the final answer.')"
### Expected behavior
The result should be parsed properly.
|
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PR_kwDOIPDwls5UkVSc
| 7,115 |
Add better warnings when ref or inputs are passed but not used
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COLLABORATOR
| null |
If a string evaluator doesn't want references, it currently silently ignores any that are passed in. This PR proposes to warn once if they're provided. It does so via a mixin.
Other options we could do:
- Separate classes for criteria with references and comparisons with references - I don't love this because all the combinations lead to class explosion and the class names themselves are ugly (CriteriaWithReferencesNoLabels, etc.)
- Raise errors if they're provided - I don't like this because it requires more checks when running evaluators over a dataset, but would be more explicit.
- Decide at calling time -> could be a lot of partial prompt insertions that makes it more brittle. Would make a single "run" potentially have two results that are fundamentally different evaluations
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| 1,786,963,890 |
PR_kwDOIPDwls5UkUyX
| 7,114 |
Add base Chain docstrings
|
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PR_kwDOIPDwls5UkSLK
| 7,113 |
Add integration for MLflow AI Gateway
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[] | null | 6 | 2023-07-04T00:13:38 | 2023-07-19T14:41:23 | 2023-07-19T14:40:56 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
- Adds integration for MLflow AI Gateway (this will be shipped in MLflow 2.5 this week).
Manual testing:
```sh
# Move to mlflow repo
cd /path/to/mlflow
# install langchain
pip install git+https://github.com/harupy/langchain.git@gateway-integration
# launch gateway service
mlflow gateway start --config-path examples/gateway/openai/config.yaml
# Then, run the examples in this PR
```
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PR_kwDOIPDwls5UkR4b
| 7,112 |
Missing self in class function
|
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[] | null | 1 | 2023-07-04T00:10:55 | 2023-07-04T00:13:01 | 2023-07-04T00:12:52 |
CONTRIBUTOR
| null |
### Description:
Class method is missing self.
<!-- Thank you for contributing to LangChain!
### Maintainer:
@baskaryan
|
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| 1,786,941,209 |
PR_kwDOIPDwls5UkP_i
| 7,111 |
Warn if reference passed but evaluator doesn't require it
|
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PR_kwDOIPDwls5UkLWo
| 7,110 |
[Document fix] Fix an expired link qa_benchmarking_pg.ipynb
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[] | null | 2 | 2023-07-03T23:19:59 | 2023-07-04T01:03:17 | 2023-07-04T01:03:16 |
CONTRIBUTOR
| null |
## Change description
- Description: Fix an expired link that points to the readthedocs site.
- Dependencies: No
- Tag maintainer: @baskaryan
|
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PR_kwDOIPDwls5UkHFp
| 7,109 |
Update sdk version
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| 1,786,864,012 |
I_kwDOIPDwls5qgWGM
| 7,108 |
STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION Custom Tools Failure
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[] | null | 4 | 2023-07-03T22:07:55 | 2023-12-19T00:50:07 | 2023-12-19T00:50:07 |
NONE
| null |
### System Info
I have been using STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION with a custom tool that takes 2 inputs, however have not been able to get the agent to produce the outputs we had before. Specifically, we're getting the intermediate, custom function input as output.
Ie instead of getting a value associated with a query "What is the `purchase total` for customer 5432 on 07-03-2023?"
We are now getting
```
{
"action": "database_tool",
"action_input": {
"customer_id": "5432",
"local_date": "2023-07-03"
}
}
```
This did not occur before this weekend. Here's more code snippet in the below example
### Who can help?
@hwcha
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I also tried simplifying the agent call, etc to no avail:
```
llm = ChatOpenAI(
temperature=0,
openai_api_key=openai.api_key
)
memory = ConversationBufferWindowMemory(
memory_key='chat_history',
k=3,
return_messages=True,
input_key='input',
output_key="output"
)
@tool(return_direct=False)
def database_tool(customer_id, local_date) -> str:
"""Useful when questions are asked about specific customer_id details,
particularly recent ones.
If date is not provided, it will default to today's date in yyyy-mm-dd format.
Format your input using the following template.
```
{{
"action": "database_action",
"action_input": {{"customer_id": "<customer id>", "local_date": "<date in yyyy-mm-dd format>"}}
}}
```
"""
db_query = """<QUERY THAT WORKS>
""".format(customer_id=customer_id, local_date=local_date)
formatted_d = get_data.query_database(query=db_query)
return formatted_d
conversational_agent = initialize_agent(
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
tools=[database_tool],
llm=llm,
verbose=True,
early_stopping_method='generate',
memory=memory,
SystemAgentPromptTemplate=prompt_template+"\n The only tool available is the database tool.",
return_intermediate_steps=True,
return_source_documents=True,
handle_parsing_errors='Check your output and make sure there is an equal number of "{" and "}"'
)
response = conversational_agent("What is the `purchase total` for customer 5432 on 07-03-2023?")
print(response['output'])
```
### Expected behavior
"The purchase total for customer 5432 is 59.55"
I should note the following also works; it's just the agent integration that's problematic:
n = {
"customer_id": "5432",
"local_date": "2023-07-03"
}
database_tool(n)
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| 1,786,841,369 |
PR_kwDOIPDwls5Uj6jl
| 7,107 |
Accept no 'reasoning' response in qa evaluator
|
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[] | null | 1 | 2023-07-03T21:40:39 | 2023-07-03T21:47:18 | 2023-07-03T21:47:17 |
COLLABORATOR
| null |
Re add since #6995 inadvertently undid #7031
|
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| 7,105 |
Log errors
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[] | null | 1 | 2023-07-03T21:35:41 | 2023-07-03T21:47:33 | 2023-07-03T21:47:32 |
COLLABORATOR
| null |
Re-add change that was inadvertently undone in #6995
|
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PR_kwDOIPDwls5Uj31d
| 7,104 |
Base language model docstrings
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[] | null | 1 | 2023-07-03T21:25:30 | 2023-07-07T20:09:11 | 2023-07-07T20:09:10 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5Uj2fP
| 7,103 |
Feat: JSONL Document Loader
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[] | null | 4 | 2023-07-03T21:17:23 | 2023-07-04T23:44:52 | 2023-07-04T21:22:43 |
CONTRIBUTOR
| null |
- Description: Adding JSONL Document Loader just like TS port of Langchain
- Issue: #6973
@rlancemartin, @eyurtsev
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| 1,786,818,574 |
PR_kwDOIPDwls5Uj1rj
| 7,102 |
Minor updates to notebook for MultiQueryRetriever
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[] | null | 1 | 2023-07-03T21:13:14 | 2023-07-04T00:32:51 | 2023-07-04T00:32:50 |
COLLABORATOR
| null |
* Add an easier-to-run example.
* Add logging per https://github.com/hwchase17/langchain/pull/6891.
* Updated params per https://github.com/hwchase17/langchain/pull/5962.
|
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PR_kwDOIPDwls5Uj1ax
| 7,101 |
Load Run Evaluator
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[] | null | 1 | 2023-07-03T21:11:43 | 2023-07-08T02:58:01 | 2023-07-08T02:57:59 |
COLLABORATOR
| null |
Current problems:
1. Evaluating LLMs or Chat models isn't smooth. Even specifying 'generations' as the output inserts a redundant list into the eval template
2. Configuring input / prediction / reference keys in the `get_qa_evaluator` function is confusing. Unless you are using a chain with the default keys, you have to specify all the variables and need to reason about whether the key corresponds to the traced run's inputs, outputs or the examples inputs or outputs.
Proposal:
- Configure the run evaluator according to a model. Use the model type and input/output keys to assert compatibility where possible. Only need to specify a reference_key for certain evaluators (which is less confusing than specifying input keys)
When does this work:
- If you have your langchain model available (assumed always for run_on_dataset flow)
- If you are evaluating an LLM, Chat model, or chain
- If the LLM or chat models are traced by langchain (wouldn't work if you add an incompatible schema via the REST API)
When would this fail:
- Currently if you directly create an example from an LLM run, the outputs are generations with all the extra metadata present. A simple `example_key` and dumping all to the template could make the evaluations unreliable
- Doesn't help if you're not using the low level API
- If you want to instantiate the evaluator without instantiating your chain or LLM (maybe common for monitoring, for instance) -> could also load from run or run type though
What's ugly:
- Personally think it's better to load evaluators one by one since passing a config down is pretty confusing.
- Lots of testing needs to be added
- Inconsistent in that it makes a separate run and example input mapper instead of the original `RunEvaluatorInputMapper`, which maps a run and example to a single input.
Example usage running the for an LLM, Chat Model, and Agent.
```
# Test running for the string evaluators
evaluator_names = ["qa", "criteria"]
model = ChatOpenAI()
configured_evaluators = load_run_evaluators_for_model(evaluator_names, model=model, reference_key="answer")
run_on_dataset(ds_name, model, run_evaluators=configured_evaluators)
```
<details>
<summary>Full code with dataset upload</summary>
```
## Create dataset
from langchain.evaluation.run_evaluators.loading import load_run_evaluators_for_model
from langchain.evaluation import load_dataset
import pandas as pd
lcds = load_dataset("llm-math")
df = pd.DataFrame(lcds)
from uuid import uuid4
from langsmith import Client
client = Client()
ds_name = "llm-math - " + str(uuid4())[0:8]
ds = client.upload_dataframe(df, name=ds_name, input_keys=["question"], output_keys=["answer"])
## Define the models we'll test over
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from langchain.tools import tool
llm = OpenAI(temperature=0)
chat_model = ChatOpenAI(temperature=0)
@tool
def sum(a: float, b: float) -> float:
"""Add two numbers"""
return a + b
def construct_agent():
return initialize_agent(
llm=chat_model,
tools=[sum],
agent=AgentType.OPENAI_MULTI_FUNCTIONS,
)
agent = construct_agent()
# Test running for the string evaluators
evaluator_names = ["qa", "criteria"]
models = [llm, chat_model, agent]
run_evaluators = []
for model in models:
run_evaluators.append(load_run_evaluators_for_model(evaluator_names, model=model, reference_key="answer"))
# Run on LLM, Chat Model, and Agent
from langchain.client.runner_utils import run_on_dataset
to_test = [llm, chat_model, construct_agent]
for model, configured_evaluators in zip(to_test, run_evaluators):
run_on_dataset(ds_name, model, run_evaluators=configured_evaluators, verbose=True)
```
</details>
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I_kwDOIPDwls5qgKgi
| 7,100 |
Issue: Error when running `make coverage`
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[] | null | 4 | 2023-07-03T21:10:50 | 2023-12-20T16:07:23 | 2023-12-20T16:07:22 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
When I run the command
```bash
make coverage
```
I get the following error:
```bash
collected 1556 items / 9 errors
INTERNALERROR> Traceback (most recent call last):
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/_pytest/config/__init__.py", line 1560, in getoption
INTERNALERROR> val = getattr(self.option, name)
INTERNALERROR> AttributeError: 'Namespace' object has no attribute 'only_extended'
INTERNALERROR>
INTERNALERROR> The above exception was the direct cause of the following exception:
INTERNALERROR>
INTERNALERROR> Traceback (most recent call last):
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/_pytest/main.py", line 269, in wrap_session
INTERNALERROR> session.exitstatus = doit(config, session) or 0
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/_pytest/main.py", line 322, in _main
INTERNALERROR> config.hook.pytest_collection(session=session)
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/pluggy/_hooks.py", line 265, in __call__
INTERNALERROR> return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/pluggy/_manager.py", line 80, in _hookexec
INTERNALERROR> return self._inner_hookexec(hook_name, methods, kwargs, firstresult)
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/pluggy/_callers.py", line 60, in _multicall
INTERNALERROR> return outcome.get_result()
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/pluggy/_result.py", line 60, in get_result
INTERNALERROR> raise ex[1].with_traceback(ex[2])
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/pluggy/_callers.py", line 39, in _multicall
INTERNALERROR> res = hook_impl.function(*args)
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/_pytest/main.py", line 333, in pytest_collection
INTERNALERROR> session.perform_collect()
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/_pytest/main.py", line 668, in perform_collect
INTERNALERROR> hook.pytest_collection_modifyitems(
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/pluggy/_hooks.py", line 265, in __call__
INTERNALERROR> return self._hookexec(self.name, self.get_hookimpls(), kwargs, firstresult)
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/pluggy/_manager.py", line 80, in _hookexec
INTERNALERROR> return self._inner_hookexec(hook_name, methods, kwargs, firstresult)
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/pluggy/_callers.py", line 60, in _multicall
INTERNALERROR> return outcome.get_result()
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/pluggy/_result.py", line 60, in get_result
INTERNALERROR> raise ex[1].with_traceback(ex[2])
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/pluggy/_callers.py", line 39, in _multicall
INTERNALERROR> res = hook_impl.function(*args)
INTERNALERROR> File "/home/mohtashimkhan/langchain/tests/unit_tests/conftest.py", line 43, in pytest_collection_modifyitems
INTERNALERROR> only_extended = config.getoption("--only-extended") or False
INTERNALERROR> File "/home/mohtashimkhan/mambaforge/envs/langchain/lib/python3.9/site-packages/_pytest/config/__init__.py", line 1571, in getoption
INTERNALERROR> raise ValueError(f"no option named {name!r}") from e
INTERNALERROR> ValueError: no option named 'only_extended'
```
I am not sure what is the root cause of this issue. I created a new conda environment and installed poetry test,test_integration and main dependencies from scratch.
### Versions:
- python: 3.9
- poetry: 1.5.1
- make: 4.3
- OS: Ubuntu 22.04.1 LTS
### Suggestion:
_No response_
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PR_kwDOIPDwls5UjuXo
| 7,099 |
Align cassio versions between examples for Cassandra integration
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[] | null | 2 | 2023-07-03T20:36:13 | 2023-08-31T00:05:53 | 2023-07-04T10:21:48 |
CONTRIBUTOR
| null |
Just reducing confusion by requiring cassio>=0.0.7 consistently across examples.
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small fix for chroma embedding function
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[] | null | 3 | 2023-07-03T20:04:57 | 2023-11-07T03:47:22 | 2023-11-07T03:47:21 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: super small fix that makes the Chroma vectorstore object more compatible with the chroma library.
In chroma when you initialize a client / collection, if you specify an embedding function, it is of type chromadb.utils.embedding_functions.SentenceTransformerEmbeddingFunction and that uses the __call__ to embed stuff. This was not compatable with langchain Chroma object because of [this line](https://github.com/hwchase17/langchain/blob/98dbea6310828ec817dd1f89dfa26a7c1c2de579/langchain/vectorstores/chroma.py#L96C57-L96C72) (the SentenceTransformerEmbeddingFunction) doesn't have an `embed_documents` functions. This PR fixes that so you can initialize a Chroma lang chain object with the same embedding_function object as you could with the chroma library.
- Description: Above
- Issue: none
- Dependencies:none
- Tag maintainer: idk
- Twitter handle: @McClainThiel
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
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See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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PR_kwDOIPDwls5UjjnD
| 7,097 |
fix retriever signatures
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PR_kwDOIPDwls5UjiRO
| 7,096 |
add trulens integration
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[] | null | 2 | 2023-07-03T19:47:27 | 2023-07-05T18:04:56 | 2023-07-05T18:04:55 |
CONTRIBUTOR
| null |
Description: Add TruLens integration.
Twitter: @trulensml
For review:
- Tracing: @agola11
- Tools: @hinthornw
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PR_kwDOIPDwls5Ujb-N
| 7,095 |
update pr tmpl
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PR_kwDOIPDwls5Ujbfg
| 7,094 |
Asyncio config in toml file
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[] | null | 1 | 2023-07-03T19:19:19 | 2023-07-03T19:22:50 | 2023-07-03T19:22:50 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Added asyncio in tomal file,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
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2. an example notebook showing its use.
Maintainer responsibilities:
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- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5UjUyr
| 7,093 |
Fix GPT4All bug w/ "n_ctx" param
|
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[] | null | 10 | 2023-07-03T18:54:49 | 2023-07-04T17:08:08 | 2023-07-04T15:53:52 |
COLLABORATOR
| null |
Running `GPT4All` per the [docs](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/gpt4all), I see:
```
$ from langchain.llms import GPT4All
$ model = GPT4All(model=local_path)
$ model("The capital of France is ", max_tokens=10)
TypeError: generate() got an unexpected keyword argument 'n_ctx'
```
It appears `n_ctx` is [no longer a supported param](https://docs.gpt4all.io/gpt4all_python.html#gpt4all.gpt4all.GPT4All.generate) in the GPT4All API from https://github.com/nomic-ai/gpt4all/pull/1090.
It now uses `max_tokens`, so I set this.
And I also set other defaults used in GPT4All client [here](https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-bindings/python/gpt4all/gpt4all.py).
Confirm it now works:
```
$ from langchain.llms import GPT4All
$ model = GPT4All(model=local_path)
$ model("The capital of France is ", max_tokens=10)
< Model logging >
"....Paris."
```
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| 1,786,624,458 |
PR_kwDOIPDwls5UjLPZ
| 7,092 |
Add Metal support to llama.cpp doc
|
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[] | null | 2 | 2023-07-03T18:17:44 | 2023-07-04T07:34:05 | 2023-07-03T19:35:39 |
CONTRIBUTOR
| null |
- Description: Add Metal support to llama.cpp doc
- Issue: #7091
- Dependencies: N/A
- Tag maintainer: @hwchase17, @dev2049
- Twitter handle: gene_wu
|
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| 1,786,607,479 |
I_kwDOIPDwls5qfXd3
| 7,091 |
DOC: enhancement with Llama-cpp document
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[] | null | 1 | 2023-07-03T18:03:36 | 2023-07-03T23:57:09 | 2023-07-03T23:57:09 |
CONTRIBUTOR
| null |
### Issue with current documentation:
The current document didn't cover the Metal support of Llama-cpp, which need different parameter to build.
Also, the default parameter of initial LLM will crash error:
```
GGML_ASSERT: .../vendor/llama.cpp/ggml-metal.m:706: false && "not implemented"
```
### Idea or request for content:
I would like to contribute a pull request to polish the document with better Metal (Apple Silicon Chip) support of Llama-cpp.
|
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PR_kwDOIPDwls5UjBF_
| 7,090 |
Add events to tracer runs
|
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[] | null | 2 | 2023-07-03T17:35:55 | 2023-07-03T19:43:44 | 2023-07-03T19:43:44 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
|
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| 1,786,507,922 |
PR_kwDOIPDwls5UiyJE
| 7,089 |
Add integration with DataForSEO SERP API
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[] | null | 1 | 2023-07-03T16:39:31 | 2023-07-05T17:42:18 | 2023-07-05T17:42:17 |
NONE
| null |
This Pull Request proposes the integration of the DataForSEO API Wrapper into the Langchain project, a direct response to the growing demand from our customers. Our API is significantly more cost-effective compared to SerpApi (similar product already integrated with Langchain), while also providing a wider range of functionalities and opportunities for software developers.
We've aimed to maintain a simple programmatic interface, while also providing features that could be useful for specific tasks such as searching Google Maps, summarizing product ratings, or fetching online results for a specific location.
**Features:**
- Support for Google, Bing, Yahoo, and more.
- Advanced functionality for fetching results from Organic Search, News, Maps, Events, Images, among others.
- Search queries can be specified in different languages and set for a variety of locations.
- Capability to filter and fetch only necessary data fields from SERP, like prices from shopping results or rating from organic elements.
- Ability to limit the number of search results returned.
Usage example:
```python
import os
from langchain.llms.openai import OpenAI
from langchain.agents.initialize import initialize_agent
from langchain.tools.base import Tool
from langchain.utilities.dataforseo_api_search import DataForSeoAPIWrapper
from langchain.agents import load_tools
#The API credentials could be found in client's panel
os.environ["DATAFORSEO_LOGIN"] = ""
os.environ["DATAFORSEO_PASSWORD"] = ""
os.environ["OPENAI_API_KEY"] = ""
search = DataForSeoAPIWrapper()
llm = OpenAI(temperature=0)
#search for first result description
output = search.run("pi value")
#prints "In decimal form, the value of pi is approximately 3.14..."
print(output)
#json results, output is dict
json_output = search.results("pi value")
#takes only top-3 results and only title, snippet, description fields from JSON, restrict output to 4 SERP elements: organic, featured_snippet,answer_box, knowlege_graph
configured_search = DataForSeoAPIWrapper(
json_result_fields=["title", "snippet","description"],
json_result_types=["organic", "featured_snippet","answer_box", "knowlege_graph"],
top_count=3)
configured_result = configured_search.results("Who is Bill Gates?")
#Google Maps search
maps_search = DataForSeoAPIWrapper(
top_count=10,
json_result_fields=["title", "value", "address", "rating", "type"],
params={"location_coordinate": "52.512,13.36,12z", "language_code": "en", "se_type": "maps"})
maps_search.results("coffee near me")
#integration with agents
tools = load_tools(["dataforseo-api-search"])
agent = initialize_agent(tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True)
result = agent.run("What is the weather in New York?")
#prints current NY weather
print(result)
#custom tool
search = DataForSeoAPIWrapper(
top_count=3,
json_result_types=["organic"],
json_result_fields=["title", "description", "type"])
tool = Tool(
name="google-search-answer",
description="My new answer tool",
func=search.run,
)
json_tool = Tool(
name="google-search-json",
description="My new json tool",
func=search.results,
)
```
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PR_kwDOIPDwls5Uitvf
| 7,088 |
Use EggTimer to simplify timeout logic
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[] | null | 6 | 2023-07-03T16:22:38 | 2023-11-10T23:34:33 | 2023-11-10T23:34:32 |
CONTRIBUTOR
| null |
## Description
[EggTimer](https://pypi.org/project/egg-timer/) is a simple Python library that makes handling timeouts easier. Instead of tracking time elapsed, start times, and performing calculations, using EggTimer is a simple as `timer = EggTimer(); timer.set(TIMEOUT)`. Timeout can be checked by calling `timer.is_expired() -> bool`.
This PR modifies the `Agent` and `BlockchainDocumentLoader` to use EggTimer.
## A note about dependencies
I don't think there's a way to make this dependency optional, since it's used by the Agent. LMK if there's something different I should do.
## Tag Maintainer
Agents: @vowelparrot
DataLoaders: @rlancemartin @eyurtsev
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PR_kwDOIPDwls5UijQo
| 7,087 |
Update chroma.py: Persist directory from client_settings if provided there
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CONTRIBUTOR
| null |
Change details:
- Description: When calling db.persist(), a check prevents from it proceeding as the constructor only sets member `_persist_directory` from parameters. But the ChromaDB client settings also has this parameter, and if the client_settings parameter is used without passing the persist_directory (which is optional), the `persist` method raises `ValueError` for not setting `_persist_directory`. This change fixes it by setting the member `_persist_directory` variable from client_settings if it is set, else uses the constructor parameter.
- Issue: I didn't find any github issue of this, but I discovered it after calling the persist method
- Dependencies: None
- Tag maintainer: vectorstore related change - @rlancemartin, @eyurtsev
- Twitter handle: Don't have one :(
*Additional discussion*: We may need to discuss the way I implemented the fallback using `or`.
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PR_kwDOIPDwls5UigUl
| 7,086 |
bump 222
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[] | null | 1 | 2023-07-03T15:38:55 | 2023-07-03T16:03:57 | 2023-07-03T16:03:56 |
COLLABORATOR
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PR_kwDOIPDwls5Uiahe
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Remove `None` default value for FAISS relevance_score_fn
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[] | null | 2 | 2023-07-03T15:21:00 | 2023-07-03T16:25:33 | 2023-07-03T16:11:49 |
CONTRIBUTOR
| null |
## Description
The type hint for `FAISS.__init__()`'s `relevance_score_fn` parameter allowed the parameter to be set to `None`. However, a default function is provided by the constructor. This led to an unnecessary check in the code, as well as a test to verify this check.
**ASSUMPTION**: There's no reason to ever set `relevance_score_fn` to `None`.
This PR changes the type hint and removes the unnecessary code.
## Tag Maintainer
@rlancemartin @eyurtsev
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PR_kwDOIPDwls5UiVgG
| 7,084 |
ChromaDB relevance score fn
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[] | null | 7 | 2023-07-03T15:11:05 | 2023-08-23T16:00:30 | 2023-07-11T16:45:50 |
CONTRIBUTOR
| null |
## Description
This PR adds a `relevance_score_fn` to `Chroma.__init__()` that allows developers to pass in a custom function to map relevance scores to [0,1]. This code follows the pattern set by `FAISS`.
Other changes made:
- `Chroma.from_{documents,texts}()` pass `kwargs` through to the constructor
## Tag maintainer
@rlancemartin @eyurtsev
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PR_kwDOIPDwls5Uh1iS
| 7,083 |
Mark some output parsers as serializable (cross-checked w/ JS)
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[] | null | 1 | 2023-07-03T13:49:15 | 2023-07-05T18:53:57 | 2023-07-05T18:53:56 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
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Maintainer responsibilities:
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| 1,786,178,650 |
I_kwDOIPDwls5qduxa
| 7,082 |
SQLDatabaseChain Double Query
|
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[] | null | 5 | 2023-07-03T13:22:53 | 2023-10-12T16:07:47 | 2023-10-12T16:07:46 |
NONE
| null |
### System Info
When trying to do an aggregation on a table (I've tried average, min, max), two duplicate queries are being generated, resulting in an error
```
syntax error line 2 at position 0 unexpected 'SELECT'.
[SQL: SELECT AVG(YEAR) FROM my_table
SELECT AVG(YEAR) FROM my_table]
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. db_chain = SQLDatabaseChain.from_llm(...)
2. db_chain.run("what is the average year?")
### Expected behavior
should return the average year
|
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PR_kwDOIPDwls5UhmoY
| 7,081 |
Harrison/parameter
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[] | null | 1 | 2023-07-03T13:10:40 | 2023-07-06T00:51:26 | 2023-07-06T00:51:25 |
COLLABORATOR
| null |
add parameter to use original question or not
|
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| 1,786,156,035 |
PR_kwDOIPDwls5Uhmb5
| 7,080 |
Harrison/conv retrieval docs
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[] | null | 1 | 2023-07-03T13:10:07 | 2023-07-05T00:17:45 | 2023-07-05T00:17:43 |
COLLABORATOR
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| 1,786,073,569 |
I_kwDOIPDwls5qdVHh
| 7,079 |
KeyError when formatting FewShotPromptTemplate
|
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[] | null | 7 | 2023-07-03T12:23:47 | 2023-07-04T10:01:18 | 2023-07-04T10:01:17 |
NONE
| null |
### System Info
LangChain: 0.0.221
Python: 3.9.17.
OS: MacOS Ventura 13.3.1 (a)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Below is the code to reproduce the error. I attempt to set up a simple few-shot prompting logic with an LLM for named entity extraction from documents. At the moment, I supply one single document with the expected entities to be extracted, and the expected answer in key-value pairs. Then I want to format the few-shot prompt object before seeding it as an input to one of the LLM chains, and this is the step where I encounter the error:
```
KeyError Traceback (most recent call last)
Cell In[1], line 56
53 new_doc = "Kate McConnell is 56 and is soon retired according to the current laws of the state of Alaska in which she resides."
54 new_entities_to_extract = ['name', 'organization']
---> 56 prompt = few_shot_prompt_template.format(
57 input_doc=new_doc,
58 entities_to_extract=new_entities_to_extract,
59 answer={}
60 )
File [~/Desktop/automarkup/.venv/lib/python3.9/site-packages/langchain/prompts/few_shot.py:123](https://file+.vscode-resource.vscode-cdn.net/Users/username/Desktop/automarkup/src/~/Desktop/automarkup/.venv/lib/python3.9/site-packages/langchain/prompts/few_shot.py:123), in FewShotPromptTemplate.format(self, **kwargs)
120 template = self.example_separator.join([piece for piece in pieces if piece])
122 # Format the template with the input variables.
--> 123 return DEFAULT_FORMATTER_MAPPING[self.template_format](template, **kwargs)
File [/opt/homebrew/Cellar/python](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Cellar/python)@3.9/3.9.17/Frameworks/Python.framework/Versions/3.9/lib/python3.9/string.py:161, in Formatter.format(self, format_string, *args, **kwargs)
160 def format(self, format_string, [/](https://file+.vscode-resource.vscode-cdn.net/), *args, **kwargs):
--> 161 return self.vformat(format_string, args, kwargs)
File [~/Desktop/automarkup/.venv/lib/python3.9/site-packages/langchain/formatting.py:29](https://file+.vscode-resource.vscode-cdn.net/Users/username/Desktop/automarkup/src/~/Desktop/automarkup/.venv/lib/python3.9/site-packages/langchain/formatting.py:29), in StrictFormatter.vformat(self, format_string, args, kwargs)
24 if len(args) > 0:
25 raise ValueError(
26 "No arguments should be provided, "
27 "everything should be passed as keyword arguments."
28 )
---> 29 return super().vformat(format_string, args, kwargs)
File [/opt/homebrew/Cellar/python](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Cellar/python)@3.9/3.9.17/Frameworks/Python.framework/Versions/3.9/lib/python3.9/string.py:165, in Formatter.vformat(self, format_string, args, kwargs)
163 def vformat(self, format_string, args, kwargs):
164 used_args = set()
--> 165 result, _ = self._vformat(format_string, args, kwargs, used_args, 2)
166 self.check_unused_args(used_args, args, kwargs)
167 return result
File [/opt/homebrew/Cellar/python](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Cellar/python)@3.9/3.9.17/Frameworks/Python.framework/Versions/3.9/lib/python3.9/string.py:205, in Formatter._vformat(self, format_string, args, kwargs, used_args, recursion_depth, auto_arg_index)
201 auto_arg_index = False
203 # given the field_name, find the object it references
204 # and the argument it came from
--> 205 obj, arg_used = self.get_field(field_name, args, kwargs)
206 used_args.add(arg_used)
208 # do any conversion on the resulting object
File [/opt/homebrew/Cellar/python](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Cellar/python)@3.9/3.9.17/Frameworks/Python.framework/Versions/3.9/lib/python3.9/string.py:270, in Formatter.get_field(self, field_name, args, kwargs)
267 def get_field(self, field_name, args, kwargs):
268 first, rest = _string.formatter_field_name_split(field_name)
--> 270 obj = self.get_value(first, args, kwargs)
272 # loop through the rest of the field_name, doing
273 # getattr or getitem as needed
274 for is_attr, i in rest:
File [/opt/homebrew/Cellar/python](https://file+.vscode-resource.vscode-cdn.net/opt/homebrew/Cellar/python)@3.9/3.9.17/Frameworks/Python.framework/Versions/3.9/lib/python3.9/string.py:227, in Formatter.get_value(self, key, args, kwargs)
225 return args[key]
226 else:
--> 227 return kwargs[key]
KeyError: "'name'"
```
And the code is:
```
from langchain import FewShotPromptTemplate, PromptTemplate
example = {
"input_doc": "John Doe recently celebrated his birthday, on 2023-06-30, turning 30. He has been working with us at ABC Corp for a very long time already.",
"entities_to_extract": ["name", "date", "organization"],
"answer": {
"name": "John Doe",
"date": "2023-06-30",
"organization": "ABC Corp"
}
}
example_template = """
User: Retrieve the following entities from the document:
Document: {input_doc}
Entities: {entities_to_extract}
AI: The extracted entities are: {answer}
"""
example_prompt = PromptTemplate(
input_variables=["input_doc", "entities_to_extract", "answer"],
template=example_template
)
prefix = """You are a capable large language model. Your task is to extract entities from a given document.
Please pay attention to all the words in the following example and retrieve the requested entities.
"""
suffix = """
User: Retrieve the requested entities from the document.
Document: {input_doc}
Entities: {entities_to_extract}
AI:
"""
few_shot_prompt_template = FewShotPromptTemplate(
examples=[example],
example_prompt=example_prompt,
prefix=prefix,
suffix=suffix,
input_variables=["input_doc", "entities_to_extract"],
example_separator="\n\n"
)
new_doc = "Kate McConnell is 56 and is soon retired according to the current laws of the state of Alaska in which she resides."
new_entities_to_extract = ['name', 'organization']
prompt = few_shot_prompt_template.format(
input_doc=new_doc,
entities_to_extract=new_entities_to_extract,
answer={}
)
```
### Expected behavior
The expected behavior is to return the formatted template in string format, instead of the error showing up.
|
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| 1,786,027,453 |
I_kwDOIPDwls5qdJ29
| 7,078 |
Serialization of OpenLLM Local Inference Models does not work.
|
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[] | null | 4 | 2023-07-03T11:56:01 | 2023-10-09T16:05:35 | 2023-10-09T16:05:35 |
NONE
| null |
### System Info
- Langchain Version 0.0.219
## Where does the problem arise:
When saving the OpenLLM model, there is a key called "llm_kwargs" which cannot be parsed when reloading the model. Instead the "llm_kwargs" should be directly inside the "llm" object without the key "llm_kwargs".
### Example
```json
"llm": {
"server_url": null,
"server_type": "http",
"embedded": true,
"llm_kwargs": {
"temperature": 0.2,
"format_outputs": false,
"generation_config": {
"max_new_tokens": 1024,
"min_length": 0,
"early_stopping": false,
"num_beams": 1,
"num_beam_groups": 1,
"use_cache": true,
"temperature": 0.2,
"top_k": 15,
"top_p": 1.0,
"typical_p": 1.0,
"epsilon_cutoff": 0.0,
"eta_cutoff": 0.0,
"diversity_penalty": 0.0,
"repetition_penalty": 1.0,
"encoder_repetition_penalty": 1.0,
"length_penalty": 1.0,
"no_repeat_ngram_size": 0,
"renormalize_logits": false,
"remove_invalid_values": false,
"num_return_sequences": 1,
"output_attentions": false,
"output_hidden_states": false,
"output_scores": false,
"encoder_no_repeat_ngram_size": 0
}
},
"model_name": "opt",
"model_id": "facebook/opt-1.3b",
"_type": "openllm"
}
```
In contrast the serialization for a OpenAI LLM looks the following:
```json
"llm": {
"model_name": "text-davinci-003",
"temperature": 0.7,
"max_tokens": 256,
"top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"n": 1,
"request_timeout": null,
"logit_bias": {},
"_type": "openai"
}
```
### Who can help?
@hwchase17 @eyurtsev @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
1. Create a OpenLLM object
2. Call `save()` on that object
3. Try to reload the previously generated OpenLLM object with `langchain.chains.loading.load_chain_from_config()`
### Expected behavior
The serialized OpenLLM model should be reloadable.
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PR_kwDOIPDwls5Ug9sc
| 7,077 |
Changed the metadata source from temporary file location to original …
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Changed the metadata source from temporary file location to original url/path in PyMuPDf
Issue: #7034
@rlancemartin , @eyurtsev
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| 1,785,956,452 |
I_kwDOIPDwls5qc4hk
| 7,075 |
I am trying to use multi retreival QA chain but not sure what version of langchain will be best?
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[] | null | 6 | 2023-07-03T11:12:00 | 2023-08-25T20:15:00 | 2023-07-10T19:30:50 |
NONE
| null |
### Issue you'd like to raise.
I am trying to develop a chatbot that can give answers from multiple namespace.That why I am try to use the MULTIRETREIVALQA chain but the langchain versions seems to be making some issues for me . Any suggestions?
### Suggestion:
_No response_
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Add serialized object to retriever start callback
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Replace this comment with:
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If you're adding a new integration, please include:
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2. an example notebook showing its use.
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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PR_kwDOIPDwls5Ug1Ca
| 7,073 |
Add tags to all callback handler methods
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Replace this comment with:
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PR_kwDOIPDwls5Ugn0o
| 7,072 |
Update base.py
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NONE
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Added Support for Multiple Dataframes
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
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PR_kwDOIPDwls5UfylK
| 7,070 |
Retryable exception for empty OpenAI embedding.
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| false | null |
[] | null | 3 | 2023-07-03T08:31:06 | 2023-07-05T19:23:46 | 2023-07-05T19:23:45 |
CONTRIBUTOR
| null |
Description:
The OpenAI "embeddings" API intermittently falls into a failure state where an embedding is returned as [ Nan ], rather than the expected 1536 floats. This patch checks for that state (specifically, for an embedding of length 1) and if it occurs, throws an ApiError, which will cause the chunk to be retried.
Issue:
I have been unable to find an official langchain issue for this problem, but it is discussed (by another user) at https://stackoverflow.com/questions/76469415/getting-embeddings-of-length-1-from-langchain-openaiembeddings
Maintainer: @dev2049
Testing:
Since this is an intermittent OpenAI issue, I have not provided a unit or integration test. The provided code has, though, been run successfully over several million tokens.
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| 1,785,597,576 |
PR_kwDOIPDwls5UfsF2
| 7,069 |
Add concurrency to GitbookLoader
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[] | null | 1 | 2023-07-03T08:14:21 | 2023-07-06T00:51:11 | 2023-07-06T00:51:10 |
CONTRIBUTOR
| null |
- Description: Fetch all pages concurrently.
- Dependencies: `scrape_all` -> `fetch_all` -> `_fetch_with_rate_limit` -> `_fetch` (might be broken currently: https://github.com/hwchase17/langchain/pull/6519)
- Tag maintainer: @rlancemartin, @eyurtsev
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