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Merged
gunthercox merged 3 commits into from
Jan 18, 2026
+40 −144
Merged

Update optional python dependencies #2433

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84368f5
Update optional python dependencies
gunthercox Jan 18, 2026
441f122
Switch back to simpler vector search code
gunthercox Jan 18, 2026
9a0534e
Allow wider langchain-redis range
gunthercox Jan 18, 2026
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168 changes: 31 additions & 137 deletions chatterbot/storage/redis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,6 +65,7 @@ def __init__(self, **kwargs):
index_name='chatterbot',
redis_url=self.database_uri,
content_field='in_response_to',
legacy_key_format=False,
metadata_schema=[
{
'name': 'conversation',
Expand Down Expand Up @@ -212,12 +213,16 @@ def filter(self, page_size=4, **kwargs):
- search_in_response_to_contains
- order_by
"""
from redisvl.query import VectorQuery
from redisvl.query.filter import Tag, Text

# https://redis.io/docs/latest/develop/interact/search-and-query/advanced-concepts/query_syntax/
filter_condition = None

ordering = kwargs.get('order_by', None)

if ordering:
ordering = ','.join(ordering)

if 'in_response_to' in kwargs:
filter_condition = Text('in_response_to') == kwargs['in_response_to']

Expand Down Expand Up @@ -255,7 +260,7 @@ def filter(self, page_size=4, **kwargs):
_query = '|'.join([
f'%%{text}%%' for text in kwargs['exclude_text_words']
])
query = Text('text') % f'-({ _query })'
query = Text('text') % f'-({_query})'
if filter_condition:
filter_condition &= query
else:
Expand Down Expand Up @@ -290,67 +295,20 @@ def filter(self, page_size=4, **kwargs):
# similar responses.
_search_query = kwargs['search_text_contains']

# Use vector similarity to find statements responding to similar contexts
embedding = self.vector_store.embeddings.embed_query(_search_query)

return_fields = [
'text', 'in_response_to', 'conversation', 'persona', 'tags', 'created_at'
]

query = VectorQuery(
vector=embedding,
vector_field_name='embedding',
return_fields=return_fields,
num_results=page_size,
filter_expression=filter_condition
documents = self.vector_store.similarity_search(
_search_query,
k=page_size, # The number of results to return
return_all=True, # Include the full document with IDs
filter=filter_condition,
sort_by=ordering
)

results = self.vector_store.index.query(query)

Document = self.get_statement_model()
documents = []

# Calculate confidence from vector distances
# Add confidence scores based on similarity ordering
# Results are ordered by similarity (best match first)
for idx, result in enumerate(results):
in_response_to = result.get('in_response_to', '')

# Redis vector_score is cosine distance (lower is better)
# Convert to confidence: confidence = 1 - distance
# If vector_score not available, use result order
vector_score = result.get('vector_score')
if vector_score is not None:
# Cosine distance ranges from 0 (identical) to 2 (opposite)
# Normalize to confidence: 1.0 (identical) to 0.0 (opposite)
confidence = max(0.0, 1.0 - (float(vector_score) / 2.0))
else:
# Fallback: use result order (first result = highest confidence)
# Start at 0.95 for first result, decay by 0.05 per position
confidence = max(0.0, 0.95 - (idx * 0.05))

# Parse timestamp
created_at_value = result.get('created_at', 0)
if isinstance(created_at_value, str):
created_at = datetime.fromtimestamp(float(created_at_value))
elif created_at_value:
created_at = datetime.fromtimestamp(float(created_at_value))
else:
created_at = datetime.now()

metadata = {
'text': result.get('text', ''),
'conversation': result.get('conversation', ''),
'persona': result.get('persona', ''),
'tags': result.get('tags', ''),
'created_at': created_at,
'confidence': confidence,
}
doc = Document(
page_content=in_response_to,
metadata=metadata,
id=result['id']
)
documents.append(doc)
for idx, doc in enumerate(documents):
# Start at 0.95 for first result, decay by 0.05 per position
confidence = max(0.0, 0.95 - (idx * 0.05))
doc.metadata['confidence'] = confidence

return [self.model_to_object(document) for document in documents]

Expand Down Expand Up @@ -379,74 +337,20 @@ def filter(self, page_size=4, **kwargs):
if 'search_in_response_to_contains' in kwargs:
_search_text = kwargs.get('search_in_response_to_contains', '')

# Get embedding for the search text
embedding = self.vector_store.embeddings.embed_query(_search_text)

# Build return fields from metadata schema
return_fields = [
'text', 'in_response_to', 'conversation', 'persona', 'tags', 'created_at'
]

# Use direct index query via RedisVL
# langchain's similarity_search has issues with filters in v0.2.4
# and may not work properly with existing indexes
# TODO: Look into similarity_search_with_score implementation
query = VectorQuery(
vector=embedding,
vector_field_name='embedding',
return_fields=return_fields,
num_results=page_size,
filter_expression=filter_condition
documents = self.vector_store.similarity_search(
_search_text,
k=page_size, # The number of results to return
return_all=True, # Include the full document with IDs
filter=filter_condition,
sort_by=ordering
)

# Execute query
results = self.vector_store.index.query(query)

# Convert results to Document objects
Document = self.get_statement_model()
documents = []

# Calculate confidence from vector distances
# Add confidence scores based on similarity ordering
# Results are ordered by similarity (best match first)
for idx, result in enumerate(results):
# Extract metadata and content
in_response_to = result.get('in_response_to', '')

# Redis vector_score is cosine distance (lower is better)
# Convert to confidence: confidence = 1 - distance
# If vector_score not available, use result order
vector_score = result.get('vector_score')
if vector_score is not None:
# Cosine distance ranges from 0 (identical) to 2 (opposite)
# Normalize to confidence: 1.0 (identical) to 0.0 (opposite)
confidence = max(0.0, 1.0 - (float(vector_score) / 2.0))
else:
# Fallback: use result order (first result = highest confidence)
# Start at 0.95 for first result, decay by 0.05 per position
confidence = max(0.0, 0.95 - (idx * 0.05))

# Convert Unix timestamp back to datetime
# Redis returns numeric fields as strings
created_at_timestamp = result.get('created_at', '0')
if created_at_timestamp and created_at_timestamp != '0':
created_at = datetime.fromtimestamp(float(created_at_timestamp))
else:
created_at = datetime.now()

metadata = {
'text': result.get('text', ''),
'conversation': result.get('conversation', ''),
'persona': result.get('persona', ''),
'tags': result.get('tags', ''),
'created_at': created_at,
'confidence': confidence,
}
doc = Document(
page_content=in_response_to,
metadata=metadata,
id=result['id']
)
documents.append(doc)
for idx, doc in enumerate(documents):
# Start at 0.95 for first result, decay by 0.05 per position
confidence = max(0.0, 0.95 - (idx * 0.05))
doc.metadata['confidence'] = confidence
else:
documents = self.vector_store.query_search(
k=page_size,
Expand Down Expand Up @@ -551,11 +455,8 @@ def update(self, statement):
client = self.vector_store.index.client
client.delete(statement.id)

# NOTE: langchain-redis has an inconsistency - it uses :: for auto-generated
# IDs but : (single colon) when keys are explicitly provided
if '::' in statement.id:
key = statement.id.split('::', 1)[1]
elif ':' in statement.id:
# Extract the key from the full ID (format: prefix:key)
if ':' in statement.id:
key = statement.id.split(':', 1)[1]
else:
# If no delimiter found, use the entire ID as the key
Expand All @@ -564,13 +465,6 @@ def update(self, statement):
ids = self.vector_store.add_texts(
[document.page_content], [metadata], keys=[key]
)

# Normalize the ID to use :: delimiter (if langchain-redis returned single colon)
if ids and ':' in ids[0] and '::' not in ids[0]:
# Replace first occurrence of single colon with double colon
normalized_id = ids[0].replace(':', '::', 1)
# Update the key in Redis to use the correct format
client.rename(ids[0], normalized_id)
else:
self.vector_store.add_documents([document])

Expand Down
2 changes: 2 additions & 0 deletions docs/conf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,8 @@

sitemap_url_scheme = '{link}'

sitemap_prettify = True

# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']

Expand Down
14 changes: 7 additions & 7 deletions pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -71,7 +71,7 @@ dependencies = [
test = [
"flake8",
"coverage",
"sphinx>=5.3,<8.2",
"sphinx>=5.3,<9.2",
"sphinx-sitemap>=2.6.0",
"huggingface_hub",
"django<=4.1,<6.0"
Expand All @@ -84,12 +84,12 @@ dev = [
"openai"
]
redis = [
"redis[hiredis]<7.0",
"langchain-redis<=0.2.5",
"langchain-huggingface<=0.1.2",
"accelerate<=1.6.0",
"sentence-transformers<=4.0.2",
"redis[hiredis]>=7.0,<7.2",
"langchain-redis<0.3.0",
"langchain-huggingface>=0.1.2,<1.3.0",
"accelerate>=1.6.0,<1.13",
"sentence-transformers>=4.0.2,<5.3.0",
]
mongodb = [
"pymongo>=4.11,<4.16",
"pymongo>=4.11,<4.17",
]