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PR_kwDOIPDwls5UGzL-
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bump version to 218
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PR_kwDOIPDwls5UGmnw
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Implement timeout cancellation on Chain base class
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NONE
| null |
**Description:** Implement base Chain cancellation policy (execute a Chain’s cancellation after n milliseconds), similar to the implementation in the JS repository
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PR_kwDOIPDwls5UGhMz
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support max_chunk_bytes in OpensearchVectorSearch to pass down to bulk
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CONTRIBUTOR
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Support `max_chunk_bytes` kwargs to pass down to `buik` helper, in order to support the request limits in Opensearch locally and in AWS.
@rlancemartin, @eyurtsev
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I_kwDOIPDwls5p_DsH
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Different results when loading Chroma() vs Chroma.from_documents
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[] | null | 6 | 2023-06-28T04:06:56 | 2023-12-14T09:25:45 | 2023-10-16T16:06:58 |
NONE
| null |
### System Info
langchain==0.0.217
python=3.10
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Hi! I am working on a 'question answering' use case. I am loading PDF docs which I am storing in the Chroma vectorDB along with the instructor embeddings. I use the following command to do that:
```
vectordb = Chroma.from_documents(documents=texts,
embedding=embedding,
persist_directory='db')
```
Here I am storing the vectordb on my local machine in the 'db' folder.
When I use this vectordb as retriever and then use RetrievalQA to ask questions I get 'X' answers.
After storing the vectordb on my local, the next time I directly load the db from the directory:
`vectordb = Chroma(persist_directory='db', embedding_function=embedding)`
When I use this vectordb as retriever and then use RetrievalQA to ask the same questions, I get different answers.
I hope I was able to explain the issue properly.
### Expected behavior
My understanding is that I should get the same answer after loading the vectordb from my local. Why am I getting different answers? Is this an issue with langchain or am I doing this incorrectly? Can you please help me understand?
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PR_kwDOIPDwls5UGSZ5
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Support AwaDB for macosx arm64 platform
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[] | null | 1 | 2023-06-28T03:50:48 | 2023-06-28T03:56:54 | 2023-06-28T03:56:54 |
CONTRIBUTOR
| null |
Upgrade the version of AwaDB for supporting macosx arm64 platform
Please review, thanks! @rlancemartin, @eyurtsev
|
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PR_kwDOIPDwls5UGKK0
| 6,852 |
chat_models: Add support for calling Poe
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CONTRIBUTOR
| null |
- Add a ChatModel to call Poe bots
- Dependencies: fastapi-poe (optional project-wide)
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5p-dD0
| 6,851 |
max_length support for HuggingFaceTextGenInference
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[] | null | 6 | 2023-06-28T01:07:18 | 2023-12-13T16:38:12 | 2023-06-28T09:31:59 |
NONE
| null |
### System Info
TL;DR
The error is reported in the error reproduction section.
Here's a guess at the solution:
HuggingFaceTextGenInference [docs](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/huggingface_textgen_inference) and [code](https://github.com/hwchase17/langchain/blob/master/langchain/llms/huggingface_text_gen_inference.py#L77-L90) don't yet support [huggingface's native max_length generation kwarg](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig.max_length)
I'm guessing adding max_length below max_new_tokens in places like [here](https://github.com/hwchase17/langchain/blob/master/langchain/llms/huggingface_text_gen_inference.py#L142)
would provide the desired behavior? Ctrl-F for max_length shows other places the addition may be required
### 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
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The code snippet below works for local models
```
pipe = pipeline("text-generation", model=hf_llm, tokenizer=tokenizer, max_new_tokens=200)
llm = HuggingFacePipeline(pipeline=pipe)
chain = RetrievalQAWithSourcesChain.from_chain_type(
llm, chain_type="stuff", retriever=db.as_retriever()
)
chain(
{"question": "What did the president say about Justice Breyer"},
return_only_outputs=True,
)
```
However, when replacing the llm definition with this snippet
```
llm = HuggingFaceTextGenInference(
inference_server_url="http://hf-inference-server:80/",
max_new_tokens=256,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.01,
repetition_penalty=1.03,
)
```
Yields this error
```
ValidationError: Input validation error: `inputs` tokens + `max_new_tokens` must be <= 1512. Given: 2342 `inputs`
tokens and 512 `max_new_tokens`
```
The code snippet that fails here works on it's own when used like this
`generated_text = llm("<|prompter|>What is the capital of Hungary?<|endoftext|><|assistant|>")`
### Expected behavior
Expecting a text based answer with no error.
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| 6,850 |
UnpicklingError: pickle data was truncated
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[] | null | 2 | 2023-06-27T23:10:44 | 2023-10-05T16:07:05 | 2023-10-05T16:07:05 |
NONE
| null |
### System Info
❯ pip list |grep unstructured
unstructured 0.7.9
❯ pip list |grep langchain
langchain 0.0.215
langchainplus-sdk 0.0.17
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.document_loaders import UnstructuredFileLoader
loader = UnstructuredFileLoader("../modules/tk.txt")
document = loader.load()
```
errors:
```
UnpicklingError Traceback (most recent call last)
Cell In[11], line 3
1 from langchain.document_loaders import UnstructuredFileLoader
2 loader = UnstructuredFileLoader("../modules/tk.txt")
----> 3 document = loader.load()
File [~/micromamba/envs/openai/lib/python3.11/site-packages/langchain/document_loaders/unstructured.py:71](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/langchain/document_loaders/unstructured.py:71), in UnstructuredBaseLoader.load(self)
69 def load(self) -> List[Document]:
70 """Load file."""
---> 71 elements = self._get_elements()
72 if self.mode == "elements":
73 docs: List[Document] = list()
File [~/micromamba/envs/openai/lib/python3.11/site-packages/langchain/document_loaders/unstructured.py:133](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/langchain/document_loaders/unstructured.py:133), in UnstructuredFileLoader._get_elements(self)
130 def _get_elements(self) -> List:
131 from unstructured.partition.auto import partition
--> 133 return partition(filename=self.file_path, **self.unstructured_kwargs)
File [~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/partition/auto.py:193](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/partition/auto.py:193), in partition(filename, content_type, file, file_filename, url, include_page_breaks, strategy, encoding, paragraph_grouper, headers, ssl_verify, ocr_languages, pdf_infer_table_structure, xml_keep_tags, data_source_metadata, **kwargs)
183 elements = partition_image(
184 filename=filename, # type: ignore
185 file=file, # type: ignore
(...)
190 **kwargs,
191 )
192 elif filetype == FileType.TXT:
--> 193 elements = partition_text(
194 filename=filename,
195 file=file,
196 encoding=encoding,
197 paragraph_grouper=paragraph_grouper,
198 **kwargs,
199 )
200 elif filetype == FileType.RTF:
201 elements = partition_rtf(
202 filename=filename,
203 file=file,
204 include_page_breaks=include_page_breaks,
205 **kwargs,
206 )
File [~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/documents/elements.py:118](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/documents/elements.py:118), in process_metadata..decorator..wrapper(*args, **kwargs)
116 @wraps(func)
117 def wrapper(*args, **kwargs):
--> 118 elements = func(*args, **kwargs)
119 sig = inspect.signature(func)
120 params = dict(**dict(zip(sig.parameters, args)), **kwargs)
File [~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/file_utils/filetype.py:493](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/file_utils/filetype.py:493), in add_metadata_with_filetype..decorator..wrapper(*args, **kwargs)
491 @wraps(func)
492 def wrapper(*args, **kwargs):
--> 493 elements = func(*args, **kwargs)
494 sig = inspect.signature(func)
495 params = dict(**dict(zip(sig.parameters, args)), **kwargs)
File [~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/partition/text.py:92](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/partition/text.py:92), in partition_text(filename, file, text, encoding, paragraph_grouper, metadata_filename, include_metadata, **kwargs)
89 ctext = ctext.strip()
91 if ctext:
---> 92 element = element_from_text(ctext)
93 element.metadata = metadata
94 elements.append(element)
File [~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/partition/text.py:104](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/partition/text.py:104), in element_from_text(text)
102 elif is_us_city_state_zip(text):
103 return Address(text=text)
--> 104 elif is_possible_narrative_text(text):
105 return NarrativeText(text=text)
106 elif is_possible_title(text):
File [~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/partition/text_type.py:86](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/partition/text_type.py:86), in is_possible_narrative_text(text, cap_threshold, non_alpha_threshold, language, language_checks)
83 if under_non_alpha_ratio(text, threshold=non_alpha_threshold):
84 return False
---> 86 if (sentence_count(text, 3) < 2) and (not contains_verb(text)) and language == "en":
87 trace_logger.detail(f"Not narrative. Text does not contain a verb:\n\n{text}") # type: ignore # noqa: E501
88 return False
File [~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/partition/text_type.py:189](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/partition/text_type.py:189), in contains_verb(text)
186 if text.isupper():
187 text = text.lower()
--> 189 pos_tags = pos_tag(text)
190 return any(tag in POS_VERB_TAGS for _, tag in pos_tags)
File [~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/nlp/tokenize.py:57](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/unstructured/nlp/tokenize.py:57), in pos_tag(text)
55 for sentence in sentences:
56 tokens = _word_tokenize(sentence)
---> 57 parts_of_speech.extend(_pos_tag(tokens))
58 return parts_of_speech
File [~/micromamba/envs/openai/lib/python3.11/site-packages/nltk/tag/__init__.py:165](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/nltk/tag/__init__.py:165), in pos_tag(tokens, tagset, lang)
140 def pos_tag(tokens, tagset=None, lang="eng"):
141 """
142 Use NLTK's currently recommended part of speech tagger to
143 tag the given list of tokens.
(...)
163 :rtype: list(tuple(str, str))
164 """
--> 165 tagger = _get_tagger(lang)
166 return _pos_tag(tokens, tagset, tagger, lang)
File [~/micromamba/envs/openai/lib/python3.11/site-packages/nltk/tag/__init__.py:107](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/nltk/tag/__init__.py:107), in _get_tagger(lang)
105 tagger.load(ap_russian_model_loc)
106 else:
--> 107 tagger = PerceptronTagger()
108 return tagger
File [~/micromamba/envs/openai/lib/python3.11/site-packages/nltk/tag/perceptron.py:169](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/nltk/tag/perceptron.py:169), in PerceptronTagger.__init__(self, load)
165 if load:
166 AP_MODEL_LOC = "file:" + str(
167 find("taggers/averaged_perceptron_tagger/" + PICKLE)
168 )
--> 169 self.load(AP_MODEL_LOC)
File [~/micromamba/envs/openai/lib/python3.11/site-packages/nltk/tag/perceptron.py:252](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/nltk/tag/perceptron.py:252), in PerceptronTagger.load(self, loc)
246 def load(self, loc):
247 """
248 :param loc: Load a pickled model at location.
249 :type loc: str
250 """
--> 252 self.model.weights, self.tagdict, self.classes = load(loc)
253 self.model.classes = self.classes
File [~/micromamba/envs/openai/lib/python3.11/site-packages/nltk/data.py:755](https://vscode-remote+ssh-002dremote-002bubuntu-002eh.vscode-resource.vscode-cdn.net/home/john/project/testscratch/python/hello-world/llm/langchain/getting_started_guide_zh/~/micromamba/envs/openai/lib/python3.11/site-packages/nltk/data.py:755), in load(resource_url, format, cache, verbose, logic_parser, fstruct_reader, encoding)
753 resource_val = opened_resource.read()
754 elif format == "pickle":
--> 755 resource_val = pickle.load(opened_resource)
756 elif format == "json":
757 import json
UnpicklingError: pickle data was truncated
```
how to fix it
### Expected behavior
no
|
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PR_kwDOIPDwls5UFbHi
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Added missing attribute value object
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[] | null | 2 | 2023-06-27T23:05:04 | 2023-06-28T01:57:50 | 2023-06-27T23:30:11 |
CONTRIBUTOR
| null |
## Description
Adds a missing type class for [AdditionalResultAttributeValue](https://docs.aws.amazon.com/kendra/latest/APIReference/API_AdditionalResultAttributeValue.html). Fixes validation failure for the query API that have `AdditionalAttributes` in the response.
cc @dev2049
cc @zhichenggeng
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I_kwDOIPDwls5p-BrK
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Issue: Installing langchain[llms] is really difficult
|
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[] | null | 2 | 2023-06-27T23:01:43 | 2023-10-05T16:07:28 | 2023-10-05T16:07:27 |
NONE
| null |
### Issue you'd like to raise.
I tried to install langchain[llms] with pip on Windows 11. The installation did not throw any errors. But trying to import langchain in a python script gives the following error:
from numexpr.interpreter import MAX_THREADS, use_vml, __BLOCK_SIZE1__
ImportError: DLL load failed while importing interpreter: The specified module could not be found.
pip list shows that numexpr=2.8.4 is installed.
Uninstalling and reinstalling numexpr did not help.
Since I had a machine where langchain was working I could determine the difference that the one machine had Visual Studio installed with C compiling capabilities.
Uninstalling and reinstalling numexpr fixed it after installing Visual Studio on the second machine.
But since the documentation does not explain that Visual Studio is required in Windows I am confused and it took me a while to figure this out. Also that the installation had no errors but still didnt worked? Maybe someone looks into that.
Please delete the issue if this is not the right place.
### Suggestion:
_No response_
|
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docstrings `document_loaders` 1
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COLLABORATOR
| null |
- Updated docstrings in `document_loaders`
- several code fixes.
- added `docs/extras/ecosystem/integrations/airtable.md`
@rlancemartin, @eyurtsev
|
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I_kwDOIPDwls5p94Sx
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Callbacks for retriever
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[] | null | 1 | 2023-06-27T22:15:16 | 2023-10-05T16:08:00 | 2023-10-05T16:07:59 |
NONE
| null |
### Feature request
Have callbacks as an argument for BaseConversationalRetrievalChain._get_docs method and BaseRetriever.get_relevant_documents
### Motivation
I am using a custom retriever which has multiple intermediate steps, and I would like to store info from some of these steps for debugging and subsequent analyses. This information is specific to the request, and it cannot be stored at an individual document level.
### Your contribution
I think this can be addressed by having an option to pass callbacks to the BaseConversationalRetrievalChain._get_docs method and BaseRetriever.get_relevant_documents. BaseCallbackHandler may have to be modified too to extend a new class RetrieverManagerMixin which can contain methods like on_retriever_start and on_retriever_end.
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PR_kwDOIPDwls5UFQng
| 6,845 |
Fix Document Similarity Check with passed Threshold
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[] | null | 12 | 2023-06-27T22:09:29 | 2023-07-17T16:57:59 | 2023-07-13T05:30:48 |
CONTRIBUTOR
| null |
Converting the Similarity obtained in the similarity_search_with_score_by_vector method whilst comparing to the passed
threshold. This is because the passed threshold is a number between 0 to 1 and is already in the relevance_score_fn format.
As of now, the function is comparing two different scoring parameters and that wouldn't work.
Dependencies
None
Issue:
Different scores being compared in similarity_search_with_score_by_vector method in FAISS.
Tag maintainer
@hwchase17
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5UFH-F
| 6,844 |
Implement async for the human tool
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[] | null | 3 | 2023-06-27T21:33:32 | 2023-11-07T03:54:00 | 2023-11-07T03:54:00 |
NONE
| null |
**Description**
Implement running the human tool asynchronously. Pretty hacky, not sure what the intent is for how this should work. I'm very open to feedback!
**Issue**
Currently the tool just raises `NotImplementedError`.
**Dependencies**
None
**Tag maintainer**
@vowelparrot
**Twitter handle**
@bencrouse
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Azure Cognitive Search: Custom index and scoring profile support
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[] | null | 7 | 2023-06-27T20:51:17 | 2023-07-27T00:58:02 | 2023-07-27T00:58:01 |
CONTRIBUTOR
| null |
Description: Adding support for custom index and scoring profile support in Azure Cognitive Search
@hwchase17
|
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| 1,777,709,551 |
PR_kwDOIPDwls5UE3hV
| 6,842 |
feat: Add `UnstructuredOrgModeLoader`
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[] | null | 1 | 2023-06-27T20:33:59 | 2023-06-27T23:34:18 | 2023-06-27T23:34:17 |
CONTRIBUTOR
| null |
### Summary
Adds `UnstructuredOrgModeLoader` for processing [Org-mode](https://en.wikipedia.org/wiki/Org-mode) documents.
### Testing
```python
from langchain.document_loaders import UnstructuredOrgModeLoader
loader = UnstructuredOrgModeLoader(
file_path="example_data/README.org", mode="elements"
)
docs = loader.load()
print(docs[0])
```
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
|
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I_kwDOIPDwls5p9Ycw
| 6,841 |
Support for Pydantic v2
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[] | null | 30 | 2023-06-27T20:24:25 | 2023-08-17T21:20:44 | 2023-08-17T00:43:05 |
CONTRIBUTOR
| null |
### Feature request
Currently, `langchain 0.0.217 depends on pydantic<2 and >=1`. Pydantic v2 is re-written in Rust and is between 5-50x faster than v1 depending on the use case. Given how much LangChain relies on Pydantic for both modeling and functional components, and given that FastAPI is now supporting (in beta) Pydantic v2, it'd be great to see LangChain handle a user-specified installation of Pydantic above v2.
The following is an example of what happens when a user specifies installing Pydantic above v2.
```bash
The conflict is caused by:
The user requested pydantic==2.0b2
fastapi 0.100.0b1 depends on pydantic!=1.8, !=1.8.1, <3.0.0 and >=1.7.4
inflect 6.0.4 depends on pydantic>=1.9.1
langchain 0.0.217 depends on pydantic<2 and >=1
```
### Motivation
Pydantic v2 is re-written in Rust and is between 5-50x faster than v1 depending on the use case. Given how much LangChain relies on Pydantic for both modeling and functional components, and given that FastAPI is now supporting (in beta) Pydantic v2, it'd be great to see LangChain handle a user-specified installation of Pydantic above v2.
### Your contribution
Yes! I'm currently opening just an issue to document my request, and because I'm fairly backlogged. But I have contributed to LangChain in the past and would love to write a pull request to facilitate this in full.
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PR_kwDOIPDwls5UEkLD
| 6,840 |
Zapier - Add better error messaging for 401 responses
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[] | null | 1 | 2023-06-27T19:26:09 | 2023-06-27T23:35:43 | 2023-06-27T23:35:42 |
CONTRIBUTOR
| null |
Description: When a 401 response is given back by Zapier, hint to the end user why that may have occurred
- If an API Key was initialized with the wrapper, ask them to check their API Key value
- if an access token was initialized with the wrapper, ask them to check their access token or verify that it doesn't need to be refreshed.
Tag maintainer: @dev2049
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PR_kwDOIPDwls5UEiGh
| 6,839 |
Enhancement : Ignore deleted messages and media in WhatsAppChatLoader
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[] | null | 1 | 2023-06-27T19:19:26 | 2023-06-27T23:36:55 | 2023-06-27T23:36:55 |
CONTRIBUTOR
| null |
- Description: Ignore deleted messages and media
- Issue: #6838
- Dependencies: No new dependencies
- Tag maintainer: @rlancemartin, @eyurtsev
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I_kwDOIPDwls5p9AJU
| 6,838 |
WhatsappChatLoader doesn't ignore deleted messages and omitted media
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[] | null | 1 | 2023-06-27T19:11:54 | 2023-06-28T02:21:59 | 2023-06-28T02:21:58 |
CONTRIBUTOR
| null |
### System Info
langchain git+https://github.com/hwchase17/langchain@8392ca602c03d3ae660d05981154f17ee0ad438e
Archcraft x86_64
Python 3.11.3
### Who can help?
@eyurtsev @dev2049
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Export the chat from WhatsApp, of a conversation with media and deleted messages.
2. The exported chat contains deleted messages and omitted media during the export. For example : `6/29/23, 12:16 am - User 4: This message was deleted` and `4/20/23, 9:42 am - User 3: <Media omitted>`.
3. Currently these messages are also processed and stored in the index.
### Expected behavior
We can avoid embedding these messages in the index.
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agent with memory unable to execute and throwing a output key error
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[] | null | 4 | 2023-06-27T18:45:58 | 2023-06-28T18:15:34 | 2023-06-28T18:15:34 |
NONE
| null |
### System Info
python - 3.9
langchain - 0.0.213
OS - Mac Monterey
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code Snippet
```
from langchain import (
LLMMathChain,
OpenAI,
SerpAPIWrapper,
SQLDatabase,
SQLDatabaseChain,
)
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA, ConversationChain
from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain import OpenAI, LLMChain
from langchain.utilities import GoogleSearchAPIWrapper
from langchain import OpenAI, LLMMathChain, SerpAPIWrapper
from langchain.agents import initialize_agent, Tool
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory, ConversationSummaryMemory, ConversationBufferWindowMemory
import time
from chainlit import AskUserMessage, Message, on_chat_start
import random
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain.vectorstores import Milvus
from langchain.docstore.document import Document
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.document_loaders import WebBaseLoader
from langchain.document_loaders import UnstructuredHTMLLoader
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from pathlib import Path
from langchain.document_loaders import WebBaseLoader
from langchain.prompts import PromptTemplate
import json
import os
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo", openai_api_key=openai_api_key)
llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)
conversational_memory = ConversationBufferWindowMemory(
memory_key='chat_history',
k=50,
return_messages=True,
input_key="question",
output_key='output'
)
# load will vector db
relevant_parts = []
for p in Path(".").absolute().parts:
relevant_parts.append(p)
if relevant_parts[-3:] == ["langchain", "docs", "modules"]:
break
def get_meta(data:str):
split_data = [item.split(":") for item in data.split("\n")]
# Creating a dictionary from the split data
result = {}
for item in split_data:
if len(item) > 1:
key = item[0].strip()
value = item[1].strip()
result[key] = value
# Converting the dictionary to JSON format
json_data = json.dumps(result)
return json_data
template = """You're an customer care representative
You have the following products in your store based on the customer question. Answer politely to customer questions on any questions on product sold in the wesbite and provide product details. Send the product webpage links for each recommendation
{context}
"chat_history": {chat_history}
Question: {question}
Answer:"""
products_path = "ecommerce__20200101_20200131__10k_data_10.csv"
loader = CSVLoader(products_path, csv_args={
'delimiter': ',',
'quotechar': '"',
})
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=4000, chunk_overlap=0, length_function = len)
sources = text_splitter.split_documents(documents)
source_chunks = []
splitter = RecursiveCharacterTextSplitter(chunk_size=3000, chunk_overlap=100, length_function = len)
for source in sources:
for chunk in splitter.split_text(source.page_content):
chunk_metadata = json_meta = json.loads(get_meta(chunk))
source_chunks.append(Document(page_content=chunk, metadata=chunk_metadata))
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key, model="ada")
products_db = Chroma.from_documents(source_chunks, embeddings, collection_name="products")
prompt = PromptTemplate(template=template, input_variables=["context", "question", "chat_history"])
product_inquery_agent = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=products_db.as_retriever(search_kwargs={"k": 4}),
chain_type_kwargs = {"prompt": prompt, "verbose": True, "memory": conversational_memory},
)
tools = [
Tool(
name="Product inquiries System",
func=product_inquery_agent.run,
description="useful for getting information about products, features, and specifications to make informed purchase decisions. Input should be a fully formed question.",
return_direct=True,
),
]
prefix = """Have a conversation with a human as a customer representative agent, answering the following questions as best you can in a polite and friendly manner. You have access to the following tools:"""
suffix = """Begin!"
{chat_history}
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history", "agent_scratchpad"],
)
llm_chain = LLMChain(llm=OpenAI(temperature=0, openai_api_key=openai_api_key), prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=conversational_memory)
```
### Expected behavior
query = "what products are available in your website ?"
response = agent_chain.run(query)
print(response)
The response should print the output captured but it throws a error with key not found
``` > Entering new chain...
Thought: I need to find out what products are available
Action: Product inquiries System
Action Input: What products are available in your website?
> Entering new chain...
> Entering new chain...
Prompt after formatting:
You're an customer care representative
You have the following products in your store based on the customer question. Answer politely to customer questions on any questions on product sold in the wesbite and provide product details. Send the product webpage links for each recommendation
Uniq Id: cc2083338a16c3fe2f7895289d2e98fe
Product Name: ARTSCAPE Etched Glass 24" x 36" Window Film, 24-by-36-Inch
Brand Name:
Asin:
Category: Home & Kitchen | Home Décor | Window Treatments | Window Stickers & Films | Window Films
Upc Ean Code:
List Price:
Selling Price: $12.99
Quantity:
Model Number: 01-0121
About Product: Make sure this fits by entering your model number. | The visual effect of textured glass and stained glass | Creates privacy / Provides UV protection | No adhesives / Applies easily | Patterns repeat to cover any size window | Made in USA
Product Specification: ProductDimensions:72x36x0inches|ItemWeight:11.2ounces|ShippingWeight:12.2ounces(Viewshippingratesandpolicies)|Manufacturer:Artscape|ASIN:B000Q3PRYA|DomesticShipping:ItemcanbeshippedwithinU.S.|InternationalShipping:ThisitemcanbeshippedtoselectcountriesoutsideoftheU.S.LearnMore|ShippingAdvisory:Thisitemmustbeshippedseparatelyfromotheritemsinyourorder.Additionalshippingchargeswillnotapply.|Itemmodelnumber:01-0121
Technical Details: show up to 2 reviews by default Product Description Artscape window films create the look of stained and etched glass. These thin, translucent films provide privacy while still allowing natural light to enter the room. Artscape films are easily applied to any smooth glass surface. They do not use adhesives and are easily removed if needed. They can be trimmed or combined to fit any size window. The images have a repeating pattern left to right and top to bottom and can be used either vertically or horizontally. These films provide UV protection and are the perfect decorative accent for windows that require continued privacy. Artscape patented products are all made in the USA. From the Manufacturer Artscape window films create the look of stained and etched glass. These thin, translucent films provide privacy while still allowing natural light to enter the room. Artscape films are easily applied to any smooth glass surface. They do not use adhesives and are easily removed if needed. They can be trimmed or combined to fit any size window. The images have a repeating pattern left to right and top to bottom and can be used either vertically or horizontally. These films provide UV protection and are the perfect decorative accent for windows that require continued privacy. Artscape patented products are all made in the USA. | 12.2 ounces (View shipping rates and policies)
Shipping Weight: 12.2 ounces
Product Dimensions:
Image: [https://images-na.ssl-images-amazon.com/images/I/51iAe3LF7FL.jpg|https://images-na.ssl-images-amazon.com/images/I/417B20oEehL.jpg|https://images-na.ssl-images-amazon.com/images/I/41J3bDg633L.jpg|https://images-na.ssl-images-amazon.com/images/I/51hhnuGWwdL.jpg|https://images-na.ssl-images-amazon.com/images/G/01/x-locale/common/transparent-pixel.jpg](https://images-na.ssl-images-amazon.com/images/I/51iAe3LF7FL.jpg%7Chttps://images-na.ssl-images-amazon.com/images/I/417B20oEehL.jpg%7Chttps://images-na.ssl-images-amazon.com/images/I/41J3bDg633L.jpg%7Chttps://images-na.ssl-images-amazon.com/images/I/51hhnuGWwdL.jpg%7Chttps://images-na.ssl-images-amazon.com/images/G/01/x-locale/common/transparent-pixel.jpg)
Variants:
Sku:
Product Url: https://www.amazon.com/ARTSCAPE-Etched-Glass-Window-Film/dp/B000Q3PRYA
Stock:
Product Details:
Dimensions:
Color:
Ingredients:
Stock:
Product Details:
Dimensions:
Color:
Ingredients:
Direction To Use:
Is Amazon Seller: Y
Size Quantity Variant:
Product Description:
Uniq Id: f8c32a45e507a177992973cf0d46d20c
Product Name: Terra by Battat – 4 Dinosaur Toys, Medium – Dinosaurs for Kids & Collectors, Scientifically Accurate & Designed by A Paleo-Artist; Age 3+ (4 Pc)
Brand Name:
Asin:
Category:
Upc Ean Code:
List Price:
Selling Price: $18.66
Quantity:
Model Number: AN4054Z
About Product: Make sure this fits by entering your model number. | 4 medium-sized dinosaurs for kids, with lifelike pose, accurate ratio, and exquisitely detailed paint | Includes: Parasaurolophus walkeri, Stegosaurus ungulatus, Pachyrhinosaurus, and euoplocephalus tutus toy dinos | Museum Quality: classic toy dinosaurs designed by an internationally renowned paleo-artist | Educational toy: dinosaur toys for kids spark curiosity about paleontology, science, and natural History | Dimensions: Dimensions: miniature figurines measure 6.25-8.5 (L) 1.5-2.25 (W) 2.25-3.5 (H) inches approximately toys 6.25-8.5 (L) 1.5-2.25 (W) 2.25-3.5 (H) inches approximately | No batteries required, just imagination! | Earth-friendly recyclable packaging | Age: suggested for ages 3+ | Collect them all! Discover the entire Terra by Battat family of animal toy figurines and dinosaur playsets!
Product Specification: ProductDimensions:8.7x3.9x3.4inches|ItemWeight:15.8ounces|ShippingWeight:1.4pounds(Viewshippingratesandpolicies)|ASIN:B07PF1R8LS|Itemmodelnumber:AN4054Z|Manufacturerrecommendedage:36months-10years
Technical Details: Go to your orders and start the return Select the ship method Ship it! | Go to your orders and start the return Select the ship method Ship it! | show up to 2 reviews by default Look out! Here come 4 incredible dinosaur toys that look like the real thing! Sought after by both kids and collectors, These scientifically precise and very collectable dinosaur replicas also feature bright, beautiful colors and look amazing in any dinosaur playset. And, they're made with high quality material, so these dinos will never go extinct! The intimidating-looking, and instantly recognizable Stegosaurus was the largest of plate-backed prehistoric herbivores. Scientists have suggested that the Stegosaurus’ iconic plates were for defense against predators, but this is unlikely as the plates were quite thin. Others have said the Stegosaurus’ spiked tail may have been used as a weapon. Terra’s Stegosaurus toy accurately recreates the formidable size and terrifying presence of this iconic predator. In particular, the large, heavily built frame and rounded backs of the mighty Stegosaurus are faithfully depicted in the dinosaur replica. This highly collectable playset also includes a toy Parasaurolophus, Stegosaurus, Pachyrhinosaurus, and euoplocephalus tutus. Collect your own miniature versions of 4 powerful and iconic dinosaurs from Terra by Battat! | 1.4 pounds (View shipping rates and policies)
Shipping Weight: 1.4 pounds
Product Dimensions:
Uniq Id: e04b990e95bf73bbe6a3fa09785d7cd0
Product Name: Woodstock- Collage 500 pc Puzzle
Brand Name:
Asin:
Category: Toys & Games | Puzzles | Jigsaw Puzzles
Upc Ean Code:
List Price:
Selling Price: $17.49
Quantity:
Model Number: 62151
About Product: Make sure this fits by entering your model number. | Puzzle has 500 pieces | Completed puzzle measure 14 x 19 | 100% officially licensed merchandise | Great for fans & puzzlers alike
Product Specification: ProductDimensions:1.9x8x10inches|ItemWeight:13.4ounces|ShippingWeight:13.4ounces(Viewshippingratesandpolicies)|ASIN:B07MX21WWX|Itemmodelnumber:62151|Manufacturerrecommendedage:14yearsandup
Technical Details: show up to 2 reviews by default 100% Officially licensed merchandise; complete puzzle measures 14 x 19 in. | 13.4 ounces (View shipping rates and policies)
Shipping Weight: 13.4 ounces
Product Dimensions:
Image: [https://images-na.ssl-images-amazon.com/images/I/61plo8Xv4vL.jpg|https://images-na.ssl-images-amazon.com/images/G/01/x-locale/common/transparent-pixel.jpg](https://images-na.ssl-images-amazon.com/images/I/61plo8Xv4vL.jpg%7Chttps://images-na.ssl-images-amazon.com/images/G/01/x-locale/common/transparent-pixel.jpg)
Variants:
Sku:
Product Url: https://www.amazon.com/Woodstock-Collage-500-pc-Puzzle/dp/B07MX21WWX
Stock:
Product Details:
Dimensions:
Color:
Ingredients:
Direction To Use:
Is Amazon Seller: Y
Size Quantity Variant:
Product Description:
"chat_history": []
Question: What products are available in your website?
Answer:
> Finished chain.
> Finished chain.
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[42], line 2
1 query = "what products are available in your website ?"
----> 2 response = agent_chain.run(query)
3 print(response)
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/chains/base.py:290, in Chain.run(self, callbacks, tags, *args, **kwargs)
288 if len(args) != 1:
289 raise ValueError("`run` supports only one positional argument.")
--> 290 return self(args[0], callbacks=callbacks, tags=tags)[_output_key]
292 if kwargs and not args:
293 return self(kwargs, callbacks=callbacks, tags=tags)[_output_key]
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/chains/base.py:166, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
--> 166 raise e
167 run_manager.on_chain_end(outputs)
168 final_outputs: Dict[str, Any] = self.prep_outputs(
169 inputs, outputs, return_only_outputs
170 )
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/chains/base.py:160, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
154 run_manager = callback_manager.on_chain_start(
155 dumpd(self),
156 inputs,
157 )
158 try:
159 outputs = (
--> 160 self._call(inputs, run_manager=run_manager)
161 if new_arg_supported
162 else self._call(inputs)
163 )
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/agents/agent.py:987, in AgentExecutor._call(self, inputs, run_manager)
985 # We now enter the agent loop (until it returns something).
986 while self._should_continue(iterations, time_elapsed):
--> 987 next_step_output = self._take_next_step(
988 name_to_tool_map,
989 color_mapping,
990 inputs,
991 intermediate_steps,
992 run_manager=run_manager,
993 )
994 if isinstance(next_step_output, AgentFinish):
995 return self._return(
996 next_step_output, intermediate_steps, run_manager=run_manager
997 )
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/agents/agent.py:850, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
848 tool_run_kwargs["llm_prefix"] = ""
849 # We then call the tool on the tool input to get an observation
--> 850 observation = tool.run(
851 agent_action.tool_input,
852 verbose=self.verbose,
853 color=color,
854 callbacks=run_manager.get_child() if run_manager else None,
855 **tool_run_kwargs,
856 )
857 else:
858 tool_run_kwargs = self.agent.tool_run_logging_kwargs()
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/tools/base.py:299, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, **kwargs)
297 except (Exception, KeyboardInterrupt) as e:
298 run_manager.on_tool_error(e)
--> 299 raise e
300 else:
301 run_manager.on_tool_end(
302 str(observation), color=color, name=self.name, **kwargs
303 )
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/tools/base.py:271, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, **kwargs)
268 try:
269 tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
270 observation = (
--> 271 self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
272 if new_arg_supported
273 else self._run(*tool_args, **tool_kwargs)
274 )
275 except ToolException as e:
276 if not self.handle_tool_error:
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/tools/base.py:414, in Tool._run(self, run_manager, *args, **kwargs)
411 """Use the tool."""
412 new_argument_supported = signature(self.func).parameters.get("callbacks")
413 return (
--> 414 self.func(
415 *args,
416 callbacks=run_manager.get_child() if run_manager else None,
417 **kwargs,
418 )
419 if new_argument_supported
420 else self.func(*args, **kwargs)
421 )
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/chains/base.py:290, in Chain.run(self, callbacks, tags, *args, **kwargs)
288 if len(args) != 1:
289 raise ValueError("`run` supports only one positional argument.")
--> 290 return self(args[0], callbacks=callbacks, tags=tags)[_output_key]
292 if kwargs and not args:
293 return self(kwargs, callbacks=callbacks, tags=tags)[_output_key]
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/chains/base.py:166, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
--> 166 raise e
167 run_manager.on_chain_end(outputs)
168 final_outputs: Dict[str, Any] = self.prep_outputs(
169 inputs, outputs, return_only_outputs
170 )
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/chains/base.py:160, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
154 run_manager = callback_manager.on_chain_start(
155 dumpd(self),
156 inputs,
157 )
158 try:
159 outputs = (
--> 160 self._call(inputs, run_manager=run_manager)
161 if new_arg_supported
162 else self._call(inputs)
163 )
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/chains/retrieval_qa/base.py:120, in BaseRetrievalQA._call(self, inputs, run_manager)
117 question = inputs[self.input_key]
119 docs = self._get_docs(question)
--> 120 answer = self.combine_documents_chain.run(
121 input_documents=docs, question=question, callbacks=_run_manager.get_child()
122 )
124 if self.return_source_documents:
125 return {self.output_key: answer, "source_documents": docs}
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/chains/base.py:293, in Chain.run(self, callbacks, tags, *args, **kwargs)
290 return self(args[0], callbacks=callbacks, tags=tags)[_output_key]
292 if kwargs and not args:
--> 293 return self(kwargs, callbacks=callbacks, tags=tags)[_output_key]
295 if not kwargs and not args:
296 raise ValueError(
297 "`run` supported with either positional arguments or keyword arguments,"
298 " but none were provided."
299 )
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/chains/base.py:168, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
166 raise e
167 run_manager.on_chain_end(outputs)
--> 168 final_outputs: Dict[str, Any] = self.prep_outputs(
169 inputs, outputs, return_only_outputs
170 )
171 if include_run_info:
172 final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id)
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/chains/base.py:233, in Chain.prep_outputs(self, inputs, outputs, return_only_outputs)
231 self._validate_outputs(outputs)
232 if self.memory is not None:
--> 233 self.memory.save_context(inputs, outputs)
234 if return_only_outputs:
235 return outputs
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/memory/chat_memory.py:34, in BaseChatMemory.save_context(self, inputs, outputs)
32 def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
33 """Save context from this conversation to buffer."""
---> 34 input_str, output_str = self._get_input_output(inputs, outputs)
35 self.chat_memory.add_user_message(input_str)
36 self.chat_memory.add_ai_message(output_str)
File /opt/miniconda3/envs/discord_dev/lib/python3.9/site-packages/langchain/memory/chat_memory.py:30, in BaseChatMemory._get_input_output(self, inputs, outputs)
28 else:
29 output_key = self.output_key
---> 30 return inputs[prompt_input_key], outputs[output_key]
KeyError: 'output' ```
|
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I_kwDOIPDwls5p8gzL
| 6,836 |
Issue: Large observation handling limit
|
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[] | null | 6 | 2023-06-27T17:57:22 | 2023-11-08T16:08:35 | 2023-11-08T16:08:34 |
NONE
| null |
### Large observation handling limit.
Hey langchain community,
I have a tool which takes a database query as input and does database query. This is similar to what `QuerySQLDataBaseTool` does. The problem is the output of the query is out of control, it can be large and the agent exceeded the token limit.
The solution I have tried:
1. Do pagination:
- Chunk the large output, summarize each chunk according to the target question
- Combine all chunks' summarization, which is much smaller than the original output.
Problems:
- Even though I did the summarization according to the target question, the summarization will still lose information.
- The pagination can be slow.
2. Vectorization:
- Chunk the large output
- Embed each chunk and put them into a Vector DB.
- Do a similarity search based on the target question, and take number of chunks within the token limit.
Problems:
- The embedding take times, so it can be slow for a single thought.
- The output of the query is semantic continuously as a a whole, the chunks can break the semantic meaning.
Does anyone have a solution for this problem? I appreciated any idea!
### Suggestion:
_No response_
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PR_kwDOIPDwls5UD6ob
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Implement Function Callback tracer
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CONTRIBUTOR
| null |
Description: I wanted to be able to redirect debug output to a function, but it wasn't very easy. I figured it would make sense to implement a `FunctionCallbackHandler`, and reimplement `ConsoleCallbackHandler` as a subclass that calls the `print` function. Now I can create a simple subclass in my project that calls `logging.info` or whatever I need.
Tag maintainer: @agola11
Twitter handle: `@andandaraalex`
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PR_kwDOIPDwls5UD2EF
| 6,834 |
Adds a chain around sympy for symbolic math
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[] | null | 2 | 2023-06-27T17:15:15 | 2023-07-12T19:17:33 | 2023-07-12T19:17:32 |
CONTRIBUTOR
| null |
- Description: Adds a new chain that acts as a wrapper around Sympy to give LLMs the ability to do some symbolic math.
- Dependencies: SymPy
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PR_kwDOIPDwls5UDm0O
| 6,833 |
Create MultiQueryRetriever
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[] | null | 2 | 2023-06-27T16:30:03 | 2023-06-30T03:44:35 | 2023-06-28T05:59:40 |
COLLABORATOR
| null |
Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on "distance". But, retrieval may produce difference results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious.
The `MultiQueryRetriever` automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the `MultiQueryRetriever` might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results.
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PR_kwDOIPDwls5UDip2
| 6,832 |
rail_parser: Allow creation from pydantic
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[] | null | 1 | 2023-06-27T16:17:44 | 2023-06-27T23:40:53 | 2023-06-27T23:40:52 |
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: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
Adds a way to create the guardrails output parser from a pydantic model.
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PR_kwDOIPDwls5UDgWu
| 6,831 |
bump version to 217
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[] | null | 1 | 2023-06-27T16:10:15 | 2023-06-27T16:39:57 | 2023-06-27T16:39:56 |
COLLABORATOR
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PR_kwDOIPDwls5UDRVP
| 6,830 |
Order messages in PostgresChatMessageHistory
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[] | null | 1 | 2023-06-27T15:32:42 | 2023-06-30T02:14:21 | 2023-06-30T01:10:28 |
CONTRIBUTOR
| null |
Fixes issue: https://github.com/hwchase17/langchain/issues/6829
This guarantees message history is in the correct order.
@hwchase17
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I_kwDOIPDwls5p7lUi
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PostgresChatMessageHistory message order isn't guaranteed
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[] | null | 1 | 2023-06-27T15:22:50 | 2023-06-30T17:13:58 | 2023-06-30T17:13:58 |
CONTRIBUTOR
| null |
### System Info
Langchain version: 0.0.208
OS version: macOS 13.4
Python version: 3.10.12
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Queries without an `order by` clause aren't guaranteed to have any particular order.
### Expected behavior
Chat history should be in order.
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| 1,777,167,767 |
I_kwDOIPDwls5p7W2X
| 6,828 |
Custom tool class not working with extra properties
|
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[] | null | 4 | 2023-06-27T14:54:25 | 2023-11-25T18:35:57 | 2023-10-05T16:10:12 |
NONE
| null |
### System Info
Hello everyone,
I am currently utilizing the OpenAIFunctions agent along with some custom tools that I've developed. I'm trying to incorporate a custom property named `source_documents` into one of these tools. My intention is to assign a value to this property within the tool and subsequently utilize this value outside of the tool.
To illustrate, this is how I invoke my custom tool: `tools = [JamesAllenRetrievalSearchTool(source_documents), OrderTrackingTool()]`. Here, `source_documents `is the property that I wish to update within the `JamesAllenRetrievalSearchTool `class.
I attempted to create a constructor within the custom tool and pass the desired variable for updating, but unfortunately, this approach was unsuccessful. If anyone has knowledge of a solution to my problem, I would greatly appreciate your assistance.
Thank you!
### 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
- [X] Tools / Toolkits
- [x] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Here is My custom tool Class:
`class JamesAllenRetrivalSearchTool(BaseTool):
name = "jamesallen-search"
description = "Use this tool as the primary source of context information. Always search for the answers using this tool first, don't make up answers yourself"
return_direct = True
args_schema: Type[BaseModel] = JamesAllenRetrivalSearchInput
def _run(self, question: str, run_manager: Optional[CallbackManagerForToolRun] = None) -> str:
knowledge_base_retrival_chain = RetrivalQAChain(prompt)
result = knowledge_base_retrival_chain.run(question)
# **HERE -> I need to update returned source_documents from the chain outside the custom tool class**
# source_documents = result["source_documents"]
# self.retrival_source_docs = source_documents
return result["result"]
async def _arun(self, question: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("Not implemented")
`
### Expected behavior
When I run the tool I expect that the answer from the chain will return and the source documents will be update.
|
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| 1,777,164,701 |
PR_kwDOIPDwls5UC_xs
| 6,827 |
Use serialized format for messages in tracer
|
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[] | null | 3 | 2023-06-27T14:52:59 | 2023-07-04T09:19:09 | 2023-07-04T09:19:09 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,776,978,548 |
I_kwDOIPDwls5p6op0
| 6,825 |
Import Error for Gpt4All
|
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[] | null | 6 | 2023-06-27T13:31:27 | 2023-11-28T16:10:20 | 2023-11-28T16:10:19 |
NONE
| null |
### System Info
Langchain version: 0.0.27 (I installed over pip install lanchain)
Python v: 3.8
OS: Windows 11
When I try to ``from langchain.llms import GPT4All`
I am getting the error that says there is no Gpt4All module.
When I check the installed library it does not exist. However when I check github repo it exist in the latest version as per today.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.llms import GPT4All
model_path = r"C:\Users\suat.atan\AppData\Local\nomic.ai\GPT4All\ggml-gpt4all-j-v1.3-groovy.bin"
llm = GPT4All(model= model_path)
llm("Where is Paris?")
```
### Expected behavior
At least the first line of the code should work.
This code should answer my prompt
|
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| 1,776,977,466 |
PR_kwDOIPDwls5UCW2x
| 6,824 |
Add `OpenAIMultiFunctionsAgent` to import list in agents directory
|
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[] | null | 2 | 2023-06-27T13:30:56 | 2023-06-30T01:34:28 | 2023-06-30T01:34:27 |
CONTRIBUTOR
| null |
- Added OpenAIMultiFunctionsAgent to the import list of the Agents directory
@hwchase17
|
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| 1,776,873,702 |
PR_kwDOIPDwls5UB_X9
| 6,823 |
WebBaseLoader: optionally raise exception in the case of http error
|
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[] | null | 1 | 2023-06-27T12:51:54 | 2023-06-27T23:44:00 | 2023-06-27T23:44:00 |
CONTRIBUTOR
| null |
- **Description**: this PR adds the possibility to raise an exception in the case the http request did not return a 2xx status code. This is particularly useful in the situation when the url points to a non-existent web page, the server returns a http status of 404 NOT FOUND, but WebBaseLoader anyway parses and returns the http body of the error message.
- **Dependencies**: none,
- **Tag maintainer**: @rlancemartin, @eyurtsev,
- **Twitter handle**: jtolgyesi
|
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| 1,776,845,482 |
I_kwDOIPDwls5p6IKq
| 6,822 |
"office365/__init__ .py" filename contains typo
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[] | null | 3 | 2023-06-27T12:42:09 | 2023-10-09T16:06:26 | 2023-10-09T16:06:26 |
NONE
| null |
### System Info
Most recent version of langchain, 0.0.216.
### 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
Causes bazel build fail due to small typo in __init__.py filename (filenames cannot have spaces).
### Expected behavior
Should rename __init__. py to __init__.py
|
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PR_kwDOIPDwls5UB3ek
| 6,821 |
Specify utilities package in SerpAPIWrapper docstring
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[] | null | 1 | 2023-06-27T12:38:31 | 2023-06-28T12:58:52 | 2023-06-28T05:26:21 |
CONTRIBUTOR
| null |
- Description: Specify utilities package in SerpAPIWrapper docstring
- Issue: Not an issue
- Dependencies: (n/a)
- Tag maintainer: @dev2049
- Twitter handle: (n/a)
|
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| 1,776,748,473 |
PR_kwDOIPDwls5UBjmZ
| 6,820 |
Synchronize metadata types between pgvector.add_texts() and pgvector.similarity_search_with_score_by_vector()
|
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[] | null | 4 | 2023-06-27T11:50:58 | 2023-11-07T03:54:13 | 2023-11-07T03:54:13 |
NONE
| null |
…similarity_search_with_score_by_vector()
pgvector.add_texts() stored metadata to postgresql is from dict to string, but pgvector.similarity_search_with_score_by_vector() get metadata just string from database, when struct Document object, report error:
```
pydantic.error_wrappers.ValidationError: 1 validation error for Document
metadata
value is not a valid dict (type=type_error.dict)
```
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I_kwDOIPDwls5p5tT4
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LLMRouterChain uses deprecated predict_and_parse method
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[] | null | 20 | 2023-06-27T11:45:08 | 2023-11-22T03:19:32 | null |
CONTRIBUTOR
| null |
### System Info
langchain v0.0.216, Python 3.11.3 on WSL2
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Follow the first example at https://python.langchain.com/docs/modules/chains/foundational/router
### Expected behavior
[This line](https://github.com/hwchase17/langchain/blob/v0.0.216/langchain/chains/llm.py#L275) gets triggered:
> The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.
As suggested by the error, we can make the following code changes to pass the output parser directly to LLMChain by changing [this line](https://github.com/hwchase17/langchain/blob/v0.0.216/langchain/chains/router/llm_router.py#L83) to this:
```python
llm_chain = LLMChain(llm=llm, prompt=prompt, output_parser=prompt.output_parser)
```
And calling `LLMChain.__call__` instead of `LLMChain.predict_and_parse` by changing [these lines](https://github.com/hwchase17/langchain/blob/v0.0.216/langchain/chains/router/llm_router.py#L58-L61) to this:
```python
cast(
Dict[str, Any],
self.llm_chain(inputs, callbacks=callbacks),
)
```
Unfortunately, while this avoids the warning, it creates a new error:
> ValueError: Missing some output keys: {'destination', 'next_inputs'}
because LLMChain currently [assumes the existence of a single `self.output_key`](https://github.com/hwchase17/langchain/blob/v0.0.216/langchain/chains/llm.py#L220) and produces this as output:
> {'text': {'destination': 'physics', 'next_inputs': {'input': 'What is black body radiation?'}}}
Even modifying that function to return the keys if the parsed output is a dict triggers the same error, but for the missing key of "text" instead. `predict_and_parse` avoids this fate by skipping output validation entirely.
It appears changes may have to be a bit more involved here if LLMRouterChain is to keep using LLMChain.
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I_kwDOIPDwls5p5qCv
| 6,818 |
Batch search for Qdrant database
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[] | null | 1 | 2023-06-27T11:37:00 | 2023-10-05T16:08:11 | 2023-10-05T16:08:10 |
NONE
| null |
### Feature request
I am proposing an enhancement for the `langchain` implementation of `qdrant`. As of the current version, `langchain` only supports single text searches. My feature proposal involves extending `langchain` to integrate the [search_batch](https://github.com/qdrant/qdrant-client/blob/master/qdrant_client/qdrant_client.py#L171) method from the `qdrant` client. This would allow us to conduct batch searches, increasing efficiency, and potentially speeding up the process for large volumes of text.
### Motivation
This feature request is born out of the need for more efficient text searches when dealing with large data sets. Currently, using `langchain` for search functionality becomes cumbersome and time-consuming due to the lack of batch search capabilities. Running single text searches one after another restricts the speed of operations and is not scalable when dealing with large text corpuses.
Given the increasing size and complexity of modern text datasets and applications, it is more pertinent than ever to have a robust and efficient method of search that can handle bulk operations. With this enhancement, we can perform multiple text searches simultaneously, thus saving considerable time and computing resources.
### Your contribution
While I would love to contribute, I simply do not have the time right now, so I, therefore, hope that the great community, currently building `langchain` sees some sense in the above-mentioned paragraphs.
|
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PR_kwDOIPDwls5UBWff
| 6,817 |
feat: prompt laboratory
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[] | null | 3 | 2023-06-27T11:17:33 | 2023-11-07T03:54:51 | 2023-11-07T03:54:51 |
NONE
| null |
- Description: implement `PromptLaboratory` similar to `ModelLaboratory`, which facilitates experimenting with different prompts on one model.
- Example usage can be checked with `docs/extras/guides/prompt_laboratory.ipynb`
- Tag maintainer: @hwchase17, @dev2049
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PR_kwDOIPDwls5UBThO
| 6,816 |
fix pinecone delete bug
|
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[] | null | 1 | 2023-06-27T11:11:17 | 2023-06-28T01:58:22 | 2023-06-27T23:50:17 |
CONTRIBUTOR
| null |
The implementation of delete in pinecone vector omits the namespace, which will cause delete failed
|
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PR_kwDOIPDwls5UBLAX
| 6,815 |
feat: implement prompt laboratory
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[] | null | 1 | 2023-06-27T10:50:25 | 2023-06-27T11:17:01 | 2023-06-27T11:15:43 |
NONE
| null |
- Description: implement `PromptLaboratory` similar to `ModelLaboratory`, which facilitates experimenting with different prompts on one model.
- Example usage can be checked with `docs/extras/guides/prompt_laboratory.ipynb`
- Tag maintainer: @hwchase17, @dev2049
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| 1,776,544,976 |
I_kwDOIPDwls5p4-zQ
| 6,814 |
Issue: The parameters passed by the OpenAI function agent seem to have a problem.
|
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[] | null | 2 | 2023-06-27T10:01:30 | 2023-07-04T07:42:15 | 2023-07-04T07:40:56 |
NONE
| null |
### System Info
langchain 0.0.200 in Debian 11
### 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
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Pydantic Model:
```py
class SortCondition(BaseModel):
field: str = Field(description="Field name")
order: str = Field(description="Sort order", enum=["desc", "asc"])
class RecordQueryCondition(BaseModel):
datasheet_id: str = Field(description="The ID of the datasheet to retrieve records from.")
filter_condition: Optional[Dict[str, str]] = Field(
description="""
Find records that meet specific conditions.
This object should contain a key-value pair where the key is the field name and the value is the lookup value. For instance: {"title": "test"}.
"""
)
sort_condition: Optional[List[SortCondition]] = Field(min_items=1,
description="Sort returned records by specific field"
)
maxRecords_condition: Optional[int] = Field(
description="Limit the number of returned values."
)
```
OpenAI return parameters:
```json
{
"datasheet_id": "dsti6VpNpuKQpHVSnh",
"sort_condition": [
{
"field": "Timestamp",
"direction": "desc" # error key!
}
],
"maxRecords_condition": 1
}
```
So, LangChain raise a error:
ValidationError: 1 validation error for RecordQueryCondition
sort_condition -> 0 -> order
field required (type=value_error.missing)
This is source code: https://github.com/xukecheng/APITable-LLMs-Enhancement-Experiments/blob/main/apitable_langchain_function.ipynb
### Expected behavior
I use the OpenAI API to define functions, so it will work properly: https://github.com/xukecheng/APITable-LLMs-Enhancement-Experiments/blob/main/apitable_openai_function.ipynb
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I_kwDOIPDwls5p4mx8
| 6,813 |
Issue[Bug]: OpenAIMultiFunctionsAgent stuck - 'tool_selection is not a valid tool'
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[] | null | 1 | 2023-06-27T09:09:28 | 2023-10-05T16:07:36 | 2023-10-05T16:07:35 |
NONE
| null |
### Issue you'd like to raise.
Hi,
I was trying to get `PythonREPL `or in this case `PythonAstREPL `to work with the `OpenAIMultiFunctionsAgent` reliably, because I came across the same problem as mentioned in this issue: https://github.com/hwchase17/langchain/issues/6364.
I applied the mentioned fix, which worked very well for fixing the REPL tool, but sadly it also broke the usage of any other tools. The agent repeatedly reports `tool_selection is not a valid tool, try another one.`
This is my code to create the agent and tools, as well as for applying the fix (i'm using chainlit for ui):
```
@cl.langchain_factory(use_async=False)
def factory():
# Initialize the OpenAI language model
model = llms[str(use_model)]
llm = ChatOpenAI(
temperature=0,
model=model,
streaming=True,
client="openai",
# callbacks=[cl.ChainlitCallbackHandler()]
)
# Initialize the SerpAPIWrapper for search functionality
search = SerpAPIWrapper(search_engine="google")
# Define a list of tools offered by the agent
tools = [
Tool(
name="Search",
func=search.run,
description="Useful when you need to answer questions about current events or if you have to search the web. You should ask targeted questions like for google."
),
CustomPythonAstREPLTool(),
WriteFileTool(),
ReadFileTool(),
CopyFileTool(),
MoveFileTool(),
DeleteFileTool(),
FileSearchTool(),
ListDirectoryTool(),
ShellTool(),
HumanInputRun(),
]
memory = ConversationTokenBufferMemory(
memory_key="memory",
return_messages=True,
max_token_limit=2000,
llm=llm
)
# needed for memory with openai functions agent
agent_kwargs = {
"extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
}
prompt = OpenAIFunctionsAgent.create_prompt(
extra_prompt_messages=[MessagesPlaceholder(variable_name="memory")],
),
print("Prompt: ", prompt[0])
cust_agent = CustomOpenAIMultiFunctionsAgent(
tools=tools,
llm=llm,
prompt=prompt[0],
# kwargs=agent_kwargs,
# return_intermediate_steps=True,
)
mrkl = AgentExecutor.from_agent_and_tools(
agent=cust_agent,
tools=tools,
memory=memory,
# kwargs=agent_kwargs,
# return_intermediate_steps=True,
)
return mrkl
# ----- Custom classes and functions ----- #
class CustomPythonAstREPLTool(PythonAstREPLTool):
name = "python"
description = (
"A Python shell. Use this to execute python commands. "
"The input must be an object as follows: "
"{'__arg1': 'a valid python command.'} "
"When using this tool, sometimes output is abbreviated - "
"Make sure it does not look abbreviated before using it in your answer. "
"Don't add comments to your python code."
)
def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]:
"""Parse an AI message."""
if not isinstance(message, AIMessage):
raise TypeError(f"Expected an AI message got {type(message)}")
function_call = message.additional_kwargs.get("function_call", {})
if function_call:
function_call = message.additional_kwargs["function_call"]
function_name = function_call["name"]
try:
_tool_input = json.loads(function_call["arguments"])
except JSONDecodeError:
print(
f"Could not parse tool input: {function_call} because "
f"the `arguments` is not valid JSON."
)
_tool_input = function_call["arguments"]
# HACK HACK HACK:
# The code that encodes tool input into Open AI uses a special variable
# name called `__arg1` to handle old style tools that do not expose a
# schema and expect a single string argument as an input.
# We unpack the argument here if it exists.
# Open AI does not support passing in a JSON array as an argument.
if "__arg1" in _tool_input:
tool_input = _tool_input["__arg1"]
else:
tool_input = _tool_input
content_msg = "responded: {content}\n" if message.content else "\n"
return _FunctionsAgentAction(
tool=function_name,
tool_input=tool_input,
log=f"\nInvoking: `{function_name}` with `{tool_input}`\n{content_msg}\n",
message_log=[message],
)
return AgentFinish(return_values={"output": message.content}, log=message.content)
class CustomOpenAIMultiFunctionsAgent(OpenAIMultiFunctionsAgent):
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date, along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
user_input = kwargs["input"]
agent_scratchpad = _format_intermediate_steps(intermediate_steps)
memory = kwargs["memory"]
prompt = self.prompt.format_prompt(
input=user_input, agent_scratchpad=agent_scratchpad, memory=memory
)
messages = prompt.to_messages()
predicted_message = self.llm.predict_messages(
messages, functions=self.functions, callbacks=callbacks
)
agent_decision = _parse_ai_message(predicted_message)
return agent_decision
```
And these are the console outputs with `langchain.debug = true`:
```
Prompt: input_variables=['memory', 'agent_scratchpad', 'input'] output_parser=None partial_variables={}
messages=[SystemMessage(content='You are a helpful AI assistant.', additional_kwargs={}), MessagesPlaceholder(variable_name='memory'), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['input'], output_parser=None, partial_variables={}, template='{input}', template_format='f-string', validate_template=True), additional_kwargs={}), MessagesPlaceholder(variable_name='agent_scratchpad')]
[chain/start] [1:chain:AgentExecutor] Entering Chain run with input:
{
"input": "write a text file to my desktop",
"memory": []
}
[llm/start] [1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:
{
"prompts": [
"System: You are a helpful AI assistant.\nHuman: write a text file to my desktop"
]
}
[llm/end] [1:chain:AgentExecutor > 2:llm:ChatOpenAI] [3.00s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "",
"generation_info": null,
"message": {
"content": "",
"additional_kwargs": {
"function_call": {
"name": "tool_selection",
"arguments": "{\n \"actions\": [\n {\n \"action_name\": \"write_file\",\n \"action\": {\n \"file_path\": \"~/Desktop/my_file.txt\",\n \"text\": \"This is the content of my file.\"\n }\n }\n ]\n}"
}
},
"example": false
}
}
]
],
"llm_output": null,
"run": null
}
[tool/start] [1:chain:AgentExecutor > 3:tool:invalid_tool] Entering Tool run with input:
"tool_selection"
[tool/end] [1:chain:AgentExecutor > 3:tool:invalid_tool] [0.0ms] Exiting Tool run with output:
"tool_selection is not a valid tool, try another one."
[llm/start] [1:chain:AgentExecutor > 4:llm:ChatOpenAI] Entering LLM run with input:
{
"prompts": [
"System: You are a helpful AI assistant.\nHuman: write a text file to my desktop\nAI: {'name': 'tool_selection', 'arguments': '{\\n \"actions\": [\\n {\\n \"action_name\": \"write_file\",\\n \"action\": {\\n \"file_path\": \"~/Desktop/my_file.txt\",\\n \"text\": \"This is the content of my file.\"\\n }\\n }\\n ]\\n}'}\nFunction: tool_selection is not a valid tool, try another one."
]
}
[llm/end] [1:chain:AgentExecutor > 4:llm:ChatOpenAI] [2.42s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "",
"generation_info": null,
"message": {
"content": "",
"additional_kwargs": {
"function_call": {
"name": "tool_selection",
"arguments": "{\n \"actions\": [\n {\n \"action_name\": \"write_file\",\n \"action\": {\n \"file_path\": \"~/Desktop/my_file.txt\",\n \"text\": \"This is the content of my file.\"\n }\n }\n ]\n}"
}
},
"example": false
}
}
]
],
"llm_output": null,
"run": null
}
[tool/start] [1:chain:AgentExecutor > 5:tool:invalid_tool] Entering Tool run with input:
"tool_selection"
[tool/end] [1:chain:AgentExecutor > 5:tool:invalid_tool] [0.0ms] Exiting Tool run with output:
"tool_selection is not a valid tool, try another one."
[llm/start] [1:chain:AgentExecutor > 6:llm:ChatOpenAI] Entering LLM run with input:
{
"prompts": [
"System: You are a helpful AI assistant.\nHuman: write a text file to my desktop\nAI: {'name': 'tool_selection', 'arguments': '{\\n \"actions\": [\\n {\\n \"action_name\": \"write_file\",\\n \"action\": {\\n \"file_path\": \"~/Desktop/my_file.txt\",\\n \"text\": \"This is the content of my file.\"\\n }\\n }\\n ]\\n}'}\nFunction: tool_selection is not a valid tool, try another one.\nAI: {'name': 'tool_selection', 'arguments': '{\\n \"actions\": [\\n {\\n \"action_name\": \"write_file\",\\n \"action\": {\\n \"file_path\": \"~/Desktop/my_file.txt\",\\n \"text\": \"This is the content of my file.\"\\n }\\n }\\n ]\\n}'}\nFunction: tool_selection is not a valid tool, try another one."
]
}
[llm/end] [1:chain:AgentExecutor > 6:llm:ChatOpenAI] [2.35s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "",
"generation_info": null,
"message": {
"content": "",
"additional_kwargs": {
"function_call": {
"name": "tool_selection",
"arguments": "{\n \"actions\": [\n {\n \"action_name\": \"write_file\",\n \"action\": {\n \"file_path\": \"~/Desktop/my_file.txt\",\n \"text\": \"This is the content of my file.\"\n }\n }\n ]\n}"
}
},
"example": false
}
}
]
],
"llm_output": null,
"run": null
}
2023-06-27 11:07:24 - Error in ChainlitCallbackHandler.on_tool_start callback: Task stopped by user
[chain/error] [1:chain:AgentExecutor] [7.86s] Chain run errored with error:
"InterruptedError('Task stopped by user')"
```
Langchain Plus run: https://www.langchain.plus/public/b6c08e7e-bdb0-4792-a291-545e055ad966/r
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5UAcVD
| 6,812 |
Update link in apis.mdx
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[] | null | 1 | 2023-06-27T08:57:04 | 2023-06-28T06:00:27 | 2023-06-28T06:00:26 |
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: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5p4Trw
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The latest version langchain encountered errors when saving Chroma locally, "error "AttributeError: 'Collection' object has no attribute 'upsert'""
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[] | null | 2 | 2023-06-27T08:25:25 | 2023-06-27T13:13:29 | 2023-06-27T13:13:28 |
NONE
| null |
### System Info
langchain 0.0.215 and langchain 0.0.216
python 3.9
chromadb 0.3.21
### Who can help?
@agola11 @hw
### Information
- [X] 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import VectorDBQA
from langchain.document_loaders import TextLoader
from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings
loader = TextLoader('state_of_the_union.txt')
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
persist_directory = 'db'
embedding = HuggingFaceInstructEmbeddings(model_name=“hkunlp/instructor-large”)
vectordb = Chroma.from_documents(documents=documents, embedding=embedding, persist_directory=persist_directory)
vectordb.persist()
vectordb = None
[state_of_the_union.txt](https://github.com/hwchase17/langchain/files/11879392/state_of_the_union.txt)
The detail error information is attached as follows,
[error_info.txt](https://github.com/hwchase17/langchain/files/11879458/error_info.txt)
I don't know why there will be a error "AttributeError: 'Collection' object has no attribute 'upsert'"
And when i degrade the langchain version to 0.0.177, everything go back normal
### Expected behavior
The document could be stored locally for the further retrieval.
|
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I_kwDOIPDwls5p4FUj
| 6,810 |
Support kwargs on GoogleSearchApiWrapper run / result
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[] | null | 1 | 2023-06-27T07:52:22 | 2023-08-31T04:06:32 | 2023-08-31T04:06:32 |
CONTRIBUTOR
| null |
### Feature request
Would you please consider supporting kwargs in GoogleSearchApiWrapper's run / result call,
https://python.langchain.com/docs/modules/agents/tools/integrations/google_search
for the extra filtering on search. for example, I'd like to add "cr" option in cse search, but it seems that I cannot pass any options to run / result method, although internal function "_google_search_results" supports passing extra option to search engine.
### Motivation
I'd like to add "cr" option in cse search, but it seems that I cannot pass any options to run / result method, although internal function "_google_search_results" supports passing extra option to search engine.
### Your contribution
If you allow me, I'd like to make pr for this change.
|
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| 6,809 |
Issue: How to iterate using agents
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[] | null | 1 | 2023-06-27T07:49:29 | 2023-10-05T16:07:41 | 2023-10-05T16:07:40 |
NONE
| null |
### Issue you'd like to raise.
I have a sentence, and I'd like to extract entities from it. On each entity, I'd like to run a custom tool for validating. Is this possible via agents? I've looked through the documentation but couldn't find any related topics
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5T_7wI
| 6,808 |
Update description in Evals notebook
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[] | null | 1 | 2023-06-27T07:26:30 | 2023-06-27T07:26:51 | 2023-06-27T07:26:50 |
CONTRIBUTOR
| null | null |
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PR_kwDOIPDwls5T_xZC
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Permit Constitutional Principles
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[] | null | 1 | 2023-06-27T06:58:29 | 2023-06-27T07:23:55 | 2023-06-27T07:23:54 |
CONTRIBUTOR
| null |
In the criteria evaluator.
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PR_kwDOIPDwls5T_qWv
| 6,806 |
feat: add model architecture back to wandb tracer
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[] | null | 1 | 2023-06-27T06:36:52 | 2023-08-07T08:12:08 | 2023-07-12T19:00:19 |
CONTRIBUTOR
| null |
# Description
This PR adds model architecture to the `WandbTracer` from the Serialized Run kwargs. This allows visualization of the calling parameters of an Agent, LLM and Tool in Weights & Biases.
1. Safely serialize the run objects to WBTraceTree model_dict
2. Refactors the run processing logic to be more organized.
- Tag maintainer: @agola11
- Twitter handle: @parambharat
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load_qa_chain with chain_type="map_reduce" can not process long document
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[] | null | 3 | 2023-06-27T06:25:37 | 2023-10-05T16:09:37 | 2023-10-05T16:09:36 |
NONE
| null |
### System Info
langchain version: 0.0.215
python version: Python 3.8.8
### Who can help?
@hwchase17 @agola11 @ey
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I take the example from https://python.langchain.com/docs/modules/chains/additional/question_answering#the-map_reduce-chain . I ignore the retrieval part and inject the whole document into `load_qa_chain` with set `chain_type="map_reduce"`：
```
from langchain.document_loaders import TextLoader
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
chain = load_qa_chain(OpenAI(temperature=0), chain_type="map_reduce")
query = "What did the president say about Justice Breyer"
chain({"input_documents": documents, "question": query}, return_only_outputs=True)
```
comes error below:
```
InvalidRequestError: This model's maximum context length is 4097 tokens, however you requested 9640 tokens (9384 in your prompt; 256 for the completion). Please reduce your prompt; or completion length.
```
when `documents` is long document, set `chain_type="map_reduce"` seems do not work, why and how to solve it?
Thanks a lot!
### Expected behavior
load_qa_chain with `chain_type="map_reduce"` setting should can process long document directly，does it？
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PR_kwDOIPDwls5T_iSN
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Update to the latest Psychic python library version
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[] | null | 1 | 2023-06-27T06:05:11 | 2023-06-28T05:26:38 | 2023-06-28T05:26:38 |
CONTRIBUTOR
| null |
Update the Psychic document loader to use the latest `psychicapi` python library version: `0.8.0`
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I_kwDOIPDwls5p3Z19
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ValueError: RedisCache only supports caching of normal LLM generations, got <class 'langchain.schema.ChatGeneration'>
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[] | null | 1 | 2023-06-27T05:41:52 | 2023-06-29T12:08:37 | 2023-06-29T12:08:36 |
NONE
| null |
### System Info
Python 3.10, Langchain > v0.0.212
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Enable caching with langchain.llm_cache = RedisCache
### Expected behavior
This works with version <= v0.0.212
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PR_kwDOIPDwls5T_WXZ
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Improve docstrings for langchain.schema.py
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CONTRIBUTOR
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PR_kwDOIPDwls5T_UyR
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Updated QA notebook
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CONTRIBUTOR
| null |
Description: `all_metadatas` was not defined, `OpenAIEmbeddings` was not imported,
Issue: #6723 the issue # it fixes (if applicable),
Dependencies: lark,
Tag maintainer: @vowelparrot , @dev2049
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PR_kwDOIPDwls5T_P7D
| 6,799 |
Clean up agent trajectory interface
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[] | null | 1 | 2023-06-27T04:26:39 | 2023-06-27T05:54:05 | 2023-06-27T05:54:04 |
CONTRIBUTOR
| null |
- Enable reference
- Enable not specifying tools at the start
- Add methods with keywords
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PR_kwDOIPDwls5T_EDu
| 6,798 |
Adjusted OpenAI cost calculation
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CONTRIBUTOR
| null |
Added parentheses to ensure the division operation is performed before multiplication. This now correctly calculates the cost by dividing the number of tokens by 1000 first (to get the cost per token), and then multiplies it with the model's cost per 1k tokens @agola11
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Intermediate combine results in output of BaseConversationalRetrievalChain
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NONE
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Add intermediate_steps from combine_docs_chain's results to the output of BaseConversationalRetrievalChain when combine_docs_chain.return_intermediate_steps is True. This can help while debugging and in some subsequent analyses.
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PR_kwDOIPDwls5T-6gz
| 6,796 |
docs: vectorstore upgrades 2
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[] | null | 1 | 2023-06-27T02:10:49 | 2023-06-27T15:38:42 | 2023-06-27T05:55:05 |
COLLABORATOR
| null |
updated vectorstores/ notebooks; added new integrations into ecosystem/integrations/
@dev2049
@rlancemartin, @eyurtsev
|
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I_kwDOIPDwls5p2qUh
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Source info in map_rerank's answer
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[] | null | 1 | 2023-06-27T01:33:01 | 2023-10-05T16:07:51 | 2023-10-05T16:07:50 |
NONE
| null |
### Feature request
Have SOURCES info in map_rerank's answer similar to the information available for 'map_reduce' and 'stuff' chain_type options.
### Motivation
Standardization of output
Indicate answer source when map-rerank is used with ConversationalRetrievalChain
### Your contribution
https://github.com/hwchase17/langchain/pull/6794
|
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PR_kwDOIPDwls5T-0gE
| 6,794 |
Source info in map_rerank's answer
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[] | null | 3 | 2023-06-27T01:32:37 | 2023-11-07T03:55:24 | 2023-11-07T03:55:23 |
NONE
| null |
Have SOURCES info in map_rerank's answer similar to the information available for 'map_reduce' and 'stuff' chain_type options.
|
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I_kwDOIPDwls5p2kpf
| 6,793 |
Azure rejects tokens sent by OpenAIEmbeddings, expects strings
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[] | null | 1 | 2023-06-27T01:01:39 | 2023-09-26T16:09:43 | 2023-09-26T16:09:43 |
NONE
| null |
### System Info
Langchain .216, OS X 11.6, Python 3.11.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Setup OpenAIEmbeddings method with Azure arguments
2. Split text with a splitter like RecursiveCharacterTextSplitter
3. Use text and embedding function in chroma.from_texts
```python
import openai
import os
from dotenv import load_dotenv, find_dotenv
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
_ = load_dotenv(find_dotenv())
API_KEY = os.environ.get('STAGE_API_KEY')
API_VERSION = os.environ.get('API_VERSION')
RESOURCE_ENDPOINT = os.environ.get('RESOURCE_ENDPOINT')
openai.api_type = "azure"
openai.api_key = API_KEY
openai.api_base = RESOURCE_ENDPOINT
openai.api_version = API_VERSION
openai.log = "debug"
sample_text = 'This metabolite causes atherosclerosis in the liver[55]. Strengths and limitations This is the first thorough bibliometric analysis of nutrition and gut microbiota research conducted on a global level.'
embed_deployment_id = 'text-embedding-ada-002'
embed_model = 'text-embedding-ada-002'
persist_directory = "./storage_openai_chunks" # will be created if not existing
embeddings = OpenAIEmbeddings(
deployment=embed_deployment_id,
model=embed_model,
openai_api_key=API_KEY,
openai_api_base=RESOURCE_ENDPOINT,
openai_api_type="azure",
openai_api_version=API_VERSION,
)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=40, chunk_overlap=10)
texts = text_splitter.split_text(sample_text)
vectordb = Chroma.from_texts(collection_name='test40',
texts=texts,
embedding=embeddings,
persist_directory=persist_directory)
vectordb.persist()
print(vectordb.get())
message='Request to OpenAI API' method=post path=https://***/openai/deployments/text-embedding-ada-002/embeddings?api-version=2023-05-15
api_version=2023-05-15 data='{"input": [[2028, 28168, 635, 11384, 264, 91882, 91711], [258, 279, 26587, 58, 2131, 948, 32937, 82, 323], [438, 9669, 1115, 374, 279, 1176], [1820, 1176, 17879, 44615, 24264], [35584, 315, 26677, 323, 18340], [438, 18340, 53499, 6217, 3495, 13375], [444, 55015, 389, 264, 3728, 2237, 13]], "encoding_format": "base64"}' message='Post details'
message='OpenAI API response' path=https://***/openai/deployments/text-embedding-ada-002/embeddings?api-version=2023-05-15 processing_ms=None request_id=None response_code=400
body='{\n "error": "/input/6 expected type: String, found: JSONArray\\n/input/5 expected type: String, found: JSONArray\\n/input/4 expected type: String, found: JSONArray\\n/input/3 expected type: String, found: JSONArray\\n/input/2 expected type: String, found: JSONArray\\n/input/1 expected type: String, found: JSONArray\\n/input/0 expected type: String, found: JSONArray\\n/input expected: null, found: JSONArray\\n/input expected type: String, found: JSONArray"\n}' headers="{'Date': 'Tue, 27 Jun 2023 00:08:56 GMT', 'Content-Type': 'application/json; charset=UTF-8', 'Content-Length': '454', 'Connection': 'keep-alive', 'Strict-Transport-Security': 'max-age=16070400; includeSubDomains', 'Set-Cookie': 'TS01bd4155=0179bf738063e38fbf3fffb70b7f9705fd626c2df1126f29599084aa69d137b77c61d6377a118a5ebe5a1f1f9f314c22a777a0e861; Path=/; Domain=.***', 'Vary': 'Accept-Encoding'}" message='API response body'
Traceback (most recent call last):
File "/Users/A/dev/python/openai/langchain_embed_issue.py", line 39, in <module>
vectordb = Chroma.from_texts(collection_name='test40',
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/langchain/vectorstores/chroma.py", line 403, in from_texts
chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/langchain/vectorstores/chroma.py", line 148, in add_texts
embeddings = self._embedding_function.embed_documents(list(texts))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/langchain/embeddings/openai.py", line 465, in embed_documents
return self._get_len_safe_embeddings(texts, engine=self.deployment)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/langchain/embeddings/openai.py", line 302, in _get_len_safe_embeddings
response = embed_with_retry(
^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/langchain/embeddings/openai.py", line 97, in embed_with_retry
return _embed_with_retry(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/tenacity/__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
^^^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/tenacity/__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/tenacity/__init__.py", line 314, in iter
return fut.result()
^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/concurrent/futures/_base.py", line 449, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/tenacity/__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/langchain/embeddings/openai.py", line 95, in _embed_with_retry
return embeddings.client.create(**kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/openai/api_resources/embedding.py", line 33, in create
response = super().create(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create
response, _, api_key = requestor.request(
^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/openai/api_requestor.py", line 298, in request
resp, got_stream = self._interpret_response(result, stream)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/openai/api_requestor.py", line 700, in _interpret_response
self._interpret_response_line(
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/openai/api_requestor.py", line 763, in _interpret_response_line
raise self.handle_error_response(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/A/anaconda3/envs/openai1/lib/python3.11/site-packages/openai/api_requestor.py", line 418, in handle_error_response
error_code=error_data.get("code"),
^^^^^^^^^^^^^^
AttributeError: 'str' object has no attribute 'get'
Process finished with exit code 1
```
### Expected behavior
OpenAIEmbeddings should return embeddings instead of an error.
Because Azure currently only accepts str input, in contrast to OpenAI which accepts tokens or strings, the input is rejected because OpenAIEmbeddings sends tokens only. Azure embedding API docs confirm this, where the request body input parameter is of type string: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/reference#embeddings
Second, after modifying openai.py to send strings, Azure complains that it currently accepts one input at a time--in other words, it doesn't accept batches of strings (or even tokens if it accepted tokens). So the for loop increment was modified to send one decoded batch of tokens (in other words, the original str chunk) at a time.
Modifying embeddings/openai.py with:
```python
# batched_embeddings = []
# _chunk_size = chunk_size or self.chunk_size
# for i in range(0, len(tokens), _chunk_size):
# response = embed_with_retry(
# self,
# input=tokens[i : i + _chunk_size],
# **self._invocation_params,
# )
# batched_embeddings += [r["embedding"] for r in response["data"]]
batched_embeddings = []
_chunk_size = chunk_size or self.chunk_size if 'azure' not in self.openai_api_type else 1
#
#
for i in range(0, len(tokens), _chunk_size):
embed_input = encoding.decode(tokens[i]) if 'azure' in self.openai_api_type else tokens[i : i + _chunk_size]
response = embed_with_retry(
self,
input=embed_input,
**self._invocation_params,
)
batched_embeddings += [r["embedding"] for r in response["data"]]
```
and re-running the code:
```text
# same code
...
message='Request to OpenAI API' method=post path=https://***/openai/deployments/text-embedding-ada-002/embeddings?api-version=2023-05-15
api_version=2023-05-15 data='{"input": "This metabolite causes atherosclerosis", "encoding_format": "base64"}' message='Post details'
message='OpenAI API response' path=https://***/openai/deployments/text-embedding-ada-002/embeddings?api-version=2023-05-15 processing_ms=27.0109 request_id=47bee143-cb00-4782-8560-f267ee839af4 response_code=200
body='{\n "object": "list",\n "data": [\n {\n "object": "embedding",\n "index": 0,\n "embedding": "5zPWu+V2e7w75Ia7HeCavKhhE71NQhA865WYvE+Y9DuB8ce8Xak7uhgQgble4z48H8L4uyePnzu2XVq8ucg+u7ZdWj28ofq7Jzd6PMFMkbvQiIq8nbuwPFJMLTxGe5i83c2lPIXQsjzPToc8taB/vZlZ7ryVjwM8jsiLPIvLfrywnBG9RjLEO2XkuTpOMz
... (removed for brevity)
/gP7uzTTC8RZf5PMOULTv2D4C7caQfvR60EbyqjZ48yqxUuzHeLzhSFJW8qDu5uwcj7zyeDnO8UMKvPNLEezxNixm6X7U3vBeDqzumrI08jzQqPDZObLzZS2c843itO9a+y7w+mJG8gChjPAIHqLqEeLg6ysUTvfqaizzT2yo77Di/u3A3azyziva8ct9VvI80Kry1n5U7ipJvvHy2FjuAQSK9"\n }\n ],\n "model": "ada",\n "usage": {\n "prompt_tokens": 7,\n "total_tokens": 7\n }\n}\n' headers="{'Date': 'Tue, 27 Jun 2023 00:20:13 GMT', 'Content-Type': 'application/json', 'Content-Length': '8395', 'Connection': 'keep-alive', 'x-ms-region': 'East US', 'apim-request-id': 'b932333d-1eb9-415a-a84b-da1c5f95433b', 'x-content-type-options': 'nosniff, nosniff', 'openai-processing-ms': '26.8461', 'access-control-allow-origin': '*', 'x-request-id': '0677d084-2449-486c-9bff-b6ef07df004f', 'x-ms-client-request-id': 'b932333d-1eb9-415a-a84b-da1c5f95433b', 'strict-transport-security': 'max-age=31536000; includeSubDomains; preload, max-age=16070400; includeSubDomains', 'X-Frame-Options': 'SAMEORIGIN', 'X-XSS-Protection': '1; mode=block'}" message='API response body'
{'ids': ['60336172-1480-11ee-b223-acde48001122', '6033621c-1480-11ee-b223-acde48001122', '60336280-1480-11ee-b223-acde48001122', '603362b2-1480-11ee-b223-acde48001122', '603362da-1480-11ee-b223-acde48001122', '603362f8-1480-11ee-b223-acde48001122', '60336370-1480-11ee-b223-acde48001122'], 'embeddings': None, 'documents': ['This metabolite causes atherosclerosis', 'in the liver[55]. Strengths and', 'and limitations This is the first', 'the first thorough bibliometric', 'analysis of nutrition and gut', 'and gut microbiota research conducted', 'conducted on a global level.'], 'metadatas': [None, None, None, None, None, None, None]}
```
Also made the following change to openai.py a few lines later, although this is untested:
```python
batched_embeddings = []
_chunk_size = chunk_size or self.chunk_size if 'azure' not in self.openai_api_type else 1
# azure only accepts str input, currently one list element at a time
for i in range(0, len(tokens), _chunk_size):
embed_input = encoding.decode(tokens[i]) if 'azure' in self.openai_api_type else tokens[i : i + _chunk_size]
response = await async_embed_with_retry(
self,
input=embed_input,
**self._invocation_params,
)
batched_embeddings += [r["embedding"] for r in response["data"]]
```
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Web Loader: Add proxy support
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CONTRIBUTOR
| null |
Proxies are helpful, especially when you start querying against more anti-bot websites.
[Proxy services](https://developers.oxylabs.io/advanced-proxy-solutions/web-unblocker/making-requests) (of which there are many) and `requests` make it easy to rotate IPs to prevent banning by just passing along a simple dict to `requests`.
CC @rlancemartin, @eyurtsev
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add async to zapier nla tools
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[] | null | 3 | 2023-06-27T00:06:52 | 2023-06-27T23:53:36 | 2023-06-27T23:53:36 |
CONTRIBUTOR
| null |
Replace this comment with:
- Description: Add Async functionality to Zapier NLA Tools
- Issue: n/a
- Dependencies: n/a
- Tag maintainer:
Maintainer responsibilities:
- Agents / Tools / Toolkits: @vowelparrot
- 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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Docs /redirects
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[] | null | 1 | 2023-06-26T23:54:58 | 2023-06-29T00:07:54 | 2023-06-29T00:07:53 |
CONTRIBUTOR
| null |
Auto-generated a bunch of redirects from initial docs refactor commit
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Change Dep Name
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[] | null | 1 | 2023-06-26T23:48:50 | 2023-07-12T06:49:50 | 2023-07-12T06:49:50 |
CONTRIBUTOR
| null |
- [X] Change dependency to langsmith
- [ ] Add langsmith to conda forge
|
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Initial Streamlit callback integration doc (md)
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[] | null | 1 | 2023-06-26T23:33:43 | 2023-06-27T18:43:50 | 2023-06-27T18:43:49 |
CONTRIBUTOR
| null |
**Description:** Add a documentation page for the Streamlit Callback Handler integration (#6315)
Notes:
- Implemented as a markdown file instead of a notebook since example code runs in a Streamlit app (happy to discuss / consider alternatives now or later)
- Contains an embedded Streamlit app -> https://mrkl-minimal.streamlit.app/ Currently this app is hosted out of a Streamlit repo but we're working to migrate the code to a LangChain owned repo
cc @dev2049 @tconkling
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`RecusiveUrlLoader` to `RecursiveUrlLoader`
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CONTRIBUTOR
| null |
Heres my attempt to contribute in a small way haha.
Twitter: https://twitter.com/waseemhnyc Not a big change but if you want to tag me 😅
Description: Small spelling error fix
Maintainers: @rlancemartin, @eyurtsev
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Rm lcp dep
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[] | null | 1 | 2023-06-26T23:10:42 | 2023-08-11T01:25:11 | 2023-08-11T01:25:11 |
CONTRIBUTOR
| null |
- Update to langsmith dependency.
- Make optional with lazy loading
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PR_kwDOIPDwls5T-cxJ
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codespell: workflow, config + some (quite a few) typos fixed
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[] | null | 6 | 2023-06-26T23:07:51 | 2023-07-12T20:20:23 | 2023-07-12T20:20:08 |
CONTRIBUTOR
| null |
Probably the most boring PR to review ;)
Individual commits might be easier to digest
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PR_kwDOIPDwls5T-JzI
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Update arize_callback.py - bug fix
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[] | null | 1 | 2023-06-26T22:02:27 | 2023-06-26T23:49:47 | 2023-06-26T23:49:46 |
CONTRIBUTOR
| null |
- Description: Bug Fix - Added a step variable to keep track of prompts
- Issue: Bug from internal Arize testing - The prompts and responses that are ingested were not mapped correctly
- Dependencies: N/A
@hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
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PR_kwDOIPDwls5T94Iu
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docstrings: `docstore` and `client`
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COLLABORATOR
| null |
updated docstrings in `docstore/` and `client/`
@baskaryan
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PR_kwDOIPDwls5T9qLp
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Add support for passing headers and search params to openai openapi chain
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CONTRIBUTOR
| null |
- Description: add support for passing headers and search params to OpenAI OpenAPI chains.
- Issue: n/a
- Dependencies: n/a
- Tag maintainer: @hwchase17
- Twitter handle: @pelaseyed
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PR_kwDOIPDwls5T9dWL
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Docs/unstructured api key
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[] | null | 1 | 2023-06-26T20:07:59 | 2023-06-27T23:54:16 | 2023-06-27T23:54:15 |
CONTRIBUTOR
| null |
### Summary
The Unstructured API will soon begin requiring API keys. This PR updates the Unstructured integrations docs with instructions on how to generate Unstructured API keys.
### Reviewers
@rlancemartin
@eyurtsev
@hwchase17
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Zapier update oauth support
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closed
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[] | null | 1 | 2023-06-26T20:07:38 | 2023-06-27T18:46:33 | 2023-06-27T18:46:33 |
CONTRIBUTOR
| null |
Description: Update documentation to
1) point to updated documentation links at Zapier.com (we've revamped our help docs and paths), and
2) To provide clarity how to use the wrapper with an access token for OAuth support
Demo:
Initializing the Zapier Wrapper with an OAuth Access Token
`ZapierNLAWrapper(zapier_nla_oauth_access_token="<redacted>")`
Using LangChain to resolve the current weather in Vancouver BC leveraging Zapier NLA to lookup weather by coords.
```
> Entering new chain...
I need to use a tool to get the current weather.
Action: The Weather: Get Current Weather
Action Input: Get the current weather for Vancouver BC
Observation: {"coord__lon": -123.1207, "coord__lat": 49.2827, "weather": [{"id": 802, "main": "Clouds", "description": "scattered clouds", "icon": "03d", "icon_url": "http://openweathermap.org/img/wn/[email protected]"}], "weather[]icon_url": ["http://openweathermap.org/img/wn/[email protected]"], "weather[]icon": ["03d"], "weather[]id": [802], "weather[]description": ["scattered clouds"], "weather[]main": ["Clouds"], "base": "stations", "main__temp": 71.69, "main__feels_like": 71.56, "main__temp_min": 67.64, "main__temp_max": 76.39, "main__pressure": 1015, "main__humidity": 64, "visibility": 10000, "wind__speed": 3, "wind__deg": 155, "wind__gust": 11.01, "clouds__all": 41, "dt": 1687806607, "sys__type": 2, "sys__id": 2011597, "sys__country": "CA", "sys__sunrise": 1687781297, "sys__sunset": 1687839730, "timezone": -25200, "id": 6173331, "name": "Vancouver", "cod": 200, "summary": "scattered clouds", "_zap_search_was_found_status": true}
Thought: I now know the current weather in Vancouver BC.
Final Answer: The current weather in Vancouver BC is scattered clouds with a temperature of 71.69 and wind speed of 3
```
|
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I_kwDOIPDwls5p0608
| 6,779 |
Request to Support other VertexAI's LLM model Support
|
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[] | null | 8 | 2023-06-26T19:48:47 | 2024-01-09T16:15:57 | 2023-06-26T23:51:22 |
NONE
| null |
### Feature request
Right now only `text-bison` model is support by Google PaLM. When tried `code-bison` its throwing below error:
```
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[39], line 1
----> 1 llm = VertexAI(model_name='code-bison')
File /opt/conda/envs/python310/lib/python3.10/site-packages/pydantic/main.py:341, in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for VertexAI
__root__
Unknown model publishers/google/models/code-bison; {'gs://google-cloud-aiplatform/schema/predict/instance/text_generation_1.0.0.yaml': <class 'vertexai.language_models._language_models._PreviewTextGenerationModel'>} (type=value_error)
```
Can we get other model supports as well?
### Motivation
Support for many other PaLM models
### Your contribution
I can update and test the code for different other models
|
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I_kwDOIPDwls5p00a3
| 6,778 |
Verbose flag not outputting anything other than Entering chain and Finished chain
|
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[] | null | 2 | 2023-06-26T19:31:15 | 2023-07-09T13:23:46 | 2023-07-09T13:23:46 |
NONE
| null |
### System Info
langchain==0.0.216 (I have had this since i started with Langchain (198)
python 3.10
### 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
- [x] Callbacks/Tracing
- [ ] Async
### Reproduction
I run this function with
```json
{
"prompt": "some prompt",
"temperature": 0.2
}
```
```python3
from typing import Any
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import PromptTemplate
from app.config.api_keys import ApiKeys
from app.repositories.pgvector_repository import PGVectorRepository
def completions_service(data) -> dict[str, Any]:
pgvector = PGVectorRepository().instance
tech_template = """Some prompt info (redacted)
{summaries}
Q: {question}
A: """
prompt_template = PromptTemplate(
template=tech_template, input_variables=["summaries", "question"]
)
qa = RetrievalQAWithSourcesChain.from_chain_type(llm=ChatOpenAI(temperature=data['temperature'],
model_name="gpt-3.5-turbo",
openai_api_key=ApiKeys.openai),
chain_type="stuff",
retriever=pgvector.as_retriever(),
chain_type_kwargs={"prompt": prompt_template},
return_source_documents=True,
verbose=True,
)
output = qa({"question": data['prompt']})
return output
```
I get this in the command line
```bash
> Entering new chain...
> Finished chain.
```
For some reason, there is a double space in the Entering line (as if there is meant to be something there) and then nothing until it says finish, I have set verbose = True but not luck
### Expected behavior
I would expect to see the embeddings, the full prompt being sent to openai etc. but I get nothing.
|
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I_kwDOIPDwls5p0zg1
| 6,777 |
Functions might not be supported through Azure OpenAI
|
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[] | null | 10 | 2023-06-26T19:28:21 | 2023-10-25T14:30:23 | 2023-06-30T06:51:49 |
NONE
| null |
### System Info
Windows 10
Name: langchain
Version: 0.0.208
Summary: Building applications with LLMs through composability
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from pydantic import BaseModel, Field
from langchain.chat_models import ChatOpenAI, AzureChatOpenAI
from langchain.agents import Tool
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import PyPDFLoader
from langchain.chains import RetrievalQA
# Here a proper Azure OpenAI service needs to be defined
OPENAI_API_KEY=" "
OPENAI_DEPLOYMENT_ENDPOINT="https://gptkbopenai.openai.azure.com/"
OPENAI_DEPLOYMENT_NAME = "gptkbopenai"
OPENAI_MODEL_NAME = "GPT4"
#OPENAI_EMBEDDING_DEPLOYMENT_NAME = os.getenv("OPENAI_EMBEDDING_DEPLOYMENT_NAME")
OPENAI_EMBEDDING_MODEL_NAME = "text-embedding-ada-002"
OPENAI_DEPLOYMENT_VERSION = "2023-03-15-preview"
OPENAI_API_TYPE = "azure"
OPENAI_API_BASE = "https://gptkbopenai.openai.azure.com/"
OPENAI_EMBEDDING_DEPLOYMENT_NAME = "text-embedding-ada-002"
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.chat_models import AzureChatOpenAI
import os
os.environ["OPENAI_API_TYPE"] = OPENAI_API_TYPE
os.environ["OPENAI_API_BASE"] = OPENAI_API_BASE
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
import faiss
# Code adapted from Langchain document comparison toolkit
class DocumentInput(BaseModel):
question: str = Field()
tools = []
files = [
{
"name": "belfast",
"path": "C:\\Users\\625050\\OneDrive - Clifford Chance LLP\\Documents\\Projects\\ChatGPT\\LeaseTest\\Belfast.pdf",
},
{
"name": "bournemouth",
"path": "C:\\Users\\625050\\OneDrive - Clifford Chance LLP\\Documents\\Projects\\ChatGPT\\LeaseTest\\Bournemouth.pdf",
}
]
llm = AzureChatOpenAI(deployment_name= "GPT4")
for file in files:
loader = PyPDFLoader(file["path"])
pages = loader.load_and_split()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(pages)
print(docs[0])
embeddings = OpenAIEmbeddings(deployment=OPENAI_EMBEDDING_DEPLOYMENT_NAME , chunk_size=1)
#embeddings = OpenAIEmbeddings(model='text-embedding-ada-002',
#deployment=OPENAI_DEPLOYMENT_NAME,
#openai_api_base=OPENAI_DEPLOYMENT_ENDPOINT,
#openai_api_type='azure',
#openai_api_key= OPENAI_API_KEY,
#chunk_size = 1
# )
# = OpenAIEmbeddings()
retriever = FAISS.from_documents(docs, embeddings).as_retriever()
# Wrap retrievers in a Tool
tools.append(
Tool(
args_schema=DocumentInput,
name=file["name"],
description=f"useful when you want to answer questions about {file['name']}",
func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
)
)
agent = initialize_agent(
agent=AgentType.OPENAI_FUNCTIONS,
tools=tools,
llm=llm,
verbose=True,
)
agent({"input": "Who are the landlords?"})
Error:
Entering new chain...
/openai/deployments/GPT4/chat/completions?api-version=2023-03-15-preview None False None None
---------------------------------------------------------------------------
InvalidRequestError Traceback (most recent call last)
Cell In[5], line 15
1 #llm = AzureChatOpenAI()#model_kwargs = {'deployment': "GPT4"},
2 #model_name=OPENAI_MODEL_NAME,
3 #openai_api_base=OPENAI_DEPLOYMENT_ENDPOINT,
4 #openai_api_version=OPENAI_DEPLOYMENT_VERSION,
5 #openai_api_key=OPENAI_API_KEY
6 #)
8 agent = initialize_agent(
9 agent=AgentType.OPENAI_FUNCTIONS,
10 tools=tools,
11 llm=llm,
12 verbose=True,
13 )
---> 15 agent({"input": "Who are the landlords?"})
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\chains\base.py:166, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
--> 166 raise e
167 run_manager.on_chain_end(outputs)
168 final_outputs: Dict[str, Any] = self.prep_outputs(
169 inputs, outputs, return_only_outputs
170 )
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\chains\base.py:160, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, include_run_info)
154 run_manager = callback_manager.on_chain_start(
155 dumpd(self),
156 inputs,
157 )
158 try:
159 outputs = (
--> 160 self._call(inputs, run_manager=run_manager)
161 if new_arg_supported
162 else self._call(inputs)
163 )
164 except (KeyboardInterrupt, Exception) as e:
165 run_manager.on_chain_error(e)
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\agents\agent.py:957, in AgentExecutor._call(self, inputs, run_manager)
955 # We now enter the agent loop (until it returns something).
956 while self._should_continue(iterations, time_elapsed):
--> 957 next_step_output = self._take_next_step(
958 name_to_tool_map,
959 color_mapping,
960 inputs,
961 intermediate_steps,
962 run_manager=run_manager,
963 )
964 if isinstance(next_step_output, AgentFinish):
965 return self._return(
966 next_step_output, intermediate_steps, run_manager=run_manager
967 )
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\agents\agent.py:762, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
756 """Take a single step in the thought-action-observation loop.
757
758 Override this to take control of how the agent makes and acts on choices.
759 """
760 try:
761 # Call the LLM to see what to do.
--> 762 output = self.agent.plan(
763 intermediate_steps,
764 callbacks=run_manager.get_child() if run_manager else None,
765 **inputs,
766 )
767 except OutputParserException as e:
768 if isinstance(self.handle_parsing_errors, bool):
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\agents\openai_functions_agent\base.py:209, in OpenAIFunctionsAgent.plan(self, intermediate_steps, callbacks, **kwargs)
207 prompt = self.prompt.format_prompt(**full_inputs)
208 messages = prompt.to_messages()
--> 209 predicted_message = self.llm.predict_messages(
210 messages, functions=self.functions, callbacks=callbacks
211 )
212 agent_decision = _parse_ai_message(predicted_message)
213 return agent_decision
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\chat_models\base.py:258, in BaseChatModel.predict_messages(self, messages, stop, **kwargs)
256 else:
257 _stop = list(stop)
--> 258 return self(messages, stop=_stop, **kwargs)
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\chat_models\base.py:208, in BaseChatModel.__call__(self, messages, stop, callbacks, **kwargs)
201 def __call__(
202 self,
203 messages: List[BaseMessage],
(...)
206 **kwargs: Any,
207 ) -> BaseMessage:
--> 208 generation = self.generate(
209 [messages], stop=stop, callbacks=callbacks, **kwargs
210 ).generations[0][0]
211 if isinstance(generation, ChatGeneration):
212 return generation.message
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\chat_models\base.py:102, in BaseChatModel.generate(self, messages, stop, callbacks, tags, **kwargs)
100 except (KeyboardInterrupt, Exception) as e:
101 run_manager.on_llm_error(e)
--> 102 raise e
103 llm_output = self._combine_llm_outputs([res.llm_output for res in results])
104 generations = [res.generations for res in results]
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\chat_models\base.py:94, in BaseChatModel.generate(self, messages, stop, callbacks, tags, **kwargs)
90 new_arg_supported = inspect.signature(self._generate).parameters.get(
91 "run_manager"
92 )
93 try:
---> 94 results = [
95 self._generate(m, stop=stop, run_manager=run_manager, **kwargs)
96 if new_arg_supported
97 else self._generate(m, stop=stop)
98 for m in messages
99 ]
100 except (KeyboardInterrupt, Exception) as e:
101 run_manager.on_llm_error(e)
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\chat_models\base.py:95, in <listcomp>(.0)
90 new_arg_supported = inspect.signature(self._generate).parameters.get(
91 "run_manager"
92 )
93 try:
94 results = [
---> 95 self._generate(m, stop=stop, run_manager=run_manager, **kwargs)
96 if new_arg_supported
97 else self._generate(m, stop=stop)
98 for m in messages
99 ]
100 except (KeyboardInterrupt, Exception) as e:
101 run_manager.on_llm_error(e)
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\chat_models\openai.py:359, in ChatOpenAI._generate(self, messages, stop, run_manager, **kwargs)
351 message = _convert_dict_to_message(
352 {
353 "content": inner_completion,
(...)
356 }
357 )
358 return ChatResult(generations=[ChatGeneration(message=message)])
--> 359 response = self.completion_with_retry(messages=message_dicts, **params)
360 return self._create_chat_result(response)
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\chat_models\openai.py:307, in ChatOpenAI.completion_with_retry(self, **kwargs)
303 @retry_decorator
304 def _completion_with_retry(**kwargs: Any) -> Any:
305 return self.client.create(**kwargs)
--> 307 return _completion_with_retry(**kwargs)
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\tenacity\__init__.py:289, in BaseRetrying.wraps.<locals>.wrapped_f(*args, **kw)
287 @functools.wraps(f)
288 def wrapped_f(*args: t.Any, **kw: t.Any) -> t.Any:
--> 289 return self(f, *args, **kw)
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\tenacity\__init__.py:379, in Retrying.__call__(self, fn, *args, **kwargs)
377 retry_state = RetryCallState(retry_object=self, fn=fn, args=args, kwargs=kwargs)
378 while True:
--> 379 do = self.iter(retry_state=retry_state)
380 if isinstance(do, DoAttempt):
381 try:
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\tenacity\__init__.py:314, in BaseRetrying.iter(self, retry_state)
312 is_explicit_retry = fut.failed and isinstance(fut.exception(), TryAgain)
313 if not (is_explicit_retry or self.retry(retry_state)):
--> 314 return fut.result()
316 if self.after is not None:
317 self.after(retry_state)
File c:\Users\625050\Anaconda3\envs\DD\lib\concurrent\futures\_base.py:451, in Future.result(self, timeout)
449 raise CancelledError()
450 elif self._state == FINISHED:
--> 451 return self.__get_result()
453 self._condition.wait(timeout)
455 if self._state in [CANCELLED, CANCELLED_AND_NOTIFIED]:
File c:\Users\625050\Anaconda3\envs\DD\lib\concurrent\futures\_base.py:403, in Future.__get_result(self)
401 if self._exception:
402 try:
--> 403 raise self._exception
404 finally:
405 # Break a reference cycle with the exception in self._exception
406 self = None
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\tenacity\__init__.py:382, in Retrying.__call__(self, fn, *args, **kwargs)
380 if isinstance(do, DoAttempt):
381 try:
--> 382 result = fn(*args, **kwargs)
383 except BaseException: # noqa: B902
384 retry_state.set_exception(sys.exc_info()) # type: ignore[arg-type]
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\langchain\chat_models\openai.py:305, in ChatOpenAI.completion_with_retry.<locals>._completion_with_retry(**kwargs)
303 @retry_decorator
304 def _completion_with_retry(**kwargs: Any) -> Any:
--> 305 return self.client.create(**kwargs)
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\openai\api_resources\chat_completion.py:25, in ChatCompletion.create(cls, *args, **kwargs)
23 while True:
24 try:
---> 25 return super().create(*args, **kwargs)
26 except TryAgain as e:
27 if timeout is not None and time.time() > start + timeout:
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\openai\api_resources\abstract\engine_api_resource.py:154, in EngineAPIResource.create(cls, api_key, api_base, api_type, request_id, api_version, organization, **params)
138 (
139 deployment_id,
140 engine,
(...)
150 api_key, api_base, api_type, api_version, organization, **params
151 )
153 print(url, headers, stream, request_id, request_timeout)
--> 154 response, a, api_key = requestor.request(
155 "post",
156 url,
157 params=params,
158 headers=headers,
159 stream=stream,
160 request_id=request_id,
161 request_timeout=request_timeout,
162 )
164 print(response, a, api_key)
166 if stream:
167 # must be an iterator
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\openai\api_requestor.py:230, in APIRequestor.request(self, method, url, params, headers, files, stream, request_id, request_timeout)
209 def request(
210 self,
211 method,
(...)
218 request_timeout: Optional[Union[float, Tuple[float, float]]] = None,
219 ) -> Tuple[Union[OpenAIResponse, Iterator[OpenAIResponse]], bool, str]:
220 result = self.request_raw(
221 method.lower(),
222 url,
(...)
228 request_timeout=request_timeout,
229 )
--> 230 resp, got_stream = self._interpret_response(result, stream)
231 return resp, got_stream, self.api_key
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\openai\api_requestor.py:624, in APIRequestor._interpret_response(self, result, stream)
616 return (
617 self._interpret_response_line(
618 line, result.status_code, result.headers, stream=True
619 )
620 for line in parse_stream(result.iter_lines())
621 ), True
622 else:
623 return (
--> 624 self._interpret_response_line(
625 result.content.decode("utf-8"),
626 result.status_code,
627 result.headers,
628 stream=False,
629 ),
630 False,
631 )
File c:\Users\625050\Anaconda3\envs\DD\lib\site-packages\openai\api_requestor.py:687, in APIRequestor._interpret_response_line(self, rbody, rcode, rheaders, stream)
685 stream_error = stream and "error" in resp.data
686 if stream_error or not 200 <= rcode < 300:
--> 687 raise self.handle_error_response(
688 rbody, rcode, resp.data, rheaders, stream_error=stream_error
689 )
690 return resp
InvalidRequestError: Unrecognized request argument supplied: functions
### Expected behavior
An answer should be generated and no error should be thrown.
|
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PR_kwDOIPDwls5T9NSI
| 6,776 |
Add tools description to OpenAI Multi Function request, and skip function call when there is no actions provided.
|
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[] | null | 3 | 2023-06-26T19:27:57 | 2023-11-07T03:55:41 | 2023-11-07T03:55:41 |
NONE
| null |
## Description
- Add tools description to OpenAI Multi-Function request, to provide more context on what the tool does.
- Skip function call to break the infinite loop of agent on continuously trying to perform `function_call` with empty `actions` /`tools` array.
## Tag Maintainer
@dev2049 @vowelparrot
## Twitter handle
ZhiHaoLow
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PR_kwDOIPDwls5T9EkZ
| 6,775 |
Fix inconsistent logging_and_data_dir parameter in AwaDB
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[] | null | 2 | 2023-06-26T19:04:10 | 2023-08-23T16:00:40 | 2023-06-29T06:06:53 |
CONTRIBUTOR
| null |
## Description
Tag maintainer: @rlancemartin, @eyurtsev
### log_and_data_dir
`AwaDB.__init__()` accepts a parameter named `log_and_data_dir`. But `AwaDB.from_texts()` and `AwaDB.from_documents()` accept a parameter named `logging_and_data_dir`. This inconsistency in this parameter name can lead to confusion on the part of the caller.
This PR renames `logging_and_data_dir` to `log_and_data_dir` to make all functions consistent with the constructor.
### embedding
`AwaDB.__init__()` accepts a parameter named `embedding_model`. But `AwaDB.from_texts()` and `AwaDB.from_documents()` accept a parameter named `embeddings`. This inconsistency in this parameter name can lead to confusion on the part of the caller.
This PR renames `embedding_model` to `embeddings` to make AwaDB's constructor consistent with the classmethod "constructors" as specified by `VectorStore` abstract base class.
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PR_kwDOIPDwls5T8ogP
| 6,774 |
Fix for #6696 : Added persist_directory to client_settings if persist_directory is not None in Chroma
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[] | null | 4 | 2023-06-26T17:49:58 | 2023-08-11T00:18:19 | 2023-08-11T00:18:19 |
NONE
| null |
Description:
1. Fixed an issue in Chroma Integration : Add persist_directory to client_settings if persist_directory is not None.
2. Added Tests for chroma integration to test persistence with client_settings.
Issue: https://github.com/hwchase17/langchain/issues/6696
Maintainers : @dev2049 @eyurtsev @rlancemartin
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PR_kwDOIPDwls5T8lF9
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Fix/persist directory in chroma db
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[] | null | 1 | 2023-06-26T17:38:42 | 2023-06-26T17:43:01 | 2023-06-26T17:39:10 |
NONE
| null |
- Description:
1. Fixed an issue in Chroma Integration : Add persist_directory to client_settings if persist_directory is not None.
2. Added Tests for chroma integration to test persistence with client_settings.
- Issue: #6696
- Maintainers : @rlancemartin @eyurtsev @dev2049
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PR_kwDOIPDwls5T8Y27
| 6,772 |
Add concurrent insertion of vector rows in the Cassandra Vector Store
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CONTRIBUTOR
| null |
Using the newest `cassIO`, the Cassandra Vector Store can now perform concurrent DB insertion in its `add_texts` method, hence improving its performance.
#### Linter unrelated errors
Running the linter, the following (likely unrelated) was observed:
```
langchain/vectorstores/mongodb_atlas.py:137: error: Argument 1 to "insert_many" of "Collection" has incompatible type "List[Dict[str, Sequence[object]]]"; expected "Iterable[Union[MongoDBDocumentType, RawBSONDocument]]" [arg-type]
langchain/vectorstores/mongodb_atlas.py:186: error: Argument 1 to "aggregate" of "Collection" has incompatible type "List[object]"; expected "Sequence[Mapping[str, Any]]" [arg-type]
langchain/vectorstores/qdrant.py: error: Name "grpc" is not defined [name-defined]
```
Happy to be pointed at a fix, if the above is somehow caused by the code in this PR!
|
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PR_kwDOIPDwls5T8TGL
| 6,771 |
Cassandra support for chat history using CassIO library
|
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CONTRIBUTOR
| null |
### Overview
This PR aims at building on #4378, expanding the capabilities and building on top of the `cassIO` library to interface with the database (as opposed to using the core drivers directly).
Usage of `cassIO` (a library abstracting Cassandra access for ML/GenAI-specific purposes) is already established since #6426 was merged, so no new dependencies are introduced.
In the same spirit, we try to uniform the interface for using Cassandra instances throughout LangChain: all our appreciation of the work by @jj701 notwithstanding, who paved the way for this incremental work (thank you!), we identified a few reasons for changing the way a `CassandraChatMessageHistory` is instantiated. Advocating a syntax change is something we don't take lighthearted way, so we add some explanations about this below.
Additionally, this PR expands on integration testing, enables use of Cassandra's native Time-to-Live (TTL) features and improves the phrasing around the notebook example and the short "integrations" documentation paragraph.
We would kindly request @hwchase to review (since this is an elaboration and proposed improvement of #4378 who had the same reviewer).
### About the __init__ breaking changes
There are [many](https://docs.datastax.com/en/developer/python-driver/3.28/api/cassandra/cluster/) options when creating the `Cluster` object, and new ones might be added at any time. Choosing some of them and exposing them as `__init__` parameters `CassandraChatMessageHistory` will prove to be insufficient for at least some users.
On the other hand, working through `kwargs` or adding a long, long list of arguments to `__init__` is not a desirable option either. For this reason, (as done in #6426), we propose that whoever instantiates the Chat Message History class provide a Cassandra `Session` object, ready to use. This also enables easier injection of mocks and usage of Cassandra-compatible connections (such as those to the cloud database DataStax Astra DB, obtained with a different set of init parameters than `contact_points` and `port`).
We feel that a breaking change might still be acceptable since LangChain is at `0.*`. However, while maintaining that the approach we propose will be more flexible in the future, room could be made for a "compatibility layer" that respects the current init method. Honestly, we would to that only if there are strong reasons for it, as that would entail an additional maintenance burden.
### Other changes
We propose to remove the keyspace creation from the class code for two reasons: first, production Cassandra instances often employ RBAC so that the database user reading/writing from tables does not necessarily (and generally shouldn't) have permission to create keyspaces, and second that programmatic keyspace creation is not a best practice (it should be done more or less manually, with extra care about schema mismatched among nodes, etc). Removing this (usually unnecessary) operation from the `__init__` path would also improve initialization performance (shorter time).
We suggest, likewise, to remove the `__del__` method (which would close the database connection), for the following reason: it is the recommended best practice to create a single Cassandra `Session` object throughout an application (it is a resource-heavy object capable to handle concurrency internally), so in case Cassandra is used in other ways by the app there is the risk of truncating the connection for all usages when the history instance is destroyed. Moreover, the `Session` object, in typical applications, is best left to garbage-collect itself automatically.
As mentioned above, we defer the actual database I/O to the `cassIO` library, which is designed to encode practices optimized for LLM applications (among other) without the need to expose LangChain developers to the internals of CQL (Cassandra Query Language). CassIO is already employed by the LangChain's Vector Store support for Cassandra.
We added a few more connection options in the companion notebook example (most notably, Astra DB) to encourage usage by anyone who cannot run their own Cassandra cluster.
We surface the `ttl_seconds` option for automatic handling of an expiration time to chat history messages, a likely useful feature given that very old messages generally may lose their importance.
We elaborated a bit more on the integration testing (Time-to-live, separation of "session ids", ...).
### Remarks from linter & co.
We reinstated `cassio` as a dependency both in the "optional" group and in the "integration testing" group of `pyproject.toml`. This might not be the right thing do to, in which case the author of this PR offer his apologies (lack of confidence with Poetry - happy to be pointed in the right direction, though!).
During linter tests, we were hit by some errors which appear unrelated to the code in the PR. We left them here and report on them here for awareness:
```
langchain/vectorstores/mongodb_atlas.py:137: error: Argument 1 to "insert_many" of "Collection" has incompatible type "List[Dict[str, Sequence[object]]]"; expected "Iterable[Union[MongoDBDocumentType, RawBSONDocument]]" [arg-type]
langchain/vectorstores/mongodb_atlas.py:186: error: Argument 1 to "aggregate" of "Collection" has incompatible type "List[object]"; expected "Sequence[Mapping[str, Any]]" [arg-type]
langchain/vectorstores/qdrant.py:16: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:19: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:20: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:22: error: Name "grpc" is not defined [name-defined]
langchain/vectorstores/qdrant.py:23: error: Name "grpc" is not defined [name-defined]
```
In the same spirit, we observe that to even get `import langchain` run, it seems that a `pip install bs4` is missing from the minimal package installation path.
Thank you!
|
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PR_kwDOIPDwls5T8Qzz
| 6,770 |
bump version to 216
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[] | null | 1 | 2023-06-26T16:38:53 | 2023-06-26T16:46:21 | 2023-06-26T16:46:19 |
COLLABORATOR
| null | null |
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| 1,775,138,732 |
PR_kwDOIPDwls5T8JjU
| 6,769 |
fix(memory): allow internal chains to use memory
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[] | null | 4 | 2023-06-26T16:15:40 | 2023-07-13T06:47:45 | 2023-07-13T06:47:45 |
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:
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Maintainer responsibilities:
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
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See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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Fixed #6768.
This is a workaround only. I think a better longer-term solution is for chains to declare how many input variables they *actually* need (as opposed to ones that are in the prompt, where some may be satisfied by the memory). Then, a wrapping chain can check the input match against the actual input variables.
@hwchase17
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I_kwDOIPDwls5pzlMa
| 6,768 |
Can't use memory for an internal LLMChain inside a SequentialChain
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[] | null | 0 | 2023-06-26T16:09:11 | 2023-07-13T06:47:46 | 2023-07-13T06:47:46 |
CONTRIBUTOR
| null |
### System Info
Langchain 0.0.214
Python 3.11.1
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create a `SequentialChain` that contains 2 `LLMChain`s, and add a memory to the first one.
2. When running, you'll get a validation error:
```
Missing required input keys: {'chat_history'}, only had {'human_input'} (type=value_error)
```
### Expected behavior
You should be able to add memory to one chain, not just the Sequential Chain
|
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I_kwDOIPDwls5pzOq7
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Unknown parsing error on custom tool + custom agent implementation
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[] | null | 5 | 2023-06-26T15:20:23 | 2023-06-26T17:15:43 | 2023-06-26T17:15:42 |
NONE
| null |
### System Info
langchain: 0.0.215
python: 3.10.11
OS: Ubuntu 18.04
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm implementing two custom tools that return column names and unique values in them. Their definition is as follows
```
def get_unique_values(column_name):
all_values = metadata[column_name]
return all_values
def get_column_names():
return COLUMN_NAMES
tools = [
Tool(
name="Get all column names",
func=get_column_names,
description="Useful for getting the names of all available columns. This doesn't have any arguments as input and simply invoking it would return the list of columns",
),
Tool(
name="Get distinct values of a column",
func=get_unique_values,
description="Useful for getting distinct values of a particular column. Knowing the distinct values is important to decide if a particular column should be considered in a given context or not. The input to this function should be a string representing the column name whose unique values are needed to be found out. For example, `gender` would be the input if you wanted to know what unique values are in `gender` column.",
),
]
```
I'm using a custom agent as well defined as follows: (The definition is quite straightforward and taken from the official docs)
```
prompt = CustomPromptTemplate(
template=template,
tools=tools,
input_variables=["input", "intermediate_steps"],
)
llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names,
)
agent_executor = AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, verbose=True
)
```
When I provide a prompt using `agent_executor.run()` I get the error pasted below.
Surprisingly, if I define the as follows, I don't get an error. Rather it doesn't follow my prompt template because there's no LLMChain used here and hence gets stuck in meaningless back and forth actions.
```
agent_executor = initialize_agent(
tools,
llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
```
Error trace
```
> Entering new chain...
Traceback (most recent call last):
File "/home/user/proj/agent_exp.py", line 245, in <module>
agent_executor.run(
File "/home/user/anaconda3/envs/proj/lib/python3.10/site-packages/langchain/chains/base.py", line 290, in run
return self(args[0], callbacks=callbacks, tags=tags)[_output_key]
File "/home/user/anaconda3/envs/proj/lib/python3.10/site-packages/langchain/chains/base.py", line 166, in __call__
raise e
File "/home/user/anaconda3/envs/proj/lib/python3.10/site-packages/langchain/chains/base.py", line 160, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/user/anaconda3/envs/proj/lib/python3.10/site-packages/langchain/agents/agent.py", line 987, in _call
next_step_output = self._take_next_step(
File "/home/user/anaconda3/envs/proj/lib/python3.10/site-packages/langchain/agents/agent.py", line 803, in _take_next_step
raise e
File "/home/user/anaconda3/envs/proj/lib/python3.10/site-packages/langchain/agents/agent.py", line 792, in _take_next_step
output = self.agent.plan(
File "/home/user/anaconda3/envs/proj/lib/python3.10/site-packages/langchain/agents/agent.py", line 345, in plan
return self.output_parser.parse(output)
File "/home/user/proj/agent_exp.py", line 102, in parse
raise OutputParserException(f"Could not parse LLM output: `{llm_output}`")
langchain.schema.OutputParserException: Could not parse LLM output: `Thought: I need to get all the column names
Action: Get all column names`
```
### Expected behavior
The agent should have called these functions appropriately and returned a list of columns that have the string datatype
|
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| 1,775,023,349 |
I_kwDOIPDwls5pzLT1
| 6,766 |
Issue: Qdrant does not support the vector_size parameter, which is a very common and frequently used parameter. I hope it can be supported.
|
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[] | null | 5 | 2023-06-26T15:12:51 | 2023-10-18T16:06:58 | 2023-10-18T16:06:57 |
NONE
| null |
### Issue you'd like to raise.
Qdrant does not support the vector_size parameter, which is a very common and frequently used parameter. I hope it can be supported.
langchain/vectorstores/qdrant.py
```
# Just do a single quick embedding to get vector size
partial_embeddings = embedding.embed_documents(texts[:1])
vector_size = len(partial_embeddings[0])
```
### Suggestion:
I hope it can be supported.
|
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| 1,775,022,677 |
PR_kwDOIPDwls5T7wI0
| 6,765 |
Fix breaking tags
|
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[] | null | 1 | 2023-06-26T15:12:34 | 2023-06-26T16:28:13 | 2023-06-26T16:28:11 |
CONTRIBUTOR
| null |
Fix tags change that broke old way of initializing agent
Closes #6756
|
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I_kwDOIPDwls5pzDdV
| 6,764 |
`EmbeddingRouterChain.from_names_and_descriptions` doesn't accept vectorstore kwargs
|
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[] | null | 4 | 2023-06-26T14:58:12 | 2023-10-02T16:05:14 | 2023-10-02T16:05:13 |
NONE
| null |
### System Info
Ubuntu 22.04.2
Python 3.10.11
langchain 0.0.215
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from pathlib import Path
from langchain.chains.router.embedding_router import EmbeddingRouterChain
from langchain.embeddings import LlamaCppEmbeddings
from langchain.vectorstores import Weaviate
model_dir = Path.home() / 'models'
llama_path = model_dir / 'llama-7b.ggmlv3.q4_0.bin'
encoder = LlamaCppEmbeddings(model_path=str(llama_path))
encoder.client.verbose = False
names_and_descriptions = [
("physics", ["for questions about physics"]),
("math", ["for questions about math"]),
]
router_chain = EmbeddingRouterChain.from_names_and_descriptions(names_and_descriptions, Weaviate, encoder,
weaviate_url='http://localhost:8080',
routing_keys=["input"])
```
### Expected behavior
I expect to be able to configure the underlying vectorizer with kwargs passed into the `from_names_and_descriptions` e.g. `weaviate_url`
|
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I_kwDOIPDwls5py7Sa
| 6,762 |
Stream a response from LangChain's OpenAI with python fastapi
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[] | null | 1 | 2023-06-26T14:40:55 | 2023-10-02T16:05:19 | 2023-10-02T16:05:18 |
NONE
| null |
### System Info
langchain == 0.0.205
python == 3.10.11
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
```
from fastapi import FastAPI, Depends, Request, Response
from typing import Any, Dict, List, Generator
import asyncio
from langchain.llms import OpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.schema import LLMResult, HumanMessage, SystemMessage
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from html import escape
app = FastAPI()
# 添加 CORS 中间件
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
allow_credentials=True,
)
q = asyncio.Queue()
stop_item = "###finish###"
class StreamingStdOutCallbackHandlerYield(StreamingStdOutCallbackHandler):
async def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Run when LLM starts running."""
# Clear the queue at the start
while not q.empty():
await q.get()
async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
"""Run on new LLM token. Only available when streaming is enabled."""
await q.put(token)
async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when LLM ends running."""
await q.put(stop_item)
async def generate_llm_stream(prompt: str) -> Generator[str, None, None]:
llm = OpenAI(temperature=0.5, streaming=True, callbacks=[StreamingStdOutCallbackHandlerYield()])
result = await llm.agenerate([prompt])
while True:
item = await q.get()
if item == stop_item:
break
yield item
@app.get("/generate-song", status_code=200)
async def generate_song() -> Response:
prompt = "Write me a song about sparkling water."
async def event_stream() -> Generator[str, None, None]:
async for item in generate_llm_stream(prompt):
escaped_chunk = escape(item).replace("\n", "<br>").replace(" ", " ")
yield f"data:{escaped_chunk}\n\n"
return StreamingResponse(event_stream(), media_type="text/event-stream")
if __name__ == "__main__":
import uvicorn
uvicorn.run("easy:app", host="0.0.0.0", port=8000, reload=True)
```
### Expected behavior
```Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 8.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
Retrying langchain.llms.openai.acompletion_with_retry.<locals>._completion_with_retry in 10.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
ERROR: Exception in ASGI application
Traceback (most recent call last):
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\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 "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\asyncio\base_events.py", line 1076, in create_connection
raise exceptions[0]
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\asyncio\base_events.py", line 1060, in create_connection
sock = await self._connect_sock(
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\asyncio\base_events.py", line 969, in _connect_sock
await self.sock_connect(sock, address)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\asyncio\selector_events.py", line 501, in sock_connect
return await fut
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\asyncio\selector_events.py", line 541, in _sock_connect_cb
raise OSError(err, f'Connect call failed {address}')
TimeoutError: [Errno 10060] Connect call failed ('108.160.166.253', 443)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\openai\api_requestor.py", line 592, in arequest_raw
result = await session.request(**request_kwargs)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\aiohttp\client.py", line 536, in _request
conn = await self._connector.connect(
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\aiohttp\connector.py", line 540, in connect
proto = await self._create_connection(req, traces, timeout)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\aiohttp\connector.py", line 901, in _create_connection
_, proto = await self._create_direct_connection(req, traces, timeout)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\aiohttp\connector.py", line 1206, in _create_direct_connection
raise last_exc
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\aiohttp\connector.py", line 1175, in _create_direct_connection
transp, proto = await self._wrap_create_connection(
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\aiohttp\connector.py", line 988, in _wrap_create_connection
raise client_error(req.connection_key, exc) from exc
aiohttp.client_exceptions.ClientConnectorError: Cannot connect to host api.openai.com:443 ssl:default [Connect call failed ('108.160.166.253', 443)]
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\uvicorn\protocols\http\httptools_impl.py", line 435, in run_asgi
result = await app( # type: ignore[func-returns-value]
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\uvicorn\middleware\proxy_headers.py", line 78, in __call__
return await self.app(scope, receive, send)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\fastapi\applications.py", line 282, in __call__
await super().__call__(scope, receive, send)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\applications.py", line 122, in __call__
await self.middleware_stack(scope, receive, send)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\middleware\errors.py", line 184, in __call__
raise exc
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\middleware\errors.py", line 162, in __call__
await self.app(scope, receive, _send)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\middleware\cors.py", line 91, in __call__
await self.simple_response(scope, receive, send, request_headers=headers)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\middleware\cors.py", line 146, in simple_response
await self.app(scope, receive, send)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\middleware\exceptions.py", line 79, in __call__
raise exc
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\middleware\exceptions.py", line 68, in __call__
await self.app(scope, receive, sender)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\fastapi\middleware\asyncexitstack.py", line 20, in __call__
raise e
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\fastapi\middleware\asyncexitstack.py", line 17, in __call__
await self.app(scope, receive, send)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\routing.py", line 718, in __call__
await route.handle(scope, receive, send)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\routing.py", line 276, in handle
await self.app(scope, receive, send)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\routing.py", line 69, in app
await response(scope, receive, send)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\responses.py", line 270, in __call__
async with anyio.create_task_group() as task_group:
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\anyio\_backends\_asyncio.py", line 662, in __aexit__
raise exceptions[0]
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\responses.py", line 273, in wrap
await func()
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\starlette\responses.py", line 262, in stream_response
async for chunk in self.body_iterator:
File "C:\AI\openai\easy.py", line 61, in event_stream
async for item in generate_llm_stream(prompt):
File "C:\AI\openai\easy.py", line 47, in generate_llm_stream
result = await llm.agenerate([prompt])
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\langchain\llms\base.py", line 287, in agenerate
raise e
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\langchain\llms\base.py", line 279, in agenerate
await self._agenerate(
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\langchain\llms\openai.py", line 355, in _agenerate
async for stream_resp in await acompletion_with_retry(
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\langchain\llms\openai.py", line 120, in acompletion_with_retry
return await _completion_with_retry(**kwargs)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\tenacity\_asyncio.py", line 88, in async_wrapped
return await fn(*args, **kwargs)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\tenacity\_asyncio.py", line 47, in __call__
do = self.iter(retry_state=retry_state)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\tenacity\__init__.py", line 325, in iter
raise retry_exc.reraise()
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\tenacity\__init__.py", line 158, in reraise
raise self.last_attempt.result()
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\concurrent\futures\_base.py", line 451, in result
return self.__get_result()
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\concurrent\futures\_base.py", line 403, in __get_result
raise self._exception
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\tenacity\_asyncio.py", line 50, in __call__
result = await fn(*args, **kwargs)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\langchain\llms\openai.py", line 118, in _completion_with_retry
return await llm.client.acreate(**kwargs)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\openai\api_resources\completion.py", line 45, in acreate
return await super().acreate(*args, **kwargs)
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 217, in acreate
response, _, api_key = await requestor.arequest(
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\site-packages\openai\api_requestor.py", line 304, in arequest
result = await self.arequest_raw(
File "C:\Users\jhrsya\Anaconda3\envs\langchain\lib\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```
|
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I_kwDOIPDwls5py44i
| 6,761 |
ConversationBufferMemory fails to capture OpenAI functions messages in LLMChain
|
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[] | null | 10 | 2023-06-26T14:36:44 | 2024-01-11T16:35:09 | null |
CONTRIBUTOR
| null |
### System Info
Adding memory to a LLMChain with OpenAI functions enabled fails because of `AIMessage` are generated instead of `FunctionMessage`
```python
self.add_message(AIMessage(content=message))
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/pas/development/advanced-stack/sandbox/sublime-plugin-maker/venv/lib/python3.11/site-packages/langchain/load/serializable.py", line 74, in __init__
super().__init__(**kwargs)
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for AIMessage
content
str type expected (type=type_error.str)
```
where `AIMessage.content` is in fact a dict.
Tested version : `0.0.215`
### Who can help?
@dev2049
@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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create a LLMChain with both functions and memory
2. Enjoy :)
### Expected behavior
When LLMChain is created with `functions` and memory, generate FunctionMessage and accept dict (or json dumps)
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| 1,774,817,963 |
I_kwDOIPDwls5pyZKr
| 6,760 |
Streaming Support For AzureOpenAI
|
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| false | null |
[] | null | 7 | 2023-06-26T13:44:29 | 2023-12-28T16:02:10 | null |
NONE
| null |
### System Info
There is a lack of support for the streaming option with AzureOpenAI. As you can see from the following article (https://thivy.hashnode.dev/streaming-response-with-azure-openai) official API support on Azure's side is present.
Specifically, when trying to utilize the streaming argument with AzureOpenAI, we recieve the following error (with gpt-35-turbo model, which is possible on direct Azure interfacing):
`InvalidRequestError: logprobs, best_of and echo parameters are not available on gpt-35-turbo model. Please remove the parameter and try again. For more details, see https://go.microsoft.com/fwlink/?linkid=2227346.`
### 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
1. Run the following code in a notebook:
```
from langchain.callbacks.base import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import (
ConversationalRetrievalChain,
LLMChain
)
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts.prompt import PromptTemplate
from langchain.llms import AzureOpenAI
from langchain.chat_models import AzureChatOpenAI
template = """Given the following chat history and a follow up question, rephrase the follow up input question to be a standalone question.
Or end the conversation if it seems like it's done.
Chat History:\"""
{chat_history}
\"""
Follow Up Input: \"""
{question}
\"""
Standalone question:"""
condense_question_prompt = PromptTemplate.from_template(template)
template = """You are a friendly, conversational retail shopping assistant. Use the following context including product names, descriptions, and keywords to show the shopper whats available, help find what they want, and answer any questions.
It's ok if you don't know the answer.
Context:\"""
{context}
\"""
Question:\"""
\"""
Helpful Answer:"""
qa_prompt= PromptTemplate.from_template(template)
llm = AzureOpenAI(deployment_name="gpt-35-turbo", temperature=0)
streaming_llm = AzureOpenAI(
streaming=True,
callback_manager=CallbackManager([
StreamingStdOutCallbackHandler()]),
verbose=True,
engine=deployment_name,
temperature=0.2,
max_tokens=150
)
# use the LLM Chain to create a question creation chain
question_generator = LLMChain(
llm=llm,
prompt=condense_question_prompt
)
# use the streaming LLM to create a question answering chain
doc_chain = load_qa_chain(
llm=streaming_llm,
#llm=llm,
chain_type="stuff",
prompt=qa_prompt
)
chatbot = ConversationalRetrievalChain(
retriever=vectorstore.as_retriever(),
combine_docs_chain=doc_chain,
question_generator=question_generator
)
# create a chat history buffer
chat_history = []
# gather user input for the first question to kick off the bot
question = input("Hi! What are you looking for today?")
# keep the bot running in a loop to simulate a conversation
while True:
result = chatbot(
{"question": question, "chat_history": chat_history}
)
print("\n")
chat_history.append((result["question"], result["answer"]))
question = input()
```
Engine is "gpt-35-turbo" and the correct environment variables are provided.
2. Input anything in the input field.
3. Error Occurs.
Results in the following error:
`InvalidRequestError: logprobs, best_of and echo parameters are not available on gpt-35-turbo model. Please remove the parameter and try again. For more details, see https://go.microsoft.com/fwlink/?linkid=2227346.`
### Expected behavior
To execute without error.
### P.S.
This is my first bug report on GitHub, so if anything is missing please let me know.
|
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AzureChatOpenAI: InvalidRequestError
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[] | null | 4 | 2023-06-26T13:32:27 | 2023-10-05T16:07:56 | 2023-10-05T16:07:55 |
NONE
| null |
### System Info
Windows 11,
python==3.10.5
langchain==0.0.215
openai==0.27.8
faiss-cpu==1.7.4
### Who can help?
@zeke
@sbusso
@deepblue
when run this example code:
https://python.langchain.com/docs/modules/model_io/models/chat/integrations/azure_chat_openai
I get the following error...
"InvalidRequestError: The API deployment for this resource does not exist. If you created the deployment within the last 5 minutes, please wait a moment and try again."
The code works if you downgrade langchain to 0.0.132
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Follow the instructions in the notebook.
https://python.langchain.com/docs/modules/model_io/models/chat/integrations/azure_chat_openai
### Expected behavior
Get a response.
|
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| 1,774,668,475 |
I_kwDOIPDwls5px0q7
| 6,758 |
vectorstores/faiss.py load_local can't set normalize_L2
|
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[] | null | 4 | 2023-06-26T12:26:30 | 2023-10-19T16:06:58 | 2023-10-19T16:06:58 |
NONE
| null |
https://github.com/hwchase17/langchain/blob/1742db0c3076772db652c747df1524cd07695f51/langchain/vectorstores/faiss.py#L458
this from method can set 'normalize_L2' for un-norm embeddings
https://github.com/hwchase17/langchain/blob/1742db0c3076772db652c747df1524cd07695f51/langchain/vectorstores/faiss.py#L588
but load_local no 'normalize_L2' argument, so cache&load cant't work as expected
please add 'normalize_L2'，or cache 'normalize_L2' to cache_file and restore it when load_local
|
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| 1,774,605,450 |
PR_kwDOIPDwls5T6UJi
| 6,757 |
Add Minimax LLM and Chat
|
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[] | null | 12 | 2023-06-26T11:55:41 | 2023-09-19T15:22:03 | 2023-09-19T15:22:02 |
NONE
| null |
- Add support for MiniMax LLM
- [Doc](https://api.minimax.chat/document/algorithm-concept?id=6433f37594878d408fc8295d)
|
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I_kwDOIPDwls5pxMbL
| 6,756 |
Recent tags change causes AttributeError: 'str' object has no attribute 'value' on initialize_agent call
|
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[] | null | 4 | 2023-06-26T11:00:29 | 2023-06-27T02:03:29 | 2023-06-26T16:28:13 |
NONE
| null |
### System Info
- Langchain: 0.0.215
- Platform: ubuntu
- Python 3.10.12
### Who can help?
@vowelparrot
https://github.com/hwchase17/langchain/blob/d84a3bcf7ab3edf8fe1d49083e066d51c9b5f621/langchain/agents/initialize.py#L54
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Fails if agent initialized as follows:
```python
agent = initialize_agent(
agent='zero-shot-react-description',
tools=tools,
llm=llm,
verbose=True,
max_iterations=30,
memory=ConversationBufferMemory(),
handle_parsing_errors=True)
```
With
```
...
lib/python3.10/site-packages/langchain/agents/initialize.py", line 54, in initialize_agent
tags_.append(agent.value)
AttributeError: 'str' object has no attribute 'value'
````
### Expected behavior
Expected to work as before where agent is specified as a string (or if this is highlighting that agent should actually be an object, it should indicate that instead of the error being shown).
|
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| 1,774,484,925 |
PR_kwDOIPDwls5T55WS
| 6,755 |
🎯Bug: format the url and path_params
|
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[] | null | 2 | 2023-06-26T10:50:34 | 2023-06-27T06:03:58 | 2023-06-27T06:03:57 |
CONTRIBUTOR
| null |
- Description: format the url and path_params correctly,
- Issue: #6753,
- Dependencies: None,
- Tag maintainer: @vowelparrot,
- Twitter handle: @0xbluesecurity
|
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