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| 2,000,603,949 |
PR_kwDOIPDwls5f0_Ce
| 13,569 |
update multi index templates
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PR_kwDOIPDwls5f0-nP
| 13,568 |
Add error rate
|
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[] | null | 1 | 2023-11-18T22:22:28 | 2023-11-21T15:51:32 | 2023-11-21T15:51:31 |
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| null |
To the in-memory outputs. Separate it out from the outputs so it's present in the dataframe.describe() results
|
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I_kwDOIPDwls53Pq9K
| 13,567 |
Upgrading to OpenAI Python 1.0+ = ImportError: Could not import openai python package.
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[] | null | 4 | 2023-11-18T22:04:33 | 2023-11-21T00:39:08 | 2023-11-21T00:39:08 |
NONE
| null |
### System Info
Using langchain 0.0.337 python, FastAPI.
When I use openai up through 0.28.1 it works fine. Upgrading to 1.0.0 or above results in the following error (when I try to use ChatOpenAI from langchain.chat_models):
"ImportError: Could not import openai python package. Please install it with `pip install openai`."
Trying to follow this notebook to integrate vision preview model:
https://github.com/langchain-ai/langchain/blob/master/cookbook/openai_v1_cookbook.ipynb
Any thoughts on what I might try? Thanks!
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] 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
Steps to reproduce:
1. install openai (1.0.0), langchain (0.0.337) & langchain-experimental (0.0.39)
2. in FastAPI route, import ChatOpenAI from langchain.chat_models
3. Use ChatOpenAI as usual (working fine w/ openai <= 0.28.1
` llm = ChatOpenAI(
temperature=temperature,
streaming=True,
verbose=True,
model_name=nameCode,
max_tokens=tokens,
callbacks=[callback],
openai_api_key=relevantAiKey,
)`
### Expected behavior
I would expect to not get a "failed import" error when the package is clearly installed.
|
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| 2,000,596,258 |
I_kwDOIPDwls53Pq0i
| 13,566 |
from_llm_and_api_docs fails on gpt-4-1106-preview
|
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NONE
| null |
### System Info
```
poetry show langchain
name : langchain
version : 0.0.259
description : Building applications with LLMs through composability
dependencies
- aiohttp >=3.8.3,<4.0.0
- async-timeout >=4.0.0,<5.0.0
- dataclasses-json >=0.5.7,<0.6.0
- langsmith >=0.0.11,<0.1.0
- numexpr >=2.8.4,<3.0.0
- numpy >=1,<2
- openapi-schema-pydantic >=1.2,<2.0
- pydantic >=1,<2
- PyYAML >=5.3
- requests >=2,<3
- SQLAlchemy >=1.4,<3
- tenacity >=8.1.0,<9.0.0
```
Python: v3.10.12
### Who can help?
@hwchase17 @agola11
With the current GPT-4 model, the invocation of `from_llm_and_api_docs` works as expected. However, when switching the model to the upcoming `gpt-4-1106-preview`, the function fails as the LLM, instead of returning the URL for the API call, returns a verbose response:
```
LLM response on_text: To generate the API URL for the user's question "basketball tip of the day", we need to include the `sport` parameter with the value "Basketball" since the user is asking about basketball. We also need to include the `event_start` parameter with today's date to get the tip of the day. Since the user is asking for a singular "tip", we should set the `limit` parameter to 1. The `order` parameter should be set to "popularity" if not specified, as per the documentation.
Given that today is 2023-11-18, the API URL would be:
http://<domain_name_hidden>/search/ai?date=2023-11-18&limit=1&order=popularity
```
The prompt should be refined or extra logic should be added to retrieve just the URL with the upcoming GPT-4 model.
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Try to get the upcoming GPT-4 model to return just the URL of the API call.
```
ERROR:root:No connection adapters were found for 'To generate the API URL for the user\'s question "<question edited>", we need to include the `sport` parameter with the value "Basketball" since the user is asking about basketball. We also need to include the `event_start` parameter with today\'s date to get the tip of the day. The `order` parameter should be set to "popularity" if not specified, as per the documentation.\n\nGiven that today is 2023-11-18, the API URL would be:\n\nhttp://<domain_removed>/search/ai?date=2023-11-18&limit=1&order=popularity'
```
### Expected behavior
The LLM to return just the URL and for Langchain to not error out.
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I_kwDOIPDwls53Pm4N
| 13,565 |
pydantic.error_wrappers.ValidationError: 1 validation error for RetrievalQA separators extra fields not permitted (type=value_error.extra)
|
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NONE
| null |
### System Info
Facing this error while executing the langchain code.
```
pydantic.error_wrappers.ValidationError: 1 validation error for RetrievalQA
separators
extra fields not permitted (type=value_error.extra)
```
Code for retrivalQA
def retrieval_qa_chain(llm, prompt, retriever):
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever = retriever,
verbose=True,
callbacks=[handler],
chain_type_kwargs={"prompt": prompt},
return_source_documents=True
)
return qa_chain
### 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
```
def retrieval_qa_chain(llm, prompt, retriever):
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever = retriever,
verbose=True,
callbacks=[handler],
chain_type_kwargs={"prompt": prompt},
return_source_documents=True
)
return qa_chain
```
```
def retrieval_qa_chain(llm, prompt, retriever):
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever = retriever,
verbose=True,
callbacks=[handler],
chain_type_kwargs={"prompt": prompt},
return_source_documents=True
)
return qa_chain
```
### Expected behavior
Need a fix for the above error
|
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https://github.com/langchain-ai/langchain/pull/13564
| 2,000,572,598 |
PR_kwDOIPDwls5f049m
| 13,564 |
bump 338, exp 42
|
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[] | null | 1 | 2023-11-18T20:42:52 | 2023-11-18T23:12:08 | 2023-11-18T23:12:08 |
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| 2,000,522,960 |
I_kwDOIPDwls53PY7Q
| 13,563 |
Bug: Weaviate raise doc_id error using with ParentDocumentRetriever
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[] | null | 2 | 2023-11-18T18:09:56 | 2023-11-18T18:33:42 | 2023-11-18T18:33:42 |
NONE
| null |
### System Info
LangChain Version: 0.0.337
Python: 3.10
### Who can help?
@hwchase17
Note: I am facing this issue with Weaviate, when I use the Chroma Vector Store it's working fine.
I am trying to use "Weaviate Vector DB" with ParentDocumentRetriever and I am getting this error during the pipeline:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[13], line 1
----> 1 retriever.get_relevant_documents("realization")
File ~/miniconda3/envs/docs_qa/lib/python3.10/site-packages/langchain/schema/retriever.py:211, in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, run_name, **kwargs)
209 except Exception as e:
210 run_manager.on_retriever_error(e)
--> 211 raise e
212 else:
213 run_manager.on_retriever_end(
214 result,
215 **kwargs,
216 )
File ~/miniconda3/envs/docs_qa/lib/python3.10/site-packages/langchain/schema/retriever.py:204, in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, run_name, **kwargs)
202 _kwargs = kwargs if self._expects_other_args else {}
203 if self._new_arg_supported:
--> 204 result = self._get_relevant_documents(
205 query, run_manager=run_manager, **_kwargs
206 )
207 else:
208 result = self._get_relevant_documents(query, **_kwargs)
File ~/miniconda3/envs/docs_qa/lib/python3.10/site-packages/langchain/retrievers/multi_vector.py:36, in MultiVectorRetriever._get_relevant_documents(self, query, run_manager)
34 ids = []
35 for d in sub_docs:
---> 36 if d.metadata[self.id_key] not in ids:
37 ids.append(d.metadata[self.id_key])
38 docs = self.docstore.mget(ids)
KeyError: 'doc_id'
```
### 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
```
import weaviate
from langchain.vectorstores.weaviate import Weaviate
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.retrievers import ParentDocumentRetriever
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.storage import RedisStore
from langchain.schema import Document
from langchain.storage._lc_store import create_kv_docstore
from langchain.storage import InMemoryStore
from langchain.vectorstores import Chroma
import redis
import os
os.environ["OPENAI_API_KEY"] = ""
client = weaviate.Client(url="https://test-n5.weaviate.network")
embeddings = OpenAIEmbeddings()
vectorstore = Weaviate(client=client, embedding=embeddings, index_name="test1".capitalize(), text_key="text", by_text=False)
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=50, chunk_overlap=1)
child_splitter = RecursiveCharacterTextSplitter(chunk_size=5, chunk_overlap=1)
store = InMemoryStore()
retriever = ParentDocumentRetriever(
vectorstore=vectorstore,
docstore=store,
child_splitter=child_splitter,
parent_splitter=parent_splitter,
id_key="doc_id"
)
docs = [
"The sun is shining brightly in the clear blue sky.",
"Roses are red, violets are blue, sugar is sweet, and so are you.",
"The quick brown fox jumps over the lazy dog.",
"Life is like a camera. Focus on what's important, capture the good times, develop from the negatives, and if things don't work out, take another shot.",
"A journey of a thousand miles begins with a single step.",
"The only limit to our realization of tomorrow will be our doubts of today.",
"Success is not final, failure is not fatal: It is the courage to continue that counts.",
"Happiness can be found even in the darkest of times if one only remembers to turn on the light."
]
docs = [Document(page_content=text) for en, text in enumerate(docs)]
retriever.add_documents(docs)
```
The output of below line below didn't contain a ID_Key for mapping the child to parent.
`vectorstore.similarity_search("realization", k=4)`
So, when I tried `retriever.get_relevant_documents("realization")` this returned the KeyError I mentioned.
### Expected behavior
The output of `vectorstore.similarity_search("realization", k=2)` should have been:
```
[Document(page_content='real', metadata={"doc_id": "fdsfsdfsdfsdfsd"}),
Document(page_content='real'), metadata={"doc_id": "rewrwetet"}]
```
but the output I got was:
[Document(page_content='real'),
Document(page_content='real')]
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| 2,000,516,549 |
I_kwDOIPDwls53PXXF
| 13,562 |
BedrockRerank using newly available Cohere embedding model
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[] | null | 2 | 2023-11-18T17:51:30 | 2023-11-22T16:15:48 | null |
NONE
| null |
### Feature request
Hi, the new Cohere embedding models are now available on Amazon Bedrock. How can we use them for their reranking capability (instead of just embedding via BedrockEmbedding class)
### Motivation
These models perform well for reranking
|
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PR_kwDOIPDwls5f0pT2
| 13,561 |
Change docs: add -U flag (upgrade) to "pip install -U duckduckgo_search"
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[] | null | 3 | 2023-11-18T16:45:28 | 2023-12-05T03:48:56 | 2023-12-05T03:48:55 |
CONTRIBUTOR
| null |
change all the lines like `pip install duckduckgo_search` to `pip install -U duckduckgo_search`
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I_kwDOIPDwls53PRrs
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Release 0.0.337 breaks MongoDBAtlasVectorSearch.from_connection_string?
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[] | null | 7 | 2023-11-18T16:43:18 | 2023-11-28T14:54:05 | 2023-11-24T01:51:00 |
NONE
| null |
### System Info
Hi there,
I have a LangChain app at https://huggingface.co/spaces/bstraehle/openai-llm-rag/blob/main/app.py. Using the latest release 0.0.337 produces the error below. Pinning the library to release 0.0.336 works as expected.
:blue_heart: LangChain, thanks!
Bernd
---
Traceback (most recent call last):
File "/home/user/app/app.py", line 129, in invoke
db = document_retrieval_mongodb(llm, prompt)
File "/home/user/app/app.py", line 91, in document_retrieval_mongodb
db = MongoDBAtlasVectorSearch.from_connection_string(MONGODB_URI,
File "/home/user/.local/lib/python3.10/site-packages/langchain/vectorstores/mongodb_atlas.py", line 109, in from_connection_string
raise ImportError(
ImportError: Could not import pymongo, please install it with `pip install pymongo`.
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce the behavior:
1. In file https://huggingface.co/spaces/bstraehle/openai-llm-rag/blob/main/requirements.txt, unpin the langchain library (or pin it to release 0.0.337).
2. Use the app at https://huggingface.co/spaces/bstraehle/openai-llm-rag with MongoDB selected to invoke `MongoDBAtlasVectorSearch.from_connection_string`, which produces the error.
### Expected behavior
When using release 0.0.337 `MongoDBAtlasVectorSearch.from_connection_string`, error "ImportError: Could not import pymongo, please install it with `pip install pymongo`." should not happen.
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PR_kwDOIPDwls5f0iI2
| 13,559 |
move streaming stdout
|
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[] | null | 1 | 2023-11-18T15:03:15 | 2023-11-18T17:24:50 | 2023-11-18T17:24:49 |
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I_kwDOIPDwls53PIoL
| 13,558 |
Response taking way to long
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[] | null | 3 | 2023-11-18T15:01:10 | 2023-11-19T03:14:01 | null |
NONE
| null |
Im building an embedded chatbot using langchain and openai its working fine but the issue is that the responses takes around 15-25 seconds and i tried to use the time library to know which line is taking this much
`import os
import sys
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.indexes import VectorstoreIndexCreator
from langchain.indexes.vectorstore import VectorStoreIndexWrapper
from langchain.vectorstores import Chroma
from cachetools import TTLCache
import time
import constants
os.environ["OPENAI_API_KEY"] = constants.APIKEY
cache = TTLCache(maxsize=100, ttl=3600) # Example: Cache up to 100 items for 1 hour
PERSIST = False
template_prompt = "If the user greets you, greet back. If there is a link in the response return it as a clickable link as if it is an a tag '<a>'. If you don't know the answer, you can say, 'I don't have the information you need, I recommend contacting our support team for assistance.' Here is the user prompt: 'On the Hawsabah platform"
def initialize_chatbot():
query = None
if len(sys.argv) > 1:
query = sys.argv[1]
if PERSIST and os.path.exists("persist"):
print("Reusing index...\n")
vectorstore = Chroma(persist_directory="persist", embedding_function=OpenAIEmbeddings())
index = VectorStoreIndexWrapper(vectorstore=vectorstore)
else:
loader = TextLoader("data/data.txt")
if PERSIST:
index = VectorstoreIndexCreator(vectorstore_kwargs={"persist_directory":"persist"}).from_loaders([loader])
else:
index = VectorstoreIndexCreator().from_loaders([loader])
chat_chain = ConversationalRetrievalChain.from_llm(
llm=ChatOpenAI(model="gpt-3.5-turbo"),
retriever=index.vectorstore.as_retriever(search_kwargs={"k": 1}),
)
chat_history = []
return chat_chain, chat_history
MAX_CONVERSATION_HISTORY = 3 # Set the maximum number of interactions to keep in the buffer
def chatbot_response(user_prompt, chat_chain, chat_history):
# Check if the response is cached
cached_response = cache.get(user_prompt)
if cached_response:
return cached_response
# Check if the user's query is a greeting or unrelated
is_greeting = check_for_greeting(user_prompt)
# Conditionally clear the conversation history
if is_greeting:
chat_history.clear()
query_with_template = f"{template_prompt} {user_prompt}'"
s = time.time()
result = chat_chain({"question": query_with_template, "chat_history": chat_history})
e = time.time()
# Append the new interaction and limit the conversation buffer to the last MAX_CONVERSATION_HISTORY interactions
chat_history.append((user_prompt, result['answer']))
if len(chat_history) > MAX_CONVERSATION_HISTORY:
chat_history.pop(0) # Remove the oldest interaction
response = result['answer']
# Cache the response for future use
cache[user_prompt] = response
print("Time taken by chatbot_response:", (e - s) * 1000, "ms")
return response`
the line result = chat_chain({"question": query_with_template, "chat_history": chat_history}) was the one taking this long i tried to figure out how to fix this but i couldnt i also tried to implement word streaming to help make it look faster but it only worked for the davinci model. Is there a way or method to make responses faster?
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| 2,000,440,136 |
PR_kwDOIPDwls5f0ez_
| 13,557 |
migrate openai audio api
|
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[] | null | 5 | 2023-11-18T14:19:07 | 2023-12-05T04:27:55 | 2023-12-05T04:27:55 |
CONTRIBUTOR
| null |
for issue https://github.com/langchain-ai/langchain/issues/13162
migrate openai audio api, as [openai v1.0.0 Migration Guide](https://github.com/openai/openai-python/discussions/742)
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I_kwDOIPDwls53PCl3
| 13,556 |
Adding DuckDuckGo search HTTPError handling
|
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[] | null | 7 | 2023-11-18T13:58:54 | 2023-11-18T14:44:22 | null |
NONE
| null |
### System Info
Bumped into HTTPError when using DuckDuckGo search wrapper in an agent, currently using `langchain==0.0.336`.
Here's the snippet of the traceback as below.
```
File "/path/to/venv/lib/python3.10/site-packages/langchain/utilities/duckduckgo_search.py", line 64, in run
snippets = self.get_snippets(query)
File "/path/to/venv/lib/python3.10/site-packages/langchain/utilities/duckduckgo_search.py", line 55, in get_snippets
for i, res in enumerate(results, 1):
File "/path/to/venv/lib/python3.10/site-packages/duckduckgo_search/duckduckgo_search.py", line 96, in text
for i, result in enumerate(results, start=1):
File "/path/to/venv/lib/python3.10/site-packages/duckduckgo_search/duckduckgo_search.py", line 148, in _text_api
resp = self._get_url("GET", "https://links.duckduckgo.com/d.js", params=payload)
File "/path/to/venv/lib/python3.10/site-packages/duckduckgo_search/duckduckgo_search.py", line 55, in _get_url
raise ex
File "/path/to/venv/lib/python3.10/site-packages/duckduckgo_search/duckduckgo_search.py", line 48, in _get_url
raise httpx._exceptions.HTTPError("")
httpx.HTTPError
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/path/to/src/single_host.py", line 179, in <module>
response = chain({"topic": "Why did Sam Altman got fired by OpenAI.",
File "/path/to/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 310, in __call__
raise e
File "/path/to/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 306, in __call__
else self._call(inputs)
File "/path/to/src/single_host.py", line 163, in _call
script = script_chain.run({"topic": inputs["topic"], "focuses": inputs["focuses"], "keypoints": keypoints})
File "/path/to/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 505, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/path/to/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 310, in __call__
raise e
File "/path/to/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 306, in __call__
else self._call(inputs)
File "/path/to/src/single_host.py", line 117, in _call
information = agent.run(background_info_search_formatted)
File "/path/to/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 505, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/path/to/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 310, in __call__
raise e
File "/path/to/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 304, in __call__
self._call(inputs, run_manager=run_manager)
File "/path/to/venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 1245, in _call
next_step_output = self._take_next_step(
File "/path/to/venv/lib/python3.10/site-packages/langchain/agents/agent.py", line 1095, in _take_next_step
observation = tool.run(
File "/path/to/venv/lib/python3.10/site-packages/langchain/tools/base.py", line 344, in run
raise e
File "/path/to/venv/lib/python3.10/site-packages/langchain/tools/base.py", line 337, in run
self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
File "/path/to/venv/lib/python3.10/site-packages/langchain/tools/base.py", line 510, in _run
self.func(
File "/path/to/venv/lib/python3.10/site-packages/langchain/tools/base.py", line 344, in run
raise e
File "/path/to/venv/lib/python3.10/site-packages/langchain/tools/base.py", line 337, in run
self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
File "/path/to/venv/lib/python3.10/site-packages/langchain/tools/ddg_search/tool.py", line 36, in _run
return self.api_wrapper.run(query)
File "/path/to/venv/lib/python3.10/site-packages/langchain/utilities/duckduckgo_search.py", line 67, in run
raise ToolException("DuckDuckGo Search encountered HTTPError.")
```
I tried to add an error handling in the method `run()` in `langchain/utilities/duckduckgo_search.py`, something look like below:
```
def run(self, query: str) -> str:
try:
snippets = self.get_snippets(query)
return " ".join(snippets)
except httpx._exceptions.HTTPError as e:
raise ToolException("DuckDuckGo Search encountered HTTPError.")
```
I have also added `handle_tool_error`, where it was copied from the langchain [documentation](https://python.langchain.com/docs/modules/agents/tools/custom_tools)
```
def _handle_error(error: ToolException) -> str:
return (
"The following errors occurred during tool execution:"
+ error.args[0]
+ "Please try another tool."
)
```
However these methods do not seems to stop and still cause the error showed in first code block above. Am I implementing this incorrectly? or should there be other mechanism to handle the error occuried?
### Who can help?
_No response_
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Adding `handle_tool_errors` and passing the `_handle_error` function into it.
```
news_tool = Tool.from_function(name="News Search",
func=news_duckduckgo.run,
description="News search to help you look up latest news, which help you to understand latest current affair, and being up-to-date.",
handle_tool_errors=_handle_error)
```
2. Does not seems to work, so I tried to change the DuckDuckGo Wrapper, as described above.
3. HTTPError still lead to abrupt stop of Agent actions.
### Expected behavior
Expecting a proper error handling method, if tool fails, Agent moves on, or try n time before moving on to next step.
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| 2,000,423,580 |
PR_kwDOIPDwls5f0bjO
| 13,555 |
improve sparql-qa-chain
|
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| false | null |
[] | null | 4 | 2023-11-18T13:38:18 | 2023-12-01T23:39:50 | null |
CONTRIBUTOR
| null |
**Description:**
* fixed the SPARQL queries for schema extraction - in some cases not all classes were loaded
* improved the prompt for creating natural language answers from the query results
* fixed the RDF example - the content was not retrieved from the graph DB due to the name not matching the one in the graph
* added an OWL example with a local file - simplified, but inspired by @LorenzBuehmann's example in #8907
**Issue:**
loosely related to issue #8907 and the discussion within PR #11240. the issues mentioned there were resolved in #9136, but made me notice another issue where classes were missing in the extracted schema
**Dependencies:**
none
**Tag maintainer:**
@baskaryan
|
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| 2,000,411,141 |
PR_kwDOIPDwls5f0ZKg
| 13,553 |
Add embedchain retriever
|
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[] | null | 3 | 2023-11-18T12:58:42 | 2023-11-20T06:25:15 | 2023-11-20T01:35:03 |
CONTRIBUTOR
| null |
**Description:**
This commit adds embedchain retriever along with tests and docs. Embedchain is a RAG framework to create data pipelines.
**Twitter handle:**
- [Taranjeet's twitter](https://twitter.com/taranjeetio) and [Embedchain's twitter](https://twitter.com/embedchain)
**Reviewer**
@hwchase17
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I_kwDOIPDwls53Oy9d
| 13,551 |
DOC: add info about how to update the template and the packages of an app created from a template
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[] | null | 4 | 2023-11-18T10:44:59 | 2023-11-18T11:06:04 | null |
NONE
| null |
### Issue with current documentation:
How to update the template and packages of an app created from a template?
I checked:
https://github.com/langchain-ai/langchain/tree/master/templates
and a couple of templates' README.mds, but this info is missing and it's not obvious for us citizen devs.
I supposed it should be done via langchain-cli, but there's no such option.
So pls. provide a solution and add it to docs.
### Idea or request for content:
How to update the template and packages of an app created from a template?
|
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PR_kwDOIPDwls5f0A3z
| 13,550 |
import error fix to quickstart.mdx
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[] | null | 3 | 2023-11-18T07:31:43 | 2023-11-20T02:33:50 | 2023-11-20T02:33:49 |
NONE
| null |
**Description**: I fixed a very small import error in the quickstart docs
(from langchain.llms import OpenAI -> from langchain.llms.openai import OpenAI)
|
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PR_kwDOIPDwls5fzu6P
| 13,549 |
feat: load all namespaces
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[] | null | 2 | 2023-11-18T04:02:59 | 2023-11-20T01:35:18 | 2023-11-20T01:35:18 |
CONTRIBUTOR
| null |
- **Description:** This change allows for the `MWDumpLoader` to load all namespaces including custom by default instead of only loading the [default namespaces](https://www.mediawiki.org/wiki/Help:Namespaces#Localisation).
- **Tag maintainer:** @hwchase17
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AIMessage in output of Qianfan with streaming enabled may lose info about 'additional_kwargs', which causes 'function_call', 'token_usage' info lost.
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[] | null | 5 | 2023-11-18T03:19:52 | 2023-11-19T14:11:55 | null |
NONE
| null |
### System Info
LangChain: 0.0.336
Python: 3.11.6
OS: Microsoft Windows [Version 10.0.19045.3693]
### Who can help?
_No response_
### 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
Should be very easy to reproduce. Just enable streaming and use function call in chat. Info about `function_call` supposed to be in `additional_kwargs` has lost. I found this issue because I wanted to use 'function call' feature.
These is a debug output from my console. As you see, output becomes an `AIMessage` with empty `content`, and `additional_kwargs` is empty.
```
[llm/end] [1:chain:AgentExecutor > 2:llm:QianfanChatEndpoint] [2.39s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "",
"generation_info": {
"finish_reason": "stop"
},
"type": "ChatGeneration",
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain",
"schema",
"messages",
"AIMessage"
],
"kwargs": {
"content": "",
"additional_kwargs": {}
}
}
}
]
],
"llm_output": {
"token_usage": {},
"model_name": "ERNIE-Bot"
},
"run": null
}
[chain/end] [1:chain:AgentExecutor] [2.40s] Exiting Chain run with output:
{
"output": ""
}
```
A quick-and-dirty hack in `QianfanChatEndpoint` can fix the issue. Please read the following code related to `first_additional_kwargs` (which is added by me).
```python
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
completion = ""
token_usage = {}
first_additional_kwargs = None
async for chunk in self._astream(messages, stop, run_manager, **kwargs):
if first_additional_kwargs is None:
first_additional_kwargs = chunk.message.additional_kwargs
completion += chunk.text
lc_msg = AIMessage(content=completion, additional_kwargs=first_additional_kwargs or {})
gen = ChatGeneration(
message=lc_msg,
generation_info=dict(finish_reason="stop"),
)
return ChatResult(
generations=[gen],
llm_output={"token_usage": {}, "model_name": self.model},
)
params = self._convert_prompt_msg_params(messages, **kwargs)
response_payload = await self.client.ado(**params)
lc_msg = _convert_dict_to_message(response_payload)
generations = []
gen = ChatGeneration(
message=lc_msg,
generation_info={
"finish_reason": "stop",
**response_payload.get("body", {}),
},
)
generations.append(gen)
token_usage = response_payload.get("usage", {})
llm_output = {"token_usage": token_usage, "model_name": self.model}
return ChatResult(generations=generations, llm_output=llm_output)
```
Similarly `_generate` probably contains the same bug.
The following is the new debug output in console. As you can see, 'function call' now works. `additional_kwargs` also contains non-empty `usage`. But `token_usage` in `llm_output` is still empty.
```
[llm/end] [1:chain:AgentExecutor > 2:llm:QianfanChatEndpointHacked] [2.21s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "",
"generation_info": {
"finish_reason": "stop"
},
"type": "ChatGeneration",
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain",
"schema",
"messages",
"AIMessage"
],
"kwargs": {
"content": "",
"additional_kwargs": {
"id": "as-zh6tasbjyb",
"object": "chat.completion",
"created": 1700274407,
"sentence_id": 0,
"is_end": true,
"is_truncated": false,
"result": "",
"need_clear_history": false,
"function_call": {
"name": "GetCurrentTime",
"arguments": "{}"
},
"search_info": {
"is_beset": 0,
"rewrite_query": "",
"search_results": null
},
"finish_reason": "function_call",
"usage": {
"prompt_tokens": 121,
"completion_tokens": 0,
"total_tokens": 121
}
}
}
}
}
]
],
"llm_output": {
"token_usage": {},
"model_name": "ERNIE-Bot"
},
"run": null
}
```
### Expected behavior
`additional_kwargs` should not be empty.
|
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| 2,000,180,229 |
PR_kwDOIPDwls5fzpKC
| 13,547 |
Implemented the option to include output from map chain in the final result of ConversationalRetrievalChain
|
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open
| false | null |
[] | null | 2 | 2023-11-18T03:01:22 | 2023-11-27T00:09:32 | null |
NONE
| null |
**Description:**
MapReduceDocumentChain contains a flag named “return_intermediate_steps” that allows intermediate steps to be outputted along with the final result. Normally, when this flag is set to True, the final result dictionary should contain a list of intermediate results produced during the map reduce process. However, when a MapReduceDocumentChain object with “return_intermediate_steps” set to True is used as a part of a ConversationalRetrievalChain object, an error will arise, making it impossible for users to obtain intermediate results of the execution of a ConversationalRetrievalChain object with map reduce.
The main cause of the issue seems to be inside _call() of class BaseConversationalRetrievalChain, where method run() is invoked on self.combine_docs_chain.
<img width="502" alt="image" src="https://github.com/langchain-ai/langchain/assets/88512829/c4f71b0d-489f-4b7f-badc-1635c03d76f0">
When a MapReduceDocumentChain object with “return_intermediate_steps” set to True is evaluated, there will be two output keys, named “output_text” and “intermediate_steps”, respectively. Therefore, _ _call_ _() should be used to evaluate such an object. However, inside the method _call() of class BaseConversationalRetrievalChain, it is hardcoded that run() is to be used to evaluate the object, resulting in this error
<img width="697" alt="image" src="https://github.com/langchain-ai/langchain/assets/88512829/4264ade0-381c-459c-86ee-7340dc201a60">
This PR modifies the existing code of the ConversationalRetrievalChain to verify if the map reduce document chain passed into combine_docs_chain has the return_intermediate_result flag set to True, and add the intermediate result of map reduce to the output of ConversationalRetrievalChain accordingly.
**Issue:**
#11209
**Dependencies:**
No new dependencies have been added.
I worked closely with @PitcairnHotdog on this PR
|
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I_kwDOIPDwls53OEI_
| 13,546 |
Issue: When using Qianfan chat model and enabling streaming, get ValueError
|
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[] | null | 4 | 2023-11-18T02:49:13 | 2023-11-28T10:29:36 | null |
NONE
| null |
### Issue you'd like to raise.
Here is my code:
"""For basic init and call"""
import os
import qianfan
from langchain.chat_models import QianfanChatEndpoint
from langchain.chat_models.base import HumanMessage
os.environ["QIANFAN_AK"] = "myak"
os.environ["QIANFAN_SK"] = "mysk"
chat = QianfanChatEndpoint(
streaming=True,
)
res = chat.stream([HumanMessage(content="给我一篇100字的睡前故事")], streaming=True)
for r in res:
print("chat resp:", r)
And after it prints two sentences, returns an error. The full error message is:
Traceback (most recent call last):
File "d:\work\qianfan_test.py", line 13, in <module>
for r in res:
File "C:\Users\a1383\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\langchain\chat_models\base.py", line 220, in stream
raise e
File "C:\Users\a1383\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\langchain\chat_models\base.py", line 216, in stream
generation += chunk
File "C:\Users\a1383\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\langchain\schema\output.py", line 94, in __add__
message=self.message + other.message,
File "C:\Users\a1383\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\langchain\schema\messages.py", line 225, in __add__
additional_kwargs=self._merge_kwargs_dict(
File "C:\Users\a1383\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\LocalCache\local-packages\Python310\site-packages\langchain\schema\messages.py", line 138, in _merge_kwargs_dict
raise ValueError(
ValueError: Additional kwargs key created already exists in this message.
I am only following the official langchain documentation:https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint
And it is not working. What have I done wrong?
### Suggestion:
_No response_
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PR_kwDOIPDwls5fzhpp
| 13,545 |
Fix for oracle schema parsing stated on the issue #7928
|
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[] | null | 1 | 2023-11-18T01:30:51 | 2023-11-20T01:35:28 | 2023-11-20T01:35:28 |
CONTRIBUTOR
| null |
- **Description:** In this pull request, we address an issue related to assigning a schema to the SQLDatabase class when utilizing an Oracle database. The current implementation encounters a bug where, upon attempting to execute a query, the alter session parse is not appropriately defined for Oracle, leading to an error,
- **Issue:** #7928,
- **Dependencies:** No dependencies,
- **Tag maintainer:** @baskaryan,
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PR_kwDOIPDwls5fzX74
| 13,544 |
Use random seed
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[] | null | 1 | 2023-11-18T00:00:50 | 2023-11-18T00:33:33 | 2023-11-18T00:33:32 |
COLLABORATOR
| null |
For default eval llm
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PR_kwDOIPDwls5fzUZy
| 13,543 |
fixed `openai_assistant` namespace
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[] | null | 1 | 2023-11-17T23:34:12 | 2023-11-18T06:51:21 | 2023-11-18T01:15:34 |
COLLABORATOR
| null |
BUG: langchain.agents.openai_assistant has a reference as
`from langchain_experimental.openai_assistant.base import OpenAIAssistantRunnable`
should be
`from langchain.agents.openai_assistant.base import OpenAIAssistantRunnable`
This prevents building of the API Reference docs
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| 2,000,035,681 |
PR_kwDOIPDwls5fzKNp
| 13,542 |
Add execution time
|
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[] | null | 1 | 2023-11-17T22:38:43 | 2023-11-18T00:04:17 | 2023-11-18T00:04:16 |
COLLABORATOR
| null |
And warn instead of raising an error, since the chain API is too inconsistent.
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PR_kwDOIPDwls5fy_1N
| 13,541 |
implement the summary buffer fix without insantiating new BaseMessage…
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[] | null | 3 | 2023-11-17T21:54:21 | 2023-12-05T00:01:17 | 2023-12-05T00:01:17 |
NONE
| null |
**Description:** Despite this class intending to summarize just enough messages to fit within a set context size, the product of this class always contains ALL messages PLUS a summary system message at the front, ultimately making token count longer rather than shorter. This commit addresses the issue by fixing the single bug where the buffer is constantly reset to the internal message store even after messages are summarized and removed.
- The error does not occur with in-memory History buffers, but instead fails when trying to access and modify an immutable database (such as a Postgres DB)
- [My organization's project repo](https://github.com/UGA-Innovation-Factory/AlfredBot) contains the code that triggered this error to be discovered, as well as output Verbose=True files for the current langchain master, my initial fix, and this revised solution that doesn't replicate any extra Lists.
**Issues:** Couldn't find one, misread the issue I thought was similar with my last pull request.
@efriis
Reason for changes in diff order:
- An index is stored with the moving summary buffer to represent up to which messages it represents
- The buffer property only returns memory outside of the summary buffer, without a duplicate List this time
- This satisfy's the need for the required member `type` for any BaseMessage constructor
- Update the prune index
- Clear index when clearing other members
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PR_kwDOIPDwls5fy-kZ
| 13,540 |
updated langchain stack img to be svg
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[] | null | 1 | 2023-11-17T21:50:46 | 2023-11-20T00:26:54 | 2023-11-20T00:26:53 |
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I_kwDOIPDwls53NURX
| 13,539 |
New update broke embeddings models
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[] | null | 3 | 2023-11-17T21:47:33 | 2023-11-18T20:07:42 | 2023-11-17T21:52:59 |
NONE
| null |
### System Info
LangChain version: 0.0.337
Python version: 3.10.13
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] 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
db = Chroma.from_documents(docs, AzureOpenAIEmbeddings())
### Expected behavior
This worked on previous versions of LangChain using OpenAIEmbeddings(), but now I get this error
BadRequestError: Error code: 400 - {'error': {'message': 'Too many inputs. The max number of inputs is 16. We hope to increase the number of inputs per request soon. Please contact us through an Azure support request at: https://go.microsoft.com/fwlink/?linkid=2213926 for further questions.', 'type': 'invalid_request_error', 'param': None, 'code': None}}
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PR_kwDOIPDwls5fy5p4
| 13,538 |
update OpenAI function agents' llm validation
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[] | null | 2 | 2023-11-17T21:32:49 | 2023-12-05T04:28:13 | 2023-12-05T04:28:13 |
CONTRIBUTOR
| null |
- **Description:** This PR modifies the LLM validation in OpenAI function agents to check whether the LLM supports OpenAI functions based on a property (`supports_oia_functions`) instead of whether the LLM passed to the agent `isinstance` of `ChatOpenAI`. This allows classes that extend `BaseChatModel` to be passed to these agents as long as they've been integrated with the OpenAI APIs and have this property set, even if they don't extend `ChatOpenAI`.
- **Issue:** N/A
- **Dependencies:** none
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PR_kwDOIPDwls5fyxK_
| 13,537 |
Override Keys Option
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[] | null | 1 | 2023-11-17T21:01:02 | 2023-11-17T21:32:44 | 2023-11-17T21:32:43 |
COLLABORATOR
| null |
Should be able to override the global key if you want to evaluate different outputs in a single run
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| 1,999,921,377 |
I_kwDOIPDwls53NGDh
| 13,536 |
Issue: Missing some input keys in langchain even when it's present - unclear how prompt args are treated
|
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[] | null | 2 | 2023-11-17T21:00:10 | 2023-11-17T21:08:12 | null |
NONE
| null |
### Issue you'd like to raise.
Why does the below code complain that extra_instructions is a missing key, even though it's learning included in input_variables=["context", "question", "extra_instructions"]?
Any help is greatly appreciated.
vectorstore = Chroma(
collection_name=collection_name,
persist_directory=chroma_db_directory,
embedding_function=embedding,
)
prompt_template = """
{extra_instructions}
{context}
{question}
Continuation:
"""
PROMPT = PromptTemplate(
template=prompt_template,
input_variables=["context", "question", "extra_instructions"],
)
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectorstore.as_retriever(
search_kwargs={"k": 1}
),
chain_type_kwargs={"verbose": True, "prompt": PROMPT},
)
### Suggestion:
_No response_
|
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| 1,999,916,188 |
PR_kwDOIPDwls5fyv03
| 13,535 |
DOCS `langchain decorators` update
|
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[] | null | 1 | 2023-11-17T20:56:10 | 2023-11-20T16:35:28 | 2023-11-20T01:30:06 |
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added disclaimer
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PR_kwDOIPDwls5fymCg
| 13,534 |
bump 337
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| 1,999,863,087 |
I_kwDOIPDwls53M30v
| 13,533 |
OpenAI Functions Extraction Chain not returning a list
|
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[] | null | 6 | 2023-11-17T20:18:52 | 2024-01-08T20:30:22 | null |
NONE
| null |
### System Info
python==3.10.13
langchain==0.0.336
pydantic==1.10.13
### Who can help?
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Noticed an error with Pydantic validation when the schema contains optional lists. Here are the steps to reproduce the issue and the error that I am getting.
1. A basic extraction scheme is defined using Pydantic.
```python
from pydantic import BaseModel, Field
class Properties(BaseModel):
person_names: Optional[List[str]] = Field([], description="The names of the people")
person_heights: Optional[List[int]] = Field([], description="The heights of the people")
person_hair_colors: Optional[List[str]] = Field([], description="The hair colors of the people")
```
2. Extraction chain is created to extract the defined fields from a document.
```python
from langchain.chat_models import ChatOpenAI
from langchain.chains import create_extraction_chain_pydantic
llm = ChatOpenAI(
temperature=0, model="gpt-3.5-turbo", request_timeout=20, max_retries=1
)
chain = create_extraction_chain_pydantic(pydantic_schema=Properties, llm=llm)
```
3. When we run the extraction on a document, sometimes the OpenAI function call does not return the `info` field as a `list` but instead as a `dict`. That creates a validation error with Pydantic, even if the extracted fields are perfectly given in the returned dictionary.
```python
inp = """Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde."""
response = chain.run(inp)
```
4. The error and the traceback are as follows
```python
File "pydantic/main.py", line 549, in pydantic.main.BaseModel.parse_raw
File "pydantic/main.py", line 526, in pydantic.main.BaseModel.parse_obj
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for PydanticSchema
info
value is not a valid list (type=type_error.list)
```
### Expected behavior
We can make the Pydantic validation pass by maybe simply casting the `info` field into a list if it is somehow returned as a dictionary by the OpenAI function call.
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| 1,999,837,560 |
I_kwDOIPDwls53Mxl4
| 13,532 |
Output parser fails with index out of range error but doesn't give actual fail reason in case of GooglePalm
|
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NONE
| null |
### System Info
- Python 3.11.5
- google-ai-generativelanguage==0.3.3
- langchain==0.0.336
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] 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
Steps to reproduce the issue:
1. Create a simple GooglePalm llm
2. Run simple chain with some medical related prompt e.g: `Tell me reasons why I am having symptoms of cold`
Error:
```
return self.parse(result[0].text)
~~~~~~^^^
IndexError: list index out of range
```
### Expected behavior
There is a proper error thrown by GooglePalm in completion object
```
Completion(candidates=[],
result=None,
filters=[{'reason': <BlockedReason.SAFETY: 1>}],
safety_feedback=[{'rating': {'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.HIGH: 4>},
'setting': {'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'threshold': <HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE: 2>}}])
```
If same is relayed to user it will be more useful.
|
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| 1,999,767,379 |
PR_kwDOIPDwls5fyO4h
| 13,531 |
Add VertexAI Chuck Norris template
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PR_kwDOIPDwls5fyAv_
| 13,529 |
fix: filtering in opensearch was not working
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NONE
| null |
- changed the filtering inside the query (boolean filtering)
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:**: I simply adjusted the boolean filtering with the proper format.
- **Issue:** the issue #
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PR_kwDOIPDwls5fx0S0
| 13,528 |
add llm kwargs as loading parameter
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NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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**Description:**
Enables runtime parameter injection while loading serialized chains.
**Issue:**
Current approach does not supports runtime credential loading if there is multiple or conditional logic with the LLM settings while loading.
For e.g. If we have serialized object like this one
```python
chain_config = {
"memory": None,
"verbose": False,
"prompt": {
"input_variables": [
"topic"
],
"output_parser": None,
"template": "Tell me a joke about {topic}:",
"template_format": "f-string",
"_type": "prompt"
},
"llm": {
"model_name": "text-davinci-003",
"temperature": 0.9,
"max_tokens": 256,
"top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"n": 1,
"best_of": 1,
"request_timeout": None,
"logit_bias": {},
"_type": "openai"
},
"output_key": "text",
"_type": "llm_chain"
}
```
and if we try to load without environment variable it raises an error because of the api key does not exists in either env variables or inside the serialized object (as expected)
```python
from langchain.chains.loading import load_chain_from_config
chain = load_chain_from_config(chain_config)
# Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. (type=value_error)
```
and what I hgumbly propose in here is using the kwargs which is already defined in current version to add LLM parameters for using as settings like this one
```python
chain = load_chain_from_config(chain_config, **{
"llm_extras": {
"openai": {
"openai_api_key": "<super_secret>"
}
}
})
```
**Dependencies:**
No extra dependency needed.
**Tag maintainer:**
I don't know you are the right person to tag but @eyurtsev I will be very appreciated if there is any direction or review. Thanks in advance!
**Twitter handle:**
This is not a big feature/fix bug but here it is `@cureef`
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I_kwDOIPDwls53L7-O
| 13,526 |
RAG but for fact checking
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CONTRIBUTOR
| null |
### Feature request
I would be very nice to be able to build something similar to RAG, but for checking an assumption quality against a knowledge base.
### Motivation
Currently, RAG allows a semantic search but does not help when it comes to evaluating the quality of a user input, based on your vector db.
The point is not to fact-check the news in a newspaper (must be very hard actually...), but to evaluate truth in a random prompt.
### Your contribution
- prompting
- PR
- doc
- discussions
|
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PR_kwDOIPDwls5fxrNN
| 13,525 |
Introduce search_kwargs for Custom Parameters in BingSearchAPIWrapper
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[] | null | 1 | 2023-11-17T17:22:28 | 2023-11-20T01:51:02 | 2023-11-20T01:51:02 |
CONTRIBUTOR
| null |
Added a `search_kwargs` field to BingSearchAPIWrapper in `bing_search.py,` enabling users to include extra keyword arguments in Bing search queries. This update, like specifying language preferences, adds more customization to searches. The `search_kwargs` seamlessly merge with standard parameters in `_bing_search_results` method.
|
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PR_kwDOIPDwls5fxYbY
| 13,524 |
updated cohere.py to support input_type for new embedding models
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[] | null | 2 | 2023-11-17T16:38:23 | 2023-11-18T00:47:21 | 2023-11-18T00:47:20 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Added input_type field to Cohere API calls for embedding. Newer models require this field be specified for embedding with the value "search_document". Older models still work.
- **Issue:** Allows any model v2.0 > to work with Langchain.
- **Dependencies:**
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** @jay_martz
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls53LhAZ
| 13,523 |
YoutubeLoader translation not working
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[] | null | 5 | 2023-11-17T16:32:37 | 2023-11-20T03:30:37 | null |
NONE
| null |
### System Info
langchain == 0.0.336
python == 3.9.6
### Who can help?
@hwchase17 @eyu
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code:
video_id = YoutubeLoader.extract_video_id("https://www.youtube.com/watch?v=DWUzWvv3xz4")
loader = YoutubeLoader(video_id, add_video_info=True,
language=["en","hi","ru"],
translation = "en")
loader.load()
Output:
[Document(page_content='में रुके शोध सजावट हाउ टू लेट सफल टीम मेंबर्स नो दैट वास नॉट फीयर कॉन्टेक्ट्स रिस्पोंड फॉर एग्जांपल एक्टिव एंड स्ट्रैंथ एंड assistant 3000 1000 कॉन्टैक्ट एंड वे नेवर रिग्रेट थे रिस्पांस यू वांट यू ऑटोमेटेकली गेट नोटिफिकेशन टुडे ओके खुफिया इज द नोटिफिकेशन पाइथन इन नोटिफिकेशन सीनेटर नोटिफिकेशन प्यार सेलिब्रेट विन रिस्पोंस वर्षीय बेटे ईमेल एस वेल एजुकेटेड व्हाट्सएप नोटिफिकेशन इफ यू वांट एनी फीयर अदर टीम मेंबर टू ऑल्सो गेट नोटिफिकेशन व्हेन यू व्हेन यू रिसीवर रिस्पांस फ्रॉम अननोन फीयर कांटेक्ट सुधर रिस्पांस सिस्टम लिफ्ट से ज़ू और यह टीम मेंबर कैन रिस्पोंड इम्युनिटी द न्यू कैंट व ईमेल ऐड्रेस आफ थे पर्सन वरीय स्वीडन से लेफ्ट से अब दूर एक पाठ 7 टारगेट्स ऑयल सुबह रायपुर दिस ईमेल एड्रेस नो अब्दुल विल गेट एनी नोटिफिकेशन फ्रॉम assistant साक्षी व्हेनेवर एनी बडी दिस पॉइंट स्ट्रेन ईमेल अब्दुल विल अलसो गेट डर्टी में अगर सब्जेक्ट लाइन रिस्पांस रिसीवड ए [संगीत]', metadata={'source': 'DWUzWvv3xz4', 'title': 'How to Notify your team member Immediately when a Lead Responds', 'description': 'Unknown', 'view_count': 56, 'thumbnail_url': 'https://i.ytimg.com/vi/DWUzWvv3xz4/hq720.jpg', 'publish_date': '2021-10-04 00:00:00', 'length': 87, 'author': '7Targets AI Sales Assistant'})]
### Expected behavior
The output video transcript should be in English.
|
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PR_kwDOIPDwls5fxEnp
| 13,521 |
Adds support for new OctoAI endpoints
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[] | null | 1 | 2023-11-17T15:47:31 | 2023-11-18T01:15:22 | 2023-11-18T01:15:22 |
CONTRIBUTOR
| null |
small fix to add support for new OctoAI LLM endpoints
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| 1,999,400,883 |
PR_kwDOIPDwls5fw_nd
| 13,520 |
Fix Astra integration tests
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[] | null | 1 | 2023-11-17T15:35:23 | 2023-11-24T08:23:48 | 2023-11-20T01:50:49 |
CONTRIBUTOR
| null |
- **Description:** Fix Astra integration tests that are failing. The `delete` always return True as the deletion is successful if no errors are thrown. I aligned the test to verify this behaviour
- **Tag maintainer:** @hemidactylus
- **Twitter handle:** nicoloboschi
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I_kwDOIPDwls53LDD2
| 13,519 |
Extract SQL information and execute
|
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[] | null | 6 | 2023-11-17T15:27:24 | 2023-11-18T01:16:24 | null |
NONE
| null |
Hello,
### System Info
Langchain Version: 0.0.336
OS: Windows
### Who can help?
No response
### Information
- [x] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [x] LLMs/Chat Models
- [x] Prompts / Prompt Templates / Prompt Selectors
- [x] Vector Stores / Retrievers
- [x] Chains
- [x] SQL Database
### Reproduction
Related to structured data.
I have predefined SQL and variable information. SQL is quite complicated when having to join multiple tables with abbreviated column names. This is a common practical situation. Is there a way to output sqltext and run them with the constraint that the user must fully fill in the variable value?
Example SQL standard information:
<html xmlns:v="urn:schemas-microsoft-com:vml"
xmlns:o="urn:schemas-microsoft-com:office:office"
xmlns:x="urn:schemas-microsoft-com:office:excel"
xmlns="http://www.w3.org/TR/REC-html40">
<head>
<meta name=ProgId content=Excel.Sheet>
<meta name=Generator content="Microsoft Excel 15">
<link id=Main-File rel=Main-File
href="file:///C:/Users/buido/AppData/Local/Temp/msohtmlclip1/01/clip.htm">
<link rel=File-List
href="file:///C:/Users/buido/AppData/Local/Temp/msohtmlclip1/01/clip_filelist.xml">
<style>
<!--table
{mso-displayed-decimal-separator:"\.";
mso-displayed-thousand-separator:"\,";}
@page
{margin:.75in .7in .75in .7in;
mso-header-margin:.3in;
mso-footer-margin:.3in;}
tr
{mso-height-source:auto;}
col
{mso-width-source:auto;}
br
{mso-data-placement:same-cell;}
td
{padding-top:1px;
padding-right:1px;
padding-left:1px;
mso-ignore:padding;
color:black;
font-size:11.0pt;
font-weight:400;
font-style:normal;
text-decoration:none;
font-family:Calibri, sans-serif;
mso-font-charset:0;
mso-number-format:General;
text-align:general;
vertical-align:bottom;
border:none;
mso-background-source:auto;
mso-pattern:auto;
mso-protection:locked visible;
white-space:nowrap;
mso-rotate:0;}
.xl65
{border:.5pt solid windowtext;}
.xl66
{text-align:center;
vertical-align:middle;
border:.5pt solid windowtext;}
.xl67
{text-align:left;
vertical-align:middle;}
.xl68
{font-size:9.0pt;
font-weight:700;
font-family:"Malgun Gothic", sans-serif;
mso-font-charset:0;
text-align:center;
vertical-align:middle;
border:.5pt solid windowtext;}
.xl69
{font-size:9.0pt;
font-family:"Malgun Gothic", sans-serif;
mso-font-charset:0;
text-align:left;
vertical-align:middle;
border:.5pt solid windowtext;
white-space:normal;}
.xl70
{font-size:9.0pt;
font-family:"Malgun Gothic", sans-serif;
mso-font-charset:0;
text-align:center;
vertical-align:middle;
border:.5pt solid windowtext;}
.xl71
{font-weight:700;
border:.5pt solid windowtext;}
-->
</style>
</head>
<body link="#0563C1" vlink="#954F72">
No | Schema | SQL Text | Intent | Condition | Example
-- | -- | -- | -- | -- | --
1 | m_data | Select sum(s.qty) from shipment_info s, product_info p where 1=1 and s.prod_id = p.prod_id and p.prod_type = #prod_type and p.prod_name = #prod_name | Total quantity of goods exported during the day | #prod_type, #prod_name | I want to calculate the total number of model AAA(#prod_name) phones(#prod_type) shipped during the day
100 | … | … | … | … | …
</body>
</html>
### Expected behavior
I expect to output SQLtext when variables are fully supplied and execute sql.
|
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| 1,999,259,475 |
PR_kwDOIPDwls5fwhzB
| 13,518 |
docs: expand the desktop threshold width
|
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PR_kwDOIPDwls5fwhG9
| 13,517 |
fix: VLLMOpenAI -- create() got an unexpected keyword argument 'api_key'
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[] | null | 2 | 2023-11-17T14:27:48 | 2023-11-20T07:50:09 | 2023-11-20T01:49:56 |
CONTRIBUTOR
| null |
The issue was occurring because of an `OpenAI` update in `Completions`. It's not accepting the `api_key` and `api_base` arguments.
The fix is we check for the openai version and if it's v1 or greater then remove these keys from args before passing them to `Compilation.create(...)` when sending from `VLLMOpenAI`
Fixed: #13507
@eyu
@efriis
@hwchase17
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I_kwDOIPDwls53Kamk
| 13,516 |
VectorStoreRetrieverMemory doesn't work with AgentExecutor
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[] | null | 3 | 2023-11-17T14:08:19 | 2023-11-17T14:35:43 | null |
NONE
| null |
### System Info
aiohttp==3.8.4
aiosignal==1.3.1
altair==5.0.1
annotated-types==0.6.0
anyio==3.7.1
asgiref==3.7.2
async-timeout==4.0.2
attrs==23.1.0
backoff==2.2.1
blinker==1.6.2
cachetools==5.3.1
certifi==2023.5.7
cffi==1.15.1
chardet==5.1.0
charset-normalizer==3.2.0
click==8.1.5
clickhouse-connect==0.6.6
coloredlogs==15.0.1
cryptography==41.0.2
dataclasses-json==0.5.9
decorator==5.1.1
deprecation==2.1.0
dnspython==2.4.0
duckdb==0.8.1
ecdsa==0.18.0
et-xmlfile==1.1.0
fastapi==0.104.1
fastapi-pagination==0.12.12
filetype==1.2.0
flatbuffers==23.5.26
frozenlist==1.4.0
gitdb==4.0.10
GitPython==3.1.32
greenlet==2.0.2
h11==0.14.0
hnswlib==0.7.0
httpcore==0.17.3
httptools==0.6.0
humanfriendly==10.0
idna==3.4
importlib-metadata==6.8.0
install==1.3.5
itsdangerous==2.1.2
Jinja2==3.1.2
joblib==1.3.1
jsonpatch==1.33
jsonpointer==2.4
jsonschema==4.18.3
jsonschema-specifications==2023.6.1
langchain==0.0.335
langsmith==0.0.64
loguru==0.7.0
lxml==4.9.3
lz4==4.3.2
Markdown==3.4.3
markdown-it-py==3.0.0
MarkupSafe==2.1.3
marshmallow==3.19.0
marshmallow-enum==1.5.1
mdurl==0.1.2
monotonic==1.6
mpmath==1.3.0
msg-parser==1.2.0
multidict==6.0.4
mypy-extensions==1.0.0
nltk==3.8.1
numexpr==2.8.4
numpy==1.25.1
olefile==0.46
onnxruntime==1.15.1
openai==0.27.8
openapi-schema-pydantic==1.2.4
openpyxl==3.1.2
overrides==7.3.1
packaging==23.1
pandas==2.0.3
pdf2image==1.16.3
pdfminer.six==20221105
Pillow==9.5.0
pinecone-client==2.2.4
posthog==3.0.1
protobuf==4.23.4
psycopg2-binary==2.9.7
pulsar-client==3.2.0
py-automapper==1.2.3
pyarrow==12.0.1
pyasn1==0.5.0
pycparser==2.21
pycryptodome==3.18.0
pydantic==2.5.0
pydantic-settings==2.1.0
pydantic_core==2.14.1
pydeck==0.8.1b0
Pygments==2.15.1
pymongo==4.6.0
Pympler==1.0.1
pypandoc==1.11
python-dateutil==2.8.2
python-docx==0.8.11
python-dotenv==1.0.0
python-jose==3.3.0
python-keycloak==2.16.6
python-magic==0.4.27
python-pptx==0.6.21
pytz==2023.3
pytz-deprecation-shim==0.1.0.post0
PyYAML==6.0
referencing==0.29.1
regex==2023.6.3
requests==2.31.0
requests-toolbelt==1.0.0
rich==13.4.2
rpds-py==0.8.11
rsa==4.9
six==1.16.0
smmap==5.0.0
sniffio==1.3.0
SQLAlchemy==2.0.19
starlette==0.27.0
streamlit==1.24.1
sympy==1.12
tabulate==0.9.0
tenacity==8.2.2
tiktoken==0.4.0
tokenizers==0.13.3
toml==0.10.2
toolz==0.12.0
tornado==6.3.2
tqdm==4.65.0
typing-inspect==0.9.0
typing_extensions==4.8.0
tzdata==2023.3
tzlocal==4.3.1
unstructured==0.8.1
urllib3==2.0.3
uvicorn==0.23.0
uvloop==0.17.0
validators==0.20.0
watchdog==3.0.0
watchfiles==0.19.0
websockets==11.0.3
xlrd==2.0.1
XlsxWriter==3.1.2
yarl==1.9.2
zipp==3.16.2
zstandard==0.21.0
### Who can help?
_No response_
### 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
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
// Omitted LLM and store retriever code
memory = VectorStoreRetrieverMemory(
retriever=retriever,
return_messages=True,
)
tool = create_retriever_tool(
retriever,
"search_egypt_mythology",
"Searches and returns documents about egypt mythology",
)
tools = [tool]
system_message = SystemMessage(
content=(
"Do your best to answer the questions. "
"Feel free to use any tools available to look up "
"relevant information, only if necessary"
)
)
prompt = OpenAIFunctionsAgent.create_prompt(
system_message=system_message,
extra_prompt_messages=[
MessagesPlaceholder(variable_name="history")
],
)
agent = OpenAIFunctionsAgent(llm=__llm, tools=tools, prompt=prompt)
chat = AgentExecutor(
agent=agent,
tools=tools,
memory=memory,
verbose=True,
return_intermediate_steps=True,
)
result = chat({"input":"my question"})
answer = result['output']
```
**The error is:
ValueError: variable history should be a list of base messages, got**
The agent works with the mongodb as chat history though, so it should have worked with the vector memory retriever:
```
mongo_history = MongoDBChatMessageHistory(
connection_string=settings.mongo_connection_str,
session_id=__get_chat_id(user_uuid),
database_name = settings.mongo_db_name,
collection_name = 'chat_history',
)
memory = ConversationBufferMemory(
chat_memory=mongo_history,
memory_key='history',
input_key="input",
output_key="output",
return_messages=True,
)
```
### Expected behavior
Vector retrieval memory should have worked like the MongoDBChatMessageHistory memory.
There is some omission about this issue in the documentation.
|
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| 1,999,214,937 |
PR_kwDOIPDwls5fwX-v
| 13,515 |
Vectorstore relevance fn fix
|
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[] | null | 4 | 2023-11-17T14:06:23 | 2023-11-26T15:53:15 | null |
NONE
| null |
**Description:**
- The changes fixes the normalization in the relevance score functions.
- Some vector databases use `cosine` to indicate `cosine similarity` while some use it to indicate `cosine distance`.
- SemaDB uses `cosine` metric to indicate `cosine distance`. https://rapidapi.com/semafind-semadb/api/semadb/details
- Elasticsearch uses `cosine` metric to indicate `cosine similarity`. https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#dense-vector-params
- Neo4j uses `cosine` metric to indicate `cosine similarity`. https://neo4j.com/docs/cypher-manual/current/indexes-for-vector-search/#indexes-vector-create
- Momento uses `cosine_similarity` metric to indicate `cosine similarity`. https://github.com/momentohq/client-sdk-python/blob/main/src/momento/requests/vector_index/similarity_metric.py
- pgvector uses `cosine distance`. https://github.com/pgvector/pgvector#pgvector
- Pinecone uses `cosine` metric to indicate `cosine similarity`. https://docs.pinecone.io/docs/indexes#distance-metrics
- FAISS does not directly support `cosine similarity` nor `cosine distance`. https://github.com/facebookresearch/faiss/blob/main/faiss/MetricType.h#L44
- TimescaleVector is built on top of pgvector, hence uses `cosine distance`.
- Chroma uses `cosine` metric to indicate `cosine distance`. https://docs.trychroma.com/usage-guide#changing-the-distance-function
- Redis uses `cosine` metric to indicate `cosine distance`. https://redis.io/docs/interact/search-and-query/advanced-concepts/vectors/
**Issue:**
https://github.com/langchain-ai/langchain/issues/11587
https://github.com/langchain-ai/langchain/issues/13437
https://github.com/langchain-ai/langchain/issues/7384
https://github.com/langchain-ai/langchain/issues/11386
https://github.com/langchain-ai/langchain/issues/12697
Twitter handle: dangoldbj
@baskaryan, @eyurtsev, @hwchase17
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I_kwDOIPDwls53JuAM
| 13,513 |
SQLiteCache - store only the hash of the prompt as key instead of the entire prompt
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[] | null | 2 | 2023-11-17T12:40:31 | 2023-11-17T12:42:48 | null |
NONE
| null |
### Feature request
I see the current SQLiteCache is storing the entire prompt message in the SQLite db.
It would be more efficient to just hash the prompt and store this as the key for cache lookup.
### Motivation
My prompt messages are often lengthy and I want to optimize the storage requirements of the Cache.
Keeping an md5 hash as the lookup key would make the lookup also faster rather than doing a string search.
### Your contribution
If this feature makes sense, I can work on this and raise a PR. Let me know.
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PR_kwDOIPDwls5fvgbk
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AstraDB: use includeSimilarity option instead of $similarity
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[] | null | 5 | 2023-11-17T12:04:40 | 2023-11-20T08:40:40 | 2023-11-20T01:54:35 |
CONTRIBUTOR
| null |
- **Description:** AstraDB is going to deprecate the `$similarity` projection property in favor of the ´includeSimilarity´ option flag. I moved all the queries to the new format.
- **Tag maintainer:** @hemidactylus
- **Twitter handle:** nicoloboschi
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PR_kwDOIPDwls5fu6l_
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Fix `MarkdownHeaderTextSplitter` not recognizing tilde-fenced code blocks
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[] | null | 3 | 2023-11-17T10:36:24 | 2023-11-28T16:52:39 | 2023-11-28T16:52:39 |
CONTRIBUTOR
| null |
- **Description:** Previously `MarkdownHeaderTextSplitter` did not consider tilde-fenced code blocks (https://spec.commonmark.org/0.30/#fenced-code-blocks). This PR fixes that.
````md
# Bug caused by previous implementation:
~~~py
foo()
# This is a comment that would be considered header
bar()
~~~
````
- **Tag maintainer:** @baskaryan
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I_kwDOIPDwls53IrUR
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Text splitters inhéritance
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[] | null | 3 | 2023-11-17T10:20:27 | 2023-12-10T10:19:01 | null |
NONE
| null |
### System Info
I am facing an issue using `MarkdownHeaderTextSplitter` class and, after looking at the code, I noticed that the problem might be present in several Text Splitters and I did not find any issue on this, so I create a new one.
I am trying to use `MarkdownHeaderTextSplitter` regarding the `TextSplitter` interface by calling the method `transform_document`.
However, the [`MarkdownHeaderTextSplitter` does not inherit from `TextSplitter`](https://github.com/langchain-ai/langchain/blob/35e04f204ba3e69356a4f8f557ea88f46d2fa389/libs/langchain/langchain/text_splitter.py#L331) and I wondered if it was a justified implementation or just an oversight.
It seems that the [`HTMLHeaderTextSplitter` is in that case too](https://github.com/langchain-ai/langchain/blob/35e04f204ba3e69356a4f8f557ea88f46d2fa389/libs/langchain/langchain/text_splitter.py#L496).
Can you give me some insight on how to use theses classes if the behavior is normal ?
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This code is a good way to reproduce what I am trying to do
```python
from langchain.text_splitter import MarkdownHeaderTextSplitter
from langchain.document_loaders import TextLoader
loader = TextLoader("test.md")
document = loader.load()
transformer = MarkdownHeaderTextSplitter([
("#", "Header 1"),
("##", "Header 2"),
("###", "Header 3"),
])
tr_documents = transformer.transform_documents(document)
```
### Expected behavior
I want this to return a list of documents (`langchain.docstore.document.Document`) splitted in the same way `MarkdownHeaderTextSplitter.split_text` does on the content of a markdown document [as presented in the documentation](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/markdown_header_metadata).
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I_kwDOIPDwls53IlOY
| 13,509 |
Issue: Getting CLOSE_WAIT and too many files open error using ChatAnthropic and StuffDocumentsChain
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NONE
| null |
### Issue you'd like to raise.
Hi, I am trying claude-2 model using the ChatAnthropic library and iterating over my data to call the model end for predictions. Since it's a chain of input, I am using StuffDocumentsChain. I am facing two issues currently.
1. CLOSE_WAIT error after the connection is established with cloud front with anthropic, it keeps creating a new connection for each iteration of data and goes into CLOSE_WAIT state after the process is completed for that iteration after some time.
2. When too many connections go into the close wait state the application gives too many files open error due to the file descriptor handling too many connections.
## Solutions Tried
1. Giving request_default_timeout to handle the CLOSE_WAIT error.
2. Tried creating a function try/catch and final to close any objects within the block.
## Code
```
import warnings
warnings.filterwarnings("ignore")
import jaconv
import numpy as np
import pandas as pd
from langchain import PromptTemplate
from fuzzysearch import find_near_matches
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatAnthropic
from langchain.document_loaders import PDFPlumberLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.schema import HumanMessage, SystemMessage
from langchain.schema.output_parser import OutputParserException
from langchain.chains import LLMChain, StuffDocumentsChain
from langchain.retrievers import BM25Retriever, EnsembleRetriever
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
import time
from json.decoder import JSONDecodeError
from loguru import logger
output_parser = StructuredOutputParser.from_response_schemas([
ResponseSchema(
name="answer",
description="answer to the user's question",
type="string"
),
])
format_instructions = output_parser.get_format_instructions()
def load_data_(file_path):
return PDFPlumberLoader(file_path).load_and_split()
def retriever_(docs):
return EnsembleRetriever(
retrievers=[
BM25Retriever.from_documents(docs, k=4),
Chroma.from_documents(
docs,
OpenAIEmbeddings(
chunk_size=200,
max_retries=60,
show_progress_bar=True
)
).as_retriever(search_kwargs={"k": 4})
],
weights=[0.5, 0.5]
)
def qa_chain():
start_qa = time.time()
qa_chain_output = StuffDocumentsChain(
llm_chain=LLMChain(
llm=ChatAnthropic(
model_name="claude-2",
temperature=0,
top_p=1,
max_tokens_to_sample=500000,
default_request_timeout=30
),
prompt=ChatPromptTemplate(
messages=[
SystemMessage(content='You are a world class & knowledgeable product catalog assistance.'),
HumanMessagePromptTemplate.from_template('Context:\n{context}'),
HumanMessagePromptTemplate.from_template('{format_instructions}'),
HumanMessagePromptTemplate.from_template('Questions:\n{question}'),
HumanMessage(content='Tips: Make sure to answer in the correct format.'),
],
partial_variables={"format_instructions": format_instructions}
)
),
document_variable_name='context',
document_prompt=PromptTemplate(
template='Content: {page_content}',
input_variables=['page_content'],
)
)
logger.info("Finished QA Chain in {}",time.time()-start_qa)
return qa_chain_output
def generate(input_data, retriever):
start_generate = time.time()
doc_query = jaconv.z2h(
jaconv.normalize(
"コード: {}\n製品の種類: {}\nメーカー品名: {}".format(
str(input_data["Product Code"]),
str(input_data["Product Type"]),
str(input_data["Product Name"])
),
"NFKC"
),
kana=False,
digit=True,
ascii=True
)
docs = RecursiveCharacterTextSplitter(
chunk_size=512,
chunk_overlap=128
).split_documents(
retriever.get_relevant_documents(doc_query)
)
docs = [
doc
for doc in docs
if len(
find_near_matches(
str(input_data["Product Code"]),
str(doc.page_content),
max_l_dist=1
)
) > 0
]
pages = list(set([str(doc.metadata["page"]) for doc in docs]))
question = (
"Analyze the provided context and understand to extract the following attributes as precise as possible for {}"
"Attributes:\n"
"{}\n"
"Return it in this JSON format: {}. "
"if no information found or not sure return \"None\"."
)
generate_output = output_parser.parse(
qa_chain().run({
"input_documents": docs,
"question": question.format(
str(input_data["Product Code"]),
'\n'.join([
f" {i + 1}. What is the value of \"{attrib}\"?"
for i, attrib in enumerate(input_data["Attributes"])
]),
str({"answer": {attrib: f"value of {attrib}" for attrib in input_data["Attributes"]}}),
)
})
)["answer"], ';'.join(pages)
logger.info("Finished QA Chain in {}",time.time()-start_generate)
return generate_output
def predict(pdf_file_path:str, csv_file_path:str,output_csv_file_path:str):
# load PDF data
start_load = time.time()
logger.info("Started loading the pdf")
documents = load_data_(pdf_file_path)
logger.info("Finished loading the pdf in {}",time.time()-start_load)
try:
start_retriever = time.time()
logger.info("Started retriever the pdf")
retriever = retriever_(documents)
logger.info("Finished retriever the pdf in {}",time.time()-start_retriever)
except Exception as e:
logger.error(f"Error in Retriever: {e}")
# Load CSV
start_load_csv = time.time()
logger.info("Started Reading Csv")
df = pd.read_csv(
csv_file_path,
low_memory=False,
dtype=object
).replace(np.nan, 'None')
logger.info("Finished Reading Csv in {}",time.time()-start_load_csv)
# Inference
index = 0
result_df = list()
start_generate = time.time()
logger.info("Started Generate Function")
for code, dfx in df.groupby('Product Code'):
start_generate_itr = time.time()
try:
logger.info("Reached index {}",index)
logger.info("Reached index code {}",code)
index = index + 1
result, page = generate(
{
'Product Code': code,
'Product Type': dfx['Product Type'].tolist()[0],
'Product Name': dfx['Product Name'].tolist()[0],
'Attributes': dfx['Attributes'].tolist()
},
retriever
)
dfx['Value'] = dfx['Attributes'].apply(lambda attrib: result.get(attrib, 'None'))
dfx['Page'] = page
logger.info("Generate Function 1 iterations {}",time.time()-start_generate_itr)
except OutputParserException as e:
dfx['Value'] = "None"
dfx['Page'] = "None"
logger.info("Generate Function 1 iterations {}",time.time()-start_generate_itr)
logger.info("JSONDecodeError Exception Occurred in {}",e)
result_df.append(dfx)
logger.info("Finished Generate Function in {}",time.time()-start_generate)
try:
result_df = pd.concat(result_df)
result_df.to_csv(output_csv_file_path)
except FileNotFoundError as e:
logger.error("FileNotFoundError Exception Occurred in {}",e)
return result_df
# df = predict(f"{PDF_FILE}", f"{CSV_FILE}","output.csv")
```
### Suggestion:
I would like to know how to handle the connection CLOSE_WAIT error since I am handling a big amount of data to be processed through Anthropic claude-2
|
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| 1,998,679,576 |
PR_kwDOIPDwls5fuhmU
| 13,508 |
Documentation: Reduce largest static image file size
|
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[] | null | 3 | 2023-11-17T09:33:06 | 2023-11-28T18:00:54 | 2023-11-28T18:00:53 |
CONTRIBUTOR
| null |
- **Description:** Reduce image asset file size used in documentation by running them via lossless image optimization ([tinypng](https://www.npmjs.com/package/tinypng-cli) was used in this case). Images wider than 1916px (the maximum width of an image displayed in documentation) where downsized.
- **Issue:** No issue is created for this, but the large image file assets caused slow documentation load times
- **Dependencies:** No dependencies affected
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I_kwDOIPDwls53IBbf
| 13,507 |
VLLMOpenAI -- create() got an unexpected keyword argument 'api_key'
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[] | null | 3 | 2023-11-17T08:56:07 | 2023-11-20T01:49:57 | 2023-11-20T01:49:57 |
NONE
| null |
### System Info
Python 3.9
langchain 0.0.336
openai 1.3.2
pandas 2.1.3
### Who can help?
@EYU
### Information
- [X] The official example notebooks/scripts
- [ ] 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
First of all, thank you for this great library !
Concerning the bug, I have a vllm openai server (0.2.1.post1) running locally started with the following command:
```
python -m vllm.entrypoints.openai.api_server --model ./zephyr-7b-beta --served-model-name zephyr-7b-beta
```
On the client side, I have this piece of code, slightly adapted from the documentation (only the model name changes).
```python
from langchain.llms import VLLMOpenAI
llm = VLLMOpenAI(
openai_api_key="EMPTY",
openai_api_base="http://localhost:8000/v1",
model_name="zephyr-7b-beta",
)
print(llm("Rome is"))
```
And I got the following error:
```text
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[19], line 6
1 llm = VLLMOpenAI(
2 openai_api_key="EMPTY",
3 openai_api_base="http://localhost:8000/v1",
4 model_name="zephyr-7b-beta",
5 )
----> 6 llm("Rome is")
File ~/softwares/miniconda3/envs/demo/lib/python3.9/site-packages/langchain/llms/base.py:876, in BaseLLM.__call__(self, prompt, stop, callbacks, tags, metadata, **kwargs)
869 if not isinstance(prompt, str):
870 raise ValueError(
871 "Argument `prompt` is expected to be a string. Instead found "
872 f"{type(prompt)}. If you want to run the LLM on multiple prompts, use "
873 "`generate` instead."
874 )
875 return (
--> 876 self.generate(
877 [prompt],
878 stop=stop,
879 callbacks=callbacks,
880 tags=tags,
881 metadata=metadata,
882 **kwargs,
883 )
884 .generations[0][0]
885 .text
886 )
File ~/softwares/miniconda3/envs/demo/lib/python3.9/site-packages/langchain/llms/base.py:656, in BaseLLM.generate(self, prompts, stop, callbacks, tags, metadata, run_name, **kwargs)
641 raise ValueError(
642 "Asked to cache, but no cache found at `langchain.cache`."
643 )
644 run_managers = [
645 callback_manager.on_llm_start(
646 dumpd(self),
(...)
654 )
655 ]
--> 656 output = self._generate_helper(
657 prompts, stop, run_managers, bool(new_arg_supported), **kwargs
658 )
659 return output
660 if len(missing_prompts) > 0:
File ~/softwares/miniconda3/envs/demo/lib/python3.9/site-packages/langchain/llms/base.py:544, in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
542 for run_manager in run_managers:
543 run_manager.on_llm_error(e)
--> 544 raise e
545 flattened_outputs = output.flatten()
546 for manager, flattened_output in zip(run_managers, flattened_outputs):
File ~/softwares/miniconda3/envs/demo/lib/python3.9/site-packages/langchain/llms/base.py:531, in BaseLLM._generate_helper(self, prompts, stop, run_managers, new_arg_supported, **kwargs)
521 def _generate_helper(
522 self,
523 prompts: List[str],
(...)
527 **kwargs: Any,
528 ) -> LLMResult:
529 try:
530 output = (
--> 531 self._generate(
532 prompts,
533 stop=stop,
534 # TODO: support multiple run managers
535 run_manager=run_managers[0] if run_managers else None,
536 **kwargs,
537 )
538 if new_arg_supported
539 else self._generate(prompts, stop=stop)
540 )
541 except BaseException as e:
542 for run_manager in run_managers:
File ~/softwares/miniconda3/envs/demo/lib/python3.9/site-packages/langchain/llms/openai.py:454, in BaseOpenAI._generate(self, prompts, stop, run_manager, **kwargs)
442 choices.append(
443 {
444 "text": generation.text,
(...)
451 }
452 )
453 else:
--> 454 response = completion_with_retry(
455 self, prompt=_prompts, run_manager=run_manager, **params
456 )
457 if not isinstance(response, dict):
458 # V1 client returns the response in an PyDantic object instead of
459 # dict. For the transition period, we deep convert it to dict.
460 response = response.dict()
File ~/softwares/miniconda3/envs/demo/lib/python3.9/site-packages/langchain/llms/openai.py:114, in completion_with_retry(llm, run_manager, **kwargs)
112 """Use tenacity to retry the completion call."""
113 if is_openai_v1():
--> 114 return llm.client.create(**kwargs)
116 retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
118 @retry_decorator
119 def _completion_with_retry(**kwargs: Any) -> Any:
File ~/softwares/miniconda3/envs/demo/lib/python3.9/site-packages/openai/_utils/_utils.py:299, in required_args.<locals>.inner.<locals>.wrapper(*args, **kwargs)
297 msg = f"Missing required argument: {quote(missing[0])}"
298 raise TypeError(msg)
--> 299 return func(*args, **kwargs)
TypeError: create() got an unexpected keyword argument 'api_key'
```
It seems that if I remove the line 158 from `langchain/llms/vllm.py`, the code is working.
### Expected behavior
I expect a completion with no error.
|
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| 1,998,469,952 |
I_kwDOIPDwls53HjtA
| 13,505 |
Sudden failure to initialize
|
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[] | null | 2 | 2023-11-17T07:42:15 | 2023-11-17T07:52:11 | null |
NONE
| null |
### System Info
I was using 0.0.182-rc.1 and I tried upgrading to the latest, 0.0.192
But I'm still getting the error.
Please note: everything was working fine before, I have made no changes.
Did openai change something?
Not sure what is going on here but it looks like its from openai side. If so how do I fix this? Do I wait for a langchain update?
Error:
/node_modules/openai
/src/error.ts:66
return new BadRequestError(status, error, message, headers);
^
Error: 400 '$.input' is invalid. Please check the API reference: https://p
latform.openai.com/docs/api-reference.
at Function.generate (/home/hedgehog/Europ/profiling-github/profiling/
server/node_modules/openai/src/error.ts:66:14)
at OpenAI.makeStatusError (/home/hedgehog/Europ/profiling-github/profi
ling/server/node_modules/openai/src/core.ts:358:21)
at OpenAI.makeRequest (/home/hedgehog/Europ/profiling-github/profiling
/server/node_modules/openai/src/core.ts:416:24)
at processTicksAndRejections (node:internal/process/task_queues:95:5)
at /home/hedgehog/Europ/profiling-github/profiling/server/node_modules
/langchain/dist/embeddings/openai.cjs:223:29
at RetryOperation._fn (/home/hedgehog/Europ/profiling-github/profiling
/server/node_modules/p-retry/index.js:50:12)
@agola11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] 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
Not sure, it broke suddenly without any changes
### Expected behavior
It would initialize properly and execute all the onModuleLoad functions in the Nest js application with the embeddings ready for use.
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| 1,998,415,070 |
I_kwDOIPDwls53HWTe
| 13,504 |
Found Potential Bug! (langchain > llm > base.py)
|
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[] | null | 3 | 2023-11-17T07:05:15 | 2023-11-20T00:38:53 | null |
NONE
| null |
### System Info
Location: langchain/lllms/base.py
I think there's a hidden error in a 'generate' method. (line 554)
Inside of the second 'if' statement, it calls the index of callbacks that may cause the potential error.
`Callbacks` is an object of `CallbackManager`, which is not iterable, so it can not be called as `callbacks[0]`. (see the source code below)
Thanks for @Seuleeee
### Who can help?
@hwchase17 @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] 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
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
langchain/lllms/base.py
```
if (
isinstance(callbacks, list)
and callbacks
and (
isinstance(callbacks[0], (list, BaseCallbackManager))
or callbacks[0] is None
)
):
```
Error message
```
Traceback (most recent call last):
File "/tmp/ipykernel_713/4197562787.py", line 10, in test_rag
result=conv_chain.run(query)
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 505, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 310, in __call__
raise e
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 304, in __call__
self._call(inputs, run_manager=run_manager)
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/conversational_retrieval/base.py", line 159, in _call
answer = self.combine_docs_chain.run(
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 510, in run
return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 310, in __call__
raise e
File "/usr/local/lib/python3.8/dist-packages/langchain/chains/base.py", line 304, in __call__
self._call(inputs, run_manager=run_manager)
...
File "/usr/local/lib/python3.8/dist-packages/langchain/llms/base.py", line 617, in generate
isinstance(callbacks[0], (list, BaseCallbackManager)
TypeError: 'CallbackManager' object is not subscriptable
```
### Expected behavior
Revise the code that has a potential error.
Tell me how can I contribute to fix this code. (test code ...etc)
|
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| 1,998,400,060 |
I_kwDOIPDwls53HSo8
| 13,503 |
implement memory for RetrievalQA.from_chain_type()
|
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[] | null | 2 | 2023-11-17T06:52:41 | 2023-11-17T07:06:51 | null |
NONE
| null |
### Feature request
can you add memory for RetrievalQA.from_chain_type(). I haven't seen any implementation of memory for this kind of RAG chain. It would be nice to have memory and ask questions in context.
### Motivation
I just can't get any memory to work with RetrievalQA.from_chain_type().
### Your contribution
Not right now... I don't have all required knowledge about LLMs
|
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| 1,998,344,562 |
PR_kwDOIPDwls5ftZXB
| 13,502 |
FIX: doc-string - langchain.vectorstores.dashvector.DashVector
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[] | null | 2 | 2023-11-17T06:09:43 | 2023-11-20T06:46:58 | 2023-11-20T02:24:05 |
CONTRIBUTOR
| null |
- **Description:** There are several mistakes in the sample code in the doc-string of `DashVector` class, and this pull request aims to correct them.
The correction code has been tested against latest version (at the time of creation of this pull request) of: `langchain==0.0.336` `dashvector==1.0.6` .
- **Issue:** No issue is created for this.
- **Dependencies:** No dependency is required for this change,
<!-- - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below), -->
- **Twitter handle:** `zeyanglin`
<!-- Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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| 13,501 |
Add rag-opensearch template
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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Adding rag-opensearch template.
|
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I_kwDOIPDwls53G26d
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Why does Redis vector store misses some keys in redistext search. although key and related details exists in index.Issue
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| false | null |
[] | null | 2 | 2023-11-17T05:05:26 | 2023-11-17T05:24:08 | null |
NONE
| null |
### Issue you'd like to raise.
**Description:**
The Redistext search feature in Redis Vector Store functions well with a small number of indexed documents (20-30). However, when the quantity of indexed documents exceeds 400-500, the performance degrades noticeably. Some keys are missed during the Redistext search, and Redis Similarity search retrieves incorrect keys.
**Steps to Reproduce:**
1. Store 400-500 documents in an Index of Redis vector store database.
2. Conduct Redistext search and observe that it is not able to find some of the stored keys.
3. Use Redis Similarity search and notice retrieval of incorrect keys, for some number of keys that are already stored in REdis vector store database.
**Additional Details:**
The issue is not present with smaller datasets (20-30 indexed documents) .
All problematic keys are confirmed to be stored in the Redis Vector Store database with double-checked hash values.
**Expected Behavior:**
Redistext search and Redis Similarity search should provide accurate results even with larger datasets.
**Environment:**
- Official Redis stack docker image (redis/redis-stack-server:latest)
- Docker version 24.0.2
**Any guidance or solutions to improve search performance would be highly appreciated.**
Thank you.
### Suggestion:
**Optimize Redistext Search and Redis Similarity Search for Larger Datasets**
**Proposed Solution:**
Given the observed performance degradation with Redistext search and Redis Similarity search when handling larger datasets (400-500 indexed documents), I suggest reassessing the Redis index similarity search and Redistext search functions. The objective is to ensure accurate results even when the dataset is extensive.
|
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| 1,998,227,516 |
I_kwDOIPDwls53Gog8
| 13,498 |
Output Parser not Work in an Agent Chain
|
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[] | null | 2 | 2023-11-17T03:53:58 | 2023-11-17T04:01:31 | null |
NONE
| null |
### Issue you'd like to raise.
I created a tool in an agent to output some data. The data contains ADMET properties and other properties. Although I have made it very clear that all properties should be kept in the tool function as well as in the output parser, I still can not get the non-ADMET properties in the final output. Here is my code:
def Func_Tool_XYZ(parameters):
print("\n", parameters)
print("触发Func_Tool_ADMET插件")
print("........正在解析ADMET属性..........")
data = {
"SMILES": "C1=CC=CC=C1C1=C(C2=CC=CC=C2)C=CC=C1",
"humanIntestinalAbsorption": "HIA+|0.73",
"caco2Permeability": "None",
"caco2PermeabilityIi": "Caco2+|0.70",
"savePath": "/profile/chemicalAppsResult/admetResult/2023/11/10/2567",
"status": "1",
"favoriteFlag": "0"
}
prompt = f"Properties: {data['SMILES']} {data['humanIntestinalAbsorption']} {data['caco2Permeability']} {data['caco2PermeabilityIi']} {data['savePath']} {data['status']} {data['favoriteFlag']}"
return {
'output': data,
'prompt': prompt
}
tools = [
Tool(
name="Tool XYZ",
func=Func_Tool_XYZ,
description="""
useful when you want to obtain the XYZ data for a molecule.
like: get the XYZ data for molecule X
The input to this tool should be a string, representing the smiles_id.
"""
)
]
class CustomOutputParser(JSONAgentOutputParser):
def parse(self, llm_output: str):
print('jdlfjdlfjdlfjsdlfdjld')
print(llm_output)
smiles = self.data["SMILES"]
return f"SMILES: {smiles}, {llm_output}"
output_parser = CustomOutputParser()
llm = OpenAI(temperature=0, max_tokens=2048)
memory = ConversationBufferMemory(memory_key="chat_history", output_key='output')
memory.clear()
agent = initialize_agent(tools,
llm,
agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
verbose=True,
memory=memory,
return_intermediate_steps=True,
output_parser=output_parser
)
pdf_id = 1111
Human_prompt = f'provide a document. PDF ID is {pdf_id}. This information helps you to understand this document, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\".'
AI_prompt = "Received. "
memory.save_context({"input": Human_prompt}, {"output": AI_prompt})
answer = agent({"input": "Get the ADMET data for molecule X."})
print(answer["output"])
And here is my output messages:
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: Tool XYZ
Action Input: molecule X
molecule X
触发Func_Tool_ADMET插件
........正在解析ADMET属性..........
Observation: {'output': {'SMILES': 'C1=CC=CC=C1C1=C(C2=CC=CC=C2)C=CC=C1', 'humanIntestinalAbsorption': 'HIA+|0.73', 'caco2Permeability': 'None', 'caco2PermeabilityIi': 'Caco2+|0.70', 'savePath': '/profile/chemicalAppsResult/admetResult/2023/11/10/2567', 'status': '1', 'favoriteFlag': '0'}, 'prompt': 'Properties: C1=CC=CC=C1C1=C(C2=CC=CC=C2)C=CC=C1 HIA+|0.73 None Caco2+|0.70 /profile/chemicalAppsResult/admetResult/2023/11/10/2567 1 0'}
Thought: Do I need to use a tool? No
AI: The ADMET data for molecule X is as follows: Human Intestinal Absorption: HIA+|0.73, Caco2 Permeability: None, Caco2 Permeability II: Caco2+|0.70, Save Path: /profile/chemicalAppsResult/admetResult/2023/11/10/2567, Status: 1, Favorite Flag: 0.
> Finished chain.
The ADMET data for molecule X is as follows: Human Intestinal Absorption: HIA+|0.73, Caco2 Permeability: None, Caco2 Permeability II: Caco2+|0.70, Save Path: /profile/chemicalAppsResult/admetResult/2023/11/10/2567, Status: 1, Favorite Flag: 0.
It seems like the non-ADMET properties have been lost.
And, it may be causing by the output parser not working, becuase if it works fine, I should get something like "jdlfjdlfjdlfjsdlfdjld" which is stated in the output parser.
So how should I do next?
### Suggestion:
_No response_
|
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| 1,998,208,034 |
I_kwDOIPDwls53Gjwi
| 13,497 |
faiss中的错误
|
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[] | null | 2 | 2023-11-17T03:29:19 | 2023-11-17T03:40:00 | null |
NONE
| null |
### System Info
When using fail, similarity_search_with_score function, the parameters filter and score_threshold together, the results will be problematic
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1.use similarity_search_with_score
2.parameter：filter and score_threshold
### Expected behavior
result is error
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| 1,998,201,016 |
PR_kwDOIPDwls5fs6i_
| 13,496 |
Fix hunyuan appid type
|
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[] | null | 4 | 2023-11-17T03:23:53 | 2023-11-20T02:23:46 | 2023-11-20T02:23:45 |
CONTRIBUTOR
| null |
- **Description: fix hunyuan appid type
- **Issue: https://github.com/langchain-ai/langchain/pull/12022#issuecomment-1815627855
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| 1,998,187,189 |
I_kwDOIPDwls53Geq1
| 13,495 |
Property Lost in an Agent Chain
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[] | null | 3 | 2023-11-17T03:09:10 | 2023-11-17T03:24:11 | null |
NONE
| null |
### Issue you'd like to raise.
I want to create an ADMET properties prediction tool in an agent. But the result is not so good. Here is my code:
def Func_Tool_XYZ(parameters):
print("\n", parameters)
print("触发Func_Tool_ADMET插件")
print("........正在解析ADMET属性..........")
data = {
"SMILES": "C1=CC=CC=C1C1=C(C2=CC=CC=C2)C=CC=C1",
"humanIntestinalAbsorption": "HIA+|0.73",
"caco2Permeability": "None",
"caco2PermeabilityIi": "Caco2+|0.70",
"savePath": "/profile/chemicalAppsResult/admetResult/2023/11/10/2567",
"status": "1",
"favoriteFlag": "0"
}
prompt = f"Properties: {data['SMILES']} {data['humanIntestinalAbsorption']} {data['caco2Permeability']} {data['caco2PermeabilityIi']} {data['savePath']} {data['status']} {data['favoriteFlag']}"
return {
'output': data,
'prompt': prompt
}
tools = [
Tool(
name="Tool XYZ",
func=Func_Tool_XYZ,
description="""
useful when you want to obtain the XYZ data for a molecule.
like: get the XYZ data for molecule X
The input to this tool should be a string, representing the smiles_id.
"""
)
]
class CustomOutputParser(JSONAgentOutputParser):
def parse(self, llm_output: str):
print('jdlfjdlfjdlfjsdlfdjld')
print(llm_output)
return llm_output
output_parser = CustomOutputParser()
llm = OpenAI(temperature=0, max_tokens=2048)
memory = ConversationBufferMemory(memory_key="chat_history", output_key='output')
memory.clear()
agent = initialize_agent(tools,
llm,
agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
verbose=True,
memory=memory,
return_intermediate_steps=True,
output_parser=output_parser
)
pdf_id = 1111
Human_prompt = f'provide a document. PDF ID is {pdf_id}. This information helps you to understand this document, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\".'
AI_prompt = "Received. "
memory.save_context({"input": Human_prompt}, {"output": AI_prompt})
answer = agent({"input": "Get the ADMET data for molecule X."})
print(answer["output"])
I got the output like this:
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: Tool XYZ
Action Input: molecule X
molecule X
触发Func_Tool_ADMET插件
........正在解析ADMET属性..........
Observation: {'output': {'SMILES': 'C1=CC=CC=C1C1=C(C2=CC=CC=C2)C=CC=C1', 'humanIntestinalAbsorption': 'HIA+|0.73', 'caco2Permeability': 'None', 'caco2PermeabilityIi': 'Caco2+|0.70', 'savePath': '/profile/chemicalAppsResult/admetResult/2023/11/10/2567', 'status': '1', 'favoriteFlag': '0'}, 'prompt': 'Properties: C1=CC=CC=C1C1=C(C2=CC=CC=C2)C=CC=C1 HIA+|0.73 None Caco2+|0.70 /profile/chemicalAppsResult/admetResult/2023/11/10/2567 1 0'}
Thought: Do I need to use a tool? No
AI: The ADMET data for molecule X is as follows: Human Intestinal Absorption: HIA+|0.73, Caco2 Permeability: None, Caco2 Permeability II: Caco2+|0.70, Save Path: /profile/chemicalAppsResult/admetResult/2023/11/10/2567, Status: 1, Favorite Flag: 0.
> Finished chain.
The ADMET data for molecule X is as follows: Human Intestinal Absorption: HIA+|0.73, Caco2 Permeability: None, Caco2 Permeability II: Caco2+|0.70, Save Path: /profile/chemicalAppsResult/admetResult/2023/11/10/2567, Status: 1, Favorite Flag: 0.
As you can see it, the property of "SMILES" has been lost, although I have made it clear that the properties in the tool should be kept as they are originally.
So what happend? How should I revise the code to make it work?
### Suggestion:
_No response_
|
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| 1,998,176,960 |
I_kwDOIPDwls53GcLA
| 13,494 |
very slow make command
|
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[] | null | 6 | 2023-11-17T02:58:21 | 2023-11-17T16:25:27 | null |
COLLABORATOR
| null |
### Issue you'd like to raise.
The `make api_docs_build` command is very slow. This command builds the API Reference documentation. This command is defined in the `Makefile`
### Suggestion:
_No response_
|
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| 1,998,146,316 |
I_kwDOIPDwls53GUsM
| 13,493 |
Adding messages to history doesn't work with custom message types
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[] | null | 3 | 2023-11-17T02:23:36 | 2023-11-17T02:44:00 | null |
NONE
| null |
### System Info
langchain 0.0.320
MacOS 13.14.1
Python 3.9.18
### Who can help?
_No response_
### 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
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm using `DynamoDBChatMessageHistory` for storing chat messages. However, I do not use `Human` or `AI` as my chat prefixes. Using pre-defined methods `add_user_message` and `add_ai_message` won't work for me. I extended the `BaseMessage` class to create a new message type. I get this error when trying to read messages from history.
```
raise ValueError(f"Got unexpected message type: {_type}")
ValueError: Got unexpected message type: User
```
Here's my code:
```
import uuid
from typing_extensions import Literal
from langchain.memory.chat_message_histories import DynamoDBChatMessageHistory
from langchain.schema.messages import BaseMessage
class UserMessage(BaseMessage):
type: Literal["User"] = "User"
class BotMessage(BaseMessage):
type: Literal["Bot"] = "Bot"
session_id = str(uuid.uuid4())
history = DynamoDBChatMessageHistory(
table_name="tableName",
session_id=session_id,
primary_key_name='sessionId'
)
# history.add_user_message("hi!") # works fine
# history.add_ai_message("whats up?") # works fine
history.add_message(UserMessage(content="Hello, I'm the user!")) # saves the message to the table with the correct message type
history.add_message(BotMessage(content="Hello, I'm the bot!!")) # doesn't run due to the above-encountered error
print(history.messages) # throws a ValueError
```
### Expected behavior
The error should not be thrown so that other execution steps are completed.
|
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| 1,998,124,732 |
PR_kwDOIPDwls5fsqoL
| 13,492 |
docs updating `AzureML` notebooks
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[] | null | 1 | 2023-11-17T02:06:03 | 2023-11-20T16:35:47 | 2023-11-20T02:07:13 |
COLLABORATOR
| null |
- Added/updated descriptions and links
|
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| 1,998,073,533 |
PR_kwDOIPDwls5fsgGl
| 13,491 |
Neptune graph updates
|
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[] | null | 2 | 2023-11-17T01:21:23 | 2023-11-17T21:49:31 | 2023-11-17T21:49:31 |
CONTRIBUTOR
| null |
## Description
This PR adds an option to allow unsigned requests to the Neptune database when using the `NeptuneGraph` class.
```python
graph = NeptuneGraph(
host='<my-cluster>',
port=8182,
sign=False
)
```
Also, added is an option in the `NeptuneOpenCypherQAChain` to provide additional domain instructions to the graph query generation prompt. This will be injected in the prompt as-is, so you should include any provider specific tags, for example `<instructions>` or `<INSTR>`.
```python
chain = NeptuneOpenCypherQAChain.from_llm(
llm=llm,
graph=graph,
extra_instructions="""
Follow these instructions to build the query:
1. Countries contain airports, not the other way around
2. Use the airport code for identifying airports
"""
)
```
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| 1,998,018,379 |
PR_kwDOIPDwls5fsUiM
| 13,490 |
TEMPLATES: Add multi-index templates
|
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[] | null | 1 | 2023-11-17T00:22:49 | 2023-11-17T10:00:13 | 2023-11-17T10:00:11 |
COLLABORATOR
| null |
One that routes and one that fuses
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PR_kwDOIPDwls5fsTPK
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Allow openai v1 in all templates that require it
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[] | null | 1 | 2023-11-17T00:16:25 | 2023-11-17T01:10:09 | 2023-11-17T01:10:08 |
COLLABORATOR
| null |
- pyproject change
- lockfiles
|
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PR_kwDOIPDwls5fsPQr
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add input_type to VoyageEmbeddings
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CONTRIBUTOR
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- **Description:** add input_type to VoyageEmbeddings
- **Issue:**
- **Dependencies:**
- **Tag maintainer:**
- **Twitter handle:**
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docs `integrations/vectorstores/` cleanup
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[] | null | 1 | 2023-11-16T23:29:14 | 2023-11-17T02:06:32 | 2023-11-17T01:51:49 |
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- updated titles to consistent format
- added/updated descriptions and links
- format heading
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PR_kwDOIPDwls5fsEbp
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Lock pydantic v1 in app template, cli 0.0.18
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PR_kwDOIPDwls5fr_4f
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renamed `google_vertex_ai_vector_search` notebook
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COLLABORATOR
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The `integrations/vectorstores/matchingengine.ipynb` example has the "Google Vertex AI Vector Search" title. This place this Title in the wrong order in the ToC (it is sorted by the file name).
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- Updated a correspondent comment in docstring
- Rerouted old URL to a new URL
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PR_kwDOIPDwls5fr-m0
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Add Batch Size kwarg to the llm start callback
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[] | null | 2 | 2023-11-16T22:43:50 | 2023-11-22T22:47:58 | 2023-11-22T22:47:57 |
COLLABORATOR
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So you can more easily use the token counts directly from the API endpoint for batch size of 1
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PR_kwDOIPDwls5fr39O
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BUG Fix app_name in cli app new
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Resolve Buffer Clearing Issue in Summary Generation
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[] | null | 9 | 2023-11-16T22:13:11 | 2023-11-17T23:15:37 | 2023-11-16T23:06:20 |
NONE
| null |
**Description:** Despite this class intending to summarize just enough messages to fit within a set context size, the product of this class always contains *ALL* messages *PLUS* a summary system message at the front, ultimately making token count longer rather than shorter. This commit addresses the issue by fixing the single bug where the buffer is constantly reset to the internal message store even after messages are summarized and removed.
**Issues:** Couldn't find one, misread the issue I thought was similar with my last pull request.
@efriis (no LLM this time, I may have drank too much of the kool-aid)
Reason for changes in diff order:
- Local member needed to store truncated message list
- Allow buffer to use local chat list if a moving summary exists
- Let the BaseMessage object have 'system' type specifically (required to pass type checking)
- Store truncated list when `prune` runs
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PR_kwDOIPDwls5frvLK
| 13,480 |
IMPROVEMENT: update assistants output and doc
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[] | null | 1 | 2023-11-16T21:46:40 | 2023-11-17T19:58:56 | 2023-11-17T19:58:55 |
COLLABORATOR
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PR_kwDOIPDwls5frfB0
| 13,479 |
Resolve Buffer Clearing Issue in Summary Generation
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[] | null | 2 | 2023-11-16T21:01:17 | 2023-11-16T21:34:37 | 2023-11-16T21:34:37 |
NONE
| null |
- **Description:** This pull request introduces critical updates to the ConversationSummaryBufferMemory class to address a bug where the summary buffer was not being cleared as expected. Due to this issue, the buffer continued to grow without respect to the max_token_limit, leading to potential performance bottlenecks and skyrocketing context token costs.
- **Issue:** #3922
- **Dependencies:** N/A
Changes:
- Addition of chat_messages List: A new list called chat_messages has been added to track the messages within the current conversation scope. This ensures a clear boundary between messages pending summarization and those already processed.
- Buffer Selection Logic: The logic for selecting the buffer contents has been refined. The system now conditionally utilizes chat_messages when moving_summary_buffer is not empty, ensuring that only new, unsummarized messages are accounted for.
- Summary Message Enhancement: Summary messages are now instantiated with an additional type="system" parameter, providing clarity and aiding in the identification of system-generated summaries.
- Buffer Management in predict_new_summary Method: The predict_new_summary method has been updated to assign the current buffer to chat_messages post-summary prediction, effectively managing the buffer's size.
- Clear Method Update: The clear method now resets the chat_messages to an empty state, which ensures that the buffer is properly cleared when needed.
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I_kwDOIPDwls53E41e
| 13,478 |
VertexAI chat-bison-32k support
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[] | null | 3 | 2023-11-16T20:58:24 | 2023-11-16T22:28:16 | 2023-11-16T22:28:16 |
NONE
| null |
### System Info
google-cloud-aiplatform = "^1.36.4"
langchain = "0.0.336"
python 3.11
```
pydantic.v1.error_wrappers.ValidationError: 1 validation error for VertexAI
__root__
Unknown model publishers/google/models/chat-bison-32k; {'gs://google-cloud-aiplatform/schema/predict/instance/text_generation_1.0.0.yaml': <class 'vertexai.preview.language_models._PreviewTextGenerationModel'>} (type=value_error)
```
```
(Pdb) model_id
'publishers/google/models/codechat-bison-32k'
(Pdb) _publisher_models._PublisherModel(resource_name=model_id)
<google.cloud.aiplatform._publisher_models._PublisherModel object at 0xffff752ba110>
resource name: publishers/google/models/codechat-bison-32k
```
Public Preview: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/chat-bison?hl=en
|name|released|status|
| -------------- | ------------ | -------------|
|chat-bison-32k | 2023-08-29 | Public Preview|
### Who can help?
@ey
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] 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
```
llm = VertexAI(
model_name="chat-bison-32k",
max_output_tokens=8192,
temperature=0.1,
top_p=0.8,
top_k=40,
verbose=True,
# streaming=True,
)
```
### Expected behavior
chat-bison-32k works
There may be some other models released recently that may also need similar update.
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PR_kwDOIPDwls5frZ2q
| 13,477 |
little hack to make elasticsearch search OK on indexes WITHOUT any "metadata" field
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[] | null | 7 | 2023-11-16T20:47:33 | 2023-11-30T20:52:33 | null |
NONE
| null |
**Description:**
This is for enabling the whole thing for people (like me) who just **don't have "metadata" field in their index**... these extra things I put them at the same level as content in the doc, so using your tools was asking me to add a useless metadata field... I prefered to share this.
I Tested it, looks ok! The search returns the expected doc without any metadata part
**Issue:**
Blocking the search if docs DONT have the "metadata" field.
**Dependencies:**
no change
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PR_kwDOIPDwls5fqzQ4
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docs: `integrations/text_embeddings/` cleanup
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[] | null | 1 | 2023-11-16T19:18:27 | 2023-11-16T20:52:26 | 2023-11-16T19:56:53 |
COLLABORATOR
| null |
Updated several notebooks:
- fixed titles which are inconsistent or break the ToC sorting order.
- added missed soruce descriptions and links
- fixed formatting
|
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| 1,997,547,851 |
I_kwDOIPDwls53EClL
| 13,475 |
Issue: minor naming discrepency of MLflowAIGateway
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[] | null | 2 | 2023-11-16T19:00:55 | 2023-11-16T19:03:14 | null |
NONE
| null |
### Issue you'd like to raise.
The MLflowAIGateway module names should be consistent with `MLflow` (upper case ML and lowercase f):
`from langchain.chat_models import ChatMLflowAIGateway`
For embeddings and completions, current module names are:
```
from langchain.llms import MlflowAIGateway
from langchain.embeddings import MlflowAIGatewayEmbeddings
```
should be:
```
from langchain.llms import MLflowAIGateway
from langchain.embeddings import MLflowAIGatewayEmbeddings
```
### Suggestion:
_No response_
|
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| 1,997,529,011 |
I_kwDOIPDwls53D9-z
| 13,474 |
inconsistency parameter requirements for chat_models.ChatMLflowAIGateway
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[] | null | 2 | 2023-11-16T18:51:10 | 2023-11-16T18:57:49 | null |
NONE
| null |
### System Info
databricks Machine Learning Runtime: 13.3
langchain==0.0.319
python == 3.9
### Who can help?
@agola11 @hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] 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. create a route following [this instruction](https://mlflow.org/docs/latest/python_api/mlflow.gateway.html)
```
gateway.create_route(
name="chat",
route_type="llm/v1/chat",
model={
"name": "llama2-70b-chat",
"provider": "mosaicml",
"mosaicml_config": {
"mosaicml_api_key": <key>
}
}
)
```
3. use the template code from the documentation page [here](https://python.langchain.com/docs/integrations/providers/mlflow_ai_gateway#chat-example)
```
from langchain.chat_models import ChatMLflowAIGateway
from langchain.schema import HumanMessage, SystemMessage
chat = ChatMLflowAIGateway(
gateway_uri="databricks",
route="chat",
params={
"temperature": 0.1
}
)
messages = [
SystemMessage(
content="You are a helpful assistant that translates English to French."
),
HumanMessage(
content="Translate this sentence from English to French: I love programming."
),
]
print(chat(messages))
```
This will complain parameter `max_tokens` is not provided. Similarly, if we update the model
```
chat = ChatMLflowAIGateway(
gateway_uri="databricks",
route="chat",
params={
"temperature": 0.1,
"max_tokens": 200
}
)
```
it complains about parameter `stop` is not provided.
However, both parameters are supposed to be non-required parameters based on MLflow's doc [here](https://mlflow.org/docs/latest/llms/gateway/index.html#chat)
### Expected behavior
expecting the example code below should be executed successfully
```
from langchain.chat_models import ChatMLflowAIGateway
from langchain.schema import HumanMessage, SystemMessage
chat = ChatMLflowAIGateway(
gateway_uri="databricks",
route="chat",
params={
"temperature": 0.1
}
)
messages = [
SystemMessage(
content="You are a helpful assistant that translates English to French."
),
HumanMessage(
content="Translate this sentence from English to French: I love programming."
),
]
print(chat(messages))
```
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| 1,997,520,825 |
PR_kwDOIPDwls5fqmIW
| 13,473 |
Update chain of note README.md
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[] | null | 1 | 2023-11-16T18:47:22 | 2023-11-16T18:47:32 | 2023-11-16T18:47:27 |
COLLABORATOR
| null | null |
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| 1,997,513,136 |
PR_kwDOIPDwls5fqkYj
| 13,472 |
Update azuresearch.py following recent change from azure-search-documents library
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[] | null | 3 | 2023-11-16T18:43:54 | 2023-12-05T04:29:21 | 2023-12-05T04:29:20 |
CONTRIBUTOR
| null |
- **Description:**
Reference library azure-search-documents has been adapted in version 11.4.0:
1. Notebook explaining Azure AI Search updated with most recent info
2. HnswVectorSearchAlgorithmConfiguration --> HnswAlgorithmConfiguration
3. PrioritizedFields(prioritized_content_fields) --> SemanticPrioritizedFields(content_fields)
4. SemanticSettings --> SemanticSearch
5. VectorSearch(algorithm_configurations) --> VectorSearch(configurations)
--> Changes now reflected on Langchain: default vector search config from langchain is now compatible with officially released library from Azure.
- **Issue:**
Issue creating a new index (due to wrong class used for default vector search configuration) if using latest version of azure-search-documents with current langchain version
- **Dependencies:** azure-search-documents>=11.4.0,
- **Tag maintainer:** ,
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| 1,997,471,993 |
PR_kwDOIPDwls5fqbjr
| 13,471 |
fix for `integratons/document_loaders` sidebar
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[] | null | 1 | 2023-11-16T18:16:25 | 2023-11-16T20:52:56 | 2023-11-16T20:02:30 |
COLLABORATOR
| null |
The current `integrations/document_loaders/` sidebar has the `example_data` item, which is a menu with a single item: "Notebook".
It is happening because the `integrations/document_loaders/` folder has the `example_data/notebook.md` file that is used to autogenerate the above menu item.
- removed an example_data/notebook.md file. Docusaurus doesn't have simple ways to fix this problem (to exclude folders/files from an autogenerated sidebar). Removing this file didn't break any existing examples, so this fix is safe.
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| 13,470 |
Bagatur/chain of note
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[] | null | 1 | 2023-11-16T17:57:35 | 2023-11-16T18:34:05 | 2023-11-16T18:34:04 |
COLLABORATOR
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| 1,997,280,488 |
PR_kwDOIPDwls5fpw4I
| 13,469 |
Add ahandle_event to _all_
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[] | null | 1 | 2023-11-16T16:39:32 | 2023-11-16T20:46:22 | 2023-11-16T20:46:21 |
COLLABORATOR
| null |
Add ahandle_event for backwards compatibility as it is used by langserve
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PR_kwDOIPDwls5fpiqT
| 13,468 |
fixed: Missing escape in llms.utils.enforce_stop_tokens()
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[] | null | 4 | 2023-11-16T16:10:33 | 2023-11-17T22:09:25 | 2023-11-16T20:39:36 |
CONTRIBUTOR
| null |
**Problem:**
The problem with the enforce_stop_tokens method as currently implemented is that it directly joins the stop words with the | character for the re.split function. This approach works well if the stop words don't contain any regex special characters. However, if they do, this can lead to unintended behavior because the special characters are interpreted as part of the regex pattern, not as literal characters.
**Solution**
To fix this issue, each stop word should be escaped so that any special characters it contains are treated as literals.
fixed: #13397
|
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PR_kwDOIPDwls5fpdUS
| 13,467 |
Bugfix: OpenAIFunctionsAgentOutputParser doesn't handle functions with no args
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[] | null | 2 | 2023-11-16T15:57:18 | 2023-11-17T00:47:06 | 2023-11-17T00:47:06 |
CONTRIBUTOR
| null |
**Description/Issue:**
When OpenAI calls a function with no args, the args are `""` rather than `"{}"`. Then `json.loads("")` blows up. This PR handles it correctly.
**Dependencies:** None
|
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PR_kwDOIPDwls5fpYpC
| 13,466 |
Introduce `search_kwargs` for Custom Parameters in BingSearchAPIWrapper
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[] | null | 1 | 2023-11-16T15:46:56 | 2023-11-17T17:14:40 | 2023-11-17T17:14:40 |
CONTRIBUTOR
| null |
Added a `search_kwargs` field to BingSearchAPIWrapper in `bing_search.py`, enabling users to include extra keyword arguments in Bing search queries. This update, like specifying language preferences, adds more customization to searches. The `search_kwargs` seamlessly merge with standard parameters in `_bing_search_results` method.
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PR_kwDOIPDwls5fpR0j
| 13,465 |
Add serialisation arguments to Bedrock and ChatBedrock
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CONTRIBUTOR
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I_kwDOIPDwls53CRYS
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ImportError: cannot import name 'Row' from 'sqlalchemy'
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[] | null | 6 | 2023-11-16T15:08:51 | 2023-12-04T15:25:32 | null |
NONE
| null |
### System Info
Version: langchain 0.0.336
Version: sqlalchemy 2.0.1
File "/Users/anonymous/code/anon/anon/utils/cache.py", line 40, in set_cache
from langchain.cache import SQLiteCache
File "/Users/anonymous/dotfiles/virtualenvs/aiproject/lib/python3.11/site-packages/langchain/cache.py", line 45, in <module>
from sqlalchemy import Column, Integer, Row, String, create_engine, select
ImportError: cannot import name 'Row' from 'sqlalchemy' (/Users/anonymous/dotfiles/virtualenvs/aiproject/lib/python3.11/site-packages/sqlalchemy/__init__.py)
### Who can help?
_No response_
### 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
from langchain.cache import SQLiteCache
### Expected behavior
Doesn't throw exception
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I_kwDOIPDwls53Bghi
| 13,461 |
ConversationalRetrievalChain
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open
| false | null |
[] | null | 5 | 2023-11-16T13:42:31 | 2023-11-16T15:38:00 | null |
NONE
| null |
### System Info
I am facing an issue during the testing of my chatbot, here is below i attached a screenshot which can show you the exact issue.
But when i hit the api again with first small letter 'what' instead of 'What' then it return correct response.
Could someone please help me identify the exact issue? thanks
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [x] LLMs/Chat Models
- [x] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [x] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
{
"question": "what are the reactions associated with hydromorphone use in animals?"
}
### Expected behavior
|
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| 1,996,800,557 |
I_kwDOIPDwls53BMIt
| 13,460 |
Issue: Sitemap Loader not fetching
|
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| false | null |
[] | null | 5 | 2023-11-16T12:56:45 | 2023-11-16T15:30:14 | null |
NONE
| null |
### Issue you'd like to raise.
Wrote the following code in Google Colab but it is unable to fetch the data. Kindly help.
from langchain.document_loaders.sitemap import SitemapLoader
sitemap_loader = SitemapLoader(web_path="https://langchain.readthedocs.io/sitemap.xml")
docs = sitemap_loader.load()
print(docs)
### Suggestion:
_No response_
|
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| 1,996,796,905 |
PR_kwDOIPDwls5foF1-
| 13,459 |
Add optional arguments to FalkorDBGraph constructor
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| false | null |
[] | null | 2 | 2023-11-16T12:54:43 | 2023-11-17T02:15:53 | 2023-11-17T02:15:40 |
CONTRIBUTOR
| null |
**Description:** Add optional arguments to FalkorDBGraph constructor
**Tag maintainer:** baskaryan
**Twitter handle:** @g_korland
|
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