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https://api.github.com/repos/langchain-ai/langchain/issues/8487
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| 1,827,868,223 |
I_kwDOIPDwls5s8w4_
| 8,487 |
Web Research Retriever error, code run in free Colab
|
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[] | null | 2 | 2023-07-30T09:28:34 | 2023-07-30T14:30:15 | 2023-07-30T14:30:15 |
NONE
| null |
### System Info
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires requests==2.27.1, but you have requests 2.31.0 which is incompatible.
Successfully installed backoff-2.2.1 chroma-hnswlib-0.7.1 chromadb-0.4.3 coloredlogs-15.0.1 dataclasses-json-0.5.13 fastapi-0.99.1 h11-0.14.0 httptools-0.6.0 humanfriendly-10.0 langchain-0.0.247 langsmith-0.0.15 marshmallow-3.20.1 monotonic-1.6 mypy-extensions-1.0.0 onnxruntime-1.15.1 openai-0.27.8 openapi-schema-pydantic-1.2.4 overrides-7.3.1 posthog-3.0.1 pulsar-client-3.2.0 pypika-0.48.9 python-dotenv-1.0.0 requests-2.31.0 starlette-0.27.0 tokenizers-0.13.3 typing-inspect-0.9.0 uvicorn-0.23.1 uvloop-0.17.0 watchfiles-0.19.0 websockets-11.0.3
### Who can help?
@agola11
Trying to run Web Research Retriever code available in [Langchain docs](https://python.langchain.com/docs/modules/data_connection/retrievers/web_research)
in free Google Colab,
after running code block:
```
user_input = "How do LLM Powered Autonomous Agents work?"
qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llm,retriever=web_research_retriever)
result = qa_chain({"question": user_input})
result
```
I got the following info log and error:
```
INFO:langchain.retrievers.web_research:Generating questions for Google Search ...
INFO:langchain.retrievers.web_research:Questions for Google Search (raw): {'question': 'What is Task Decomposition in LLM Powered Autonomous Agents?', 'text': LineList(lines=['1. How do LLM powered autonomous agents utilize task decomposition?\n', '2. Can you explain the concept of task decomposition in LLM powered autonomous agents?\n'])}
INFO:langchain.retrievers.web_research:Questions for Google Search: ['1. How do LLM powered autonomous agents utilize task decomposition?\n', '2. Can you explain the concept of task decomposition in LLM powered autonomous agents?\n']
INFO:langchain.retrievers.web_research:Searching for relevat urls ...
INFO:langchain.retrievers.web_research:Searching for relevat urls ...
INFO:langchain.retrievers.web_research:Search results: [{'title': "LLM Powered Autonomous Agents | Lil'Log", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Agent System Overview In a LLM-powered autonomous agent system, ... Task decomposition can be done (1) by LLM with simple prompting like\xa0...'}]
INFO:langchain.retrievers.web_research:Searching for relevat urls ...
INFO:langchain.retrievers.web_research:Search results: [{'title': "LLM Powered Autonomous Agents | Lil'Log", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1." , "What are the subgoals for achieving XYZ?" , (2)\xa0...'}]
INFO:langchain.retrievers.web_research:New URLs to load: ['https://lilianweng.github.io/posts/2023-06-23-agent/']
INFO:langchain.retrievers.web_research:Indexing new urls...
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
[<ipython-input-13-efc9ad25b93a>](https://localhost:8080/#) in <cell line: 5>()
3 logging.getLogger("langchain.retrievers.web_research").setLevel(logging.INFO)
4 user_input = "What is Task Decomposition in LLM Powered Autonomous Agents?"
----> 5 docs = web_research_retriever.get_relevant_documents(user_input)
4 frames
[/usr/local/lib/python3.10/dist-packages/langchain/schema/retriever.py](https://localhost:8080/#) in get_relevant_documents(self, query, callbacks, tags, metadata, **kwargs)
191 except Exception as e:
192 run_manager.on_retriever_error(e)
--> 193 raise e
194 else:
195 run_manager.on_retriever_end(
[/usr/local/lib/python3.10/dist-packages/langchain/schema/retriever.py](https://localhost:8080/#) in get_relevant_documents(self, query, callbacks, tags, metadata, **kwargs)
184 _kwargs = kwargs if self._expects_other_args else {}
185 if self._new_arg_supported:
--> 186 result = self._get_relevant_documents(
187 query, run_manager=run_manager, **_kwargs
188 )
[/usr/local/lib/python3.10/dist-packages/langchain/retrievers/web_research.py](https://localhost:8080/#) in _get_relevant_documents(self, query, run_manager)
202 html2text = Html2TextTransformer()
203 logger.info("Indexing new urls...")
--> 204 docs = loader.load()
205 docs = list(html2text.transform_documents(docs))
206 docs = self.text_splitter.split_documents(docs)
[/usr/local/lib/python3.10/dist-packages/langchain/document_loaders/async_html.py](https://localhost:8080/#) in load(self)
134 """Load text from the url(s) in web_path."""
135
--> 136 results = asyncio.run(self.fetch_all(self.web_paths))
137 docs = []
138 for i, text in enumerate(results):
[/usr/lib/python3.10/asyncio/runners.py](https://localhost:8080/#) in run(main, debug)
31 """
32 if events._get_running_loop() is not None:
---> 33 raise RuntimeError(
34 "asyncio.run() cannot be called from a running event loop")
35
RuntimeError: asyncio.run() cannot be called from a running event loop
```
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
```
! pip install langchain openai chromadb google-api-python-client
import os
import logging
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chat_models.openai import ChatOpenAI
from langchain.retrievers.web_research import WebResearchRetriever
from langchain.utilities import GoogleSearchAPIWrapper
os.environ["OPENAI_API_KEY"] = "****"
os.environ["GOOGLE_CSE_ID"] = "*****"
os.environ["GOOGLE_API_KEY"] = "******"
# Vectorstore
vectorstore = Chroma(embedding_function=OpenAIEmbeddings(),persist_directory="./chroma_db_oai")
# LLM
llm = ChatOpenAI(temperature=0)
# Search
search = GoogleSearchAPIWrapper()
from langchain.chains import RetrievalQAWithSourcesChain
user_input = "How do LLM Powered Autonomous Agents work?"
qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llm,retriever=web_research_retriever)
result = qa_chain({"question": user_input})
result
```
### Expected behavior
It works as shown in docs' page.
|
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I_kwDOIPDwls5s8vyE
| 8,486 |
no support for loading 70B models via llamacpp
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[] | null | 3 | 2023-07-30T09:12:42 | 2023-11-07T16:06:33 | 2023-11-07T16:06:32 |
NONE
| null |
### System Info
LangChain 0.0.242
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
tried to load https://huggingface.co/TheBloke/StableBeluga2-70B-GGML model to work with lanchain's Llamacpp :
`llm = LlamaCpp(model_path="./stablebeluga2-70b.ggmlv3.q4_0.bin", n_gpu_layers=n_gpu_layers,
n_batch=n_batch, n_ctx=8192, input={"temperature": 0.01},n_threads=8)
llm_chain = LLMChain(llm=llm, prompt=prompt)`
i see that there is no support in passing n_gqa=8 parameter that according to [https://github.com/abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) should be used for 70B models
the error i get is :
error loading model: llama.cpp: tensor 'layers.0.attention.wk.weight' has wrong shape; expected 8192 x 8192, got 8192 x 1024
### Expected behavior
model should be loaded successfuly
|
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PR_kwDOIPDwls5WurEQ
| 8,485 |
add llama index retriever test
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[] | null | 3 | 2023-07-30T08:33:23 | 2023-11-17T02:53:34 | 2023-11-17T02:53:34 |
CONTRIBUTOR
| null |
# What
- add llama index retriever test
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: add llama index retriever test
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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IteratorChain
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[] | null | 2 | 2023-07-30T07:43:44 | 2023-11-05T16:04:59 | 2023-11-05T16:04:58 |
NONE
| null |
### Feature request
I propose the addition of a foundational chain feature, namely, the IteratorChain to LangChain. Unlike a convenience function, this feature is intended to enhance the flexibility and usability of LangChain by allowing it to handle collections of inputs for any existing chain, propagating them sequentially or asynchronously.
```python
from langchain.chains import IteratorChain, LLMChain
llm_chain = LLMChain(...)
iterator_chain = IteratorChain(llm_chain)
inputs = [{"text": "Hello"}, {"text": "World"}]
outputs = iterator_chain.run(inputs)
```
In the current LangChain framework, when dealing with lists or collections of inputs, developers are required to manually loop through the input list and call the run method for each item. This approach not only results in more code but also creates challenges with LangChain's features like LangSmith logging, especially when lists are dynamically generated from previous chains.
The IteratorChain would encapsulate this looping, resulting in cleaner code and better integration with LangSmith logging and other LangChain features.
### Motivation
The proposed IteratorChain could address a current issue I'm having when dealing with nested lists of inputs. Let's consider the current SequentialChain setup below:
```python
SequentialChain(
chains=[
ChainA(input_variables=["inputA"], output_key="outputA"),
TransformChain(input_variables=["outputA", "auxiliary_input"], output_variables=["outputB"], transform=process_A_to_B),
TransformChain(input_variables=["outputB", "auxiliary_input"], output_variables=["outputC"], transform=refine_B_to_C),
TransformChain(input_variables=["outputC", "auxiliary_input"], output_variables=["final_output"], transform=process_C_to_final)
],
input_variables=["inputA", "auxiliary_input"],
output_variables=["final_output"]
)
```
In this setup, process_A_to_B and refine_B_to_C are both creating lists of inputs for further chains. However, these lists of inputs are currently not being processed elegantly. We have to loop manually over the list and call the underlying chain for each item. This not only leads to cumbersome code, but also hinders proper interaction with the LangSmith logging feature.
```python
def process_A_to_B(params) -> List[Dict[str, Any]]:
...
for item in items:
chainB = SomeChain(...)
output = chainB.run({"input": item, "auxiliary_input": auxiliary_input})
...
return {"outputB": outputs}
```
```python
def refine_B_to_C(params) -> List[Dict[str, Any]]:
...
for item in items:
chainC = AnotherChain(...)
output = chainC.run({"input": item, "auxiliary_input": auxiliary_input})
...
return {"outputC": outputs}
```
The addition of an IteratorChain feature would address these issues. It will encapsulate the manual loop and make the list handling process more intuitive and efficient, ensuring proper integration with LangSmith logging and other LangChain features.
### Your contribution
I'm prepared to contribute to the development of this feature, if decided that it is a good addition and is not already possible using existing capabilities.
|
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| 8,483 |
ConnectionResetError: [Errno 54] Connection reset by peer
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[] | null | 4 | 2023-07-30T07:07:22 | 2023-11-09T16:08:15 | 2023-11-09T16:08:14 |
NONE
| null |
### System Info
I use this code:
```
search = GoogleSearchAPIWrapper()
tool = Tool(
name="Google Search",
description="Search Google for recent results.",
func=search.run,
)
tool.run("Obama's first name?")
```
The result looks fine. But when I use the code below
```
search = GoogleSearchAPIWrapper()
tools = [
Tool(
name="google-search",
func=search.run,
description="useful when you need to search google to answer questions about current events"
)
]
agent = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
max_iterations=6)
response = agent("What is the latest news about the Mars rover?")
print(response)
```
I get this error:
```
File /opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/googleapiclient/http.py:191, in _retry_request(http, num_retries, req_type, sleep, rand, uri, method, *args, **kwargs)
189 try:
190 exception = None
--> 191 resp, content = http.request(uri, method, *args, **kwargs)
192 # Retry on SSL errors and socket timeout errors.
193 except _ssl_SSLError as ssl_error:
File /opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/httplib2/__init__.py:1724, in Http.request(self, uri, method, body, headers, redirections, connection_type)
1722 content = b""
1723 else:
-> 1724 (response, content) = self._request(
1725 conn, authority, uri, request_uri, method, body, headers, redirections, cachekey,
1726 )
1727 except Exception as e:
1728 is_timeout = isinstance(e, socket.timeout)
File /opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/httplib2/__init__.py:1444, in Http._request(self, conn, host, absolute_uri, request_uri, method, body, headers, redirections, cachekey)
1441 if auth:
1442 auth.request(method, request_uri, headers, body)
-> 1444 (response, content) = self._conn_request(conn, request_uri, method, body, headers)
1446 if auth:
1447 if auth.response(response, body):
File /opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/site-packages/httplib2/__init__.py:1396, in Http._conn_request(self, conn, request_uri, method, body, headers)
1394 pass
1395 try:
-> 1396 response = conn.getresponse()
1397 except (http.client.BadStatusLine, http.client.ResponseNotReady):
1398 # If we get a BadStatusLine on the first try then that means
1399 # the connection just went stale, so retry regardless of the
1400 # number of RETRIES set.
1401 if not seen_bad_status_line and i == 1:
File /opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/http/client.py:1348, in HTTPConnection.getresponse(self)
1346 try:
1347 try:
-> 1348 response.begin()
1349 except ConnectionError:
1350 self.close()
File /opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/http/client.py:316, in HTTPResponse.begin(self)
314 # read until we get a non-100 response
315 while True:
--> 316 version, status, reason = self._read_status()
317 if status != CONTINUE:
318 break
File /opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/http/client.py:277, in HTTPResponse._read_status(self)
276 def _read_status(self):
--> 277 line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
278 if len(line) > _MAXLINE:
279 raise LineTooLong("status line")
File /opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/socket.py:669, in SocketIO.readinto(self, b)
667 while True:
668 try:
--> 669 return self._sock.recv_into(b)
670 except timeout:
671 self._timeout_occurred = True
File /opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/ssl.py:1241, in SSLSocket.recv_into(self, buffer, nbytes, flags)
1237 if flags != 0:
1238 raise ValueError(
1239 "non-zero flags not allowed in calls to recv_into() on %s" %
1240 self.__class__)
-> 1241 return self.read(nbytes, buffer)
1242 else:
1243 return super().recv_into(buffer, nbytes, flags)
File /opt/homebrew/anaconda3/envs/common_3.8/lib/python3.8/ssl.py:1099, in SSLSocket.read(self, len, buffer)
1097 try:
1098 if buffer is not None:
-> 1099 return self._sslobj.read(len, buffer)
1100 else:
1101 return self._sslobj.read(len)
ConnectionResetError: [Errno 54] Connection reset by peer
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
search = GoogleSearchAPIWrapper()
tools = [
Tool(
name="google-search",
func=search.run,
description="useful when you need to search google to answer questions about current events"
)
]
agent = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
max_iterations=6)
response = agent("What is the latest news about the Mars rover?")
print(response)
```
### Expected behavior
Hope to know the reason and how to prevent the issue.
|
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| 1,827,822,489 |
PR_kwDOIPDwls5Wulc_
| 8,482 |
Fix documentation for from_documents signature
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closed
| false | null |
[] | null | 1 | 2023-07-30T06:25:33 | 2023-07-30T20:24:44 | 2023-07-30T20:24:44 |
CONTRIBUTOR
| null |
Docs for from_documents() were outdated as seen in https://github.com/langchain-ai/langchain/issues/8457 .
fixes #8457
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
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- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 8,481 |
FallbackChatOpenAI
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[] | null | 2 | 2023-07-30T06:04:45 | 2023-11-13T06:05:41 | 2023-11-13T06:05:40 |
NONE
| null |
# Description
This PR presents the class `GptTurboWith16kFallback` that extends `ChatOpenAI` with a fallback mechanism for larger language models when encountering a "maximum context length" error.
## Issue
This addition aligns with the discussion [here](https://github.com/langchain-ai/langchain/discussions/1802). If an existing solution or further discussions regarding token-specific fallback mechanisms are available, I'd appreciate being directed to them.
## Dependencies
No additional dependencies.
## Additional context
While this PR provides a solution for automatic fallbacks when handling larger input contexts, it is merely a stepping stone. The goal is to broaden this concept and allow for fallback models with larger context capabilities across different models, not just ChatOpenAI. We aim to refine this idea and warmly welcome further contributions.
## Maintainers
@baskaryan, @hwchase17
## Proposed changes
`GptTurboWith16kFallback` introduces a `fallback_model`attribute. This attribute specifies a larger language model to switch to when encountering a "maximum context length" error. The default fallback_model is "gpt-3.5-turbo-16k-0613", and it can be customized during class initialization.
Overriding of the `_generate `method from `ChatOpenAI`. If a "maximum context length" error occurs during the generation, a new `GptTurboWith16kFallback` instance is created with the fallback_model and a new generation attempt is made.
Moreover, it's plausible that the existing retry logic can handle this situation. If there's a way to leverage that, I'd love to understand it better.
I highly appreciate your feedback and contributions to enhancing this concept. As a new OSS contributor, I'm seeking your understanding and assistance in making this contribution more effective. Thank you.
|
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3 different google search API varies a lot
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[] | null | 2 | 2023-07-30T06:00:36 | 2023-11-13T16:07:20 | 2023-11-13T16:07:19 |
NONE
| null |
### System Info
Langchain 0.0.239
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The results I got from 3 different Google Search API Wrapper. The same thing happenes to the Agent with tool.
```python
from langchain.utilities import GoogleSerperAPIWrapper
search = GoogleSerperAPIWrapper()
search.run("What's the Tesla stock today?")
```
'266.44 +10.73 (4.20%)'
```python
from langchain.utilities import SerpAPIWrapper
search = SerpAPIWrapper()
print(search.run("What's the Tesla stock today?"))
```
Tesla, Inc. is an American multinational automotive and clean energy company headquartered in Austin, Texas. Tesla designs and manufactures electric vehicles, stationary battery energy storage devices from home to grid-scale, solar panels and solar roof tiles, and related products and services.
```python
from langchain.utilities import GoogleSearchAPIWrapper
search = GoogleSearchAPIWrapper()
search.run("What's the Tesla stock today?")
```
"In this week's video, I cover need-to-know news items related to Tesla (NASDAQ: TSLA) during the week of July 24. Today's video will focus on what Tesla\xa0... TSLA | Complete Tesla Inc. stock news by MarketWatch. View real-time stock prices ... Here's What One Survey Revealed About Their Perception Of Elon Musk. Tesla is accelerating the world's transition to sustainable energy with electric cars, solar and integrated renewable energy solutions for homes and\xa0... Tesla's mission is to accelerate the world's transition to sustainable energy. Today, Tesla builds not only all-electric vehicles but also infinitely\xa0... Get Tesla Inc (TSLA:NASDAQ) real-time stock quotes, news, price and financial ... Please contact cnbc support to provide details about what went wrong. Feb 2, 2023 ... Tesla stock soared 41% in January, its best month since October 2021, leaving investors breathless and wondering what to do next. TSLA: Get the latest Tesla stock price and detailed information including TSLA news, historical charts and ... What are analysts forecasts for Tesla stock? Aug 25, 2022 ... The question now is what do Tesla investors expect the stock to do after the split. Tesla (ticker: TSLA) stock on Thursday was trading at\xa0... Aug 25, 2022 ... The electric car company completed a 3-for-1 stock split after the closing bell Wednesday. So one share now costs a third of what it did a day\xa0... Jun 30, 2023 ... Doubling your money isn't easy, and doubling it in just six months is even more difficult, so investors now have to decide: Is It time to take\xa0..."
### Expected behavior
I except the result from these 3 API should be similar.
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add test to ensure values in time weighted retriever are updated
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[] | null | 1 | 2023-07-30T05:43:27 | 2023-07-30T18:42:25 | 2023-07-30T18:42:25 |
CONTRIBUTOR
| null |
# What
- add test to ensure values in time weighted retriever are updated
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: add test to ensure values in time weighted retriever are updated
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @MlopsJ
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
|
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PR_kwDOIPDwls5WuiYN
| 8,478 |
doc(prompts): Add redirects in Prompt subcategories pages
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[] | null | 2 | 2023-07-30T05:09:55 | 2023-07-31T18:07:06 | 2023-07-31T02:38:52 |
CONTRIBUTOR
| null |
- Description: Fixes broken links in some Prompts subcategories in documentation (Example Selectors, Prompt Templates)
- Issue: #8477 (Fixes #8477)
- Dependencies: None
- Tag maintainer: @baskaryan
- Twitter handle: [@BharatR123](https://twitter.com/BharatR123)
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Fixes broken links in some Prompts subcategories in documentation (Example Selectors, Prompt Templates)
- Issue: #8477,
- Dependencies: None,
- Tag maintainer: @baskaryan,
- Twitter handle: [@BharatR123](https://twitter.com/BharatR123)
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,827,802,229 |
I_kwDOIPDwls5s8gx1
| 8,477 |
DOC: Broken Links in Prompts Sub Categories Pages
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[] | null | 0 | 2023-07-30T04:41:57 | 2023-07-31T02:38:53 | 2023-07-31T02:38:53 |
CONTRIBUTOR
| null |
### Issue with current documentation:
The INFO: Python Guide links in both https://docs.langchain.com/docs/components/prompts/prompt-template and https://docs.langchain.com/docs/components/prompts/example-selectors are both broken (similar to #8105)
### Idea or request for content:
The pages have simply been moved from https://python.langchain.com/docs/modules/prompts/ to https://python.langchain.com/docs/modules/model_io/prompts/, so setting up corresponding redirects should fix it
I can open up a PR with the corresponding redirects myself
|
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PR_kwDOIPDwls5Wugg-
| 8,476 |
add xml agent
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[] | null | 1 | 2023-07-30T04:09:40 | 2023-07-30T14:22:16 | 2023-07-30T14:22:16 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5WugHb
| 8,475 |
add anthropic functions wrapper
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[] | null | 1 | 2023-07-30T03:56:54 | 2023-07-30T14:23:47 | 2023-07-30T14:23:46 |
COLLABORATOR
| null |
a cheeky wrapper around claude that adds in function calling support (kind of, hence it going in experimental)
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PR_kwDOIPDwls5WuaUN
| 8,474 |
Update doc-string in few shot template
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[] | null | 1 | 2023-07-30T02:30:59 | 2023-07-31T02:39:15 | 2023-07-31T02:39:14 |
COLLABORATOR
| null |
Partial update of doc-string, need to update other instances in documentation
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PR_kwDOIPDwls5WuZ7L
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ChatPromptTemplate: clean up doc-string
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[] | null | 1 | 2023-07-30T02:17:10 | 2023-07-31T03:11:06 | 2023-07-31T03:11:05 |
COLLABORATOR
| null |
Minor doc-string clean up
|
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| 1,827,763,902 |
I_kwDOIPDwls5s8Xa-
| 8,472 |
unsupported operand type(s) for +: 'SystemMessage' and 'HumanMessage'
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[] | null | 5 | 2023-07-30T02:14:01 | 2023-11-05T16:05:09 | 2023-11-05T16:05:08 |
NONE
| null |
### System Info
Langchain version: 0.0.247
python version: 3.11.0
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
You can reproduce this issue according following link:
https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining
```
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.schema import HumanMessage, AIMessage, SystemMessage
prompt = SystemMessage(content="You are a nice pirate")
new_prompt = (
prompt
+ HumanMessage(content="hi")
+ AIMessage(content="what?")
+ "{input}"
)
```
prompy + HumanMessage(content="hi") will generate this issue
### Expected behavior
operand + for 'SystemMessage' and 'HumanMessage' should be support
|
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PR_kwDOIPDwls5WuWkK
| 8,471 |
fix links generation
|
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[] | null | 1 | 2023-07-30T00:45:11 | 2023-07-30T01:31:34 | 2023-07-30T01:31:33 |
COLLABORATOR
| null | null |
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| 1,827,743,550 |
PR_kwDOIPDwls5WuV4K
| 8,470 |
prompt cleanup
|
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[] | null | 2 | 2023-07-30T00:33:25 | 2023-07-30T17:47:32 | 2023-07-30T17:47:31 |
COLLABORATOR
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| 1,827,714,420 |
I_kwDOIPDwls5s8LV0
| 8,469 |
AzureCognitiveSearchRetriever Issue
|
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[] | null | 1 | 2023-07-29T23:12:59 | 2023-11-04T16:04:30 | 2023-11-04T16:04:29 |
NONE
| null |
### System Info
langchain-version: 0.0.246
AzureCognitiveSearchRetriever always return top 10 results as supposed to what was specified for top k
<img width="1158" alt="image" src="https://github.com/langchain-ai/langchain/assets/19245478/cb0e7317-8eee-4d9b-b71c-95c217451b42">
### Who can help?
_No response_
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- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
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- [ ] 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
retriever = AzureCognitiveSearchRetriever(api_version=api_version, top_k=3)
results = retriever.get_relevant_documents(chat_input.to_string())
print(retriever.top_k)
print(len(results))
### Expected behavior
The two print results are not the same
|
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PR_kwDOIPDwls5WuKQx
| 8,468 |
add initial anthropic agent
|
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COLLABORATOR
| null | null |
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PR_kwDOIPDwls5WuEd6
| 8,467 |
runnable.bind().bind() should combine kwargs, instead of nesting wrappers
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[] | null | 1 | 2023-07-29T21:05:03 | 2023-07-29T22:48:31 | 2023-07-29T22:48:30 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5Wt7L2
| 8,465 |
Refactor for extraction docs
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[] | null | 5 | 2023-07-29T19:41:37 | 2023-08-05T17:09:15 | 2023-08-05T17:09:14 |
COLLABORATOR
| null |
Refactor for the extraction use case documentation
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PR_kwDOIPDwls5Wt14w
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Redis Meta data Filter changes
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[] | null | 7 | 2023-07-29T18:46:39 | 2023-09-05T21:00:17 | 2023-09-05T21:00:16 |
NONE
| null |
- Description: Adding Metadata filter to Redis,
- Issue: 3967,
- Tag maintainer: @rlancemartin, @eyurtsev,
- Twitter handle: nareshr8
Testing code:
```
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.redis import Redis
from langchain.document_loaders import TextLoader
embeddings = OpenAIEmbeddings()
loader = TextLoader("./untitled.txt")
documents = loader.load()
# Editing metadata of document
documents[0].metadata = {"about":"langchain",'by':'text'}
# Inform the list of metadatakeys for indexing in redis for search
rds = Redis.from_documents(documents, embeddings,redis_url="redis://localhost:6379", index_name="sample",metadata_keys=['about','by'])
# Filter the documents based on metadata
rds.similarity_search('What should be used to load data from a source as Document',filter={"about":"langchain",'by':'text'})
```
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PR_kwDOIPDwls5Wt0bQ
| 8,463 |
BaseLoaderAsRetriever
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[] | null | 3 | 2023-07-29T18:32:22 | 2023-08-08T23:51:11 | 2023-08-08T23:51:11 |
COLLABORATOR
| null |
`BaseLoaderAsRetriever`, `QueryMixin` implementation; updated Arxiv to new base; ut-s; examples
Now, if Loader derives from BaseLoaderAsRetriever (not from BaseLoader) it can be used as a Retriever.
`Arxiv` is used as an example of `BaseLoaderAsRetriever`.
@hwchase17
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I_kwDOIPDwls5s7lhH
| 8,462 |
Issue: OpenAI Bad Gateway results in Error in on_retry: asyncio.run() cannot be called from a running event loop (coroutine 'AsyncRunManager.on_retry' was never awaited) inside openai.acompletion_with_retry
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[] | null | 14 | 2023-07-29T17:11:33 | 2023-09-25T09:44:18 | 2023-09-25T09:44:18 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I just saw a novel error, which appears to be triggered by a failed OpenAI API call (inside an asynchronous block) which is causing an asyncio.run() inside an asyncio.run(). Error pasted below. Is this my (user) error? Or possibly a problem with the acompletion_with_retry() implementation?
```
2023-07-29 05:53:14,838 INFO message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=None request_id=None response_code=502
2023-07-29 05:53:14,838 INFO error_code=502 error_message='Bad gateway.' error_param=None error_type=cf_bad_gateway message='OpenAI API error received' stream_error=False
2023-07-29 05:53:14,839 WARNING Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised APIError: Bad gateway. {"error":{"code":502,"message":"Bad gateway.","param":null,"type":"cf_bad_gateway"}} 502 {'error': {'code': 502, 'message': 'Bad gateway.', 'param': None, 'type': 'cf_bad_gateway'}} <CIMultiDictProxy('Date': 'Sat, 29 Jul 2023 05:53:14 GMT', 'Content-Type': 'application/json', 'Content-Length': '84', 'Connection': 'keep-alive', 'X-Frame-Options': 'SAMEORIGIN', 'Referrer-Policy': 'same-origin', 'Cache-Control': 'private, max-age=0, no-store, no-cache, must-revalidate, post-check=0, pre-check=0', 'Expires': 'Thu, 01 Jan 1970 00:00:01 GMT', 'Server': 'cloudflare', 'CF-RAY': '7ee3120dab9f1084-ORD', 'alt-svc': 'h3=":443"; ma=86400')>.
2023-07-29 05:53:14,839 ERROR Error in on_retry: asyncio.run() cannot be called from a running event loop
/usr/local/python-modules/tenacity/__init__.py:338: RuntimeWarning: coroutine 'AsyncRunManager.on_retry' was never awaited
self.before_sleep(retry_state)
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
```
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5Wttm8
| 8,461 |
improve notebooks
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[] | null | 1 | 2023-07-29T17:01:09 | 2023-07-29T19:49:12 | 2023-07-29T19:49:11 |
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| null | null |
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PR_kwDOIPDwls5Wtao2
| 8,460 |
Implement Runnable for Tools
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[] | null | 1 | 2023-07-29T12:23:31 | 2023-07-29T17:01:19 | 2023-07-29T17:01:19 |
COLLABORATOR
| null |
- Make _arun optional
- Pass run_manager to inner chains in tools that have them
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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[] | null | 2 | 2023-07-29T11:19:47 | 2023-11-04T16:04:36 | 2023-11-04T16:04:35 |
NONE
| null |
### System Info
I'm trying to use the tutorial on langchain but I get this error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[11], line 1
----> 1 from langchain.document_loaders import TextLoader
2 print(langchain.__version__)
3 loader = TextLoader("Scribbles.txt")
File [~/Dropbox/Personal/islington_news/myenv/lib/python3.9/site-packages/langchain/__init__.py:6](https://file+.vscode-resource.vscode-cdn.net/Users/davidelks/Dropbox/Personal/~/Dropbox/Personal/islington_news/myenv/lib/python3.9/site-packages/langchain/__init__.py:6)
3 from importlib import metadata
4 from typing import Optional
----> 6 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
7 from langchain.cache import BaseCache
8 from langchain.chains import (
9 ConversationChain,
10 LLMBashChain,
(...)
16 VectorDBQAWithSourcesChain,
17 )
File [~/Dropbox/Personal/islington_news/myenv/lib/python3.9/site-packages/langchain/agents/__init__.py:2](https://file+.vscode-resource.vscode-cdn.net/Users/davidelks/Dropbox/Personal/~/Dropbox/Personal/islington_news/myenv/lib/python3.9/site-packages/langchain/agents/__init__.py:2)
1 """Interface for agents."""
----> 2 from langchain.agents.agent import (
3 Agent,
4 AgentExecutor,
5 AgentOutputParser,
...
851 if not isinstance(cls, _GenericAlias):
--> 852 return issubclass(cls, self.__origin__)
853 return super().__subclasscheck__(cls)
TypeError: issubclass() arg 1 must be a class
```
I can't provide the version of langchain because I get this error. (I've got 0.0.247 from pip install.)
Running on MacOs Ventura. Python: 3.9.15
### Who can help?
@elksie5000
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.document_loaders import TextLoader
print(langchain.__version__)
loader = TextLoader("Scribbles.txt")
from langchain.indexes import VectorstoreIndexCreator
index = VectorstoreIndexCreator().from_loaders([loader])
### Expected behavior
The index to be created.
|
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I_kwDOIPDwls5s7IhS
| 8,457 |
VectorStore.from_documents() takes 3 positional arguments but 4 were given
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[] | null | 2 | 2023-07-29T10:53:33 | 2023-07-30T06:26:23 | 2023-07-30T06:26:23 |
CONTRIBUTOR
| null |
### System Info
... % python --version
Python 3.11.4
... % pip show langchain | grep Version
Version: 0.0.247
### Who can help?
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When following the langchain docs [here](https://python.langchain.com/docs/integrations/vectorstores/qdrant#qdrant-cloud), there will be an error thrown:
```py
qdrant = Qdrant.from_documents(
docs,
embeddings,
url,
prefer_grpc=True,
api_key=api_key,
collection_name="test",
)
```
error:
```
Traceback (most recent call last):
File "...myscript.py", line 29, in <module>
qdrant = Qdrant.from_documents(
^^^^^^^^^^^^^^^^^^^^^^
TypeError: VectorStore.from_documents() takes 3 positional arguments but 4 were given
```
Is it related to https://github.com/langchain-ai/langchain/pull/7910 ?
### Expected behavior
QDrant being initialized properly.
|
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PR_kwDOIPDwls5WtTZt
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Create cr.yml
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[] | null | 1 | 2023-07-29T09:43:03 | 2023-07-29T09:43:50 | 2023-07-29T09:43:50 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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Wfh/ref links
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[] | null | 1 | 2023-07-29T06:56:48 | 2023-07-29T15:44:33 | 2023-07-29T15:44:32 |
COLLABORATOR
| null |
Improve link matching regex
Fix broken experimental imports
fix rockset and spacy broken imports
|
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Add instruction how to resolve dependency failures for dev
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[] | null | 3 | 2023-07-29T06:56:20 | 2023-11-13T06:04:42 | 2023-11-13T06:04:41 |
NONE
| null |
Encountered the error "fastavro (1.7.4) not supporting PEP 517 build" while following instructions in CONTRIBUTING.md. Suggest the text how to resolve it.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
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| 1,827,339,234 |
PR_kwDOIPDwls5WtBbS
| 8,452 |
Wfh/update guide imports
|
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[] | null | 1 | 2023-07-29T06:09:54 | 2023-07-29T06:20:39 | 2023-07-29T06:12:10 |
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I_kwDOIPDwls5s6u64
| 8,451 |
SitemapLoader: set proxy
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open
| false | null |
[] | null | 2 | 2023-07-29T06:02:55 | 2024-01-08T09:30:58 | null |
NONE
| null |
### Feature request
I am behind company firewall and I need to set proxy to `SitemapLoader`
### Motivation
I am behind company firewall
### Your contribution
Eg.:
```python
sitemap_loader = SitemapLoader(
web_path="https://langchain.readthedocs.io/sitemap.xml",
https_proxy="https://my.proxy.io/"
)
docs = sitemap_loader.load()
```
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PR_kwDOIPDwls5WtAqQ
| 8,450 |
Partial formatting for chat messages
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[] | null | 1 | 2023-07-29T06:02:20 | 2023-07-29T06:08:34 | 2023-07-29T06:08:33 |
COLLABORATOR
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| 1,827,318,961 |
I_kwDOIPDwls5s6qyx
| 8,449 |
GooglePalm requires google.generativeai?
|
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[] | null | 3 | 2023-07-29T04:55:22 | 2023-11-15T16:06:58 | 2023-11-15T16:06:57 |
NONE
| null |
### System Info
```python
from langchain.llms.base import LLM
from langchain.llms import GooglePalm
```
This throws an error saying it requires `google.generativeai`; pervious it used to work, something changed and is it documented?
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
pip install langchain
```
```python
from langchain.llms.base import LLM
from langchain.llms import GooglePalm
```
### Expected behavior
Should load without errors?
|
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I_kwDOIPDwls5s6qbA
| 8,448 |
Object of type 'FieldInfo' is not JSON serializable
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[] | null | 2 | 2023-07-29T04:47:30 | 2023-07-29T08:50:24 | 2023-07-29T08:50:23 |
NONE
| null |
### System Info
```shell
$ langchain.__version__
'0.0.234'
$ uname -a
Linux codespaces-92388d 5.15.0-1042-azure #49-Ubuntu SMP Tue Jul 11 17:28:46 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux
$ python
Python 3.10.8 (main, Jun 15 2023, 01:39:58) [GCC 9.4.0] on linux
```
### Who can help?
@hwchase17 @vowe
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The project address is: https://github.com/eunomia-bpf/trace-agent. When uncomment the line [# args_schema=AnalyseInput,](https://github.com/eunomia-bpf/trace-agent/blob/a0c1ff74017e1e47a900c9371833eb2cca1705ef/iminder/tools.py#L38) in the file `trace-agent/iminder/tools.py`, and then run the project using `python -m iminder pid`, the following error occurs:
```shell
$ python -m iminder 480
Traceback (most recent call last):
File "/usr/local/python/3.10.8/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/local/python/3.10.8/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/workspaces/trace-agent/iminder/__main__.py", line 11, in <module>
bot.run([f"Obtain the resource usage of the process whose pid is {pid} over a period of time, "
File "/workspaces/trace-agent/iminder/autogpt.py", line 61, in run
return self.agent.run(tasks)
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/experimental/autonomous_agents/autogpt/agent.py", line 93, in run
assistant_reply = self.chain.run(
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/base.py", line 445, in run
return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/base.py", line 243, in __call__
raise e
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/base.py", line 237, in __call__
self._call(inputs, run_manager=run_manager)
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/llm.py", line 92, in _call
response = self.generate([inputs], run_manager=run_manager)
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/llm.py", line 101, in generate
prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/chains/llm.py", line 135, in prep_prompts
prompt = self.prompt.format_prompt(**selected_inputs)
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/prompts/chat.py", line 155, in format_prompt
messages = self.format_messages(**kwargs)
File "/workspaces/trace-agent/iminder/prompt.py", line 130, in format_messages
misc_messages = self._format_misc_messages(**kwargs)
File "/workspaces/trace-agent/iminder/prompt.py", line 67, in _format_misc_messages
base_prompt = SystemMessage(content=self.construct_full_prompt(**kwargs))
File "/workspaces/trace-agent/iminder/prompt.py", line 58, in construct_full_prompt
full_prompt += f"\n\n{get_prompt(self.tools)}"
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/experimental/autonomous_agents/autogpt/prompt_generator.py", line 184, in get_prompt
prompt_string = prompt_generator.generate_prompt_string()
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/experimental/autonomous_agents/autogpt/prompt_generator.py", line 113, in generate_prompt_string
f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/experimental/autonomous_agents/autogpt/prompt_generator.py", line 84, in _generate_numbered_list
command_strings = [
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/experimental/autonomous_agents/autogpt/prompt_generator.py", line 85, in <listcomp>
f"{i + 1}. {self._generate_command_string(item)}"
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/experimental/autonomous_agents/autogpt/prompt_generator.py", line 50, in _generate_command_string
output += f", args json schema: {json.dumps(tool.args)}"
File "/usr/local/python/3.10.8/lib/python3.10/site-packages/langchain/tools/base.py", line 418, in args
return self.args_schema.schema()["properties"]
File "pydantic/main.py", line 664, in pydantic.main.BaseModel.schema
File "pydantic/schema.py", line 188, in pydantic.schema.model_schema
File "pydantic/schema.py", line 582, in pydantic.schema.model_process_schema
File "pydantic/schema.py", line 623, in pydantic.schema.model_type_schema
File "pydantic/schema.py", line 249, in pydantic.schema.field_schema
File "pydantic/schema.py", line 217, in pydantic.schema.get_field_info_schema
File "pydantic/schema.py", line 992, in pydantic.schema.encode_default
File "pydantic/schema.py", line 991, in genexpr
File "pydantic/schema.py", line 996, in pydantic.schema.encode_default
File "pydantic/json.py", line 90, in pydantic.json.pydantic_encoder
TypeError: Object of type 'FieldInfo' is not JSON serializable
```
### Expected behavior
In [trace-agent/iminder/tools.py](https://github.com/eunomia-bpf/trace-agent/blob/main/iminder/tools.py), I have defined two custom tools: one is called `sample`, and the other is called `analyse_process`. Both tools have only one input parameter, but of different types. `sample` takes an integer as input, while `analyse_process` takes a string. Strangely, `sample` works as expected, but `analyse_process` does not. My expectation was that both of them would function correctly.
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PR_kwDOIPDwls5Ws5yg
| 8,447 |
Fix ChromaDB integration -> docker container instructions
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[] | null | 4 | 2023-07-29T03:46:02 | 2023-07-31T04:56:51 | 2023-07-31T04:11:56 |
CONTRIBUTOR
| null |
## Description
This PR handles modifying the Chroma DB integration's documentation.
It modifies the **Docker container** example to fix the instructions mentioned in the documentation.
In the current documentation, the below `client.reset()` line causes a runtime error:
```py
...
client = chromadb.HttpClient(settings=Settings(allow_reset=True))
client.reset() # resets the database
collection = client.create_collection("my_collection")
...
```
`Exception: {"error":"ValueError('Resetting is not allowed by this configuration')"}`
This is due to the Chroma DB server needing to have the `allow_reset` flag set to `true` there as well.
This is fixed by adding the `ALLOW_RESET=TRUE` to the `docker-compose` file environment variable to the docker container before spinning it
## Issue
This fixes the runtime error that occurs when running the docker container example code
## Tag Maintainer
@rlancemartin, @eyurtsev
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PR_kwDOIPDwls5WszN-
| 8,446 |
log output parser
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[] | null | 1 | 2023-07-29T02:05:27 | 2023-07-29T06:53:46 | 2023-07-29T06:53:45 |
COLLABORATOR
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| 1,827,254,929 |
PR_kwDOIPDwls5WswHj
| 8,445 |
Optional strategies for criteria evaluator
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[] | null | 2 | 2023-07-29T01:15:42 | 2023-10-31T13:29:02 | 2023-10-31T13:29:02 |
COLLABORATOR
| null |
Add option to configure eval strategies for the criteria evaluator.
Now supported:
- Binary (Y or N)
- Score (0-9)
- Confidence (extremely confident no -> extremely confident yes) basically a likert scale
|
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I_kwDOIPDwls5s6bJT
| 8,444 |
Huggingface_Pipeline for summarization returning generalized response
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[] | null | 6 | 2023-07-29T01:15:26 | 2023-08-02T12:38:17 | 2023-08-02T12:38:17 |
NONE
| null |
### System Info
Python = 3.9
Langchain = 0.0.245
### Who can help?
@hwchase17
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I checked the recent update for summarization pipeline with memory as:
```
from langchain.llms import HuggingFacePipeline
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
from langchain.memory import ConversationSummaryBufferMemory
import torch
summarize_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
summarize_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn", padding_side="left")
pipe_summary = pipeline("summarization", model=summarize_model, tokenizer=summarize_tokenizer) #, max_new_tokens=500, min_new_tokens=300
hf_summary = HuggingFacePipeline(pipeline=pipe_summary)
memory=ConversationSummaryBufferMemory(llm=hf_summary, max_token_limit=10)
```
Then, added chat history to the memory and observed the memory afterwards as:
```
memory.save_context({"input": "hi"}, {"output": "whats up"})
memory.save_context({"input": "not much you"}, {"output": "not much"})
memory.save_context({"input": "what's my name"}, {"output": "AJ"})
memory.load_memory_variables({})
```
It returned:
```
{'history': "System: The human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential. Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary. The human asks the AI: Why do you think artificial Intelligence is a Force for good? The AI: Because artificial intelligence will help human reach their potential.\nHuman: what's my name\nAI: AJ"}
```
This doesn't summarize the actual chat history, but returns a generalized text: `The human asks what the AI thinks of artificial intelligence. The AI thinks artificial intelligence is a force for good because it will help humans reach their full potential. Progressively summarize the lines of conversation provided, adding onto the previous summary returning a new summary. The human asks the AI: Why do you think artificial Intelligence is a Force for good? The AI: Because artificial intelligence will help human reach their potential.`
### Expected behavior
This would pass wrong prompt to the LLM for question-answering. The expectation is the summary of the chat history.
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PR_kwDOIPDwls5WswDF
| 8,443 |
Update supabase docstrings
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[] | null | 1 | 2023-07-29T01:14:22 | 2023-07-29T06:08:15 | 2023-07-29T06:08:14 |
COLLABORATOR
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PR_kwDOIPDwls5WssnH
| 8,442 |
add notebook for sql query
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[] | null | 1 | 2023-07-29T00:31:01 | 2023-07-29T00:45:00 | 2023-07-29T00:44:59 |
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PR_kwDOIPDwls5WsqlT
| 8,441 |
bump and fix
|
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| 1,827,201,525 |
I_kwDOIPDwls5s6OH1
| 8,440 |
DOC: Contributor docs are inaccurate
|
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[] | null | 2 | 2023-07-28T23:26:09 | 2023-07-31T18:01:18 | 2023-07-31T18:01:18 |
CONTRIBUTOR
| null |
### Issue with current documentation:
## Description
The installation instructions in the Contributor docs are not working and should be updated. For example:
```bash
> poetry install -E all
Installing dependencies from lock file
Extra [all] is not specified.
```
```bash
> poetry install --with dev
Group(s) not found: dev (via --with)
```
### Idea or request for content:
_No response_
|
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| 1,827,182,330 |
PR_kwDOIPDwls5Wsg6M
| 8,439 |
Fixed exports for NeptuneOpenCypherQAChain
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[] | null | 1 | 2023-07-28T22:51:29 | 2023-07-31T03:36:23 | 2023-07-31T03:36:23 |
CONTRIBUTOR
| null |
## Description
The imports for `NeptuneOpenCypherQAChain` are failing. This PR adds the chain class to the `__init__.py` file to fix this issue.
## Maintainers
@dev2049
@krlawrence
|
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PR_kwDOIPDwls5WsVbe
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mv dropbox
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I_kwDOIPDwls5s54sh
| 8,436 |
Issue: Indexing and querying an XML file
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NONE
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PR_kwDOIPDwls5WsPQj
| 8,435 |
add experimental ref
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PR_kwDOIPDwls5WsL-e
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Add support for Anyscale Endpoint, with llama2 70B model for now
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[] | null | 1 | 2023-07-28T21:04:36 | 2023-07-28T22:09:00 | 2023-07-28T22:08:21 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
Adding support or Anyscale Endpoint, with default llama2 70B model for now
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I_kwDOIPDwls5s5uVg
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FewShotPromptTemplate example formating bug
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[] | null | 2 | 2023-07-28T20:48:54 | 2023-11-03T16:05:41 | 2023-11-03T16:05:40 |
NONE
| null |
### System Info
- MacOS 13.4.1 (c)
- Intel Core i9
#### Version
- Python 3.8.17
- Langchain 0.0.245
#### Context
I am trying build a prompt that convert latex string generated by an OCR algo to a text describing that latex. When using the `FewShotPromptTemplate`, the curly brackets in the latex string are somehow interpreted as key to a dict.
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
#### Code
```
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.prompts.prompt import PromptTemplate
examples = [
{
"latex": """\sum_{i=1}^{n}""",
"doc": """taking sum from 1 to n"""
}
]
example_template = """
latex: {latex}
doc: {doc}
"""
prefix = """ Convert the latex
"""
suffix = """
User: {latex}
AI: """
example_prompt = PromptTemplate(input_variables=["latex", "doc"], template="Question: {latex}\n{doc}")
few_shot_prompt_template = FewShotPromptTemplate(
examples=examples,
example_prompt=example_prompt,
prefix=prefix,
suffix=suffix,
input_variables=["latex"],
example_separator="\n\n"
)
print(example_prompt.format(**examples[0]))
print(few_shot_prompt_template.format(latex="\frac{a}{b}"))
```
### Expected behavior
#### Error
The PromptTemplate.format works fine, but the FewShotPromptTemplate fails.
```
---> 34 print(few_shot_prompt_template.format(latex="\frac{a}{b}"))
File [~/Library/Caches/pypoetry/virtualenvs/expression-engine-OXFJOYa8-py3.8/lib/python3.8/site-packages/langchain/prompts/few_shot.py:123](https://file+.vscode-resource.vscode-cdn.net/Users/LLM/~/Library/Caches/pypoetry/virtualenvs/expression-engine-OXFJOYa8-py3.8/lib/python3.8/site-packages/langchain/prompts/few_shot.py:123), in FewShotPromptTemplate.format(self, **kwargs)
120 template = self.example_separator.join([piece for piece in pieces if piece])
122 # Format the template with the input variables.
--> 123 return DEFAULT_FORMATTER_MAPPING[self.template_format](template, **kwargs)
File [/usr/local/opt/python](https://file+.vscode-resource.vscode-cdn.net/usr/local/opt/python)@3.8/Frameworks/Python.framework/Versions/3.8/lib/python3.8/string.py:163, in Formatter.format(self, format_string, *args, **kwargs)
162 def format(self, format_string, [/](https://file+.vscode-resource.vscode-cdn.net/), *args, **kwargs):
--> 163 return self.vformat(format_string, args, kwargs)
File [~/Library/Caches/pypoetry/virtualenvs/expression-engine-OXFJOYa8-py3.8/lib/python3.8/site-packages/langchain/utils/formatting.py:29](https://file+.vscode-resource.vscode-cdn.net/Users/LLM/~/Library/Caches/pypoetry/virtualenvs/expression-engine-OXFJOYa8-py3.8/lib/python3.8/site-packages/langchain/utils/formatting.py:29), in StrictFormatter.vformat(self, format_string, args, kwargs)
24 if len(args) > 0:
25 raise ValueError(
26 "No arguments should be provided, "
...
227 return args[key]
228 else:
--> 229 return kwargs[key]
KeyError: 'i=1'
```
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PR_kwDOIPDwls5Wr7Sj
| 8,432 |
Issue warning for `OSSContentFormatter` usage
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[] | null | 1 | 2023-07-28T19:49:47 | 2023-07-28T19:56:29 | 2023-07-28T19:50:05 |
CONTRIBUTOR
| null |
# Description
`OSSContentFormatter` will use the new formatter `GPT2ContentFormatter` and issue a warning to switch to it as the name more properly describes what model it should be used for. Those using `OSSContentFormatter` will not have any breaking changes as this is simply a name change, and the logic is still the same.
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PR_kwDOIPDwls5Wr11R
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update get_num_tokens_from_messages model
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[] | null | 2 | 2023-07-28T19:26:05 | 2023-07-29T03:07:09 | 2023-07-28T22:07:04 |
CONTRIBUTOR
| null |
(#8430)
@hwchase17, @baskaryan
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I_kwDOIPDwls5s5Yg3
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Issue: Azure/OpenAI get_num_tokens_from_messages returns wrong prompt tokens
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[] | null | 1 | 2023-07-28T19:22:44 | 2023-07-29T01:13:36 | 2023-07-29T01:13:36 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
OpenAI Cookbook
- https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
- `gpt-3.5-turbo-0301` set `tokens_per_message = 4` and `tokens_per_name = -1`
- `gpt-3.5-turbo-*` set `tokens_per_message = 3` and `tokens_per_name = 1`
- `gpt-3.5-turbo` redirect to `gpt-3.5-turbo-0613`
- `gpt-4` redirect to `gpt-4-0613`
```
if model in {
"gpt-3.5-turbo-0613",
"gpt-3.5-turbo-16k-0613",
"gpt-4-0314",
"gpt-4-32k-0314",
"gpt-4-0613",
"gpt-4-32k-0613",
}:
tokens_per_message = 3
tokens_per_name = 1
elif model == "gpt-3.5-turbo-0301":
tokens_per_message = 4 # every message follows <|start|>{role/name}\n{content}<|end|>\n
tokens_per_name = -1 # if there's a name, the role is omitted
elif "gpt-3.5-turbo" in model:
print("Warning: gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613.")
return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0613")
elif "gpt-4" in model:
print("Warning: gpt-4 may update over time. Returning num tokens assuming gpt-4-0613.")
return num_tokens_from_messages(messages, model="gpt-4-0613")
```
LangChain
- https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chat_models/openai.py#L541-L548
- `gpt-3.5-turbo` and `gpt-3.5-turbo-*` set `tokens_per_message = 4` and `tokens_per_name = -1`
```
if model.startswith("gpt-3.5-turbo"):
# every message follows <im_start>{role/name}\n{content}<im_end>\n
tokens_per_message = 4
# if there's a name, the role is omitted
tokens_per_name = -1
elif model.startswith("gpt-4"):
tokens_per_message = 3
tokens_per_name = 1
```
- https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chat_models/openai.py#L508-L515
- `gpt-3.5-turbo` redirect to `gpt-3.5-turbo-0301`
- `gpt-4` redirect to `gpt-4-0314`
```
if model == "gpt-3.5-turbo":
# gpt-3.5-turbo may change over time.
# Returning num tokens assuming gpt-3.5-turbo-0301.
model = "gpt-3.5-turbo-0301"
elif model == "gpt-4":
# gpt-4 may change over time.
# Returning num tokens assuming gpt-4-0314.
model = "gpt-4-0314"
```
### Suggestion:
Follow the OpenAI Cookbook
- `gpt-3.5-turbo-0301` set `tokens_per_message = 4` and `tokens_per_name = -1`
- `gpt-3.5-turbo-*` set `tokens_per_message = 3` and `tokens_per_name = 1`
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PR_kwDOIPDwls5WrqUN
| 8,428 |
Expose text-bison as chat model
|
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[] | null | 5 | 2023-07-28T18:50:07 | 2023-07-31T18:15:45 | 2023-07-31T18:15:45 |
CONTRIBUTOR
| null |
#8427
|
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I_kwDOIPDwls5s5NKb
| 8,427 |
Expose Vertex text model as chat model
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[] | null | 2 | 2023-07-28T18:49:53 | 2023-11-03T16:05:29 | 2023-11-03T16:05:28 |
CONTRIBUTOR
| null |
### Feature request
Expose text-bison as chat model since it might be useful for some applications.
### Motivation
Sometimes it might be interesting to compare text-bison vs chat-bison for chat scenarios.
### Your contribution
yes, I'm happy to do it.
|
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| 8,426 |
expose function for converting messages to messages
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PR_kwDOIPDwls5WrRgL
| 8,425 |
remove code
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PR_kwDOIPDwls5WrBmz
| 8,424 |
ChatPromptTemplate: minor fix in doc string
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[] | null | 1 | 2023-07-28T16:50:42 | 2023-07-28T17:01:15 | 2023-07-28T17:01:14 |
COLLABORATOR
| null |
Minor fix in doc-string to use `ai` rather than `assistant`
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PR_kwDOIPDwls5Wq211
| 8,423 |
PromptTemplate update documentation and expand kwarg
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[] | null | 1 | 2023-07-28T16:13:42 | 2023-07-28T18:11:50 | 2023-07-28T18:11:49 |
COLLABORATOR
| null |
# PromptTemplate
* Update documentation to highlight the classmethod for instantiating a prompt template.
* Expand kwargs in the classmethod to make parameters easier to discover
This PR got reverted here: https://github.com/langchain-ai/langchain/pull/8395/files
Unit tests got coupled due to slight change in serialization of prompt templates.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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| 1,826,633,655 |
PR_kwDOIPDwls5Wqn2j
| 8,422 |
Add RoPE Scaling params from llamacpp
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[] | null | 2 | 2023-07-28T15:28:55 | 2023-07-28T21:42:42 | 2023-07-28T21:42:42 |
CONTRIBUTOR
| null |
Description:
Just adding parameters from `llama-python-cpp` that support RoPE scaling.
@hwchase17, @baskaryan
sources:
papers and explanation:
https://kaiokendev.github.io/context
llamacpp conversation:
https://github.com/ggerganov/llama.cpp/discussions/1965
Supports models like:
https://huggingface.co/conceptofmind/LLongMA-2-13b
img from
https://llama-cpp-python.readthedocs.io/en/latest/api-reference/#llama_cpp.Llama
- [x] lint and format
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I_kwDOIPDwls5s4DOE
| 8,421 |
Different Model to generate thought and answer from Agent.
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[] | null | 1 | 2023-07-28T15:28:07 | 2023-11-03T16:05:57 | 2023-11-03T16:05:56 |
NONE
| null |
### Issue you'd like to raise.
Is there anyway to use a different model to generate AGENT THOUGHTS and AGENT FINAL ANSWER?
For example: I want to to use GPT3.5 for generate thoughts and GPT4 to generate the final answer.
### Suggestion:
_No response_
|
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I_kwDOIPDwls5s3w06
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langchain with LlamaCpp for QA from txt files fails on Chroma part
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[] | null | 4 | 2023-07-28T14:45:53 | 2023-10-13T12:13:43 | 2023-07-28T19:57:45 |
NONE
| null |
### System Info
Firstly sorry if I am posting this in wrong place but I felt like it belongs to here.
I am trying use LlamaCpp for QA for txt documents but on Chromadb I am getting following error
I couldn't find a way to solve this:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-13-9bd0ee703b23>](https://localhost:8080/#) in <cell line: 1>()
----> 1 db = Chroma.from_documents(texts, embeddings, persist_directory='db')
7 frames
[/usr/local/lib/python3.10/dist-packages/langchain/vectorstores/chroma.py](https://localhost:8080/#) in from_documents(cls, documents, embedding, ids, collection_name, persist_directory, client_settings, client, collection_metadata, **kwargs)
576 texts = [doc.page_content for doc in documents]
577 metadatas = [doc.metadata for doc in documents]
--> 578 return cls.from_texts(
579 texts=texts,
580 embedding=embedding,
[/usr/local/lib/python3.10/dist-packages/langchain/vectorstores/chroma.py](https://localhost:8080/#) in from_texts(cls, texts, embedding, metadatas, ids, collection_name, persist_directory, client_settings, client, collection_metadata, **kwargs)
540 **kwargs,
541 )
--> 542 chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
543 return chroma_collection
544
[/usr/local/lib/python3.10/dist-packages/langchain/vectorstores/chroma.py](https://localhost:8080/#) in add_texts(self, texts, metadatas, ids, **kwargs)
173 embeddings = None
174 if self._embedding_function is not None:
--> 175 embeddings = self._embedding_function.embed_documents(list(texts))
176
177 if metadatas:
[/usr/local/lib/python3.10/dist-packages/langchain/embeddings/llamacpp.py](https://localhost:8080/#) in embed_documents(self, texts)
108 List of embeddings, one for each text.
109 """
--> 110 embeddings = [self.client.embed(text) for text in texts]
111 return [list(map(float, e)) for e in embeddings]
112
[/usr/local/lib/python3.10/dist-packages/langchain/embeddings/llamacpp.py](https://localhost:8080/#) in <listcomp>(.0)
108 List of embeddings, one for each text.
109 """
--> 110 embeddings = [self.client.embed(text) for text in texts]
111 return [list(map(float, e)) for e in embeddings]
112
[/usr/local/lib/python3.10/dist-packages/llama_cpp/llama.py](https://localhost:8080/#) in embed(self, input)
810 A list of embeddings
811 """
--> 812 return list(map(float, self.create_embedding(input)["data"][0]["embedding"]))
813
814 def _create_completion(
[/usr/local/lib/python3.10/dist-packages/llama_cpp/llama.py](https://localhost:8080/#) in create_embedding(self, input, model)
774 tokens = self.tokenize(input.encode("utf-8"))
775 self.reset()
--> 776 self.eval(tokens)
777 n_tokens = len(tokens)
778 total_tokens += n_tokens
[/usr/local/lib/python3.10/dist-packages/llama_cpp/llama.py](https://localhost:8080/#) in eval(self, tokens)
469 raise RuntimeError(f"llama_eval returned {return_code}")
470 # Save tokens
--> 471 self.input_ids[self.n_tokens : self.n_tokens + n_tokens] = batch
472 # Save logits
473 rows = n_tokens if self.params.logits_all else 1
ValueError: could not broadcast input array from shape (8,) into shape (0,)
```
Code:
```
#installation
!pip install langchain PyPDF2 huggingface_hub chromadb llama-cpp-python
#download the model
!git clone https://github.com/ggerganov/llama.cpp.git
%cd llama.cpp
!curl -L https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/resolve/main/llama-2-7b-chat.ggmlv3.q4_K_M.bin -o ./models/llama-2-7b-chat.ggmlv3.q4_K_M.bin
!LLAMA_METAL=1 make
```
```
from langchain.llms import LlamaCpp
from langchain.embeddings import LlamaCppEmbeddings
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know or you can't help, don't try to make up an answer.
{context}
Question: {question}
Answer:"""
prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
model_path = '/content/llama.cpp/models/llama-2-7b-chat.ggmlv3.q4_K_M.bin'
llm = LlamaCpp(model_path=model_path)
embeddings = LlamaCppEmbeddings(model_path=model_path)
llm_chain = LLMChain(llm=llm, prompt=prompt)
loader = TextLoader(txt_file_path)
docs = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
texts = text_splitter.split_documents(docs)
db = Chroma.from_documents(texts, embeddings, persist_directory='db')
question = 'summerize this document'
similar_doc = db.similarity_search(question, k=1)
context = similar_doc[0].page_content
query_llm = LLMChain(llm=llm, prompt=prompt)
response = query_llm.run({"context": context, "question": question})
```
versions:
```
langchain==0.0.246
chromadb==0.4.3
```
Is there any alternative way to do what i want to achieve?
### Who can help?
@hwchase17 @agola11
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
- Upload a text file
- run all the code to download the model
- replace model_path with downloaded model
- run the rest
### Expected behavior
working
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I_kwDOIPDwls5s26C9
| 8,419 |
Working with Langchain I get nlkt errors telling me: Package "tokenizers" not found in index and Packaage "taggers" not found in index
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[] | null | 8 | 2023-07-28T12:23:04 | 2023-11-03T16:06:43 | 2023-11-03T16:06:42 |
NONE
| null |
### Issue you'd like to raise.
I'm trying to load some documents, powerpoints and text to train my custom LLm using Langchain.
When I run it I come to a weird error message where it tells I don't have "tokenizers" and "taggers" packages (folders).
I've read the docs, asked Langchain chatbot, pip install nltk, uninstall, pip install nltk without dependencies, added them with nltk.download(), nltk.download("punkt"), nltk.download("all"),... Did also manually put on the path: nltk.data.path = ['C:\Users\zaesa\AppData\Roaming\nltk_data'] and added all the folders. Added the tokenizers folder and taggers folder from the github repo: [](https://github.com/nltk/nltk_data/tree/gh-pages/packages). Everything. Also asked on the Github repo. Nothing, no success.
Here the code of the file I try to run:
`
from nltk.tokenize import sent_tokenize
from langchain.document_loaders import UnstructuredPowerPointLoader, TextLoader, UnstructuredWordDocumentLoader
from dotenv import load_dotenv, find_dotenv
import os
import openai
import sys
import nltk
nltk.data.path = ['C:\\Users\\zaesa\\AppData\\Roaming\\nltk_data']
nltk.download(
'punkt', download_dir='C:\\Users\\zaesa\\AppData\\Roaming\\nltk_data')
sys.path.append('../..')
_ = load_dotenv(find_dotenv()) # read local .env file
openai.api_key = os.environ['OPENAI_API_KEY']
folder_path_docx = "DB\\ DB VARIADO\\DOCS"
folder_path_txt = "DB\\BLOG-POSTS"
folder_path_pptx_1 = "DB\\PPT DAY JUNIO"
folder_path_pptx_2 = "DB\\DB VARIADO\\PPTX"
loaded_content = []
for file in os.listdir(folder_path_docx):
if file.endswith(".docx"):
file_path = os.path.join(folder_path_docx, file)
loader = UnstructuredWordDocumentLoader(file_path)
docx = loader.load()
loaded_content.extend(docx)
for file in os.listdir(folder_path_txt):
if file.endswith(".txt"):
file_path = os.path.join(folder_path_txt, file)
loader = TextLoader(file_path, encoding='utf-8')
text = loader.load()
loaded_content.extend(text)
for file in os.listdir(folder_path_pptx_1):
if file.endswith(".pptx"):
file_path = os.path.join(folder_path_pptx_1, file)
loader = UnstructuredPowerPointLoader(file_path)
slides_1 = loader.load()
loaded_content.extend(slides_1)
for file in os.listdir(folder_path_pptx_2):
if file.endswith(".pptx"):
file_path = os.path.join(folder_path_pptx_2, file)
loader = UnstructuredPowerPointLoader(file_path)
slides_2 = loader.load()
loaded_content.extend(slides_2)
print(loaded_content[0].page_content)
print(nltk.data.path)
installed_packages = nltk.downloader.Downloader(
download_dir='C:\\Users\\zaesa\\AppData\\Roaming\\nltk_data').packages()
print(installed_packages)
sent_tokenize("Hello. How are you? I'm well.")
`
When running the file I get:
`
[nltk_data] in index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading taggers: Package 'taggers' not found in
[nltk_data] index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading taggers: Package 'taggers' not found in
[nltk_data] index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading taggers: Package 'taggers' not found in
[nltk_data] index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading taggers: Package 'taggers' not found in
[nltk_data] index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading tokenizers: Package 'tokenizers' not found
[nltk_data] in index
[nltk_data] Error loading taggers: Package 'taggers' not found in
[nltk_data] index
- HERE SOME TEXT -
['C:\\Users\\zaesa\\AppData\\Roaming\\nltk_data']
dict_values([<Package perluniprops>, <Package mwa_ppdb>, <Package punkt>, <Package rslp>, <Package porter_test>, <Package snowball_data>, <Package maxent_ne_chunker>, <Package moses_sample>, <Package bllip_wsj_no_aux>, <Package word2vec_sample>, <Package wmt15_eval>, <Package spanish_grammars>, <Package sample_grammars>, <Package large_grammars>, <Package book_grammars>, <Package basque_grammars>, <Package maxent_treebank_pos_tagger>, <Package averaged_perceptron_tagger>, <Package averaged_perceptron_tagger_ru>, <Package universal_tagset>, <Package vader_lexicon>, <Package lin_thesaurus>, <Package movie_reviews>, <Package problem_reports>, <Package pros_cons>, <Package masc_tagged>, <Package sentence_polarity>, <Package webtext>, <Package nps_chat>, <Package city_database>, <Package europarl_raw>, <Package biocreative_ppi>, <Package verbnet3>, <Package pe08>, <Package pil>, <Package crubadan>, <Package gutenberg>, <Package propbank>, <Package machado>, <Package state_union>, <Package twitter_samples>, <Package semcor>, <Package wordnet31>, <Package extended_omw>, <Package names>, <Package ptb>, <Package nombank.1.0>, <Package floresta>, <Package comtrans>, <Package knbc>, <Package mac_morpho>, <Package swadesh>, <Package rte>, <Package toolbox>, <Package jeita>, <Package product_reviews_1>, <Package omw>, <Package wordnet2022>, <Package sentiwordnet>, <Package product_reviews_2>, <Package abc>, <Package wordnet2021>, <Package udhr2>, <Package senseval>, <Package words>, <Package framenet_v15>, <Package unicode_samples>, <Package kimmo>, <Package framenet_v17>, <Package chat80>, <Package qc>, <Package inaugural>, <Package wordnet>, <Package stopwords>, <Package verbnet>, <Package shakespeare>, <Package ycoe>, <Package ieer>, <Package cess_cat>, <Package switchboard>, <Package comparative_sentences>, <Package subjectivity>, <Package udhr>, <Package pl196x>, <Package paradigms>, <Package gazetteers>, <Package timit>, <Package treebank>, <Package sinica_treebank>, <Package opinion_lexicon>, <Package ppattach>, <Package dependency_treebank>, <Package reuters>, <Package genesis>, <Package cess_esp>, <Package conll2007>, <Package nonbreaking_prefixes>, <Package dolch>, <Package smultron>, <Package alpino>, <Package wordnet_ic>, <Package brown>, <Package bcp47>, <Package panlex_swadesh>, <Package conll2000>, <Package universal_treebanks_v20>, <Package brown_tei>, <Package cmudict>, <Package omw-1.4>, <Package mte_teip5>, <Package indian>, <Package conll2002>, <Package tagsets>])
`
And here is how my folders structure from nltk_data looks like:
<img width="841" alt="nltk-screenshot" src="https://github.com/langchain-ai/langchain/assets/29057173/c094ec97-6e9b-4a3c-83ae-afbe08af3380">
<img width="842" alt="taggers-screenshot" src="https://github.com/langchain-ai/langchain/assets/29057173/e943a0cc-6897-4a59-9d23-0c7e5c080f37">
<img width="835" alt="tokeenizers-screenshot" src="https://github.com/langchain-ai/langchain/assets/29057173/184d69f3-d8a9-42e1-a792-a78044f54076">
<img width="838" alt="punkt-screenshot" src="https://github.com/langchain-ai/langchain/assets/29057173/e1158619-1fdc-4fd5-b014-53ddc802e9c4">
### Suggestion:
I have fresh installed nltk with no dependencies. The version is the latest. The support team from NLTK doesn't know what is wrong. It seems everything is fine. So, it has to be a bug or something coming from Langchain that I'm not able to see. Really appreciate any help. Need to make this work! Thank you.
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I_kwDOIPDwls5s20eK
| 8,418 |
Question: How can I include SystemMessage with RetrievalQA
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[] | null | 5 | 2023-07-28T12:05:32 | 2023-08-01T06:22:50 | 2023-08-01T06:22:50 |
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| null |
I'd like to incorporate this 'system_message' with each whenever I call 'qa.run(prompt)'.
How is this possible. Can someone help?
Here is the code I wrote to initialize the LLM and the RetrievalQA:
```from langchain.chat_models import ChatOpenAI
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain.prompts import ChatPromptTemplate
from langchain.chains import RetrievalQA, LLMChain
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
llm = ChatOpenAI(
openai_api_key=OPENAI_API_KEY,
model_name = 'gpt-3.5-turbo',
temperature=0.0
)
system_message = [SystemMessage(
content='You are a Virtual Vet. '
'You should help clients with their concerns about their pets and provide helpful solutions.'
'You can ask questions to help you understand and diagnose the problem.'
'You should only talk within the context of problem.'
'If you are unsure of how to help, you can suggest the client to go to the nearest clink of their place.'
'You should talk on German, unless the client talks in English.')]
conversational_memory = ConversationBufferWindowMemory(
memory_key='chat_history',
k=2,
return_messages=True
)
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type='stuff',
retriever=vectorstore.as_retriever(),
)
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PR_kwDOIPDwls5WpLiZ
| 8,417 |
Bug Fix - #8415
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[] | null | 5 | 2023-07-28T10:49:15 | 2023-08-01T00:08:46 | 2023-08-01T00:08:46 |
CONTRIBUTOR
| null |
- Issue: #8415
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PR_kwDOIPDwls5WpHHg
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Minor improvements to rockset vectorstore
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[] | null | 2 | 2023-07-28T10:34:44 | 2023-07-31T16:55:00 | 2023-07-31T16:55:00 |
CONTRIBUTOR
| null |
This PR makes minor improvements to our python notebook, and adds support for `Rockset` workspaces in our vectorstore client.
@rlancemartin, @eyurtsev
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I_kwDOIPDwls5s2TJ4
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GPTCache object should be created during or before the first lookup
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[] | null | 1 | 2023-07-28T10:24:52 | 2023-08-02T15:17:50 | 2023-08-02T15:17:50 |
CONTRIBUTOR
| null |
### System Info
langchain==0.0.245
gptcache==0.1.37
### 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
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from gptcache import Cache
from gptcache.adapter.api import init_similar_cache
from langchain.cache import GPTCache
# In the first llm predict call the cache is not initialized and always returns None
def init_gptcache(cache_obj: Cache, llm_str: str):
init_similar_cache(cache_obj=cache_obj, data_dir=f"similar_cache_{llm_str}")
langchain.llm_cache = GPTCache(init_gptcache)
llm = OpenAI(model_name="text-davinci-002", temperature=0.2)
llm.predict("tell me a joke")
```
### Expected behavior
It should first create the gptcache object and check the cache. This becomes a problem if I use external dbs for gptcache for my langchain app.
Please assign this issue to me. I am willing to contribute on this one.
|
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PR_kwDOIPDwls5Wo1l9
| 8,414 |
Devcontainer README -> Clarification.
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[] | null | 2 | 2023-07-28T09:50:11 | 2023-07-28T22:09:42 | 2023-07-28T22:09:42 |
CONTRIBUTOR
| null |
- Description: The contribution guidlelines using devcontainer refer to the main repo and not the forked repo. We should create our changes in our own forked repo, not on langchain/main
- Issue: Just documentation
- Dependencies: N/A,
- Tag maintainer: @baskaryan
- Twitter handle: @levalencia
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5s1p-C
| 8,413 |
Issue: LangChain is fairly consistently dropping the first one or two characters of the chain answer.
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[] | null | 7 | 2023-07-28T08:53:41 | 2023-07-31T22:00:04 | 2023-07-31T22:00:03 |
NONE
| null |
### Issue you'd like to raise.
My Chatbot is fairly straight forward. It uses MosaicMLInstructorEmbeddings() as the embedding and MosaicML() as the model. I have a file that I want to retrieve from. When outputting an answer, for instance:
What is AliBi?
It will give a response like:
Answer: LiBi (Attention with Linear Biases) dispenses with position embeddings for tokens in transformer-based NLP models, instead encoding position information by biasing the query-key attention scores proportionally to each token pair’s distance. ALiBi yields excellent extrapolation to unseen sequence lengths compared to other position embedding schemes. We leverage this extrapolation capability by training with shorter sequence lengths, which reduces the memory and computation load.
Or when I give it:
Answer: arded checkpointing is a feature in distributed systems that allows for the checkpointing of the state of the system to be divided into multiple shards. This is useful for systems that have a large amount of data or compute to perform.
I have noticed that the chatbot fairly consistently drops the first or first two characters and I'm wondering if this is a bug with LangChain, MosaicMLInstructorEmbeddings, or MosaicML.
Here is my chatbot file:
https://github.com/KuuCi/examples/blob/support-bot/examples/end-to-end-examples/support_chatbot/chatbot.py
### Suggestion:
_No response_
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PR_kwDOIPDwls5WobGm
| 8,412 |
Fix GitLoader to handle repeated load calls
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[] | null | 2 | 2023-07-28T08:45:49 | 2023-07-31T04:27:20 | 2023-07-31T04:27:20 |
CONTRIBUTOR
| null |
**Description: a description of the change**
In this pull request, GitLoader has been updated to handle multiple load calls, provided the same repository is being cloned. Previously, calling `load` multiple times would raise an error if a clone URL was provided.
Additionally, a check has been added to raise a ValueError when attempting to clone a different repository into an existing path.
New tests have also been introduced to verify the correct behavior of the GitLoader class when `load` is called multiple times.
Lastly, the GitPython package, a dependency for the GitLoader class, has been added to the project dependencies (pyproject.toml and poetry.lock).
**Issue: the issue # it fixes (if applicable)**
None
**Dependencies: any dependencies required for this change**
GitPython
**Tag maintainer: for a quicker response, tag the relevant maintainer (see below)**
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5WoNlm
| 8,411 |
Updating docstings in utilities
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[] | null | 3 | 2023-07-28T08:08:43 | 2023-07-31T23:34:54 | 2023-07-31T23:34:54 |
CONTRIBUTOR
| null |
Updating docstrings on utility packages
@baskaryan
|
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PR_kwDOIPDwls5WoNSn
| 8,410 |
bump 246
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PR_kwDOIPDwls5WoLpU
| 8,409 |
added top_k and use_mmr to add_texts
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[] | null | 1 | 2023-07-28T08:03:10 | 2023-07-28T08:03:42 | 2023-07-28T08:03:42 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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| 1,825,790,074 |
PR_kwDOIPDwls5WnxIF
| 8,408 |
doc(prompts): Add redirect to fix broken link on Prompts Page
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[] | null | 2 | 2023-07-28T06:25:07 | 2023-07-30T04:45:38 | 2023-07-28T23:08:06 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with: -->
- Description: Fixes a broken link in https://docs.langchain.com/docs/components/prompts/ with a redirect
- Issue: #8105
- Dependencies: none
- Tag maintainer: @baskaryan
- Twitter handle: [@BharatR123](https://twitter.com/BharatR123)
<!--
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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| 1,825,780,100 |
I_kwDOIPDwls5s0zGE
| 8,407 |
Issue: prefix `Use ` being added to agent action causing InvalidTool to be invoked again and again
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[] | null | 7 | 2023-07-28T06:15:22 | 2023-12-23T08:22:46 | null |
NONE
| null |
### Issue you'd like to raise.
I have added the tools named A_tool, default_tool to a `ZeroShotAgent`.
The output from one of the execution looks like:
```
Thought: We need to use the A_tool.
Action: Use A_tool
Observation: Use A_tool is not a valid tool, try another one.
Thought:We need to use the default_tool since A_tool is not a valid tool.
Action: Use default_tool
Action Input: None
Observation: Use default_tool is not a valid tool, try another one.
```
I am not sure why the prefix `Use ` is getting added to Action.....ideally action should be just the name of the tool [`A_tool`,` default_tool`]
This is causing InvalidTool to be invoked again and again.
What can i do to fix this issue?
### Suggestion:
_No response_
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I_kwDOIPDwls5s0ysz
| 8,406 |
Issue: Unable to define "output_key" param of the LLM chain class for a csv agent while binding to SequentialChain class
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[] | null | 2 | 2023-07-28T06:13:52 | 2023-08-02T04:54:40 | 2023-08-02T04:54:40 |
NONE
| null |
### Issue you'd like to raise.
Currently, I'm using SequentialChain class to combine two steps in my workflow.
Step1: I'm using the LLM through prompt to identify the intent of the question posed by the user.
Step2: I'm using the csv based agent to answer the question posed by the user based on the csv file, but my aim is to answer the question only if the intent of the question is a textual response.
Below is the code snippets I have used to create the SequentialChain
`model = AzureOpenAI(temperature=0,deployment_name="",openai_api_key="",openai_api_version="",openai_api_base="",
)`
`template = """
You will help me identify the intent with the following examples and instructions.
Give your response in this format {{"Intent":"<identified intent>",
"Question":"<Input question>"}}
### Instructions
# Different possible intents are textResponse, BarChart, LineChart.
# If the question doesn't come under any of the intents, identify it as a None intent.
####
### Examples
Question: What is the total count of stores in 2022?
Intent: textResponse
Question: What is the split of sale amount for each sale type?
Intent: BarChart
Question: What is the monthly trend of sales amount in 2022?
Intent: LineChart
Question: {input}
"""`
`prompt = PromptTemplate(
input_variables=["input"],
template=template,
)`
`chain_one = LLMChain(llm=model, prompt=prompt, output_key = "intent")`
`agent = create_csv_agent(
AzureOpenAI(temperature=0.5,top_p = 0.5,deployment_name="",openai_api_key="",openai_api_version="",openai_api_base="",),
<csv file path>,
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
handle_parsing_errors=True
)`
`agent.agent.llm_chain.prompt.template = """
You are a friendly assistant who is warm and thoughtful and never rude or curt.
Possible Intents are: textResponse, LineChart, BarChart, None
You will act further only if the {intent} is textResponse, else your Final Answer will be I cannot respond to your query.
If {intent} is textResponse use the python_repl_ast to answer the question.
You should use the tools below to answer the question posed of you:
python_repl_ast: A Python shell. Use this to execute python commands.
You should use the python_repl_ast to answer the question posed of you. You are working with a pandas dataframe in Python.
The name of the dataframe is `df`.
Input to python_repl_ast should be a valid python command.
Give your Final Answer in this format {{"output":"Final Answer"}}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do.
Action: the action to take, should be one of [python_repl_ast]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question.
This is the result of `print(df.head())`:{df_head}
Begin!
Question: {input}
{agent_scratchpad}
"""`
`input_var = agent.agent.llm_chain.prompt.input_variables`
`input_var.append("intent")`
This was done to append my input variables to already pre-defined ones for the csv agent.
`agent.agent.llm_chain.output_key = "FinalAnswer"`
`chain_two = agent`
`overall_chain = SequentialChain(chains=[chain_one, chain_two],input_variables=["input"],
output_variables=["intent","FinalAnswer"],verbose=True)`
`overall_chain.run(input = "count of total stores in 2022")`
Now, when I run the above code I get the following error:
**validation error for SequentialChain __root__ Expected output variables that were not found: {'FinalAnswer'}. (type=value_error)**
As far as I understood the langchain documentation [(https://python.langchain.com/docs/modules/chains/foundational/sequential_chains)]
the output_key must be defined for each LLM hit for the model to tag the response to that key, hence here I have provided the output key to the agent through the llm_chain.output_key property. But still the code throws error that output variables were not found.
Is this a bug in langchain while binding the csv agents to SequentialChain class or am I missing something? Can someone please help?
### Suggestion:
_No response_
|
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| 1,825,765,474 |
I_kwDOIPDwls5s0vhi
| 8,405 |
Add more CodeSegmenters
|
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[] | null | 2 | 2023-07-28T06:06:12 | 2023-11-07T16:17:57 | null |
NONE
| null |
### Feature request
I'm interested in the code segmenters created. So far we have python and javascript. I'm looking for a typescript one in particular, the JS one marks my typescript as invalid. But in general, having the language support shown in the text splitter Language enum would be ideal but with full on parsing.
### Motivation
This call is ok to start:
`RecursiveCharacterTextSplitter.get_separators_for_language(Language.JS)`
but when we're splitting by the word function we're cutting off `export async` or `default`, and especially JsDoc, which is probably the best think to start a chunk of a function with.
Splitting by keywords like this is kinda clunky as a long term solution because of the above cases and others, having something that's more carefully splitting files up would be better.
### Your contribution
Maybe instead of a parser like eprisma, there's just a generic one that's powered by an LLM.
|
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PR_kwDOIPDwls5WnokO
| 8,404 |
Bagatur/nuclia
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[] | null | 5 | 2023-07-28T05:50:59 | 2023-08-28T07:09:55 | 2023-08-05T17:44:43 |
COLLABORATOR
| null | null |
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| 1,825,748,285 |
PR_kwDOIPDwls5WnoRe
| 8,403 |
add: license file to subproject
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[] | null | 3 | 2023-07-28T05:49:24 | 2023-11-13T19:48:22 | 2023-11-13T19:48:22 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
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- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
hi!
This is pretty straight-forward: The sdist package does not contain the license file (which is needed by e.g. conda) because the package is built from the subdir and can't see the license.
I _copied_ the license but since I'm unfamiliar with the projects direction, I'm not sure that's correct.
thanks!
|
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| 1,825,725,238 |
PR_kwDOIPDwls5WnjkC
| 8,402 |
update experimental
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[] | null | 1 | 2023-07-28T05:22:46 | 2023-07-28T20:01:37 | 2023-07-28T20:01:36 |
COLLABORATOR
| null |
some changes were made to experimental, porting them over
|
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PR_kwDOIPDwls5WniIa
| 8,401 |
add test for ensemble retriever to ensure multiple retrievers
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[] | null | 1 | 2023-07-28T05:13:45 | 2023-09-19T22:42:55 | null |
CONTRIBUTOR
| null |
Add tests to ensemble retriever to ensure it works with combination of multiple retrievers
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: Add tests to ensemble retriever to ensure it works with combination of multiple retrievers
- Issue: None
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @Mlopsj
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
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PR_kwDOIPDwls5WndUz
| 8,400 |
Sql tool cardinality distinct
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[] | null | 8 | 2023-07-28T04:49:07 | 2023-11-11T20:39:11 | null |
NONE
| null |
- Description: This adds functionality to the SQLDatabaseToolkit agent_toolkit to address a shortcoming in the agent's capability to better understand and utilize the data in the database for filtering when required. Previously, the model would hallucinate filter values or just hard code exactly what a user asks for, which often times would return no results. This Loop was fine for users used to working with data who could iteratively prompt to get distinct values and subsequently ask another question to filter, but not as useful for users who are not experienced with working with databases and SQL. This change makes the natural language agent a bit more autonomous and self healing by being better able to gain an understanding of the data it's working with.
- Issue: N/A,
- Dependencies: none,
- Tag maintainer: @hinthornw (changing a tool/toolkit/agent), @hwchase17, @baskaryan (changing a prompt), also made a tweak to the makefile for lint_diff (should a check for only changed files not [follow imports](https://mypy.readthedocs.io/en/stable/running_mypy.html#following-imports)?)
- Twitter handle: @bpafoshizle
[Unit Test](https://github.com/bpafoshizle/langchain/blob/f093d0c7d948186fc3501301105603cdc5a0c00e/libs/langchain/tests/unit_tests/test_sql_database.py#L128)
[Example notebook](https://github.com/bpafoshizle/langchain/blob/sql-tool-cardinality-distinct/docs/extras/integrations/toolkits/sql_database.ipynb)
Passed format, lint (without following imports), and tests (including test I added).
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I_kwDOIPDwls5s0dmM
| 8,399 |
Questions Regarding load_summarize_chain Implementation and LLM Models Usage
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[] | null | 2 | 2023-07-28T04:43:43 | 2023-12-07T16:07:05 | 2023-12-07T16:07:04 |
NONE
| null |
### Issue you'd like to raise.
Hello, I'm currently using the load_summarize_chain function to summarize extensive documents, specifically those about 250 pages or more. In this process, I'm employing the map_reduce chain type along with a custom prompt.
The process works as follows:
In the initial step, the chain sends parallel requests to LLM to summarize chunks of data.
Upon receiving all responses, the summary chain collects them, combines them into a single prompt, and generates the final output.
I'm curious about two aspects of this process:
In the second step, the chain employs the StuffDocumentsChain method to create the final summary despite setting mapreduce in chaintype. Is it possible to use an alternative chain type for this? If yes, could you recommend any?
Would it be feasible to utilize different Language Learning Models (LLMs) in the two steps? For instance, could I use GPT3.5 for the first step and GPT4 for generating the final summary?
I appreciate your help and look forward to your response.
Thanks in advance!
### Suggestion:
_No response_
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PR_kwDOIPDwls5WncA1
| 8,398 |
refactor `agents/agent_toolkits` into `toolkits`
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[] | null | 5 | 2023-07-28T04:40:11 | 2023-10-10T21:24:08 | 2023-10-10T21:24:07 |
COLLABORATOR
| null |
This refactoring follows LangChain's flat-module hierarchy.
@hwchase17, @baskaryan
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PR_kwDOIPDwls5WnXUf
| 8,397 |
Improve README.md Documentation
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[] | null | 1 | 2023-07-28T04:08:23 | 2023-08-01T17:39:51 | 2023-08-01T17:19:52 |
CONTRIBUTOR
| null |
This pull request improves the README.md documentation for better readability and clarity. The updates provide a clear introduction to LangChain's purpose, organized sections for each use case, and detailed explanations of core concepts. The formatting is consistent, and interactive links enhance user navigation. Additionally, we've added contributing guidelines and clear contact information. These changes aim to empower users, both newcomers and experienced, to utilize LangChain effectively and build powerful applications with Large Language Models.
@baskaryan
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I_kwDOIPDwls5s0Tqg
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can you provide agent examples for multiple rounds of dialogue?
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[] | null | 1 | 2023-07-28T03:45:01 | 2023-11-03T16:05:37 | 2023-11-03T16:05:36 |
NONE
| null |
For example: we have two tool, one is search tool, used to search transaction, the other is date tool
for search tool, we have three parameters, if user input less parameters, search tool will feedback user to input missing information, then user will provide supplementary information, agent need to combine dialogue history to route to search tool until the information is complete, finally search tool return the query results.
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PR_kwDOIPDwls5WnTWk
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Revert "PromptTemplate update documentation and expand kwargs (#8234)"
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[] | null | 1 | 2023-07-28T03:39:32 | 2023-07-28T04:11:12 | 2023-07-28T04:11:11 |
COLLABORATOR
| null |
fyi @eyurtsev was failing a unit test
|
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PR_kwDOIPDwls5WnQyX
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Bagatur/from texts bug fix
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[] | null | 1 | 2023-07-28T03:24:14 | 2023-07-28T04:52:39 | 2023-07-28T04:52:38 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,825,595,680 |
I_kwDOIPDwls5s0GEg
| 8,393 |
how can i get response of agent
|
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[] | null | 4 | 2023-07-28T02:39:03 | 2023-11-03T16:06:06 | 2023-11-03T16:06:06 |
NONE
| null |
i just wrote a custom agent to classify intent to choose different tool, but i don't know how to get response from agent, when i print answer, it shows Agent stopped due to iteration limit or time limit. Code is shown as below, thanks
tools = [SearchTool(),DateTool()]
agent = IntentAgent(tools=tools, llm=llm)
agent_exec = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, max_iterations=1)
answer=agent_exec.run(prompt)
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I_kwDOIPDwls5sz_Ue
| 8,392 |
DOC: Custom LLM Wrappers not functional
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[] | null | 1 | 2023-07-28T02:06:13 | 2023-08-06T18:32:16 | 2023-08-06T18:32:16 |
NONE
| null |
### Issue with current documentation:
I have tried to make a wrapper around my LLMs but the class cant be instantiated.
Can't even get the example here to work:
https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm
Can someone help out with this?
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls5szxhW
| 8,391 |
chain.arun() for summarization no faster than chain()
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[] | null | 8 | 2023-07-28T01:09:54 | 2023-07-28T05:12:04 | 2023-07-28T05:12:04 |
NONE
| null |
### System Info
(h2ogpt) jon@pseudotensor:~/h2ogpt$ pip freeze | grep langchain
langchain==0.0.235
langchainplus-sdk==0.0.20
Python 3.10
(h2ogpt) jon@pseudotensor:~/h2ogpt$ lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 20.04.6 LTS
Release: 20.04
Codename: focal
### Who can help?
@agola11
### 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
- [x] Chains
- [ ] Callbacks/Tracing
- [x] Async
### Reproduction
https://github.com/h2oai/h2ogpt/pull/551/commits/cc3331d897f4f7bab13c7f9644e7a7d7cd35031e
The above shows my introduction of async from before not having it.
The text generation inference server is set to have a large concurrency, but is showing requests are coming in back-to-back.
### Expected behavior
I expect the summarization part to be parallel, like stated here:
https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/combine_documents/map_reduce.py#L210-L213
But perhaps I misunderstand something. Or perhaps it's not really parallel:
https://github.com/langchain-ai/langchain/issues/1145#issuecomment-1586234397
There's lots of discussion w.r.t. hitting rate limit with OpenAI:
https://github.com/langchain-ai/langchain/issues/2465
https://github.com/langchain-ai/langchain/issues/1643
So I presume this works, but I'm not seeing it. In OpenAI case it seems to be done via batching, which is possible in HF TGI server but not implemented. But I would have thought that all the reduction tasks could have been in parallel with asyncio.
https://github.com/langchain-ai/langchain/pull/1463#issuecomment-1566391189
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I_kwDOIPDwls5szcQ5
| 8,390 |
DOC: how to setup APIs in my project to receive requests and returns responses from and to end devices
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[] | null | 3 | 2023-07-28T00:17:12 | 2023-10-27T17:50:35 | null |
CONTRIBUTOR
| null |
### Issue with current documentation:
I would like to deploy my langchain project in a shared host server , so I will need tobe able APIs to receive request and return response to the end devices. I think this feature is already done , however, I do not how to implement it in my project
### Idea or request for content:
_No response_
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PR_kwDOIPDwls5Wmfi5
| 8,389 |
Minor fixes to enhance notebook usability:
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[] | null | 1 | 2023-07-28T00:07:14 | 2023-07-31T22:16:31 | 2023-07-29T00:10:04 |
CONTRIBUTOR
| null |
- Install langchain
- Set Pinecone API key and environment as env vars
- Create Pinecone index if it doesn't already exist
---
- Description: Fix a couple minor issues I came across when running this notebook,
- Issue: the issue # it fixes (if applicable),
- Dependencies: none,
- Tag maintainer: @rlancemartin @eyurtsev,
- Twitter handle: @zackproser (certainly not necessary!)
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PR_kwDOIPDwls5WmNN4
| 8,388 |
add kwargs to llm runnables
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[] | null | 4 | 2023-07-27T22:56:05 | 2023-07-28T13:38:31 | 2023-07-28T08:13:11 |
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| 8,387 |
Update tot.ipynb
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[] | null | 1 | 2023-07-27T22:49:51 | 2023-07-27T23:44:41 | 2023-07-27T23:44:41 |
CONTRIBUTOR
| null |
Spelling error fix
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5szBNr
| 8,386 |
qa_chain with retriever and input_documents.
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[] | null | 1 | 2023-07-27T22:35:13 | 2023-11-02T16:04:35 | 2023-11-02T16:04:34 |
NONE
| null |
### System Info
I wonder if the input_documents are combined with relevant_docuemnts from retriever? I have been comparing results, and although wiki_doc provide different context, it is not reflected in response! I have tried both version, and the information by "input_doc" is not reflected in result1. I looked up in example, and I didn't find example where both "retriever" and "input_documents" are used together, it is either this OR that.
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
qa_chain = RetrievalQA.from_chain_type(llm, retriever=ret_para, chain_type="stuff")
result1 = qa_chain({"input_documents": wiki_doc, "query": query})
result2 = qa_chain({"query": query})
### Who can help?
@agola11
### 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 is:
1-load any db
2-ret = db.as_retriever()
3-llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
4-qa_chain = RetrievalQA.from_chain_type(llm, retriever=ret_para, chain_type="stuff")
5-Create any additional_doc from some other sources
6-result1 = qa_chain({"input_documents": additional_doc, "query": query})
7-result2 = qa_chain({"query": query})
### Expected behavior
It is expected that result1 and result2 have different output since they have different contexts, if input_documents is properly combined with relevant_documents from retriever.
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I_kwDOIPDwls5sy_td
| 8,385 |
Exllama integration to run GPTQ models
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[] | null | 6 | 2023-07-27T22:26:51 | 2023-12-18T22:45:50 | null |
NONE
| null |
### Feature request
An integration of exllama in Langchain to be able to use 4-bit GPTQ weights, designed to be fast and memory-efficient on modern GPUs.
### Motivation
The benchmarks on the official repo speak for themselves:
https://github.com/turboderp/exllama#results-so-far
### Your contribution
There is a fork that uses exllama with langchain here :
https://github.com/CoffeeVampir3/exllama-langchain-example/tree/master
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Harrison/memory docs
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[] | null | 1 | 2023-07-27T21:36:31 | 2023-07-28T00:18:21 | 2023-07-28T00:18:20 |
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PR_kwDOIPDwls5Wl7cF
| 8,382 |
Add LLaMa Formatter and AzureML Chat Endpoint
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[] | null | 2 | 2023-07-27T21:20:37 | 2023-07-31T23:26:25 | 2023-07-31T23:26:25 |
CONTRIBUTOR
| null |
## Description
Microsoft and Meta recently [announced their collaboration](https://blogs.microsoft.com/blog/2023/07/18/microsoft-and-meta-expand-their-ai-partnership-with-llama-2-on-azure-and-windows/) on LLaMa2. This PR extends the current LLM wrapper and introduces a new Chat Model wrapper for AzureML to support LLaMa2.
## Dependencies
No dependencies added :)
## Twitter Handles
[@matthew_d13](https://twitter.com/matthew_d13)
[@prakhar_in](https://twitter.com/prakhar_in)
maintainers - @hwchase17, @baskaryan
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