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| 1,791,756,774 |
PR_kwDOIPDwls5U0lXj
| 7,281 |
Allow passing api key into OpenAIWhisperParser
|
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[] | null | 3 | 2023-07-06T15:12:26 | 2023-07-10T23:38:00 | 2023-07-07T19:07:46 |
CONTRIBUTOR
| null |
This just allows the user to pass in an api_key directly into OpenAIWhisperParser. Very simple addition.
@rlancemartin
@eyurtsev
|
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I_kwDOIPDwls5qy3YU
| 7,280 |
Callback for VertexAI to monitor cost and token consumption
|
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[] | null | 5 | 2023-07-06T14:50:29 | 2023-12-25T16:03:03 | null |
NONE
| null |
### Feature request
It would be nice to have a similar function as `get_openai_callback()` for VertexAI. This actually gives the input tokens, output tokens and cost of using OpenAI models:
```python
with get_openai_callback() as cb:
llm = OpenAI(temperature=0)
chat = ChatOpenAI(temperature=0)
emb = OpenAIEmbeddings()
output_llm = llm("As I was saying,")
print(output_llm)
# System message + Human Message
messages = [
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love programming.")
]
output_chat = chat(messages)
print(output_chat)
print(cb)
```
I would like to have:
```python
with get_vertexai_callback() as cb:
llm = VertexAI(temperature=0)
chat = ChatVertexAI(temperature=0)
emb = VertexAIEmbeddings()
print(llm("As I was saying,"))
# System message + Human Message
messages = [
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love programming.")
]
print(chat(messages))
```
### Motivation
I would like to monitor my usage of VertexAI models
### Your contribution
I already read quite a bit the openai version of the callback but if anyone has already thought about how to do it with vertex I would be really curious :). If someone else also planned to do it we could merge efforts!
|
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PR_kwDOIPDwls5U0VsQ
| 7,279 |
Small updates to run_evaluator after adding dataset.data_type
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[] | null | 4 | 2023-07-06T14:31:37 | 2023-07-08T01:39:07 | 2023-07-08T01:39:06 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @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_kwDOIPDwls5U0N4e
| 7,278 |
Add new chain qa_with_references_and_verbatim
|
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CONTRIBUTOR
| null |
# Description:
This chain returns the selected documents used for the response. Then, it's possible to extract all meta-data
for these documents, to generate a markdown link or other stuff.
It's not easy to manage the links of sources. Langchain proposed a qa_with_source, but this chain returns only a
list of URL. It's not possible to extract other informations about the metadata, like the filename or others stuff.
I propose a solution to this problem: return the Documents, and let development do what it wants with it.
This pull-request proposes a new implementation of the original chain `qa_with_source`.
Now, the implementation `qa_with_reference` returns the list of documents used for the response.
Then, it's possible to use all meta-data for these documents, to generate a Markdown link (with filename/title and URL) or what you want.
To do that, I inject a UUID in the meta-data, and ask to return a list of UUID in the response. Then, it's easy to find the corresponding document.
In another [branch](https://github.com/pprados/langchain/tree/pprados/qa_with_references_rewrite_qa_with_sources),
I have reimplemented the origin `qa_with_source` to extend the `qa_with_reference`.
# Tag maintainer:
- @baskaryan
# Dependencies:
- no
# Twitter handle:
- @pprados
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I_kwDOIPDwls5qyfjJ
| 7,277 |
Allow filter to be passed in ElasticKnnSearch knn_search and knn_hybrid_search
|
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[] | null | 1 | 2023-07-06T13:56:39 | 2023-09-20T14:35:44 | 2023-09-20T14:35:43 |
NONE
| null |
You can pass filter to an kNN in elasticsearch.
This is currently implemented in the langchain wrapper in the exact KNN search.
But is not yet implemented in the approximate KNN search.
Add filter param to [_default_knn_query](https://github.com/jeffvestal/langchain/blob/10f34bf62e2d53f0b1a7b15ba21c2328b64862cd/langchain/vectorstores/elastic_vector_search.py#L398), [knn_search](https://github.com/jeffvestal/langchain/blob/10f34bf62e2d53f0b1a7b15ba21c2328b64862cd/langchain/vectorstores/elastic_vector_search.py#L432), [knn_hybrid_search](https://github.com/jeffvestal/langchain/blob/10f34bf62e2d53f0b1a7b15ba21c2328b64862cd/langchain/vectorstores/elastic_vector_search.py#L488).
cc: @jeffvestal, @derickson
### Suggestion:
_No response_
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Fix `make docs_build` and related scripts
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CONTRIBUTOR
| null |
**Description: a description of the change**
Fixed `make docs_build` and related scripts which caused errors. There are several changes.
First, I made the build of the documentation and the API Reference into two separate commands. This is because it takes less time to build. The commands for documents are `make docs_build`, `make docs_clean`, and `make docs_linkcheck`. The commands for API Reference are `make api_docs_build`, `api_docs_clean`, and `api_docs_linkcheck`.
It looked like `docs/.local_build.sh` could be used to build the documentation, so I used that. Since `.local_build.sh` was also building API Rerefence internally, I removed that process. `.local_build.sh` also added some Bash options to stop in error or so. Futher more added `cd "${SCRIPT_DIR}"` at the beginning so that the script will work no matter which directory it is executed in.
`docs/api_reference/api_reference.rst` is removed, because which is generated by `docs/api_reference/create_api_rst.py`, and added it to .gitignore.
Finally, the description of CONTRIBUTING.md was modified.
**Issue: the issue # it fixes (if applicable)**
https://github.com/hwchase17/langchain/issues/6413
**Dependencies: any dependencies required for this change**
`nbdoc` was missing in group docs so it was added. I installed it with the `poetry add --group docs nbdoc` command. I am concerned if any modifications are needed to poetry.lock. I would greatly appreciate it if you could pay close attention to this file during the review.
**Tag maintainer**
- General / Misc / if you don't know who to tag: @baskaryan
If this PR needs any additional changes, I'll be happy to make them!
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| 1,791,522,651 |
PR_kwDOIPDwls5UzyjO
| 7,275 |
fix pydantic parser: JSONDecodeError
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[] | null | 3 | 2023-07-06T13:03:35 | 2023-09-05T01:58:31 | 2023-09-05T01:58:30 |
CONTRIBUTOR
| null |
## Problem
The **double quatations (")** which cannot use `parser.parse()` correctly.
For example, completion from chain response as following:
```python
text = """
Answer: {
"product_name": "Control4® T3 Series 7\" Tabletop Touch Screen",
"manufactured_date": "2015-10-09",
"size_inch": "7.0\" × 5.1\" × 0.75\"",
"resolution": "1280 × 800",
"contrast": "",
"operation_temperature": "32 ~ 104˚F (0˚ ~ 40˚C)",
"power_supply": "PoE (IEEE802.3af) 100VAC ~ 240VAC, 50-60 Hz International power supply adapters included",
"sunlight_readable": false,
"antiglare": false,
"low_power_consumption": false,
"high_brightness": false,
"wide_temperature": false,
"fast_response": true,
"screen_features": ["Capacitive touch", "Camera: 720p", "HD camera", "Speakers and microphone", "Video intercom integration", "Audio intercom integration"]
}
"""
# Just take the parse function :)
try:
# Greedy search for 1st json candidate.
match = re.search(
r"\{.*\}", text.strip(), re.MULTILINE | re.IGNORECASE | re.DOTALL
)
json_str = ""
if match:
json_str = match.group()
json_object = json.loads(json_str, strict=False)
except Exception as e:
print(e)
```
## Wrong Result
```
Expecting ',' delimiter: line 2 column 43 (char 44)
```
## Solution
Let's add a `remove_illegal_quatations` function which will:
1. **Remove the illegal quatations** which not display in `first, second, third, last time`
2. Remeber there will `List` value, we should'nt remove here. ex: `["Capacitive touch", "Camera: 720p", ..."]`
3. Remeber there will `true/false` which will not have `""`. ex: ` "sunlight_readable": false`
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PR_kwDOIPDwls5UzsRF
| 7,274 |
Use correct Language for html_splitter
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[] | null | 1 | 2023-07-06T12:46:58 | 2023-07-06T13:24:26 | 2023-07-06T13:24:26 |
CONTRIBUTOR
| null |
`html_splitter` was using `Language.MARKDOWN`.
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I_kwDOIPDwls5qx_fw
| 7,273 |
[FEATURE] SOLR Based Vector Store
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[] | null | 7 | 2023-07-06T12:44:39 | 2023-10-29T17:35:57 | null |
CONTRIBUTOR
| null |
### Feature request
This adds support for Apache SOLRs vector search capabilities
(https://solr.apache.org/guide/solr/latest/query-guide/dense-vector-search.html)
### Motivation
As SOLR is a commonly used search index and now offers this feature, it is important to allow SOLR users to be able to integrate seamlessly with LangChain (and the associated benefits).
### Your contribution
I can try submitting a PR
|
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I_kwDOIPDwls5qx32N
| 7,272 |
'Qdrant.from_documents' Memory overflow
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[] | null | 8 | 2023-07-06T12:25:00 | 2023-10-16T16:06:24 | 2023-10-16T16:06:23 |
NONE
| null |
### Issue you'd like to raise.
```
def handle_embeddings(payload):
loader = UnstructuredPDFLoader(payload["filepath"])
documents = loader.load()
text_splitter = SpacyTextSplitter(pipeline=payload["language"], chunk_size=1536, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings(**payload)
qdrant = Qdrant.from_documents(
docs, embeddings,
path=path,
collection_name=collection_name,
)
import dramatiq
from dramatiq.brokers.redis import RedisBroker
from tasks import handle_embeddings
redis_broker = RedisBroker(url='redis://redis.helloreader.docker/10')
dramatiq.set_broker(redis_broker)
@dramatiq.actor(max_retries = 0)
def handle_embeddings_task(payload):
result = handle_embeddings(payload)
return result
```
Due to the time-consuming nature of embeddings and storing them in a vector database, I opted for asynchronous queue tasks to handle them. However, I noticed that when processing documents of size 30 MB, the memory usage of the queue task kept increasing until it eventually crashed due to overflow. At this point, I investigated and found that the memory overflow occurred even before the embeddings interface was called, indicating that the issue was with the `Qdrant.from_documents` method. I have been searching for the root cause for a while but haven't found it yet.
### Suggestion:
I hope someone who is familiar with the `Qdrant.from_documents` method or has knowledge of other possible causes can help me resolve this issue.
The document size of approximately 30 MB corresponds to approximately 560,000 tokens.
During the process, I tried using Dramatiq, Celery, and RQ, and encountered the same issue with all of them. Therefore, we can exclude the possibility of the issue being specific to these queue tools.
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PR_kwDOIPDwls5Uy9XT
| 7,271 |
Added number_of_head_rows to pandas agent parameters
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[] | null | 1 | 2023-07-06T10:33:03 | 2023-07-07T06:24:22 | 2023-07-07T06:17:26 |
CONTRIBUTOR
| null |
Description: Added number_of_head_rows as a parameter to pandas agent. number_of_head_rows allows the user to select the number of rows to pass with the prompt when include_df_in_prompt is True. This gives the ability to control the token length and can be helpful in dealing with large dataframe.
Tag maintainer: @hinthornw @baskaryan
Twitter handle: @activenikhilg
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PR_kwDOIPDwls5Uy2At
| 7,270 |
Generic OpenAI fn chain
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[] | null | 1 | 2023-07-06T10:12:39 | 2023-07-07T09:44:54 | 2023-07-07T09:44:53 |
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I_kwDOIPDwls5qxDMA
| 7,269 |
Langchain Tools and Agents
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[] | null | 5 | 2023-07-06T10:11:15 | 2023-12-01T16:09:13 | 2023-12-01T16:09:12 |
NONE
| null |
### Issue you'd like to raise.
I am using Tools and Agents to query on different vectorstores. But when I am asking question which is not from the vectorstore.It responds i dont know. So is there any approach i can try where if the answer is not from the vectorstore i can carry out the conversation like chatgpt. If Yes? Can you Please let me know how we can integrate this
### Suggestion:
_No response_
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| 1,791,159,677 |
I_kwDOIPDwls5qwu19
| 7,268 |
langchain + gpt4all 执行异常，总提示参数问题
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[] | null | 3 | 2023-07-06T09:22:31 | 2023-10-14T20:11:07 | 2023-10-14T20:11:06 |
NONE
| null |
### System Info
LangChain v0.0.225, Windows10, Python 3.10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
一个来自langchain的事例代码如下：
from langchain import PromptTemplate, LLMChain
from langchain.llms import GPT4All
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
local_path = (
"ggml-gpt4all-j-v1.3-groovy.bin" # replace with your desired local file path
)
callbacks = [StreamingStdOutCallbackHandler()]
llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
llm_chain.run(question)
执行的结果如下：
TypeError: GPT4All.generate() got an unexpected keyword argument 'n_ctx'
当我加上参数 max_tokens=200 时：llm = GPT4All(model=local_path, max_tokens=200, callbacks=callbacks, verbose=True)
出现如下结果：
ValidationError: 1 validation error for GPT4All
max_tokens
extra fields not permitted (type=value_error.extra)
### Expected behavior
callbacks = [StreamingStdOutCallbackHandler()]
llm = GPT4All(model=local_path, max_tokens=200, callbacks=callbacks, verbose=True)
llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
llm_chain.run(question)
#可以输出正确结果
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PR_kwDOIPDwls5Uydb7
| 7,267 |
FLARE Example notebook: switch to named arg to pass pydantic validation
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[] | null | 1 | 2023-07-06T09:05:53 | 2023-08-31T00:05:49 | 2023-07-06T13:32:01 |
CONTRIBUTOR
| null |
Adding the name of the parameter to comply with latest requirements by Pydantic usage for BaseModels.
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PR_kwDOIPDwls5UyVjV
| 7,266 |
refactor BedrockEmbeddings class
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[] | null | 1 | 2023-07-06T08:43:14 | 2023-07-06T15:56:31 | 2023-07-06T15:56:31 |
CONTRIBUTOR
| null |
#### Description
refactor BedrockEmbeddings class to clean code as below:
1. inline content type and accept
2. rewrite input_body as a dictionary literal
3. no need to declare embeddings variable, so remove it
#### Tag maintainer
- General / Misc / if you don't know who to tag: @baskaryan
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PR_kwDOIPDwls5UyIyk
| 7,264 |
Remove whitespace in filename
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[] | null | 1 | 2023-07-06T08:05:03 | 2023-07-07T00:10:05 | 2023-07-06T15:55:43 |
CONTRIBUTOR
| null |
Rename `langchain/tools/office365/__init__ .py` to `langchain/tools/office365/__init__.py`
@baskaryan
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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PR_kwDOIPDwls5UyHL2
| 7,263 |
Fix inconsistent behavior of `CharacterTextSplitter` when changing `keep_separator`
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[] | null | 1 | 2023-07-06T08:00:08 | 2023-07-06T13:30:04 | 2023-07-06T13:30:04 |
CONTRIBUTOR
| null |
- Description:
- When `keep_separator` is `True` the `_split_text_with_regex()` method in `text_splitter` uses regex to split, but when `keep_separator` is `False` it uses `str.split()`. This causes problems when the separator is a special regex character like `.` or `*`. This PR fixes that by using `re.split()` in both cases.
- Issue: #7262
- Tag maintainer: @baskaryan
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I_kwDOIPDwls5qwNg6
| 7,262 |
Inconsistent behavior of `CharacterTextSplitter` when changing `keep_separator` for special regex characters
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[] | null | 1 | 2023-07-06T07:57:36 | 2023-07-06T13:54:13 | 2023-07-06T13:54:13 |
CONTRIBUTOR
| null |
### System Info
LangChain v0.0.225, Windows, Python 3.10
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The behavior for `CharacterTextSplitter` when changing `keep_separator` when using normal characters is like this:
```python
text_splitter = CharacterTextSplitter(
chunk_size=4,
chunk_overlap=0,
separator="_",
keep_separator=False,
)
text_splitter.split_text("foo_bar_baz_123")
# ['foo', 'bar', 'baz', '123']
```
```python
text_splitter = CharacterTextSplitter(
chunk_size=4,
chunk_overlap=0,
separator="_",
keep_separator=True,
)
text_splitter.split_text("foo_bar_baz_123")
# ['foo', '_bar', '_baz', '_123']
```
However, when using special regex characters like `.` or `*` the splitter breaks when `keep_separator` is `False`.
```python
text_splitter = CharacterTextSplitter(
chunk_size=4,
chunk_overlap=0,
separator=r"\.",
keep_separator=False,
)
text_splitter.split_text("foo.bar.baz.123")
# ['foo.bar.baz.123']
```
The special characters should be escaped, otherwise it raises an error. For example, the following code raises an error.
```python
text_splitter = CharacterTextSplitter(
chunk_size=4,
chunk_overlap=0,
separator=r"?",
keep_separator=True,
)
text_splitter.split_text("foo?bar?baz?123")
```
I'll make a PR to fix this.
Also, the documentation never mentions that the separator should be a regex, I only found out the hard way after getting regex errors on one of the `RecursiveTextSplitter` splitters after updating LangChain. I think we should add a note about this in the documentation or the code.
### Expected behavior
```python
text_splitter = CharacterTextSplitter(
chunk_size=4,
chunk_overlap=0,
separator=r"\.",
keep_separator=False,
)
text_splitter.split_text("foo.bar.baz.123")
# ['foo', 'bar', 'baz', '123']
```
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PR_kwDOIPDwls5UyDbj
| 7,261 |
docs: Fixed typos in Amazon Kendra Retriever documentation
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[] | null | 1 | 2023-07-06T07:49:01 | 2023-07-06T15:56:53 | 2023-07-06T15:56:52 |
CONTRIBUTOR
| null |
## Description
Fixed to the official service name Amazon Kendra.
## Tag maintainer
@baskaryan
|
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I_kwDOIPDwls5qwJa4
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Chroma DB Error
|
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[] | null | 11 | 2023-07-06T07:46:56 | 2023-11-24T20:56:06 | null |
CONTRIBUTOR
| null |
### System Info
Error: Please install chromadb as a dependency with, e.g. `npm install -S chromadb`
at Function.imports (file:///home/aqib/backend-11/vision/node_modules/langchain/dist/vectorstores/chroma.js:160:19)
at processTicksAndRejections (node:internal/process/task_queues:96:5)
at async Chroma.ensureCollection (file:///home/aqib/backend-11/vision/node_modules/langchain/dist/vectorstores/chroma.js:61:42)
at async Chroma.addVectors (file:///home/aqib/backend-11/vision/node_modules/langchain/dist/vectorstores/chroma.js:88:28)
at async Chroma.addDocuments (file:///home/aqib/backend-11/vision/node_modules/langchain/dist/vectorstores/chroma.js:56:9)
at async Function.fromDocuments (file:///home/aqib/backend-11/vision/node_modules/langchain/dist/vectorstores/chroma.js:145:9)
at async add (file:///home/aqib/backend-11/vision/vectorStore/db.js:24:5)
at async run (file:///home/aqib/backend-11/vision/vectorStore/insertDocs.js:6:3)
node:internal/process/promises:279
triggerUncaughtException(err, true /* fromPromise */);
^
**_Got this when I console Logged the error_**
ReferenceError: fetch is not defined
at Object.<anonymous> (/home/aqib/backend-11/vision/node_modules/chromadb/dist/main/generated/runtime.js:17:24)
at Module._compile (node:internal/modules/cjs/loader:1196:14)
at Object.Module._extensions..js (node:internal/modules/cjs/loader:1250:10)
at Module.load (node:internal/modules/cjs/loader:1074:32)
at Function.Module._load (node:internal/modules/cjs/loader:909:12)
at Module.require (node:internal/modules/cjs/loader:1098:19)
at require (node:internal/modules/cjs/helpers:108:18)
at Object.<anonymous> (/home/aqib/backend-11/vision/node_modules/chromadb/dist/main/generated/api.js:17:19)
at Module._compile (node:internal/modules/cjs/loader:1196:14)
at Object.Module._extensions..js (node:internal/modules/cjs/loader:1250:10)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Call any Chroma method to reproduce this issue
### Expected behavior
It should've inserted the documents
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I_kwDOIPDwls5qwEYi
| 7,259 |
Issue: confusion about `Document` or string input for `embed_documents` function
|
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[] | null | 3 | 2023-07-06T07:32:37 | 2023-10-05T17:49:36 | 2023-10-05T17:49:10 |
NONE
| null |
### Issue you'd like to raise.
I'm using the `OpenAIEmbeddings` object to create embeddings for my list of documents. While it expects a list of strings, the `embed_documents` function seems misleading as to accept a list of documents. As a result, passing a list of documents raises `AttributeError` since `Document` objects do not have a `replace` method.
### Suggestion:
1. Add a `replace` method to `Document` (or)
2. Extend the function `embed_documents` to handle `Document` objects (or)
3. Rename the function to suit handling a list of strings
I will be glad to implement one of them as a first issue!
|
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| 1,790,972,427 |
PR_kwDOIPDwls5Ux7dr
| 7,258 |
docs: Update documentation for Wikipedia tool to use WikipediaQueryRun
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[] | null | 1 | 2023-07-06T07:23:17 | 2023-07-06T13:29:39 | 2023-07-06T13:29:38 |
CONTRIBUTOR
| null |
**Description**
In the following page, "Wikipedia" tool is explained.
https://python.langchain.com/docs/modules/agents/tools/integrations/wikipedia
However, the WikipediaAPIWrapper being used is not a tool. This PR updated the documentation to use a tool WikipediaQueryRun.
**Issue**
None
**Tag maintainer**
Agents / Tools / Toolkits: @hinthornw
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PR_kwDOIPDwls5Uxzhm
| 7,257 |
Update supabase match_docs ddl and notebook to use expected id type
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[] | null | 1 | 2023-07-06T06:56:23 | 2023-07-06T13:22:42 | 2023-07-06T13:22:42 |
CONTRIBUTOR
| null |
- Description: Switch supabase match function DDL to use expected uuid type instead of bigint
- Issue: https://github.com/hwchase17/langchain/issues/6743, https://github.com/hwchase17/langchain/issues/7179
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: https://twitter.com/ShantanuNair
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PR_kwDOIPDwls5UxxqI
| 7,256 |
Add HumanInputChatModel
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[] | null | 1 | 2023-07-06T06:49:09 | 2023-07-06T13:21:04 | 2023-07-06T13:21:03 |
CONTRIBUTOR
| null |
- Description: This is a chat model equivalent of HumanInputLLM. An example notebook is also added.
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: N/A
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I_kwDOIPDwls5qvtk3
| 7,255 |
OpenAI Functions Agent not asking required parameter value
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[] | null | 1 | 2023-07-06T06:19:57 | 2023-10-12T16:06:00 | 2023-10-12T16:05:59 |
NONE
| null |
I was using openai functions agent with custom functions , the custom function(loan eligibility) needs three augments , state, age and income .
when i run the agent with question "how much can i borrow in state CA?" , it is directly calling function without asking age and income from the user .
below is the error
pydantic.error_wrappers.ValidationError: 2 validation errors for LoanEligibilitySchema
age
field required (type=value_error.missing)
income
field required (type=value_error.missing)
how to fix this ?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5qvWi6
| 7,254 |
Structured Chat Agent cannot save_context when memory has additional input variables
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[] | null | 1 | 2023-07-06T04:49:18 | 2023-10-12T16:06:05 | 2023-10-12T16:06:04 |
CONTRIBUTOR
| null |
### System Info
LangChain 0.0.225, Python 3.10, Windows
### Who can help?
@hwchase17
### 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
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am creating a structured chat agent using an `AgentExecutor.from_agent_and_tools`. I have added a custom variable `new_variable` to `input_variables`, and followed the instructions on how to add memory as per here: https://python.langchain.com/docs/modules/agents/agent_types/structured_chat
`def get_agent_executor(llm: ChatOpenAI, tools: list[Tool], chat_history: MessagesPlaceholder, memory: ConversationBufferMemory) -> AgentExecutor:
input_variables = ["input", "agent_scratchpad", "chat_history", "new_variable"]
prefix = CUSTOM_PREFIX
suffix = CUSTOM_SUFFIX
custom_prompt = StructuredChatAgent.create_prompt(tools, prefix=prefix, suffix=suffix,
input_variables=input_variables, memory_prompts=[chat_history])
llm_chain = LLMChain(llm=llm, prompt=custom_prompt, verbose=True)
convo_agent = StructuredChatAgent(llm_chain=llm_chain)
agent_executor = AgentExecutor.from_agent_and_tools(agent=convo_agent, tools=tools, verbose=True, max_iterations=1,
memory=memory, memory_prompts=[chat_history], input_variables=input_variables,
handle_parsing_errors="Check your output and make sure it is a markdown code snippet of a json blob with a single action!")
return agent_executor`
This agent crashes every time at the end of the first iteration:
`final_outputs: Dict[str, Any] = self.prep_outputs(
self.memory.save_context(inputs, outputs)
input_str, output_str = self._get_input_output(inputs, outputs)
prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
raise ValueError(f"One input key expected got {prompt_input_keys}")
ValueError: One input key expected got ['example_good_page', 'input']`
### Expected behavior
save_context without hiccup
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PR_kwDOIPDwls5UxTOR
| 7,253 |
Add verbose parameter for llamacpp
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[] | null | 2 | 2023-07-06T04:35:08 | 2023-07-07T20:44:28 | 2023-07-07T19:08:25 |
CONTRIBUTOR
| null |
**Title:** Add verbose parameter for llamacpp
**Description:**
This pull request adds a 'verbose' parameter to the llamacpp module. The 'verbose' parameter, when set to True, will enable the output of detailed logs during the execution of the Llama model. This added parameter can aid in debugging and understanding the internal processes of the module.
The verbose parameter is a boolean that prints verbose output to stderr when set to True. By default, the verbose parameter is set to True but can be toggled off if less output is desired. This new parameter has been added to the `validate_environment` method of the `LlamaCpp` class which initializes the `llama_cpp.Llama` API:
```python
class LlamaCpp(LLM):
...
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
...
model_param_names = [
...
"verbose", # New verbose parameter added
]
...
values["client"] = Llama(model_path, **model_params)
...
```
**Issue:**
Not applicable (If there is an issue that this PR resolves, please link to it here)
**Dependencies:**
No new dependencies introduced.
**Maintainer:**
Tagging @hinthornw, as this change relates to Tools / Toolkits.
**Twitter handle:**
(If you want a shout-out on Twitter and have a Twitter handle, mention it here.)
This change does not introduce any new features or integrations, so no new tests or notebooks are provided. However, existing tests should cover this new parameter.
Maintainers, please review at your earliest convenience. Thank you for considering this contribution!
|
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| 1,790,785,354 |
I_kwDOIPDwls5qvTdK
| 7,252 |
How to parse docx/pdf file which contains text table and image?Also, we need to classify text table and image, maybe operations are different?thanks
|
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[] | null | 2 | 2023-07-06T04:35:02 | 2023-10-12T16:06:10 | 2023-10-12T16:06:09 |
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| 1,790,771,160 |
I_kwDOIPDwls5qvP_Y
| 7,251 |
How to disable the OpenAI initialization when you're not using an OpenAI model
|
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[] | null | 2 | 2023-07-06T04:19:04 | 2023-10-14T20:11:12 | 2023-10-14T20:11:12 |
NONE
| null |
### Feature request
how to disable the OpenAI initialization when you're not using an OpenAI model.
[Please check this issue](https://github.com/hwchase17/langchain/issues/7189#issuecomment-1621931461)
### Motivation
I am trying to build a VectorstoreIndexCreator using the following configuration
embeddings= SentenceTransformerEmbeddings embeddings
vectorstore_cls = Chroma
llm = HuggingfaceHub Model
Note: I am not using any openai model as llm or embedding purpose
here is the code
```
from langchain.indexes import VectorstoreIndexCreator
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.llms import HuggingFaceHub
from langchain.document_loaders import PyPDFLoader
loader = PyPDFLoader("pdffile.pdf")
embeddings = SentenceTransformerEmbeddings(model_name="paraphrase-MiniLM-L6-v2")
model = HuggingFaceHub(repo_id="facebook/mbart-large-50",
model_kwargs={"temperature": 0, "max_length":200},
huggingfacehub_api_token=HUGGING_FACE_API_KEY)
index = VectorstoreIndexCreator(
embedding=embeddings,
vectorstore_cls=Chroma,
text_splitter=CharacterTextSplitter(chunk_size=100,chunk_overlap=0)
).from_loaders([loader])
result = index.query(llm=model,question=query,chain_type="refine")
```
But still iam getting the open_ai key dependency error when i run the code
```
Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. (type=value_error)
```
using langchain version: langchain==0.0.219
### Your contribution
To disable the OpenAI initialization when you're not using an OpenAI model
|
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| 1,790,757,303 |
PR_kwDOIPDwls5UxNJS
| 7,250 |
Rename prompt_template => _DEFAULT_GRAPH_QA_TEMPLATE and PROMPT => GRAPH_QA_PROMPT to make consistent with the rest of the files
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[] | null | 2 | 2023-07-06T04:03:20 | 2023-07-07T06:30:14 | 2023-07-07T06:17:40 |
CONTRIBUTOR
| null |
Rename prompt_template => _DEFAULT_GRAPH_QA_TEMPLATE to make consistent with the rest of the file.
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| 1,790,747,965 |
I_kwDOIPDwls5qvKU9
| 7,249 |
Does LlamaCPP currently not support the gpu acceleration of mps when n_gpu_layer to 1?
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[] | null | 0 | 2023-07-06T03:50:02 | 2023-07-06T03:56:20 | 2023-07-06T03:56:20 |
NONE
| null |
### System Info
langchain : 0.0.223
os: mac Ventura 13.4.1 max
python: 3.11.3
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have compiled the llama.cpp project into mps, and tested the support for gpu acceleration under mps through the command, but when calling the cpp model in langchain, I found， When I set n_gpu_layer to 1, the gpu acceleration of mps is not turned on， below is the code and output by it.
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
llm=LlamaCpp(model_path='./zh-models/33B/ggml-model-q4_K.bin', n_ctx="2048", n_gpu_layers=1, callback_manager = callback_manager, verbose = True)
llm('tell me a joke')
`llama_model_load_internal: format = ggjt v3 (latest)
llama_model_load_internal: n_vocab = 49954
llama_model_load_internal: n_ctx = 2048
llama_model_load_internal: n_embd = 6656
llama_model_load_internal: n_mult = 256
llama_model_load_internal: n_head = 52
llama_model_load_internal: n_layer = 60
llama_model_load_internal: n_rot = 128
llama_model_load_internal: ftype = 2 (mostly Q4_0)
llama_model_load_internal: n_ff = 17920
llama_model_load_internal: model size = 30B
llama_model_load_internal: ggml ctx size = 0.14 MB
llama_model_load_internal: mem required = 19884.88 MB (+ 3124.00 MB per state)
llama_new_context_with_model: kv self size = 3120.00 MB
AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 |
`
### Expected behavior
support mps gpu acceleration
|
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| 1,790,694,441 |
PR_kwDOIPDwls5Uw_mb
| 7,248 |
Re-use Trajectory Evaluator
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[] | null | 1 | 2023-07-06T02:46:49 | 2023-07-06T14:00:26 | 2023-07-06T14:00:25 |
COLLABORATOR
| null |
Use the trajectory eval chain in the run evaluation implementation and update the prepare inputs method to apply to both asynca nd sync
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PR_kwDOIPDwls5Uw7xH
| 7,247 |
Make tqdm for OpenAIEmbeddings optional
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[] | null | 1 | 2023-07-06T02:25:59 | 2023-07-06T03:36:02 | 2023-07-06T03:36:01 |
CONTRIBUTOR
| null |
- Description: I have added a `show_progress_bar` parameter (defaults.to `False`) to the `OpenAIEmbeddings`. If the user sets `show_progress_bar` to `True`, a progress bar will be displayed.
- Issue: #7246
- Dependencies: N/A
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: N/A
|
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| 1,790,672,805 |
I_kwDOIPDwls5qu3-l
| 7,246 |
Make tqdm optional for OpenAIEmbeddings
|
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[] | null | 1 | 2023-07-06T02:21:17 | 2023-07-06T03:58:55 | 2023-07-06T03:58:55 |
CONTRIBUTOR
| null |
### Feature request
Add `show_progress_bar` within `OpenAIEmbeddings` class.
### Motivation
Simply speaking,
1. Showing progress bar within existing progress bar is generally not a good practice. Most of time it will break.
2. There might be people who want to keep their console quiet.
### Your contribution
I will make a PR
|
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PR_kwDOIPDwls5UwvJB
| 7,245 |
fix conversational retrieval docs
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| 1,790,596,768 |
PR_kwDOIPDwls5UwqID
| 7,244 |
bump ver to 225
|
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[] | null | 1 | 2023-07-06T01:04:27 | 2023-07-06T01:22:19 | 2023-07-06T01:22:18 |
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I_kwDOIPDwls5qua8K
| 7,243 |
DOC: Pinecone functions need more documentation
|
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[] | null | 1 | 2023-07-06T00:15:48 | 2023-10-12T16:06:20 | 2023-10-12T16:06:20 |
CONTRIBUTOR
| null |
### Issue with current documentation:
A continuation of #7126,
Pinecone features in Langchain that are not mentioned in Langchain's documentation. https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone
### Idea or request for content:
Add documentation for the other Pinecone functions.
|
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I_kwDOIPDwls5quXiA
| 7,242 |
Async callback has issues
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[] | null | 2 | 2023-07-06T00:03:04 | 2023-07-06T12:43:26 | 2023-07-06T09:12:31 |
NONE
| null |
### System Info
Langchain Version: 0.0.224
Platform: Mac
Python Version: 3.10.9
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [x] Callbacks/Tracing
- [x] Async
### Reproduction
Copy/Paste of example snippet from official documentation:
https://python.langchain.com/docs/modules/callbacks/how_to/async_callbacks
import asyncio
from typing import Any, Dict, List
from langchain.chat_models import ChatOpenAI
from langchain.schema import LLMResult, HumanMessage
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
from dotenv import load_dotenv
load_dotenv()
class MyCustomSyncHandler(BaseCallbackHandler):
def on_llm_new_token(self, token: str, **kwargs) -> None:
print(f"Sync handler being called in a `thread_pool_executor`: token: {token}")
class MyCustomAsyncHandler(AsyncCallbackHandler):
"""Async callback handler that can be used to handle callbacks from langchain."""
async def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Run when chain starts running."""
print("zzzz....")
await asyncio.sleep(0.3)
class_name = serialized["name"]
print("Hi! I just woke up. Your llm is starting")
async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when chain ends running."""
print("zzzz....")
await asyncio.sleep(0.3)
print("Hi! I just woke up. Your llm is ending")
# To enable streaming, we pass in `streaming=True` to the ChatModel constructor
# Additionally, we pass in a list with our custom handler
chat = ChatOpenAI(
max_tokens=25,
streaming=True,
callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],
)
async def main():
await chat.agenerate([[HumanMessage(content="Tell me a joke")]])
if __name__ == '__main__':
asyncio.run(main())
#####################################################
Output:
zzzz....
Error in MyCustomAsyncHandler.on_llm_start callback: 'name'
Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 1.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 2.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 8.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in 16.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
Traceback (most recent call last):
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/aiohttp/connector.py", line 980, in _wrap_create_connection
return await self._loop.create_connection(*args, **kwargs) # type: ignore[return-value] # noqa
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/asyncio/base_events.py", line 1097, in create_connection
transport, protocol = await self._create_connection_transport(
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/asyncio/base_events.py", line 1127, in _create_connection_transport
await waiter
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/asyncio/sslproto.py", line 534, in data_received
ssldata, appdata = self._sslpipe.feed_ssldata(data)
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/asyncio/sslproto.py", line 188, in feed_ssldata
self._sslobj.do_handshake()
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/ssl.py", line 975, in do_handshake
self._sslobj.do_handshake()
ssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:997)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/openai/api_requestor.py", line 592, in arequest_raw
result = await session.request(**request_kwargs)
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/aiohttp/client.py", line 536, in _request
conn = await self._connector.connect(
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/aiohttp/connector.py", line 540, in connect
proto = await self._create_connection(req, traces, timeout)
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/aiohttp/connector.py", line 901, in _create_connection
_, proto = await self._create_direct_connection(req, traces, timeout)
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/aiohttp/connector.py", line 1206, in _create_direct_connection
raise last_exc
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/aiohttp/connector.py", line 1175, in _create_direct_connection
transp, proto = await self._wrap_create_connection(
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/aiohttp/connector.py", line 982, in _wrap_create_connection
raise ClientConnectorCertificateError(req.connection_key, exc) from exc
aiohttp.client_exceptions.ClientConnectorCertificateError: Cannot connect to host api.openai.com:443 ssl:True [SSLCertVerificationError: (1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:997)')]
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/pnsvk/my-apps/p_y/llm-apps/async_callbacks.py", line 49, in <module>
asyncio.run(main())
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete
return future.result()
File "/Users/pnsvk/my-apps/p_y/llm-apps/async_callbacks.py", line 45, in main
res = await chat.agenerate([[HumanMessage(content="Tell me a joke")]])
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/langchain/chat_models/base.py", line 191, in agenerate
raise exceptions[0]
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/langchain/chat_models/base.py", line 292, in _agenerate_with_cache
return await self._agenerate(
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 408, in _agenerate
async for stream_resp in await acompletion_with_retry(
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 95, in acompletion_with_retry
return await _completion_with_retry(**kwargs)
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/tenacity/_asyncio.py", line 86, in async_wrapped
return await fn(*args, **kwargs)
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/tenacity/_asyncio.py", line 48, in __call__
do = self.iter(retry_state=retry_state)
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/tenacity/__init__.py", line 362, in iter
raise retry_exc.reraise()
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/tenacity/__init__.py", line 195, in reraise
raise self.last_attempt.result()
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/concurrent/futures/_base.py", line 451, in result
return self.__get_result()
File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
raise self._exception
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/tenacity/_asyncio.py", line 51, in __call__
result = await fn(*args, **kwargs)
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 93, in _completion_with_retry
return await llm.client.acreate(**kwargs)
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/openai/api_resources/chat_completion.py", line 45, in acreate
return await super().acreate(*args, **kwargs)
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 217, in acreate
response, _, api_key = await requestor.arequest(
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/openai/api_requestor.py", line 304, in arequest
result = await self.arequest_raw(
File "/Users/pnsvk/my-venv-3.10/lib/python3.10/site-packages/openai/api_requestor.py", line 609, in arequest_raw
raise error.APIConnectionError("Error communicating with OpenAI") from e
openai.error.APIConnectionError: Error communicating with OpenAI
### Expected behavior
As mentioned in the official documentation:
zzzz....
Hi! I just woke up. Your llm is starting
Sync handler being called in a `thread_pool_executor`: token:
Sync handler being called in a `thread_pool_executor`: token: Why
Sync handler being called in a `thread_pool_executor`: token: don
Sync handler being called in a `thread_pool_executor`: token: 't
Sync handler being called in a `thread_pool_executor`: token: scientists
Sync handler being called in a `thread_pool_executor`: token: trust
Sync handler being called in a `thread_pool_executor`: token: atoms
Sync handler being called in a `thread_pool_executor`: token: ?
Sync handler being called in a `thread_pool_executor`: token:
Sync handler being called in a `thread_pool_executor`: token: Because
Sync handler being called in a `thread_pool_executor`: token: they
Sync handler being called in a `thread_pool_executor`: token: make
Sync handler being called in a `thread_pool_executor`: token: up
Sync handler being called in a `thread_pool_executor`: token: everything
Sync handler being called in a `thread_pool_executor`: token: .
Sync handler being called in a `thread_pool_executor`: token:
zzzz....
Hi! I just woke up. Your llm is ending
LLMResult(generations=[[ChatGeneration(text="Why don't scientists trust atoms? \n\nBecause they make up everything.", generation_info=None, message=AIMessage(content="Why don't scientists trust atoms? \n\nBecause they make up everything.", additional_kwargs={}, example=False))]], llm_output={'token_usage': {}, 'model_name': 'gpt-3.5-turbo'})
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PR_kwDOIPDwls5Uwc5n
| 7,241 |
Add missing token_max in reduce.py acombine_docs
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[] | null | 1 | 2023-07-06T00:00:33 | 2023-07-06T01:02:46 | 2023-07-06T01:02:45 |
CONTRIBUTOR
| null |
Replace this comment with:
- Description: reduce.py reduce chain implementation's acombine_docs call does not propagate token_max. Without this, the async call will end up using 3000 tokens, the default, for the collapse chain.
- Tag maintainer: @hwchase17 @agola11 @baskaryan
- Twitter handle: https://twitter.com/ShantanuNair
Related PR: https://github.com/hwchase17/langchain/pull/7201 and https://github.com/hwchase17/langchain/pull/7204
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PR_kwDOIPDwls5UwZHg
| 7,240 |
Add function call params to invocation params
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I_kwDOIPDwls5quEr6
| 7,239 |
Add Document Index Information for QA Inline Citations
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NONE
| null |
### Feature request
Include source document index information in the combine document chain prompt. For example in `_get_inputs` of `StuffDocumentChain` we could make the following change (in addition to corresponding changes to `format_document`):
```
# From
doc_strings = [format_document(doc, self.document_prompt) for doc in docs]
# To
doc_strings = [format_document(doc, i, self.document_prompt) for i, doc in enumerate(docs)]
```
### Motivation
The point of this change is to enable QA based chains (e.g. `ConversationalRetrievalChain`) to easily do inline citations using the source document's index.
## Example
### Prompt
```
Context:
[1] Harrison went to Harvard.
[2] Ankush went to Princeton.
[3] Emma went to Yale.
Question:
Where did Harrison and Emma go to college?
```
### Response
```
Harrison went to Harvard【1】 and Emma went to Yale【3】.
```
This type of structure is also found in popular "QA" models like Bing Chat and ChatGPT's Browse feature (WebGPT etc.). I feel like there should at least be high-level options to enable something like this without having to make custom modifications/extensions to the existing chains.
Without explicitly including these document indices, I find prompting the LLM to cite documents by index could lead to hallucinated citations (e.g. with retrieved `k=4` documents it cites "[8]")
## More Details
As far as I can tell, existing QA implementations in Langchain seem to return source documents separately (i.e. `return_source_documents=True`), or at the end of the response (e.g. `{'output_text': ' The president thanked Justice Breyer for his service.\nSOURCES: 30-pl'}` as in [document-qa-with-sources](https://python.langchain.com/docs/modules/chains/additional/question_answering#document-qa-with-sources)) rather than provide them in-line.
Even newer approaches using OpenAI's Functions API e.g. from `create_citation_fuzzy_match_chain` and `create_qa_with_sources_chain` do not provide this option.
The approach of [QA with sources chain](https://python.langchain.com/docs/modules/chains/additional/openai_functions_retrieval_qa) has a drawback that the LLM has to generate the entire source of the document (e.g. full URL). This is slower because we can just reference the index of the document instead. Also, this is prone to hallucination especially with `gpt-3.5` where fake sources (like URLs) could be generated.
Similarly, the method in [Question-Answering Citations](https://python.langchain.com/docs/modules/chains/additional/qa_citations) provides quotes from source documents, but doesn't actually identify which document they're from. Referencing documents by index should help reduce hallucination and generation speed here as well.
### Your contribution
I'm happy to assist with this, but first I'd like to gather feedback on the idea. It's possible that there are existing approaches or best practices that I'm not familiar with, which could facilitate inline citations without additional implementation. If there are any recommendations on how to proceed with this, I'd be interested in having a discussion around that.
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PR_kwDOIPDwls5UwJld
| 7,238 |
remove arize nb
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[] | null | 1 | 2023-07-05T22:34:15 | 2023-07-05T22:34:21 | 2023-07-05T22:34:20 |
COLLABORATOR
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was causing some issues with docs build
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PR_kwDOIPDwls5UwInm
| 7,237 |
Harrison/update anthropic
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| 1,790,420,088 |
PR_kwDOIPDwls5UwDFT
| 7,236 |
Update Hugging Face Hub notebook
|
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[] | null | 1 | 2023-07-05T22:04:17 | 2023-07-06T00:45:02 | 2023-07-06T00:45:02 |
CONTRIBUTOR
| null |
Description: `flan-t5-xl` hangs, updated to `flan-t5-xxl`. Tested all stabilityai LLMs- all hang so removed from tutorial. Temperature > 0 to prevent unintended determinism.
Issue: #3275
Tag maintainer: @baskaryan
|
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PR_kwDOIPDwls5UwBmi
| 7,235 |
Skip evaluation of unfinished runs
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[] | null | 1 | 2023-07-05T21:57:48 | 2023-07-05T23:35:21 | 2023-07-05T23:35:20 |
COLLABORATOR
| null |
Cut down on errors logged
|
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| 1,790,377,708 |
PR_kwDOIPDwls5Uv5iz
| 7,234 |
Updating for anthropic v0.3.2
|
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[] | null | 3 | 2023-07-05T21:34:04 | 2023-07-05T22:34:40 | 2023-07-05T22:34:39 |
CONTRIBUTOR
| null |
In the latest version of Anthropic-python-sdk - the api_url variable in the anthropic.Client has been updated to base_url variable. But if we access the same using langchain - it results in an error as explained in the - #6400 . In this pull request, I have updated the variable, which helps us to stay upto mark with Anthropic v0.3.2
Maintainer responsibilities:
- Models / Prompts: @hwchase17, @baskaryan
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I_kwDOIPDwls5qtvFn
| 7,233 |
OpenAPISpec functions can get stuck in a loop
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[] | null | 2 | 2023-07-05T21:31:57 | 2023-10-12T16:06:26 | 2023-10-12T16:06:25 |
NONE
| null |
### System Info
langchain: '0.0.218'
windows 10
### Who can help?
@dev2049 @homanp
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run the following:
```py
from langchain.chains.openai_functions.openapi import get_openapi_chain
chain = get_openapi_chain("https://chat-web3-plugin.alchemy.com/openapi.yaml")
chain.run("DOES NOT MATTER")
```
Results in endless loop
```shell
Attempting to load an OpenAPI 3.0.0 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.0.0 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.0.0 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.0.0 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.0.0 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.0.0 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.0.0 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Attempting to load an OpenAPI 3.0.0 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
...
```
### Expected behavior
Loop should break
|
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| 1,790,365,763 |
PR_kwDOIPDwls5Uv2z6
| 7,232 |
ZeroShotAgent prompt fixes
|
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[] | null | 5 | 2023-07-05T21:27:48 | 2023-12-01T15:59:05 | 2023-11-07T03:47:04 |
NONE
| null |
- Description: Reformatting prompt for ZeroShotAgent. The current prompt for the ZeroShotAgent presents limitations when getting the response from the agents. The purpose of this PR is to be more specific in its prompt in order to achieve the response in the expected format.
- Issue: None
- Dependencies: None
- Tag maintainer: @hinthornw (for Agents) @hwchase17 @baskaryan (for Prompts)
- Twitter handle: @goyoreyes
|
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PR_kwDOIPDwls5UvxdA
| 7,231 |
store template and template args in prompt output
|
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[] | null | 4 | 2023-07-05T21:16:13 | 2023-07-07T00:58:17 | 2023-07-07T00:58:13 |
CONTRIBUTOR
| null |
This exposes the original template + variables used in prompts to downstream consumers such as the callbacks system or LLM libraries. It does this by altering the template-formatting system to use a subclass of `str` rather than just a `str`. This is the most pythonic way I could find to accomplish this (There is also UserString, but that doesn't subclass `str` so it fails `isinstance` checks)
What this specifically allows is that libraries that monitor API libraries like the OpenAI SDK, can look at the prompt that the API was called with (a completion text or a messages array) and inspect the objects to pull out the template (on `.template`) or the template arguments (on `.template_args`)
(I spent some time trying to do this with callbacks, but the problem I ran into is that you have access to the prompt + variables in `on_chain_start` but there isn't an easy way to tie this back to the actual request sent to the OpenAI and this data doesn't seem to be captured by `on_llm_start`, and these are called in a way that makes it hard to capture any data and have it visible from the openai `create()` methods)
If this approach feels right, I can go ahead and write some unit tests for it as a part of this PR.
Not sure who to tag for this, so tagging @baskaryan and @hwchase17
|
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PR_kwDOIPDwls5UvvK-
| 7,230 |
docs: tutorials update
|
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COLLABORATOR
| null |
updated `tutorials.mdx`:
- added a link to new `Deeplearning AI` course on LangChain
- added links to other tutorial videos
- fixed format
@baskaryan, @hwchase17
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PR_kwDOIPDwls5UvqUp
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feat: support generate as an early stopping method for `OpenAIFunctionsAgent`
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[] | null | 2 | 2023-07-05T21:00:37 | 2023-07-11T15:54:15 | 2023-07-11T13:25:02 |
CONTRIBUTOR
| null |
This PR proposes an implementation to support `generate` as an `early_stopping_method` for the new `OpenAIFunctionsAgent` class.
The motivation behind is to facilitate the user to set a maximum number of actions the agent can take with `max_iterations` and force a final response with this new agent (as with the `Agent` class).
The following changes were made:
- The `OpenAIFunctionsAgent.return_stopped_response` method was overwritten to support `generate` as an `early_stopping_method`
- A boolean `with_functions` parameter was added to the `OpenAIFunctionsAgent.plan` method
This way the `OpenAIFunctionsAgent.return_stopped_response` method can call the `OpenAIFunctionsAgent.plan` method with `with_function=False` when the `early_stopping_method` is set to `generate`, making a call to the LLM with no functions and forcing a final response from the `"assistant"`.
- Relevant maintainer: @hinthornw
- Twitter handle: @aledelunap
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Harrison/scene xplain
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[] | null | 1 | 2023-07-05T20:57:44 | 2023-07-05T22:34:51 | 2023-07-05T22:34:50 |
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fix arize nb
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[] | null | 1 | 2023-07-05T20:55:43 | 2023-07-05T20:55:51 | 2023-07-05T20:55:48 |
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PR_kwDOIPDwls5Uviax
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use LlamCpp as LlamaCppEmbeddings base, without model reloading
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[] | null | 2 | 2023-07-05T20:41:24 | 2023-07-08T18:52:26 | 2023-07-08T18:52:25 |
NONE
| null |
Just create LlamCpp, set embedding to True and when you create LlamaCppEmbeddings, just tranfer your llm as llm parametr, without model path
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Use LlamCpp as LlamaCppEmbeddings base
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Use LlamCpp as LlamaCppEmbeddings base, without model reloading, just create LlamaCpp, then transfer it to LlamaCppEmbeddings as "llm" parametr
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Use LlamaCpp as base for LlamaCppEmbeddings
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[] | null | 1 | 2023-07-05T19:57:07 | 2023-07-05T20:04:10 | 2023-07-05T19:57:53 |
NONE
| null |
Use LlamCpp as LlamaCppEmbeddings base, without model reloading, just create LlamaCpp, then transfer it to LlamaCppEmbeddings as "llm" parametr
|
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I_kwDOIPDwls5qtE72
| 7,223 |
Salesforce XGen integration
|
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[] | null | 1 | 2023-07-05T19:54:04 | 2023-07-06T04:53:07 | 2023-07-06T04:53:07 |
CONTRIBUTOR
| null |
### Feature request
Still new here so I mimicked the setup of `llm/llamacpp.py` wrapper for the draft of Salesforce's new LLM [XGen](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/91).
### Motivation
Expand the LLM sets integrated in Langchain.
### Your contribution
PR #7221
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| 1,790,201,579 |
PR_kwDOIPDwls5UvR4V
| 7,222 |
docs: update docs strings for base unstructured loaders
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[] | null | 1 | 2023-07-05T19:54:03 | 2023-07-05T21:02:27 | 2023-07-05T21:02:27 |
CONTRIBUTOR
| null |
### Summary
Updates the docstrings for the unstructured base loaders so more useful information appears on the integrations page. If these look good, will add similar docstrings to the other loaders.
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
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PR_kwDOIPDwls5UvRuM
| 7,221 |
Draft: Starting Salesforce XGen model integration
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[] | null | 2 | 2023-07-05T19:53:25 | 2023-07-05T22:04:53 | 2023-07-05T22:04:53 |
CONTRIBUTOR
| null |
Description: Salesforce recently released the LLM [XGen](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/91). This change is to begin integration of the model into Langchain.
Issue: the issue # it fixes (if applicable),
Dependencies: transformers
Tag maintainer: @hwchase17 @baskaryan
Still new here so marking as Draft to ensure further development is okay. Mimicked design of llamacpp wrapper to start.
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I_kwDOIPDwls5qtD6m
| 7,220 |
Issue: Issue with MutliPromptRouter with memory destination chains
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[] | null | 2 | 2023-07-05T19:50:23 | 2023-10-12T16:06:31 | 2023-10-12T16:06:30 |
NONE
| null |
### Issue you'd like to raise.
Hi I found myself having to do a "hack" when using MultiPromptChain.
In particular when ny destination chains take more than one parameter into the template at runtime for example "{current"_timestamp}"
I was getting the following exception
```
File "/home/xxx/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 149, in __call__
inputs = self.prep_inputs(inputs)
File "/home/xxx/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 258, in prep_inputs
self._validate_inputs(inputs)
File "/home/xyz/venv/lib/python3.10/site-packages/langchain/chains/base.py", line 103, in _validate_inputs
raise ValueError(f"Missing some input keys: {missing_keys}")
ValueError: Missing some input keys: {'current_timestamp'}
```
I had the below configuration:
```
destinations = [f"{p['name']}: {p['description']}" for p in tools.prompt_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(destinations=destinations_str,
current_timestamp=datetime.now().isoformat())
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input"],
output_parser=RouterOutputParser(),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)
chain = MultiPromptChain(
router_chain=router_chain,
destination_chains=tools.chains,
default_chain=default_chain
verbose=True,
)
```
which had to be modified
first:
I used modified router template
```
MY_MULTI_PROMPT_ROUTER_TEMPLATE = """\
Given a raw text input to a language model select the model prompt best suited for \
the input. You will be given the names of the available prompts and a description of \
what th
MY_MULTI_PROMPT_ROUTER_TEMPLATE = """\
Given a raw text input to a language model select the model prompt best suited for \
the input. You will be given the names of the available prompts and a description of \
what the prompt is best suited for. You may also revise the original input if you \
think that revising it will ultimately lead to a better response from the language \
model.
<< FORMATTING >>
Return a markdown code snippet with a JSON object formatted to look like:
```json
{{{{
"destination": string \\ name of the prompt to use or "DEFAULT"
"next_inputs": dict \\ dictionary of the with two fields "input" containing original input and current_timestamp containing {current_timestamp}
}}}}
```
REMEMBER: "destination" MUST be one of the candidate prompt names specified below OR
it can be "DEFAULT" if the input is not well suited for any of the candidate prompts.
REMEMBER: "next_inputs" can just be the original input if you don't think any \
modifications are needed.
<< CANDIDATE PROMPTS >>
{destinations}
<< INPUT >>
{{input}}
<< OUTPUT >>
"""e prompt is best suited for. You may also revise the original input if you \
think that revising it will ultimately lead to a better response from the language \
model.
<< FORMATTING >>
Return a markdown code snippet with a JSON object formatted to look like:
```json
{{{{
"destination": string \\ name of the prompt to use or "DEFAULT"
"next_inputs": dict \\ dictionary of the with two fields "input" containing original input and current_timestamp containing {current_timestamp}
}}}}
```
REMEMBER: "destination" MUST be one of the candidate prompt names specified below OR
it can be "DEFAULT" if the input is not well suited for any of the candidate prompts.
REMEMBER: "next_inputs" can just be the original input if you don't think any \
modifications are needed.
<< CANDIDATE PROMPTS >>
{destinations}
<< INPUT >>
{{input}}
<< OUTPUT >>
"""
```
I also had to change the implementation of RouterOutputParser where I changed
line 102 from
```
parsed["next_inputs"] = {self.next_inputs_inner_key: parsed["next_inputs"]}
to
parsed["next_inputs"] = parsed["next_inputs"]
```
This has allowed me to return and pass the desired next_inputs dict including current_timestamp into the destination chain
my chain initialization has changed to the following
```
destinations = [f"{p['name']}: {p['description']}" for p in tools.prompt_infos]
destinations_str = "\n".join(destinations)
router_template = MY_MULTI_PROMPT_ROUTER_TEMPLATE.format(destinations=destinations_str,
current_timestamp=datetime.now().isoformat())
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input"],
output_parser=MyRouterOutputParser(next_inputs_inner_key="input", next_inputs_type=dict),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)
chain = MultiPromptChain(
router_chain=router_chain,
destination_chains=tools.chains,
default_chain=tools.chains[ConversationStage.CASUAL.value],
verbose=True,
)
```
so I managed to pass the correct parameters to the destination chain but then I faced a new issue which was
File "/home/xxx/venv/lib/python3.10/site-packages/langchain/memory/utils.py", line 21, in get_prompt_input_key
raise ValueError(f"One input key expected got {prompt_input_keys}")
ValueError: One input key expected got ['current_timestamp', 'input']
after investigation I had to define a new MyConversationBufferMemory
```
class MyConversationBufferMemory(ConversationBufferMemory):
other_keys: list = []
@property
def memory_variables(self) -> List[str]:
"""Will always return list of memory variables.
:meta private:
"""
return [self.memory_key] + self.other_keys
```
and instead of creating my memory in this way
`memory = ConversationBufferMemory(memory_key="chat_history", chat_memory=get_history(user_id))
`
I have created it in this way
`memory = MyConversationBufferMemory(memory_key="chat_history", chat_memory=get_history(user_id),other_keys=['current_timestamp'])`
this has finally allowed me to get get the response from the destination chain.
If someone has followed, would you thing that there is a better way of doing it?
### Suggestion:
_No response_
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PR_kwDOIPDwls5Uu9Yx
| 7,219 |
Added retries for Vertex LLM
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[] | null | 2 | 2023-07-05T18:58:35 | 2023-07-06T13:38:02 | 2023-07-06T13:38:02 |
CONTRIBUTOR
| null |
#7217
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| 1,790,096,009 |
PR_kwDOIPDwls5Uu6Z3
| 7,218 |
added template repo for CI/CD deployment on Google Cloud Run
|
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[] | null | 1 | 2023-07-05T18:48:34 | 2023-07-06T13:38:39 | 2023-07-06T13:38:39 |
CONTRIBUTOR
| null |
Replace this comment with:
- Description: added documentation for a template repo that helps dockerizing and deploying a LangChain using a Cloud Build CI/CD pipeline to Google Cloud build serverless
- Issue: None,
- Dependencies: None,
- Tag maintainer: @baskaryan,
- Twitter handle: EdenEmarco177
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.
|
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| 1,790,092,753 |
I_kwDOIPDwls5qsqXR
| 7,217 |
Add retries to VertexAI models
|
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[] | null | 1 | 2023-07-05T18:45:48 | 2023-07-10T08:52:37 | 2023-07-10T08:52:37 |
CONTRIBUTOR
| null |
### Feature request
Add retry policy to VertexAI models.
### Motivation
E.g., when trying to run a summarization chain on many chunks (I reproduce the error with 99 chunks), an exception `ResourceExhausted: 429 Quota exceeded` might be returned by Vertex.
### Your contribution
yes, I'll submit a PR shortly.
|
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PR_kwDOIPDwls5Uu5lt
| 7,216 |
Update milvus and zilliz docstring
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[] | null | 1 | 2023-07-05T18:45:18 | 2023-07-05T21:03:51 | 2023-07-05T21:03:51 |
CONTRIBUTOR
| null |
Description:
Updating the docstrings for Milvus and Zilliz so that they appear correctly on https://integrations.langchain.com/vectorstores. No changes done to code.
Maintainer:
@baskaryan
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I_kwDOIPDwls5qsgcE
| 7,215 |
DOC: DOCUMENT interface in Javascript but not in Python
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[] | null | 2 | 2023-07-05T18:15:21 | 2023-07-05T18:41:14 | 2023-07-05T18:41:14 |
NONE
| null |
### Issue with current documentation:
More of a clarification than an issue. In the javascript documentation, it describes the Document interface. https://js.langchain.com/docs/modules/schema/document. Does an equivalent thing exist in the Python version. When I try 'from langchain.document import Document` in python, it throws an error?
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5Uungj
| 7,214 |
Harrison/dataforseo
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[] | null | 1 | 2023-07-05T17:42:25 | 2023-07-05T20:02:03 | 2023-07-05T20:02:02 |
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| null | null |
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PR_kwDOIPDwls5Uudkz
| 7,213 |
Remove extra base model
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[] | null | 1 | 2023-07-05T17:07:39 | 2023-07-05T18:02:29 | 2023-07-05T18:02:28 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @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_kwDOIPDwls5Uuay9
| 7,212 |
Update scenexplain.py to use new API endpoint
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[] | null | 1 | 2023-07-05T16:59:43 | 2023-07-05T20:57:16 | 2023-07-05T20:57:16 |
CONTRIBUTOR
| null |
- Description: Update SceneXplain API endpoint URL (taken from official docs: https://scenex.jina.ai/api)
- Issue: The old sceneXplain API endpoint: "https://us-central1-causal-diffusion.cloudfunctions.net/describe" is deprecated and now returns 500 internal error codes.
- Dependencies: N/A
- Tag maintainer: @hinthornw
- Twitter handle: @deoxykev
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PR_kwDOIPDwls5UuG9Z
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Master test build
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[] | null | 1 | 2023-07-05T16:02:12 | 2023-07-05T16:02:21 | 2023-07-05T16:02:21 |
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!
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_kwDOIPDwls5UuDEV
| 7,210 |
add apply method for async
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[] | null | 1 | 2023-07-05T15:51:02 | 2023-08-10T23:07:10 | 2023-08-10T23:07:10 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5UuAwI
| 7,209 |
Add 'eval' tag
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[] | null | 1 | 2023-07-05T15:43:44 | 2023-07-05T23:28:35 | 2023-07-05T23:28:34 |
COLLABORATOR
| null |
Add an "eval" tag to traced evaluation runs
Most of this PR is actually https://github.com/hwchase17/langchain/pull/7207 but I can't diff off two separate PRs
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PR_kwDOIPDwls5Ut9e0
| 7,207 |
Add tags support for langchaintracer
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[] | null | 2 | 2023-07-05T15:35:02 | 2023-07-05T23:19:05 | 2023-07-05T23:19:04 |
COLLABORATOR
| null | null |
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| 1,789,794,212 |
PR_kwDOIPDwls5Ut4vt
| 7,206 |
Send evaluator logs to new session
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[] | null | 1 | 2023-07-05T15:23:50 | 2023-07-05T23:15:31 | 2023-07-05T23:15:29 |
COLLABORATOR
| null |
Also stop specifying "eval" mode since explicit project modes are deprecated
|
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| 1,789,785,245 |
PR_kwDOIPDwls5Ut2yN
| 7,205 |
add tqdm to embeddings
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[] | null | 1 | 2023-07-05T15:18:19 | 2023-07-05T16:04:24 | 2023-07-05T16:04:23 |
COLLABORATOR
| null |
for longer running embeddings, can be helpful to visualize
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PR_kwDOIPDwls5UtnOb
| 7,204 |
add token max parameter
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| 1,789,690,233 |
PR_kwDOIPDwls5Uth8_
| 7,203 |
bump version to 224
|
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| 1,789,678,902 |
PR_kwDOIPDwls5UtfmF
| 7,202 |
Update in_memory.py to fix "TypeError: keywords must be strings"
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[] | null | 1 | 2023-07-05T14:23:19 | 2023-07-05T16:48:39 | 2023-07-05T16:48:38 |
CONTRIBUTOR
| null |
Update in_memory.py to fix "TypeError: keywords must be strings" on certain dictionaries
Simple fix to prevent a "TypeError: keywords must be strings" error I encountered in my use case.
@baskaryan
Thanks! Hope useful!
|
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PR_kwDOIPDwls5UtZ3h
| 7,201 |
propogate token max
|
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PR_kwDOIPDwls5UtWbx
| 7,200 |
more formatting
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| 1,789,623,893 |
I_kwDOIPDwls5qq35V
| 7,199 |
Undesired outputs when using map_reduce
|
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[] | null | 2 | 2023-07-05T13:55:41 | 2023-07-08T17:20:32 | 2023-07-08T17:20:32 |
NONE
| null |
### System Info
There seem to be some hallucinations involving the president and Michael Jackson.
I use the following, where `data` is loaded using `UnstructuredURLLoader(urls).load()` and `urls` is just a list of URLs I'm interested in. Needless to say that none of the URLs involve Michael Jackson (or the president, for that matter)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
chain = RetrievalQAWithSourcesChain.from_chain_type(
llm= HuggingFaceHub(
repo_id="tiiuae/falcon-7b-instruct",
model_kwargs={"max_new_tokens": 500}
),
chain_type="map_reduce",
retriever=FAISS.from_documents(doc_splitter.split_documents(data),
HuggingFaceEmbeddings()).as_retriever()
)
```
followed by
```
prompt = "some text unrelated to Michael Jackson."
chain({"question": prompt}, return_only_outputs=True)
```
I believe this occurs as part of the `map_reduce.py` file:
```
result, extra_return_dict = self.reduce_documents_chain.combine_docs(
result_docs, callbacks=callbacks, **kwargs
)
```
### Expected behavior
should have provided some answer w.r.t to the provided data, stored in FAISS vectorbase
|
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| 1,789,614,225 |
PR_kwDOIPDwls5UtRgw
| 7,198 |
fix weird bold issues in docs
|
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| 1,789,429,006 |
PR_kwDOIPDwls5Usow3
| 7,196 |
add preset ability to textgen llm
|
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[] | null | 1 | 2023-07-05T12:15:15 | 2023-07-07T06:41:25 | 2023-07-07T06:41:25 |
CONTRIBUTOR
| null |
add an ability for textgen llm to work with preset provided by text gen webui API.
@hwchase17, @baskaryan
|
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| 1,789,422,146 |
PR_kwDOIPDwls5UsnRW
| 7,195 |
Fixed the confluence loader ".csv" files loading issue
|
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[] | null | 2 | 2023-07-05T12:10:51 | 2023-07-06T21:21:44 | 2023-07-06T21:21:44 |
CONTRIBUTOR
| null |
- Description: Sometimes there are csv attachments with the media type "application/vnd.ms-excel". These files failed to be loaded via the xlrd library. It throws a corrupted file error. I fixed it by separately processing excel files using pandas. Excel files will be processed just like before.
- Dependencies: pandas, os, io
|
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| 1,789,406,993 |
PR_kwDOIPDwls5Usj-5
| 7,194 |
fix: missing parameter in POST/PUT/PATCH HTTP requests
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[] | null | 5 | 2023-07-05T12:01:27 | 2023-07-05T16:48:42 | 2023-07-05T16:47:31 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
@hinthornw
|
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| 1,789,401,709 |
I_kwDOIPDwls5qqBpt
| 7,193 |
ConversationalRetrievalChain return only the prompt not the answer
|
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[] | null | 4 | 2023-07-05T11:58:18 | 2023-11-20T16:06:21 | 2023-11-20T16:06:21 |
NONE
| null |
### System Info
**Python : Python 3.9.13
Langchain: langchain==0.0.219
OS : Ubuntu**
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.chains import ConversationalRetrievalChain
from langchain.vectorstores import Chroma
from langchain.embeddings import SentenceTransformerEmbeddings
embeddings = SentenceTransformerEmbeddings(model_name="paraphrase-MiniLM-L6-v2")
from langchain.document_loaders import PyPDFLoader
loader = PyPDFLoader("file.pdf")
documents = loader.load()
from langchain.llms import HuggingFaceHub
model = HuggingFaceHub(repo_id="facebook/mbart-large-50",
model_kwargs={"temperature": 0, "max_length":200},
huggingfacehub_api_token=HUGGING_FACE_API_KEY)
from langchain.text_splitter import CharacterTextSplitter
text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
texts=text_splitter.split_documents(documents)
db = Chroma.from_documents(texts, embeddings)
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 1})
qa = ConversationalRetrievalChain.from_llm(model,retriever)
chat_history= []
query = "sample query"
result = qa({"question": query,"chat_history":chat_history})
print("\nResult of ConversationalRetrievalChainMethod")
print(result)
```
It return the **result** as follows
{'question': 'sample question', 'chat_history': [], '**answer': "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, don't try to make up an answer.**"}
### Expected behavior
Expecting the answer in the result. But it returns the prompt itself now
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| 1,789,340,428 |
I_kwDOIPDwls5qpysM
| 7,192 |
kwargs are forgot to send to huggingface pipeline call
|
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[] | null | 3 | 2023-07-05T11:18:41 | 2023-12-19T00:50:02 | 2023-12-19T00:50:02 |
NONE
| null |
https://github.com/hwchase17/langchain/blob/e27ba9d92bd2cc4ac9ed7439becb2d32816fc89c/langchain/llms/huggingface_pipeline.py#L169
should modified to
#response = self.pipeline(prompt)
response = self.pipeline(prompt, **kwargs)
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I_kwDOIPDwls5qpo-p
| 7,191 |
Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
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[] | null | 3 | 2023-07-05T10:53:22 | 2023-10-12T16:06:36 | 2023-10-12T16:06:35 |
NONE
| null |
### Issue you'd like to raise.
When using AgentType.OPENAI_FUNCTIONS, the error message "openai.error.InvalidRequestError: 'Gmail: Find Email' does not match '^[a-zA-Z0-9_-]{1,64}$' - '[functions.6.name](http://functions.6.name/)'" suggests that the name you are using for the function ('Gmail: Find Email') does not adhere to the naming conventions.
For AgentType.OPENAI_FUNCTIONS, function names can only contain alphanumeric characters, underscores (_), and hyphens (-). The name must be between 1 and 64 characters long.
To resolve this issue, make sure the name you provide for the function complies with the naming rules mentioned above.
If you need further assistance, please provide more details about the specific function you're trying to use, and I'll be happy to help you further.
### Suggestion:
_No response_
|
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I_kwDOIPDwls5qpnSa
| 7,190 |
Issue: Missing some input keys: {'question'} when using ConversationalRetrievalChain
|
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| false | null |
[] | null | 3 | 2023-07-05T10:50:07 | 2023-10-18T10:09:39 | 2023-07-06T13:05:01 |
NONE
| null |
### Issue you'd like to raise.
I have written a code to add the memory to ConversationalRetrievalChain but I am getting an error **"Missing some input keys: {'question'}"**. Below is the snippet of my code
memory = ConversationBufferMemory(
memory_key="chat_history",
input_key="question"
)
chatTemplate = """
Answer the question based on the chat history(delimited by <hs></hs>) and context(delimited by <ctx> </ctx>) below.
-----------
<ctx>
{context}
</ctx>
-----------
<hs>
{chat_history}
</hs>
-----------
Question: {question}
Answer:
"""
promptHist = PromptTemplate(
input_variables=["context", "question", "chat_history"],
template=chatTemplate
)
retriever = chatDb.as_retriever(search_type="similarity", search_kwargs={"k": 2})
qa = ConversationalRetrievalChain.from_llm(
llm=get_openai_model(), chain_type="stuff", retriever=retriever, return_source_documents=True,
verbose = True,
combine_docs_chain_kwargs={'prompt': promptHist},
memory = memory,
)
result = qa({"query": prompt["prompt"]})
### Suggestion:
_No response_
|
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| 1,789,283,892 |
I_kwDOIPDwls5qpk40
| 7,189 |
Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. (type=value_error)
|
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[] | null | 7 | 2023-07-05T10:43:47 | 2023-12-06T04:31:44 | 2023-10-30T16:06:08 |
NONE
| null |
### System Info
I am trying to build a **VectorstoreIndexCreator** using the following configuration
embeddings= **SentenceTransformerEmbeddings** embeddings
vectorstore_cls = **Chroma**
llm = **HuggingfaceHub** Model
Note: I am not using any **openai** model as **llm** or **embedding** purpose
here is the code
```
from langchain.indexes import VectorstoreIndexCreator
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.llms import HuggingFaceHub
from langchain.document_loaders import PyPDFLoader
loader = PyPDFLoader("pdffile.pdf")
embeddings = SentenceTransformerEmbeddings(model_name="paraphrase-MiniLM-L6-v2")
model = HuggingFaceHub(repo_id="facebook/mbart-large-50",
model_kwargs={"temperature": 0, "max_length":200},
huggingfacehub_api_token=HUGGING_FACE_API_KEY)
index = VectorstoreIndexCreator(
embeddings=embeddings,
vectorstore_cls=Chroma,
text_splitter=CharacterTextSplitter(chunk_size=100,chunk_overlap=0)
).from_loaders([loader])
result = index.query(llm=model,qustion=query,chain_type="refine")
```
But still iam getting the open_ai key dependency error when i run the code
```
Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. (type=value_error)
```
using langchain version: **langchain==0.0.219**
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.indexes import VectorstoreIndexCreator
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.llms import HuggingFaceHub
from langchain.document_loaders import PyPDFLoader
loader = PyPDFLoader("pdffile.pdf")
embeddings = SentenceTransformerEmbeddings(model_name="paraphrase-MiniLM-L6-v2")
model = HuggingFaceHub(repo_id="facebook/mbart-large-50",
model_kwargs={"temperature": 0, "max_length":200},
huggingfacehub_api_token=HUGGING_FACE_API_KEY)
index = VectorstoreIndexCreator(
embeddings=embeddings,
vectorstore_cls=Chroma,
text_splitter=CharacterTextSplitter(chunk_size=100,chunk_overlap=0)
).from_loaders([loader])
result = index.query(llm=model,qustion=query,chain_type="refine")
```
### Expected behavior
Dont show any openai error
|
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| 1,789,266,410 |
I_kwDOIPDwls5qpgnq
| 7,188 |
Load Chroma database: Invalid Input Error: No magic bytes found at end of file 'database/vectors/chroma-embeddings.parquet'
|
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[] | null | 2 | 2023-07-05T10:33:28 | 2023-10-12T16:06:41 | 2023-10-12T16:06:40 |
NONE
| null |
### System Info
Python 3.11.4
duckdb==0.8.1
chromadb==0.3.26
langchain==0.0.221
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I persisted my Chroma database in a EC2 instance, where I have all these files:
chroma-collections.parquet
chroma-embeddings.parquet
index/
I download these files for testing the database in my local machine, but I got the error:
> Invalid Input Error: No magic bytes found at end of file 'database/vectors/chroma-embeddings.parquet'
When I tried to:
```
from langchain.vectorstores import Chroma
db = Chroma(embedding_function=embedding_function,
persist_directory=persist_directory,
collection_name=collection_name)
```
Can I download these persisted files and test them on another machine?
### Expected behavior
To read all the embeddings
|
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| 1,789,229,545 |
I_kwDOIPDwls5qpXnp
| 7,187 |
DOC: https://python.langchain.com/docs/modules/model_io/models/llms/integrations/sagemaker
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[] | null | 3 | 2023-07-05T10:10:48 | 2023-10-09T09:18:13 | null |
NONE
| null |
### Issue with current documentation:
https://python.langchain.com/docs/modules/model_io/models/llms/integrations/sagemaker
This example is not working. I've just copy pasted the code from here and ran it in my notebook instance. I'm using falcon-40b-instruct model.
The error I'm getting is as follows
Error raised by inference endpoint: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (422) from primary with message "Failed to deserialize the JSON body into the target type: missing field `inputs` at line 1 column 509".
### Idea or request for content:
_No response_
|
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| 1,789,214,352 |
PR_kwDOIPDwls5Ur6F9
| 7,186 |
make textstat optional in the flyte callback handler
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[] | null | 1 | 2023-07-05T10:02:26 | 2023-07-05T17:15:57 | 2023-07-05T17:15:56 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
This PR makes the `textstat` library optional in the Flyte callback handler.
@hinthornw, would you mind reviewing this PR since you merged the flyte callback handler code previously?
|
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| 1,789,198,867 |
PR_kwDOIPDwls5Ur2ui
| 7,185 |
optimize pgvector `add_texts`
|
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CONTRIBUTOR
| null |
- Description: At the moment, inserting new embeddings to pgvector is querying all embeddings every time as the defined `embeddings` relationship is using the default params, which sets `lazy="select"`. This change drastically improves the performance and adds a few additional cleanups:
* remove `collection.embeddings.append` as it was querying all embeddings on insert, replace with `collection_id` param
* centralize storing logic in add_embeddings function to reduce duplication
* remove boilerplate
- Issue: No issue was opened.
- Dependencies: None.
- Tag maintainer: this is a vectorstore update, so I think @rlancemartin, @eyurtsev
- Twitter handle: @falmannaa
|
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| 1,789,191,768 |
I_kwDOIPDwls5qpOZY
| 7,184 |
Issue: RetrievalQAWithSourcesChain gives error 'too many values to unpack (expected 2)' after running.
|
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[] | null | 2 | 2023-07-05T09:49:42 | 2023-08-16T20:30:16 | 2023-08-16T20:30:16 |
NONE
| null |
Hello,
I'm using _langchain_ for QA with court case documents. More specifically, the RetrievalQAWithSourcesChain to retrieve the answer and document source information. However, when running the chain with embedded documents, I get the following error:
```
ValueError: too many values to unpack (expected 2)
Traceback:
response = qa({"question": pregunta}, return_only_outputs=True)
File "C:\Anaconda3\envs\iagen_3_10\lib\site-packages\langchain\chains\base.py", line 166, in __call__
raise e
File "C:\Anaconda3\envs\iagen_3_10\lib\site-packages\langchain\chains\base.py", line 160, in __call__
self._call(inputs, run_manager=run_manager)
File "C:\Anaconda3\envs\iagen_3_10\lib\site-packages\langchain\chains\qa_with_sources\base.py", line 132, in _call
answer, sources = re.split(r"SOURCES:\s", answer)
```
The passed documents are the sections from the court case. I added the following **metadata** fields:
1. Source: PDF file name.
2. Section: Name of the section
3. Section_chunk: Numeral value used for identification in case the section was divided into chunks.
4. Page: Page where the section chunk starts.
The documents are passed as retriever to the chain with FAISS (FAISS.from_documents(documents, self.embeddings)).
I tried out two approaches (both resulting in the same error):
1. providing the _load_qa_chain_ as chain
2. creating it using the class method **_.from_chain_type_**
My question is why does this error ocurrs. And also, if the type of metadata used may cause the errors.
Thank you in advance!
|
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| 1,789,126,857 |
PR_kwDOIPDwls5UrnI2
| 7,183 |
Add docs for setting MapReduceChain token_max arg
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[] | null | 7 | 2023-07-05T09:14:03 | 2023-11-02T00:50:00 | 2023-11-02T00:50:00 |
CONTRIBUTOR
| null |
- Description: Add docs on how to set and use the token_max arg for MapReduceChains
- Issue: Several issues have been repeatedly raised regarding this, since it is non-intuitive how to pass this arg. This makes usage with larger models such as gpt-3.5-turbo-16k much easier. I also explain how it's a sliding scale between how much the inputs will be summarized vs tokens left for generation.
- Tag maintainer: @baskaryan @hwchase17
- Twitter handle: More PRs incoming! :) https://twitter.com/ShantanuNair
Related Github discussion: https://github.com/hwchase17/langchain/discussions/2746
Related issues: https://github.com/hwchase17/langchain/issues/1349, https://github.com/hwchase17/langchain/issues/7043, https://github.com/hwchase17/langchain/issues/434, https://github.com/hwchase17/langchain/issues/6397, https://github.com/hwchase17/langchain/issues/6714, https://github.com/hwchase17/langchain/issues/6191
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I_kwDOIPDwls5qo-Jj
| 7,182 |
does llms.AzureOpenAI support gpt4 or gpt-32k?
|
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[] | null | 2 | 2023-07-05T09:13:02 | 2023-10-12T16:06:46 | 2023-10-12T16:06:45 |
NONE
| null |
### System Info
llm = AzureOpenAI(
model_name="gpt-4-32k",
engine="gpt-4-32k"
)
llm("tell me a joke")
Exception:
The completion operation does not works with the specific model, gpt-4-32k, pls choose different model....
Environment:
LangChain:0.0.218
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Environment:
LangChain:0.0.218
llm = AzureOpenAI(
model_name="gpt-4-32k",
engine="gpt-4-32k"
)
llm("tell me a joke")
run the code above
### Expected behavior
the program should work
|
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I_kwDOIPDwls5qosUj
| 7,181 |
SentenceTransformersTokenTextSplitter Doesn't Preserve Text
|
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[] | null | 4 | 2023-07-05T08:29:31 | 2023-10-12T16:06:51 | 2023-10-12T16:06:50 |
NONE
| null |
### System Info
```
from langchain.text_splitter import SentenceTransformersTokenTextSplitter
splitter = SentenceTransformersTokenTextSplitter(
tokens_per_chunk=64,
chunk_overlap=0,
model_name='intfloat/e5-base-v2',
add_start_index=True,
)
text = "- afrikaans\n- العربية\n- azərbaycanca\n- বাংলা\n- беларуская\n- bosanski\n- čeština\n- deutsch\n- eesti\n- ελληνικά\n- español\n- فارسی\n- français\n- gaeilge\n- 한국어\n- հայերեն\n- bahasa indonesia\n- עברית\n- jawa\n- kurdî\n- latviešu\n- lietuvių\n- македонски\n- malti\n- मराठी\n- مصرى\n- bahasa melayu\n- nederlands\n- 日本語\n- oʻzbekcha / ўзбекча\n- ਪੰਜਾਬੀ\n- پنجابی\n- پښتو\n- português\n- română\n- русский\n- simple english\n- کوردی\n- suomi\n- తెలుగు\n- ไทย\n- türkçe\n- українська\n- اردو\n- tiếng việt\n- 粵語\n- 中文\nedit links\ncoordinates: 41°43′32″n 49°56′49″w / 41.72556°n 49.94694°w / 41.72556; -49.94694\nfrom wikipedia, the free encyclopedia\n2023 submersible implosion in the atlantic\n|date||18 june 2023|\n|location||north atlantic ocean, near the wreck of the titanic|\n|coordinates||41°43′32″n 49°56′49″w / 41.72556°n 49.94694°w / 41.72556; -49.94694|\n|type||maritime disaster|\n|cause||failure of the pressure hull|\n|participants||5 passengers|\n|outcome||submersible destroyed by implosion|\n|deaths||5 (see fatalities)|\non 18 june 2023, titan, a submersible operated by american tourism and expeditions company oceangate, imploded during an expedition to view the wreck of the titanic in the north atlantic ocean off the coast of newfoundland, canada.on board titan, a submersible operated by american tourism and expeditions company oceangate, were stockton rush, the ceo of american tourism and expeditions company oceangate; paul-henri nargeolet, a french deep sea explorer and the titanic| expert; hamish harding, a british billionaire businessman; shahzada dawood, a pakistani-british billionaire businessman; and dawood's son suleman.communication with titan, a submersible operated by american tourism and expeditions company oceangate, was lost 1 hour and 45 minutes into imploded submersible operated by american tourism and expeditions company oceangate, dive.authorities were alerted when titan, a submersible operated by american tourism and expeditions company oceangate, failed to resurface at the scheduled time later that day.after titan, a submersible operated by american tourism and expeditions company oceangate, had been missing for four days, a remotely operated underwater vehicle (rov) discovered a debris field containing parts of titan, a submersible operated by american tourism and expeditions company oceangate,, about 500 metres (1,600 ft) from the bow of the titanic|.the search area was informed by the united states navy's (usn)"
```
```
"".join(splitter.split_text(text)) == text
False
```
Additional characters are added
```
len("".join(splitter.split_text(text)))
2534
len(text)
2426
```
Newlines are stripped
```
text[:50]
'- afrikaans\n- العربية\n- azərbaycanca\n- বাংলা\n- бел'
splitter.split_text(text)[0][:50]
'- afrikaans - العربية - azərbaycanca - বাংলা - бел'
```
Special tokens are added
```
text[193:293]
'awa\n- kurdî\n- latviešu\n- lietuvių\n- македонски\n- malti\n- मराठी\n- مصرى\n- bahasa melayu\n- nederlands\n-'
"".join(splitter.split_text(text))[200:300]
'awa - kurdi - latviesu - lietuviu - македонски -malti - [UNK] - مصرى - bahasa melayu - nederlands - '
```
Recommended improvement: call
`tokenizer(text, return_offsets_mapping=True)`
This will allow selection of N tokens and allow reconstruction without the need for and allow reconstruction without the use of tokenizer.decode (which is not perfectly invertible).
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Please see my code above
### Expected behavior
Please see my code above
|
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PR_kwDOIPDwls5UrSpr
| 7,180 |
fix: prevent adding an empty string to the result queue in AsyncIteratorCallbackHandler
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[] | null | 1 | 2023-07-05T08:17:18 | 2023-07-05T17:00:36 | 2023-07-05T17:00:35 |
CONTRIBUTOR
| null |
- Description: Modify the code for AsyncIteratorCallbackHandler.on_llm_new_token to ensure that it does not add an empty string to the result queue.
- Tag maintainer: @agola11
When using AsyncIteratorCallbackHandler with OpenAIFunctionsAgent, if the LLM response function_call instead of direct answer, the AsyncIteratorCallbackHandler.on_llm_new_token would be called with empty string.
see also: langchain.chat_models.openai.ChatOpenAI._generate
An alternative solution is to modify the langchain.chat_models.openai.ChatOpenAI._generate and do not call the run_manager.on_llm_new_token when the token is empty string.
I am not sure which solution is better.
@hwchase17
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I_kwDOIPDwls5qoaR-
| 7,179 |
SupabaseVectorStore.from_documents returns APIError: {'code': '22P02'
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[] | null | 4 | 2023-07-05T07:44:06 | 2023-10-25T16:07:47 | 2023-10-25T16:07:46 |
NONE
| null |
### System Info
Langchain version: 0.0.221
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When trying to index documents return API error
`APIError: {'code': '22P02', 'details': None, 'hint': None, 'message': 'invalid input syntax for type bigint: "54d7bd9c-9822-40ca-ade6-ae173b65d34e"'}`
This code perfectly worked on previous versions of Langchain.
After upgrading, got the error.
Supabase backend didn't change.
`
# load required libraries
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import SupabaseVectorStore
from supabase import Client, create_client
import os
from dotenv import load_dotenv
# load document from web
url = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase"
loader = WebBaseLoader(url)
documents = loader.load()
# split documents into chunks
docs_splitter = RecursiveCharacterTextSplitter(chunk_size=2500,chunk_overlap=250)
splitted_docs = docs_splitter.split_documents(documents=documents)
# initialize embeddings model
embeddings = OpenAIEmbeddings()
# initialize vector store
supabase_url = os.getenv('SUPABASE_URL')
supabase_key = os.getenv('SUPABASE_KEY')
supabase = create_client(supabase_url, supabase_key)
# save values to supabase
vector_store = SupabaseVectorStore.from_documents(documents=splitted_docs, embedding=embeddings, client=supabase)
`
### Expected behavior
document embeddings should be saved in a Supabase database
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